signifies the number of metastases.

Variability in radiologic interpretation in cancer patients has been well documented, for instance when evaluating the RECIST response [8] and even when using conventional imaging, which most radiologists have the greatest amount of experience in interpreting. The lack of sensitivity and specificity of conventional imaging for identifying prostate cancer metastases has also been well documented [9]. For instance, in the POPSTAR trial, even with [18F]-NaF PET bone scans, there was considerable understaging as distant progression-free survival (PFS) was about 40% at 2 years compared to 89–100% continued remission at the sites of irradiation [6]. This indicates that smaller deposits of disease had not been visible when metastasis-directed therapy was administered.

PSMA PET tracers significantly improve sensitivity [3]. However, due to limited access and difficulty in interpretation, the research community has largely opted to continue basing eligibility and response assessment on conventional imaging. In the ORIOLE trial, for example, conventional imaging formed the basis for treatment. The protocol specified that [ 18F]DCFPyL-PET images were evaluated and compared to bone scans, but additional sites of suspected metastatic disease from the PET scan were not considered for treatment by SBRT nor required to undergo further evaluation [2]. This design resulted in the ability to analyze outcomes in patients whose PET-detected disease was fully treated (i.e., PET scan did not detect additional sites of disease beyond what was visible on conventional imaging) compared to those in whom some metastatic disease was left untreated, and it was noted that the former group had greater progression-free survival. Thus, future oligometastatic protocols are likely to rely on PSMA PET imaging. However, false positives will continue to be an important consideration since benign conditions such as Paget's disease have been reported to result in false-positive PSMA radiotracer uptake [10], and interpretation can be challenging for this relatively newer imaging modality.

Access to PET scans for prostate cancer patients remains a major limitation in community practice and in academic centers. A recent publication found that in a tertiary medical center, there were disparities in PET scanning, with African American prostate cancer patients less likely to undergo PSMA PET scan compared to non-Hispanic white patients [11]. In the community oncology setting, differences in health insurance coverage and imaging facility capabilities may exacerbate the lack of equitable access to PET imaging. Community oncologists may also have decreased access to multidisciplinary care via participation in tumor boards. In one survey of community practices, 53.8% of physicians reported participating in tumor boards weekly, while 42% participated less than once per week, with less attendance from medical oncologists compared to radiation oncologists [12]. In our experience, tumor board review was key in gaining confidence for inclusion of patients and treatment of oligometastatic sites, even if PET imaging was not available, and lack of access to optimal imaging should not preclude patients from accessing the potential benefit of metastasis-directed therapy.

While a major focus has been placed on determining the number of metastatic lesions that define the oligometastatic disease state, it seems that controversy surrounds whether a conventional imaging modality can adequately establish a patient as having the specified number of metastases. Bone scanning has been suggested to yield inconclusive results in about 16% of cases [13]. MRI has often been used to enhance detection of osseous metastases or clarify inconclusive bone scan findings. However, in a study of findings from pelvic MRI performed in 3765 patients for evaluation of presumed localized prostate cancer, 74% of patients had bone abnormalities, which were only rarely confirmed to be metastases [14]. This calls into question the use of MRI to adjudicate findings from conventional imaging. Bone biopsies may be helpful in confirming a bone metastasis, but even in experienced centers with specific protocols designed to maximize the yield, the detection rates from bone biopsy performed to obtain cancer tissue have been reported to be less than 80% [15], and in most community centers without expertise the yield will be lower. Therefore, bone biopsy may not have a high enough sensitivity to be used to exclude the presence of metastatic cancer. Some clinical characteristics may be helpful in selecting metastases for greater yield, including the size of the lesion in the bone, presence of a soft tissue component, intensity of scintigraphic uptake, or a newly apparent area of disease involvement, but without communication between oncologists and radiologists, optimal target selection is less likely to occur.

Overall, our experience raises concern about a potential lack of uniformity in the population of patients who are subject to protocols for oligometastatic prostate cancer, and how the results can subsequently be translated into clinical practice. There is no defined algorithm for how to confirm oligometastases identified in a prostate cancer patient using conventional imaging. We found that simple clinical factors may help guide clinicians as to when additional scrutiny is warranted. In this research, subjects with PSA over 10 were less likely to be deemed oligometastatic after further imaging or imaging review, although there were four subjects who were found eligible with PSA ranging from 10 to 45. Similarly, subjects were less likely to have metastases at all when PSA was less than 1, though four subjects were deemed to have oligometastatic disease at this PSA level, only one of whose disease was detected on a PET scan. While clearly not enough to define an oligometastatic state, higher or lower PSA should at least raise clinical suspicion and trigger additional imaging, consultation with radiology, or potentially a biopsy to better clarify the extent of the disease. Where PSMA PET scans are not available, any indeterminate bone findings, or a discordance between bone scanning and CT, or between the volume of disease and PSA, may warrant additional imaging and/or biopsy before metastasis-directed therapy is undertaken.

#### **5. Conclusions**

Defining the oligometastatic state depends on accurate interpretation of imaging. In our experience, prostate cancer patients initially thought to be oligometastatic were frequently reclassified when additional imaging was ordered to clarify indeterminate findings. Physicians should be encouraged to thoroughly review imaging, utilizing tumor boards or additional imaging modalities when appropriate, prior to applying metastasisdirected strategies for their patients who appear to have oligometastatic prostate cancer.

**Author Contributions:** Conceptualization: T.B.D. and S.D., Methodology: T.B.D. and S.K., Data Curation: T.B.D. and S.K., Writing—Original Draft Preparation: T.B.D. and S.K., Writing—Review and Editing: T.B.D., S.K., N.G., S.L., S.K.P., J.W. and S.D. All authors have read and agreed to the published version of the manuscript.

**Funding:** Conduct of the clinical trial was supported by research funds from Bayer Pharmaceuticals to City of Hope for an investigator sponsored trial.

**Institutional Review Board Statement:** This study was conducted with approval by the City of Hope IRB (protocol #17085), and was conducted according to the guidelines of the Declaration of Helsinki.

**Informed Consent Statement:** All subjects included and treated in the study provided informed consent; IRB exemption was granted to include information from patients who failed screening and, therefore, did not sign informed consent to enroll onto the study.

**Data Availability Statement:** Data sharing not applicable. This study is still accruing patients. Data sharing is not applicable to this article since full data from the clinical trial will be shared upon completion of the study.

**Conflicts of Interest:** T.D. has received consulting income from Astellas, AstraZeneca, Bayer, Exelixis, Janssen, Pfizer, and Sanofi. S.P. has received institutional research funding from Eisai, Genentech, Roche, Exelixis, Pfizer, Crispr, and Allogene. He has also received travel expenses from Crispr and Roche.

#### **References**


**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

**Luis Meza 1, Matthew Feng 1, Kyle Lee 1, Rubens Sperandio <sup>2</sup> and Sumanta Kumar Pal 1,\***


**Abstract:** The introduction of targeted therapy (TT) and immuno-oncology (IO) agents have revolutionized the treatment of metastatic renal cell carcinoma (mRCC). However, despite the significant improvements in survival and clinical response yielded by these agents, a significant percentage of patients still experience progressive disease. Evidence now suggests that microorganisms living in the gut (i.e., the gut microbiome) could be used as a biomarker for response and may also have utility in increasing response to these treatments. In this review, we present an overview of the role of the gut microbiome in cancer and its potential implications in the treatment of mRCC.

**Keywords:** renal cell carcinoma; gut microbiome; translational research

#### **1. Introduction**

Approximately 82,000 new cases of kidney cancer will be diagnosed in the United States during 2023, with varying rates of progression to metastatic disease [1]. While treatment options for localized disease have remained largely unchanged, significant advances have occurred in the treatment landscape of metastatic renal cell carcinoma (mRCC). The last couple of decades have seen an explosion in the number of U.S. Food and Drug Administration (FDA) approvals for this disease setting with multiple targeted-therapy agents (TT), and immune checkpoint inhibitors (ICIs) being now available for this patient population. TT agents can be divided into (a) inhibitors of vascular endothelial growth factor (VEGF) signaling, which include drugs such as sunitinib, pazopanib, axitinib, cabozantinib and levantinib, and (b) inhibitors of the mammalian target of rapamycin (mTOR), represented by everolimus and temsirolimus [2–7]. In contrast, ICIs block coinhibitory molecules such as programmed death-1 (PD-1), the programmed death ligand-1 (PD-L1) and the cytotoxic T-lymphocyte activating protein-4 (CTLA-4) [8].

Despite the numerous available options for patients with mRCC with the use of the previously mentioned agents, either as monotherapy or in combination, response to these regimens remains heterogeneous, with some patients achieving a complete response (CR) while others experience progressive disease (PD). Moreover, the 5-year survival rate for patients in this stage is only 15% [9]. Therefore, selecting the approach that will yield the most benefit for a given patient remains a significant challenge [10]. Despite multiple efforts to identify biomarkers predictive of response, such as the gene expression signatures from the IMmotion 151 trial, tumor mutational burden (TMB), and PD-L1 expression, the International mRCC Database Consortium (IMDC) risk model remains the only predictive biomarker to be prospectively validated in a phase 3 trial to date. There is, therefore, a need to increase our understanding of the biological processes underlying the development and evolution of RCC to develop novel biomarkers of response that will allow for treatment selection in an individualized manner.

In recent years, fueled by the advent of next-generation sequencing technologies, there has been an increased interest in the evaluation of the gut microbiome and its role in cancer. Multiple studies now show that certain bacterial species might be associated

**Citation:** Meza, L.; Feng, M.; Lee, K.; Sperandio, R.; Pal, S.K. The Gut Microbiome and Metastatic Renal Cell Carcinoma. *J. Clin. Med.* **2023**, *12*, 1502. https://doi.org/10.3390/ jcm12041502

Academic Editor: Hiroshi Tanaka

Received: 15 January 2023 Revised: 6 February 2023 Accepted: 11 February 2023 Published: 14 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

with the development of certain cancers such as lung, melanoma and colon, as well as with treatment response to currently available regimens [11]. In the setting of RCC, there have also been efforts to characterize the role of the gut microbiome. Here, we provide an overview of the role of the gut microbiome in cancer with a special focus on RCC. In addition, we highlight the ongoing trials in the field and discuss the importance of intra– inter-institutional collaboration for creating a solid working framework for microbiome studies in the future.

#### **2. Gut Microbiome**

It is estimated that the human body is composed of around 3.7 × 1013 human cells [12]. In addition to these cells, the healthy human body also comprises a plethora of microbes including bacteria, viruses and fungi which are collectively known as the microbiome. Revised estimates suggest that these organisms amount to at least 3.8 × 1013 cells, accounting for approximately half of the total number of cells present in the body, and are intrinsically involved in the regulation and maintenance of human health [13,14]. However, although these organisms can be found in multiple tissues throughout the human body, such as in the skin, oral mucosa, and gastrointestinal tract, it is this last one, particularly the colon, that hosts the highest number of bacteria, exceeding all other organs by two orders of magnitude [13]. It is well established that the gut microbiome plays an integral role in a number of physiologic functions that include the metabolism and uptake of nutrients, the preservation of the intestinal barrier, and modulation of the immune system [14]. Indeed, it is now known that there is a complex interplay between the gut microbiota and the immune system of the host that impacts both local immunity and peripheral white blood cell dynamics [15–22].

It has been hypothesized that intestinal microbes confer many metabolic capabilities needed for the preservation of the host's immune homeostasis and that alterations of the gut microbiome composition (dysbiosis) could lead to immune alterations contributing to the development of a number of systemic disorders [23,24] Notably, numerous studies have shown its association with a number of inflammatory and autoimmune conditions such as inflammatory bowel disease and lupus nephritis, while a number of persuasive interventional studies have further demonstrated that microbiome modulating strategies, such as fecal microbiota transplantation (FMT), can induce remission of some of these conditions and modulate treatment response [25–29].

#### **3. Gut Microbiome and Cancer**

It is therefore not surprising that given the successes in establishing associations between the gut microbiome and several diseases, subsequent studies have sought to determine its influence in the context of cancer. Interestingly, and despite the increased interest in examining the role of the microbiome in cancer seen in recent years, there are historical reports dating back to 1868 suggesting a link between the presence of certain microbes and oncogenesis [11,30,31]. Among the microbes reported to have a role in carcinogenesis are viruses such as the Epstein–Barr, human papilloma, and hepatitis viruses and bacteria such as *Helicobacter pylori* [32,33]. Nevertheless, the path to characterization of other microbiome–cancer associations has been largely truncated by technical challenges of the time. Encouragingly, the advent of new laboratory techniques and technologies such as next-generation genomic sequencing is helping us to deepen our understanding of the contribution of bacteria present in the gut to the development of cancer and their influence in response to anti-cancer systemic therapies and their associated toxicities [34].

It is through the incorporation of these new technologies that pivotal investigations have been able to show the presence of distinct microbial profiles in the gut of cancer patients compared with their cancer-free counterparts [34,35]. Moreover, the preponderance of preclinical and clinical evidence now suggests that gut dysbiosis plays key role in the natural history of a number of malignancies including colorectal cancer, hepatocellular carcinoma, melanoma and breast cancer [36–41]. Furthermore, the influence of the gut microbiome has been investigated in the setting of different systemic therapy approaches, such as chemotherapy, stem cell transplantation and immunotherapy, where it has been shown to modulate toxicity and treatment response [31,42–46]. Particularly, significant efforts have been dedicated to investigating the association between the gut microbiome and immune-related adverse events (irAEs). Evidence now suggests that differences in gut microbiome profiles exist between patients who experience irAEs and those who do not [47–49]. This finding could potentially be used to develop biomarkers to predict their occurrence prior to initiation of therapy, as well as devising interventions to abrogate these events once they ensue [49].

Notably, associations between certain bacterial species and response to immune checkpoint blockade (anti-CTLA-4 and anti-PD-1) have also been demonstrated across different cancer types, suggesting the presence of "responder" and "non-responder" gut microbiome profiles [50–53] Indeed, there have been several efforts to recapitulate these favorable profiles through interventions such as FMT or bacterial supplementation that have shown some success in enhancing therapeutic response and overcoming resistance [50,54–57]. Likewise, dietary changes such as a higher fiber intake have also been associated with an increased benefit from ICIs in preclinical and clinical models [58]. All of the compounding evidence has resulted in the inclusion of "polymorphic microbes" as a new emerging hallmark of cancer [59,60]. However, despite these encouraging data, the cellular and molecular underpinnings that critically regulate these interactions are yet to be completely elucidated.

Although not fully understood, it is thought that the gut microbiome influences host immunity and carcinogenesis through positive and negative interaction with other recognized hallmarks of cancer [59]. This is mediated by a number of mechanisms including (1) direct DNA damage and the disruption of systems that aim to maintain genomic integrity, (2) production of ligand mimetics that stimulate epithelial proliferation, (3) secretion of gut hormones, (4) elicitation of immune responses through cross-reactive microbial and tumor-associated antigens and (5) shifts in the gut ecosystem causing changes in the levels of microbial metabolites [34,61–66]. Whereas it is certainly challenging to ascertain which of these factors has the biggest influence in the context of cancer, there is an increasing body of evidence suggesting that microbial metabolites and secondary metabolites not only play a key role in the onset and development of numerous malignancies, but could also be drivers of response of systemic treatment, namely immunotherapy [48,50,51,54,67]. One such group of metabolites are short-chain fatty acids (SCFAs), such as butyrate and propionate, which originate from the bacterial fermentation of non-digestible carbohydrates, and have been implicated in the reduction of inflammation and regulation of CD4+ and CD8+ T cells [66,68–73]. Moreover, butyrate has also been shown to have a role in tumor suppression through the up- and down-regulation of genes involved in carcinogenesis [66,74,75]. Indeed, this SCFA seems to induce a pro-apoptotic effect through the increased expression of genes such as *Bax* and *Bak*, and has been proposed to have an additional tumor suppressing effect by regulating the *Wnt/*β-catenin signaling pathway and by reducing the expression of anti-apoptotic genes such as *Bcl-2* [73,74,76–78].

#### **4. Gut Microbiome and Renal Cell Carcinoma**

The treatment of metastatic renal cell carcinoma has changed dramatically over the past decades with the introduction of targeted treatment strategies with tyrosine kinase inhibitors such as sunitinib, pazopanib and cabozantinib and more recently with the approval of ICIs that target inhibitory molecules such as PD-1, PD-L1 and CTDLA-4 [8]. The use of this latter treatment modality, either alone or in combination with TT, has further improved the outcome of patients with mRCC and is currently the standard of care for first-line treatment of this disease. However, unlike other malignancies such as non-small cell lung cancer and melanoma, where the use of ICIs can be guided by PD-L1 tumor expression or tumor mutational burden, there are currently no validated biomarkers to predict response in patients with mRCC receiving ICIs [79–82]. Moreover, despite the improvements in efficacy seen with current treatment approaches, up to 60% of patients

receiving these regimens fail to respond [83]. Hence, there is increasing need for both biomarkers of response that will allow us to identify the group of patients that will benefit the most from these treatments, and interventions that can allow us to maximize the benefit conferred by these approaches.

Given this context, as well as the large body of evidence linking the gut microbiome with the host's immune system and treatment response to ICIs in other malignancies, the role of the gut microbiome in mRCC and its potential as a biomarker of response and an intervention to improve treatment effectiveness are also being studied. Initial observations from several studies, the majority of which were retrospective in nature, have sought to indirectly determine the impact of gut dysbiosis in treatment response to ICIs by assessing for changes in the context of antibiotic treatment. Overall, the resulting evidence indicates that treatment with antibiotics is associated with decreased overall survival (OS), progression-free survival (PFS) and objective response rate (ORR) in patients with mRCC treated with standard-of-care ICIs [84]. Moreover, a study by De Rosa and colleagues further suggested that antibiotic treatment was associated with an alteration in the composition of the intestinal microbiota and the taxonomic beta diversity. Namely, this study noted an over-representation of bacteria, such as *Erysipelotrichaceae bacterium* and *Clostridium hathewayi*, suggesting that akin to the observations made for other cancer types, gut dysbiosis could also affect treatment response in RCC [85].

Additional studies further extended this line of inquiry and aimed to delineate this effect by assessing the impact of baseline gut microbiome profiles in patients receiving ICIs. This was performed by collecting stool specimens prior to the initiation of treatment and looking for the relative abundance of different bacteria using whole genome sequencing (WGS). These studies found that an increase in microbial diversity, as well as in relative abundance of certain bacterial species such as *Akkermansia muciniphila* and *Bifidobacterium spp.*, was associated with response to ICIs [50,85,86]. In contrast, data published by Park and colleagues who evaluated a cohort of NSCLC and RCC patients showed that a lack of treatment response was associated with an over-representation of the *Enterocloster* genus [85]. Further work presented by Alves during the 2022 ESMO symposium supported these findings, noting that not only was the baseline overrepresentation of the *Enterocloster* genus linked with a lack of treatment response but that those patients who do respond to ICIs exhibited a decrease in the *Enterocloster* genus representation after treatment [87].

Preclinical models have been in turn devised to evaluate the impact of gut microbiome interventions in treatment response and have shown that the direct administration of bacterial species associated with response in previous studies, such as *Bifidobacterium* and *Akkermancia muciniphila*, could delay tumor progression and restore treatment efficacy in mice treated with an immune checkpoint blockade [50,88]. Interestingly, it has also been shown that bacterial supplementation with *Clostridium butyricum MIYAIRI 588 (CBM 588)*, a probiotic bacterium, could lead to an increase in relative abundance of previously identified "beneficial bacteria" such as *Bifidobacterium* and *Lactobacillus* in mice, while also enhancing the intestinal barrier function [89].

Current studies in humans have intended to harness this effect to achieve an increased response to treatment and a reduction in treatment-related side effects using several strategies including (1) bacterial supplementation, (2) fecal microbiota transplantation and (3) diet modulation.

The first randomized clinical trial in this space was conducted by Dizman and colleagues. In the study, twenty patients with mRCC who were initiating VEGF-TKIs in any line of therapy were randomized to a probiotic-supplemented arm receiving a Bifidobacterium-containing yogurt, or a probiotic-restricted arm. Notably, all patients enrolled to the intervention arm reached detectable levels of *Bifidobacterium animalis*. Although no difference in clinical benefit was seen between these arms, whole metagenome sequencing identified that *Barnesiella intestinihominis* and *Akkermansia muciniphila* were significantly more abundant in patients achieving clinical benefit [90].

Another study was then carried out by the same group evaluating the effect of live bacterial supplementation with CBM588 in treatment naïve mRCC patients receiving ipilimumab with nivolumab for first-line treatment [91]. A total of 30 patients were randomized in a 2:1 fashion to the probiotic-containing and probiotic restricted arms, respectively. Despite the robust preclinical and clinical rationale behind its' primary endpoint of characterizing the effect of CBM588 on the relative abundance of *Bifidobacterium* spp., this endpoint was not met [54,91]. However, a significant advantage in PFS was seen in those receiving live bacterial supplementation over those receiving ipilimumab with nivolumab alone (12.7 vs. 2.5 months, hazard ratio 0.15, 95% CI 0.05–0.47, *p* < 0.001). Additionally, a comparable safety profile was seen among the two groups, with grade 3 and 4 adverse events being reported in 50% and 52% of patients in the control and intervention arms, respectively.

Despite this encouraging PFS signal, a remaining question is whether the effects of CBM588 will also be relevant in the context of newer combination strategies combining ICIs and TKIs. This is especially true in light of our growing understanding of the effect of TKIs in immune responses with several pieces of evidence suggesting that common TKI-driven effects such as VEGF blockage or more specific activity such as inhibition of MET and the TAM kinases, as seen with cabozantinib, could play an immunomodulatory role [92–95]. To answer this question, and given the encouraging safety profile seen in the aforementioned trial, a currently ongoing study will evaluate the effect of CBM588 in treatment-naïve patients receiving treatment with a combination of cabozantinib plus nivolumab as first-line therapy for mRCC [96] (Figure 1).

**Figure 1.** Study design for the phase I cabozantinib plus nivolumab +/− CBM588 trial [97].

FMT represents another microbiome-directed intervention with increasing momentum in the treatment of mRCC. Although there are still limited published data regarding the effect of this approach in this disease, current evidence suggests that FMT could improve mucosa-associated invariant T (MAIT) cell function in this patient population and boost immune surveillance against opportunistic pathogens that might be of relevance in the setting of cancer-mediated immunosuppression [98]. Furthermore, this intervention is also being evaluated as a way to reduce treatment-related toxicity. In a study conducted by Ianiro et al., FMT was employed to reduce TKI-induced diarrhea in mRCC patients. In his study, patients treated with donor-FMT showed a significant clinical improvement in TKI-induced diarrhea symptoms when compared to those receiving placebo [99]. Another interesting study is the currently ongoing PERFORM trial, one that will evaluate the prevention of treatment toxicity with immunotherapy utilizing this approach [100].

Beyond this, FMT is being evaluated as a tool to improve and induce response to ICIs in the TACITO and MITRIC trials, respectively. The TACITO trial is a randomized control trial of 50 mRCC patients to receive FMT or placebo and will evaluate the number of participants free of tumor progression [101]. In contrast, the MITRIC trial will enroll patients with solid tumors (including RCC) that have failed to respond to treatment. This is a single-arm, open-label study that will enroll 20 patients who will receive FMT from ICI-responders after experiencing progressive disease while on therapy with PD1/PD-L1 blockers and/or CTLA4-blockers [102]. The rationale behind these trials derives from pre-clinical evidence showing that FMT from patients responding to ICIs can successfully rescue primary resistance in RCC tumor-bearing mice [85]. Moreover, similar concepts have already been successfully implemented in cohorts of immunotherapy-refractory patients with melanoma [56,57].

Finally, dietary interventions are also underway in the KETOREIN trial. This is a non-randomized four-arm design that aims to evaluate a ketogenic diet used concomitantly with nivolumab plus ipilimumab in mRCC patients. This trial will evaluate objective response rate as its primary outcome and will enroll a total of 60 patients to one of four arms detailed in Figure 2 [103]. Results from this trial will build upon previously published pre-clinical data from Ferrere et al. suggesting that a ketogenic diet shifts the balance of the gut microbiota from tolerogenic to immunogenic bacteria (e.g., *Akkermancia muciniphila*) and induces an antineoplastic effect mediated by 3-hydroxybutyrate [104].

**Figure 2.** Study design for the KETOREIN trial.

#### **5. Challenges and Opportunities**

Historically, challenges related to the characterization of the microbiome were mostly attributable to technical limitations, especially considering that not all regular bacterial species are amenable to culture processes, and that cultivating viruses and fungi can be even more challenging. Moreover, body environments other than the gut are less colonized and have yielded disappointing results. It was only more recently, with the advent of advanced molecular techniques such as DNA sequencing and fluorescence insitu hybridization of stool, blood and saliva samples, as well as intra-tumoral analysis, that a broader characterization of the human microbiome became independent from culture

methods [105,106]. The most utilized tool as a strategy to surpass the challenge of obtaining reliable and high-quality samples is sequencing the 16S rRNA gene, which is present only in prokaryotic cells, with the drawback of identifying only bacteria [106]. Notwithstanding, even when high-throughput sequencing technologies are increasingly available, up to 50% of functional diversity remains unknown, a fact that is further complicated when including non-reference populations [105].

Furthermore, although evolutionary advances in next-generation sequencing technology have ushered in a new understanding of the interplay between the gut microbiome, immunity and cancer, several challenges are notable and represent barriers for its incorporation in routine clinical practice. Among these challenges are the lack of uniformity across the methodologies used for microbiome analysis (e.g., stool collection kits, probiotic restriction in the control arms, etc.), an issue that could explain the modest overlap in gut-microbiome profiles associated with response across studies (Table 1). Hence, the development and validation of a reference framework would be a promising approach to be incorporated in microbiome research that could facilitate collaboration and the comparison of results.

**Table 1.** Studies evaluating gut microbiome composition and treatment response in mRCC patients.


Another important challenge is the limited sample size of most studies. Considering that microbiome profiling can be influenced by factors such as age, diet, socioeconomic status, geography and ethnicity, large sets of data are needed to identify and fully capture this heterogeneity [107]. Joint efforts analogous to The Cancer Genome Atlas (TCGA) could prove beneficial in better understanding the immune–microbiome interface. Initiated in 2006, TCGA consisted of a collaboration across multiple institutions and with the labor of a myriad of multidisciplinary specialists to collect and analyze data from over 20,000 samples across 33 different cancer types to elucidate genomic aspects of cancer. A similar approach would be an important step in microbiome research, with the collection of information from multiple centers, including academic and community sites, able to create a more robust database and provide the foundation for insights into different microbiome compositions. Ongoing population-wide initiatives such as The Human Microbiome Project (HMP) in the United States, the Metagenomes of the Human Intestinal Tract (MetaHIT) in Europe, and a diabetes cohort in China have already managed to survey around 2000 individuals [108–110].

The City of Hope is one medical group primed to help in this collaborative effort. With over 30 different locations across Southern California, this network is well positioned to conduct studies that collect samples representative of a broad population. Not only accounting for the diverse ethnic backgrounds present in the state of California, but also socioeconomic and cultural factors, can help broaden the resident microbiota. Additionally, the institution recently broadened its area of influence and cancer care beyond its original regional borders by acquiring the Cancer Treatment Centers of America group, which has a well-established presence in Georgia, Illinois and Arizona. This will hopefully allow for nation-wide studies that will provide a wider look at the composition of what constitutes a

normal microbiome and will help better determine the changes seen during treatment and survey differences across various patient groups.

Admittedly, this collaborative endeavor would require the contribution of experts in many areas of biomedical research. Physicians and patient care personnel would identify eligible candidates to provide samples. Basic science researchers would identify strains, elucidate molecular pathways, and understand the gut microbiome's modulatory effects. In turn, bioinformaticians and data scientists would play a role in identifying correlations and scrutinizing data. With recent studies relating the gut microbiome to cancer treatment response and toxicity, it is particularly important for basic scientists to use animal models to understand the mechanism behind these findings. Communication across all levels of the chain of care is required to streamline such an effort and translate findings to patient care and the clinical setting.

With broad patient samples, physicians from multiple sites, and basic science labs working together, we can broaden our understanding of the mechanisms driving microbiome modulatory effects and use this knowledge to provide more personalized treatment options for patients. For example, in a certain cancer population, if malnutrition or a poor microbiota diversity is identified, we might be able to correct the course of treatment and increase the odds of response and perhaps extend survival by administering live bacterial products, as early-phase data have suggested, with larger confirmatory trials underway [111].

#### **6. Conclusions and Future Directions**

In summary, the gut microbiome represents an area of emerging interest in oncology. Difficulties faced during initial efforts for the characterization of the vast array of microorganisms that reside in the human body have now been largely addressed by the introduction and use of next-generation sequencing technologies. It is now well accepted that the microorganisms living in the gut have an impact across many disease settings, including cancer, and studies have further implicated the gut microbiome as a potential biomarker for response in many cancer types including mRCC.

Furthermore, randomized clinical trials in the mRCC space have produced encouraging results supporting the use of microbiome-based interventions to increase the effectiveness of systemic therapy and reduce toxicity. Ongoing clinical trials are seeking to validate these findings in larger cohorts, as well as address other clinically relevant questions, including the effect of dietary interventions in treatment outcomes. However, much work remains to be done before microbiome-based interventions can have a tangible impact in routine clinical practice.

Namely, there is an unmet need for longitudinal microbiome data at the individual and population level that can provide insights into the heterogeneity of the gut microbiome across different patient populations. Hence, future research efforts should aim to include diverse patient populations, as well as carefully annotated correlatives including genomic, epigenomic and metabolomic data, all of which will help us elucidate the factors driving differences between patient cohorts. Admittedly, such projects will necessitate large intra- and inter-institutional collaborations which remain, to date, a largely unfulfilled opportunity.

**Author Contributions:** Conceptualization, L.M., M.F., K.L., R.S. and S.K.P.; Data Curation, L.M., M.F., K.L., R.S. and S.K.P.; Writing—Original Draft Preparation L.M., M.F., K.L., R.S. and S.K.P.; Writing—Review & Editing, L.M., M.F., K.L., R.S. and S.K.P.; Visualization, L.M., M.F., K.L., R.S. and S.K.P.; Supervision, S.K.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** No funding was received for the preparation of this manuscript.

**Conflicts of Interest:** Luis Meza, Matthew Feng, Kyle Lee, Rubens Sperandio and Sumanta K. Pal have no conflict of interest that might be relevant to the contents of this manuscript.

#### **References**


**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

### *Opinion* **Proposed Implementation of a Patient-Centered Self-Assessment Tool for Patients with Neuroendocrine Tumors among Academic and Community Practice Sites: The City of Hope Model**

**Christiana Joy Crook 1, Lisa Yen 2, Kathleen Ta 1, Misagh Karimi 3, Danny Nguyen 4, Richard T. Lee <sup>5</sup> and Daneng Li 1,\***


**Abstract:** Neuroendocrine tumors are a rare type of cancer found in hormone-producing cells throughout the body. Research on disease-specific patient education assessments in this population is lacking. We previously demonstrated the feasibility and validity of NET VITALS, a patient-centered self-assessment designed to improve patients' knowledge of their neuroendocrine tumor diagnosis/treatment and facilitate communication with their physician. In this report, we provide a brief overview of patient assessments that have been used for patients with neuroendocrine tumors. We summarize NET VITALS and present a proposed infrastructure for its implementation into standard clinical care in both academic and community practice settings at City of Hope. Incorporating NET VITALS into standard of care treatment for patients with neuroendocrine tumors may improve patients' overall clinical care experience.

**Keywords:** neuroendocrine tumors; self-assessment; NET VITALS; implementation science

### **1. Introduction**

Neuroendocrine tumors (NETs) are hormone-producing tumors that develop from endocrine cells throughout the body [1]. Although the incidence of NETs is rising, NETs are a rare diagnosis, with 8.3 cases per 100,000 individuals diagnosed in the United States in 2018 [2,3]. Given that NETs often present with nonspecific symptoms, such as diarrhea, bloating, abdominal cramping, and flushing [4,5], delays in diagnosis are common, with patients reporting a median of 9.2 years between the development of symptoms and final diagnosis [6]. As a result, patients often present with advanced or metastatic disease at diagnosis [7,8].

The relatively small number of patients with NETs may be a contributing factor to the lack of research regarding education and treatment experiences of this patient population. Patients with NETs often report poor clinical experiences, with many patients expressing frustration with the lack of information provided and poor communication with their treating physician [7–9]. Educational tools for patients with breast, prostate, and liver cancers have demonstrated improvements in patient understanding and satisfaction with the information received from their treating physician [10,11]. These positive results suggest that patients with NETs could also benefit from patient-centric educational tools.

**Citation:** Crook, C.J.; Yen, L.; Ta, K.; Karimi, M.; Nguyen, D.; Lee, R.T.; Li, D. Proposed Implementation of a Patient-Centered Self-Assessment Tool for Patients with Neuroendocrine Tumors among Academic and Community Practice Sites: The City of Hope Model. *J. Clin. Med.* **2023**, *12*, 1229. https:// doi.org/10.3390/jcm12031229

Academic Editor: Bruno Annibale

Received: 15 December 2022 Revised: 30 January 2023 Accepted: 1 February 2023 Published: 3 February 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

In this report, we provide a brief overview of patient-centered self-assessments, with an emphasis on tools specifically designed for patients with NETs. We describe NET VITALS, a patient-centered self-assessment tool created by NET patient advocates and physicians, and propose a strategy for its implementation at City of Hope. Our goal is to establish a robust clinical infrastructure for the implementation of NET VITALS that could improve the treatment experience of patients with NETs and contribute to an increase in patients' overall well-being.

#### **2. Patient-Centered Assessments**

Patient self-assessments have been developed for a variety of situations. Here, we describe disease-agnostic and disease-specific self-assessments that have been used among patients with NETs.

#### *2.1. Disease-Agnostic Patient Assessments: Quality of Life*

Many disease-agnostic patient self-assessments are designed to assess quality of life. A well-known example is the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire-Core 30 (QLQ-C30) [12]. This 30-item questionnaire has been used among patients with a variety of diagnoses [13–15]. Other examples of patient-centered quality of life tools include the Health Outcomes Tool, Attitude Scale, Now vs Later tool, Prognosis and Treatment Perceptions Questionnaire, and Short Form 36 health survey questionnaire [16–21]. These tools allow patients to clearly identify treatment goals and current health status and can provide physicians with clarity regarding patients' preferences, which can potentially improve patient–physician communication. Moreover, these tools have successfully been used in NET patient populations in the contexts of clinical trials and observational studies, emphasizing their utility [22–29]. However, patients' knowledge of their disease and treatment is not analyzed with these tools, leaving a gap in patient education. While these tools can increase patient awareness and potentially improve patient–physician communication, they lack disease-specific questions that may provide greater insight for both the patient and physician.

#### *2.2. NET-Specific Patient Assessments*

Although there are numerous examples of disease-specific patient educational tools and assessments [10,11,30], there are few examples of assessments designed specifically for patients with NETs. The International Neuroendocrine Cancer Alliance (INCA) conducted a global survey of patients with NETs in 2014 that investigated patients' knowledge of their disease and their perspective on disease burden and treatment experience [7,8,31]. While this anonymous survey provided the NET research community with additional details regarding patient perspectives on their NET disease burden, it did not allow for any followup with patients, nor did it provide patients with a way to improve communication with their physician. In 2017, INCA conducted another international survey of patients/families, healthcare providers, and patient advocates and reported that patients with NETs continue to struggle with a lack of information [32]. Only 30% of patients stated that they were provided with sufficient information from their healthcare provider at diagnosis. However, 59% of healthcare providers surveyed believed that they provided patients with sufficient information, highlighting a lack of communication between patients with NETs and their healthcare providers.

The EORTC created a quality of life questionnaire for patients with gastrointestinal NETs (EORTC QLQ-GINET21); this 21-item questionnaire has been validated in patients with liver, pancreatic, and other gastrointestinal NETs [33,34]. This questionnaire is often used in conjunction with the EORTC QLQ-C30 to obtain a more comprehensive picture of a NET patient's health-related quality of life [23,26,27,35]. While the EORTC QLQ-GINET21 has allowed patients with gastrointestinal NETs to provide their physicians with information about their current quality of life, some of the questions may not be relevant for patients with non-functional tumors, and patients with non-gastrointestinal NETs are

not included in the target patient population. Spolverato et al. designed a quality of life questionnaire specifically for patients with NET liver metastases that incorporated elements from the EORTC QLQ-C30, EORTC QLQ-GINET21, and Norfolk Quality of Life tool for NETs [36,37]. While this questionnaire is useful for this patient population, it does not assess patient knowledge or perception of information received.

To address the issue of patient satisfaction with information received, Bouma et al. developed a web-based information system designed to improve patient satisfaction with the amount and quality of information they were able to access about their diagnosis [38]. Although an initial feasibility study suggested that patients experienced an improvement in quality of life and were satisfied with the application, a randomized controlled trial comparing the web-based application to standard of care treatment found no difference in perception of information received or satisfaction with information received [39]. However, a 26-week multidisciplinary educational intervention for patients recently diagnosed with NETs reported improvements in patients' general self-efficacy and health-related quality of life [28], suggesting that educational interventions in this patient population require further optimization to maximize their benefit to patients.

The NET Cancer Health Storylines mobile application was developed to allow patients with NETs to track the frequency and severity of their symptoms and monitor additional health outcomes such as nutritional concerns, medications, and sleep [40]. Adams et al. investigated the health-related quality of life of patients using the application who were receiving somatostatin analog treatment; they observed a decrease in reported physical symptoms on the EORTC QLQ-C30 and EORTC QLQ-GINET21 over time, suggesting that the act of tracking symptoms may improve patients' perception of changes in symptoms [41].

In terms of treatment planning, Wagner et al. designed a multicriteria decision analysis framework for NET patients and physicians to use together when deciding on treatment plans [42]. Although this framework is limited in its treatment options, it provides an example of cooperative decision making, which has the potential to provide NET patients with increased autonomy and feelings of improved communication with their physician.

In summary, there is a lack of NET-specific tools that have the goal of increasing patients' self-knowledge of their diagnosis and treatment journey.

#### **3. NET VITALS**

In light of complaints from NET patients that they did not have enough information about their diagnosis and treatment from their physician [7–9], we decided to create a tool to allow patients to address these issues. Patient advocates from the Learn Advocate Connect Neuroendocrine Tumor Society (LACNETS) collaborated with NET physicians from City of Hope to create NET VITALS, a patient-centered self-assessment tool [43,44]. NET VITALS comprises six sections: diagnosis information, pathology/functional status/symptoms, imaging and diagnostic information, laboratory test results, surgery and treatments received, and additional information (genetic testing information, level of social support) (Figure 1 and Figure S1) [43]. The goal of NET VITALS is to give patients a sense of autonomy and control as they navigate their NET diagnosis and treatment odyssey.

In 2019, we introduced NET VITALS to patients attending the Los Angeles NET Education Conference [43]. Patients were invited to complete NET VITALS after attending a seminar that explained how to fill it out. The feasibility of NET VITALS was demonstrated, with an 88.3% response rate (68 out of 77 patients) and a median of 85.7% of items completed. NET patients were satisfied with NET VITALS as a potential tool to use with their physicians, with 74.6% of patients agreeing that NET VITALS was a useful communication tool and 76.3% of patients indicating that they would recommend NET VITALS to someone else. In terms of disease and treatment knowledge, NET VITALS highlighted areas where NET patients may not have as much knowledge about their diagnosis or treatment, including tumor Ki-67 index, grade, functional status, differentiation status, and receipt of liver-directed therapy. These gaps in knowledge suggest that NET VITALS could be

used to spur communication between patients and physicians to increase NET patients' knowledge of their disease.

**Figure 1.** Sections of NET VITALS.

Given the feasibility and high level of patient satisfaction with NET VITALS in our preliminary cross-sectional survey study, we present a suggested infrastructure for implementing NET VITALS in the clinic.

#### **4. Implementation of NET VITALS: The City of Hope Model**

NET patient advocates, physicians experienced in treating patients with NETs across the City of Hope enterprise, and healthcare providers with experience integrating patient assessments into clinical care were approached to determine the best way to implement NET VITALS into clinical practice. A proposed model for NET VITALS integration is shown in Figure 2.

**Figure 2.** Proposed implementation of NET VITALS.

In this model, NET VITALS will be built into the list of intake tasks for patients with NETs seeking consultation at City of Hope. Intake coordinators will direct patients to the LACNETS website to download and complete NET VITALS. Once completed, the patient will send a copy of NET VITALS to the intake coordinator to scan and upload into the patient's medical record for physician review prior to the consultation appointment.

For patients unable to access the LACNETS website, the intake coordinator will mail a paper copy of NET VITALS to the patient for completion prior to their appointment. If completion of NET VITALS prior to the consultation appointment is not feasible, patients will be given a paper copy of NET VITALS upon check-in on the day of their appointment. Paper copies of NET VITALS will be scanned and uploaded into the patient's medical record on the day of their initial consultation at City of Hope.

#### **5. Opportunities and Challenges of NET VITALS Integration within the City of Hope Enterprise**

The implementation model for NET VITALS will be piloted initially on the City of Hope main Duarte campus and select community practice sites throughout southern California. NET VITALS will also continue to be promoted through the LACNETS website and outreach platform. Once the feasibility of this model has been demonstrated, expansion to all City of Hope locations across the enterprise will be performed.

Strengths of the proposed infrastructure include the strong relationships between the academic and community centers of City of Hope. With a primary center in Duarte, the City of Hope Orange County Lennar Foundation Cancer Center in Irvine, over two dozen community practice sites across southern California, and three Cancer Treatment Centers of America locations in Phoenix, Chicago, and Atlanta, the potential for collaboration is enormous. The connectivity of the main campus at Duarte and various community practice locations has been previously demonstrated [45]. This interconnectivity allows physicians from satellite clinics that may not see many patients with NETs on a routine basis to have access to specialists at other locations to better understand their patient's diagnosis and develop an optimal treatment plan. City of Hope's new relationship with Cancer Treatment Centers of America furthers this connectivity, allowing patients at all locations to have the opportunity to potentially benefit from NET VITALS.

A potential challenge of this implementation strategy is its reliance on electronic medical records. To complete NET VITALS before their appointment, patients are expected to have access to their online patient portal and the LACNETS website (which also implies Internet access). We have suggested alternative pathways to complete NET VITALS that are not dependent on Internet access to ensure that all patients have an opportunity to complete this assessment. Additionally, City of Hope is in the process of promoting access to the online patient portal, which includes converting Cancer Treatment Centers of America to the electronic medical record system used by City of Hope to ensure uniform access across the enterprise. The integration of NET VITALS into the electronic medical record could allow care teams to easily compare patients' responses to pre-existing data and track patients' care over time.

Another perceived limitation may be the inability of patients to complete all sections of NET VITALS before their appointment. However, patients are not expected to be familiar with everything covered in NET VITALS [44]. Identifying areas where knowledge is lacking allows patients to have a more guided discussion with their physician during their appointment, potentially improving overall patient–physician communication.

#### **6. Conclusions**

Patient-centered self-assessments, such as NET VITALS, may help increase patients' knowledge about their NET diagnosis/treatment and promote dialogue with their physician and healthcare providers. Identifying and implementing a strategy for the incorporation of NET VITALS into clinical care can be significant to care teams that strive to provide the best personalized care possible for patients with NETs.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/jcm12031229/s1, Figure S1: NET VITALS.

**Author Contributions:** Conceptualization, C.J.C., L.Y., K.T., R.T.L. and D.L.; investigation, C.J.C., L.Y., K.T., M.K., D.N., R.T.L. and D.L.; writing—original draft preparation, C.J.C., K.T. and D.L.; writing—review and editing, C.J.C., L.Y., K.T., M.K., D.N., R.T.L. and D.L.; supervision, D.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors would like to acknowledge Giovanna Joyce Imbesi for her role in the development of NET VITALS and the patients and families who participated in the 2019 feasibility/satisfaction study of NET VITALS.

**Conflicts of Interest:** Daneng Li reports research funding to his institution from AstraZeneca and Brooklyn ImmunoTherapeutics. He serves as a consultant and has received honoraria from Adagene, Advanced Accelerator Applications, Coherus, Delcath, Eisai, Exelixis, Genentech, Ipsen Biopharmaceuticals, Lexicon, Merck, MiNA Therapeutics, QED, Servier, Sun Pharma, and TerSera Therapeutics, all outside the submitted work. Lisa Yen serves as a consultant and has received honoraria from Ipsen Biopharmaceuticals and Sun Pharma. The remaining authors declare no conflict of interest.

#### **References**


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### *Perspective* **A Systems Biology Approach for Addressing Cisplatin Resistance in Non-Small Cell Lung Cancer**

**Sravani Ramisetty 1, Prakash Kulkarni 1,2, Supriyo Bhattacharya 3, Arin Nam 4, Sharad S. Singhal 1, Linlin Guo 1, Tamara Mirzapoiazova 1, Bolot Mambetsariev 1, Sandeep Mittan 5, Jyoti Malhotra 6, Evan Pisick 7, Shanmuga Subbiah 8, Swapnil Rajurkar 9, Erminia Massarelli 1, Ravi Salgia <sup>1</sup> and Atish Mohanty 1,\***


**Abstract:** Translational research in medicine, defined as the transfer of knowledge and discovery from the basic sciences to the clinic, is typically achieved through interactions between members across scientific disciplines to overcome the traditional silos within the community. Thus, translational medicine underscores 'Team Medicine', the partnership between basic science researchers and clinicians focused on addressing a specific goal in medicine. Here, we highlight this concept from a City of Hope perspective. Using cisplatin resistance in non-small cell lung cancer (NSCLC) as a paradigm, we describe how basic research scientists, clinical research scientists, and medical oncologists, in true 'Team Science' spirit, addressed cisplatin resistance in NSCLC and identified a previously approved compound that is able to alleviate cisplatin resistance in NSCLC. Furthermore, we discuss how a 'Team Medicine' approach can help to elucidate the mechanisms of innate and acquired resistance in NSCLC and develop alternative strategies to overcome drug resistance.

**Keywords:** drug resistance; cisplatin; non-small cell lung cancer; group behavior; IDPs; phenotype switching; mathematical modeling

### **1. Introduction**

Cancer is one of the major contributors to global mortality. According to Cancer Statistics, which is published every year [1], 1.9 million new cancer cases and 609,360 deaths from cancer are estimated in 2022 in the US alone, which is about 1670 deaths a day. Among all the cancer types, lung cancer (both small and non-small cell) is the most prevalent, and it is estimated that a total of 236,740 people will be diagnosed with lung cancer in 2022, which is 1 in 16 people in the US alone [2–5]. Lung cancer is generally a disease of middle-aged and elderly smokers, usually with several comorbid smoking-related conditions, such as emphysema, chronic bronchitis, widespread atherosclerosis, and degenerative disorders of the central nervous system (CNS) and other organs [6–8]. Despite significant developments in preventing, screening, and treating lung cancer over the past decade, innate and acquired

**Citation:** Ramisetty, S.; Kulkarni, P.; Bhattacharya, S.; Nam, A.; Singhal, S.S.; Guo, L.; Mirzapoiazova, T.; Mambetsariev, B.; Mittan, S.; Malhotra, J.; et al. A Systems Biology Approach for Addressing Cisplatin Resistance in Non-Small Cell Lung Cancer. *J. Clin. Med.* **2023**, *12*, 599. https://doi.org/10.3390/jcm 12020599

Academic Editor: Paola Concolino

Received: 6 December 2022 Revised: 6 January 2023 Accepted: 10 January 2023 Published: 11 January 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

resistance to chemotherapeutic agents and radiation remains a vexing problem, and success in increasing the life expectancy of patients is limited.

A vast majority (~85%) of lung cancer patients have a group of histological subtypes collectively known as non-small cell lung cancer (NSCLC). Among the various subtypes, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LSCC) are the most common subtypes [9]. LUAD is mostly driven by driver oncogenes, such as EGFR, KRAS (G12C), MET, ALK, etc., against which targeted therapies are available. On the other hand, no known targetable driver oncogenes have been identified for LSCC; thus, the therapeutic options for LSSC patients are limited.

NSCLC patients are offered a broad range of genotoxic drugs, such as cisplatin or carboplatin alone or in combination with immunotherapy [10], and often respond to treatment initially. However, most patients develop drug resistance, and numerous mechanisms underlying drug resistance have been identified, mostly in preclinical models of the disease [11–13]. Unfortunately, many of these mechanisms do not always hold in vivo and, very often, are not effective in the clinic even if they appear promising in the in vivo models. Therefore, a collaborative effort integrating the preclinical studies with clinical outcomes could help to better understand the mechanism of drug resistance.

Here, we summarize the concept of 'Integrating Clinical and Translational Research Networks—Building Team Medicine' from a City of Hope perspective (Figure 1). Using cisplatin resistance in NSCLC as a paradigm, we describe how basic research scientists with expertise in fields as varied as cancer biology, cell and molecular biology, biochemistry, biophysics, structural biology, and mathematical and computational biology; clinical research scientists; and medical oncologists working together with a true 'Team Medicine' spirit, uncovered a non-genetic mechanism underlying cisplatin resistance in NSCLC and identified carfilzomib (CFZ), a previously approved proteasome inhibitor, to alleviate resistance. The team was led by the Department Chair, a thoracic oncologist who helped coordinate the team's efforts, much like a tumor board that comprises clinical specialists, nurses, and care providers does in a hospital setting. Furthermore, we discuss how this 'Team Medicine' approach also helped explore novel treatment strategies that could potentially preclude, attenuate, or at least delay, the onset of cisplatin resistance in these patients.

**Figure 1.** Schematic representing the 'Team Medicine' approach.

#### **2. Mechanisms of Drug Resistance**

Drug resistance is the major obstacle to long-term patient survival [14]. Cancer cells can escape therapy and exhibit drug resistance by different routes and many of these routes remain unpredictable and difficult to characterize [15]. A better understanding of the molecular mechanisms that help in tumor progression and drug resistance is essential in designing cancer subtype specific treatments. Drug resistance is defined as the inheritable ability of the cells to survive clinically relevant drug concentrations [16]. Drug resistance can either pre-exist before the start of the treatment, referred to as innate resistance, or develop in response to the treatment and is referred to as acquired resistance [15–19]. Innate drug resistance is typically thought to involve genetic mutations, while acquired resistance is generally believed to be due to both genetic and non-genetic/epigenetic changes. In either case, resistance to therapy is associated with metastatic disease and poor survival rates in patients [16,20–22]. Regardless, however, numerous mechanisms [23,24] that promote drug resistance have been reported in the literature, adding to the resistance conundrum (Figure 2).

**Figure 2.** Schematic representation of acquired resistance. Acquired resistance can arise through the Darwinian section or Lamarckian induction. Path of drug resistance where a tumor cell can become resistant purely due to genetic changes through non-genetic alteration in a particular genotype or through initial non-genetic changes combined with genetic mutations.

Cancer drug resistance is multi-factorial and not solely driven by genetic mechanisms [25]. In fact, an increasing body of evidence shows that non-genetic mechanisms, such as lineage plasticity [26] (change in cell identity), epigenetic factors that regulate gene expression, and phenotype plasticity, contribute to cancer drug resistance [27]. Cancer cells escape the drug assault by two phenomena; 'tolerance', which is the ability of the cell to survive transient exposure to high drug concentration, and 'persistence', which is the ability of a subpopulation of a clonal population to survive exposure to high concentrations of a drug [27]. Drug-tolerant persisters (DTP) remain major factors in cancer relapse and in developing drug resistance [28]. Persistence is observed in low frequency in tumor cells with reduced proliferation rate and metabolism that helps them in tolerating drug insult. The genetic makeup of the DTPs is indistinguishable from the bulk tumor population and the resistance exhibited by them reverts to the sensitive state upon drug removal [29–34]. However, Shaffer et al. [35] showed that persisters cells exhibit significant variability at the single-cell level. Furthermore, these variabilities eventually decide the fate of the cell regarding whether to irreversibly become resistant to drug treatment.

Two phenomena that determine whether a given cancer cell population will undergo non-genetic evolution of drug resistance are epigenetic heterogeneity and epigenetic plasticity [27,36–38]. Epigenetic heterogeneity refers to the overall variability in the epigenetic landscape across a given cell population, which is influenced by both cell-intrinsic and cell-extrinsic stimuli. Epigenetic plasticity, on the other hand, is the capacity of a cell to alter its epigenetic state in response to either internal or external stimuli [27,36–38]. It is crucial to understand that both epigenetic heterogeneity and epigenetic plasticity are not completely independent variables; for example, various cancer cell types exhibit heterogeneity because the epigenetic state of the population is more plastic.

A stable mechanism of non-genetic resistance can result in the pre-existence of resistant clones in the subpopulation, in which case drug resistance simply emerges through Darwinian selection and is completely dependent on epigenetic heterogeneity. Alternatively, the stable origin of a non-genetic resistance can also be a result of gradual Darwinian or Lamarckian induction (Figure 2).

Recent evidence indicates that the genetic and non-genetic mechanisms of drug resistance are not mutually exclusive but indeed co-exist (meaning that both evolutionary phenomena of Darwinian selection and Lamarckian induction may be active) within a given cancer type and drive the drug resistance that eventually led to therapy failure. The genetic/nongenetic duality as described in the review is believed to be a major contributor to the complexity of drug resistance [39]. Designing drugs that target only the genetic mutations is like playing a whack-a-mole game where the player has zero chance of winning, even after multiple attempts. Thus, it is important to gain a deeper understanding of the relative contributions of genetic and non-genetic mechanisms especially, by understanding how, why, and when these non-genetic alterations occur so that one can hit the desired target consistently.

In addition to genetic mutations/epigenetic changes, protein interaction networks (PIN) also contribute to drug resistance [40–42]. PIN dynamics are orchestrated by the hub proteins, which are typically intrinsically disordered proteins (IDPs). IDPs lack 3D structure but exist as conformational ensembles. Indeed, ~80% of cancer-associated proteins, for example, p53, cyclins, MYC, SOX2, paxillin, etc., are IDPs [43]. This article focuses on acquired resistance to cisplatin resistance in NSCLC.

#### **3. Cisplatin Resistance in NSCLC**

Cisplatin is one of the platinum-based frontline chemotherapeutic agents used to treat solid tumors in a wide spectrum of cancers, including lung, ovarian, colorectal, head and neck, and testicular [44–46]. Cisplatin delivers its attack by entering cancer cells and binding to DNA, thus forming DNA adducts. These adducts block transcription and DNA synthesis, which activates the intracellular signal transduction that helps to eliminate the tumor lesions [43]. Patients usually have a good initial response to cisplatin-based chemotherapy but relapse later, because the development of acquired or innate resistance markedly reduces its clinical effectiveness [46–50]. Various molecular mechanisms, such as altered DNA repair and the cellular accumulation of the drug, as well as the cytoplasmic inactivation of the drug, are a few of many pathways through which patients usually develop resistance to cisplatin. The goal of personalized medicine is to develop better responses to the drug in the clinic. Here in this review, we will comprehensively discuss the non-genetic mechanisms of cisplatin resistance in NSCLC [51,52]. Cellular resistance to cisplatin may conceivably be based upon the overexpression or inactivation of certain oncogenes both in genetic and epigenetic pathways [47,49,53]. One such epigenetic mechanism involves focal adhesion complex (FA) and the components that contribute to cisplatin-resistance in NSCLC.

#### *3.1. Focal Adhesion and Cisplatin Resistance*

Focal adhesions (FAs) are contact points for a cell that interact with the extracellular matrix (ECM) and regulate diverse cellular processes, such as apoptosis, proliferation, migration, and differentiation. The principal components of FAs are integrin, paxillin, focal adhesion kinase (FAK), SRC (Src Oncogene), talin, tensin, vinculin, and actin [54]. Integrins function as transmembrane receptors for extracellular ligands and transduce biochemical signals into the cell. Integrins, when bound to ligands, are shown to be involved in a variety of signaling pathways, such as the cell cycle; the organization of the intracellular cytoskeleton; and in mediating the translocation of new receptors to the cell membranes with an α and a β subunit [55,56]. In mammals, there are 24α and 9β integrins among which integrin β4 (ITGB4) is believed to be unique due to its >1000 amino acid cytoplasmic domain when compared to other β-forms that typically have cytoplasmic domains of ~59 amino acids [57]. Furthermore, the unique property of ITGB4 is that it heterodimerizes with ITGα6 as well as ITGα7 [52,58]. However, ITGβ4 and its role in cisplatin resistance remained poorly understood until recently.

Another important component of the FA complex is paxillin (PXN). Human PXN is a 68-kDa (591 amino acids) protein [59]. The LUAD upregulation of PXN is associated with tumor progression and metastasis [60,61]. The phosphorylation of PXN leads to the activation of the downstream pathways of MAPF/ERK, resulting in cisplatin resistance [50]. PXN contains an N-terminus proline-rich region that anchors SH3-containing proteins along with five leucine-rich residues (LD domains 1–5) with a consensus sequence of LD*X*LLXXL [62,63]. The LD2-LD4 region includes sequences for the recruitment of signaling and structural molecules, such as FAK, vinculin, and Crk [62,64,65]. This region has also been reported to interact with integrin; more specifically, integrin α4 (ITGA4). Interestingly, PXN is an IDP [66]. The C-terminal region of PXN is believed to be involved in anchoring PXN to the plasma membrane and targeting to FA complex. The C-terminal of the FA complex harbor Cysteine-Histidine-enriched Lin11/Isl1/Mec3 (LIM) domains that form zinc fingers, suggesting that PXN could bind DNA and act as a transcription factor [67]. In addition to LD domains, LIM domains contain the SH3 domain and SH2 domain that forms a docking site for many tyrosine and threonine kinases and recruit additional enzymes into the complex, eventually leading to the activation of canonical signaling through the Ras-mitogen-activated protein kinase (MAPK), phosphoinositide-3-kinase (PI3K)-Akt, and phospholipase C-gamma (PLC-γ) pathways.

Our recent work showed that NSCLC tumor tissue has the heterogenous expression of PXN/ITGB4, and patients with the increased expression of both these genes have poor overall survival [50]. Furthermore, the cell lines that were identified to be cisplatin-resistant were also observed to have elevated levels of ITGB4/PXN. The knocking down of ITGB4 and PXN attenuated cell growth and enhanced apoptosis in 2D and 3D cultures. Interestingly, the double knockdown affected the expression of several genes, including USP1/VDAC1. Chromatin immunoprecipitation revealed a reduced binding of acetylated H3K27 at the promoter region of USP1 on the knocking down of ITGB4/PXN, highlighting the epigenetic regulation of various genes by these two proteins. Further, the knocking down of USP1 and VDAC1 generated a similar phenotype as the knockdown of ITGB4/PXN (Figure 3). The suppression of VDAC1 resulted in increased mitochondrial respiration and the generation of reactive oxygen species, leading to DNA damage, whereas the suppression of USP1 affected the DNA repair caused by adduct formation induced by cisplatin. Thus, these results highlighted the important role of the FA-associated complex-associated genes in cisplatin resistance and suggested that disrupting the interactions between the key components could potentially alleviate cisplatin resistance.

**Figure 3.** Schematic depicting the interaction between ITGB4 and PXN regulating downstream proteins USP1 and VDAC1 at the transcriptional level to coordinate cisplatin resistance; taken from Reprinted/adapted with permission from Ref. [51].

#### *3.2. Mathematical Modeling Suggests Bistability Drives Phenotypic Switching*

Our work also highlighted the role of ITGB4 in defining tumor heterogeneity. An immunohistochemistry analysis of patient samples and NSCLC cell lines confirmed the differential expression of ITGB4 [52]. Therefore, ITGB4 was used as a marker to sort low and high ITGB4-expressing NSCLC cells. Interestingly, the low ITGB4-expressing cells, after a few days in culture, were able to recreate the heterogeneous population of ITGB4 expressing cells, but the cells sorted for high ITGB4 failed to recreate the heterogeneous expression of ITGB4. These results were suggestive that low ITGB4-expressing cells have more plasticity to recreate the heterogeneity compared to high ITGB4-expressing cells. Further, RNA seq analysis carried out on the ITGB4 knockdown cells suggested a bistable relation between the microRNA 1-3p and ITGB4. A mathematical model developed based on the expression of these two genes indicated bistability; in a mixture of heterogeneous cells, some cells express high ITGB4/low microRNA 1-3p, low ITGB4/high microRNA 1-3p, or an equal expression of both ITGB4 and miRNA 1-3p (intermediary cells). Intermediary cells can shift in either direction depending on the environmental cues, for example, they could increase ITGB4 expression to tolerate cisplatin toxicity, and in absence of a drug, they could return to normal or low expressing subtypes [52].

#### *3.3. Novel Alternatives to Alleviate Cisplatin Resistance*

To identify small molecules that could potentially disrupt the interaction between PXN and FAK and, hence, perturb the focal adhesion complex and its downstream signaling, we used an in silico screening approach to screen a library of FDA-approved compounds. The screen identified several compounds that were found to sensitize the platinum-resistant NSCLC cells. Of these, carfilzomib (CFZ) was the most efficacious (IC50 in the low nanomolar range) and was able to induce DNA damage and apoptosis in NSCLC cells [68]. Furthermore, CFZ was also found to significantly inhibit migration, wound-healing, and ITGB4 expression at sublethal doses. Altogether, the data revealed an alternative and more efficient approach to treating lung cancer patients with cisplatin resistance.

#### *3.4. Group Behavior and Phenotypic Switching Enable NSCLC Cells to Evade Chemotherapy*

Phenotypic plasticity is critical for cancer cells to adapt themselves and survive [69]. Because of phenotypic plasticity, cancer cells are adept at switching their phenotypes in response to either intrinsic or extrinsic (environmental) cues. Thus, phenotypic plasticity enables cancer cells to undergo epithelial-to-mesenchymal transition (EMT) in order to facilitate distant metastasis; switch from being drug-sensitive to becoming drug-tolerant and, eventually, -resistant; or acquire stem cell-like characteristics. These phenomena help cancer cells to adapt to the fitness landscape and withstand drug treatment. Emerging evidence also indicates that both genetic and non-genetic mechanisms play crucial roles in the adaptability or cooperation between cancer cells to withstand stressful conditions. Hata et al. [30] provided clinical evidence showing that drug-resistant cells can both preexist and can evolve from drug-tolerant cells. If so, how does the co-existence of drugsensitive and drug-tolerant/resistant clones impact their ability to cooperate or compete (group behavior) to evade drug toxicity [70,71]?

To address this question, we employed an approach that embodied both experimental methods and mathematical modeling, again using the cisplatin treatment of NSCLC cell lines [53]. Cisplatin-sensitive H23 and cisplatin-tolerant H2009 NSCLC cells were cocultured and monitored in real-time in order to discern differences in their behavior. The two cell line cultures were grown as either monotypic (grown by themselves) or as heterotypic cultures (co-culture of tolerant and sensitive cells) in different ratios 1:1, 2:1, 4:1, and 8:1 and their growth rates were monitored in real time using a live cell imaging system. The data revealed that the tolerant cell proliferation was suppressed in the presence of sensitive cells at a 1:1 ratio and the proliferation could be rescued by increasing the fraction of tolerant cells in the co-cultures (Figure 4). The experiment was also repeated for the alternative ratios where the sensitive cells were increased, keeping the tolerant cells constant and the same result was observed, i.e., tolerant cell growth was inhibited by sensitive cells; however, the addition of a drug or increase in the ratio of tolerant cells in the population favored the growth of tolerant cells.

**Figure 4.** Behavior of cisplatin-sensitive and tolerant NSCLC cells in the 2D co-culture.

Considering the key observations from these in vitro studies, such as (i) sensitive cells inhibiting tolerant cell proliferation in a co-culture in the absence of cisplatin, (ii) the suppressive effect being stronger upon longer incubation compared to shorter incubation, and (iii) the competition by the sensitive cells being eliminated in the presence of cisplatin, a new mathematical approach called the Phenotypic Switch Model with Stress Response (PSMSR) was developed to fit the observed growth curves, the model conglomerate concepts from chemical reaction kinetics, and the cooperative behavior of drug-tolerant phenotypes in the community. A distinguishing feature of the PSMSR model is that it considers the ability of cancer cells to switch phenotypes. In addition to several testable predictions, the most important takeaway from the modeling exercise is that a small population of the tolerant cells may help the drug-sensitive cells to sustain proliferation. However, high levels of or continuous drug treatment, such stress removal mechanisms, may be insufficient to sustaining sensitive cell viability. Thus, it follows that it is essential to turn off phenotypic switching in such situations and allow the sensitive cells to become extinct and the tolerant cells to proliferate, which is the fundamental basis of 'adaptive' therapy or intermittent therapy strategy [72,73].

#### **4. Conclusions and Future Perspective**

In the cancer world, drug attrition rates are notorious—several drugs are effective in preclinical studies but only a few are approved for clinical use [74]. Furthermore, while most approved cancer drugs do help in improving the life expectancy of the patients, cancer cells often develop resistance against these therapies and relapse as resistant and metastatic diseases. Moreover, underlying mechanisms remain poorly understood.

The prescribed strategy that a physician follows—administering the maximal dose continuously in the shortest possible time—can lead to counterproductive and potentially adverse outcomes, such as drug resistance through genetic and non-genetic mechanisms, as discussed above. This led to exploring the emergence of alternative therapies and approaches [75–78], for example, adaptive/intermittent therapies [70,79]. The basic principle of intermittent therapy is to administer a lower therapeutic dose of the drug than the maximally tolerated dose to maintain a stable disease. The major advantage of intermittent therapy is an improved quality of life for the patient due to low drug dosage and, thus, fewer side effects. By keeping the drug doses low and intermittent (with drug holidays in between), the proliferation of resistant subclones can be delayed. Some of the success stories of intermittent therapy have been seen in rectal, pediatric sarcoma, prostate, and breast cancer [80–85].

Based on our observations and those of others from the literature, we believe that maintaining a stable disease may be more prudent. A good example is a study by Klotz et [78], where they treated 20 patients with advanced prostate cancer with intermittent endocrine therapy (diethylstilbesterol in 19 cases and flutamide in 1 case). These patients were treated until a clinical response was demonstrated, with a mean initial treatment duration of 10 months (range 2–70 months). The treatment was then stopped and re-started when tumors relapsed, with mean interval times of 8 months (range 1–24 months). All relapsed patients responded to the re-administration of the drug. Patients had a better quality of life during the drug holidays of the treatment. Indeed, subsequent studies [86], including a meta-analysis (Marlon et al.) [87], also concluded that intermittent androgen deprivation can be considered as an option for recurrent or metastatic prostate cancer.

Therefore, the data from our studies on cisplatin resistance in NSCLC not only lend further credence to the paradigm of intermittent strategy to maintain stable disease but also underscore the nuances and benefits of a 'Team Medicine' approach from a systems biology perspective.

**Author Contributions:** Conceptualization, S.R. (Sravani Ramisetty) and A.M; resources R.S.; writing original draft preparation, S.R. (Sravani Ramisetty), P.K. and A.M.; writing—review and editing, visualization, P.K., S.B., A.N., S.S.S., L.G., T.M., B.M., S.M., J.M., E.P., S.S., S.R. (Swapnil Rajurkar), E.M., R.S. and A.M.; supervision, P.K., A.M. and R.S.; project administration, R.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported in part by grants from the United States Department of Defense (W81XWH-22-0331).

**Institutional Review Board Statement:** Not applicable for studies not involving humans or animals.

**Informed Consent Statement:** Not applicable for studies not involving humans.

**Data Availability Statement:** The data are available upon request from the corresponding author.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Abbreviations**

CFZ, carfilzomib; DTP drug-tolerant persisters; FA, focal adhesion; FAK, focal adhesion kinase; IDP, intrinsically disordered proteins; ITGB4, integrin beta 4; LSCC, lung squamous cell carcinoma; LUAD, lung adenocarcinoma; MAPK, Ras-mitogen-activated protein kinase, NSCLC, non-small cell lung cancer; PI3K, Phosphoinositide 3-Kinase; PIN, protein interaction networks; PLC-γ, phospholipase C-gamma; PSMR, phenotypic switch model with stress response; PXN, paxillin.

#### **References**


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### *Perspective* **Addressing Drug Resistance in Cancer: A Team Medicine Approach**

**Prakash Kulkarni 1,2, Atish Mohanty 1, Supriyo Bhattacharya 3, Sharad Singhal 1, Linlin Guo 1, Sravani Ramisetty 1, Tamara Mirzapoiazova 1, Bolot Mambetsariev 1, Sandeep Mittan 4, Jyoti Malhotra 5, Naveen Gupta 6, Pauline Kim 7, Razmig Babikian 1, Swapnil Rajurkar 6, Shanmuga Subbiah 8, Tingting Tan 9, Danny Nguyen 10, Amartej Merla 11, Sudarsan V. Kollimuttathuillam 12, Tanyanika Phillips 13, Peter Baik 14, Bradford Tan 14, Pankaj Vashi 14, Sagun Shrestha 15, Benjamin Leach 16, Ruchi Garg 17, Patricia L. Rich 17, F. Marc Stewart 1, Evan Pisick <sup>14</sup> and Ravi Salgia 1,\***


**Abstract:** Drug resistance remains one of the major impediments to treating cancer. Although many patients respond well initially, resistance to therapy typically ensues. Several confounding factors appear to contribute to this challenge. Here, we first discuss some of the challenges associated with drug resistance. We then discuss how a 'Team Medicine' approach, involving an interdisciplinary team of basic scientists working together with clinicians, has uncovered new therapeutic strategies. These strategies, referred to as intermittent or 'adaptive' therapy, which are based on eco-evolutionary principles, have met with remarkable success in potentially precluding or delaying the emergence of drug resistance in several cancers. Incorporating such treatment strategies into clinical protocols could potentially enhance the precision of delivering personalized medicine to patients. Furthermore, reaching out to patients in the network of hospitals affiliated with leading academic centers could help them benefit from such innovative treatment options. Finally, lowering the dose of the drug and its frequency (because of intermittent rather than continuous therapy) can also have a significant impact on lowering the toxicity and undesirable side effects of the drugs while lowering the financial burden carried by the patient and insurance providers.

**Citation:** Kulkarni, P.; Mohanty, A.; Bhattacharya, S.; Singhal, S.; Guo, L.; Ramisetty, S.; Mirzapoiazova, T.; Mambetsariev, B.; Mittan, S.; Malhotra, J.; et al. Addressing Drug Resistance in Cancer: A Team Medicine Approach. *J. Clin. Med.* **2022**, *11*, 5701. https://doi.org/ 10.3390/jcm11195701

Academic Editor: Claude Lambert

Received: 30 August 2022 Accepted: 23 September 2022 Published: 27 September 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Keywords:** drug resistance; drug tolerance; eco-evolutionary; intermittent therapy; adaptive therapy; continuous therapy; Team Medicine; intrinsically disordered proteins

#### **1. Introduction**

Cancer is a major healthcare crisis and one of the leading causes of death in the world. In 2020 alone, worldwide, there were ~18 million cancer cases, and cancer accounted for nearly 10 million deaths, or nearly one in six deaths, in the same year, Sung et al., 2020 [1]. In less than two decades from now (by 2040), the number of new cancer cases per year is expected to rise to >27 million and the number of cancer-related deaths to >16 million [2]. These whopping numbers underscore the magnitude of the health care crisis and the economic burden of this burgeoning problem across the globe. Although the disease presents several challenges depending on the cancer type and, in some cases, ethnicity, the emergence of drug resistance remains a major common concern in treating all cancer patients.

Several confounding factors appear to be contributing to this challenge. Here, we first discuss some of the challenges associated with drug resistance in cancer. We then discuss how a 'Team Medicine' approach [3], involving an interdisciplinary team of scientists with expertise in physics, biophysics, mathematics, evolutionary biology, bioinformatics, data science, computational biology, and cancer biology, working with clinicians, has provided new opportunities and new therapeutic strategies that could potentially preclude or delay the emergence of drug resistance in several cancers. We conclude by proposing a few innovations to our approach in treating cancer: (1) the novel therapeutic strategies such as intermittent drug treatment at moderate dosage as opposed to continuous treatment at high dosage and (2) leveraging precise knowledge of the tumor phenotypic landscape in designing personalized therapy through deeper consideration of genetic, epigenetic, and transcriptomic information.

#### **2. Is Drug Resistance Genetic or, Are Non-Genetic Mechanisms Involved?**

For well over a century, since Theodore Boveri's ground-breaking observations in the early 1900s (*Zur Frage der Entstehung maligner Tumouren*), cancer has been thought to be a genetic disease [4–7]. In fact, today, a genetic basis underlying the origin of cancer, its progression through distant metastasis, and the emergence of drug resistance is practically common knowledge. The following excerpt from an influential review in Nature Medicine by Vogelstein and Kinzler [8], titled "Cancer genes and the pathways they control", underscores the prevailing ethos: "*The revolution in cancer research can be summed up in a single sentence: cancer is, in essence, a genetic disease. In the last decade, many important genes responsible for the genesis of various cancers have been discovered, their mutations precisely identified, and the pathways through which they act characterized*." (Our bold for emphasis). Pursuant to this landmark review, a decade later, Vogelstein and Kinzler [9] further emphasized the genetic nature of cancer in a perspective article, "The path to cancer—three strikes and you're out", in the leading medical journal, New England Journal of Medicine. The authors wrote "*Focusing on driver-gene mutations and the pathways they control has rendered complex cancer-genome landscapes intelligible. In solid tumors of adults, alterations in as few as three driver genes appear to suffice for a cell to evolve into an advanced cancer.*" (Our bold is for emphasis).

Highly influential articles like these and countless others have helped to firmly establish the genetic basis of cancer and, much like the modern synthesis in evolutionary theory [10], provide a conceptual framework to understand cancer and its link to Darwinian evolution. Furthermore, specific mutations have been leveraged as hallmarks for a conclusive diagnosis and the staging of specific cancer types, and highly potent drugs that specifically bind to the mutant target oncoproteins have been developed, adding to the precision with which individual patients are treated [11–13].

However, contrary to the prevailing wisdom, emerging evidence indicates that mechanisms such as epigenetic modifications and protein interaction network (PIN) rewiring defined as changes in the interaction of individual proteins in signaling pathways in response to environmental changes—can also contribute to various aspects of cancer, including its origin, progression, and the emergence of drug resistance [14–20], underscoring the role of non-genetic mechanisms. It is now evident that both non-genetic and genetic mechanisms are involved, especially in acquired drug resistance and, furthermore, as discussed below, the irreversible resistance to a drug that can be acquired via an intermediate, reversible tolerant state via non-genetic mechanisms.

#### **3. Discerning Drug Tolerance and Resistance**

Perhaps one of the main reasons for our failure to overcome drug resistance in cancer, whether innate or acquired, may have to do with the difficulty in how we perceive the phenomenon. Unfortunately, and erroneously (even if inadvertently), it is assumed that drug resistance, tolerance, and persistence are synonymous or equivalent and hence, are used indiscriminately [21]. However, these are quite distinct and nuanced phenomena, as elegantly demonstrated in microbiology.

In bacteria for example, resistance is defined as the ability of an organism to grow at high concentrations within the presence of a drug. Resistance is typically due to mutations and is heritable transgenerationally. On the other hand, tolerance is more generally used to describe the ability, whether inherited or not, to survive transient exposure to high concentrations of a drug, and persistence is defined as the ability of a subpopulation to survive long-term exposure to high concentrations of a drug. Persistence is typically observed when the majority of the population is rapidly killed following drug treatment while a subpopulation persists for a much longer period of time [22,23] (Figure 1). Since rigorous definitions are lacking in the cancer field, the term 'resistance' remains ambiguous or confusing at best; thus, this further adds to the difficulty of defining a patient's response to a drug. For example, if a patient does not respond to a drug, is the patient's tumor tolerant and hence potentially reversible? or is it truly resistant and hence irreversible? Perhaps, publicly available databases dedicated to cancer drug resistance could help alleviate some of the confusion [24].

**Figure 1.** Bacterial Persistence. (**A**) Biphasic time-kill curve in bacterial populations exposed to antibiotics: faster killing rate of sensitive cell (green dotted line) followed by a slower killing rate (red dotted line) of the persisters. In contrast, the antibiotic-resistant population continues to grow in the presence of antibiotic (blue curve). (**B**) (top) An isogenic population of antibiotic sensitive cells can give rise to persisters via non-genetic/phenotypic plasticity. These slow cycling persisters survive in the antibiotic treatment and tend to resume growth and generate a new population identical to the original population upon antibiotic removal (bottom). Persisters and non-persisters can switch among one another; the switching rate can be influenced by external stress factors. (**C**) Non-genetic heterogeneity of a key regulator of persistence (say X) in an isogenic population may give rise to two (or more) subpopulations that may continue switching stochastically among themselves to maintain persistence [25].

#### **4. Current Treatment Strategies May Be Counterproductive**

To further complicate the issue, in addition to being taken for granted that cancer is a genetic disease, it is also believed that cancers arise by Darwinian evolution, involving a reiterative process of clonal expansion, genetic diversification, and selection within the adaptive landscapes of the tissue ecosystems they inhabit [26]. Therefore, while therapeutic intervention can destroy cancer clones and erode their habitats, the same intervention, especially when administered continuously, may also inadvertently provide a potent selective pressure for the expansion of drug-resistant phenotypes. However, recent developments in sequencing and omics technologies, coupled with theoretical advancements, have provided an expanded understanding of the cancer phenotypic landscape. They highlight non-genetic mechanisms that enable cancer cells to reversibly adapt to their environment, unlike genetic mechanisms that are irreversible, underscoring the dire need to reconcile the two mechanisms. Additionally, plastic phenotypes such as cancer stem cells (CSCs) are also well-recognized entities contributing to drug resistance in many cancers [27–29]. Therefore, the dynamic heterogeneity and the dynamic transitions between CSCs and non-CSCs and their significance in metastasis and drug resistance warrant a deeper understanding of the underlying mechanisms.

Indeed, a recent article by Sui Huang [30] in a Special Issue of *Trends in Cancer* entitled, 'Quantitative Cancer Biology', further emphasizes the need to reconcile non-genetic plasticity with somatic evolution in cancer: "Posttreatment progression of tumors is commonly explained by somatic Darwinian evolution (i.e., selection of cells carrying genetic mutations that create more aggressive cell traits). But cancer genome and transcriptome analyses now paint a picture far more complex, prompting us to see beyond the Darwinian scheme: non-genetic cell phenotype plasticity explained by alternative stable gene expression states ('attractors'), may also produce aggressive phenotypes that can be selected for, without mutations. Worse, treatment may even induce cell state transitions into more malignant attractors." (Our bold for emphasis).

#### **5. Emergence of Irreversible Drug Resistance via a Potentially Reversible Tolerant State**

Because they exhibit a high degree of phenotypic plasticity [31], cancer cells can switch on cell-autonomous traits such as persistence and quorum sensing when stressed [32]. Cancer cells exhibiting a persister trait are slow growing and can give rise to tolerant cells that, as discussed above, play an important role in the emergence of true drug resistance [33,34]. To comprehend how drug resistance may evolve from an intermediate tolerant state, it is helpful to view cancer from Waddington's [35] epigenetic landscape perspective (Figure 2). The concept of a "landscape" represents a high-dimensional state space where each phenotype acts as an "attractor" determined by the underlying PIN and is buffered against environmental fluctuations. Cellular PINs are organized following scale-free (rather than random) configurations. Therefore, PINs follow a power law distribution, wherein a few nodes (referred to as hubs) have numerous edges (connections), while most nodes have few or very few edges. Scale-free networks are resilient to random node failures; however, they are susceptible to targeted attacks on hubs. PINs serve as the main conduit of information flow with crucial roles in cellular decision-making [17,36]. PINs can determine the fate that a cell can realize and can robustly establish its phenotype because they are minimally frustrated [37]. Frustration is defined as the inability of the system to simultaneously minimize the competing interaction energies between its components [38]. In cancer cells, PIN frustration can be a viable mechanism of achieving phenotypic plasticity besides epigenetic changes. As discussed in the next paragraph, PIN frustration is driven by a special class of proteins with high structural flexibility and an ability to interact with multiple partners.

**Figure 2.** Schematic illustration of Waddington's epigenetic landscape [35]. The ball rolling down the hill (the x axis) represents a pluripotent cell that differentiates as it rolls down the valleys. The fate of the cell is decided by the attractors that reside at the bottom of the hill (the y axis). The valleys are separated by ridges that preclude transdifferentiation [39].

Intrinsically disordered proteins (IDPs) are proteins that lack a rigid 3D structure and exist as ensembles of interconverting conformations [40]. Because they are highly malleable, IDPs interact with multiple partners and thus occupy hub positions in PINs. Noise contributed in part by the conformational dynamics of IDPs ('conformational noise') plays an important role in cell-fate specification [17,18,36]. Nonetheless, in response to stress, especially in conjunction with post-translational modifications such as phosphorylation, IDPs engage in promiscuous interactions and drive phenotypic transitions by PIN rewiring [17,18,36,41]. This heuristic can uncover new attractors in the system—including "cancer attractors," defined as hidden stable states of PINs [42,43]—and cause phenotypic switching. Upon stress withdrawal, IDPs reconfigure the PIN to return to the original phenotype, highlighting the reversible nature of phenotypic switching. However, if stressful conditions persist, chronic stress can result in persistent network frustration, which is relieved by acquiring specific DNA mutations and/or other genetic alterations, making the phenotypic change permanent [32,44]. Thus, it follows that, non-genetic mechanisms can eventually lead to acquired resistance via irreversible genetic changes at the individual cell level. Further, as discussed below, this stepwise trajectory to drug resistance also highlights an unprecedented opportunity to preclude or delay it by controlling stress (by manipulating drug dose/time) experienced by the tolerant tumor cells.

#### **6. Intermittent or 'Adaptive' Therapy—An Eco-Evolutionary Principles-Based Therapeutic Strategy to Preclude or Delay Onset of Drug Resistance**

The rationale for this treatment strategy is based on the principles of ecology and evolution. Within the tumor microenvironment (TME), cancer cells reside with several other cell types that cohabit in this space. By producing growth factors and proinflammatory cytokines to promote angiogenesis, these cells create an ecosystem that enables the malignant cell population to grow and flourish. Therefore, group behavior, an emergent property defined as the collective actions performed by the individuals in the group as a whole, imposes costs and benefits to the participating individuals that can be recast as a game pay-off matrix. Thus, evolutionary game theory, which provides an elegant conceptual framework to capture the frequency-dependent nature of ecosystem dynamics, can be used to model tumor progression and dynamics. In fact, game theory can also be leveraged to discern the games cancer cells play by either cooperating or competing in the absence or presence of stress (selective pressure). Therefore, treatment options that

consider the strategies cancer cells adopt to deal with drug effects have been developed and are referred to as intermittent or 'adaptive' therapy [45].

Typically, such treatment protocols call for initial therapies to induce adaptive changes in the tumor environment such that the proliferation of resistant clones is markedly suppressed for extended periods. In this paradigm, it is recommended that therapy is administered in small doses to attenuate tumor growth but has just enough to improve the symptoms. In other words, it is recommended that a minimal dose of the treatment (that is necessary and not at the maximum tolerated) must be used to achieve the desired result. Furthermore, treatment is administered intermittently (in alternate cycles) rather than continuously (given at every scheduled time) so that a drug-sensitive tumor population will be sustained at the expense of the resistant ones. In addition, drug combinations/epigenetic modifiers may be used in the intermittent/adaptive therapy regimen if necessary. Although, in this treatment strategy, the tumor is not completely eradicated, and it is likely that the tumor progresses between treatments; it is also likely that the tumor cells will continue to be sensitive to therapy and therefore delay or may even preclude the onset of drug-resistant disease (Figure 3) and thus, prolong overall survival.

**Figure 3.** Continuous Monotherapy versus Intermittent Combination Therapy. (**A**) In continuous monotherapy, the idea is to eradicate the tumor as quickly as possible. However, this strategy can give rise to resistance, and resistant cells are expected to propagate over time (top). By contrast, combination therapy applied intermittently (bottom) could induce 'adaptive strategies' to change the tumor environment in such a way that the proliferation of the resistant clones can be suppressed for prolonged periods of time. Therapy is applied in small doses to reduce the tumor population only sufficient enough to improve the symptoms. Furthermore, treatment is intermittent so that drug-sensitive cells will proliferate at the expense of the resistant ones. (**B**,**C**) Although the tumor will increase in size between treatments, the extant tumor cells will continue to be sensitive to therapy [21].

Indeed, intermittent therapy for prostate and breast cancer [46], melanoma [47], rectal cancer [48], and pediatric sarcomas [49] are currently being evaluated in the clinic with promising results. In some other cancers, e.g., non-small cell lung cancer, our preclinical data obtained using a Team Medicine approach also indicate that such strategies may prove successful as well [50]. They could also have a significant impact on mutant KRAS inhibitors, such as sotorasib, which was recently approved for lung cancer treatment and is promising but is already being reported to develop resistance [51–56].

Despite the promise and initial success, more research is warranted to gain a deeper understanding of intermittent/adaptive therapy and of the side effects, if any, especially when drug combinations are to be used. For example, in one study, it was reported that when a tumor is sensitive to two or more drugs, the simultaneous application of these drugs could result in the emergence of cells resistant to both therapies. However, when these drugs were applied one at a time, a subpopulation of cells was sensitive to one or the other drug, delaying the emergence of double-resistant cell clones [57]. Conversely, in another study on lung cancer, it was observed that concurrent targeting of multiple kinases, rather than a single kinase, resulted in the complete (100%) inhibition of tumor growth. The latter strategy was only effective when intermittent and not continuous therapy was administered. One possibility for this dramatic inhibition is likely due to the lack of adaptability of the tumor cells to the changing fitness threshold imposed by selection [58].

Two randomized trials have investigated intermittent dosing regimens with BRAF and MEK inhibitors for the treatment of BRAF-mutated advanced malignant melanoma. Gonzalez-Cao et al. [59] reported lower median progression-free survival (PFS; 6.9 months versus 16.2 months; *p* = 0.079) with the intermittent use of vemurafenib and cobimetinib when compared to continuous dosing in 70 patients with treatment-naïve advanced melanoma. No statistical difference was observed for overall survival (OS) or in objective response rates (OSS). In another randomized, open-label, phase two trial, comparing continuous versus intermittent BRAF and MEK inhibition in patients with BRAFmutated melanoma, Algazi et al. [60] reported that continuous dosing was associated with a statistically significant improvement in median PFS compared with intermittent dosing (9.0 months versus 5.5 months, *p* = 0.064, pre-specified two-sided α = 0.2). Even though there was a PFS difference between the two groups, no differences were observed in the OS and ORR. This could possibly be due to the finding that intermittent dosing was associated with longer survival after progression (HR 0.76; 80% CI 0.78 to 1.00). Maio et al. [61] improved efficacy with intermittent MEK inhibition when combined with anti-PD-1 immunotherapy (pembrolizumab) in patients with advanced or metastatic BRAF-mutated solid tumors (36% colorectal cancer and 10% melanoma). ORR was reported to be 8% effective with concurrent and 28% effective with the intermittent dosing groups, respectively. Several trials have investigated the role of intermittent androgen deprivation therapy (ADT) in the treatment of advanced prostate cancer. These trials have reported that intermittent ADT has similar clinical outcomes when compared to continuous ADT with no statistically significant differences in OS, cancer-specific survival, or PFS [62–64]. However, intermittent ADT is associated with an improved quality of life and a lower risk of adverse events [63–65]. Ongoing trials are now investigating intermittent ADT in combination with additional therapies, such as radiation or immunotherapy, that can potentially further increase the time of systemic treatment [66]. Thus, the clinical trials so far, which have investigated intermittent dosing regimens, have yielded mixed results, highlighting the complexity of translation preclinical studies into human trials and the challenges of selecting the optimal dosing regimen. Future trials should focus on exploring this approach in biomarker-selected populations, as well as elucidating which subgroups of patients may benefit most from this approach.

Since intermittent therapy relies on drug-sensitive cells to suppress the proliferation of the tolerant cells, the success of such therapies is dependent on the initial population of the two cell types within the tumor. The hypothetical scenario shown in Figure 4, where the three models represent the three patients, may help better appreciate the underlying nuances. Here, Patient 1, with the highest number of sensitive cells compared to tolerant cells, will have more prolonged progression-free survival in response to drug treatment. The second patient with an equal number of sensitive and tolerant populations will have shorter progression-free survival, while Patient 3, with the highest number of tolerant cells, will have the shortest progression-free survival. The purpose of intermittent therapy is to prevent the development of drug refractory-resistant clones and is based on the presumption that the sensitive cells, in the absence of the drug, will compete with resistant cells and grow faster to suppress the growth of resistant cells. Thus, the more percentage of sensitive cells, the better the response to the intermittent therapy, and, thus, in scenarios like model 1 and model 2, the success rate will be higher compared to model 3. Moreover, the inherent assumption in intermittent therapy, that the sensitive and tolerant cell types are competitive, may not hold universally. Depending on the tissue/cancer type, more complex ecological relationships may exist among the different cell types (such as cooperation or competition, depending on the stress level/drug dosage) that may require more fine-tuned, dynamic adjustments to drug schedules/dosages as the therapy progresses and the tumor phenotypic landscape evolves.

**Figure 4.** The cartoon representing the importance of tumor heterogeneity on therapeutic approach, continuous verses intermittent.

Thus, it follows that, before initiating a therapeutic strategy, a detailed genetic (and possibly transcriptomic and epigenetic) analysis of the patient's tumor is imperative. For example (Figure 5), patients with an RAF mutation are likely to respond better to BRAF inhibitors, while patients with BRAF and MEK mutations may respond less to the same inhibitors, and patients with MEK and PI3K mutations will not respond to BRAF inhibitors at all. Likewise, patients with activated RTK signaling are unlikely to respond to the BRAF inhibitor drug treatment effectively, as the tumor will likely take advantage of the bypass signaling through the AKT -mTOR pathway to overcome the drug effect. Therefore, pretrial validation of the mutations in the tumor by NGS and the expression of the activated signaling need to be determined so that patients with a similar mutational profile or expression status may be grouped into a specific cohort. As illustrated in Figure 5, patients in each of the four cohorts can be treated with continuous or intermittent therapy, and the statistical significance can be derived to determine the best therapeutic approach. However, comparing intermittent versus continuous treatment between the two cohorts will give insignificant information. Figure 5 also suggests the role of Team Medicine, where basic help from scientists can identify those signaling pathways that need to be targeted for effective therapy through experiments, clinicians and bioinformaticians can help to validate the basic study by analyzing thousands of public data and help to look for specific mutations that can contribute to the pathways, molecular pathologists can help in

determining the expression of these proteins in the tumor biopsies, and finally, clinicians, aided by cumulative information and precise mathematical models, can design the drug treatment strategy.

**Figure 5.** A schematic representing the pretreatment preparation for choosing the best treatment strategy. RTK, receptor tyrosine kinase; Ras, Ras protooncogene; Raf. Raf protooncogene, serine/threonine kinase; MEK, MAP kinase-ERK kinase; ERK, extracellular regulated MAP kinase; PI3K, phosphatidylinositol 3-kinase; AKT, AKT serine/threonine kinase 1; mTOR, Mechanistic Target Of Rapamycin Kinase; BRAF, B-Raf Proto-Oncogene, Serine/Threonine Kinase; NGS, next generation sequencing.

#### **7. Concluding Remarks**

There exist multiple mechanisms that regulate phenotypic switching and drug resistance, even within a given cancer type. Furthermore, although promising in some cases, challenges still remain with regard to intermittent therapy, as discussed above. Thus, a better understanding of the mechanisms can help us to design the most effective therapeutic approach. Nonetheless, from the foregoing, it is obvious that these exciting developments in medical oncology, which expound the virtues of modern translational research, can only be made possible by a true Team Medicine approach, including basic scientists and clinicians. By incorporating treatment strategies based on the principles of ecology and evolution in clinical protocols, and by reaching out to patients who frequent those hospitals that are part of the network formed by academic centers rather than the academic centers themselves, we can enhance the precision in which we deliver personalized medicine to all our patients, regardless of their economic status or their ability to access advanced medical centers. We trust that our integrated efforts at the City of Hope, in conjunction with the cancer treatment centers of America, shall serve as a good example to those who wish to adopt this paradigm. Last but not least, lowering the dose of the drug and its frequency (because of intermittent rather than continuous therapy) can also have a significant impact on lowering the toxicity and undesirable side effects of the drugs while lowering the financial burden carried by the patient and insurance providers [67].

**Author Contributions:** Conceptualization, P.K. (Prakash Kulkarni) and R.S.; resources, R.S.; writing original draft preparation, P.K. (Prakash Kulkarni) and R.S.; writing—review and editing, A.M. (Atish Mohanty), S.B., S.S. (Sharad Singhal), L.G., S.R. (Sravani Ramisetty), T.M., B.M., S.M., J.M., N.G., P.K. (Pauline Kim), R.B., S.R. (Swapnil Rajurkar), S.S. (Shanmuga Subbiah), T.T., D.N., A.M. (Amartej Merla), S.V.K., T.P., P.B., B.T., P.V., S.S. (Sagun Shrestha) and B.L., R.G., P.L.R., F.M.S., E.P.; visualization, P.K. (Prakash Kulkarni) and R.S.; supervision, P.K. (Prakash Kulkarni) and R.S.; project administration, P.K. (Prakash Kulkarni) and R.S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported in part by Robert and Kathleen Henderson via the Robert and Kathleen Henderson Lung Cancer Research Accelerator Fund at the City of Hope and by William and Anna Tenenblatt via The William & Anna Tenenblatt Foundation.

**Institutional Review Board Statement:** Not applicable for studies not involving humans or animals.

**Informed Consent Statement:** Not applicable for studies not involving humans.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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