Historical perspective and future directions: computational science in immuno-oncology

Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life.

Spatial genomics enables multi-modal study of clonal heterogeneity in tissues

The state and behaviour of a cell can be influenced by both genetic and environmental factors. In particular, tumour progression is determined by underlying genetic aberrations^1-4 as well as the makeup of the tumour microenvironment^5,6. Quantifying the contributions of these factors requires new technologies that can accurately measure the spatial location of genomic sequence together with phenotypic readouts. Here we developed slide-DNA-seq, a method for capturing spatially resolved DNA sequences from intact tissue sections. We demonstrate that this method accurately preserves local tumour architecture and enables the de novo discovery of distinct tumour clones and their copy number alterations. We then apply slide-DNA-seq to a mouse model of metastasis and a primary human cancer, revealing that clonal populations are confined to distinct spatial regions. Moreover, through integration with spatial transcriptomics, we uncover distinct sets of genes that are associated with clone-specific genetic aberrations, the local tumour microenvironment, or both. Together, this multi-modal spatial genomics approach provides a versatile platform for quantifying how cell-intrinsic and cell-extrinsic factors contribute to gene expression, protein abundance and other cellular phenotypes.

SMARCA4 inactivation promotes lineage-specific transformation and early metastatic features in the lung

SMARCA4/BRG1 encodes for one of two mutually exclusive ATPases present in mammalian SWI/SNF chromatin remodeling complexes and is frequently mutated in human lung adenocarcinoma. However, the functional consequences of SMARCA4 mutation on tumor initiation, progression, and chromatin regulation in lung cancer remain poorly understood. Here, we demonstrate that loss of Smarca4 sensitizes CCSP+ cells within the lung in a cell-type dependent fashion to malignant transformation and tumor progression, resulting in highly advanced dedifferentiated tumors and increased metastatic incidence. Consistent with these phenotypes, Smarca4-deficient primary tumors lack lung lineage transcription factor activities and resemble a metastatic cell state. Mechanistically, we show that Smarca4 loss impairs the function of all three classes of SWI/SNF complexes, resulting in decreased chromatin accessibility at lung lineage motifs and ultimately accelerating tumor progression. Thus, we propose that the SWI/SNF complex - via Smarca4 - acts as a gatekeeper for lineage-specific cellular transformation and metastasis during lung cancer evolution.

Epigenomic State Transitions Characterize Tumor Progression in Mouse Lung Adenocarcinoma

Regulatory networks that maintain functional, differentiated cell states are often dysregulated in tumor development. Here, we use single-cell epigenomics to profile chromatin state transitions in a mouse model of lung adenocarcinoma (LUAD). We identify an epigenomic continuum representing loss of cellular identity and progression toward a metastatic state. We define co-accessible regulatory programs and infer key activating and repressive chromatin regulators of these cell states. Among these co-accessibility programs, we identify a pre-metastatic transition, characterized by activation of RUNX transcription factors, which mediates extracellular matrix remodeling to promote metastasis and is predictive of survival across human LUAD patients. Together, these results demonstrate the power of single-cell epigenomics to identify regulatory programs to uncover mechanisms and key biomarkers of tumor progression.

Abstract PR08: Leveraging single-cell epigenomics to uncover regulatory programs in lung adenocarcinoma

A hallmark of cancer development is the loss of cellular identity due to the dysregulation of regulatory networks that maintain functional, differentiated states. Chromatin state has been linked to this control of cellular identity and developmental processes; however, the mechanisms by which these regulatory landscapes are disrupted during cancer progression are not well understood. To this end, we leveraged an optimized single-cell ATAC-sequencing (scATAC-seq) strategy using a combinatorial indexing-based technology to assess chromatin state changes that occur in a mouse model of lung adenocarcinoma (LUAD). We utilized a well-established LUAD mouse model in which mice conditionally express the KrasG12D mutation and lose p53 expression in alveolar type II cells following Cre-mediated recombination, termed the KP model. In this model, KP tumors progress from early-stage hyperplasias to metastatic, advanced adenocarcinomas without acquiring additional somatic mutations, suggesting that these phenotypic transitions may be driven in part by epigenetic mechanisms. Using scATAC-seq, we profiled 16,044 normal lung, primary KP tumor, and metastatic cells to assess chromatin state changes across LUAD progression. We found that LUAD tumor evolution was characterized by a heterogeneous epigenomic continuum that was associated with loss of alveolar identity and progression to metastasis. Interestingly, we identified a reproducible metastatic state that was homogeneous, suggesting that cancer cells funnel towards a stable epigenetic state. In addition, we utilized novel computational tools to assess combinatorial transcription factor (TF)-driven programs that delineate stages of cancer progression in the KP model. From these analyses, we identified a clear transition point that corresponded to progressive gain of RUNX2 transcription factor activity. Using CRISPR activation and knockout strategies, we found that RUNX2 regulated the chromatin accessibility of several regulatory programs involved in extracellular matrix remodeling, suggesting that RUNX2 signaling is a critical event in LUAD progression and metastasis. In addition, gene signatures derived from this RUNX-activated state were highly predictive of survival in human LUAD. Together, these results demonstrated the power of single-cell epigenomics to identify TF-driven regulatory programs as key biomarkers of tumor progression.

This abstract is also being presented as Poster A40.

Citation Format: Lindsay M. LaFave, Vinay Kartha, Sai Ma, Kevin Meli, Isabella Del Priore, Caleb Lareau, Venkat Sanker, Santiago Naranjo, Peter Westcott, Zachary Chiang, Alison Brack, Travis Law, Aviv Regev, Jason D Buenrostro, Tyler Jacks. Leveraging single-cell epigenomics to uncover regulatory programs in lung adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference on the Evolving Landscape of Cancer Modeling; 2020 Mar 2-5; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2020;80(11 Suppl):Abstract nr PR08.