Failed immune responses across multiple pathologies share pan-tumor and circulating lymphocytic targets
Anne Monette, … , Igor Jurisica, Réjean Lapointe
Anne Monette, … , Igor Jurisica, Réjean Lapointe
Published March 26, 2019
Citation Information: J Clin Invest. 2019;129(6):2463-2479. https://doi.org/10.1172/JCI125301.
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Research Article Immunology Oncology

Failed immune responses across multiple pathologies share pan-tumor and circulating lymphocytic targets

Abstract

Tumor-infiltrating lymphocytes (TILs) are widely associated with positive outcomes, yet carry key indicators of a systemic failed immune response against unresolved cancer. Cancer immunotherapies can reverse their tolerance phenotypes while preserving tumor reactivity and neoantigen specificity shared with circulating immune cells. We performed comprehensive transcriptomic analyses to identify gene signatures common to circulating and TILs in the context of clear cell renal cell carcinoma. Modulated genes also associated with disease outcome were validated in other cancer types. Through comprehensive bioinformatics analyses, we identified practical diagnostic markers and actionable targets of the failed immune response. On circulating lymphocytes, 3 genes (LEF1, FASLG, and MMP9) could efficiently stratify patients from healthy control donors. From their associations with resistance to cancer immunotherapies and microbial infections, we uncovered not only pan-cancer, but pan-pathology, failed immune response profiles. A prominent lymphocytic matrix metallopeptidase cell migration pathway is central to a panoply of diseases and tumor immunogenicity, correlates with multi-cancer recurrence, and identifies a feasible noninvasive approach to pan-pathology diagnoses. The differentially expressed genes we have identified warrant future investigation into the development of their potential in noninvasive precision diagnostics and precision pan-disease immunotherapeutics.

Authors

Anne Monette, Antigoni Morou, Nadia A. Al-Banna, Louise Rousseau, Jean-Baptiste Lattouf, Sara Rahmati, Tomas Tokar, Jean-Pierre Routy, Jean-François Cailhier, Daniel E. Kaufmann, Igor Jurisica, Réjean Lapointe

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Figure 7

Additive prognostic pan-cancer DEGs stratify multi-cancer recurring ccRCC patients having activated CD8+ T cell profiles.

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Additive prognostic pan-cancer DEGs stratify multi-cancer recurring ccRC...
(A) DEGs from the validation cohort were compared among cdPBLs (n = 12) and ptPBLs with (n = 10) or without (n = 18) recurring multi-cancers. P, 2-way ANOVA with Tukey post test; *, P < 0.05; **, P < 0.01; ****, P < 0.0001; boxes, upper and lower quartiles; whiskers, all points maxima to minima; +, mean; line, median. Functional classifications of DEG groups are listed above and below, and the literature was used to (B) segregate DEGs according to tolerance or activation phenotypes. Correlograms (Spearman method) using normalized –ΔCt qRT-PCR expression values for visualization of 2 groups of pan-cancer and T cell–polarizing DEGs, with differences observed between all patients with ccRCC vs. control donors and patients with vs. without recurring cancers (Student t test, P < 0.05) (red, increased expression; green, decreased expression). Only MMP9 is significantly increased in multi-cancer patients relative to all others. (C) Pan-cancer DEG combinations tested for additive prognostic effects using TCGA KIRK data set. Only MMP9, LEF1, PF4V1, TIMP1, and TMEFF1 demonstrate additive prognostic effects, and these cluster in correlograms (as above) enquiring pan-cancer DEGs with combinatorial effects on prognosis. Kaplan-Meier plots P, log-rank. (D) Venn diagram illustrating that ptPBLs have more differentially represented exon-exon PSR junctions relative to TILs; both are relative to TIICs (P < 0.05; ANOVA, Transcriptome Analysis Console v.3, Affymetrix), with 8% overlap of total PSR junctions between ptPBLs and TILs and 47% of all pan-cancer DEGs having shared ptPBL and TIL PSR junction identity (see Supplemental Table 8).