Biology subjects

Marusyk, A.

Publications and source records attributed to Marusyk, A..

At least 19 recordsLinked to original sources

bioRxiv2026

A reference-calibrated spatial-statistical framework for quantifying stroma-tumor cell spatial associations from histology and its geometric limits

Quantifying interactions in biological tissues requires determining whether an interaction exists, the distance over which it operates, and its strength. Existing spatial-statistical approaches can characterize spatial organization but often require interaction range or interaction models to be specified a priori, making them less suited to settings where these quantities are themselves unknown. Here, we present a practical framework that recovers the presence, range, and association strength of spatial bias toward a tissue structure directly from segmented histology. The framework combines four complementary statistics, the resource selection function, nearest-structure distance distributions, the cross J-function, and the radial distribution function, with specimen-specific reference states that preserve tissue stromal architecture while spanning a continuum of known spatial biases. Across controlled in silico tissues, we show that tissue stromal architecture imposes geometric limits on the range of recoverable interactions and that event fraction independently constrains statistical sensitivity. These spatial associations establish fundamental bounds on inference and demonstrate that spatial statistics must be interpreted relative to tissue-specific reference states rather than absolute scales. We apply the framework to proliferating tumor cells relative to CAF-rich stroma during tyrosine kinase inhibitor therapy in an ALK+ NSCLC model and identify a strong stroma-associated proliferation bias extending approximately 30-60 micrometers. The framework broadly applies to any histological setting where events and reference structures can be defined, providing a practical approach to quantify localized interactions directly from tissue stromal architecture.

cancer biology↗

bioRxiv2026

Pharmacokinetic Acceleration via CYP3A4 Hyperactivation as a Clinically Actionable Mechanism of Targeted Therapy Resistance in NSCLC

Resistance of cancers to targeted therapies is traditionally framed as a tumor-intrinsic phenomenon, mediated by tumor cell-intrinsic or microenvironmental mechanisms. Here, we identify a tumor-extrinsic, systemic resistance mechanism resulting from hyperactivation of the hepatic cytochrome P450 enzyme, CYP3A4. This tumor-extrinsic resistance mechanism can function independently of, or in tandem with, tumor-intrinsic resistance. Focusing on experimental mouse models of targetable lung cancer, we find that xenobiotic-mediated induction of CYP3A4 results in accelerated drug metabolism and a drastic reduction in systemic and tumor-drug exposure in vivo. CYP3A4 activation can be triggered by chemically unrelated xenobiotics, leading to resistance to a wide range of targeted therapies, including ALK, EGFR, and KRASG12C inhibitors. Retrospective analysis of clinical cohorts suggests that variability in CYP3A4 activity might be a major contributor to variability in clinical outcomes. While higher CYP3A4 activity leads to sub-therapeutic tumor drug exposure and shorter progression-free survival, reduced drug metabolism is expected to result in supratherapeutic exposure and increased systemic toxicity. To address the consequences of abnormal CYP3A4 activity, we utilized mathematical modeling to demonstrate that drug concentrations can be restored through the optimization of dosing amounts and intervals. Further, we show that tumor sensitivity to targeted therapies can be rescued through pharmacological inhibition of CYP3A4. Our findings establish systemic metabolic variability as a bona fide resistance and toxicity driver, providing a translational framework for personalized dosing to maximize both safety and efficacy.

cancer biology↗

bioRxiv2026

Mega-frequency mutagenesis: generation of non-random precise mutations with extremely high frequency upon adaptation of cancer cells to drugs and stress

Acquiring drug resistance is a major problem in cancer treatment. As cancers adapt to chemotherapy, chromatin landscape becomes altered in subpopulations of persister cells to acquire gene expression patterns that provide drug resistance. The increased level of stress-induced random mutagenesis in cancer has also been linked to acquisition of drug resistance. Here we show that during adaptation to conventional cytotoxic chemotherapies or targeted therapies, tens of thousands of mutations are generated, and the probability of acquisition of these mutations at specific positions can reach 50% and even higher. A large fraction of these mutations is highly recurrent and non-random. The patterns of the recurrent mutations are specific to the drug target and are unrelated to the chemical nature of the drug. Surprisingly, these mutations are progressively generated at the non-dividing pseudo-senescence stage following drug exposure, and at this stage, selection is not involved in their accumulation. Notably, these mutations are highly enriched within or near binding motifs of certain transcription factors, like KLF9, IRF1 and others. Therefore, a mechanism for precise generation of mutations at specific positions with extremely high rates appears to be triggered upon drug adaptation, and these precise mutations may affect activities of a set of transcription factors.

cancer biology↗

bioRxiv2026

Selection for targeted therapy resistance leads to an indirect selection for higher phenotypic plasticity and enhanced evolvability to orthogonal stressors

Acquired resistance to targeted therapies is a primary barrier to durable cancer control. Resistance frequently coincides with stem-like features and EMT-associated cell-state changes (partial EMT), yet whether these programs directly cause resistance or instead enable escape by increasing phenotypic plasticity remains debated. Integrating computational modeling, functional experimental assays, and lineage tracing, we investigated how cell-state plasticity shapes acquired resistance to ALK inhibition in ALK+ lung cancer models. Our results support a model in which drug exposure selects for cells with higher phenotypic plasticity, progressively increasing their representation as resistance emerges. Consequently, resistant populations show enhanced capacity to adapt to orthogonal therapeutic and environmental stressors and exhibit heightened metastatic potential. Bulk ATAC-seq showed that highly plastic cells have increased chromatin accessibility at regulators of EMT and stemness. Consistent with this, boosting plasticity via Yamanaka-factor induction or EMT-factor expression reduced ALKi sensitivity over time. In contrast, constraining plasticity (SOX2 knockdown or epigenetic inhibition) reduced long-term resistance outgrowth and prolonged ALKi response. Together, our results indicate that targeted therapy indirectly selects for cells with increased phenotypic plasticity, providing the substrate from which multifactorial resistance and metastatic competence evolve. Further, it suggests that constraining plasticity could delay resistance and extend response durability.

cancer biology↗

bioRxiv2025

Evolutionary antifragile therapy

A population of cells within a tumor can be described as antifragile (the opposite of fragile) if they derive a benefit from fluctuations or perturbations in environmental conditions induced by treatment. We hypothesized that treatment fluctuations may either promote (antifragile tumor) or inhibit (fragile tumor) the evolution of treatment resistance to targeted therapies. Analysis of the convexity of dose response curves provides a direct prediction of response to prescribed fluctuations in treatment. Theory predicts that continuous treatment protocols (i.e. zero prescribed fluctuations in treatment) will outperform uneven protocols when dose response is convex. We apply this theory to predict in vivo response to targeted therapy and validate these predictions by experimentally testing high/low intermittent dosing (uneven dosing) and continuous dosing (even dosing) schedules. Convexity (and its inverse, concavity) explains two phenomena: dose response is a convex (fragile) function but resistance onset rate is a concave (antifragile) function. Thus, we design, propose, and validate alternative treatment schedules which maximize response while maintaining prolonged sensitivity to treatment. These analyses provide supporting evidence that fluctuations alter the evolutionary trajectory of tumors in response to targeted therapy, even without altering the cumulative dose. This insight is used to design alternative, switching treatment protocols to limit the evolution of resistance, a process which we call evolutionary antifragile therapy.

cancer biology↗

bioRxiv2024

Trastuzumab deruxtecan combination strongly enhances responses and overcomes sotorasib resistance in KRASG12C-mutant NSCLC

IntroductionRecent advances in the treatment of KRAS-mutant non-small cell lung cancer (NSCLC) have led to the development of KRASG12C inhibitors, such as sotorasib and adagrasib. However, resistance and disease progression remain significant challenges. In this study, we investigated the therapeutic potential of combining trastuzumab deruxtecan (T-DXd), an anti-HER2 antibody-drug conjugate, with sotorasib in KRASG12C-mutant NSCLC, while also evaluating HER2 expression in NSCLC samples. MethodsThe HER2 expression dependence of xenograft responses to sotorasib, T-DXd, and their combination was evaluated in therapy-naive and sotorasib-treated tumors by immunohistochemistry (IHC). Also, we analyzed 191 clinical (pre- or on-treatment) and rapid autopsy (post-treatment) samples from 31 patients with driver-positive and driver-negative advanced stage NSCLC, assessing HER2 expression using interpretation guidelines developed for breast cancer (BC) and gastroesophageal adenocarcinoma (GEA). ResultsIn the majority of preclinical models, including sotorasib-resistant tumors, the sotorasib-T-DXd combination induced stronger and more durable responses compared to monotherapies. The strong effect of the combination therapy was likely attributable to sotorasib-induced adaptive HER2 upregulation; stronger HER2 expression in sotorasib-treated tumors was linked with stronger responses. Although HER2 expression was higher in samples from patients with KRASG12C-mutant NSCLC compared to NSCLC with other driver mutations or no drivers, the difference was not statistically significant. HER2 IHC score discrepancy was also observed between BC and GEA interpretation guidelines. ConclusionsOur results support the potential clinical utility of the sotorasib-T-DXd combination, including tumors with intrinsic and acquired resistance to sotorasib monotherapy. Since the strengths of the responses depend on HER2 expression levels, successful clinical implementation necessitates optimizing patient selection. Our results highlight the complexities of accurate HER2 interpretation in NSCLC and highlight the need for standardized testing methods.

cancer biology↗

bioRxiv2024

The adaptive state determines the impact of mutations on evolving populations

Darwinian evolution results from an interplay between stochastic diversification of heritable phenotypes, impacting the chance of survival and reproduction, and fitness-based selection. The ability of populations to evolve and adapt to environmental changes depends on rates of mutational diversification and the distribution of fitness effects of random mutations. In turn, the distribution of fitness effects of stochastic mutations can be expected to depend on the adaptive state of a population. To systematically study the impact of the interplay between the adaptive state of a population on the ability of asexual populations to adapt, we used a spatial agent-based model of a neoplastic population adapting to a selection pressure of continuous exposure to targeted therapy. We found favorable mutations were overrepresented at the extinction bottleneck but depleted at the adaptive peak. The model-based predictions were tested using an experimental cancer model of an evolution of resistance to a targeted therapy. Consistent with the models prediction, we found that enhancement of the mutation rate was highly beneficial under therapy but moderately detrimental under the baseline conditions. Our results highlight the importance of considering population fitness in evaluating the fitness distribution of random mutations and support the potential therapeutic utility of restricting mutational variability. SIGNIFICANCE STATEMENTThe ability of a population to adapt and evolve is heavily influenced by the effects of random mutations on individuals. However, these effects can vary depending on the existing fitness level of the population. Using the development of cancer treatment resistance as an example, our research shows that populations nearing extinction can benefit from an increased rate of mutation. In contrast, mutations have a neutral or harmful effect on well-adapted populations. These findings suggest that new therapeutic strategies that manipulate mutation rates based on a populations current state of adaptation could be effective in preventing cancer and antimicrobial resistance.

evolutionary biology↗

bioRxiv2024

Cancer-associated fibroblasts confer ALK inhibitor resistance in EML4-ALK-driven lung cancer via concurrent integrin and MET signaling

Cancer-associated fibroblasts (CAFs) are associated with tumor progression and modulate drug sensitivity of cancer cells. However, the underlying mechanisms are often incompletely understood and crosstalk between tumor cells and CAFs involves soluble secreted as well as adhesion proteins. Interrogating a panel of non-small cell lung cancer (NSCLC) cell lines driven by EML4-ALK fusions, we observed substantial CAF-mediated drug resistance to clinical ALK tyrosine kinase inhibitors (TKIs). Array-based cytokine profiling of fibroblast-derived conditioned- media identified HGF-MET signaling as a major contributor to CAF-mediated paracrine resistance that can be overcome by MET TKIs. However, Cell Type specific labeling using Amino acid Precursors (CTAP)-based expression and phosphoproteomics in direct coculture also highlighted a critical role for the fibronectin-integrin pathway. Flow cytometry analysis confirmed activation of integrin {beta}1 (ITGB1) in lung cancer cells by CAF coculture. Treatment with pharmacological inhibitors, cancer cell-specific silencing or CRISPR-Cas9-mediated knockout of ITGB1 overcame adhesion protein-mediated resistance. Concurrent targeting of MET and integrin signaling effectively abrogated CAF-mediated resistance of EML4-ALK-driven NSCLC cells to ALK TKIs in vitro. Consistently, combination of the ALK TKI alectinib with the MET TKI capmatinib and/or the integrin inhibitor cilengitide was significantly more efficacious than single agent treatment in suppressing tumor growth using an in vivo EML4-ALK-dependent allograft mouse model of NSCLC. In summary, these findings emphasize the complexity of resistance-associated crosstalk between CAFs and cancer cells, which can involve multiple concurrent signaling pathways, and illustrate how comprehensive elucidation of paracrine and juxtacrine resistance mechanisms can inform on more effective therapeutic approaches.

cancer biology↗

bioRxiv2024

Peristromal niches protect lung cancers from targeted therapies through a combined effect of multiple molecular mediators

Targeted therapies directed against oncogenic signaling addictions, such as inhibitors of ALK in ALK+ NSCLC often induce strong and durable clinical responses. However, they are not curative in metastatic cancers, as some tumor cells persist through therapy, eventually developing resistance. Therapy sensitivity can reflect not only cell-intrinsic mechanisms but also inputs from stromal microenvironment. Yet, the contribution of tumor stroma to therapeutic responses in vivo remains poorly defined. To address this gap of knowledge, we assessed the contribution of stroma-mediated resistance to therapeutic responses to the frontline ALK inhibitor alectinib in xenograft models of ALK+ NSCLC. We found that stroma-proximal tumor cells are partially protected against cytostatic effects of alectinib. This effect is observed not only in remission, but also during relapse, indicating the strong contribution of stroma-mediated resistance to both persistence and resistance. This therapy-protective effect of the stromal niche reflects a combined action of multiple mechanisms, including growth factors and extracellular matrix components. Consequently, despite improving alectinib responses, suppression of any individual resistance mechanism was insufficient to fully overcome the protective effect of stroma. Focusing on shared collateral sensitivity of persisters offered a superior therapeutic benefit, especially when using an antibody-drug conjugate with bystander effect to limit therapeutic escape. These findings indicate that stroma-mediated resistance might be the major contributor to both residual and progressing disease and highlight the limitation of focusing on suppressing a single resistance mechanism at a time.

cancer biology↗

bioRxiv2023

Paracrine enhancement of tumor cell proliferation provides indirect stroma-mediated chemoresistance via acceleration of tumor recovery between chemotherapy cycles.

The ability of tumors to survive therapy reflects both cell-intrinsic and microenvironmental mechanisms. Across many cancers, including triple-negative breast cancer (TNBC), a high stroma/tumor ratio correlates with poor survival. In many contexts, this correlation can be explained by the direct reduction of therapy sensitivity by stroma-produced paracrine factors. We sought to explore whether this direct effect contributes to the link between stroma and poor responses to chemotherapies. Our in vitro studies with panels of TNBC cell line models and stromal isolates failed to detect a direct modulation of chemoresistance. At the same time, consistent with prior studies, we observed treatment-independent enhancement of tumor cell proliferation by fibroblast-produced secreted factors. Using spatial statistics analyses, we found that proximity to stroma is often associated with enhanced tumor cell proliferation in vivo. Based on these observations, we hypothesized an indirect link between stroma and chemoresistance, where stroma-augmented proliferation potentiates the recovery of residual tumors between chemotherapy cycles. To evaluate the feasibility of this hypothesis, we developed a spatial agent-based model of stroma impact on proliferation/death dynamics. The model was quantitatively parameterized using inferences from histological analyses and experimental studies. We found that the observed enhancement of tumor cell proliferation within stroma-proximal niches can enable tumors to avoid elimination over multiple chemotherapy cycles. Therefore, our study supports the existence of a novel, indirect mechanism of environment-mediated chemoresistance that might contribute to the negative correlation between stromal content and poor therapy outcomes.

cancer biology↗

bioRxiv2020

Understanding the Evolutionary Games in NSCLC Microenvironment

While initially highly successful, targeted therapies eventually fail as populations of tumor cells evolve mechanisms of resistance, leading to resumption of tumor growth. Historically, cell-intrinsic mutational changes have been the major focus of experimental and clinical studies to decipher origins of therapy resistance. While the importance of these mutational changes is undeniable, a growing body of evidence suggests that non-cell autonomous interactions between sub-populations of tumor cells, as well as with non-tumor cells within tumor microenvironment, might have a profound impact on both short term sensitivity of cancer cells to therapies, as well as on the evolutionary dynamics of emergent resistance. In contrast to well established tools to interrogate the functional impact of cell-intrinsic mutational changes, methodologies to understand non-cell autonomous interactions are largely lacking. Evolutionary Game Theory (EGT) is one of the main frameworks to understand the dynamics that drive frequency changes in interacting competing populations with different phenotypic strategies. However, despite a few notable exceptions, the use of EGT to understand evolutionary dynamics in the context of evolving tumors has been largely confined to theoretical studies. In order to apply EGT towards advancing our understanding of evolving tumor populations, we decided to focus on the context of the emergence of resistance to targeted therapies, directed against EML4-ALK fusion gene in lung cancers, as clinical responses to ALK inhibitors represent a poster child of limitations, posed by evolving resistance. To this end, we have examined competitive dynamics between differentially labelled therapy-naive tumor cells, cells with cell-intrinsic resistance mechanisms, and cells with cell-extrinsic resistance, mediated by paracrine action of hepatocyte growth factor (HGF), within in vitro game assays in the presence or absence of front-line ALK inhibitor alectinib. We found that producers of HGF were the fittest in every pairwise game, while also supporting the proliferation of therapy-naive cells. Both selective advantage of these producer cells and their impact on total population growth was a linearly increasing function of the initial frequency of producers until eventually reaching a plateau. Resistant cells did not significantly interact with the other two phenotypes. These results provide insights on reconciling selection driven emergence of subpopulations with cell non-cell autonomous resistance mechanisms, with lack of evidence of clonal dominance of these subpopulations. Further, our studies elucidate mechanisms for co-existence of multiple resistance strategies within evolving tumors. This manuscript serves as a technical report and will be followed up with a research paper in a different journal.

cancer biology↗

bioRxiv2020

Antifragile therapy

Antifragility is a recently coined word used to describe the opposite of fragility. Systems or organisms can be described as antifragile if they derive a benefit from systemic variability, volatility, randomness, or disorder. Herein, we introduce a mathematical framework to quantify the fragility or antifragility of cancer cell lines in response to treatment variability. This framework enables straightforward prediction of the optimal dose treatment schedule for a range of treatment schedules with identical cumulative dose. We apply this framework to non-small-cell lung cancer cell lines with evolved resistance to ten anti-cancer drugs. We show the utility of this antifragile framework when applied to 1) treatment resistance, and 2) collateral sensitivity of sequential monotherapies.

cancer biology↗

bioRxiv2020

Dose dependent evolutionary game dynamics modulate competitive release in cancer therapy

Therapeutic strategies for tumor control have traditionally assumed that maximizing reduction in tumor volume correlates with clinical efficacy. Unfortunately, this rapid decrease in tumor burden is almost invariably followed by the emergence of therapeutic resistance. Evolutionary based treatment strategies attempt to delay resistance via judicious treatments that maintain a significant treatable subpopulation. While these strategies have shown promise in recent clinical trials, they often rely on biological conjecture and intuition to derive parameters. In this study we experimentally measure the frequency-dependent interactions between a gefitinib resistant non-small cell lung cancer (NSCLC) population and its sensitive ancestor via the evolutionary game assay. We show that cost of resistance is insufficient to accurately predict competitive exclusion and that frequency-dependent growth rate measurements are required. In addition, we show that frequency-dependent growth rate changes may ultimately result in a safe harbor for resistant populations to safely accumulate, even those with significant cost of resistance. Using frequency-dependent growth rate data we then show that gefitinib treatment results in competitive exclusion of the ancestor, while absence of treatment results in a likely, but not guaranteed exclusion of the resistant strain. Finally, using our empirically derived growth rates to constrain simulations, we demonstrate that incorporating ecological growth effects can dramatically change the predicted time to sensitive strain extinction. In addition, we show that higher drug concentrations may not lead to the optimal reduction in tumor burden. Taken together, these results highlight the potential importance of frequency-dependent growth rate data for understanding competing populations, both in the laboratory and the clinic.

cancer biology↗

bioRxiv2020

Spontaneous Cell Fusions as a Mechanism of Parasexual Recombination in Tumor Cell Populations

Initiation and progression of cancers reflect the underlying process of somatic evolution, which follows a Darwinian logic, i.e., diversification of heritable phenotypes provides a substrate for natural selection, resulting in the outgrowth of the most fit subpopulations. Although somatic evolution can tap into multiple sources of diversification, it is assumed to lack access to (para)sexual recombination - a key diversification mechanism throughout all strata of life. Based on observations of spontaneous fusions involving cancer cells, reported genetic instability of polypoid cells, and precedence of fusion-mediated parasexual recombination in fungi, we asked whether cell fusions could serve as a source of parasexual recombination in cancer cell populations. Using differentially labelled tumor cells, we found evidence of low-frequency, spontaneous cell fusions between carcinoma cells in multiple cell line models of breast cancer both in vitro and in vivo. While some hybrids remained polyploid, many displayed partial ploidy reduction, generating diverse progeny with heterogeneous inheritance of parental alleles, indicative of partial recombination. Hybrid cells also displayed elevated levels of phenotypic plasticity, which may further amplify the impact of cell fusions on the diversification of phenotypic traits. Using mathematical modeling, we demonstrated that the observed rates of spontaneous somatic cell fusions may enable populations of tumor cells to amplify clonal heterogeneity, thus facilitating the exploration of larger areas of the adaptive landscape, relative to strictly asexual populations, which may substantially accelerate a tumors ability to adapt to new selective pressures.

cancer biology↗

bioRxiv2020

High school Internship Program in Integrated Mathematical Oncology (HIP IMO) - five-year experience at Moffitt Cancer Center

Modern cancer research, and the wealth of data across multiple spatial and temporal scales, has created the need for researchers that are well-versed in the life sciences (cancer biology, developmental biology, immunology), medical sciences (oncology) and natural sciences (mathematics, physics, engineering, computer sciences). College undergraduate education is traditionally provided in disciplinary silos, which creates a steep learning curve at the graduate and postdoctoral levels that increasingly bridge multiple disciplines. Numerous colleges have begun to embrace interdisciplinary curricula, but students who double-major in mathematics (or other quantitative sciences) and biology (or medicine) remain scarce. We identified the need to educate junior and senior high school students about integrating mathematical and biological skills, through the lens of mathematical oncology, to better prepare students for future careers at the interdisciplinary interface. The High school Internship Program in Integrated Mathematical Oncology (HIP IMO) at Moffitt Cancer Center has so far trained 59 students between 2015 and 2019. We report here on the program structure, training deliverables, curriculum, and outcomes. We hope to promote such interdisciplinary educational activities early in a students career.

scientific communication and education↗

bioRxiv2019

Collagen Production and Niche Engineering: A Novel Strategy for Cancer Cells to Survive Acidosis and Evolve

Ductal Carcinoma in situ (DCIS) is an avascular disease characterized by profound acidosis. Pre-malignant cells within this niche must adapt to acidosis to survive and thrive. A component of this acid-adaptation involves extracellular matrix remodeling leading to niche construction and remodeling. Using discovery proteomics, we identified that collagen producing enzyme PLODs are upregulated in acid-adapted breast cancer cells. Second harmonic generation microscopy showed significant collagen deposition within DCIS lesions of patients. In vitro analyses identified that acid-adaptation involves production of rare collagens that can be regulated by Ras and SMAD pathway. Secretome analysis showed upregulation ECM remodeling enzymes such as TGM2 and LOXL2. Comparison of acid induced collagens in vitro and in patient data showed correlation between rare collagens production and survival of patients. We conclude acidosis induces collagen production by cancer cells and promote growth independent of basal membrane attachment. The independently produced collagen can be used for niche construction and engineering as an adaptation strategy of cancer cells to survive and evolve.

cancer biology↗

bioRxiv2018

Resistance to ALK targeting therapies as a gradual Darwinian adaptation to inhibitor specific selective pressures

Despite high initial efficacy, therapies that target oncogenic kinases eventually fail in advanced, metastatic cancers. This failure in initially responsive tumors is the direct result of the evolution of drug resistance under therapy-imposed selective pressures. In contrast to the massive body of experimental research on the molecular mechanisms of resistance, understanding of its evolutionary origins and dynamics remains fragmented. Using a combination of experimental studies and mathematical modeling, we sought to dissect the evolution of resistance to different clinical ALK inhibitors in an experimental model of ALK positive NSCLC. We found that resistance can originate from heterogeneous, weakly resistant, sub-populations with variable sensitivity to different ALK inhibitors. Instead of the commonly assumed stochastic single hit (epi) mutational transition, or drug-induced reprogramming, we found evidence of a hybrid scenario, of gradual, multifactorial development through acquisition of multiple cooperating genetic and epigenetic adaptive changes, amplified by selection. Additionally, we found that intermediate resistance phenotypes might present unique, temporally restricted collateral sensitivities, absent in therapy naive or fully resistant cells, suggesting new opportunities for therapeutic interference.

cancer biology↗

bioRxiv2018

The impact of tumor stromal architecture on therapy response and clinical progression

-- Advances in molecular oncology research culminated in the development of targeted therapies that act on defined molecular targets either on tumor cells directly (such as inhibitors of oncogenic kinases), or indirectly by targeting the tumor microenvironment (such as anti-angiogenesis drugs). These therapies can induce strong clinical responses, when properly matched to patients. Unfortunately, most targeted therapies ultimately fail as tumors evolve resistance. Tumors consist not only of neoplastic cells, but also of stroma, whereby \"stroma\" is the umbrella term for non-tumor cells and extracellular matrix (ECM) within the tumor microenvironment, possibly excluding immune cells1. We know that tumor stroma is an important player in the development of resistance. We also know that stromal architecture is spatially complex, differs from patient to patient and changes with therapy. However, to this date we do not understand the link between spatial and temporal changes in stromal architecture and response of tumors to therapy, in space and time. In this project we sought to address this gap of knowledge using a combination of mathematical and statistical modeling, experimental in vivo studies, and analysis of clinical samples in therapies that target tumor cells directly (in lung and breast cancers) and indirectly (in kidney cancer). This knowledge will inform therapy choices and offer new angles for therapeutic interventions. Our main question is: how does spatial architecture of stroma impact the emergence or evolution of resistance to targeted therapies, and how can we use this knowledge clinically?

cancer biology↗