Journal of Clinical Medicine

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10 pages, 2419 KiB

Open AccessArticle

Evolved Resistance to Placental Invasion Secondarily Confers Increased Survival in Melanoma Patients

by Yasir Suhail, Junaid Afzal and Kshitiz

J. Clin. Med. 2021, 10(4), 595; https://doi.org/10.3390/jcm10040595 - 5 Feb 2021

Cited by 9 | Viewed by 2927

Abstract

Mammals exhibit large differences in rates of cancer malignancy, even though the tumor formation rates may be similar. In placental mammals, rates of malignancy correlate with the extent of placental invasion. Our Evolved Levels of Invasibility (ELI) framework links these two phenomena identifying [...] Read more.

Mammals exhibit large differences in rates of cancer malignancy, even though the tumor formation rates may be similar. In placental mammals, rates of malignancy correlate with the extent of placental invasion. Our Evolved Levels of Invasibility (ELI) framework links these two phenomena identifying genes that potentially confer resistance in stromal fibroblasts to limit invasion, from trophoblasts in the endometrium, and from disseminating melanoma in the skin. Herein, using patient data from The Cancer Genome Atlas (TCGA), we report that these anti-invasive genes may be crucial in melanoma progression in human patients, and that their loss is correlated with increased cancer spread and lowered survival. Our results suggest that, surprisingly, these anti-invasive genes, which have lower expression in humans compared to species with non-invasive placentation, may potentially prevent stromal invasion, while a further reduction in their levels increases the malignancy and lethality of melanoma. Our work links evolution, comparative biology, and cancer progression across tissues, indicating new avenues for using evolutionary medicine to prognosticate and treat human cancers. Full article

(This article belongs to the Special Issue Latest Developments in Mathematical Oncology and Cancer Systems Biology)

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16 pages, 2427 KiB

Open AccessArticle

Multi-Stability and Consequent Phenotypic Plasticity in AMPK-Akt Double Negative Feedback Loop in Cancer Cells

by Adithya Chedere, Kishore Hari, Saurav Kumar, Annapoorni Rangarajan and Mohit Kumar Jolly

J. Clin. Med. 2021, 10(3), 472; https://doi.org/10.3390/jcm10030472 - 26 Jan 2021

Cited by 6 | Viewed by 3341

Abstract

Adaptation and survival of cancer cells to various stress and growth factor conditions is crucial for successful metastasis. A double-negative feedback loop between two serine/threonine kinases AMPK (AMP-activated protein kinase) and Akt can regulate the adaptation of breast cancer cells to matrix-deprivation stress. [...] Read more.

Adaptation and survival of cancer cells to various stress and growth factor conditions is crucial for successful metastasis. A double-negative feedback loop between two serine/threonine kinases AMPK (AMP-activated protein kinase) and Akt can regulate the adaptation of breast cancer cells to matrix-deprivation stress. This feedback loop can significantly generate two phenotypes or cell states: matrix detachment-triggered pAMPK^high/ pAkt^low state, and matrix (re)attachment-triggered pAkt^high/ pAMPK^low state. However, whether these two cell states can exhibit phenotypic plasticity and heterogeneity in a given cell population, i.e., whether they can co-exist and undergo spontaneous switching to generate the other subpopulation, remains unclear. Here, we develop a mechanism-based mathematical model that captures the set of experimentally reported interactions among AMPK and Akt. Our simulations suggest that the AMPK-Akt feedback loop can give rise to two co-existing phenotypes (pAkt^high/ pAMPK^low and pAMPK^high/pAkt^low) in specific parameter regimes. Next, to test the model predictions, we segregated these two subpopulations in MDA-MB-231 cells and observed that each of them was capable of switching to another in adherent conditions. Finally, the predicted trends are supported by clinical data analysis of The Cancer Genome Atlas (TCGA) breast cancer and pan-cancer cohorts that revealed negatively correlated pAMPK and pAkt protein levels. Overall, our integrated computational-experimental approach unravels that AMPK-Akt feedback loop can generate multi-stability and drive phenotypic switching and heterogeneity in a cancer cell population. Full article

(This article belongs to the Special Issue Latest Developments in Mathematical Oncology and Cancer Systems Biology)

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19 pages, 1768 KiB

Open AccessArticle

Hybrid E/M Phenotype(s) and Stemness: A Mechanistic Connection Embedded in Network Topology

by Satwik Pasani, Sarthak Sahoo and Mohit Kumar Jolly

J. Clin. Med. 2021, 10(1), 60; https://doi.org/10.3390/jcm10010060 - 26 Dec 2020

Cited by 27 | Viewed by 3687

Abstract

Metastasis remains an unsolved clinical challenge. Two crucial features of metastasizing cancer cells are (a) their ability to dynamically move along the epithelial–hybrid–mesenchymal spectrum and (b) their tumor initiation potential or stemness. With increasing functional characterization of hybrid epithelial/mesenchymal (E/M) phenotypes along the [...] Read more.

Metastasis remains an unsolved clinical challenge. Two crucial features of metastasizing cancer cells are (a) their ability to dynamically move along the epithelial–hybrid–mesenchymal spectrum and (b) their tumor initiation potential or stemness. With increasing functional characterization of hybrid epithelial/mesenchymal (E/M) phenotypes along the spectrum, recent in vitro and in vivo studies have suggested an increasing association of hybrid E/M phenotypes with stemness. However, the mechanistic underpinnings enabling this association remain unclear. Here, we develop a mechanism-based mathematical modeling framework that interrogates the emergent nonlinear dynamics of the coupled network modules regulating E/M plasticity (miR-200/ZEB) and stemness (LIN28/let-7). Simulating the dynamics of this coupled network across a large ensemble of parameter sets, we observe that hybrid E/M phenotype(s) are more likely to acquire stemness relative to “pure” epithelial or mesenchymal states. We also integrate multiple “phenotypic stability factors” (PSFs) that have been shown to stabilize hybrid E/M phenotypes both in silico and in vitro—such as OVOL1/2, GRHL2, and NRF2—with this network, and demonstrate that the enrichment of hybrid E/M phenotype(s) with stemness is largely conserved in the presence of these PSFs. Thus, our results offer mechanistic insights into recent experimental observations of hybrid E/M phenotype(s) that are essential for tumor initiation and highlight how this feature is embedded in the underlying topology of interconnected EMT (Epithelial-Mesenchymal Transition) and stemness networks. Full article

(This article belongs to the Special Issue Latest Developments in Mathematical Oncology and Cancer Systems Biology)

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42 pages, 4472 KiB

Open AccessArticle

Data Driven Mathematical Model of Colon Cancer Progression

by Arkadz Kirshtein, Shaya Akbarinejad, Wenrui Hao, Trang Le, Sumeyye Su, Rachel A. Aronow and Leili Shahriyari

J. Clin. Med. 2020, 9(12), 3947; https://doi.org/10.3390/jcm9123947 - 5 Dec 2020

Cited by 16 | Viewed by 4895

Abstract

Every colon cancer has its own unique characteristics, and therefore may respond differently to identical treatments. Here, we develop a data driven mathematical model for the interaction network of key components of immune microenvironment in colon cancer. We estimate the relative abundance of [...] Read more.

Every colon cancer has its own unique characteristics, and therefore may respond differently to identical treatments. Here, we develop a data driven mathematical model for the interaction network of key components of immune microenvironment in colon cancer. We estimate the relative abundance of each immune cell from gene expression profiles of tumors, and group patients based on their immune patterns. Then we compare the tumor sensitivity and progression in each of these groups of patients, and observe differences in the patterns of tumor growth between the groups. For instance, in tumors with a smaller density of naive macrophages than activated macrophages, a higher activation rate of macrophages leads to an increase in cancer cell density, demonstrating a negative effect of macrophages. Other tumors however, exhibit an opposite trend, showing a positive effect of macrophages in controlling tumor size. Although the results indicate that for all patients the size of the tumor is sensitive to the parameters related to macrophages, such as their activation and death rate, this research demonstrates that no single biomarker could predict the dynamics of tumors. Full article

(This article belongs to the Special Issue Latest Developments in Mathematical Oncology and Cancer Systems Biology)

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24 pages, 4329 KiB

Open AccessArticle

Bayesian Information-Theoretic Calibration of Radiotherapy Sensitivity Parameters for Informing Effective Scanning Protocols in Cancer

by Heyrim Cho, Allison L. Lewis and Kathleen M. Storey

J. Clin. Med. 2020, 9(10), 3208; https://doi.org/10.3390/jcm9103208 - 5 Oct 2020

Cited by 3 | Viewed by 2017

Abstract

With new advancements in technology, it is now possible to collect data for a variety of different metrics describing tumor growth, including tumor volume, composition, and vascularity, among others. For any proposed model of tumor growth and treatment, we observe large variability among [...] Read more.

With new advancements in technology, it is now possible to collect data for a variety of different metrics describing tumor growth, including tumor volume, composition, and vascularity, among others. For any proposed model of tumor growth and treatment, we observe large variability among individual patients’ parameter values, particularly those relating to treatment response; thus, exploiting the use of these various metrics for model calibration can be helpful to infer such patient-specific parameters both accurately and early, so that treatment protocols can be adjusted mid-course for maximum efficacy. However, taking measurements can be costly and invasive, limiting clinicians to a sparse collection schedule. As such, the determination of optimal times and metrics for which to collect data in order to best inform proper treatment protocols could be of great assistance to clinicians. In this investigation, we employ a Bayesian information-theoretic calibration protocol for experimental design in order to identify the optimal times at which to collect data for informing treatment parameters. Within this procedure, data collection times are chosen sequentially to maximize the reduction in parameter uncertainty with each added measurement, ensuring that a budget of n high-fidelity experimental measurements results in maximum information gain about the low-fidelity model parameter values. In addition to investigating the optimal temporal pattern for data collection, we also develop a framework for deciding which metrics should be utilized at each data collection point. We illustrate this framework with a variety of toy examples, each utilizing a radiotherapy treatment regimen. For each scenario, we analyze the dependence of the predictive power of the low-fidelity model upon the measurement budget. Full article

(This article belongs to the Special Issue Latest Developments in Mathematical Oncology and Cancer Systems Biology)

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23 pages, 11525 KiB

Open AccessArticle

Quantifying the Landscape and Transition Paths for Proliferation–Quiescence Fate Decisions

by Zihao Chen and Chunhe Li

J. Clin. Med. 2020, 9(8), 2582; https://doi.org/10.3390/jcm9082582 - 10 Aug 2020

Cited by 2 | Viewed by 2705

Abstract

The cell cycle, essential for biological functions, experiences delicate spatiotemporal regulation. The transition between G1 and S phase, which is called the proliferation–quiescence decision, is critical to the cell cycle. However, the stability and underlying stochastic dynamical mechanisms of the proliferation–quiescence decision have [...] Read more.

The cell cycle, essential for biological functions, experiences delicate spatiotemporal regulation. The transition between G1 and S phase, which is called the proliferation–quiescence decision, is critical to the cell cycle. However, the stability and underlying stochastic dynamical mechanisms of the proliferation–quiescence decision have not been fully understood. To quantify the process of the proliferation–quiescence decision, we constructed its underlying landscape based on the relevant gene regulatory network. We identified three attractors on the landscape corresponding to the G0, G1, and S phases, individually, which are supported by single-cell data. By calculating the transition path, which quantifies the potential barrier, we built expression profiles in temporal order for key regulators in different transitions. We propose that the two saddle points on the landscape characterize restriction point (RP) and G1/S checkpoint, respectively, which provides quantitative and physical explanations for the mechanisms of Rb governing the RP while p21 controlling the G1/S checkpoint. We found that Emi1 inhibits the transition from G0 to G1, while Emi1 in a suitable range facilitates the transition from G1 to S. These results are partially consistent with previous studies, which also suggested new roles of Emi1 in the cell cycle. By global sensitivity analysis, we identified some critical regulatory factors influencing the proliferation–quiescence decision. Our work provides a global view of the stochasticity and dynamics in the proliferation–quiescence decision of the cell cycle. Full article

(This article belongs to the Special Issue Latest Developments in Mathematical Oncology and Cancer Systems Biology)

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20 pages, 3677 KiB

Open AccessArticle

MicroRNA-222 Regulates Melanoma Plasticity

by Maria Chiara Lionetti, Filippo Cola, Oleksandr Chepizhko, Maria Rita Fumagalli, Francesc Font-Clos, Roberto Ravasio, Saverio Minucci, Paola Canzano, Marina Camera, Guido Tiana, Stefano Zapperi and Caterina A. M. La Porta

J. Clin. Med. 2020, 9(8), 2573; https://doi.org/10.3390/jcm9082573 - 8 Aug 2020

Cited by 9 | Viewed by 2754

Abstract

Melanoma is one of the most aggressive and highly resistant tumors. Cell plasticity in melanoma is one of the main culprits behind its metastatic capabilities. The detailed molecular mechanisms controlling melanoma plasticity are still not completely understood. Here we combine mathematical models of [...] Read more.

Melanoma is one of the most aggressive and highly resistant tumors. Cell plasticity in melanoma is one of the main culprits behind its metastatic capabilities. The detailed molecular mechanisms controlling melanoma plasticity are still not completely understood. Here we combine mathematical models of phenotypic switching with experiments on IgR39 human melanoma cells to identify possible key targets to impair phenotypic switching. Our mathematical model shows that a cancer stem cell subpopulation within the tumor prevents phenotypic switching of the other cancer cells. Experiments reveal that hsa-mir-222 is a key factor enabling this process. Our results shed new light on melanoma plasticity, providing a potential target and guidance for therapeutic studies. Full article

(This article belongs to the Special Issue Latest Developments in Mathematical Oncology and Cancer Systems Biology)

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24 pages, 1549 KiB

Open AccessArticle

Mathematical Model of Muscle Wasting in Cancer Cachexia

by Suzan Farhang-Sardroodi and Kathleen P. Wilkie

J. Clin. Med. 2020, 9(7), 2029; https://doi.org/10.3390/jcm9072029 - 28 Jun 2020

Cited by 7 | Viewed by 4366

Abstract

Cancer cachexia is a debilitating condition characterized by an extreme loss of skeletal muscle mass, which negatively impacts patients’ quality of life, reduces their ability to sustain anti-cancer therapies, and increases the risk of mortality. Recent discoveries have identified the myostatin/activin A/ActRIIB pathway [...] Read more.

Cancer cachexia is a debilitating condition characterized by an extreme loss of skeletal muscle mass, which negatively impacts patients’ quality of life, reduces their ability to sustain anti-cancer therapies, and increases the risk of mortality. Recent discoveries have identified the myostatin/activin A/ActRIIB pathway as critical to muscle wasting by inducing satellite cell quiescence and increasing muscle-specific ubiquitin ligases responsible for atrophy. Remarkably, pharmacological blockade of the ActRIIB pathway has been shown to reverse muscle wasting and prolong the survival time of tumor-bearing animals. To explore the implications of this signaling pathway and potential therapeutic targets in cachexia, we construct a novel mathematical model of muscle tissue subjected to tumor-derived cachectic factors. The model formulation tracks the intercellular interactions between cancer cell, satellite cell, and muscle cell populations. The model is parameterized by fitting to colon-26 mouse model data, and the analysis provides insight into tissue growth in healthy, cancerous, and post-cachexia treatment conditions. Model predictions suggest that cachexia fundamentally alters muscle tissue health, as measured by the stem cell ratio, and this is only partially recovered by anti-cachexia treatment. Our mathematical findings suggest that after blocking the myostatin/activin A pathway, partial recovery of cancer-induced muscle loss requires the activation and proliferation of the satellite cell compartment with a functional differentiation program. Full article

(This article belongs to the Special Issue Latest Developments in Mathematical Oncology and Cancer Systems Biology)

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11 pages, 1190 KiB

Open AccessArticle

Tumor Volume Dynamics as an Early Biomarker for Patient-Specific Evolution of Resistance and Progression in Recurrent High-Grade Glioma

by Daniel J. Glazar, G. Daniel Grass, John A. Arrington, Peter A. Forsyth, Natarajan Raghunand, Hsiang-Hsuan Michael Yu, Solmaz Sahebjam and Heiko Enderling

J. Clin. Med. 2020, 9(7), 2019; https://doi.org/10.3390/jcm9072019 - 27 Jun 2020

Cited by 11 | Viewed by 3445

Abstract

Recurrent high-grade glioma (HGG) remains incurable with inevitable evolution of resistance and high inter-patient heterogeneity in time to progression (TTP). Here, we evaluate if early tumor volume response dynamics can calibrate a mathematical model to predict patient-specific resistance to develop opportunities for treatment [...] Read more.

Recurrent high-grade glioma (HGG) remains incurable with inevitable evolution of resistance and high inter-patient heterogeneity in time to progression (TTP). Here, we evaluate if early tumor volume response dynamics can calibrate a mathematical model to predict patient-specific resistance to develop opportunities for treatment adaptation for patients with a high risk of progression. A total of 95 T1-weighted contrast-enhanced (T1post) MRIs from 14 patients treated in a phase I clinical trial with hypo-fractionated stereotactic radiation (HFSRT; 6 Gy × 5) plus pembrolizumab (100 or 200 mg, every 3 weeks) and bevacizumab (10 mg/kg, every 2 weeks; NCT02313272) were delineated to derive longitudinal tumor volumes. We developed, calibrated, and validated a mathematical model that simulates and forecasts tumor volume dynamics with rate of resistance evolution as the single patient-specific parameter. Model prediction performance is evaluated based on how early progression is predicted and the number of false-negative predictions. The model with one patient-specific parameter describing the rate of evolution of resistance to therapy fits untrained data (

R^{2} = 0.70

). In a leave-one-out study, for the nine patients that had T1post tumor volumes ≥1 cm³, the model was able to predict progression on average two imaging cycles early, with a median of 9.3 (range: 3–39.3) weeks early (median progression-free survival was 27.4 weeks). Our results demonstrate that early tumor volume dynamics measured on T1post MRI has the potential to predict progression following the protocol therapy in select patients with recurrent HGG. Future work will include testing on an independent patient dataset and evaluation of the developed framework on T2/FLAIR-derived data. Full article

(This article belongs to the Special Issue Latest Developments in Mathematical Oncology and Cancer Systems Biology)

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30 pages, 2737 KiB

Open AccessArticle

Mathematical Model Predicts Effective Strategies to Inhibit VEGF-eNOS Signaling

by Qianhui Wu and Stacey D. Finley

J. Clin. Med. 2020, 9(5), 1255; https://doi.org/10.3390/jcm9051255 - 26 Apr 2020

Cited by 16 | Viewed by 5638

Abstract

The endothelial nitric oxide synthase (eNOS) signaling pathway in endothelial cells has multiple physiological significances. It produces nitric oxide (NO), an important vasodilator, and enables a long-term proliferative response, contributing to angiogenesis. This signaling pathway is mediated by vascular endothelial growth factor (VEGF), [...] Read more.

The endothelial nitric oxide synthase (eNOS) signaling pathway in endothelial cells has multiple physiological significances. It produces nitric oxide (NO), an important vasodilator, and enables a long-term proliferative response, contributing to angiogenesis. This signaling pathway is mediated by vascular endothelial growth factor (VEGF), a pro-angiogenic species that is often targeted to inhibit tumor angiogenesis. However, inhibiting VEGF-mediated eNOS signaling can lead to complications such as hypertension. Therefore, it is important to understand the dynamics of eNOS signaling in the context of angiogenesis inhibitors. Thrombospondin-1 (TSP1) is an important angiogenic inhibitor that, through interaction with its receptor CD47, has been shown to redundantly inhibit eNOS signaling. However, the exact mechanisms of TSP1′s inhibitory effects on this pathway remain unclear. To address this knowledge gap, we established a molecular-detailed mechanistic model to describe VEGF-mediated eNOS signaling, and we used the model to identify the potential intracellular targets of TSP1. In addition, we applied the predictive model to investigate the effects of several approaches to selectively target eNOS signaling in cells experiencing high VEGF levels present in the tumor microenvironment. This work generates insights for pharmacologic targets and therapeutic strategies to inhibit tumor angiogenesis signaling while avoiding potential side effects in normal vasoregulation. Full article

(This article belongs to the Special Issue Latest Developments in Mathematical Oncology and Cancer Systems Biology)

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14 pages, 1468 KiB

Open AccessArticle

Integrative Analysis and Machine Learning Based Characterization of Single Circulating Tumor Cells

by Arvind Iyer, Krishan Gupta, Shreya Sharma, Kishore Hari, Yi Fang Lee, Neevan Ramalingam, Yoon Sim Yap, Jay West, Ali Asgar Bhagat, Balaram Vishnu Subramani, Burhanuddin Sabuwala, Tuan Zea Tan, Jean Paul Thiery, Mohit Kumar Jolly, Naveen Ramalingam and Debarka Sengupta

J. Clin. Med. 2020, 9(4), 1206; https://doi.org/10.3390/jcm9041206 - 22 Apr 2020

Cited by 24 | Viewed by 7759

Abstract

We collated publicly available single-cell expression profiles of circulating tumor cells (CTCs) and showed that CTCs across cancers lie on a near-perfect continuum of epithelial to mesenchymal (EMT) transition. Integrative analysis of CTC transcriptomes also highlighted the inverse gene expression pattern between PD-L1 [...] Read more.

We collated publicly available single-cell expression profiles of circulating tumor cells (CTCs) and showed that CTCs across cancers lie on a near-perfect continuum of epithelial to mesenchymal (EMT) transition. Integrative analysis of CTC transcriptomes also highlighted the inverse gene expression pattern between PD-L1 and MHC, which is implicated in cancer immunotherapy. We used the CTCs expression profiles in tandem with publicly available peripheral blood mononuclear cell (PBMC) transcriptomes to train a classifier that accurately recognizes CTCs of diverse phenotype. Further, we used this classifier to validate circulating breast tumor cells captured using a newly developed microfluidic system for label-free enrichment of CTCs. Full article

(This article belongs to the Special Issue Latest Developments in Mathematical Oncology and Cancer Systems Biology)

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15 pages, 2648 KiB

Open AccessArticle

A CTC-Cluster-Specific Signature Derived from OMICS Analysis of Patient-Derived Xenograft Tumors Predicts Outcomes in Basal-Like Breast Cancer

by Hariprasad Thangavel, Carmine De Angelis, Suhas Vasaikar, Raksha Bhat, Mohit Kumar Jolly, Chandandeep Nagi, Chad J. Creighton, Fengju Chen, Lacey E. Dobrolecki, Jason T. George, Tanya Kumar, Noor Mazin Abdulkareem, Sufeng Mao, Agostina Nardone, Mothaffar Rimawi, C. Kent Osborne, Michael T. Lewis, Herbert Levine, Bing Zhang, Rachel Schiff, Mario Giuliano and Meghana V. Trivedi Show full author list Hide full author list

J. Clin. Med. 2019, 8(11), 1772; https://doi.org/10.3390/jcm8111772 - 24 Oct 2019

Cited by 35 | Viewed by 5626

Abstract

Circulating tumor cell clusters (CTCcl) have a higher metastatic potential compared to single CTCs and predict long-term outcomes in breast cancer (BC) patients. Because of the rarity of CTCcls, molecular characterization of primary tumors that give rise to CTCcl hold significant promise for [...] Read more.

Circulating tumor cell clusters (CTCcl) have a higher metastatic potential compared to single CTCs and predict long-term outcomes in breast cancer (BC) patients. Because of the rarity of CTCcls, molecular characterization of primary tumors that give rise to CTCcl hold significant promise for better diagnosis and target discovery to combat metastatic BC. In our study, we utilized the reverse-phase protein array (RPPA) and transcriptomic (RNA-Seq) data of 10 triple-negative BC patient-derived xenograft (TNBC PDX) transplantable models with CTCs and evaluated expression of upregulated candidate protein Bcl2 (B-cell lymphoma 2) by immunohistochemistry (IHC). The sample-set consisted of six CTCcl-negative (CTCcl−) and four CTCcl-positive (CTCcl+) models. We analyzed the RPPA and transcriptomic profiles of CTCcl− and CTCcl+ TNBC PDX models. In addition, we derived a CTCcl-specific gene signature for testing if it predicted outcomes using a publicly available dataset from 360 patients with basal-like BC. The RPPA analysis of CTCcl+ vs. CTCcl− TNBC PDX tumors revealed elevated expression of Bcl2 (false discovery rate (FDR) < 0.0001, fold change (FC) = 3.5) and reduced acetyl coenzyme A carboxylase-1 (ACC1) (FDR = 0.0005, FC = 0.3) in CTCcl+ compared to CTCcl− tumors. Genome-wide transcriptomic analysis of CTCcl+ vs. CTCcl− tumors revealed 549 differentially expressed genes associated with the presence of CTCcls. Apoptosis was one of the significantly downregulated pathways (normalized enrichment score (NES) = −1.69; FDR < 0.05) in TNBC PDX tumors associated with CTCcl positivity. Two out of four CTCcl+ TNBC PDX primary tumors had high Bcl2 expression by IHC (H-score > 34); whereas, only one of six CTCcl− TNBC PDX primary tumors met this criterion. Evaluation of epithelial-mesenchymal transition (EMT)-specific signature did not show significant differences between CTCcl+ and CTCcl− tumors. However, a gene signature associated with the presence of CTCcls in TNBC PDX models was associated with worse relapse-free survival in the publicly available dataset from 360 patients with basal-like BC. In summary, we identified the multigene signature of primary PDX tumors associated with the presence of CTCcls. Evaluation of additional TNBC PDX models and patients can further illuminate cellular and molecular pathways facilitating CTCcl formation. Full article

(This article belongs to the Special Issue Latest Developments in Mathematical Oncology and Cancer Systems Biology)

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20 pages, 1899 KiB

Open AccessArticle

Phenotypic Switching of Naïve T Cells to Immune-Suppressive Treg-Like Cells by Mutant KRAS

by Arjun Kalvala, Pierre Wallet, Lu Yang, Chongkai Wang, Haiqing Li, Arin Nam, Anusha Nathan, Isa Mambetsariev, Valeriy Poroyko, Hanlin Gao, Peiguo Chu, Martin Sattler, Andrea Bild, Edwin R. Manuel, Peter P. Lee, Mohit Kumar Jolly, Prakash Kulkarni and Ravi Salgia

J. Clin. Med. 2019, 8(10), 1726; https://doi.org/10.3390/jcm8101726 - 18 Oct 2019

Cited by 31 | Viewed by 5665

Abstract

Oncogenic (mutant) Ras protein Kirsten rat sarcoma viral oncogene homolog (KRAS) promotes uncontrolled proliferation, altered metabolism, and loss of genome integrity in a cell-intrinsic manner. Here, we demonstrate that CD4⁺ T cells when incubated with tumor-derived exosomes from mutant (MT) KRAS non-small-cell [...] Read more.

Oncogenic (mutant) Ras protein Kirsten rat sarcoma viral oncogene homolog (KRAS) promotes uncontrolled proliferation, altered metabolism, and loss of genome integrity in a cell-intrinsic manner. Here, we demonstrate that CD4⁺ T cells when incubated with tumor-derived exosomes from mutant (MT) KRAS non-small-cell lung cancer (NSCLC) cells, patient sera, or a mouse xenograft model, induce phenotypic conversion to FOXP3⁺ Treg-like cells that are immune-suppressive. Furthermore, transfecting T cells with MT KRAS cDNA alone induced phenotypic switching and mathematical modeling supported this conclusion. Single-cell sequencing identified the interferon pathway as the mechanism underlying the phenotypic switch. These observations highlight a novel cytokine-independent, cell-extrinsic role for KRAS in T cell phenotypic switching. Thus, targeting this new class of Tregs represents a unique therapeutic approach for NSCLC. Since KRAS is the most frequently mutated oncogene in a wide variety of cancers, the findings of this investigation are likely to be of broad interest and have a large scientific impact. Full article

(This article belongs to the Special Issue Latest Developments in Mathematical Oncology and Cancer Systems Biology)

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19 pages, 2717 KiB

Open AccessArticle

Monitoring and Determining Mitochondrial Network Parameters in Live Lung Cancer Cells

by Tamara Mirzapoiazova, Haiqing Li, Anusha Nathan, Saumya Srivstava, Mohd W. Nasser, Frances Lennon, Brian Armstrong, Isa Mambetsariev, Peiguo G. Chu, Srisairam Achuthan, Surinder K. Batra, Prakash Kulkarni and Ravi Salgia

J. Clin. Med. 2019, 8(10), 1723; https://doi.org/10.3390/jcm8101723 - 18 Oct 2019

Cited by 7 | Viewed by 3999

Abstract

Mitochondria are dynamic organelles that constantly fuse and divide, forming dynamic tubular networks. Abnormalities in mitochondrial dynamics and morphology are linked to diverse pathological states, including cancer. Thus, alterations in mitochondrial parameters could indicate early events of disease manifestation or progression. However, finding [...] Read more.

Mitochondria are dynamic organelles that constantly fuse and divide, forming dynamic tubular networks. Abnormalities in mitochondrial dynamics and morphology are linked to diverse pathological states, including cancer. Thus, alterations in mitochondrial parameters could indicate early events of disease manifestation or progression. However, finding reliable and quantitative tools for monitoring mitochondria and determining the network parameters, particularly in live cells, has proven challenging. Here, we present a 2D confocal imaging-based approach that combines automatic mitochondrial morphology and dynamics analysis with fractal analysis in live small cell lung cancer (SCLC) cells. We chose SCLC cells as a test case since they typically have very little cytoplasm, but an abundance of smaller mitochondria compared to many of the commonly used cell types. The 2D confocal images provide a robust approach to quantitatively measure mitochondrial dynamics and morphology in live cells. Furthermore, we performed 3D reconstruction of electron microscopic images and show that the 3D reconstruction of the electron microscopic images complements this approach to yield better resolution. The data also suggest that the parameters of mitochondrial dynamics and fractal dimensions are sensitive indicators of cellular response to subtle perturbations, and hence, may serve as potential markers of drug response in lung cancer. Full article

(This article belongs to the Special Issue Latest Developments in Mathematical Oncology and Cancer Systems Biology)

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Review

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25 pages, 2245 KiB

Open AccessReview

Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities

by Angela M. Jarrett, Danial Faghihi, David A. Hormuth II, Ernesto A. B. F. Lima, John Virostko, George Biros, Debra Patt and Thomas E. Yankeelov

J. Clin. Med. 2020, 9(5), 1314; https://doi.org/10.3390/jcm9051314 - 2 May 2020

Cited by 50 | Viewed by 5248

Abstract

Optimal control theory is branch of mathematics that aims to optimize a solution to a dynamical system. While the concept of using optimal control theory to improve treatment regimens in oncology is not novel, many of the early applications of this mathematical technique [...] Read more.

Optimal control theory is branch of mathematics that aims to optimize a solution to a dynamical system. While the concept of using optimal control theory to improve treatment regimens in oncology is not novel, many of the early applications of this mathematical technique were not designed to work with routinely available data or produce results that can eventually be translated to the clinical setting. The purpose of this review is to discuss clinically relevant considerations for formulating and solving optimal control problems for treating cancer patients. Our review focuses on two of the most widely used cancer treatments, radiation therapy and systemic therapy, as they naturally lend themselves to optimal control theory as a means to personalize therapeutic plans in a rigorous fashion. To provide context for optimal control theory to address either of these two modalities, we first discuss the major limitations and difficulties oncologists face when considering alternate regimens for their patients. We then provide a brief introduction to optimal control theory before formulating the optimal control problem in the context of radiation and systemic therapy. We also summarize examples from the literature that illustrate these concepts. Finally, we present both challenges and opportunities for dramatically improving patient outcomes via the integration of clinically relevant, patient-specific, mathematical models and optimal control theory. Full article

(This article belongs to the Special Issue Latest Developments in Mathematical Oncology and Cancer Systems Biology)

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2 pages, 186 KiB

Open AccessErratum

Erratum: Iyer, A., et al. Integrative Analysis and Machine Learning Based Characterization of Single Circulating Tumor Cells. J. Clin. Med. 2020, 9, 1206

by Arvind Iyer, Krishan Gupta, Shreya Sharma, Kishore Hari, Yi Fang Lee, Neevan Ramalingam, Yoon Sim Yap, Jay West, Ali Asgar Bhagat, Balaram Vishnu Subramani, Burhanuddin Sabuwala, Tuan Zea Tan, Jean Paul Thiery, Mohit Kumar Jolly, Naveen Ramalingam and Debarka Sengupta

J. Clin. Med. 2021, 10(2), 370; https://doi.org/10.3390/jcm10020370 - 19 Jan 2021

Cited by 1 | Viewed by 2103

Abstract

In the published manuscript [...] Full article

(This article belongs to the Special Issue Latest Developments in Mathematical Oncology and Cancer Systems Biology)

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