The prognostic landscape of genes and infiltrating immune cells across human cancers

Abstract

Molecular profiles of tumors and tumor-associated cells hold great promise as biomarkers of clinical outcomes. However, existing datasets are fragmented and difficult to analyze systematically. Here we present a pan-cancer resource and meta-analysis of expression signatures from ~18,000 human tumors with overall survival outcomes across 39 malignancies. Using this resource, we identified a FOXM1 regulatory network as a major predictor of adverse outcomes, and found that expression of favorably prognostic genes, including KLRB1, largely reflect tumor-associated leukocytes. By applying CIBERSORT, a computational approach for inferring leukocyte representation in bulk tumor transcriptomes, we identified complex associations between 22 distinct leukocyte subsets and cancer survival. For example, tumor-associated neutrophil and plasma cell signatures emerged as significant but opposite predictors of survival for diverse solid tumors, including breast and lung adenocarcinomas. This resource and associated analytical tools (http://precog.stanford.edu) may help delineate prognostic genes and leukocyte subsets within and across cancers, shed light on the impact of tumor heterogeneity on cancer outcomes, and discover biomarkers and therapeutic targets.

Somatic mutation-associated T follicular helper cell elevation in lung adenocarcinoma

“…Indeed, various systems-based analyses have highlighted Tfh as key modulators of the immune landscape across cancer types. 10,13 Here, we establish that Tfh are elevated in LUAD tissue and correlate with elevated plasma cells in TLOs. Furthermore, elevated levels of Tfh can be observed in Stage I tumours, and correspond to increased mutational burden and immunogenic CT antigen expression.…”

“…These genes are specifically weighted to Tfh cells in the CIBERSORT LM22 signature and are commonly used to define Tfh populations by flow cytometry. 13 Of note, BCL6 , encoding the defining transcription factor for Tfh differentiation, showed no elevation in tumour tissue nor did TCF7 (encoding TCF1), PRDM1 (BLIMP-1), or ICOS (ICOS) (Fig S1), all of which have been shown to regulate Tfh differentiation via BCL6 and other key transcription factors. 20,21 ICOS was in fact reduced in tumour tissue, possibly due to the transient and temporally-specific nature of ICOS-dependent Tfh promotion in the TLO.…”

“…In order to identify the immune cell types most significantly enriched in tumour tissue, expression profiles were analyzed using CIBERSORT, a deconvolution algorithm that infers relative cell content of 22 immune cell types. 13 Tumour enrichment of cell types was measured by fold change in tumour versus non-malignant lung tissue. The most dramatically increased immune cell subsets were regulatory T cells (Tregs) (mean fold change (FC) = 9.21), naive macrophages (M0) (mean FC = 5.11), and T follicular helper cells (Tfh) (FC = 4.33) (Figure 1A).…”

“…13 Outliers were identified and removed via nonlinear regression followed by false discovery correction (FDR-BH p < 0.01). 14 …”

Trial watch: Peptide-based vaccines in anticancer therapy

“…55-57 Once processed by the proteasome, these proteins can give rise to antigens that are not covered by central or peripheral tolerance and hence can be productively presented by dendritic cells (DCs) to T lymphocytes to drive an adaptive immune response. 55,58-64 Such antigens are commonly known as “tumor-associated antigens” (TAAs). 55,65 A large list of TAAs with sequences that bind human MHC Class I or II molecules and the TCR can be found at http://cvc.dfci.harvard.edu/tadb (the Tantigen database).…”