Biology subjects

Basanta, D.

Publications and source records attributed to Basanta, D..

7 recordsLinked to original sources

bioRxiv2018

Hypoxia increases the tempo of evolution in the peri-necrotic niche in glioblastoma

BackgroundLow oxygen in tumours have long been associated with poor prognosis and metastatic disease, precise reasons for which remain poorly understood. Somatic evolution drives cancer progression and treatment resistance. This process is fuelled not only by genetic and epigenetic mutation, but by selection resulting from the interactions between tumour cells, normal cells and physical microenvironment. The ecological habitat tumour cells inhabit influences evolutionary dynamics but impact on tempo of evolution is less clear. MethodsWe explored this complex dialogue with a combined clinical-theoretical approach. Using an agent-based-model, we simulated proliferative hierarchy under heterogeneous oxygen availability. Predictions were compared against clinical data derived from histology samples taken from glioblastoma patients, stained to elucidate areas of hypoxia / necrosis, and p53 expression heterogeneity. ResultsSimulation results indicate cell division in hypoxic environments is effectively upregulated, and that low-oxygen niches provide new avenues for tumour cells to spread. Analysis of human data indicates cell division isnt decreased in low-oxygen regions, despite evidence of significant physiological stress. This is consistent with simulation, suggesting hypoxia is a crucible that effectively warping evolutionary velocity, making deleterious mutations more likely than in well-oxygenated regions. ConclusionsResults suggest hypoxic regions alter evolutionary tempo, driving mutations which fuel tumour heterogeneity..

cancer biology↗

bioRxiv2018

Fractionated follow-up chemotherapy delays the onset of resistance in bone metastatic prostate cancer

Prostate cancer to bone metastases are almost always lethal. This results from the ability of metastatic prostate cancer cells to co-opt bone remodeling leading to what is known as the vicious cycle. Understanding how tumor cells can disrupt bone homeostasis through their interactionswith the stroma and how metastatic tumors respond to treatment is key to the development of new treatments for what remains an incurable disease. Here we describe an evolutionary game theoretical model of both the homeostatic bone remodeling and its co-option by prostate cancer metastases. This model extends past the evolutionary aspects typically considered in game theoretical models by also including ecological factors such as the physical microenvironment of the bone. Our model recapitulates the current paradigm of the \"vicious cycle\" driving tumor growth and sheds light on the interactions of heterogeneous tumor cells with the bone microenvironment and treatment response. Our results show that resistant populations naturally become dominant in the metastases under conventional cytotoxic treatment and that novel schedules could be used to better control the tumor and the associated bone disease compared to the current standard of care. Specifically, we introduce fractionated follow up therapy - chemotherapy where dosage is administered initially in one solid block followed by alternating smaller doeses and holidays - and argue that it is better than either a continuous application or a periodic one. Furthermore, we also show that different regimens of chemotherapy can lead to different amounts of pathological bone that are known to correlate with poor quality of life for bone metastatic prostate cancer patients.

cancer biology↗

bioRxiv2017

Size Matters: Metastatic cluster size and stromal recruitment in the establishment of successful prostate cancer to bone metastases

Prostate cancer (PCa) impacts over 180,000 men every year in the US alone with 26,000 patients expected to succumb to the disease (cancer.gov). The primary cause of death is metastasis, with secondary lesions most commonly occurring in the skeleton. Prostate cancer to bone metastasis is an important yet poorly understood process that is difficult to explore with experimental techniques alone. To this end we have utilized a hybrid (discrete-continuum) cellular automata (HCA) model of normal bone matrix homeostasis that allowed us to investigate how metastatic PCa can disrupt the bone microenvironment. Our previously published results showed that PCa cells can recruit mesenchymal stem cells (MSCs) that give rise to bone building osteoblasts. MSCs are also thought to be complicit in the establishment of successful bone metastases (1). Here we have explored aspects of early metastatic colonization and shown that the size of PCa clusters needs to be within a specific range to become successfully established: sufficiently large to maximize success but not too large to risk failure through competition amongst cancer and stromal cells for scarce resources. Furthermore, we show that MSC recruitment can promote the establishment of a metastasis and compensate for relatively low numbers of PCa cells seeding the bone microenvironment. Combined, our results highlight the utility of computational models that capture the complex and dynamic dialogue between cells during the initiation of active metastases.

cancer biology↗

bioRxiv2017

Dark selection for JAK/STAT-inhibitor resistance in chronic myelomonocytic leukemia

Acquired therapy resistance to cancer treatment is a common and serious clinical problem. The classic U-shape model for the emergence of resistance supposes that: (1) treatment changes the selective pressure on the treatment-naive tumour; (2) this shifting pressure creates a proliferative or survival difference between sensitive cancer cells and either an existing or de novo mutant; (3) the resistant cells then out-compete the sensitive cells and - if further interventions (like drug holidays or new drugs or dosage changes) are not pursued - take over the tumour: returning it to a state dangerous to the patient. The emergence of ruxolitinib resistance in chronic myelomonocytic leukemia (CMML) seems to challenge the classic model: we see the global properties of resistance, but not the drastic change in clonal architecture expected with the selection bottleneck. To study this, we explore three population-level models as alternatives to the classic model of resistance. These three effective models are designed in such a way that they are distinguishable based on limited experimental data on the time-progression of resistance in CMML. We also propose a candidate reductive implementation of the proximal cause of resistance to ground these effective theories. With these reductive implementations in mind, we also explore the impact of oxygen diffusion and spatial structure more generally on the dynamics of CMML in the bone marrow concluding that, even small fluctuations in oxygen availability can seriously impact the efficacy of ruxolitinib. Finally, we look at the ability of spatially distributed cytokine signaling feedback loops to produce a relapse in symptoms similar to what we observe in the clinic.

cancer biology↗

bioRxiv2017

Stromal Reactivity Differentially Drives Tumor Cell Evolution and Prostate Cancer Progression

We implemented a hybrid multiscale model of carcinogenesis that merges data from biology and pathology on the microenvironmental regulation of prostate cancer (PCa) cell behavior. It recapitulates the biology of stromal influence in prostate cancer progression. Our data indicate that the interactions between the tumor cells and reactive stroma shape the evolutionary dynamics of PCa cells and explain overall tumor aggressiveness. We show that the degree of stromal reactivity, when coupled with the current clinical biomarkers, significantly improves PCa prognostication, both for death and recurrence, that may alter treatment decisions. We also show that stromal reactivity correlates directly with tumor growth but inversely modulates tumor evolution. This suggests that the aggressive stromal independent PCa may be an inevitable evolutionary result of poor stromal reactivity. It also suggests that purely tumor centric metrics of aggressiveness may be misleading in terms on clinical outcome.

cancer biology↗

bioRxiv2017

Cancer associated fibroblasts and alectinib switch the evolutionary games that non-small cell lung cancer plays

Heterogeneity in strategies for survival and proliferation among the cells which constitute a tumour is a driving force behind the evolution of resistance to cancer therapy. The rules mapping the tumours strategy distribution to the fitness of individual strategies can be represented as an evolutionary game. We develop a game assay to measure effective evolutionary games in co-cultures of non-small cell lung cancer cells which are sensitive and resistant to the anaplastic lymphoma kinase inhibitor Alectinib. The games are not only quantitatively different between different environments, but targeted therapy and cancer associated fibroblasts qualitatively switch the type of game being played by the in-vitro population from Leader to Deadlock. This observation provides empirical confirmation of a central theoretical postulate of evolutionary game theory in oncology: we can treat not only the player, but also the game. Although we concentrate on measuring games played by cancer cells, the measurement methodology we develop can be used to advance the study of games in other microscopic systems by providing a quantitative description of non-cell-autonomous effects.

cancer biology↗

bioRxiv2016

Homeostasis Back and Forth: An Eco-Evolutionary Perspective of Cancer

The role of genetic mutations in cancer is indisputable: they are a key source of tumor heterogeneity and drive its evolution to malignancy. But the success of these new mutant cells relies on their ability to disrupt the homeostasis that characterizes healthy tissues. Mutated clones unable to break free from intrinsic and extrinsic homeostatic controls will fail to establish a tumor. Here we will discuss, through the lens of mathematical and computational modeling, why an evolutionary view of cancer needs to be complemented by an ecological perspective in order to understand why cancer cells invade and subsequently transform their environment during progression. Importantly, this ecological perspective needs to account for tissue homeostasis in the organs that tumors invade, since they perturb the normal regulatory dynamics of these tissues, often co-opting them for its own gain. Furthermore, given our current lack of success in treating advanced metastatic cancers through tumor centric therapeutic strategies, we propose that treatments that aim to restore homeostasis could become a promising venue of clinical research. This eco-evolutionary view of cancer requires mechanistic mathematical models in order to both integrate clinical with biological data from different scales but also to detangle the dynamic feedback between the tumor and its environment. Importantly, for these models to be useful, they need to embrace a higher degree of complexity than many mathematical modelers are traditionally comfortable with.

cancer biology↗