Next Article in Journal
Analysis of Expression of Programmed Cell Death Ligand 1 (PD-L1) and BRAFV600E Mutation in Thyroid Cancer
Next Article in Special Issue
High Serum Levels of CCL20 Are Associated with Recurrence and Unfavorable Overall Survival in Advanced Melanoma Patients Receiving Immunotherapy
Previous Article in Journal
Rectal Cancer: Are 12 Lymph Nodes the Limit?
Previous Article in Special Issue
Real-Life Study of the Benefit of Concomitant Radiotherapy with Cemiplimab in Advanced Cutaneous Squamous Cell Carcinoma (cSCC): A Retrospective Cohort Study
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Medical Needs and Therapeutic Options for Melanoma Patients Resistant to Anti-PD-1-Directed Immune Checkpoint Inhibition

1
Skin Cancer Center, Department of Dermatology and National Center for Tumor Diseases (NCT), University Hospital Heidelberg, 69120 Heidelberg, Germany
2
Department of Dermatology, University Hospital Essen, 45147 Essen, Germany
3
German Cancer Consortium (DKTK), Partner Site Essen, 69120 Heidelberg, Germany
4
Bristol-Myers Squibb GmbH & Co. KGaA, 80636 Munich, Germany
5
Department of Dermatology, Venereology and Allergology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
6
Department of Dermatology, Skin Cancer Center at the University Cancer Centre and National Center for Tumor Diseases, Faculty of Medicine and University Hospital Carl Gustav Carus, Technical University Dresden, 01062 Dresden, Germany
7
Department of Dermatology, Elbe-Kliniken, 21614 Buxtehude, Germany
8
Department of Internal Medicine III, Hematology and Oncology, University Hospital Regensburg, 93053 Regensburg, Germany
9
Bavarian Cancer Research Center (BZKF), 93053 Regensburg, Germany
10
Department for Radiation Oncology, Martin-Luther University Halle-Wittenberg, 06108 Halle, Germany
11
Department of Radiology, University Hospital Regensburg, 93053 Regensburg, Germany
12
Department of Dermatology, Johannes Wesling Medical Center, Ruhr University Bochum, 32429 Minden, Germany
*
Author to whom correspondence should be addressed.
Cancers 2023, 15(13), 3448; https://doi.org/10.3390/cancers15133448
Submission received: 27 May 2023 / Revised: 26 June 2023 / Accepted: 27 June 2023 / Published: 30 June 2023
(This article belongs to the Special Issue Novel Developments on Skin Cancer Diagnostics and Treatment)

Abstract

:

Simple Summary

Immune checkpoint blockade has dramatically improved the outcomes of patients with melanoma. Available long-term updates of clinical studies show a sustained clinical benefit for patients treated with PD-1 inhibitors such as nivolumab or pembrolizumab or for those treated with a combination of nivolumab and ipilimumab. However, about 40–50% of patients acquire resistance to therapy within five years from the start of anti-PD-1 therapy. This review assesses available definitions of the resistance and patterns of response to PD-1 immunotherapy and summarizes the potential underlying mechanisms. The available data on resistance to PD-1 therapy, medical needs and therapeutic options for melanoma patients resistant to ICI are discussed for the metastatic setting, including brain metastases, as well as for the adjuvant and neo-adjuvant settings.

Abstract

Available 4- and 5-year updates for progression-free and for overall survival demonstrate a lasting clinical benefit for melanoma patients receiving anti-PD-directed immune checkpoint inhibitor therapy. However, at least one-half of the patients either do not respond to therapy or relapse early or late following the initial response to therapy. Little is known about the reasons for primary and/or secondary resistance to immunotherapy and the patterns of relapse. This review, prepared by an interdisciplinary expert panel, describes the assessment of the response and classification of resistance to PD-1 therapy, briefly summarizes the potential mechanisms of resistance, and analyzes the medical needs of and therapeutic options for melanoma patients resistant to immune checkpoint inhibitors. We appraised clinical data from trials in the metastatic, adjuvant and neo-adjuvant settings to tabulate frequencies of resistance. For these three settings, the role of predictive biomarkers for resistance is critically discussed, as well as are multimodal therapeutic options or novel immunotherapeutic approaches which may help patients overcome resistance to immune checkpoint therapy. The lack of suitable biomarkers and the currently modest outcomes of novel therapeutic regimens for overcoming resistance, most of them with a PD-1 backbone, support our recommendation to include as many patients as possible in novel or ongoing clinical trials.

1. Introduction

Melanoma is a life-threatening skin cancer arising from the oncogenic transformation of melanocytes with a tendency for widespread metastasis, causing around 57,000 deaths per year worldwide [1,2]. Before the development of immune checkpoint inhibition (ICI), almost all patients with metastatic melanoma died from the disease, with a median overall survival (OS) of only 6–9 months [2]. Treatment with ICI is currently the standard of care in advanced melanoma, independent of the tumor’s mutational status [3,4]. PD-1 blockade and, even more, combined PD-1 and CTLA-4 inhibition can promote durable, long-term remissions leading to 5-year OS rates of 34–44% for PD-1 inhibitors and 52% OS rates for combined ICI [5,6,7]. However, a significant proportion of advanced melanoma patients harbor or develop resistance to ICI therapy [8]. The 5-year rates of progression-free survival (PFS) range from 36% for combined nivolumab plus ipilimumab and 29% for monotherapy with the PD-1 inhibitor nivolumab to 8% for monotherapy with the CTLA-4 inhibitor ipilimumab [7]. In another landmark Phase III trial comparing the PD-1 inhibitor pembrolizumab with ipilimumab, the 4-year PFS rates for treatment-naïve melanoma patients were 27% and 8%, respectively [6]. For the newly approved ICI combination of the LAG-3 antibody relatlimab plus nivolumab, 2-year PFS data on first-line advanced melanoma again show an advantage of the ICI combination therapy over nivolumab monotherapy [9].
The efficacy of ICI in the unresectable metastatic setting led to its testing and approval in the adjuvant setting after the resection of metastasis in stage IV and III and after the resection of high-risk primary melanoma (Stage IIB/C) [10,11,12,13,14,15]. Recurrence-free survival (RFS) could be significantly improved with ICI mono- and combination therapy, however, without a proven OS benefit so far except for that of combined ICI with ipilimumab and nivolumab in stage IV disease compared to the placebo [14]. In this study, patients treated with ICI combination therapy revealed a significant RFS benefit with a HR of 0.25 (95% CI, 0.13–0.48) compared to those on the placebo and a HR of 0.41 (95% CI, 0.22–0.78) compared to those on nivolumab monotherapy. However, after 4 years, 36% (combined ICI) and 69% (anti-PD-1) of patients eventually progressed. Analyses of the CheckMate-238 study revealed a 5-year RFS rate of 50% for melanoma stage IIIB-IV patients receiving adjuvant nivolumab [13]. For patients with melanoma stage IIIA-IIID, the Keynote-054 study reported a RFS rate of 55% after 4.9 years with adjuvant pembrolizumab [15]. In stage IIB/C disease, again with adjuvant pembrolizumab, after 2 years about 19% of patients in the Keynote-716 study relapsed [10]. On the basis of these studies, pembrolizumab and nivolumab monotherapy were approved by EMA and pembrolizumab, nivolumab as well as ipilimumab monotherapy were approved by the FDA for the adjuvant treatment of melanoma.
Thus, depending on the stage of the disease, the majority of melanoma patients receiving ICI still experience refractoriness to or a relapse despite anti-PD-1 therapy at some point. This review summarizes the medical needs and therapeutic options for melanoma patients resistant to ICI.

2. Materials and Methods

To review the current knowledge on ICI resistance and to develop an expert opinion on the medical needs and therapeutic options for ICI-resistant patients, experts from leading German skin cancer centers were convened. A kick-off meeting was held online in November 2020 to share insights and experiences on how to advance, diagnostically and therapeutically, melanoma patients who fail anti-PD-1 therapy.. The board included physicians from dermato-oncology, oncology, radiotherapy, and radiology. Based on this input, a present narrative review was generated; the content and recommendations were agreed upon during a virtual consensus conference held in December 2022, involving the entire author panel.
The final manuscript was reviewed by the interdisciplinary panel. Standard recommendations to enhance the quality of evidence-based judgements were followed [16].

3. Definitions and Patterns of Response and Resistance to Immunotherapy in Melanoma

Different types of response and resistance patterns can be distinguished, as summarized in Figure 1 [17]. Response can be assessed radiologically using the classical RECIST1.1 criteria. Here, a responder (complete response (CR) or partial response (PR)) shows a decrease of at least 30% in tumor size without the occurrence of new lesions. Under ICI therapy, however, tumor shrinkage can be seen in some patients even though single new metastases occur, especially in the first weeks of treatment. Therefore, the iRECIST criteria were developed where patients with new lesions are defined as unconfirmed disease progression and can be transformed into responders without further progression in a confirmative CT scan at least 4 weeks later [18]. Moreover, an increase in size of existing lesions on radiologic images might not be based on real tumor progression but may be attributed to inflammation triggered by therapy [19] and hence, stabilize or regress later without further treatment [20]. These phenomena are called pseudoprogression. Meta-analyses reveal the overall incidence of pseudoprogression in solid tumors to be about 6% during or after ICI [21] but up to date, there are no predictive biological or clinical markers available [22].
For the classification of resistance in advanced disease, a distinction between primary resistance as non-response to therapy (i.e., disease progression (PD) or stable disease (SD) lasting less than 6 months) and secondary resistance after initial benefit (CR, PR or at least a stabilization of the disease (SD) for more than six months) is commonly made in clinical practice [23]. Due to the lack of consistent and widely accepted resistance definitions, a multi-stakeholder task force under the auspices of the Society for Immunotherapy of Cancer (SITC) has been established through consensus clinical definitions of resistance to PD-1 inhibitors in three distinct clinical scenarios: primary and secondary resistance (Figure 2), and progression after treatment discontinuation [23]. The latter has its main impact in (neo)adjuvant therapy but also for patients who have discontinued therapy due to toxicity or after having received a CR. This form of resistance could have elements of either primary or secondary resistance as defined above. The consensus considers resistances to anti-PD-1 in the metastatic, adjuvant and neo-adjuvant settings separately and incorporates relevant parameters such as a sufficient treatment duration, the pharmacodynamics of the compound (in this case anti-PD-1) and the mandatory diagnostics.
In the neo-adjuvant setting prior to resection, a clinical response according to RECIST 1.1 can be assessed via imaging [23,24]. After resection, an assessment of the pathological response is possible via a histologic review of the specimen [25]. Patients with a major pathological response (MPR, i.e., CR, near CR or major PR, ≤10% viable tumor cells) and recurrence after surgery may be classified as having secondary resistance, whereas patients without pathological response are classified as having primary resistance (Figure 2) [23]. This validation possibility is missing in the adjuvant setting, requiring a matched definition, focusing on whether or not the observed relapse occurs < or ≥12 weeks after the last dose. The 12-week criterion is based on the pharmacological assumption that anti-PD-1 inhibitors will still sufficiently occupy their receptors for this period of time after stopping therapy [26], defining an “early relapse” (primary resistance) or, conversely, a “late relapse” [23]. (Figure 2) Despite the significant progress in the consistent and comprehensive definition of anti-PD-1-related resistance provided by the SITC consensus, certain limitations and uncertainties remain, such as the value of serological markers including S100 or CT-DNA, organ specificity (local–distant metastasis; affected organs), but also the extent (single, oligo, or multiple) and dynamics of metastasis.

4. Potential Mechanisms of Resistance to Systemic ICI Therapy

Primary resistance reflects the “immunologic invisibility” of tumor cells. This can be due to the characteristics of the tumor cells as well as the immune system. Secondary resistance refers to an adaptive situation in which immunologic interaction leads to a selection of “immunologically invisible” tumor cells, which might occur due to the acquisition of new escape mechanisms of the selection of pre-existing immunologically invisible cells. Thus, secondary resistance is most likely due to tumor cell-intrinsic changes.
The underlying mechanisms are diverse, and similar mechanisms have been described for primary and secondary resistance (Figure 3). They can occur at the level of tumor cells (intrinsic mechanisms), e.g., defects in antigen presentation due to a low mutation load or the downregulation of MHC molecules, or alterations in interferon receptor signaling. These alterations can either result in increased type 1 interferon signaling, leading to NOS2 expression and resistance [27], or to a decrease in interferon gamma signaling [28]. Furthermore, metabolomic changes have been described in tumor cells to lead to an immunosuppressive microenvironment, such as one involving glutamine uptake and catabolism [29,30] or the exclusion of T-cells via the upregulation of beta-catenin in tumor cells [31].
Mechanisms can also occur at the level of immune cells (extrinsic mechanisms). Here, T-cells play a major role, e.g., due to the blockade of T-cell activity via hypoxia or soluble factors such as TGF beta. Moreover, the induction and recruitment of immunosuppressive cell types, e.g., regulatory T-cells (Treg) or myeloid-derived suppressor cells (MDSC) [32,33,34], as well as the presence of B-cells in tertiary lymphoid organs are important escape mechanisms [35]. These mechanisms can moreover influence each other, and usually the precise alterations are not clear in the individual case. However, there might be surrogate markers to predict the response to a checkpoint blockade or to explain secondary resistance. Among them are PD-L1 expression on the tumor as well as tumor-associated immune cells, the presence of a T-cell-inflamed tumor microenvironment signature or interferon-gamma signature [36], or a high mutational burden in the tumor cells as described in the “cancer immunogram” [37].

5. Resistance in the Metastatic Setting

5.1. Systemic Approaches

While patients with PD as the best overall response (BOR) to ICI clearly belong to the group of primary resistance, it is difficult to provide exact numbers for the frequency of acquired resistance. However, rates might be indirectly estimated from long-term PFS rates (Table 1). The estimate of the acquired resistance rate (ARr) may be deduced from PFS rates (PFSr) and PD rates (PDr); ARr(%) = 100% − PFSr(%) − PDr(%). This parameter was newly developed in this review. As shown in Table 1, primary resistance occurs in approximately 30% of melanomas treated with PD-1 monotherapy and 25% of melanomas treated with the combination therapy of ipilimumab and nivolumab, whereas acquired resistance occurs in between 40% and almost 50% of melanomas under ICI therapy. The ARr depends on the depth of response in patients receiving PD-1 monotherapy; 17% of patients with CR as the best response and 54% of patients with PR as the best response eventually develop secondary resistance [7]. In patients treated with the ICI combination therapy, the risk of ARr appears to be independent of the depth of response and occurs in 20% of patients without progression within the first year after ipilimumab plus nivolumab [38]. Therefore, in patients treated with ICI combination therapy (as opposed to anti-PD-1 monotherapy), a CR does not appear to be superior to a PR in maintaining the ICI response.
Predictive biomarkers would be desirable, especially to identify primary resistant tumors and to choose upfront an alternative treatment to ICI or to delineate the reason for secondary resistance allowing rational combinations. However, as mentioned in chapter 4, no tumor-derived biomarker has been established in clinical routines yet, and few blood-derived biomarkers are used. Among them, patients with increased serum lactate dehydrogenase (LDH) levels are more likely to be primarily resistant to ICI therapy [7,43,44]. The amount of circulating tumor DNA (ctDNA) in the peripheral blood was shown to correlate with the ICI response; however, ctDNA and LDH levels might simply correlate with the tumor burden [45]. In addition, the composition of peripheral blood immune cells, especially the neutrophil–lymphocyte ratio and the number of myeloid-derived suppressor cells (MDSC) play a role [46]. High levels of soluble immune checkpoints such as sPD1 and sLAG3 have been shown to correlate with PD as BOR [47,48]. A clinically easily usable predictive biomarker would be the detection of autoantibodies; first results demonstrate that potential antibody profiles correlate with treatment response [49].
Several Phase III pivotal trials have tested various ICI combinations with varying success to decrease the number of primary resistance patients. The already approved combination of relatlimab and nivolumab within the Relativity-047 trial demonstrated an advantage of combination therapy over anti-PD-1 monotherapy, with 30% and 42% of patients revealing PD as BOR, respectively [50]. Other combinations tested, such as bempegaldesleukin plus nivolumab and the oncolytic virus talimogene laherparepvec (TVEC) plus pembrolizumab have not shown a significant benefit for patients with advanced melanoma [51,52]. A combination of PD-1 inhibition with targeted BRAF/MEK inhibition, e.g., within the Combi-I and ImSpire 150 trial, revealed a similarly low primary resistance rate as BRAF/MEK inhibition alone but marginal improvement in the development of secondary resistance [53,54,55]. However, these trials compared BRAF/MEK inhibition to BRAF/MEK inhibition plus ICI. The ImSpire 150 trial demonstrated a significant PFS improvement which led to FDA approval, whereas the COMBIi and the Keynote 22 study revealed only a trend of improved PFS without statistical significance.
Numerous Phase I–III trials for ICI-resistant patients are ongoing, with some first results reported (Table 2). The reported ORRs range between 8 and 67% with a median PFS (if reported) of 4–5 months. However, some demonstrate quite encouraging durations of response, e.g., for lifileucel, an autologous tumor-infiltrating lymphocyte (TIL) product, with the median duration of response still not reached after a median follow up of 28 months (range 2.2–35.2+ months) [56]. In the LEAP-004 study investigating the tyrosine kinase inhibitor lenvatinib plus pembrolizumab, a duration of response of 8.3 months was noted [57]. As summarized in Table 2, the studies for ICI-resistant patients are mainly Phase I and II. There is only one Phase III study, and results of this study have not been fully presented yet.
For the treatment with ipilimumab combinations after anti-PD1 failure, only a few studies have been performed. In the Illuminate studies, ipilimumab was combined with the Toll-like receptor agonist tilsotolimod, but the combination therapy was not superior to ipilimumab alone in the Phase III trial (Table 2) [70]. Concerning the comparison of ipilimumab monotherapy or combination to anti-PD-1 therapy, only retrospective analyses are available [70]. A multivariate analysis of 355 melanoma patients resistant to PD-1 (72% with primary resistance, 28% with secondary resistance) revealed that ICI combination therapy is superior to ipilimumab monotherapy in terms of response rates (odds ratio (OR) = 2.72 (95% CI = 1.5–4.93; p = 0.0009) and overall survival (OR = 0.61 (0.43–0.86; p = 0.0054), interestingly, without higher toxicity (≥grade 3 toxicity, 31% for ICI combination and 33% for ipilimumab monotherapy) [70]. A prospective Phase II study comparing ipilimumab monotherapy to a ipilimumab/nivolumab combination in PD-1 refractory patients is ongoing (SWOG S1616; NCT03033576).
Recently, ipilimumab monotherapy was compared with TIL therapy in an investigator-initiated trial with almost 170 patients. Here, PFS was 7.2 months for TIL therapy and 3.1 months for ipilimumab monotherapy (HR 0.5; 95%CI 0.35–0.72). However, overall survival was not superior and toxicity was high with all patients in the TIL arm experiencing grade 4 adverse events [71]. Another Phase II study investigated the use of TIL in the PD-1 refractory setting [71]. This approach will be further proceeded with the intent of approval by the FDA.
In BRAF V600-mutated patients, there is also the option to switch between ICI and BRAF-targeted therapy. Recent prospective sequencing studies clearly showed that patients benefit from initial ICI followed (in case of progression) by BRAF-directed targeted therapy compared to the opposite sequence [72].

5.2. Local Approaches

Local ablative therapies such as radiotherapy may be used, either in conjunction with systemic therapy or alone, in the case of limited disease progression in the metastatic setting. Radiation therapy induces DNA damage, autophagy and necrosis in tumor cells, leading to damage-associated molecular patterns (DAMP) and the stimulation of dendritic cells, which present tumor antigens and activate cytotoxic T-cells [73].
A recently published European consensus paper provides indication-overarching definitions for oligometastatic disease [74]. Using a system of 17 factors to characterize oligometastatic disease, the authors of the consensus paper concluded that patients with induced oligoprogression might have long-term survival when local treatment is combined with effective systemic treatment such as immunotherapy for melanoma and is used repeatedly [74,75]. However, in oligometastatic disease, the question is if a single progressing lesion develops because of secondary resistance just in that metastasis or if it is the first progressing metastasis of resistant disease. The literature is scarce on the efficacy of local ablative therapies in extracranial metastases progressing after PD-(L)1 therapy. In a retrospective study evaluating 294 patients with ICI and following solitary progression, almost half of the patients treated for solitary progression after prior response to ICI had no subsequent progression after a median follow up of 3.5 years [75]. In patients with solitary progression after the cessation of ICI, combining local therapy and ICI rechallenge was not associated with improved OS compared to the case with local treatment alone.

5.3. Rechallenge

Concerning rechallenge (defined as “repeated treatment with the same therapeutic class following disease progression in patients who had a clinical benefit with prior treatment for unresectable or metastatic disease”) with anti-PD-1 therapy, only retrospective analyses and small post hoc analyses of clinical trials have been published so far [76]. Within the 7-year follow up of the Phase III Keynote-006 trial, 16 patients who received a pembrolizumab rechallenge at progression after initial disease control (SD/PR/CR) were reported [77]. After rechallenge, nine patients achieved a response (56%; 4 CR, 5 PR) and five reached disease stabilization, with a 2-year PFS of 62.5%. However, from the first reports on these patients, it needs to be stated that none of the progress has been histologically verified; patients with solitary progression were additionally treated locally and 2two CRs at rechallenge were actually from adjuvant therapies [6,78]. In the largest-to-date retrospective analysis of 34 patients with metastatic melanoma who had discontinued anti-PD-1 monotherapy due to progressive disease and were rechallenged with PD-1 monotherapy, only 5 of 34 patients (14.7%) responded to retreatment [79]. This fits the results of a recent meta-analysis including seven reports on anti-PD-1 rechallenge after anti-PD-1 failure with 85 patients in total with an ORR of 15.5% and a PFS of 8.2 months [76,80]. However, it needs to be mentioned that none of the patients progressing under adjuvant anti-PD1 who continued first-line PD-1 inhibition benefitted from therapy [81]. Hence, patients might benefit from rechallenge after an interval therapy.

6. Resistance in Melanoma Brain Metastases

Primary and acquired resistance due to new or progressive brain metastases are common problems in metastatic melanoma patients. As registrational trials (Table 1) usually exclude patients with brain metastases or limit inclusion to those with non-active, non-symptomatic brain metastases, data for response patterns are rare, particularly for patients with symptomatic CNS metastases [82]. In recent years, a few Phase II trials have investigated the activity of ICI in asymptomatic and symptomatic melanoma brain metastases (MBM). Activated cytotoxic T-cells appear to be able to cross the blood–brain barrier, therefore providing a mechanistic rationale behind the efficacy of ICI in brain metastases [83]. In asymptomatic patients with MBM, intracranial ORR is in a similar range as that in patients with extracerebral metastases, i.e., 51–54% for nivolumab plus ipilimumab and 21%–26% for anti-PD-1 monotherapy. Intracranial PFS per a blinded, independent central review was 53% for the ICI combination after 5 years [41,42,58]. In contrast, efficacy is much lower in patients with symptomatic MBM, with an intracranial ORR of 17% and 6%, respectively; intracranial PFS was 28% after 3 years for the ICI combination therapy [41,42]. Since symptomatic MBM often requires the use of corticosteroids, this could also contribute to the lower efficacy of ICI in this patient population.
The estimated rates of intracranial primary resistance to ICI therapy remain high in MBM; they are between 30 and 40% of asymptomatic patients, and 60–70% of symptomatic patients treated with combined nivolumab and ipilimumab progress early [41,42]. For PD-1 monotherapy, the rate of PD as BOR was as high as 76% in asymptomatic patients and 80% in symptomatic patients [42,58]. Acquired resistance for patients with brain metastases can be estimated from recent data from the ABC study—18% for ipilimumab plus nivolumab and 12% for nivolumab in asymptomatic patients at 5 years (Table 1).
In tumors harboring BRAF V600 mutations, BRAF-directed therapy is also an option. In the COMBI-MB trial, response rates of intracerebral metastases to dabrafenib + trametinib were approximately 50%, and the PFS was approximately 5 months [84]. Studies are ongoing to investigate the combination of ICI- and BRAF-targeted therapy, such as the TRICOTEL study [85] and the SWOG S200 study (NCT04511013). There are also approaches combining ICI with antiangiogenic treatments in MBM, such as atezolizumab plus bevacizumab (NCT03175432).
New treatment options and combinations are therefore needed. Single-dose radiosurgery of melanoma brain metastases achieves 1-year local control rates of 86% [86,87] and comparable outcomes are achieved in larger metastases with hypofractionated radiosurgery delivered in three to five fractions [88]. Retrospective analyses suggest that the combination of stereotactic radiotherapy and immune checkpoint inhibitors achieves a survival benefit without increased toxicity compared to stereotactic radiotherapy or immune checkpoint inhibitors alone [89,90,91,92,93,94,95,96,97]. The combination strategy of immune checkpoint inhibitors plus stereotactic radiotherapy is being prospectively investigated in several studies (NCT03340129, NCT02974803 and NCT03430947). Radiosurgery might also be an option in oligoprogression with brain metastases [82]. Other interesting combination partners might include targeted therapies. Hyperactivation of the PI3K–AKT pathway, loss of PTEN expression, and activation of the oxidative phosphorylation (OXPHOS) metabolic pathway in melanoma brain metastases compared to metastases outside the brain might present a druggable resistance mechanism [98,99,100,101,102,103,104]. However, in PD-1-resistant patients with MBM, monotherapy with the PI3K inhibitor buparlisib did not lead to intracranial responses [105].

7. Resistance in the Adjuvant Setting

For ICI therapy in the adjuvant setting, the definition of resistance differs compared to the metastatic stage (Figure 2), as there is no imaging or advanced clinical method to assess response to therapy in the presence of residual (micro)metastatic disease. Therefore, the only feasible method to assess resistance is to determine the time from therapy initiation or therapy discontinuation to relapse. For adjuvant therapy, the SITC Taskforce suggested to define primary resistance/early relapse as recurrence at <12 weeks and late relapse as recurrence at ≥12 weeks after the termination of therapy (Figure 2) [23].
In six different adjuvant Phase II–III trials in patients with stage II to IV melanoma, varying recurrence rates were reported, impacted by the recurrence risk at the melanoma stage and by different follow-up times from 1–2 years in stage II to up to 4–5 years in stage III and IV (Table 3). Therapy with PD-1 inhibitors resulted in recurrence rates of between 69% (stage IV at 4 years), 45% (stage III at 5 years) and 19% (stage II at 2 years). For combined immune checkpoint blockade with a PD-1 and a CTLA-4 inhibitor, recurrence rates decreased to 36% after 4 years in stage IV melanoma; the addition of a lower dose and the use of a different schedule for ipilimumab has not shown a benefit in stage III/IV disease (Table 3) [106,107,108,109].
The most common type of recurrence in patients with stage III and IV disease was distant recurrence. Although this is not surprising for stage IV disease, this was also seen for stage III patients within the Keynote-054 trial, with 63% of all recurrences happening at distant sites (mainly lymph nodes, lung and liver) and 36% of all recurrences being loco-regional only at 3.5 years [111]. For stage II patients, loco-regional and distant recurrences were similarly distributed, with 4–6% at 1 year and 9% at 2 years [10,11,112]. Most frequent distant sites of metastases here were the lung, brain and lymph nodes. It is tempting to speculate that the different patterns of recurrence may relate to different mechanisms of resistance which might even be of relevance to the subsequent treatment approaches.
Concerning predictive biomarkers, the detection of ctDNA pre-treatment (with a prevalence of 16% in the adjuvant setting of stage III/IV disease) was recently shown to correlate with an increased recurrence rate within the CheckMate-915 trial (HR 1.87 for RFS; HR 2.86 for DMFS) independent of the treatment arm. An improved prediction of RFS was accomplished when the ctDNA level was combined with other biomarkers from the tumor microenvironment such as CD8+ T-cells, PD-L1, interferon-gamma (IFN-g) gene expression signature and tumor mutational burden (TMB), but also Breslow level and clinical stage [113]. In line with this, within the CheckMate-238 and Keynote-054 studies a numerical prolongation of RFS for PD-1-positive tumors was demonstrated [106,107]. In addition, an evaluation by Weber and colleagues of an IFN g gene expression signature, TMB, and CD8+ T-cell infiltration showed an association with improved RFS [114].
According to the definition by the SITC Immunotherapy Resistance Taskforce, primary resistance in adjuvant therapy could be roughly estimated from recurrence rates at 15 months (12 months of treatment + 12 weeks). Deduced from the Kaplan–Meier curves for RFS, under anti-PD-1 monotherapy, this would be about 14% of patients with stage II disease (Keynote-716), 30% of patients with stage III disease (Keynote-054) and about 54% of patients with stage IV disease (IMMUNED). In a retrospective multicenter evaluation, the median time to recurrence in stage III and IV disease patients under adjuvant anti-PD-1 therapy was 4.6 months (95% CI 0.3–35.7), where 76% experienced recurrence during adjuvant PD-1 after a median of 3.2 months and 24% experienced recurrence following treatment termination after a median of 12.5 months [81]. For patients who relapse in the adjuvant setting, further therapy selections should consider whether the relapse occurred during or after the end of adjuvant therapy, hence depending on primary or secondary resistance. Retreatment (defined as “repeated treatment with the same therapeutic class following relapse after adjuvant treatment has ended”), dose escalation (defined as treatment with the same therapeutic class) and the use of an additional agent following disease progression are possible treatment options [76,115].
Prospective data on retreatment after the failure of adjuvant ICI therapy are currently limited. Within the Keynote-054 trial, patients were retreated with pembrolizumab if the recurrence occurred more than six months after the completion of adjuvant pembrolizumab therapy. Briefly, 20 of 47 patients who developed this late relapse were retreated; of these, 9 had unresectable stage IV disease and were evaluable for response. The activity was low, with only one responder (CR 1/9; SD 3/9; PD 5/9) and a median PFS of 4.1 months [116].
In a multicenter retrospective analysis of 850 patients treated with adjuvant PD-1 inhibition in stage III or IV [81], about 17% of patients experienced recurrence within a median time of 4.6 months. Out of 136 patients with cutaneous melanoma, 104 (76%) experienced recurrence upon therapy after a median of 3.2 months, and 32 (24%) experienced recurrence after the completion of adjuvant therapy at a median time of 12.5 months. Eighty-nine (65%) patients received systemic therapy after recurrence. Of those who experienced recurrence on adjuvant treatment, none (0/6) responded to anti-PD-1 alone, 24% (8/33) responded to ipilimumab ± anti-PD-1 and 78% (18/23) responded to BRAF/MEK inhibition. Of those who recurred after adjuvant therapy, 40% (2/5) responded to anti-PD-1 retreatment in monotherapy, another 40% (2/5) responded to ipilimumab-based therapy and 90% (9/10) responded to BRAF/MEK inhibition [81]. Hence, anti-PD-1 retreatment in monotherapy might have clinical utility in patients with late relapses only. Patients with early relapses will need second-line treatments comparable to those for advanced melanoma.
In another retrospective analysis [70], out of 44 melanoma patients who relapsed after adjuvant anti-PD-1 monotherapy, 1/8 (13%) patients treated with ipilimumab monotherapy and 13/36 (36%) patients treated with ipilimumab plus PD-1 inhibitors responded to therapy, suggesting an advantage of ICI combination treatment after the failure of adjuvant anti-PD-1 [70].
Of note is that an increasing number of interventional trials are allowing the recruitment of patients with previous adjuvant therapy, often restricted to a minimum interval of recurrence-free survival after the completion of adjuvant therapy. Subgroup analyses of such patients will provide further insights into the resistance mechanism.

8. Lessons from the Neoadjuvant Studies

With neoadjuvant treatment, the response to systemic treatment can be assessed directly, and hence, primary resistant melanoma can easily be identified. This can be carried out radiologically before surgery as in advanced disease, but also and more precisely via a pathologic review of the surgical specimen. Interestingly, radiological and pathological responses differ a lot (Supplementary Table S1), especially concerning the rate of complete or near-complete responses. Even though a metastasis might still be seen via imaging (PR or SD according to RECIST1.1), the presence of equal to or less than 10% of viable tumor cells are per definition a major pathological response (MPR). Pooled analysis from four neoadjuvant immunotherapy trials in resectable macroscopic stage III disease revealed that patients achieving a response, and especially a MPR have a durable benefit whereas non-responders have a high risk of secondary resistance and recurrence after surgical resection [117]. The highest MPR rates were found with neoadjuvant ICI combination therapy with a 61% rate after two cycles of ipilimumab and nivolumab [117] and a 66% rate in a small study with relatlimab and nivolumab [118] as opposed to only a 21% rate with anti-PD-1 monotherapy [117] (Supplementary Table S1).
This means on the contrary that primary resistance rates are lower with ICI combination therapy with a pNR rate of 25% and 27% for nivolumab plus ipilimumab or relatlimab, respectively, and up to 66% for anti-PD-1 monotherapy [117,118].
However, in the only small trial comparing ICI combination therapy with anti-PD-1 monotherapy in the neoadjuvant setting, the difference in progression-free and overall survival was not statistically significant [119]. Importantly, patients responding to neoadjuvant immunotherapy might not need further adjuvant treatment. This was demonstrated in the PRADO study, where only patients with pathological non-response received adjuvant therapy after resection. Nevertheless, patients with a pPR not achieving a MPR had a much higher recurrence rate after surgery and might have benefitted from further adjuvant therapy to prevent secondary resistance. Hence, a pathological response might lead to further adjuvant therapy after resection. In primary melanoma, a first neoadjuvant trial with pembrolizumab was conducted on patients with desmoplastic melanoma [120], which is known to be very responsive to immunotherapy due to the high mutational burden [121]. Here, in 16 of the 29 treated patients (55%), a pCR was seen with no relapses after surgery so far.
There is some evidence that melanoma resistance can be better overcome via neoadjuvant than adjuvant immunotherapy as the stimulation of the immune response seems to result in a better expansion of tumor-resident T-cell clones in the presence of macroscopic tumors [122]. Just recently, the first randomized clinical trial was presented that compared an adjuvant with a neoadjuvant plus adjuvant pembrolizumab in more than 300 patients with resectable stage III or IV melanoma [123]. After two years, the event-free survival (event defined as not receiving surgery or adjuvant therapy, progression or death) was 72% in the perioperative and only 49% in the adjuvant treatment arm (HR, 0.58; 95% CI, 0.39–0.87). However, the OS was not statistically different between the arms. A comparison of the Kaplan–Meier curves shows that in the adjuvant arm there was especially more primary resistance based on the SITC classification criteria. This definitely supports the idea of overcoming anti-PD1 resistance with neoadjuvant therapy. Moreover, neoadjuvant therapy may also allow the escalation or de-escalation of therapy based on response; i.e., patients who have resistant disease may need escalated therapy after surgery, and vice versa. However, this should be substantiated by further randomized clinical trials, at best those including combined ICI regimens.

9. PD-1/PD-L1 Resistant Melanoma: Limitations, Perspectives, Conclusions

Immunotherapy is today of fundamental importance in treating melanoma, in the (neo)adjuvant as well as in the advanced setting. However, for those patients who relapse or progress during or after ICI therapy, further therapeutic options are urgently needed. The different patterns of recurrence in the adjuvant setting and of resistance in the advanced setting are presumably associated with different, diverse mechanisms of resistance to therapy with high interindividual and maybe also intraindividual differences. A better understanding of cancer resistance mechanisms is needed to develop rational strategies in clinics. However, multiple Phase I and II trials on ICI-resistant advanced/metastatic melanoma show only modest outcomes (Table 2). Most of these trials employ a PD-(L)1 backbone to demonstrate synergistic efficacy via the combined blockade of additional immunological pathways. Rational combination strategies other than PD1 plus CTLA4 or LAG3 comprise the investigation of further immune checkpoints such as TIGIT and TIM3, of epigenetic modifiers such as histone deacetylase (HDAC) inhibitors, modified cytokines (IL-2 derivatives), cytokine blockers (tocilizumab, TGFb), kinase inhibitors (lenvatinib, BRAFi+MEKi), intralesionals (TLR, oncolytic viruses), MDSC/Treg blockers, and vaccines, as well as of modifiers of tumor metabolomics. Novel biomarkers with which to characterize the immune system (e.g., microbiome, HLA, cytokines) and tumor cells/microenvironment (e.g., IFN g signature and signaling pathways) are important for an improved understanding of extrinsic and intrinsic resistance mechanisms and the rational design of therapeutic approaches [124,125,126]. Here, neoadjuvant trials might help to rapidly select the best combination therapy, at least to overcome primary resistance [127].
In the meantime, clinicians have to choose among the treatment options which are available in a type of “trial and error” approach. In addition to a switch between systemic therapies, i.e., the classical sequential use of second-, third-line and subsequent salvage therapy, these options are (i) “re-treatment”—defined as “repeated treatment with the same therapeutic class following relapse after adjuvant treatment has ended” [76,115];(ii) “rechallenge”—recently defined as “repeated treatment with the same therapeutic class following disease progression in patients who had clinical benefit with prior treatment for unresectable or metastatic disease” [76,115]; (iii) “treatment beyond progression”, i.e., treatment beyond RECIST 1.1. (or iRECIST)-defined PD [128]; or (iv) a combination with local therapies in the case of oligoprogression. However, data on the outcomes of all these approaches remain sparse and are so far deduced mainly from retrospective studies.
Therefore, the aim for all of us should be to include as many patients as possible in clinical and translational trials testing therapies to overcome anti-PD-1 resistance. Moreover, it is warranted that prospective clinical trials also continue data capturing after progression to provide evidence on the sequencing and efficacy of subsequent treatments.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/cancers15133448/s1. Table S1: Frequencies of response and resistance in selected neoadjuvant immunotherapy trials on melanoma (ICI monotherapy, or combined ICI).

Author Contributions

Conceptualization, T.S. and R.G.; methodology, J.C.H., L.Z., T.S. and R.G.; data curation, J.C.H., L.Z., T.S. and R.G.; writing—original draft preparation, all authors; writing—review and editing, all authors; visualization, J.C.H., L.Z., T.S. and R.G. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by Bristol Myers Squibb.

Institutional Review Board Statement

This narrative review did not require ethical approval.

Acknowledgments

Medical writing support was provided by Ecc-Oncology, Trier, Germany, and editing support was provided by Sergey Sulima, of Bristol Myers Squibb and funded by Bristol Myers Squibb.

Conflicts of Interest

J.C.H. reports grants or contracts from Bristol Myers Squibb, Sanofi, and Sun Pharma; consulting fees from Onkowissen and Glaxo Smith Kline; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Amgen, Bristol Myers Squibb, Glaxo Smith Kline, Immunocore, Merck Sharp & Dohme, Novartis, Pierre Fabre, Sanofi, and Sun Pharma; support for attending meetings and/or travel from Bristol Myers Squibb, Iovance Biotherapeutics, and Sun Pharma; participation in a data safety monitoring board or advisory board for Merck Sharp & Dohme, Pierre Fabre, Sun Pharma, Bristol Myers Squibb, Immunocore, Novartis, Philogen, Sanofi, and Nekvax. L.Z. reports payment or honoraria for lectures, presentations, speakers’ bureaus, manuscript writing or educational events from Bristol Myers Squibb, Merck Sharp & Dohme, Novartis, Pierre Fabre, Sanofi, and Sun Pharma; support for attending meetings and/or travel from Merck Sharp & Dohme, Bristol Myers Squibb, Pierre Fabre, Sanofi, Sun Pharma and Novartis; participation in a data safety monitoring board or advisory board for Bristol Myers Squibb, Merck Sharp & Dohme, Novartis, Pierre Fabre, Sanofi, and Sun Pharma. T.S. reports employment as a senior manager and medical advisor and stock or stock options at Bristol Myers Squibb. T.E. reports payment or honoraria for lectures, presentations, speakers bureaus’, manuscript writing or educational events and participation in advisory boards for Bristol Myers Squibb, Merck Sharp & Dohme, Novartis, Pierre Fabre, Sanofi, Immunocore, CureVac and Almirall Hermal; an unpaid leadership position as board member of DeCOG. F.M. reports consulting fees from Novartis, Roche, Bristol Myers Squibb, Merck Sharp & Dohme, Pierre Fabre, Sanofi, and Immunocore; payment or honoraria for lectures, presentations, speakers’ bureaus, manuscript writing or educational events from Novartis, Roche, Bristol Myers Squibb, Merck Sharp & Dohme, Pierre Fabre, Sanofi, and Immunocore; support for attending meetings and/or travel from Novartis, Roche, Bristol Myers Squibb, Merck Sharp & Dohme, Pierre Fabre, Sanofi, and Immunocore; participation in a data safety monitoring board or advisory board for Novartis, Roche, Bristol Myers Squibb, Merck Sharp & Dohme, Pierre Fabre, Sanofi, and Immunocore. A.R. reports grants or contracts from Bristol Myers Squibb and Novartis; payment or honoraria for lectures, presentations, speakers’ bureaus, manuscript writing or educational events from Bristol Myers Squibb and Novartis; an unpaid leadership (DeCOG committee Translational Research & Biobank). D.V. reports grants or contracts from Merck, Pfizer, and AstraZeneca; payment or honoraria for lectures, presentations, speakers’ bureaus, manuscript writing or educational events from Roche, Astra Zeneca, Sun Pharma, Merck, Lilly, Ferring, Takeda, and Sanofi; participation in an advisory board for Boehringer, Bristol Myers Squibb, Chugai, Merck, and Roche. C.W. reports payment or honoraria for lectures, presentations, speakers’ bureaus, manuscript writing or educational events from Bristol Myers Squibb, Merck Sharp & Dohme, Pierre Fabre, Novartis, Sun Pharma, Medac, Sanofi, and Recordati; support for attending meetings and/or travel from Bristol Myers Squibb, Novartis, Medac, Pierre Fabre, and Sun Pharma; participation in a data safety monitoring board or advisory board for Bristol Myers Squibb, Sun Pharma, Sanofi, and Novartis. R.G. reports grants or contracts from Novartis, Sun Pharma, Amgen, Sanofi/Regeneron, Merck-Serono, Kyowa-Kirin, and Almirall-Hermal; payment or honoraria for lectures, presentations, speakers’ bureaus, manuscript writing or educational events from Roche Pharma, Bristol Myers Squibb, Novartis, Merck Sharp & Dohme, Almirall-Hermal, Amgen, Merck-Serono, Sanofi/Regeneron, Pierre Fabre; support for attending meetings and/or travel from Sun Pharma, Pierre Fabre, and Boehringer-Ingelheim; participation in a data safety monitoring board or advisory board for Roche Pharma, Bristol Myers Squibb, Novartis, Merck Sharp & Dohme, Almirall-Hermal, Amgen, Pierre Fabre, Merck-Serono, Sun Pharma, Merck-Serono, Sanofi/Regeneron, Immunocore, 4SC and Delcath; an unpaid leadership position as the chairman of Arbeitsgemeinschaft Dermatologische Onkologie (ADO). All other authors declare no conflicts of interest. The funder had the following involvements in the review: contributions to the conceptualization; the decision to publish, in agreement with the Steering Committee; and the provision of funding for manuscript preparation and submission.

References

  1. Ferlay, J.; Colombet, M.; Soerjomataram, I.; Parkin, D.M.; Pineros, M.; Znaor, A.; Bray, F. Cancer statistics for the year 2020: An overview. Int. J. Cancer 2021, 149, 778–789. [Google Scholar] [CrossRef] [PubMed]
  2. Vasan, N.; Baselga, J.; Hyman, D.M. A view on drug resistance in cancer. Nature 2019, 575, 299–309. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Silva, J.L.; Cino, E.A.; Soares, I.N.; Ferreira, V.F.; De Oliveira, G.A.P. Targeting the Prion-like Aggregation of Mutant p53 to Combat Cancer. Acc. Chem. Res. 2018, 51, 181–190. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Capodanno, Y.; Chen, Y.; Schrader, J.; Tomosugi, M.; Sumi, S.; Yokoyama, A.; Hiraoka, N.; Ohki, R. Cross-talk among MEN1, p53 and Notch regulates the proliferation of pancreatic neuroendocrine tumour cells by modulating INSM1 expression and subcellular localization. Neoplasia 2021, 23, 979–992. [Google Scholar] [CrossRef] [PubMed]
  5. Levine, A.J. p53: 800 million years of evolution and 40 years of discovery. Nat. Rev. Cancer 2020, 20, 471–480. [Google Scholar] [CrossRef] [PubMed]
  6. Lane, D.P. Cancer. p53, guardian of the genome. Nature 1992, 358, 15–16. [Google Scholar] [CrossRef]
  7. Cheok, C.F.; Lane, D.P. Exploiting the p53 Pathway for Therapy. Cold Spring Harb. Perspect. Med. 2017, 7, a026310. [Google Scholar] [CrossRef]
  8. Venot, C.; Maratrat, M.; Sierra, V.; Conseiller, E.; Debussche, L. Definition of a p53 transactivation function-deficient mutant and characterization of two independent p53 transactivation subdomains. Oncogene 1999, 18, 2405–2410. [Google Scholar] [CrossRef] [Green Version]
  9. Vousden, K.H.; Lu, X. Live or let die: The cell’s response to p53. Nat. Rev. Cancer 2002, 2, 594–604. [Google Scholar] [CrossRef] [Green Version]
  10. Zhu, J.; Zhang, S.; Jiang, J.; Chen, X. Definition of the p53 functional domains necessary for inducing apoptosis. J. Biol. Chem. 2000, 275, 39927–39934. [Google Scholar] [CrossRef] [Green Version]
  11. Bode, A.M.; Dong, Z. Post-translational modification of p53 in tumourigenesis. Nat. Rev. Cancer 2004, 4, 793–805. [Google Scholar] [CrossRef]
  12. Prives, C. Signaling to p53: Breaking the MDM2-p53 circuit. Cell 1998, 95, 5–8. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Honda, R.; Tanaka, H.; Yasuda, H. Oncoprotein MDM2 is a ubiquitin ligase E3 for tumor suppressor p53. FEBS Lett. 1997, 420, 25–27. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Yin, Y.; Stephen, C.W.; Luciani, M.G.; Fahraeus, R. p53 Stability and activity is regulated by Mdm2-mediated induction of alternative p53 translation products. Nat. Cell Biol. 2002, 4, 462–467. [Google Scholar] [CrossRef] [PubMed]
  15. Ohki, R.; Kawase, T.; Ohta, T.; Ichikawa, H.; Taya, Y. Dissecting functional roles of p53 N-terminal transactivation domains by microarray expression analysis. Cancer Sci. 2007, 98, 189–200. [Google Scholar] [CrossRef]
  16. Suzuki, S.; Tsutsumi, S.; Chen, Y.; Ozeki, C.; Okabe, A.; Kawase, T.; Aburatani, H.; Ohki, R. Identification and characterization of the binding sequences and target genes of p53 lacking the 1st transactivation domain. Cancer Sci. 2020, 111, 451–466. [Google Scholar] [CrossRef]
  17. Sakaguchi, K.; Saito, S.; Higashimoto, Y.; Roy, S.; Anderson, C.W.; Appella, E. Damage-mediated phosphorylation of human p53 threonine 18 through a cascade mediated by a casein 1-like kinase. Effect on Mdm2 binding. J. Biol. Chem. 2000, 275, 9278–9283. [Google Scholar] [CrossRef] [Green Version]
  18. Shieh, S.Y.; Taya, Y.; Prives, C. DNA damage-inducible phosphorylation of p53 at N-terminal sites including a novel site, Ser20, requires tetramerization. EMBO J. 1999, 18, 1815–1823. [Google Scholar] [CrossRef]
  19. Unger, T.; Juven-Gershon, T.; Moallem, E.; Berger, M.; Vogt Sionov, R.; Lozano, G.; Oren, M.; Haupt, Y. Critical role for Ser20 of human p53 in the negative regulation of p53 by Mdm2. EMBO J. 1999, 18, 1805–1814. [Google Scholar] [CrossRef]
  20. Dumaz, N.; Milne, D.M.; Meek, D.W. Protein kinase CK1 is a p53-threonine 18 kinase which requires prior phosphorylation of serine 15. FEBS Lett. 1999, 463, 312–316. [Google Scholar] [CrossRef] [Green Version]
  21. Banin, S.; Moyal, L.; Shieh, S.; Taya, Y.; Anderson, C.W.; Chessa, L.; Smorodinsky, N.I.; Prives, C.; Reiss, Y.; Shiloh, Y.; et al. Enhanced phosphorylation of p53 by ATM in response to DNA damage. Science 1998, 281, 1674–1677. [Google Scholar] [CrossRef] [PubMed]
  22. Shieh, S.Y.; Ahn, J.; Tamai, K.; Taya, Y.; Prives, C. The human homologs of checkpoint kinases Chk1 and Cds1 (Chk2) phosphorylate p53 at multiple DNA damage-inducible sites. Genes Dev. 2000, 14, 289–300. [Google Scholar] [CrossRef] [PubMed]
  23. Higashimoto, Y.; Saito, S.; Tong, X.H.; Hong, A.; Sakaguchi, K.; Appella, E.; Anderson, C.W. Human p53 is phosphorylated on serines 6 and 9 in response to DNA damage-inducing agents. J. Biol. Chem. 2000, 275, 23199–23203. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Soubeyrand, S.; Schild-Poulter, C.; Haché, R.J. Structured DNA promotes phosphorylation of p53 by DNA-dependent protein kinase at serine 9 and threonine 18. Eur. J. Biochem. 2004, 271, 3776–3784. [Google Scholar] [CrossRef] [PubMed]
  25. Chehab, N.H.; Malikzay, A.; Stavridi, E.S.; Halazonetis, T.D. Phosphorylation of Ser-20 mediates stabilization of human p53 in response to DNA damage. Proc. Natl. Acad. Sci. USA 1999, 96, 13777–13782. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Matlashewski, G.J.; Tuck, S.; Pim, D.; Lamb, P.; Schneider, J.; Crawford, L.V. Primary structure polymorphism at amino acid residue 72 of human p53. Mol. Cell Biol. 1987, 7, 961–963. [Google Scholar] [CrossRef]
  27. Fan, R.; Wu, M.T.; Miller, D.; Wain, J.C.; Kelsey, K.T.; Wiencke, J.K.; Christiani, D.C. The p53 codon 72 polymorphism and lung cancer risk. Cancer Epidemiol. Biomark. Prev. 2000, 9, 1037–1042. [Google Scholar]
  28. Tommiska, J.; Eerola, H.; Heinonen, M.; Salonen, L.; Kaare, M.; Tallila, J.; Ristimaki, A.; von Smitten, K.; Aittomaki, K.; Heikkila, P.; et al. Breast cancer patients with p53 Pro72 homozygous genotype have a poorer survival. Clin. Cancer Res. 2005, 11, 5098–5103. [Google Scholar] [CrossRef] [Green Version]
  29. Ozeki, C.; Sawai, Y.; Shibata, T.; Kohno, T.; Okamoto, K.; Yokota, J.; Tashiro, F.; Tanuma, S.; Sakai, R.; Kawase, T.; et al. Cancer susceptibility polymorphism of p53 at codon 72 affects phosphorylation and degradation of p53 protein. J. Biol. Chem. 2011, 286, 18251–18260. [Google Scholar] [CrossRef] [Green Version]
  30. el-Deiry, W.S.; Kern, S.E.; Pietenpol, J.A.; Kinzler, K.W.; Vogelstein, B. Definition of a consensus binding site for p53. Nat. Genet. 1992, 1, 45–49. [Google Scholar] [CrossRef]
  31. Fischer, M. Census and evaluation of p53 target genes. Oncogene 2017, 36, 3943–3956. [Google Scholar] [CrossRef] [Green Version]
  32. Oda, E.; Ohki, R.; Murasawa, H.; Nemoto, J.; Shibue, T.; Yamashita, T.; Tokino, T.; Taniguchi, T.; Tanaka, N. Noxa, a BH3-only member of the Bcl-2 family and candidate mediator of p53-induced apoptosis. Science 2000, 288, 1053–1058. [Google Scholar] [CrossRef]
  33. Ohki, R.; Nemoto, J.; Murasawa, H.; Oda, E.; Inazawa, J.; Tanaka, N.; Taniguchi, T. Reprimo, a new candidate mediator of the p53-mediated cell cycle arrest at the G2 phase. J. Biol. Chem. 2000, 275, 22627–22630. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Kawase, T.; Ichikawa, H.; Ohta, T.; Nozaki, N.; Tashiro, F.; Ohki, R.; Taya, Y. p53 target gene AEN is a nuclear exonuclease required for p53-dependent apoptosis. Oncogene 2008, 27, 3797–3810. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Ezawa, I.; Sawai, Y.; Kawase, T.; Okabe, A.; Tsutsumi, S.; Ichikawa, H.; Kobayashi, Y.; Tashiro, F.; Namiki, H.; Kondo, T.; et al. Novel p53 target gene FUCA1 encodes a fucosidase and regulates growth and survival of cancer cells. Cancer Sci. 2016, 107, 734–745. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Asano, Y.; Kawase, T.; Okabe, A.; Tsutsumi, S.; Ichikawa, H.; Tatebe, S.; Kitabayashi, I.; Tashiro, F.; Namiki, H.; Kondo, T.; et al. IER5 generates a novel hypo-phosphorylated active form of HSF1 and contributes to tumourigenesis. Sci. Rep. 2016, 6, 19174. [Google Scholar] [CrossRef]
  37. Chen, Y.; Takikawa, M.; Tsutsumi, S.; Yamaguchi, Y.; Okabe, A.; Shimada, M.; Kawase, T.; Sada, A.; Ezawa, I.; Takano, Y.; et al. PHLDA1, another PHLDA family protein that inhibits Akt. Cancer Sci. 2018, 109, 3532–3542. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Kawase, T.; Ohki, R.; Shibata, T.; Tsutsumi, S.; Kamimura, N.; Inazawa, J.; Ohta, T.; Ichikawa, H.; Aburatani, H.; Tashiro, F.; et al. PH domain-only protein PHLDA3 is a p53-regulated repressor of Akt. Cell 2009, 136, 535–550. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Mehta, A.; Haber, J.E. Sources of DNA double-strand breaks and models of recombinational DNA repair. Cold Spring Harb. Perspect. Biol. 2014, 6, a016428. [Google Scholar] [CrossRef] [Green Version]
  40. Blackford, A.N.; Jackson, S.P. ATM, ATR, and DNA-PK: The Trinity at the Heart of the DNA Damage Response. Mol. Cell 2017, 66, 801–817. [Google Scholar] [CrossRef] [Green Version]
  41. Vilenchik, M.M.; Knudson, A.G. Endogenous DNA double-strand breaks: Production, fidelity of repair, and induction of cancer. Proc. Natl. Acad. Sci. USA 2003, 100, 12871–12876. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Marechal, A.; Zou, L. DNA damage sensing by the ATM and ATR kinases. Cold Spring Harb. Perspect. Biol. 2013, 5, a012716. [Google Scholar] [CrossRef] [PubMed]
  43. Novak, B.; Sible, J.C.; Tyson, J.J. Checkpoints in the Cell Cycle. In Encyclopedia of Systems Biology; Wiley: Hoboken, NJ, USA, 2003; pp. 254–259. [Google Scholar]
  44. Finn, K.; Lowndes, N.F.; Grenon, M. Eukaryotic DNA damage checkpoint activation in response to double-strand breaks. Cell Mol. Life Sci. 2012, 69, 1447–1473. [Google Scholar] [CrossRef] [PubMed]
  45. Bieging, K.T.; Mello, S.S.; Attardi, L.D. Unravelling mechanisms of p53-mediated tumour suppression. Nat. Rev. Cancer 2014, 14, 359–370. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Chatterjee, N.; Walker, G.C. Mechanisms of DNA damage, repair, and mutagenesis. Environ. Mol. Mutagen 2017, 58, 235–263. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Panier, S.; Boulton, S.J. Double-strand break repair: 53BP1 comes into focus. Nat. Rev. Mol. Cell Biol. 2014, 15, 7–18. [Google Scholar] [CrossRef]
  48. Roos, W.P.; Kaina, B. DNA damage-induced cell death by apoptosis. Trends Mol. Med. 2006, 12, 440–450. [Google Scholar] [CrossRef]
  49. Shiloh, Y.; Ziv, Y. The ATM protein kinase: Regulating the cellular response to genotoxic stress, and more. Nat. Rev. Mol. Cell Biol. 2013, 14, 197–210. [Google Scholar] [CrossRef]
  50. Bakkenist, C.J.; Kastan, M.B. DNA damage activates ATM through intermolecular autophosphorylation and dimer dissociation. Nature 2003, 421, 499–506. [Google Scholar] [CrossRef]
  51. Cheng, Q.; Chen, J. Mechanism of p53 stabilization by ATM after DNA damage. Cell Cycle 2010, 9, 472–478. [Google Scholar] [CrossRef] [Green Version]
  52. Hofmann, T.G.; Glas, C.; Bitomsky, N. HIPK2: A tumour suppressor that controls DNA damage-induced cell fate and cytokinesis. Bioessays 2013, 35, 55–64. [Google Scholar] [CrossRef] [PubMed]
  53. Chen, J. The Cell-Cycle Arrest and Apoptotic Functions of p53 in Tumour Initiation and Progression. Cold Spring Harb. Perspect. Med. 2016, 6, a026104. [Google Scholar] [CrossRef] [Green Version]
  54. Emran, T.B.; Shahriar, A.; Mahmud, A.R.; Rahman, T.; Abir, M.H.; Siddiquee, M.F.; Ahmed, H.; Rahman, N.; Nainu, F.; Wahyudin, E.; et al. Multidrug Resistance in Cancer: Understanding Molecular Mechanisms, Immunoprevention and Therapeutic Approaches. Front. Oncol. 2022, 12, 891652. [Google Scholar] [CrossRef] [PubMed]
  55. Haider, T.; Pandey, V.; Banjare, N.; Gupta, P.N.; Soni, V. Drug resistance in cancer: Mechanisms and tackling strategies. Pharmacol. Rep. 2020, 72, 1125–1151. [Google Scholar] [CrossRef]
  56. Hopkins, J.L.; Lan, L.; Zou, L. DNA repair defects in cancer and therapeutic opportunities. Genes Dev. 2022, 36, 278–293. [Google Scholar] [CrossRef] [PubMed]
  57. Janic, A.; Valente, L.J.; Wakefield, M.J.; Di Stefano, L.; Milla, L.; Wilcox, S.; Yang, H.; Tai, L.; Vandenberg, C.J.; Kueh, A.J.; et al. DNA repair processes are critical mediators of p53-dependent tumor suppression. Nat. Med. 2018, 24, 947–953. [Google Scholar] [CrossRef]
  58. Williams, A.B.; Schumacher, B. p53 in the DNA-Damage-Repair Process. Cold Spring Harb. Perspect. Med. 2016, 6, a026070. [Google Scholar] [CrossRef] [Green Version]
  59. Roy, S.; Tomaszowski, K.H.; Luzwick, J.W.; Park, S.; Li, J.; Murphy, M.; Schlacher, K. p53 orchestrates DNA replication restart homeostasis by suppressing mutagenic RAD52 and POLθ pathways. Elife 2018, 7, e31723. [Google Scholar] [CrossRef]
  60. Wang, Y.H.; Ho, T.L.F.; Hariharan, A.; Goh, H.C.; Wong, Y.L.; Verkaik, N.S.; Lee, M.Y.; Tam, W.L.; van Gent, D.C.; Venkitaraman, A.R.; et al. Rapid recruitment of p53 to DNA damage sites directs DNA repair choice and integrity. Proc. Natl. Acad. Sci. USA 2022, 119, e2113233119. [Google Scholar] [CrossRef]
  61. Lodovichi, S.; Cervelli, T.; Pellicioli, A.; Galli, A. Inhibition of DNA Repair in Cancer Therapy: Toward a Multi-Target Approach. Int. J. Mol. Sci. 2020, 21, 6684. [Google Scholar] [CrossRef]
  62. Oliver, T.G.; Mercer, K.L.; Sayles, L.C.; Burke, J.R.; Mendus, D.; Lovejoy, K.S.; Cheng, M.H.; Subramanian, A.; Mu, D.; Powers, S.; et al. Chronic cisplatin treatment promotes enhanced damage repair and tumour progression in a mouse model of lung cancer. Genes Dev. 2010, 24, 837–852. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  63. Wang, Q.E.; Milum, K.; Han, C.; Huang, Y.W.; Wani, G.; Thomale, J.; Wani, A.A. Differential contributory roles of nucleotide excision and homologous recombination repair for enhancing cisplatin sensitivity in human ovarian cancer cells. Mol. Cancer 2011, 10, 24. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Stefanski, C.D.; Keffler, K.; McClintock, S.; Milac, L.; Prosperi, J.R. APC loss affects DNA damage repair causing doxorubicin resistance in breast cancer cells. Neoplasia 2019, 21, 1143–1150. [Google Scholar] [CrossRef]
  65. Kettner, N.M.; Vijayaraghavan, S.; Durak, M.G.; Bui, T.; Kohansal, M.; Ha, M.J.; Liu, B.; Rao, X.; Wang, J.; Yi, M.; et al. Combined Inhibition of STAT3 and DNA Repair in Palbociclib-Resistant ER-Positive Breast Cancer. Clin. Cancer Res. 2019, 25, 3996–4013. [Google Scholar] [CrossRef] [Green Version]
  66. Xiong, Q.; Jiang, X.; Liu, X.; Zhou, P.; Ding, K. Prediction of IER5 structure and function using a bioinformatics approach. Mol. Med. Rep. 2019, 19, 4631–4636. [Google Scholar] [CrossRef] [PubMed]
  67. Williams, M.; Lyu, M.S.; Yang, Y.L.; Lin, E.P.; Dunbrack, R.; Birren, B.; Cunningham, J.; Hunter, K. Ier5, a novel member of the slow-kinetics immediate-early genes. Genomics 1999, 55, 327–334. [Google Scholar] [CrossRef]
  68. Yamano, S.; Kimura, M.; Chen, Y.; Imamoto, N.; Ohki, R. Nuclear import of IER5 is mediated by a classical bipartite nuclear localization signal and is required for HSF1 full activation. Exp. Cell Res. 2020, 386, 111686. [Google Scholar] [CrossRef]
  69. Kawase, T.; Chen, Y.; Ohki, R. IER5 Is a p53-Regulated Activator of HSF1 That Contributes to Promotion of Cancer. In Heat Shock Proteins; Springer: Berlin/Heidelberg, Germany, 2019; Volume 17, p. 20. [Google Scholar]
  70. Zheng, J.J.; He, Y.; Liu, Y.; Li, F.S.; Cui, Z.; Du, X.M.; Wang, C.P.; Wu, Y.M. Novel role of PAF1 in attenuating radiosensitivity in cervical cancer by inhibiting IER5 transcription. Radiat. Oncol. 2020, 15, 131. [Google Scholar] [CrossRef]
  71. Wu, Z.; Wang, D.; Zeng, F.; Zhang, Y.; Zhu, G.; Ma, Y.; Song, B.; Lui, S.; Wu, M. High IER5 Gene Expression Is Associated with Poor Prognosis in Glioma Patients. Front. Cell Dev. Biol. 2021, 9, 679684. [Google Scholar] [CrossRef]
  72. Cirelli, C.; Tononi, G. Gene expression in the brain across the sleep-waking cycle. Brain Res. 2000, 885, 303–321. [Google Scholar] [CrossRef]
  73. Li, M.J.; Wang, W.W.; Chen, S.W.; Shen, Q.; Min, R. Radiation dose effect of DNA repair-related gene expression in mouse white blood cells. Med. Sci. Monit. 2011, 17, BR290–BR297. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Zeng, F.; Hon, C.C.; Sit, W.H.; Chow, K.Y.; Hui, R.K.; Law, I.K.; Ng, V.W.; Yang, X.T.; Leung, F.C.; Wan, J.M. Molecular characterization of Coriolus versicolor PSP-induced apoptosis in human promyelotic leukemic HL-60 cells using cDNA microarray. Int. J. Oncol. 2005, 27, 513–523. [Google Scholar] [CrossRef] [PubMed]
  75. Okada, A.; Kushima, K.; Aoki, Y.; Bialer, M.; Fujiwara, M. Identification of early-responsive genes correlated to valproic acid-induced neural tube defects in mice. Birth Defects Res. A Clin. Mol. Teratol. 2005, 73, 229–238. [Google Scholar] [CrossRef] [PubMed]
  76. Kis, E.; Szatmari, T.; Keszei, M.; Farkas, R.; Esik, O.; Lumniczky, K.; Falus, A.; Safrany, G. Microarray analysis of radiation response genes in primary human fibroblasts. Int. J. Radiat. Oncol. Biol. Phys. 2006, 66, 1506–1514. [Google Scholar] [CrossRef]
  77. Ding, K.K.; Shang, Z.F.; Hao, C.; Xu, Q.Z.; Shen, J.J.; Yang, C.J.; Xie, Y.H.; Qiao, C.; Wang, Y.; Xu, L.L.; et al. Induced expression of the IER5 gene by gamma-ray irradiation and its involvement in cell cycle checkpoint control and survival. Radiat. Environ. Biophys. 2009, 48, 205–213. [Google Scholar] [CrossRef]
  78. Tavakoli, H.; Manoochehri, M.; Modarres Mosalla, S.M.; Ghafori, M.; Karimi, A.A. Dose-dependent and gender-related radiation-induced transcription alterations of Gadd45a and Ier5 inhuman lymphocytes exposed to gamma ray emitted by (60)Co. Radiat. Prot. Dosim. 2013, 154, 37–44. [Google Scholar] [CrossRef] [Green Version]
  79. Skorokhod, A.; Bachmann, J.; Giese, N.A.; Martignoni, M.E.; Krakowski-Roosen, H. Real-imaging cDNA-AFLP transcript profiling of pancreatic cancer patients: Egr-1 as a potential key regulator of muscle cachexia. BMC Cancer 2012, 12, 265. [Google Scholar] [CrossRef] [Green Version]
  80. Wouters, J.; Stas, M.; Govaere, O.; Van den Eynde, K.; Vankelecom, H.; van den Oord, J.J. Gene expression changes in melanoma metastases in response to high-dose chemotherapy during isolated limb perfusion. Pigment Cell Melanoma Res. 2012, 25, 454–465. [Google Scholar] [CrossRef]
  81. Ishikawa, Y.; Kawabata, S.; Sakurai, H. HSF1 transcriptional activity is modulated by IER5 and PP2A/B55. FEBS Lett. 2015, 589, 1150–1155. [Google Scholar] [CrossRef] [Green Version]
  82. Toma-Jonik, A.; Vydra, N.; Janus, P.; Widlak, W. Interplay between HSF1 and p53 signaling pathways in cancer initiation and progression: Non-oncogene and oncogene addiction. Cell Oncol. 2019, 42, 579–589. [Google Scholar] [CrossRef] [Green Version]
  83. Yu, X.P.; Wu, Y.M.; Liu, Y.; Tian, M.; Wang, J.D.; Ding, K.K.; Ma, T.; Zhou, P.K. IER5 is involved in DNA Double-Strand Breaks Repair in Association with PAPR1 in Hela Cells. Int. J. Med. Sci. 2017, 14, 1292–1300. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  84. Nakamura, S.; Nagata, Y.; Tan, L.; Takemura, T.; Shibata, K.; Fujie, M.; Fujisawa, S.; Tanaka, Y.; Toda, M.; Makita, R.; et al. Transcriptional repression of Cdc25B by IER5 inhibits the proliferation of leukemic progenitor cells through NF-YB and p300 in acute myeloid leukemia. PLoS ONE 2011, 6, e28011. [Google Scholar] [CrossRef] [PubMed]
  85. Ding, L.; Zhao, X.; Xiong, Q.; Jiang, X.; Liu, X.; Ding, K.; Zhou, P. Cdc25B is transcriptionally inhibited by IER5 through the NF-YB transcription factor in irradiation-treated HeLa cells. Toxicol. Res. 2021, 10, 875–884. [Google Scholar] [CrossRef]
  86. Doi, K.; Takeuchi, H.; Sakurai, H. PP2A-B55 and its adapter proteins IER2 and IER5 regulate the activity of RB family proteins and the expression of cell cycle-related genes. FEBS J. 2023, 290, 745–762. [Google Scholar] [CrossRef]
  87. Abbas, T.; Dutta, A. p21 in cancer: Intricate networks and multiple activities. Nat. Rev. Cancer 2009, 9, 400–414. [Google Scholar] [CrossRef] [PubMed]
  88. Koster, R.; di Pietro, A.; Timmer-Bosscha, H.; Gibcus, J.H.; van den Berg, A.; Suurmeijer, A.J.; Bischoff, R.; Gietema, J.A.; de Jong, S. Cytoplasmic p21 expression levels determine cisplatin resistance in human testicular cancer. J. Clin. Investig. 2010, 120, 3594–3605. [Google Scholar] [CrossRef] [Green Version]
  89. Xia, X.; Ma, Q.; Li, X.; Ji, T.; Chen, P.; Xu, H.; Li, K.; Fang, Y.; Weng, D.; Weng, Y.; et al. Cytoplasmic p21 is a potential predictor for cisplatin sensitivity in ovarian cancer. BMC Cancer 2011, 11, 399. [Google Scholar] [CrossRef] [Green Version]
  90. Johnson, G.G.; Sherrington, P.D.; Carter, A.; Lin, K.; Liloglou, T.; Field, J.K.; Pettitt, A.R. A novel type of p53 pathway dysfunction in chronic lymphocytic leukemia resulting from two interacting single nucleotide polymorphisms within the p21 gene. Cancer Res. 2009, 69, 5210–5217. [Google Scholar] [CrossRef] [Green Version]
  91. Roman-Gomez, J.; Castillejo, J.A.; Jimenez, A.; Gonzalez, M.G.; Moreno, F.; Rodriguez Mdel, C.; Barrios, M.; Maldonado, J.; Torres, A. 5’ CpG island hypermethylation is associated with transcriptional silencing of the p21(CIP1/WAF1/SDI1) gene and confers poor prognosis in acute lymphoblastic leukemia. Blood 2002, 99, 2291–2296. [Google Scholar] [CrossRef]
  92. Anuranjani Bala, M. Concerted action of Nrf2-ARE pathway, MRN complex, HMGB1 and inflammatory cytokines—Implication in modification of radiation damage. Redox Biol. 2014, 2, 832–846. [Google Scholar] [CrossRef] [Green Version]
  93. Ahmadinejad, F.; Geir Moller, S.; Hashemzadeh-Chaleshtori, M.; Bidkhori, G.; Jami, M.S. Molecular Mechanisms behind Free Radical Scavengers Function against Oxidative Stress. Antioxidants 2017, 6, 51. [Google Scholar] [CrossRef] [PubMed]
  94. No, J.H.; Kim, Y.B.; Song, Y.S. Targeting nrf2 signaling to combat chemoresistance. J. Cancer Prev. 2014, 19, 111–117. [Google Scholar] [CrossRef] [Green Version]
  95. Nguyen, T.; Huang, H.C.; Pickett, C.B. Transcriptional regulation of the antioxidant response element. Activation by Nrf2 and repression by MafK. J. Biol. Chem. 2000, 275, 15466–15473. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  96. Jyrkkanen, H.K.; Kuosmanen, S.; Heinaniemi, M.; Laitinen, H.; Kansanen, E.; Mella-Aho, E.; Leinonen, H.; Yla-Herttuala, S.; Levonen, A.L. Novel insights into the regulation of antioxidant-response-element-mediated gene expression by electrophiles: Induction of the transcriptional repressor BACH1 by Nrf2. Biochem. J. 2011, 440, 167–174. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  97. Kaspar, J.W.; Niture, S.K.; Jaiswal, A.K. Nrf2:INrf2 (Keap1) signaling in oxidative stress. Free Radic. Biol. Med. 2009, 47, 1304–1309. [Google Scholar] [CrossRef] [Green Version]
  98. Canning, P.; Sorrell, F.J.; Bullock, A.N. Structural basis of Keap1 interactions with Nrf2. Free Radic. Biol. Med. 2015, 88, 101–107. [Google Scholar] [CrossRef] [Green Version]
  99. Jaramillo, M.C.; Zhang, D.D. The emerging role of the Nrf2-Keap1 signaling pathway in cancer. Genes Dev. 2013, 27, 2179–2191. [Google Scholar] [CrossRef] [Green Version]
  100. Jeddi, F.; Soozangar, N.; Sadeghi, M.R.; Somi, M.H.; Samadi, N. Contradictory roles of Nrf2/Keap1 signaling pathway in cancer prevention/promotion and chemoresistance. DNA Repair 2017, 54, 13–21. [Google Scholar] [CrossRef]
  101. Chen, W.; Sun, Z.; Wang, X.J.; Jiang, T.; Huang, Z.; Fang, D.; Zhang, D.D. Direct interaction between Nrf2 and p21(Cip1/WAF1) upregulates the Nrf2-mediated antioxidant response. Mol. Cell. 2009, 34, 663–673. [Google Scholar] [CrossRef] [Green Version]
  102. Villeneuve, N.F.; Sun, Z.; Chen, W.; Zhang, D.D. Nrf2 and p21 regulate the fine balance between life and death by controlling ROS levels. Cell Cycle 2009, 8, 3255–3256. [Google Scholar] [CrossRef] [Green Version]
  103. Dodson, M.; de la Vega, M.R.; Cholanians, A.B.; Schmidlin, C.J.; Chapman, E.; Zhang, D.D. Modulating NRF2 in Disease: Timing Is Everything. Annu. Rev. Pharmacol. Toxicol. 2019, 59, 555–575. [Google Scholar] [CrossRef]
  104. Kavitha, K.; Thiyagarajan, P.; Rathna Nandhini, J.; Mishra, R.; Nagini, S. Chemopreventive effects of diverse dietary phytochemicals against DMBA-induced hamster buccal pouch carcinogenesis via the induction of Nrf2-mediated cytoprotective antioxidant, detoxification, and DNA repair enzymes. Biochimie 2013, 95, 1629–1639. [Google Scholar] [CrossRef]
  105. Tian, B.; Lu, Z.N.; Guo, X.L. Regulation and role of nuclear factor-E2-related factor 2 (Nrf2) in multidrug resistance of hepatocellular carcinoma. Chem. Biol. Interact. 2018, 280, 70–76. [Google Scholar] [CrossRef] [PubMed]
  106. Zhang, H.; Steed, A.; Co, M.; Chen, X. Cancer stem cells, epithelial-mesenchymal transition, ATP and their roles in drug resistance in cancer. Cancer Drug Resist. 2021, 4, 684–709. [Google Scholar] [CrossRef] [PubMed]
  107. Liu, Y.P.; Zheng, C.C.; Huang, Y.N.; He, M.L.; Xu, W.W.; Li, B. Molecular mechanisms of chemo- and radiotherapy resistance and the potential implications for cancer treatment. MedComm 2021, 2, 315–340. [Google Scholar] [CrossRef]
  108. Levine, A.J. p53, the cellular gatekeeper for growth and division. Cell 1997, 88, 323–331. [Google Scholar] [CrossRef] [Green Version]
  109. Stenvinkel, P.; Meyer, C.J.; Block, G.A.; Chertow, G.M.; Shiels, P.G. Understanding the role of the cytoprotective transcription factor nuclear factor erythroid 2-related factor 2-lessons from evolution, the animal kingdom and rare progeroid syndromes. Nephrol. Dial. Transplant. 2020, 35, 2036–2045. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  110. Li, L.Y.; Guan, Y.D.; Chen, X.S.; Yang, J.M.; Cheng, Y. DNA Repair Pathways in Cancer Therapy and Resistance. Front. Pharmacol. 2020, 11, 629266. [Google Scholar] [CrossRef] [PubMed]
  111. Mansoori, B.; Mohammadi, A.; Davudian, S.; Shirjang, S.; Baradaran, B. The Different Mechanisms of Cancer Drug Resistance: A Brief Review. Adv. Pharm. Bull. 2017, 7, 339–348. [Google Scholar] [CrossRef]
  112. Righetti, S.C.; Della Torre, G.; Pilotti, S.; Ménard, S.; Ottone, F.; Colnaghi, M.I.; Pierotti, M.A.; Lavarino, C.; Cornarotti, M.; Oriana, S.; et al. A comparative study of p53 gene mutations, protein accumulation, and response to cisplatin-based chemotherapy in advanced ovarian carcinoma. Cancer Res. 1996, 56, 689–693. [Google Scholar]
  113. Lavarino, C.; Pilotti, S.; Oggionni, M.; Gatti, L.; Perego, P.; Bresciani, G.; Pierotti, M.A.; Scambia, G.; Ferrandina, G.; Fagotti, A.; et al. p53 gene status and response to platinum/paclitaxel-based chemotherapy in advanced ovarian carcinoma. J. Clin. Oncol. 2000, 18, 3936–3945. [Google Scholar] [CrossRef] [PubMed]
  114. King, T.C.; Akerley, W.; Fan, A.C.; Moore, T.; Mangray, S.; Hsiu Chen, M.; Safran, H. p53 mutations do not predict response to paclitaxel in metastatic nonsmall cell lung carcinoma. Cancer 2000, 89, 769–773. [Google Scholar] [CrossRef] [PubMed]
  115. Kandioler-Eckersberger, D.; Ludwig, C.; Rudas, M.; Kappel, S.; Janschek, E.; Wenzel, C.; Schlagbauer-Wadl, H.; Mittlböck, M.; Gnant, M.; Steger, G.; et al. TP53 mutation and p53 overexpression for prediction of response to neoadjuvant treatment in breast cancer patients. Clin. Cancer Res. 2000, 6, 50–56. [Google Scholar]
  116. Sengeløv, L.; Horn, T.; Steven, K. p53 nuclear immunoreactivity as a predictor of response and outcome following chemotherapy for metastatic bladder cancer. J. Cancer Res. Clin. Oncol. 1997, 123, 565–570. [Google Scholar] [CrossRef] [PubMed]
  117. Varna, M.; Bousquet, G.; Plassa, L.F.; Bertheau, P.; Janin, A. TP53 status and response to treatment in breast cancers. J. Biomed. Biotechnol. 2011, 2011, 284584. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  118. Galeaz, C.; Totis, C.; Bisio, A. Radiation Resistance: A Matter of Transcription Factors. Front. Oncol. 2021, 11, 662840. [Google Scholar] [CrossRef] [PubMed]
  119. Amaria, R.N.; Reddy, S.M.; Tawbi, H.A.; Davies, M.A.; Ross, M.I.; Glitza, I.C.; Cormier, J.N.; Lewis, C.; Hwu, W.J.; Hanna, E.; et al. Neoadjuvant immune checkpoint blockade in high-risk resectable melanoma. Nat. Med. 2018, 24, 1649–1654. [Google Scholar] [CrossRef]
  120. Kendra, K.L.; Moon, J.; Eroglu, Z.; Hu-Lieskovan, S.; Carson, W.E.; Wada, D.A.; Plaza, J.A.; In, G.K.; Ikeguchi, A.; Hyngstrom, J.R.; et al. Neoadjuvant PD-1 blockade in patients with resectable desmoplastic melanoma (SWOG 1512). J. Clin. Oncol. 2022, 40, 16. [Google Scholar] [CrossRef]
  121. Eroglu, Z.; Zaretsky, J.M.; Hu-Lieskovan, S.; Kim, D.W.; Algazi, A.; Johnson, D.B.; Liniker, E.; Ben, K.; Munhoz, R.; Rapisuwon, S.; et al. High response rate to PD-1 blockade in desmoplastic melanomas. Nature 2018, 553, 347–350. [Google Scholar] [CrossRef]
  122. Blank, C.U.; Rozeman, E.A.; Fanchi, L.F.; Sikorska, K.; van de Wiel, B.; Kvistborg, P.; Krijgsman, O.; van den Braber, M.; Philips, D.; Broeks, A.; et al. Neoadjuvant versus adjuvant ipilimumab plus nivolumab in macroscopic stage III melanoma. Nat. Med. 2018, 24, 1655–1661. [Google Scholar] [CrossRef]
  123. Patel, S.O.; Prieto, M.; Lowe, V.; Buchbinder, M.; Chen, E.; Hyngstrom, Y.; Lao, J.; Truong, C.D.; Chandra, T.; Kendra, S.; et al. LBA6—Neoadjvuant versus adjuvant pembrolizumab for resected stage III-IV melanoma (SWOG S1801). Ann. Oncol. 2022, 33, S808–S869. [Google Scholar] [CrossRef]
  124. Buder-Bakhaya, K.; Hassel, J.C. Biomarkers for Clinical Benefit of Immune Checkpoint Inhibitor Treatment-A Review from the Melanoma Perspective and Beyond. Front. Immunol. 2018, 9, 1474. [Google Scholar] [CrossRef] [PubMed]
  125. Subrahmanyam, P.B.; Dong, Z.; Gusenleitner, D.; Giobbie-Hurder, A.; Severgnini, M.; Zhou, J.; Manos, M.; Eastman, L.M.; Maecker, H.T.; Hodi, F.S. Distinct predictive biomarker candidates for response to anti-CTLA-4 and anti-PD-1 immunotherapy in melanoma patients. J. Immunother. Cancer 2018, 6, 18. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  126. Ouwerkerk, W.; van den Berg, M.; van der Niet, S.; Limpens, J.; Luiten, R.M. Biomarkers, measured during therapy, for response of melanoma patients to immune checkpoint inhibitors: A systematic review. Melanoma Res. 2019, 29, 453–464. [Google Scholar] [CrossRef]
  127. Amaria, R.N.; Menzies, A.M.; Burton, E.M.; Scolyer, R.A.; Tetzlaff, M.T.; Antdbacka, R.; Ariyan, C.; Bassett, R.; Carter, B.; Daud, A.; et al. Neoadjuvant systemic therapy in melanoma: Recommendations of the International Neoadjuvant Melanoma Consortium. Lancet Oncol. 2019, 20, e378–e389. [Google Scholar] [CrossRef]
  128. Mushti, S.L.; Mulkey, F.; Tang, S.; Singh, H.; Lemery, S.J.; Goldberg, K.B.; Sridhara, R.; Keegan, P.; Kluetz, P.G.; Pazdur, R.; et al. Immune Response Evaluation and Treatment with Immune Checkpoint Inhibitors Beyond Clinical Progression: Response Assessments for Cancer Immunotherapy. Curr. Oncol. Rep. 2020, 22, 116. [Google Scholar] [CrossRef]
Figure 1. Schematic response and patterns of resistance to immunotherapy in the metastatic setting considering the SITC consensus’ clinical definition of resistance to PD-(L)1 inhibitors [23]. Primary resistance (blue line) indicates non-response to therapy including that of patients with PD or SD lasting less than 6 months. Secondary resistance (purple line) indicates resistance after initial benefit (CR, PR, and SD lasting > 6 months) and may comprise singular, oligoprogression or multiple progression emerging from a residual and/ or newly formed tumor mass. * As an extension of this rule, a progression occurring after a substantial initial benefit (PR and CR) within 6 months after the start of treatment is also considered “secondary”.
Figure 1. Schematic response and patterns of resistance to immunotherapy in the metastatic setting considering the SITC consensus’ clinical definition of resistance to PD-(L)1 inhibitors [23]. Primary resistance (blue line) indicates non-response to therapy including that of patients with PD or SD lasting less than 6 months. Secondary resistance (purple line) indicates resistance after initial benefit (CR, PR, and SD lasting > 6 months) and may comprise singular, oligoprogression or multiple progression emerging from a residual and/ or newly formed tumor mass. * As an extension of this rule, a progression occurring after a substantial initial benefit (PR and CR) within 6 months after the start of treatment is also considered “secondary”.
Cancers 15 03448 g001
Figure 2. SITC scenarios and definitions of primary and secondary resistance to PD-1 therapy in the adjuvant, neo-adjuvant and metastatic treatment settings (modified to [23]). Abbreviations: ICI, immune checkpoint inhibition; MPR, major pathological response. * Complete or partial response, or stable disease for 6 months. ** As an extension of this rule, a progression occurring after a substantial benefit (PR and CR), within 6 months after the start of the treatment start is also considered “secondary”.
Figure 2. SITC scenarios and definitions of primary and secondary resistance to PD-1 therapy in the adjuvant, neo-adjuvant and metastatic treatment settings (modified to [23]). Abbreviations: ICI, immune checkpoint inhibition; MPR, major pathological response. * Complete or partial response, or stable disease for 6 months. ** As an extension of this rule, a progression occurring after a substantial benefit (PR and CR), within 6 months after the start of the treatment start is also considered “secondary”.
Cancers 15 03448 g002
Figure 3. Mechanisms of resistance.
Figure 3. Mechanisms of resistance.
Cancers 15 03448 g003
Table 1. Estimated frequencies of primary/acquired resistance in selected trials on advanced/metastatic melanoma (ICI monotherapy, or combined ICI). Listed are fully published data for long-term (≥4 years) follow up. To deduce acquired resistance rates, PFS rates at 4 years (48 months) and 5 years (60 months) are listed with their 95% confidence intervals (CI) (as far as reported). The rate of patients with progression (PDr) as the best overall response (BOR) serves as an estimate of the frequency of primary resistance. The estimate of the acquired resistance rate (ARr) may be deduced from PFS rates (PFSr) and PDr; ARr(%) = 100% − PFSr(%) − PDr(%).
Table 1. Estimated frequencies of primary/acquired resistance in selected trials on advanced/metastatic melanoma (ICI monotherapy, or combined ICI). Listed are fully published data for long-term (≥4 years) follow up. To deduce acquired resistance rates, PFS rates at 4 years (48 months) and 5 years (60 months) are listed with their 95% confidence intervals (CI) (as far as reported). The rate of patients with progression (PDr) as the best overall response (BOR) serves as an estimate of the frequency of primary resistance. The estimate of the acquired resistance rate (ARr) may be deduced from PFS rates (PFSr) and PDr; ARr(%) = 100% − PFSr(%) − PDr(%).
StudyTreatment Regimens Trial PhasePatientsNORR, % (CI)PD Rate % (CI)PFS Rate, 4 yrs
% (CI)
PFS Rate, 5 yrs
% (CI)
AR Rate
%(For 4 yr/5 yr PFS)
1st line ICI monotherapy (anti-PD-1)
Keynote-001 [39] (NCT01295827)(i) Pem (2 mg/kg q3w or 10 mg/kg q2w or 10 mg/kg q3w)IBAdv., treatment-naïve, BRAF±(i) 15152 (40–57)25 (22–29)352940/44
Keynote-006 [6]
(NCT01866319)
(i) Pem 10 mg/kg q2w vs. 10 mg/kg q3w vs. (ii) Ipi 3 mg/kg q3w x4IIIAdv., treatment-naïve, BRAF±(i) 55642 (38–44,58–60)2923 (19–27)nr48/nr
(ii) 27817 (12–21)387 (3–13)-55/nr
CheckMate-066 [40] (NCT01721772)(i) Nivo 3 mg/kg q2w
vs. Dacarbazine 1000 mg/m²
IIIAdv., treatment-naïve, BRAF wild type(i) 21042 (36–46,58–60)32292839/40
(ii) 20814 (10–20)503347/47
CheckMate-067 [7] (NCT01844505)(i) Nivo 3 mg/kg q2w (vs. Nivo + Ipi) vs. (ii) Ipi 3 mg/kg q3w x4IIIAdv., treatment-naïve, BRAF±(i) 31645 (39–47,58–60)38nr29nr/48
(ii) 31519 (15–24)50nr8nr/42
1st-line ICI combination therapy (anti-PD-1 + anti-CTLA-4)
CheckMate-067 [7] (NCT01844505)Nivo vs. (i) Nivo 1 mg/kg q2w + Ipi 3 mg/kg vs. (ii) Ipi 3 mg/kg q3w x4IIIAdv., treatment-naïve, BRAF±(i) 31458 (50–57,61–64)24nr36nr/40
(ii) 31519 (15–24)50nr8nr/42
1st-line ICI therapy in patients with asymptomatic brain metastasis (anti-PD-1 ± anti-CTLA-4) for intracranial outcome parameters
CheckMate-204 [41] (NCT02320058)(i) Nivo 1 mg/kg q2w + Ipi 3 mg/kg a IIAdv., treatment-naïve b ABM, BRAF±(i) 10154 (40–57,61–64)30nrnr-
ABC [42]
(NCT02374242)
Nivo 1 mg/kg q2w + Ipi 3 mg/kg
vs. Nivo 3 mg/kg
IIAdv., treatment-naïve ABM, BRAF±275930nr52nr/18
192174nr14nr/12
a Same treatment regimen as that in CheckMate-067; b previously approved adjuvant systemic therapies were allowed, including ipilimumab, if the last dose was administered at least 6 months before the first dose of the study drug was (criterium applies to CheckMate-204 trial only). Previous use of BRAF and MEK inhibitors in the advanced setting was allowed (after a washout period; 5 patients received previous BRAF/MEK inhibitors). Abbreviations: ABM, asymptomatic brain metastases; BRAF±, patients with BRAF mutations and wild-type BRAF; CI, 95% confidence interval; Ipi, ipilimumab; mets, metastasis; Nivo, nivolumab; nr, not reported; ORR, overall response rate; Pem, pembrolizumab; PD, progressive disease; PFS, median progression-free survival; yr/yrs, year(s).
Table 2. Efficacy outcomes reported for Phase I–II trials in advanced/metastatic, ICI-resistant melanoma.
Table 2. Efficacy outcomes reported for Phase I–II trials in advanced/metastatic, ICI-resistant melanoma.
Trial (NCT n°)Treatment RegimensTrial PhasePatientsNPrimary EndpointORR, % (CI)PFS Median, mts (HR (CI))OS Med., mts (HR (CI))
2nd-line combination therapy (anti-PD-1 backbone)
LEAP-004 [57] (NCT03776136)Lenvatinib 20 od + Pem 200 mg q3wIIAdv., PD-(L)1 pre-treated (PD upon/after therapy a) [58], BRAF±103ORR 33 (17–53) b;
23 (13–35) c
4.2 (3.8–7.1)14.0 (10.8-nr)
IRB17-0686 [59]
(NCT02743819)
Ipi 1 mg/kg q3w × 4 + Pem 200 mg q3wIIAdv., PD-(L)1 pre-treated (PD upon therapy d = Primary
resistance), BRAF±
70ORR31 (nr-nr)4.7 (2.8–8.3)nr
CA224-020 [60]
(NCT01968109)
Relatlimab 80 mg q2w + Nivo 240 mg q2wI–IIAdv., PD-(L)1 pre-treated (PD upon ther. = Prim. resistance), BRAF±68Safety, ORR12 (nr-nr)nrnr
SYNERGY-001 [61]
(NCT02521870)
SD-101 2 mg/kg q1-3w + Pem 200 mg q3wI–IIAdvanced, PD-(L)1-pre-treated (PD upon/after ther.), BRAF±23ORR20 (nr-nr)nrnr
2014-0922 [62]
(NCT02500576)
Cryopreserved TILe + IL-2 (Aldesleukin) (high dose/low dose) + Pem 200 mg q3wI–IIMetastatic, un-/pre-treated (13 out of 14 pts were PD-1-pre-treated), BRAF±14ORR14 (nr-nr)3.9 (nr-nr)/2.1 (nr-nr)9.7 (nr-nr)/
8.8 (nr-nr)
PV-10-MM-1201 [63]
(NCT02557321)
PV-10 (intralesional) + Pem 2 mg/kg q3wIMetastatic, ICI-pre-treated
(PD upon/after therapy), BRAF±
13Safety31 (nr-nr)nrnr
4SC-202-2-2017 [64]
(NCT03278665)
Domatinostat + Pem 2 mg/kg q3wI–IIMetastatic, ICI-pre-treated
(PD upon/after therapy), BRAF±
40Safety8 (nr-nr)nrnr
16-1080.cc [65]
(NCT03200847)
all-trans-Retinoic acid + Pem 200 mg q3wI–IIMetastatic, ICI pre-treated
(PD upon/after therapy), BRAF±
24Safety67 (nr-nr)20.3nr
Lipo-MERIT [66,67] (NCT02410733)FixVak (RNA vaccine) ± anti-PD-1 IMetastatic, ICI-pre-treated
(PD on/after therapy), BRAF±
42Safety16 (FV mono)
35 (FV + PD1)
nrnr
2nd-line combination therapy (anti-CTLA-4 backbone)
ILLUMINATE-204 [68] (NCT02644967)Tilsotolimod 8 mg/kg q1-6w + Ipi 3 m/kg q3w x4I–IIAdvanced, PD-1-pre-treated
(PD on/after therapy), BRAF±
62Safety, ORR22 (12–37)5.1 (3.7–7.0)21.0 (9.8-nr)
ILLUMINATE-301 [69] (NCT03445533)Tilsotolimod 8 mg/kg q1-6w + Ipi 3 m/kg q3w x4 vs. IpiIIIAdvanced, PD-1-pre-treated481OS and ORR9 (nr-nr)nrnr
2nd-line monotherapy
C144-01 [56] (NCT02360579)Lifileucel (i.e., autologous, cryo-preserved TIL e) + IL-2 x6IIAdvanced, PD-1-pre-treated, BRAF±66ORR36 (nr-nr)nrnr
Dutch [56] (NCT02278887)TIL (i.e., autologous, cryo-preserved TIL e) vs. IpiIIIAdvanced, progression after the maximal one line of pre-treatment (no Ipi), BRAF±; approximately 90% of patients had PD-1 pre-treatment in both arms84
84
PFS48.8
21.4
7.2 (4.2–13.1)/3.1 (3.0–4.3), HR 0.05, p < 0.00125.8 (18.2-nr) /18.9 (13.8–32.6), HR 0.83, p = 0.39
a Upon therapy or ≤12 weeks after last dose of an anti-PD-(L)1 agent given alone or in combination (including with anti-CTLA-4 therapy) for ≥2 doses; b PD upon prior anti-PD-1 plus CTLA-4 therapy; c primary resistance to prior anti-PD-(L)1 monotherapy; d PD (or stable disease lasting ≥24 weeks during treatment with an anti-PD-(L)1 antibody as the treatment regimen) immediately prior to recruitment to this study or PD within ≤6 months of adjuvant anti-PD1 antibody administration; e harvested TIL re-administered in patients after lymphodepleting chemotherapy comprising cyclophosphamide and fludarabine phosphate. Abbreviations: BEMPEG, Bempegaldesleukin; BICR, blinded, independent central review; BRAF±, mutated BRAF and wild-type BRAF; CI, 95% confidence interval; FV, FixVak; IA, investigator-assessed; ICI, immune checkpoint inhibitor; Ipi, Ipilimumab; MDSC, myeloid-derived suppressor cells; Nivo, Nivolumab; NR, not reported (and/or: not reached); od, once daily; ORR, overall response rate; OS, median overall survival; PD, progressive disease; Pem, Pembrolizumab; PFS, median progression-free survival; PV-10, 10% rose bengal disodium for injection; SD-101, synthetic CpG-ODN agonist of TLR 9; TIL, tumor-infiltrating lymphocytes.
Table 3. Estimated frequencies of resistance in selected trials investigating the adjuvant use of ICI in melanoma (ICI monotherapy, or combined ICI).
Table 3. Estimated frequencies of resistance in selected trials investigating the adjuvant use of ICI in melanoma (ICI monotherapy, or combined ICI).
StudyTreatment Regimens Trial PhasePatients (Stage Distribution)NRFS rate, % (CI)Recurrence Rate, % (5 yr/*3 yr/4 yr) aPatterns of Recurrence: Local vs. (//) Distant Only % (n/ntotal) b
Stage III/IV
Keynote-054 [15] (NCT02362594)(i) Pem 200 mg q3w vs. III100% III(i) 51455 at 5 yrs (51–60)4514 (74/514)//28 (143/514)
(ii) Placebo100% III(ii) 50538 at 5 yrs (34–43)6219 (96/505//41 (206/505))
CheckMate-238 [13] (NCT02388906) (i) Nivo 3mg/kg q2w vs. III81% III, 18% IV(i) 45350 at 5 yrs (not reported)nr 50
(ii) Ipi 10mg/kg q3w x481% III, 19% IV(ii) 45339 at 5 yrs (not reported)nr 61
CheckMate-915 [110] (NCT03068455) (i) Nivo 480 mg q2w vs. (ii) NivoIII86% III, 13% IV(i) 92463 at 2 yrs A (60–66)-nr//nr
240 mg q2w + Ipi 1 mg/kg q6w87% III, 13% IV(ii) 92065 at 2 yrs (61–68)-nr//nr
IMMUNED [14] (NCT02523313)(i) Nivo 3mg/kg q2w vs.II100% IV(i) 5931 at 3 /4 yrs (20–41,58–60)69*/69*14 (8/59)//(42 (25/59)
(ii) Nivo 1mg/kg q2w + Ipi 100% IV(ii) 5664 at 3 /4 yrs (49–76)36*/36*4 (2/56)//16 (9/56)
3 mg/kg q3w x4 vs. (iii) Placebo100% IV(iii) 5215 at 3 /4 yrs (7–27)85*/85*25 (13/52)//46 (24/52)
Stage II
Keynote-716 [10] (NCT03553836)(i) Pem 200 mg q3w vs. III63% IIB, 35% IIC(i) 48781 at 2 yr (not reported)--
(ii) Placebo65% IIB, 35% IIC(ii) 48973 at 2 yr (not reported)--
CheckMate-76K [11] (NCT04099251)(i) Nivo 480 mg q4w vs.III60% IIB, 40% IIC(i) 52689 at 1 year (86–92)--
(ii) Placebo62% IIB, 38% IIC(ii) 26479 at 1 year (74–84)--
a The recurrence rate (Rr), estimated for trials with either 3-year or 4-year RFS rates reported, is calculated in the following way: Rr(%) = 100% − RFSr(%); b patterns of disease recurrence also comprise, apart from local-only recurrence and new distant-only metastasis as reported above in this table, more complex disease recurrence patterns. However, these (more complex) patterns, comprising, e.g., parallel/concomitant local and distant relapse, but also “regional” (or “loco-regional”) relapse, are reported in a non-standardized way across the different trials and publications listed here. Readers are advised to check the cited publications for additional data. RFS rate at 3 years (yrs). Abbreviations: ICI, immune checkpoint inhibitor; Ipi, ipilimumab; Nivo, nivolumab; Pem, pembrolizumab; RFS, recurrence-free survival. Asterisk (*): the 3yr/4yr recurrence rate of the IMMUNED-study [15]. The spacer “(//)” hints at the systematic order of the following data lines: local vs. distant, meaning “local” can be found left from “//” and “distant” on the right hand side.
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.

Share and Cite

MDPI and ACS Style

Hassel, J.C.; Zimmer, L.; Sickmann, T.; Eigentler, T.K.; Meier, F.; Mohr, P.; Pukrop, T.; Roesch, A.; Vordermark, D.; Wendl, C.; et al. Medical Needs and Therapeutic Options for Melanoma Patients Resistant to Anti-PD-1-Directed Immune Checkpoint Inhibition. Cancers 2023, 15, 3448. https://doi.org/10.3390/cancers15133448

AMA Style

Hassel JC, Zimmer L, Sickmann T, Eigentler TK, Meier F, Mohr P, Pukrop T, Roesch A, Vordermark D, Wendl C, et al. Medical Needs and Therapeutic Options for Melanoma Patients Resistant to Anti-PD-1-Directed Immune Checkpoint Inhibition. Cancers. 2023; 15(13):3448. https://doi.org/10.3390/cancers15133448

Chicago/Turabian Style

Hassel, Jessica C., Lisa Zimmer, Thomas Sickmann, Thomas K. Eigentler, Friedegund Meier, Peter Mohr, Tobias Pukrop, Alexander Roesch, Dirk Vordermark, Christina Wendl, and et al. 2023. "Medical Needs and Therapeutic Options for Melanoma Patients Resistant to Anti-PD-1-Directed Immune Checkpoint Inhibition" Cancers 15, no. 13: 3448. https://doi.org/10.3390/cancers15133448

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop