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Genome Biology Papers on Immunotherapy Response Models, NSCLC Metastasis, SV Pathogenicity

A Dana-Farber Cancer Institute-led team presents a mouse model approach for following tumor responses to immune checkpoint blockade immunotherapy, including cancer clone and bulk tumor growth models. Using a clone label-based barcoding approach called ClonTracer, barcode sequencing, and corresponding software, the researchers tracked colorectal cancer responses to anti-PD1, anti-CTLA4, or combination immunotherapy drugs in mouse models, identifying enhance immune cell infiltration during initial responses and a rise in existing, more resistant clones over time. "We find that tumors derived from the same clonal populations showed heterogeneous [immune checkpoint blockade] response and diverse response patterns," they write, highlighting the "value of monitoring clonal constitution and tumor microenvironment over time to optimize [immune checkpoint blockade] response and to design new combination therapies."

Researchers at the University of Texas MD Anderson Cancer Center and elsewhere share findings from an integrated exome sequencing, RNA sequencing, methylation, and immunohistochemistry (IHC) profiling analysis of primary and metastatic non-small cell lung cancers (NSCLC). By analyzing new multi-omic data on eight NSCLC tumors and matched normal samples — in combination with available exome sequences for nearly three dozen more paired primary and metastatic NSCLC samples and transcriptome data on a handful of metastatic tumor or mouse model samples — the team retraced the somatic mutations, immune shifts, and other features that tend to coincide with NSCLC metastasis. "Metastasis is a molecularly late event, and immunosuppression driven by different molecular events, including somatic copy number aberration, may be a common characteristic of tumors with metastatic plasticity," the authors report. 

A team from Yale University, the University of Texas Health Science Center, and the California Institute of Technology outlines a framework for evaluating and predicting the pathogenicity of germline or somatic structural variants (SV) in the context of the tissues involved. The researchers' machine learning pipeline, known as SVFX, produces SV pathogenicity scores based on tissue-specific genomic, epigenomic, and conservation clues — an approach they applied to samples from healthy individuals or those affected by cancer or other conditions. "[S]Vs are often neglected as a consequence of the technical challenges associated with their identification and interpretation," the authors write. "We addressed this challenge by building a new framework that utilizes tissue-specific genomic and epigenomic features to quantify the pathogenicity of SVs."