ONCO Subgroup Contributed Talks

Tuesday, June 15 at 10:30pm (PDT)
Wednesday, June 16 at 06:30am (BST)
Wednesday, June 16 02:30pm (KST)

SMB2021 SMB2021 Follow Tuesday (Wednesday) during the "CT05" time block.
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Maalavika Pillai

Indian Institute of Science, Bangalore
"Systems-level analysis of phenotypic plasticity and heterogeneity in melanoma"
Phenotypic (i.e. non-genetic) heterogeneity in melanoma drives dedifferentiation, recalcitrance to targeted therapy and immunotherapy, and consequent tumor relapse and metastasis. Various markers or regulators associated with distinct phenotypes in melanoma have been identified, but, how does a network of interactions among these regulators give rise to multiple “attractor” states and phenotypic switching remains elusive. Here, we inferred a network of transcription factors (TFs) that act as master regulators for gene signatures of diverse cell-states in melanoma. Dynamical simulations of this network predicted how this network can settle into different “attractors” (TF expression patterns), suggesting that TF network dynamics drives the emergence of phenotypic heterogeneity. These simulations can recapitulate major phenotypes observed in melanoma and explain de-differentiation trajectory observed upon BRAF inhibition. Our systems-level modeling framework offers a platform to understand trajectories of phenotypic transitions in the landscape of a regulatory TF network and identify novel therapeutic strategies targeting melanoma plasticity.

Jill Gallaher

Moffitt Cancer Center
"Using adaptive therapy to characterize collective and individual characteristics of metastases"
Evolutionary-designed therapies, such as adaptive therapy, have been shown to be useful for late stage cancer to delay treatment resistance by exploiting competition between sensitive and resistant cells by alternating between drug applications and drug-free vacations. In addition, a single cycle of adaptive therapy could be used as a tool to probe tumor dynamics. In this work, we propose a framework for estimating individual and collective components of a metastatic system using tumor dynamics during adaptive therapy. We use a system of off-lattice agent-based models to represent individual metastatic lesions within independent domains, but subject to the same systemic therapy. We find the first cycle of adaptive therapy delineates several features of the metastatic system. Larger metastases have longer cycles, more drug resistance slows the cycles, and a faster cell turnover speeds up drug response time and slows the regrowth time. The number of metastases does not affect cycle times, as dynamics are dominated by the largest tumors rather than the aggregate. Changes in individual metastases gives insight on heterogeneity amongst metastases and can guide treatment. Generally, systems with more intertumor heterogeneity had better success with continuous therapy, while systems with more intratumor heterogeneity responded better to adaptive therapy.

Joshua Bull

Wolfson Centre for Mathematical Biology, University of Oxford
"Novel spatial statistics describe phenotype transitions in an agent-based model of tumour associated macrophages"
Tumour associated macrophages adopt a range of phenotypes based on microenvironmental cues, with opposite ends of a spectrum of behaviours often summarised as “M1” (anti-tumour) and “M2” (pro-tumour). Transitions in macrophage phenotype play a role in cancer progression: for example, chemotactic gradients generated by macrophages of different phenotypes may be responsible for movement of tumour cells towards external vasculature and subsequent metastasis [1].We present an agent-based model based on [1] in which individual blood vessels, macrophages, tumour cells and stromal cells are resolved. Diffusible species in the tumour microenvironment determine macrophage phenotype, which we describe as a continuous variable. This variable governs phenotype specific macrophage behaviours such as phagocytosis and production of tumour cell chemoattractants. Our model reproduces patterns of macrophage localisation described in [1].Considering simulated macrophage locations as a point pattern, we develop novel spatial statistical techniques which account for points labelled with a continuous variable and hence identify how macrophage phenotype determines spatial localisation. This work suggests that spatial statistics accounting for real-valued labels could be used to better describe multiplexed imaging data, in which high or low expression of multiple markers can be identified from variations in stain intensity.[1] Arwert et al, 2018. doi:10.1016/j.celrep.2018.04.007.

Hosted by SMB2021 Follow
Virtual conference of the Society for Mathematical Biology, 2021.