Long range personalized cancer treatment strategies incorporating evolutionary dynamics

Generalized Evolutionary Classifier for Evolutionary Guided Precision Medicine

PURPOSE

Current precision medicine (CPM) matches patients to therapies using traditional biomarkers, but inevitably resistance develops. Dynamic precision medicine (DPM) is a new evolutionary guided precision medicine (EGPM) approach undergoing translational development. It tracks intratumoral genetic heterogeneity and evolutionary dynamics, adapts as frequently as every 6 weeks, plans proactively for future resistance development, and incorporates multiple therapeutic agents. Simulations indicated DPM can significantly improve long-term survival and cure rates in a cohort of 3 million virtual patients representing a variety of clinical scenarios. Given the cost and invasiveness of monitoring subclones frequently, we sought to determine the value of a short DPM window of only two 6-week adaptations (moves).

METHODS

In a new simulation, nearly 3 million virtual patients, differing in DPM input parameters of initial subclone compositions, drug sensitivities, and growth and mutational kinetics, were simulated as previously described. Each virtual patient was treated with CPM, DPM, and DPM for two moves followed by CPM.

RESULTS

The first two DPM moves provide similar average benefit to a 5-year, 40-move sequence in the full virtual population. If the first two moves are identical for DPM and CPM, patients will not benefit from DPM (65% negative predictive value). A patient subset (20%) in which 2-move DPM and 40-move DPM provide closely similar outcomes has extraordinary predicted benefit (hazard ratio of DPM/CPM 0.03).

CONCLUSION

The first two DPM moves provide most of the clinical benefit of DPM, reducing the duration required for subclone monitoring. This also leads to an evolutionary classifier selecting patients who will benefit: those in whom DPM and CPM recommendations differ early. These advances bring DPM (and potentially other EGPM approaches) closer to potential clinical testing.

Mathematical modeling framework enhances clinical trial design for maintenance treatment in oncology

Clinical trials are costly and time-intensive endeavors, with a high rate of drug candidate failures. Moreover, the standard approaches often evaluate drugs under a limited number of protocols. In oncology, where multiple treatment protocols can yield divergent outcomes, addressing this issue is crucial. Here, we present a computational framework that simulates clinical trials through a combination of mathematical and statistical models. This approach offers a means to explore diverse treatment protocols efficiently and identify optimal strategies for oncological drug administration. We developed a computational framework with a stochastic mathematical model as its core, capable of simulating virtual clinical trials closely recapitulating the clinical scenarios. Testing our framework on the landmark SOLO-1 clinical trial investigating Poly-ADP-Ribose Polymerase maintenance treatment in high-grade serous ovarian cancer, we demonstrate that managing toxicity through treatment interruptions or dose reductions does not compromise treatment's clinical benefits. Additionally, we provide evidence suggesting that further reduction of hematological toxicity could significantly improve the clinical outcomes. The value of this computational framework lies in its ability to expedite the exploration of new treatment protocols, delivering critical insights pivotal to shaping the landscape of upcoming clinical trials.

Unraveling Vulnerabilities in Endocrine Therapy-Resistant HER2+/ER+ Breast Cancer

Breast tumors overexpressing human epidermal growth factor receptor (HER2) confer intrinsic resistance to endocrine therapy (ET), and patients with HER2/estrogen receptor-positive (HER2+/ER+) breast cancer (BCa) are less responsive to ET than HER2-/ER+. However, real-world evidence reveals that a large subset of patients with HER2+/ER+ receive ET as monotherapy, positioning this treatment pattern as a clinical challenge. In the present study, we developed and characterized 2 in vitro models of ET-resistant (ETR) HER2+/ER+ BCa to identify possible therapeutic vulnerabilities. To mimic ETR to aromatase inhibitors (AIs), we developed 2 long-term estrogen deprivation (LTED) cell lines from BT-474 (BT474) and MDA-MB-361 (MM361). Growth assays, PAM50 subtyping, and genomic and transcriptomic analyses, followed by validation and functional studies, were used to identify targetable differences between ET-responsive parental and ETR-LTED HER2+/ER+ cells. Compared to their parental cells, MM361 LTEDs grew faster, lost ER, and increased HER2 expression, whereas BT474 LTEDs grew slower and maintained ER and HER2 expression. Both LTED variants had reduced responsiveness to fulvestrant. Whole-genome sequencing of aggressive MM361 LTEDs identified mutations in genes encoding transcription factors and chromatin modifiers. Single-cell RNA sequencing demonstrated a shift towards non-luminal phenotypes, and revealed metabolic remodeling of MM361 LTEDs, with upregulated lipid metabolism and ferroptosis-associated antioxidant genes, including GPX4. Combining a GPX4 inhibitor with anti-HER2 agents induced significant cell death in both MM361 and BT474 LTEDs. The BT474 and MM361 AI-resistant models capture distinct phenotypes of HER2+/ER+ BCa and identify altered lipid metabolism and ferroptosis remodeling as vulnerabilities of this type of ETR BCa.

Unraveling Vulnerabilities in Endocrine Therapy-Resistant HER2+/ER+ Breast Cancer

Background

Breast tumors overexpressing human epidermal growth factor receptor (HER2) confer intrinsic resistance to endocrine therapy (ET), and patients with HER2/ estrogen receptor-positive (HER2+/HR+) breast cancer (BCa) are less responsive to ET than HER2-/ER+. However, real-world evidence reveals that a large subset of HER2+/ER+ patients receive ET as monotherapy, positioning this treatment pattern as a clinical challenge. In the present study, we developed and characterized two distinct in vitro models of ET-resistant (ETR) HER2+/ER+ BCa to identify possible therapeutic vulnerabilities.

Methods

To mimic ETR to aromatase inhibitors (AI), we developed two long-term estrogen-deprived (LTED) cell lines from BT-474 (BT474) and MDA-MB-361 (MM361). Growth assays, PAM50 molecular subtyping, genomic and transcriptomic analyses, followed by validation and functional studies, were used to identify targetable differences between ET-responsive parental and ETR-LTED HER2+/ER+ cells.

Results

Compared to their parental cells, MM361 LTEDs grew faster, lost ER, and increased HER2 expression, whereas BT474 LTEDs grew slower and maintained ER and HER2 expression. Both LTED variants had reduced responsiveness to fulvestrant. Whole-genome sequencing of the more aggressive MM361 LTED model system identified exonic mutations in genes encoding transcription factors and chromatin modifiers. Single-cell RNA sequencing demonstrated a shift towards non-luminal phenotypes, and revealed metabolic remodeling of MM361 LTEDs, with upregulated lipid metabolism and antioxidant genes associated with ferroptosis, including GPX4. Combining the GPX4 inhibitor RSL3 with anti-HER2 agents induced significant cell death in both the MM361 and BT474 LTEDs.

Conclusions

The BT474 and MM361 AI-resistant models capture distinct phenotypes of HER2+/ER+ BCa and identify altered lipid metabolism and ferroptosis remodeling as vulnerabilities of this type of ETR BCa.

Optimal Strategy and Benefit of Pulsed Therapy Depend On Tumor Heterogeneity and Aggressiveness at Time of Treatment Initiation

Therapeutic resistance is a fundamental obstacle in cancer treatment. Tumors that initially respond to treatment may have a preexisting resistant subclone or acquire resistance during treatment, making relapse theoretically inevitable. Here, we investigate treatment strategies that may delay relapse using mathematical modeling. We find that for a single-drug therapy, pulse treatment-short, elevated doses followed by a complete break from treatment-delays relapse compared with continuous treatment with the same total dose over a length of time. For tumors treated with more than one drug, continuous combination treatment is only sometimes better than sequential treatment, while pulsed combination treatment or simply alternating between the two therapies at defined intervals delays relapse the longest. These results are independent of the fitness cost or benefit of resistance, and are robust to noise. Machine-learning analysis of simulations shows that the initial tumor response and heterogeneity at the start of treatment suffice to determine the benefit of pulsed or alternating treatment strategies over continuous treatment. Analysis of eight tumor burden trajectories of breast cancer patients treated at Memorial Sloan Kettering Cancer Center shows the model can predict time to resistance using initial responses to treatment and estimated preexisting resistant populations. The model calculated that pulse treatment would delay relapse in all eight cases. Overall, our results support that pulsed treatments optimized by mathematical models could delay therapeutic resistance.

How Should Cancer Models Be Constructed?

Choosing and optimizing treatment strategies for cancer requires capturing its complex dynamics sufficiently well for understanding but without being overwhelmed. Mathematical models are essential to achieve this understanding, and we discuss the challenge of choosing the right level of complexity to address the full range of tumor complexity from growth, the generation of tumor heterogeneity, and interactions within tumors and with treatments and the tumor microenvironment. We discuss the differences between conceptual and descriptive models, and compare the use of predator-prey models, evolutionary game theory, and dynamic precision medicine approaches in the face of uncertainty about mechanisms and parameter values. Although there is of course no one-size-fits-all approach, we conclude that broad and flexible thinking about cancer, based on combined modeling approaches, will play a key role in finding creative and improved treatments.

In Vitro Anticancer Effects of Stilbene Derivatives: Mechanistic Studies on HeLa and MCF-7 Cells

BACKGROUND AND OBJECTIVE

The growing prevalence of cancer and the resulting chemoresistance exert a huge burden on healthcare systems and impose a great challenge to public health around the world. In efforts to develop new chemotherapeutic agents for cancer treatment, a class of heterocyclic compounds i.e. triazine-based molecules were investigated as anticancer agents.

MATERIALS AND METHODS

New triazine hybrids of stilbene were synthesized and evaluated as anticancer agents for cervical (HeLa) and breast (MCF-7) carcinoma cells. The compound (7e), sodium (E)-6,6'-(ethene-1,2- diyl)bis(3-((4-chloro-6-((3-luorophenyl)amino)-1,3,5-triazin-2-yl)amino)benzenesulfonate) was found to be most potent among synthesized derivatives and was explored further for detailed mechanistic studies.

RESULTS

In a set comprised of twelve derivatives, compound 7e, sodium (E)-6,6'-(ethene-1,2-diyl)bis(3-((4- chloro-6-((3-luorophenyl)amino)-1,3,5-triazin-2-yl)amino)benzenesulfonate) was found most potent inhibitor for HeLa and MCF-7 cells.

DISCUSSION

The present study has revealed that compound 7e may activate mitochondrial pathway of apoptosis in HeLa and MCF-7 cells which was assessed by DNA binding studies, estimation of the release of Lactate Dehydrogenase (LDH), fluorescence imaging, production of Reactive Oxygen Species (ROS) in cancer cells, analysis of cell cycle by flow cytometry, change in Mitochondrial Membrane Potential (MMP) and activation of caspase-9 and caspase-3.

CONCLUSION

Compound 7e may serve as a lead in designing new anticancer compounds based on stilbene scaffold.

Transcompp: understanding phenotypic plasticity by estimating Markov transition rates for cell state transitions

MOTIVATION

Gradual population-level changes in tissues can be driven by stochastic plasticity, meaning rare stochastic transitions of single-cell phenotype. Quantifying the rates of these stochastic transitions requires time-intensive experiments, and analysis is generally confounded by simultaneous bidirectional transitions and asymmetric proliferation kinetics. To quantify cellular plasticity, we developed Transcompp (Transition Rate ANalysis of Single Cells to Observe and Measure Phenotypic Plasticity), a Markov modeling algorithm that uses optimization and resampling to compute best-fit rates and statistical intervals for stochastic cell-state transitions.

RESULTS

We applied Transcompp to time-series datasets in which purified subpopulations of stem-like or non-stem cancer cells were exposed to various cell culture environments, and allowed to re-equilibrate spontaneously over time. Results revealed that commonly used cell culture reagents hydrocortisone and cholera toxin shifted the cell population equilibrium toward stem-like or non-stem states, respectively, in the basal-like breast cancer cell line MCF10CA1a. In addition, applying Transcompp to patient-derived cells showed that transition rates computed from short-term experiments could predict long-term trajectories and equilibrium convergence of the cultured cell population.

AVAILABILITY AND IMPLEMENTATION

Freely available for download at http://github.com/nsuhasj/Transcompp.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Extensive subclonal mutational diversity in human colorectal cancer and its significance

Human colorectal cancers (CRCs) contain both clonal and subclonal mutations. Clonal driver mutations are positively selected, present in most cells, and drive malignant progression. Subclonal mutations are randomly dispersed throughout the genome, providing a vast reservoir of mutant cells that can expand, repopulate the tumor, and result in the rapid emergence of resistance, as well as being a major contributor to tumor heterogeneity. Here, we apply duplex sequencing (DS) methodology to quantify subclonal mutations in CRC tumor with unprecedented depth (104) and accuracy (<10-7). We measured mutation frequencies in genes encoding replicative DNA polymerases and in genes frequently mutated in CRC, and found an unexpectedly high effective mutation rate, 7.1 × 10-7. The curve of subclonal mutation accumulation as a function of sequencing depth, using DNA obtained from 5 different tumors, is in accord with a neutral model of tumor evolution. We present a theoretical approach to model neutral evolution independent of the infinite-sites assumption (which states that a particular mutation arises only in one tumor cell at any given time). Our analysis indicates that the infinite-sites assumption is not applicable once the number of tumor cells exceeds the reciprocal of the mutation rate, a circumstance relevant to even the smallest clinically diagnosable tumor. Our methods allow accurate estimation of the total mutation burden in clinical cancers. Our results indicate that no DNA locus is wild type in every malignant cell within a tumor at the time of diagnosis (probability of all cells being wild type, 10-308).

Treatment resistance in urothelial carcinoma: an evolutionary perspective

The emergence of treatment-resistant clones is a critical barrier to cure in patients with urothelial carcinoma. Setting the stage for the evolution of resistance, urothelial carcinoma is characterized by extensive mutational heterogeneity, which is detectable even in patients with early stage disease. Chemotherapy and immunotherapy both act as selective pressures that shape the evolutionary trajectory of urothelial carcinoma throughout the course of the disease. A detailed understanding of the dynamics of evolutionary drivers is required for the rational development of curative therapies. Herein, we describe the molecular basis of the clonal evolution of urothelial carcinomas and the use of genomic approaches to predict treatment responses. We discuss various mechanisms of resistance to chemotherapy with a focus on the mutagenic effects of the DNA dC->dU-editing enzymes APOBEC3 family of proteins. We also review the evolutionary mechanisms underlying resistance to immunotherapy, such as the loss of clonal tumour neoantigens. By dissecting treatment resistance through an evolutionary lens, the field will advance towards true precision medicine for urothelial carcinoma.

Combination Therapy and the Evolution of Resistance: The Theoretical Merits of Synergism and Antagonism in Cancer

The search for effective combination therapies for cancer has focused heavily on synergistic combinations because they exhibit enhanced therapeutic efficacy at lower doses. Although synergism is intuitively attractive, therapeutic success often depends on whether drug resistance develops. The impact of synergistic combinations (vs. antagonistic or additive combinations) on the process of drug-resistance evolution has not been investigated. In this study, we use a simplified computational model of cancer cell numbers in a population of drug-sensitive, singly-resistant, and fully-resistant cells to simulate the dynamics of resistance evolution in the presence of two-drug combinations. When we compared combination therapies administered at the same combination of effective doses, simulations showed synergistic combinations most effective at delaying onset of resistance. Paradoxically, when the therapies were compared using dose combinations with equal initial efficacy, antagonistic combinations were most successful at suppressing expansion of resistant subclones. These findings suggest that, although synergistic combinations could suppress resistance through early decimation of cell numbers (making them "proefficacy" strategies), they are inherently fragile toward the development of single resistance. In contrast, antagonistic combinations suppressed the clonal expansion of singly-resistant cells, making them "antiresistance" strategies. The distinction between synergism and antagonism was intrinsically connected to the distinction between offensive and defensive strategies, where offensive strategies inflicted early casualties and defensive strategies established protection against anticipated future threats. Our findings question the exclusive focus on synergistic combinations and motivate further consideration of nonsynergistic combinations for cancer therapy.Significance: Computational simulations show that if different combination therapies have similar initial efficacy in cancers, then nonsynergistic drug combinations are more likely than synergistic drug combinations to provide a long-term defense against the evolution of therapeutic resistance. Cancer Res; 78(9); 2419-31. ©2018 AACR.

Clinical laboratory and imaging evidence for effectiveness of agarose-agarose macrobeads containing stem-like cells derived from a mouse renal adenocarcinoma cell population (RMBs) in treatment-resistant, advanced metastatic colorectal cancer: Evaluation of a biological-systems approach to cancer therapy (U.S. FDA IND-BB 10091; NCT 02046174, NCT 01053013)

Objective

The complexity, heterogeneity and capacity of malignant neoplastic cells and tumors for rapid change and evolution suggest that living-cell-based biological-systems approaches to cancer treatment are merited. Testing this hypothesis, the tumor marker, metabolic activity, and overall survival (OS) responses, to the use of one such system, implantable macrobeads [RENCA macrobeads (RMBs)], in phase I and IIa clinical trials in advanced, treatment-resistant metastatic colorectal cancer (mCRC) are described here.

Methods

Forty-eight mCRC patients (30 females; 18 males), who had failed all available, approved treatments, underwent RMB implantation (8 RMB/kg body weight) up to 4 times in phase I and phase IIa open-label trials. Physicals, labs [tumor and inflammation markers, lactate dehydrogenase (LDH)] and positron emission tomography-computed tomography (PET-CT) imaging to measure number/volume and metabolic activity of the tumors were performed pre- and 3-month-post-implantation to evaluate safety and initial efficacy (as defined by biological responses). PET-CT maximum standard uptake value (SUV_max) (baseline and d 90; SUV_max ≥2.5), LDH, and carcinoembryonic antigen (CEA) and/or cancer antigen 19-9 (CA 19-9) response (baseline, d 30 and/or d 60) were assessed and compared to OS.

Results

Responses after implantation were characterized by an at least 20% decrease in CEA and/or CA 19-9 in 75% of patients. Fluorodeoxyglucose (FDG)-positive lesions (phase I, 39; 2a, 82) were detected in 37/48 evaluable patients, with 35% stable volume and stable or decreased SUV (10) plus four with necrosis; 10, increased tumor volume, SUV. LDH levels remained stable and low in Responders (R) (d 0-60, 290.4-333.9), but increased steadily in Non-responders (NR) (d 0-60, 382.8-1,278.5) (d 60, P=0.050). Responders to RMBs, indicated by the changes in the above markers, correlated with OS (R mean OS=10.76 months; NR mean OS=4.9 months; P=0.0006).

Conclusions

The correlations of the tumor marker, tumor volume and SUV changes on PET-CT, and LDH levels themselves, and with OS, support the concept of a biological response to RMB implantation and the validity of the biological-systems approach to mCRC. A phase III clinical trial is planned.

Evolutionary dynamics and significance of multiple subclonal mutations in cancer

For the last 40 years the authors have collaborated on trying to understand the complexities of human cancer by formulating testable mathematical models that are based on mutation accumulation in human malignancies. We summarize the concepts encompassed by multiple mutations in human cancers in the context of source, accumulation during carcinogenesis and tumor progression, and therapeutic consequences. We conclude that the efficacious treatment of human cancer by targeted therapy will involve individualized, uniquely directed specific agents singly and in simultaneous combinations, and take into account the importance of targeting resistant subclonal mutations, particularly those subclones with alterations in DNA repair genes, DNA polymerase, and other genes required to maintain genetic stability.