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. 2022 Aug 26:13:917118.
doi: 10.3389/fgene.2022.917118. eCollection 2022.

Pancancer analysis of a potential gene mutation model in the prediction of immunotherapy outcomes

Lishan Yu  1   2 , Caifeng Gong  3
Affiliations

Pancancer analysis of a potential gene mutation model in the prediction of immunotherapy outcomes

Lishan Yu et al. Front Genet. .

Abstract

Background: Immune checkpoint blockade (ICB) represents a promising treatment for cancer, but predictive biomarkers are needed. We aimed to develop a cost-effective signature to predict immunotherapy benefits across cancers. Methods: We proposed a study framework to construct the signature. Specifically, we built a multivariate Cox proportional hazards regression model with LASSO using 80% of an ICB-treated cohort (n = 1661) from MSKCC. The desired signature named SIGP was the risk score of the model and was validated in the remaining 20% of patients and an external ICB-treated cohort (n = 249) from DFCI. Results: SIGP was based on 18 candidate genes (NOTCH3, CREBBP, RNF43, PTPRD, FAM46C, SETD2, PTPRT, TERT, TET1, ROS1, NTRK3, PAK7, BRAF, LATS1, IL7R, VHL, TP53, and STK11), and we classified patients into SIGP high (SIGP-H), SIGP low (SIGP-L) and SIGP wild type (SIGP-WT) groups according to the SIGP score. A multicohort validation demonstrated that patients in SIGP-L had significantly longer overall survival (OS) in the context of ICB therapy than those in SIGP-WT and SIGP-H (44.00 months versus 13.00 months and 14.00 months, p < 0.001 in the test set). The survival of patients grouped by SIGP in non-ICB-treated cohorts was different, and SIGP-WT performed better than the other groups. In addition, SIGP-L + TMB-L (approximately 15% of patients) had similar survivals to TMB-H, and patients with both SIGP-L and TMB-H had better survival. Further analysis on tumor-infiltrating lymphocytes demonstrated that the SIGP-L group had significantly increased abundances of CD8+ T cells. Conclusion: Our proposed model of the SIGP signature based on 18-gene mutations has good predictive value for the clinical benefit of ICB in pancancer patients. Additional patients without TMB-H were identified by SIGP as potential candidates for ICB, and the combination of both signatures showed better performance than the single signature.

Keywords: biomarker; gene mutation signature; immune checkpoint blockade; immunotherapy; statistical modeling.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The prevalence of 18 selected genes mutated among patients and the proportion of patients with the different number of genes mutated in the MSK-TMB-training cohort and the MSK-TMB-test cohort, the proportion of patients with different cancer types and Kaplan–Meier (KM) curves of SIGP in MSK-TMB-training. (A) Prevalence of the 18 selected genes mutated among patients; (B) Proportion of patients with the different number of genes mutated; (C) Proportion of patients with different cancer types in MSK-TMB-training (n = 1329); (D) KM curves of SIGP in MSK-TMB-training.
FIGURE 2
FIGURE 2
Kaplan–Meier (KM) curves of SIGP in two ICB-treated cohorts and two non-ICB-treated cohorts: MSK-TMB-test, ALLEN, MSK-IMPACT and TCGA. (A) KM curves of SIGP in MSK-TMB-test; (B) KM curves of SIGP in ALLEN; (C) KM curves of SIGP in MSK-IMPACT; (D) KM curves of SIGP in TCGA.
FIGURE 3
FIGURE 3
Kaplan–Meier curves of TMB combined with SIGP (three groups) in MSK-TMB-training and MSK-TMB-test. (A) KM curves of SIGP + TMB in MSK-TMB-training; (B) KM curves of SIGP + TMB in MSK-TMB-test.
FIGURE 4
FIGURE 4
Medians and means of CIBERSORT estimates of abundances in a mixed cell population for 22 TIL cell types and boxplots of CIBERSORT estimates of abundances for CD8+ T cells and NK cell activated in the TCGA cohort. (A) Medians of CIBERSORT estimates of abundances for immune cell types; (B) Means of CIBERSORT estimates of abundances for immune cell types; (C) Boxplot for CD8+ T cells (Wilcoxon test); (D) Boxplot for NK cell activated (Wilcoxon test).
See this image and copyright information in PMC

References

    1. Ackerman A., Klein O., McDermott D. F., Wang W., Ibrahim N., Lawrence D. P., et al. (2014). Outcomes of patients with metastatic melanoma treated with immunotherapy prior to or after BRAF inhibitors. Cancer 120 (11), 1695–1701. 10.1002/cncr.28620 - DOI - PubMed
    1. Adachi K., Tamada K. (2015). Immune checkpoint blockade opens an avenue of cancer immunotherapy with a potent clinical efficacy. Cancer Sci. 106 (8), 945–950. 10.1111/cas.12695 - DOI - PMC - PubMed
    1. Algamal Z. Y., Lee M. H. (2015). Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification. Expert Syst. Appl. 42 (23), 9326–9332. 10.1016/j.eswa.2015.08.016 - DOI
    1. Bagaev A., Kotlov N., Nomie K., Svekolkin V., Gafurov A., Isaeva O., et al. (2021). Conserved pan-cancer microenvironment subtypes predict response to immunotherapy. Cancer Cell. 39 (6), 845–865.e7. 10.1016/j.ccell.2021.04.014 - DOI - PubMed
    1. Bai X., Wu D. H., Ma S. C., Wang J., Tang X. R., Kang S., et al. (2020). Development and validation of a genomic mutation signature to predict response to PD-1 inhibitors in non-squamous NSCLC: A multicohort study. J. Immunother. Cancer 8 (1), e000381. 10.1136/jitc-2019-000381 - DOI - PMC - PubMed