Biology subjects

Gatenby, R.

Publications and source records attributed to Gatenby, R..

18 recordsLinked to original sources

bioRxiv2025

HIF-1α and RhoA Drive Enhanced Motility and Aerotaxis of Polyaneuploid Prostate Cancer Cells in Hypoxia

Most cancer deaths result from metastasis, yet only a rare subset of tumor cells can complete this process. Among these, polyaneuploid cancer cells (PACCs), which arise via endoreplication under stressors such as hypoxia, are implicated as metastatic drivers, but how they acquire this potential is poorly understood. Here, we show that prostate cancer-derived PACCs exhibit features predictive of invasion and intravasation. Time-lapse fluorescence microscopy and single-cell tracking under hypoxia revealed that PACCs migrated significantly farther than Non-PACCs, consistent with local invasion. PACC trajectories showed a strong tendency to migrate toward oxygen, consistent with aerotaxis and predictive of intravasation. siRNA-mediated knockdown demonstrated that the enhanced motility and aerotaxis of hypoxic PACCs require both HIF-1and RhoA, with RhoA expression suppressed upon HIF-1 inhibition. Altogether, we propose a HIF-1[->] RhoA [->] motility/aerotaxis mechanism enabling PACCs to escape hypoxic cores, invade tissue, and access vasculature, highlighting them as a uniquely invasive subpopulation with implications for anti-metastatic therapies.

cancer biology↗

bioRxiv2024

Goliath clades and in vivo tracking of clonal dynamics show three phases of UV-induced skin carcinogenesis

While the genetic paradigm of cancer etiology has proven powerful, it remains incomplete as evidenced by the widening spectrum of non-cancer cell-autonomous "hallmarks" of cancer. Studies have demonstrated the commonplace presence of high oncogenic mutational burdens in homeostatically-stable epithelia. Hence, the presence of driver mutations alone does not result in cancer. Here, we reveal additional forces governing the eco-evolutionary dynamics of carcinogenesis. Using a UV-driven mouse model of cutaneous squamous cell carcinoma, we tested our central hypothesis that cancer initiation occurs in three phases: 1) tissue disruption and the emergence of unusually large "goliath" clades (ecological driver), 2) clonal selection within a subset of these goliaths as evidenced by the presence of areas of unusually high local densities of cells (termed "micro-lumps") with higher mutational burdens (evolutionary driver), and 3) emergence of macroscopic lesions. We tracked these tissue level ecological and evolutionary drivers of cancer initiation via in-vivo serial imaging and 3-D reconstruction of fluorescently labeled keratinocyte clades, yielding 25,085 clade measurements from 14 mice over 3 months, and 14,525 clades from 4 of these mice over 6 months. While median and mean clade sizes differed little between UV and non-UV exposure (cc. 65,000 {micro}m3), our ecological survey revealed the emergence of large goliath clades (> cc. 4.2x106 {micro}m3), almost exclusively within UV-exposed skin. Goliath clades emerged by month 2, increased dramatically in number by months 3-4, and plateaued between months 5-6. Goliath clades arose as serpentine structures, intercalating between adjacent keratinocytes. Unexpectedly, targeted DNA sequencing revealed mutations with very low variant allele frequencies within clades, but substantial differences among clades, suggesting that positive selection for these mutations is superfluous to the development of goliaths early in carcinogenesis. scRNAseq analysis of both bulk skin and sorted clades revealed epidermal de-differentiation and immune suppression as early events. By month 7, mutational burdens were significantly larger in goliath clades, particularly for those with micro-lumps. Finally, lesions began to emerge between months 6 and 7, only in UV-exposed skin. To confirm that goliath clades are orders of magnitude more likely to spawn lesions, we randomly selected 21 goliath clades at months 6-7, prior to the time of the emergence of detectable lesions, and followed them over time. Remarkably, 2 of these developed into macroscopic lesions. Our adaptation of the Drake equation estimated the probability of this to be <10-6. Taken together, our results support the presence of three phases of cancer initiation, the earliest of which presage the acquisition of driver mutations and also explains why cancers are relatively rare in relation to the degree of somatic mosaicism present in UV-exposed skin.

cancer biology↗

bioRxiv2024

Migrastatic Therapy as a Potential Game-Changer inAdaptive Cancer Treatment

Adaptive therapy, which anticipates and counters the evolution of resistance in cancer cells, has gained significant traction, especially following the success of the Zhang et al.s protocol in treating metastatic castrate-resistant prostate cancer. While several adaptive therapies have now advanced to clinical trials, none currently incorporates migrastatics, i.e. treatments designed to inhibit cancer cell metastasis. In this study, we propose integrating migrastatics into adaptive therapy protocols and evaluate the potential benefits of using a game-theoretic spatial model. Our results demonstrate that combining adaptive therapy with migrastatics effectively delays the onset of metastasis and reduces both the number and size of metastases across most cancer scenarios analyzed. This approach not only extends the time to the first metastasis but also enhances the overall efficacy of adaptive therapies. Our findings suggest a promising new direction for cancer treatment, where adaptive therapy, in combination with migrastatic agents, can target both the evolution of resistance and the metastatic spread of cancer cells.

cancer biology↗

bioRxiv2024

Evolutionary double-bind treatment using radiotherapy and NK cell-based immunotherapy in prostate cancer

Evolution-informed therapies exploit ecological and evolutionary consequences of drug resistance to inhibit the expansion of treatment-resistant populations and prolong time to progression. One strategy, termed an evolutionary double-bind, uses an initial therapy to elicit a specific adaptive response by the cancer cells, which is then selectively targeted by a follow-on therapy. Here we examine the combination of radiation therapy and immunotherapy as a quantifiable double-bind strategy. Radiotherapy (RT) induces lethal double-strand DNA breaks, but cancer cells can adapt by upregulating DNA damage response pathways. While this evolutionary strategy increases resistance to DNA damaging agents, it also results in enhanced expression of natural killer (NK) cell ligands potentially increasing vulnerability to an immune response. Using a radiation-resistant human prostate carcinoma cell line (22Rv1), we demonstrate that RT-resistant cells upregulate NK cell ligands, including major histocompatibility complex class I chain-related protein A/B (MICA/B), and poliovirus receptors (PVR1, PVRL2) with a 2-fold increase in sensitivity to NK cell mediated killing. We investigated this potential evolutionary double bind through in vitro studies and evolution-based mathematical models. Radiotherapy alone slowed overall growth but strongly selected for RT-resistant cells. NK cell therapy alone suppressed the RT-resistant population but with a surviving population of radiation-sensitive cells. These dynamics were framed mathematically, and model simulation predicted optimal tumour control would be achieved through initial RT rapidly followed by NK-based immunotherapy. Subsequent experiments confirmed the model prediction. We conclude that radiotherapy and NK cell-based immunotherapy produces an evolutionary double bind that can be exploited in heterogenous tumours to limit RT resistance.

cancer biology↗

bioRxiv2023

Testing Adaptive Therapy Protocols using Gemcitabine and Capecitabine on a Mouse Model of Endocrine-Resistant Breast Cancer

Highly effective cancer therapies often face limitations due to acquired resistance and toxicity. Adaptive therapy, an ecologically inspired approach, seeks to control therapeutic resistance and minimize toxicity by leveraging competitive interactions between drug-sensitive and drug-resistant subclones, prioritizing patient survival and quality of life over maximum cell kill. In preparation for a clinical trial in breast cancer, we used large populations of MCF7 cells to rapidly generate endocrine-resistance breast cancer cell line. We then mimicked second line therapy in ER+ breast cancers by treating the endocrine-resistant MCF7 cells in a mouse xenograft model to test adaptive therapy with capecitabine, gemcitabine, or the combination of those two drugs. Dose-modulation adaptive therapy with capecitabine alone increased survival time relative to MTD, but not statistically significant (HR: 0.22, 95% CI 0.043-1.1 P = 0.065). However, when we alternated the drugs in both dose modulation (HR = 0.11, 95% CI: 0.024 - 0.55, P = 0.007) and intermittent adaptive therapies significantly increased survival time compared to high dose combination therapy (HR = 0.07, 95% CI: 0.013 - 0.42; P = 0.003). Overall, survival time increased with reduced dose for both single drugs (P < 0.01) and combined drugs (P < 0.001). Adaptive therapy protocols resulted in tumors with lower proportions of proliferating cells (P = 0.0026) and more apoptotic cells (P = 0.045). The results show that Adaptive therapy outperforms high-dose therapy in controlling endocrine-resistant breast cancer, favoring slower-growing tumors, and showing promise in two-drug alternating regimens.

cancer biology↗

bioRxiv2023

Converting genetic information to non-equilibrium cellular thermodynamics in enzyme-catalyzed reactions

Living systems use genomic information to maintain a stable highly ordered state far from thermodynamic equilibrium but the specific mechanisms and general principles governing the interface of genetics and thermodynamics has not been extensively investigated. Genetic information is quantified in unitless bits termed "Shannon entropy", which does not directly relate to thermodynamic entropy or energy. Thus, it is unclear how the Shannon entropy of genetic information is converted into thermodynamic work necessary to maintain the non-equilibrium state of living systems. Here we investigate the interface of genetic information and cellular thermodynamics in enzymatic acceleration of a chemical reaction S+E[->]ES[->]E+P, where S and E are substrate and enzyme, ES is the enzyme substrate complex and P product. The rate of any intracellular chemical reaction is determined by probability functions at macroscopic (Boltzmann distribution of the reactant kinetic energies governed by temperature) or microscopic (overlap of reactant quantum wave functions) scales - described, respectively, by the Arrhenius and Knudsen equations. That is, the reaction rate, in the absence of a catalyst, is governed by temperature which determines the kinetic energy of the interacting molecules. Genetic information can act upon a when the encoded string of amino acids folds into a 3-deminsional structure that permits a lock/key spatial matching with the reactants. By optimally superposing the reactants wave functions, the information in the enzyme increases the reaction rate by up to15 orders of magnitude under isothermal conditions. In turn, the accelerated reaction rate alters the intracellular thermodynamics environment as the products are at lower Gibbs free energy which permits thermodynamic work (Wmax = -{Delta}G). Mathematically and biologically, the critical event that allows genetic information to produce thermodynamic work is the folding of the amino acid string specified by the gene into a 3-dimensional shape determined by its lowest energy state. Biologically, this allows the amino acid string to bind substrate and place them in an optimal spatial orientation. These key-lock are mathematically characterized by Kullback-Leibler Divergence and the interactions with the reaction channel now represent Fisher Information (the second derivative Kullback-Leibler divergence), which can take on the units of the process to which it is applied. Interestingly, Shannon is typically derived by "coarse graining" Shannon information. Thus, living system, by acting at a quantum level, "fine grain" Shannon information

biophysics↗

bioRxiv2023

Logistic tumor-population growth and ghost-points symmetry

The observed time evolution of a population is well approximated by a logistic function in many research fields, including oncology, ecology, chemistry, demography, economy, linguistics, and artificial neural networks. Initial growth is exponential at a constant rate and capped at a limit size, i.e., the carrying capacity. In mathematical oncology, the carrying capacity has been postulated to be co-evolving and thus patient-specific. As the relative tumor-over-carrying capacity ratio may be predictive and prognostic for tumor growth and treatment response dynamics, it is paramount to estimate it from limited clinical data. We show that exploiting the logistic functions rotation symmetry can help estimate the populations growth rate and carry capacity from fewer data points than conventional regression approaches. We test this novel approach against a classic oncology database of logistic tumor growth, achieving a 30% to 40% reduction in the time necessary to correctly estimate the logistic growth rate and carrying capacity. Our results will improve tumor dynamics forecasting and augment the clinical decision-making process.

cancer biology↗

bioRxiv2023

Experimental and Phylogenetic Evidence for Correlated Gene Expression Evolution between Dermal and Endometrial Fibroblasts: implications for the evolution of cancer malignancy

Changes in transcriptional gene expression is a dominant mode of evolution, mostly driven by mutations at cis-regulatory regions. Mutations can affect gene expression in multiple cell types if the same cis-regulatory elements are used by different cell types. As a consequence, changes in gene expression in one cell type may be associated with similar gene expression changes in another cell type. Correlated gene expression change can explain correlated character evolution, as for instance the correlation between placental invasion and vulnerability to cancer malignancy. Here we test this hypothesis using a comparative and an experimental data set. Specifically, we investigate gene expression in dermal skin fibroblasts (SF) and uterine endometrial stomal fibroblasts (ESF). The comparative dataset consists of transcriptomes from cultured SF and ESF from 9 mammalian species. We calculated the independent phylogenetic contrasts (PIC) for each gene and cell type. We find that evolutionary changes in gene expression in SF and ESF are highly correlated, supporting the hypothesis that the correlated gene expression changes are a prevalent feature of gene expression evolution. The experimental data set derives from a SCID mouse strain that was selected for slow cancer growth which led to substantial changes in the SF compared to wild type SCID mice. We isolated SF and ESF from wild type and evolved SCID mice and compared their gene expression profiles. We find a significant correlation between the gene expression contrasts of SF and ESF, which supports the hypothesis that gene expression variation in SF and ESF is correlated. We discuss the implications of these findings for the hypothesized correlation between placental invasiveness and vulnerability to metastatic cancer.

evolutionary biology↗

bioRxiv2023

Learning to Adapt - Deep Reinforcement Learning in Treatment-Resistant Prostate Cancer

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.

cancer biology↗

bioRxiv2023

Adaptive therapy for ovarian cancer: An integrated approach to PARP inhibitor scheduling

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 tumors 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 from in vitro Incucyte 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 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 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 experiments in vivo confirm 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 biology↗

bioRxiv2022

Breast Cancer Reaction-Diffusion from Spectral-Spatial Analysis in Immunohistochemistry

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.

cancer biology↗

bioRxiv2021

Coordination games in cancer

We propose a model of cancer initiation and progression where tumor growth is modulated by an evolutionary coordination game. Evolutionary games of cancer are widely used to model frequency-dependent cell interactions with the most studied games being the Prisoners Dilemma and public goods games. Coordination games, by their more obscure and less evocative nature, are left understudied, despite the fact that, as we argue, they offer great potential in understanding and treating cancer. In this paper we present the conditions under which coordination games between cancer cells evolve, we propose aspects of cancer that can be modeled as results of coordination games, and explore the ways through which coordination games of cancer can be exploited for therapy.

cancer biology↗

bioRxiv2020

The Harsh Microenvironment in Early Breast Cancer Select for a Warburg Phenotype

The harsh microenvironment of ductal carcinoma in situ (DCIS) exerts strong evolutionary selection pressures on cancer cells. We hypothesize that the poor metabolic conditions near the ductal center foment the emergence of a Warburg Effect (WE) phenotype, wherein cells rapidly ferment glucose to lactic acid, even in normoxia. To test this hypothesis, we subjected pre-malignant breast cancer cells to different microenvironmental selection pressures using combinations of hypoxia, acidosis, low glucose, and starvation for many months, and isolated single clones for metabolic and transcriptomic profiling. The two harshest conditions selected for constitutively expressed WE phenotypes. RNA-seq analysis of WE clones identified the transcription factors NF[kcy]B and KLF4 as potential inducers of the WE phenotype. NF[kcy]B was highly phosphorylated in the glycolytic clones. In stained DCIS samples, KLF4 expression was enriched in the area with the harshest microenvironmental conditions. We simulated in vivo DCIS phenotypic evolution using a mathematical model calibrated from the in vitro results. The WE phenotype emerged in the poor metabolic conditions near the necrotic core. We propose that harsh microenvironments within DCIS select for a Warburg phenotype through constitutive transcriptional reprogramming, thus conferring a survival advantage and facilitating further growth and invasion.

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↗

bioRxiv2020

Turnover modulates the need for a cost of resistance in adaptive therapy

"Control and conquer" - this is the philosophy behind adaptive therapy, which seeks to exploit intra-tumoural competition to avoid, or at least, delay the emergence of therapy resistance in cancer. Motivated by promising results from theoretical, experimental and, most recently, a clinical study in prostate cancer, there is an increasing interest in extending this approach to other cancers. As such, it is urgent to understand the characteristics of a cancer which determine whether it will respond well to adaptive therapy, or not. A plausible candidate for such a selection criterion is the fitness cost of resistance. In this paper, we study a simple competition model between sensitive & resistant cell populations to investigate whether the presence of a cost is a necessary condition for adaptive therapy to extend the time to progression beyond that of a standard-of-care continuous therapy. We find that for tumours close to their environmental carrying capacity such a cost of resistance is not required. However, for tumours growing far from carrying capacity, a cost may be required to see meaningful gains. Notably, we show that in such cases it is important to consider the cell turnover in the tumour and we discuss its role in modulating the impact of a cost of resistance. Overall, our work helps to clarify under which circumstances adaptive therapy may be beneficial, and suggests that turnover may play an unexpectedly important role in the decision making process.

cancer biology↗

bioRxiv2019

Prostate-Specific Antigen Dynamics Predict Individual Responses to Intermittent Androgen Deprivation

BackgroundIntermittent androgen deprivation therapy (IADT) is an attractive treatment approach for biochemically recurrent prostate cancer (PCa), whereby cycling treatment on and off can reduce cumulative dose, limit toxicities, and delay development of treatment resistance. To optimize treatment within the context of ongoing intratumoral evolution, underlying mechanisms of resistance and actionable biomarkers need to be identified.\n\nMethodsWe have developed a quantitative framework to simulate enrichment of prostate cancer stem cell (PCaSC) dynamics during treatment as a plausible mechanism of resistance evolution.\n\nResultsSimulated dynamics of PCaSC and non-stem cancer cells demonstrate that stem cell proliferation patterns correlate with longitudinal serum prostate-specific antigen (PSA) measurements in 70 PCa patients undergoing multiple cycles of IADT. By learning the dynamics from each treatment cycle, individual model simulations predict evolution of resistance in the subsequent IADT cycle with a sensitivity and specificity of 57% and 94%, respectively and an overall accuracy of 90%. Additionally, we evaluated the potential benefit of docetaxel for IADT in biochemically recurrent PCa. Model simulations based on response dynamics from the first IADT cycle identify patients who would or would not benefit from concurrent docetaxel in subsequent cycles.\n\nConclusionOur results demonstrate the feasibility and potential value of adaptive clinical trials guided by patient-specific mathematical models of intratumoral evolutionary dynamics continuously updated with each treatment cycle.\n\nTranslational RelevanceCompared to continuous androgen deprivation therapy, intermittent androgen deprivation (IADT) has been shown to reduce toxicity and delay time to progression in prostate cancer. While numerous mathematical models have been developed to study the response to both continuous and intermittent androgen deprivation, very few have identified actionable biomarkers of resistance and exploited them to predict how patients will or will not respond to subsequent treatment. Here, we identify prostate-specific antigen (PSA) dynamics as the first such biomarker. Mechanistic mathematical modeling of prostate cancer stem cell dynamics that dictate prostate-specific antigen serum levels predicts individual responses to IADT with 90% overall accuracy and can be used to develop patient-specific adaptive treatment protocols, and potentially identify patients that may benefit from concurrent chemotherapy. Model results demonstrate the feasibility and potential value of adaptive clinical trials guided by patient-specific mathematical models of intratumoral evolutionary dynamics continuously updated with each treatment cycle.

cancer biology↗

bioRxiv2018

Acidity promotes tumor progression by altering macrophage phenotype in prostate cancer

Tumors rapidly ferment glucose to lactic acid even in the presence of oxygen, and coupling high glycolysis with poor perfusion leads to extracellular acidification. Here we demonstrate that acidity, independent from lactate, augments the pro-tumor phenotype of macrophages. We used zwitterionic buffers to show that activating macrophages at pH 6.8 in vitro enhanced an IL-4-driven phenotype as measured by gene expression, cytokine profiling, and functional assays. These results were recapitulated in vivo wherein neutralizing intratumoral acidity reduced the pro-tumor phenotype of macrophages, while also decreasing tumor incidence and invasion in the TRAMP model of prostate cancer. These results were recapitulated using an in silico mathematical model that simulate macrophage responses to environmental signals. By turning off acid-induced cellular responses, our in silico mathematical modeling shows that acid-resistant macrophages can limit tumor progression. In summary, this study suggests that tumor acidity contributes to prostate carcinogenesis by altering the state of macrophage activation.

immunology↗

bioRxiv2018

Connecting Patterns of Tumor Growth with Sex Differences in Extreme Survivorship for Primary Glioblastoma Patients

BackgroundSex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences.\n\nMethodsCombining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients. Pretreatment MR images of 494 glioblastoma patients (299 males and 195 females) were segmented to quantify tumor volumes. Cox proportional hazard (CPH) models and Students t-tests were used to assess which variables were associated with survival outcomes.\n\nResultsAmong males, tumor (T1Gd) radius was a predictor of overall survival (HR=1.027, p=0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR=1.011, p<0.001). Female extreme survivors had significantly smaller tumors (T1Gd) (p=0.010 t-test), but tumor size was not correlated with female overall survival (p=0.955 CPH). Both male and female extreme survivors had significantly lower tumor cell net proliferation rates than other patients (M p=0.004, F p=0.001, t-test). Additionally, extent of resection, tumor laterality, and IDH1 mutation status were also found to have sex-specific effects on overall survival.\n\nConclusionDespite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes, which emphasizes the importance of considering sex as a biological factor when determining patient prognosis and treatment approach.

cancer biology↗