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Breast, Lung Cancer Outcomes Informed by Gene Expression of Individual Infiltrating T Cells

NEW YORK (GenomeWeb) – Two studies from independent research teams have demonstrated the feasibility of teasing out gene expression patterns in tumor-infiltrating lymphocytes in breast and lung cancer, respectively, using single-cell RNA sequencing.

For one of the studies, published online today in Nature Medicine, a team from the University of Melbourne and elsewhere sequenced the RNA of 6,311 individual, flow-sorted T cells isolated from two primary breast cancer cases. Both tumors had been classified as triple-negative, owing to their lack of estrogen receptor, progesterone receptor, and HER2 expression.

The researchers tracked down 10 gene expression clusters formed by the individual T cells, which were relatively similar in both of the breast cancer cases, uncovering widespread heterogeneity of infiltrating T cells within and between the triple-negative breast cancer (TNBC) cases profiled.

"Our study highlights the potential of single-cell genomics to increase our understanding of the tumor microenvironment and potentially identify new immunotherapy targets," senior authors Sherene Loi, Paul Neeson, and Laura Mackay, all researchers at the University of Melbourne, and their co-authors wrote. "This data also provides a resource of transcriptome data from [breast cancer]-infiltrating T cells that can be interrogated with a web-based tool by the wider research community."

The T cell expression data and additional information are available online.

For example, one of the CD8A+ T cells clusters encompassed so-called tissue-resident memory T (T-RM) features — a phenotype marked by enhanced ITGAE expression and lower-than-usual expression of other genes. Proliferation-related features marked another cluster of CD8A+CD103+ T cells, which the group characterized in greater detail using in vitro functional assays, bulk RNA sequencing, and T cell profiling in eight normal breast tissue samples.

The relative proportions of these T cell clusters appeared to offer some insights into breast cancer outcomes, the team reported. And in an expanded analysis that included expression data for 329 primary TNBC cases profiled by the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), the group saw an apparent association between patient outcomes and a T-RM signature established from the initial, singlecell analysis.

"[The breast cancer] T cell infiltrate quantity and quality, including the presence of the T-RM cell subset, are key to improved therapeutic and prognostic outcomes, particularly for patients with TNBC," the authors wrote. "Our study highlights the potential of single-cell genomics to increase our understanding of the tumor microenvironment and potentially identify new immunotherapy targets." 

For a related but independent study in the same issue of Nature Medicine, a team led by researchers at Peking University sequenced the RNA of 12,346 single T cells from tumor, normal adjacent tissue, or blood samples representing 14 treatment-naïve non-small cell lung cancer (NSCLC) cases: 11 adenocarcinomas and three squamous cell carcinomas. To that, they added alpha and beta T cell receptor sequences for 8,038 individual cells, encompassing more than 5,000 distinct T cell receptor sequences and just over 3,000 that were found more than once.

From the expression and T cell lineage patterns gleaned from those data, the researchers tracked down distinct, migratory effector T cell clones that differed from one tissue to the next, along with a regulatory T cell signature that seemed to mark the lung adenocarcinoma cases with particularly poor outcomes.

On the other hand, by characterizing expression signatures associated with T cell exhaustion and the lead up to it, the team detected a 'pre-exhausted' T cell state that corresponded to better outcomes in the NSCLC cases, while offering a potential avenue for stratifying and treating specific lung cancer subsets.

"We … identified two CD8+ T cell clusters probably preceding exhaustion, with both clusters predicting better prognosis in [lung adenocarcinoma]," Peking University researcher Zemin Zhang, the study's senior author, and his co-authors wrote. "Since highly exhausted T cells are resistant to checkpoint inhibition due to their epigenetic changes, the possible 'pre-exhausted' cells might provide alternative T cell subsets for immunotherapy targeting." 

That team is also making its data available online, through an interactive site that includes analysis and visualization tools.