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 6

Iterative DEG combination testing defining minimal gene sets required for stratifying patients from control donors according to circulating CD8+ T cells.

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Iterative DEG combination testing defining minimal gene sets required fo...
Normalized –ΔCt qRT-PCR DEG expression values from individual and pooled CD8+ ptPBLs and cdPBLs were used for PCA using applying the Euclidean distance metric and complete linkage clustering method (R programming language; R-studio) (n = 69). (A) Patients are stratified using 32 DEGs including pan-cancer (ICOS, PF4V1, IFNG, LAG3, TIGIT, CDA, PDK4, KLF4, PIM2, TIMP1, IGF2BP3, IL23A, LEF1, and TCF7), T cell–polarizing (FASLG, ZEB2, EOMES, CCR5, TOX, PRDM1, BATF, FOXO1, CD28, and CD27), adhesion (JAM3, SELP), and immune checkpoint DEGs (CD160, CD244, PDCD1, TIM-3, BTLA, and NT5E). (B) Patients are stratified using 12 DEGs including pan-cancer (CDA, PDK4, KLF4, and IGF2BP3) and adhesion (JAM3, SELP) DEGs. (C) Patients are stratified using 3 DEGs (pan-cancer, MMP9, and LEF1; T cell polarizing, FASLG). Boxes with a pale yellow background highlight PCA-stratified control donors used to calculate the percentage of patient stratification. (D) Graph representing the percentage of patient stratification from DEG groups in (A–C) and in Supplemental Figure 5, with representative numbers of pan-cancer genes among groups at the bottom (n = 66, nonduplicate samples). (E and F) Venn diagrams demonstrating overlaps between (E) CD8+ ccRCC ptPBL DEGs, CD8+ ccRCC TIL DEGs, CD8+ HIV elite controllers, and PBMC from patients infected with bacteria and (F) effect of pan-cancer pipeline on enhancing CD8+ DEG identity. dupe, duplicate sample; misclas., misclassified benign kidney lesion; n, number of pooled samples; other, other DEGs; pan-can, pan-cancer; ub-fig., associated sub-figure.