Image analysis reveals molecularly distinct patterns of TILs in NSCLC associated with treatment outcome.

Despite known histological, biological, and clinical differences between lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), relatively little is known about the spatial differences in their corresponding immune contextures. Our study of over 1000 LUAD and LUSC tumors revealed that computationally derived patterns of tumor-infiltrating lymphocytes (TILs) on H&E images were different between LUAD (N = 421) and LUSC (N = 438), with TIL density being prognostic of overall survival in LUAD and spatial arrangement being more prognostically relevant in LUSC. In addition, the LUAD-specific TIL signature was associated with OS in an external validation set of 100 NSCLC treated with more than six different neoadjuvant chemotherapy regimens, and predictive of response to therapy in the clinical trial CA209-057 (n = 303). In LUAD, the prognostic TIL signature was primarily comprised of CD4+ T and CD8+ T cells, whereas in LUSC, the immune patterns were comprised of CD4+ T, CD8+ T, and CD20+ B cells. In both subtypes, prognostic TIL features were associated with transcriptomics-derived immune scores and biological pathways implicated in immune recognition, response, and evasion. Our results suggest the need for histologic subtype-specific TIL-based models for stratifying survival risk and predicting response to therapy. Our findings suggest that predictive models for response to therapy will need to account for the unique morphologic and molecular immune patterns as a function of histologic subtype of NSCLC

Interactions between Phenotypic Switching, Gene Network Dynamics and Evolutionary Dynamics in Growing Cell Populations

Gene expression is a stochastic biological processes that controls the different phenotypes of an organism depending on the environment. High-resolution single cell measurements show that genetically identical cells can be different from each other even in a homogeneous environment, leading a spectrum of phenotypes with different cellular fitnesses that can reversibly switch between phenotypes. How population-level properties emerge from single cell behavior and how mutants emerge and spread in such populations is unclear. In this thesis, we developed mathematical models to study (i) the effect of time-delay needed for a mutation in a regulator gene influence an effector protein and the long term population fitness, (ii) how optimum population fitness behaves as a function of the growth rates of the various phenotypes for different durations in fluctuating environments. Our work shows a paradoxical outcome of evolution where mutations in a regulator gene interact with gene network dynamics and evolutionary dynamics, giving rise to permanent decrease in population fitness. In the other scenario of fluctuating environments, a previously predicted optimum exists for wider parameter regime if the environmental durations are long and narrow regime for short environmental durations. We also find that a mutant, which randomly evolved to match its phenotypic switching rates with environmental switching rates, can sweep the population if the predicted optimum matches with the assumed one, but not otherwise.Physics, Department o

Deep computational image analysis of immune cell niches reveals treatment-specific outcome associations in lung cancer

Abstract The tumor immune composition influences prognosis and treatment sensitivity in lung cancer. The presence of effective adaptive immune responses is associated with increased clinical benefit after immune checkpoint blockers. Conversely, immunotherapy resistance can occur as a consequence of local T-cell exhaustion/dysfunction and upregulation of immunosuppressive signals and regulatory cells. Consequently, merely measuring the amount of tumor-infiltrating lymphocytes (TILs) may not accurately reflect the complexity of tumor-immune interactions and T-cell functional states and may not be valuable as a treatment-specific biomarker. In this work, we investigate an immune-related biomarker (PhenoTIL) and its value in associating with treatment-specific outcomes in non-small cell lung cancer (NSCLC). PhenoTIL is a novel computational pathology approach that uses machine learning to capture spatial interplay and infer functional features of immune cell niches associated with tumor rejection and patient outcomes. PhenoTIL’s advantage is the computational characterization of the tumor immune microenvironment extracted from H&E-stained preparations. Association with clinical outcome and major non-small cell lung cancer (NSCLC) histology variants was studied in baseline tumor specimens from 1,774 lung cancer patients treated with immunotherapy and/or chemotherapy, including the clinical trial Checkmate 057 (NCT01673867)