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Induction of cell death represents a primary goal of most anticancer treatments. Despite the efficacy of such approaches, a small population of “persisters” develop evasion strategies to therapy-induced cell death. While previous studies have identified mechanisms of resistance to apoptosis, the mechanisms by which persisters dampen other forms of cell death, such as pyroptosis, remain to be elucidated. Pyroptosis is a form of inflammatory cell death that involves formation of membrane pores, ion gradient imbalance, water inflow, and membrane rupture. Herein, we investigate mechanisms by which cancer persisters resist pyroptosis, survive, then proliferate in the presence of tyrosine kinase inhibitors (TKI). Lung, prostate, and esophageal cancer persister cells remaining after treatments exhibited several hallmarks indicative of pyroptosis resistance. The inflammatory attributes of persisters included chronic activation of inflammasome, STING, and type I interferons. Comprehensive metabolomic characterization uncovered that TKI-induced pyroptotic persisters display high methionine consumption and excessive taurine production. Elevated methionine flux or exogenous taurine preserved plasma membrane integrity via osmolyte-mediated effects. Increased dependency on methionine flux decreased the level of one carbon metabolism intermediate S-(5′-adenosyl)-L-homocysteine, a determinant of cell methylation capacity. The consequent increase in methylation potential induced DNA hypermethylation of genes regulating metal ion balance and intrinsic immune response. This enabled thwarting TKI resistance by using the hypomethylating agent decitabine. In summary, the evolution of resistance to pyroptosis can occur via a stepwise process of physical acclimation and epigenetic changes without existing or recurrent mutations.Significance:Methionine enables cancer cells to persist by evading pyroptotic osmotic lysis, which leads to genome-wide hypermethylation that allows persisters to gain proliferative advantages.

Adaptive therapies that alternate between drug applications and drug-free vacations can exploit competition between sensitive and resistant cells to maximize the time to progression. However, optimal dosing schedules depend on the properties of metastases, which are often not directly measurable in clinical practice. Here, we proposed a framework for estimating features of metastases through tumor response dynamics during the first adaptive therapy treatment cycle. Longitudinal PSA levels in sixteen metastatic castration resistant prostate cancer patients undergoing adaptive androgen deprivation treatment were analyzed to investigate relationships between cycle dynamics and clinical variables such as Gleason score, the change in the number of metastases over a cycle, and the total number of cycles over the course of treatment. The first cycle of adaptive therapy, which consists of a response period (applying therapy until 50% PSA reduction), and a regrowth period (removing treatment until reaching initial PSA levels), delineated several features of the computational metastatic system: larger metastases had longer cycles; a higher proportion of drug resistant cells slowed the cycles; and a faster cell turnover rate sped up drug response time and slowed regrowth time. The number of metastases did not affect cycle times, as response dynamics were dominated by the largest tumors rather than the aggregate. In addition, systems with higher inter-metastasis heterogeneity responded better to continuous therapy and correlated with dynamics from patients with high or low Gleason scores. Conversely, systems with higher intra-metastasis heterogeneity responded better to adaptive therapy and correlated with dynamics from patients with intermediate Gleason scores.

Standard-of-care treatment regimes have long been designed to for maximal cell kill, yet these strategies often fail when applied to treatment–resistant tumors, resulting in patient relapse. Adaptive treatment strategies have been developed as an alternative approach, harnessing intra-tumoral competition to suppress the growth of treatment resistant populations, to delay or even prevent tumor progression. Following recent clinical implementations of adaptive therapy, it is of significant interest to optimise adaptive treatment protocols. We propose the application of deep reinforcement learning models to provide generalised solutions within adaptive drug scheduling, and demonstrate this framework can outperform the current adaptive protocols, extending time to progression by up to a quarter. This strategy is robust to varying model parameterisations, and the underlying tumor model. We demonstrate the deep learning framework can produce interpretable, adaptive strategies based on a single tumor burden threshold, replicating and informing a novel, analytically–derived optimal treatment strategy with no knowledge of the underlying mathematical tumor model. This approach is highly relevant beyond the simple, analytically–tractable tumor model considered here, demonstrating the capability of deep learning frameworks to help inform and develop treatment strategies in complex settings. Finally, we propose a pathway to integrate mechanistic modelling with DRL to tailor generalist treatment strategies to individual patients in the clinic, generating personalised treatment schedules that consistently outperform clinical standard-of-care protocols.

Induction of cell death represents a primary goal of most anticancer treatments. Despite the efficacy of such approaches, a small population of “persisters” develop evasion strategies to therapy-induced cell death. While previous studies have identified mechanisms of resistance to apoptosis, the mechanisms by which persisters dampen other forms of cell death, such as pyroptosis, remain to be elucidated. Pyroptosis is a form of inflammatory cell death that involves formation of membrane pores, ion gradient imbalance, water inflow, and membrane rupture. Herein, we investigate mechanisms by which cancer persisters resist pyroptosis, survive, then proliferate in the presence of tyrosine kinase inhibitors (TKI). Lung, prostate, and esophageal cancer persister cells remaining after treatments exhibited several hallmarks indicative of pyroptosis resistance. The inflammatory attributes of persisters included chronic activation of inflammasome, STING, and type I interferons. Comprehensive metabolomic characterization uncovered that TKI-induced pyroptotic persisters display high methionine consumption and excessive taurine production. Elevated methionine flux or exogenous taurine preserved plasma membrane integrity via osmolyte-mediated effects. Increased dependency on methionine flux decreased the level of one carbon metabolism intermediate S-(5′-adenosyl)-L-homocysteine, a determinant of cell methylation capacity. The consequent increase in methylation potential induced DNA hypermethylation of genes regulating metal ion balance and intrinsic immune response. This enabled thwarting TKI resistance by using the hypomethylating agent decitabine. In summary, the evolution of resistance to pyroptosis can occur via a stepwise process of physical acclimation and epigenetic changes without existing or recurrent mutations.Significance:Methionine enables cancer cells to persist by evading pyroptotic osmotic lysis, which leads to genome-wide hypermethylation that allows persisters to gain proliferative advantages.

Supplemental Materials Methods with additional references.

Supplemental Figures and Tables to augment (1) the mathematical modeling (providing details on specific therapeutics); (2) patient characteristics (treatment history, clinical and predicted response), (3) synergistic activities identified by EMMA; and (4) data processing guides

Multiple myeloma remains treatable but incurable. Despite a growing armamentarium of effective agents, choice of therapy, especially in relapse, still relies almost exclusively on clinical acumen. We have developed a system, Ex vivo Mathematical Myeloma Advisor (EMMA), consisting of patient-specific mathematical models parameterized by an ex vivo assay that reverse engineers the intensity and heterogeneity of chemosensitivity of primary cells from multiple myeloma patients, allowing us to predict clinical response to up to 31 drugs within 5 days after bone marrow biopsy. From a cohort of 52 multiple myeloma patients, EMMA correctly classified 96% as responders/nonresponders and correctly classified 79% according to International Myeloma Working Group stratification of level of response. We also observed a significant correlation between predicted and actual tumor burden measurements (Pearson r = 0.5658, P < 0.0001). Preliminary estimates indicate that, among the patients enrolled in this study, 60% were treated with at least one ineffective agent from their therapy combination regimen, whereas 30% would have responded better if treated with another available drug or combination. Two in silico clinical trials with experimental agents ricolinostat and venetoclax, in a cohort of 19 multiple myeloma patient samples, yielded consistent results with recent phase I/II trials, suggesting that EMMA is a feasible platform for estimating clinical efficacy of drugs and inclusion criteria screening. This unique platform, specifically designed to predict therapeutic response in multiple myeloma patients within a clinically actionable time frame, has shown high predictive accuracy in patients treated with combinations of different classes of drugs. The accuracy, reproducibility, short turnaround time, and high-throughput potential of this platform demonstrate EMMA's promise as a decision support system for therapeutic management of multiple myeloma. Cancer Res; 77(12); 3336–51. ©2017 AACR.

Individual patient EMMA predicitons of actual responses to therapy (companion to Figure 3i).

Free Article from Systems Biologists Seek Fuller Integration of Systems Biology Approaches in New Cancer Research Programs

Systems biology takes an interdisciplinary approach to the systematic study of complex interactions in biological systems. This approach seeks to decipher the emergent behaviors of complex systems rather than focusing only on their constituent properties. As an increasing number of examples illustrate the value of systems biology approaches to understand the initiation, progression, and treatment of cancer, systems biologists from across Europe and the United States hope for changes in the way their field is currently perceived among cancer researchers. In a recent EU-US workshop, supported by the European Commission, the German Federal Ministry for Education and Research, and the National Cancer Institute of the NIH, the participants discussed the strengths, weaknesses, hurdles, and opportunities in cancer systems biology. Cancer Res; 70(1); 12–3

The evolution of drug resistance leads to treatment failure and tumor progression. Intermittent androgen deprivation therapy (IADT) helps responsive cancer cells compete with resistant cancer cells in intratumoral competition. However, conventional IADT is population-based and ignores the heterogeneous phenotypes of individual patients. To address this challenge, we developed a time-varied, mixed-effect, and generative Lotka-Volterra (tM-GLV) model to account for the heterogeneity of the evolution mechanism and the pharmacokinetics of individual patients. Then, we proposed a reinforcement learningenabled individualized IADT framework, namely, I2ADT, to learn the patient-specific tumor dynamics and derive the optimal drug administration policy. Experiments with clinical trial data demonstrated that the proposed I2ADT can significantly prolong the time to progression of prostate cancer patients with reduced cumulative drug dosage. This research elucidates the application of reinforcement learning techniques to identify personalized adaptive cancer therapy.

Toxicity and emerging drug resistance are important challenges in PARP inhibitor (PARPi) treatment of ovarian cancer. Recent research has shown that evolutionary-inspired treatment algorithms which adapt treatment to the tumor’s treatment response (adaptive therapy) can help to mitigate both. Here, we present a first step in developing an adaptive therapy protocol for PARPi treatment by combining mathematical modelling and wet-lab experiments to characterize the cell population dynamics under different PARPi schedules. Using data fromin vitroIncucyte Zoom time-lapse microscopy experiments and a step-wise model selection process we derive a calibrated and validated ordinary differential equation model, which we then use to test different plausible adaptive treatment schedules. Our model can accurately predict thein vitrotreatment dynamics, even to new schedules, and suggests that treatment modifications need to be carefully timed, or one risks losing control over tumour growth, even in the absence of any resistance. This is because our model predicts that multiple rounds of cell division are required for cells to acquire sufficient DNA damage to induce apoptosis. As a result, adaptive therapy algorithms that modulate treatment but never completely withdraw it are predicted to perform better in this setting than strategies based on treatment interruptions. Pilot experimentsin vivoconfirm this conclusion. Overall, this study contributes to a better understanding of the impact of scheduling on treatment outcome for PARPis and showcases some of the challenges involved in developing adaptive therapies for new treatment settings.

Cancer is a prevalent disease, and while many significant advances have been made, the ability to accurately predict how an individual tumor will grow – and ultimately respond to therapy – remains limited. We use spatial-spectral analysis of 20 patients accrued to a phase II study of preoperative SABR with 9.5 x 3 Gy for early-stage breast cancer whose tissues were stained with multiplex immunofluorescence. We employ the reaction-diffusion framework to compare the data-deduced two-point correlation function and the corresponding spatial power spectral distribution with the theoretically predicted ones. A single histopathological slice suffices to characterize the reaction-diffusion equation dynamics through its power spectral density giving us an interpretative key in terms of infiltration and diffusion of cancer on a per-patient basis. This novel approach tackles model-parameter-inference problems for tumor infiltration and can immediately inform clinical treatments.

We present a unique database of tumor samples from 399 multiple myeloma (MM) patients, whose functional genomic landscape was assessed by integrating ex vivo drug sensitivity to 138 drugs, clinical variables, cytogenetics, mutational profiles and transcriptomes. These analyses revealed a novel MM transcriptomic topology that, when associated with ex vivo drug sensitivity, generates “footprints” having both mechanistic and predictive applications. We validated the transcriptomic footprint for the nuclear export inhibitor selinexor in two clinical trials, BOSTON and MCC17814, and showed such a gene signature can accurately classify clinical response and suggest mechanism of resistance. Finally, we propose a novel evolutionary-based therapeutic strategy involving sequential therapy of monoclonal antibody daratumumab and selinexor, based on anti-correlative transcriptomic footprints, which was validated in two clinical trials (STOMP and XPORT-MM-028). Collectively, this database and computational framework can be leveraged to inform underlying biology, and to identify novel therapeutic strategies to improve treatment of MM.

We developed a novel modeling paradigm to specifically recapitulate 3D tissue at the resolution of CT imaging and yet capture single cell information: the population-based model (PBM). This PBM method allowed us to seed a model with individual cells that operate at the CT scale of cubic millimeters and grow to the scale of 10 cubic centimeters or more. We used our PBM to investigate how well-known tumor traits of increased resource consumption and angiogenesis maybe differentially selected by a growing tumor in different densities of normal tissue. We found that tumor phenotypes that reduced consumption and increased angiogenesis grew the fastest in low density normal tissue, and vice versa in high density normal tissue. We ran parameter sweeps of this model using temporal patient data from the NLST dataset, which consisted of untreated tumors in the low-density lung periphery. We saw that specific movement rate and proliferation rate parameters were required to fit the growth speed of the tumors, while a low consumption and high angiogenic phenotype was required to fit the radial density distribution of the tumors. Citation Format: Rafael Ramon Bravo, Mark Robertson-Tessi, Alexander R. Anderson, Robert Gatenby, Scott Antonia, Jhanelle Gray, Amer Beg, Matthew Schabath. The impact of normal tissue density on tumor growth and evolution in a 3D whole-tumor model of lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 853.

The purpose of this study is to use the dynamics of commonly collected biomarker levels for prostate cancer to identify the underlying cancer cell subtype composition and help predict response to treatment. In castrate-sensitive prostate cancer, the current standard of care is continuous maximum-tolerated dose (MTD) of anti-androgen compounds, which successfully reduces the tumor burden. However, continuous use often leads to treatment resistance. Alternatively, adaptive therapy approaches have been used clinically to delay or prevent resistant outgrowth. In adaptive therapy, a drug is given to maintain a tumor size by killing enough treatment-sensitive cells to shrink the burden but keep enough sensitive cells to suppress the outgrowth of resistant cells. Clinical trials using adaptive therapy approaches have shown longer tumor control compared to MTD for castrate-resistant disease and, more recently, shown feasibility for control in castrate-sensitive disease. This approach relies on using prostate-specific antigen (PSA) as a systemic biomarker as a surrogate for total tumor burden. Because PSA is a product of testosterone metabolism by androgen receptors (AR) in cells, and both testosterone and AR can vary over time and amongst cells, we can use a mathematical model to couple PSA, testosterone, and AR-dependency to dissociate systemic biomarkers from tumor burden and uncover potential mechanisms of resistance. We build a system of ordinary differential equations to investigate the emergence of different resistance mechanisms for metastatic castrate-sensitive prostate cancer patients. We first fit the equations to testosterone dynamics alone. Then, fixing those parameters, we fit to the dynamics of PSA assuming cell subtypes consisting of testosterone-dependent cells, testosterone producers, and AR-overexpressing cells. From the best fits, we can estimate the tumor cell subtype compositions for each patient and test different treatment strategies. We find that patients with tightly correlated testosterone and PSA levels tend to have mostly testosterone-dependent cells and continued robust responses to each round of treatment. Less correlated testosterone and PSA dynamics was usually associated with changes in PSA nadirs, peaks, or rates of return after stopping treatment over subsequent rounds of adaptive therapy. By fitting to both PSA and testosterone dynamics, we can distinguish between tumors that maintain testosterone-dependent cells from those with increasing resistant cell types. This allows for better quantification of the underlying tumor composition to help optimize therapies that target those cell types and better maintain tumor control. Citation Format: Jill Gallaher, Maximilian Strobl, Jeffrey West, Mark Robertson-Tessi, Robert Gatenby, Jingsong Zhang, Alexander R. A. Anderson. Predicting progression in metastatic castrate-sensitive prostate cancer by coupling PSA and testosterone dynamics to a mechanistic mathematical model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 852.

PARP inhibitors (PARPis) are revolutionizing the treatment of ovarian cancer. Yet for many patients these improvements come at the cost of physical and financial toxicity and responses are typically temporary due to emerging drug resistance. A growing body of pre-clinical and clinical work suggests that when cure is unlikely, it is possible to delay progression and reduce drug use through patient-specific drug scheduling. So-called 'adaptive therapy' dynamically adjusts treatment to preserve drug-sensitive cells which interfere with resistant cells through competition. In a prior study, we developed a mathematical model to describe the treatment response of ovarian cancer cells to the PARPi olaparib in vitro, and we proposed a candidate adaptive PARPi algorithm. Here, we extend our model to capture the dynamics in patients and use it to study the feasibility and potential benefit of adaptive PARPi administration in practice. Our prior model posited that treatment induces cell cycle arrest that moves cells from the proliferating subset of the population to an arrested compartment. The model predicted that while there is scope for treatment reductions, these need to be carefully timed and prolonged treatment breaks should be avoided. Based on these results we proposed an adaptive treatment algorithm in which the olaparib dose is switched between high and low doses, depending on the tumor’s growth rate. To test the translational potential of this strategy, we retrospectively collected data from 53 ovarian cancer patients who received olaparib at the H Lee Moffitt Cancer Center between 2014 and 2021. Using serum CA-125 as a proxy for tumor burden, we first examined whether our mathematical model could capture the patients’ longitudinal dynamics. While the response of some patients was consistent with what we had observed in vitro, in others there was evidence of the emergence of a distinct drug-resistant population, and we extended our mathematical model to account for this. After calibrating and validating the model with the patient data, we tested whether these patients would have benefited from adaptive PARPi treatment. Our simulations suggest that our proposed algorithm is feasible and provides a means for reducing treatment in a patient-specific manner. In addition, in a subset of patients we predict that adaptive therapy could delay progression. Overall, this work corroborates the potential for adaptive PARPi therapy and helps to identify outstanding challenges on the way to clinical translation. Citation Format: Maximilian A. Strobl, Alexandra L. Martin, Christopher Gallagher, Mehdi Damaghi, Mark Robertson-Tessi, Robert Gatenby, Robert M. Wenham, Philip K. Maini, Alexander R. Anderson. Adaptive treatment scheduling of PARP inhibitors in ovarian cancer: Using mathematical modeling to assess clinical feasibility and estimate potential benefits. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5694.

Background:Abiraterone acetate is an effective treatment for metastatic castrate-resistant prostate cancer (mCRPC), but evolution of resistance inevitably leads to progression. We present a pilot study in which abiraterone dosing is guided by evolution-informed mathematical models to delay onset of resistance.Methods:In the study cohort, abiraterone was stopped when PSA was 50% PSA decline after initial abiraterone administration and met trial eligibility requirements but chose standard of care (SOC) dosing.Results:17 subjects were enrolled in the adaptive therapy group and 16 in the SOC group. All SOC subjects have progressed, but four patients in the study cohort remain stably cycling (range 53–70 months). The study cohort had significantly improved median time to progression (TTP; 33.5 months; pαRS/αSR) of sensitive and resistant populations, a critical factor in intratumoral evolution, was two- to threefold higher than pre-trial estimates. Computer simulations of intratumoral evolutionary dynamics in the four long-term survivors found that, due to the larger value for αRS/αSR, cycled therapy significantly decreased the resistant population. Simulations in subjects who progressed predicted further increases in OS could be achieved with prompt abiraterone withdrawal after achieving 50% PSA reduction.Conclusions:Incorporation of evolution-based mathematical models into abiraterone monotherapy for mCRPC significantly increases TTP and OS. Computer simulations with updated parameters from longitudinal trial data can estimate intratumoral evolutionary dynamics in each subject and identify strategies to improve outcomes.Funding:Moffitt internal grants and NIH/NCI U54CA143970-05 (Physical Science Oncology Network).

Background: To achieve better prostate cancer control and to delay the emergency of treatment resistance, we developed an evolutionary game theory model using Lotka-Volterra equations with three competing prostate cancer "species": T+, Tp, and T-. T+ prostate cancer cells depend on exogenous androgen; Tp cells express CYP17A1, produce and depend on androgen; and T- cells are androgen-independent and abiraterone (abi)-resistant. We applied this model to guide the on and off treatment cycles with abi for metastatic castration resistant prostate cancer (mCRPC). At the first interim analysis with 11 patients, this approach was shown to prolong the time to cancer progression with less than 50% drug usage compared to the conventional continuous abi (Nat Commun. 2017). Here we present the updated data of this phase 2a study with a median follow up of 5.8 years. Methods: Men with asymptomatic or minimal symptomatic mCRPC were enrolled after they achieved > 50% PSA reduction with abi as a frontline therapy for mCRPC. The primary objective is feasibility and is measured by the percentage of abi responsive men who remain to be responsive to abi (defined as > 50% decline of the pre Abi PSA) after completing 2 adaptive treatment cycles. The secondary objective is to assess the clinical benefits by comparing the radiographic progression free survival (rPFS) and overall survival (OS) in men undergoing adaptive Abi therapy to a contemporaneous cohort of patients who met trial eligibility but received continuous abi dosing as standard of care (SOC). Computer simulations of each patient’s clinical course estimated critical evolutionary parameters and then tested alternative strategies for individuals in both cohorts. Result: 17 evaluable patients were enrolled at Moffitt Cancer Center between June 2015 and January 2019. 5 of the 17 patients have completed at least 5 adaptive therapy cycles and 4 remain on study treatment without imaging progression for > 52 months at the data cut off on 1/4/2022. The study is closed for enrollment after demonstrating the feasibility of adaptive therapy. 16 SOC patients treated with continuous abi for mCRPC between 2015 and 2018 were identified through retrospective chart reviewed. Clinical features like Gleason score and metastasis burden are similar between the two groups. Evolution-based application of abiraterone significantly improved (p Conclusions: Adaptive Abi therapy is feasible in men who responded to Abi as a frontline therapy for mCRPC. The updated data with longer follow up demonstrated OS as well as rPFS benefits of our adaptive therapy approach with less drug usage compared to the conventional continuous Abi. Clinical trial information: NCT02415621 Citation Format: Jingsong Zhang, Joel S. Brown, Jessica Cunningham, Evan Adler, Robert Gatenby. A phase 2a evolutionarily informed, mathematically guided adaptive abiraterone trial in metastatic castrate resistant prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr CT159.

Although most metastatic cancers now have at least one effective therapy, evolution of resistance almost invariably leads to treatment failure and patient death. While often viewed as the result of a “resistance mutation,” many cancer cell resistance strategies simply involve increased expression of genes (e.g., xenobiotic metabolism) already in the human genome. Furthermore, clinical treatments to block the resistance mechanism (e.g. the MDR proteins) have generally been unsuccessful reflecting the multiplicity of available strategies. In an evolutionary context, however, the mere presence of a resistant phenotype is not sufficient for treatment failure. Rather, tumor progression requires proliferation of the resistant phenotype at a rate sufficient to produce the billions of cells necessary for clinically evident progressive disease. Importantly, the necessary molecular, cellular, and population dynamics necessary for cancer progression during treatment are deeply connected because there are often significant phenotypic costs from synthesis, maintenance, and operation of the molecular machinery of resistance that can reduce cellular proliferation and invasion particularly in the substrate-poor environment often present in clinical cancers. Evolution-based cancer treatment assumes that resistant phenotypes are invariably present prior to initiation of therapy and seeks to exploit evolutionary dynamics to delay or prevent proliferation of these cells. A specific example of this approach is adaptive therapy which is used in clinical settings in which cure is not achievable. Similar to widely accepted practices in pest management, adaptive therapy reduces or periodically withdraws treatment to maintain a stable population of treatment-sensitive cells that can use their fitness advantage (i.e. absence of the cost of resistance) to suppress proliferation of resistant phenotypes. This approach has been investigated in both pre-clinical and clinical conditions. A clinical trial in metastatic castrate-resistant prostate cancer (mCRPC) has been completed and will be discussed. Finally, when cure from a cancer treatment is possible but rare, evolutionary dynamics, based on observations in Anthropocene extinctions, can theoretically be used to increase the probability of complete eradication of the malignant population. Citation Format: Robert Gatenby. Harnessing evolutionary dynamics for control and cure of metastatic cancers [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr IA001.

Background: The impact of chronic UV exposure on clonal dynamics and genomic diversity remains unclear. Our central hypothesis is that skin cancer is induced not by solely accumulation of somatic mutations, but rather a combination of mutations and disruption of the spatial and temporal constraints imposed by the skin’s 3-D architecture. Here we characterize clonal dynamics and transcriptional signatures during skin carcinogenesis using multicolor lineage tracing. Methods: We generated a K14Cre-ERT2 Confetti mice with inducible fluorophore (flr) expression. Mice were UV-irradiated for 3 months. Clones were 3-D digitized using confocal microscopy (z-stacks) and clone volumes estimated computationally. scRNAseq was used to compare UV-exposed (EXP) vs. non-exposed (NON) epidermis vs. skin tumors. Results: We generated 914 serial images of the EXP/NON skin over the course of 6 months following initiation of UV. We analyzed 16,135 clones from the EXP and 21,506 clones from the NON skin. We classified clone sizes into 3 classes represented by the small ( 500,000 μm3). The median size of clones does not differ between UV treatments and does not change with time. However, clones from EXP samples have significantly greater mean size than NON ones. Their mean sizes differed by some 1.5-fold, with an over 6-fold increase in variance, resulting in the sizes distribution to be highly skewed towards large clones with a long, narrow tail. Goliath clones are rarely present in the NON skin; however, they increase in number dramatically by months 3-4, plateauing between months 5-6. Using 3 ecological metrics (clone size, clone numbers, and coefficient of variation) we see phase shifts, which primarily distinguish months 1 & 2 from months 3 & 4. scRNAseq of EXP/NON epidermis and tumors revealed differential representation of 16 clusters, the majority of which could be mapped to previously defined keratinocyte populations. We observe dynamic changes to these clusters when progressing from normal skin to chronically exposed skin, and then to tumors. EXP clusters were associated with expression of cystatins (Scfa 3, BC100530), and alarmins/proliferative keratins (Krt16, Krt6a), which have been associated with skin injury. Clusters expressing cystatins and alarmins also increased in tumors. Flr-expressing keratinocytes harvested from large clones in EXP epidermis exhibited altered keratinocyte differentiation (downregulation of Krt77, Loricrin and Nfkbia, upregulation of cystatin), inflammation (downregulation of Nfkbia), and upregulation of metabolic regulators (carbonic anhydrase II and retinol transport (Rbp1)). Genes differentially expressed in exposed skin and retained in tumors may be required for carcinogenesis, while those expressed only in exposed skin likely required for adaptive responses to UV. Our findings have important implications for understanding cancer through an eco-evolutionary framework and designing novel approaches to cancer prevention. Citation Format: Stanislav Avdieiev, Leticia Tordesillas, Omar Chavez Chiang, Zhihua Chen, Luiza Silva Simoes, Y. Ann Chen, Noemi Andor, Robert Gatenby, Elsa R. Flores, Joel S. Brown, Kenneth Y. Tsai. In vivo tracking of clonal dynamics during UV-induced skin carcinogenesis [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr PR004.

PARP inhibitors (PARPis) represent a great advancement in the treatment of ovarian cancer, yet these drugs often fail after a few months due to emerging drug resistance. A recent clinical trial in prostate cancer showed that evolutionary-inspired, adaptive drug scheduling significantly delayed time to progression. This approach adaptively skipped treatment to maintain a pool of drug-sensitive cells that suppressed resistant cells through competition. Here, we present results from a combined modelling and experimental study in which we investigated whether adaptive therapy could delay resistance to the PARPi olaparib in ovarian cancer.We performed a series of in vitro experiments in which we used Incucyte Zoom time-lapse microscopy to characterize the cell population dynamics under different PARPi schedules. Leveraging these data we developed an ordinary differential equation mathematical model of treatment response, and used this model to test different plausible adaptive treatment schedules. Our model can accurately predict the in vitro treatment dynamics, even to new schedules, and suggests that treatment modifications need to be carefully timed, or one risks losing control over tumor growth, even in the absence of any resistance. This is because multiple rounds of cell division are required for cells to acquire sufficient DNA damage to induce apoptosis. As a result, adaptive therapy algorithms that modulate treatment but never completely withdraw it are predicted to perform better in this setting than strategies based on treatment interruptions. Subsequent experiments confirm this prediction in vivo. To conclude, we present preliminary work aiming at investigating the scope for clinical translation of our results by confronting our model with longitudinal CA-125 data from 53 ovarian cancer patients receiving olaparib at Moffitt. Overall, this study contributes to a better understanding of the impact of scheduling on treatment outcome for PARPis, and showcases some of challenges involved in developing adaptive therapies for new treatment settings. Citation Format: Maximilian Strobl, Mehdi Damaghi, Alexandra Martin, Samantha Byrne, Mark Robertson-Tessi, Robert Gatenby, Robert Wenham, Philip Maini, Alexander R.A. Anderson. Integrating mathematical modelling and wet-lab experiments to examine the scope for adaptive treatment scheduling of PARP inhibitors in ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B009.

The era of big data in oncology has led to the promise of precision medicine for individual patients. However, many therapy decisions continue to be made based on “one size fits most” approaches, primarily since there exist few theoretical and practical tools to deal with a patient’s data over time. In parallel with this growing interest in personalized medicine, cancer is being increasingly recognized as an eco-evolutionary system that adapts to treatments, suggesting that static therapy regimens are often doomed to eventual failure. Here, we present preliminary results from a novel pilot clinical trial (NCT04343365), the Evolutionary Tumor Board (ETB), which uses eco-evolutionary theory (based on experiments and modeling) to assist with clinical decision making for each patient. We developed an informational and computational framework for applying evolutionary therapy approaches to individual patients in a dynamic fashion, using their clinical data in real time. The framework relies on detailed data curation and imaging measurements for each patient, as well as a mathematical modeling approach that accounts for multi-lesion tumor growth, treatment-induced death, and the evolution of resistance. The models are calibrated by historical datasets of similar patients, as well as the patient’s own temporal data. We use a “Phase i trial” approach to account for prediction uncertainty and provide decision support for therapy options available to the patient at any given time point. Crucially, this is presented in a way that harmonizes with the treating oncologist’s intuition. Fifteen patients at Moffitt have been enrolled into the ETB, many of whom have proceeded through the entire process, including follow-up analysis. The ETB generated outcome predictions and therapy recommendations for each case, and subsequent follow-up predictions and recommendations. Our current results demonstrate that the ETB approach has provided both novel and useful decision support for the clinicians. At the same time, numerous opportunities for further research and development have been identified. Our efforts show that there are both challenges and opportunities in the area of personalized therapy, particularly in the context of real-time clinical care. Early results from the ETB show great promise for improving patient outcomes in cancer using mathematical modeling and evolutionary therapy. Citation Format: Mark Robertson-Tessi, Joel Brown, Maria Poole, Kimberly Luddy, Andriy Marusyk, Jill Gallaher, Jeffrey West, Matthew Johnson, Heiko Enderling, Rikesh Makanji, Joaquim Farinhas, Robert Gatenby, Damon Reed, Christine Chung, Alexander Anderson. Evolutionary Tumor Board: Implementing dynamic personalized therapy using evolutionary theory and mathematical modeling for clinical decision support [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr PR010.

This abstract is being presented as a short talk in the scientific program. A full abstract is available in the Proffered Abstracts section (PR004) of the Conference Proceedings. Citation Format: Stanislav Avdieiev, Leticia Tordesillas, Omar Chavez Chiang, Zhihua Chen, Luiza Silva Simoes, Y. Ann Chen, Noemi Andor, Robert Gatenby, Elsa R. Flores, Joel S. Brown, Kenneth Y. Tsai. In vivo tracking of clonal dynamics during UV-induced skin carcinogenesis [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr A017.

This abstract is being presented as a short talk in the scientific program. A full abstract is available in the Proffered Abstracts section (PR010) of the Conference Proceedings. Citation Format: Mark Robertson-Tessi, Joel Brown, Maria Poole, Kimberly Luddy, Andriy Marusyk, Jill Gallaher, Jeffrey West, Matthew Johnson, Heiko Enderling, Rikesh Makanji, Joaquim Farinhas, Robert Gatenby, Damon Reed, Christine Chung, Alexander Anderson. Evolutionary Tumor Board: Implementing dynamic personalized therapy using evolutionary theory and mathematical modeling for clinical decision support [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B006.

Receptor tyrosine kinase inhibitors (TKI) are effective anti-cancer treatments. However, evolution of resistance almost inevitably occurs due to pre-existing mutations or development of therapy-tolerant persister cells. Here, we combined time-lapse imaging with longitudinal DNA/RNA sequencing, metabolomics and genome-wide DNA methylation profiling to decipher mechanisms of TKI-induced persisters. Whole exome sequencing demonstrated that persisters were genetically indistinguishable from their parental counterparts, suggesting non-genetic mechanisms were involved. Using untargeted metabolomics, we uncovered that persisters were deficient in methionine metabolism intermediates, including S-(5'-adenosyl)-L-homocysteine (SAH), a major determinant DNA methylation. Examining the consumption of a panel of isotope labeled extracellular amino acids indicated that persisters don’t exhibit deregulation in methionine and cysteine transport. Instead, persisters preferentially consume methionine and cystine to support their survival. Profiling of wide-genome DNA methylation suggested that these metabolic changes was associated with a unique global DNA hypermethylation. We then discovered that promotor regions of genes encoding a range of trace element transporters were uniquely hypermethylated. Through further functional analysis, we then unraveled that these metabolic and epigenetic changes collectively facilitate emergence of a phenotype characterized by methionine/cystine-dependent resistance to osmotic stress. Thus, evolution of TKI resistance can develop by metabolism-dependent epigenetic modifications. Unraveling the detailed mechanisms of these metabolic and epigenetic strategies in cancer cell persisters is crucial to prevent cancer recurrence. Citation Format: Asmaa El-Kenawi, Anders Berglund, Joel Brown, Robert Gatenby. Metabolism-epigenetics interplay drives cancer cell persistence during receptor tyrosine kinase inhibition [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B031.

Evolution-informed therapy exploits the ecological and evolutionary consequences of drug resistance to inhibit the expansion of treatment resistant populations prolonging time to progression. One strategy, termed an evolutionary double bind, uses an initial therapy to elicit a specific adaptive mechanism that is then selectively targeted by a follow-on therapy. Typically, the second therapy is most effective in the recurrent setting and less effective when given upfront. Here we examine combining radiation therapy followed by immunotherapy as a double bind strategy. Radiotherapy (RT) can induce lethal double-strand DNA breaks in tumor cells and radiosensitivity is governed by the cells’ DNA repair capabilities. Radioresistant cancer cells upregulate DNA damage response pathways, which are mediated by ataxia telangiectasia mutated (ATM) and ataxia telangiectasia and Rad3-related (ATR) proteins. However, successful adaptations to RT and other DNA damaging agents can increase natural killer (NK) cell ligand expression on tumor cells increasing their vulnerability to the immune system. We evolved two cell lines, derived from a single population (22Rv1), with differing RT sensitivities (RT-sensitive and RT-resistant). We demonstrate RT-resistant cells upregulate NK cell ligands, including major histocompatibility complex class I chain-related protein A/B (MICA/B), poliovirus receptor (PVR), and PVRL2, resulting in a 2-fold increase in sensitivity to NK cell mediated killing. We conducted a multidisciplinary investigation of this potential evolutionary double bind through in vitro studies of radiation-sensitive and radiation-resistant cells treated with RT, NK cell therapy alone, or in combinations to parameterize evolution-based mathematical models. Mathematical modeling quantified three potential aspects of a double-bind: cost of resistance, inter-specific competition between RT-sensitive and RT-resistant lines, and preferential targeting of RT-resistant lines by NK cells. Despite a slower intrinsic growth rate, the RT-resistant population out competed the RT-sensitive cells in co-cultures at all seeding frequencies in the absence of treatment. Radiotherapy slowed overall growth but strongly selected for RT-resistant cells. NK cell therapy alone suppressed the RT-resistant population but maintained a residual population of radiation-sensitive cells. RT followed by NK cells was the most effective at reducing overall tumor burden, even in cases with an initially large RT-resistant fraction. We conclude RT followed by immunotherapy produces an evolutionary double bind that can be exploited in heterogenous tumors to limit RT resistance. Furthermore, model simulations predict extinction of both populations by sequential RT and NK cell therapy is achievable. Citation Format: Kimberly A. Luddy, Jeffery West, Mark Robertson-Tessi, Alexander R.A. Anderson, Taylor M. Bursell, Laure Marignol, Cliona O'Farrelly, Robert Gatenby. Evolutionary games in radiotherapy resistance: Exploiting an immunologic double bind in advanced prostate cancer [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B003.

Multiple myeloma (MM) remains treatable but incurable. Despite a growing armamentarium of effective agents, choice of therapy, especially in relapse, still relies almost exclusively on clinical acumen. We have developed a system, EMMA (Ex vivo Mathematical Myeloma Advisor), consisting of patient-specific mathematical models parameterized by an ex vivo assay that reverse engineers the intensity and heterogeneity of chemosensitivity of primary cells from MM patients, allowing us to predict clinical response to up to 31 drugs within 5 days post-bone marrow biopsy. From a cohort of 52 MM patients, EMMA correctly classified 96% as responders/non-responders and correctly classified 79% according to IMWG stratification of level of response. We also observed a significant correlation between predicted and actual tumor burden measurements (Pearson r=0.5658, P

Malignant tumors exhibit altered metabolism and consume higher levels of glucose compared to surrounding normal tissue, resulting in acidic extracellular microenvironment. Acidity in the microenvironment is a critical stress factor and selection force for the evolution of aggressive tumor types. There are several acid sensing cell surface receptors and ion channels (ASICs) that can sense acidity in the microenvironment; among them proton sensing G protein-coupled receptors (GPCRs) such as OGR1 (GPR68), TDAG8 (GPR65), GPR4 and GPR132 form a major class of acid receptors. Our previous studies demonstrated that acid adaptation is associated with survival mechanisms like chronic activation of autophagy and redistribution of the lysosomal proteins to the plasma membrane. When grown under acidic pH, breast cancer cells accumulate lipids as revealed by staining with Nile Red and perilipin 2, a protein that coats adiposomes or lipid droplets. These are dynamic organelles that store neutral lipids surrounded by a shell of proteins and a phospholipid monolayer. The lipids stored in adiposomes are produced de novo, as acid-induced lipogenic phenotype is maintained, even if cells are grown with de-lipidated serum. Inhibition of fatty acid synthesis was selectively toxic under acidic conditions and attenuated adiposome accumulation. Among the acid sensing receptors, TDAG8 and OGR1 are highly expressed in a panel of breast cancer cell lines compared to non-malignant breast epithelial cells. Highly invasive and acid tolerant MDA-MB-231 cells express significantly higher levels of TDAG8 while MCF7 and T47D cells have high levels of OGR1. We investigated the role of these receptors in transducing the acid signal that results in the accumulation of lipid droplets. CRISPR/Cas9 mediated depletion of the major acid sensors in breast cancer cell lines MCF7 and T47D showed that depletion of OGR1, not TDAG8, specifically inhibited acid induced adiposome accumulation. Additionally, inducing the cells with ogerin, an allosteric activator of OGR1 resulted in adiposomogenesis. Further, OGR1 knock out cells showed inhibition in cell growth under acidic growth conditions compared to OGR1 expressing cells. OGR1 knockout cells were defective in acid induced autophagy. OGR1 is coupled with Gq/11 and, upon ligand (H+) binding, triggers activation of phospholipase C stimulating the formation of the second messenger IP3. Adiposome formation was inhibited in presence of PLC inhibitors, Edelfosine or U73122. These results indicate that OGR1 is the major acid sensing GPCR in these cells that mediate adiposomogenesis. Accumulation of adiposomes is a highly regulated process related to storing autophagic products, and appears to be important in cell survival in acid stress. Taken together, increased dependence on lipid metabolism by cancer cells under acidic conditions reveals novel therapeutic vulnerabilities. Citation Format: Smitha Pillai, Michael Langsen, Jonathan Nguyen, Jonathan Wojtkowiak, Marilyn Bui, Robert Gatenby, Robert Gillies. Acid sensing G protein-coupled receptor OGR1 is required for acid induced adiposomogenesis in breast cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1846.

The diversity in cancer cell characteristics is an area of great interest in the practice of clinical oncology. By now, we know that tumors can be composed of different subpopulations of cells that can harbor very different characteristics (both in terms of genotype and phenotype), thereby exhibiting differential biologies including degrees of differentiation, growth rates, and response to various therapeutic interventions [1]. Intertumor heterogeneity across various malignancies and lesions (i.e., different biological characteristics between various lesions of the same malignancy or between different histological subtypes) has been relatively well described compared to intratumor heterogeneity (i.e., different biological characteristics within the subpopulations of neoplastic and non-neoplastic cells that successively evolve within a tumor mass) which, though recognized, has been relatively less well investigated probably due to paucity of available techniques and methodologies. This regional intratumoral heterogeneity is manifested at the cellular level by multiple subpopulations with different genetic and phenotypic characteristics. Several newer tools (e.g., laser capture microdissection) have been utilized to study this phenomenon on a cellular basis in an apparently homogeneous tumor [2, 3]. There is also tremendous temporal and spatial variation in the environment (and, therefore, environmental selection forces) that drive regional phenotypic evolution, largely as a result of variability of vascular density and blood flow. The prognostic significance of tumor heterogeneity has been emphasized in several malignancies, and in fact, grading systems have evolved in certain tumors based upon this characteristic [4–9]. To cite an example of its importance, in a study designed to determine genetic profiles of breast carcinoma cells, CD44+ cells had an active transforming growth factor beta (TGF-beta) pathway that correlated with decreased patient survival and poor prognosis compared to CD24+ cells [7]. It is important to note that heterogeneity is typically generated prior to clinical detection of the tumor, and that phenotypic diversity probably generally increases with tumor size, in parallel with the potential for genetic mutation to occur with each mitosis and the development of hypoxia, acidosis, ischemia, and necrosis.