» Articles » PMID: 33508232

Meta-analysis of Tumor- and T Cell-intrinsic Mechanisms of Sensitization to Checkpoint Inhibition

Abstract

Checkpoint inhibitors (CPIs) augment adaptive immunity. Systematic pan-tumor analyses may reveal the relative importance of tumor-cell-intrinsic and microenvironmental features underpinning CPI sensitization. Here, we collated whole-exome and transcriptomic data for >1,000 CPI-treated patients across seven tumor types, utilizing standardized bioinformatics workflows and clinical outcome criteria to validate multivariable predictors of CPI sensitization. Clonal tumor mutation burden (TMB) was the strongest predictor of CPI response, followed by total TMB and CXCL9 expression. Subclonal TMB, somatic copy alteration burden, and histocompatibility leukocyte antigen (HLA) evolutionary divergence failed to attain pan-cancer significance. Dinucleotide variants were identified as a source of immunogenic epitopes associated with radical amino acid substitutions and enhanced peptide hydrophobicity/immunogenicity. Copy-number analysis revealed two additional determinants of CPI outcome supported by prior functional evidence: 9q34 (TRAF2) loss associated with response and CCND1 amplification associated with resistance. Finally, single-cell RNA sequencing (RNA-seq) of clonal neoantigen-reactive CD8 tumor-infiltrating lymphocytes (TILs), combined with bulk RNA-seq analysis of CPI-responding tumors, identified CCR5 and CXCL13 as T-cell-intrinsic markers of CPI sensitivity.

Citing Articles

Exome sequencing shows same pattern of clonal tumor mutational burden, intratumor heterogenicity and clonal neoantigen between autologous tumor and Vigil product.

Willoughby D, Bognar E, Stanbery L, Nagel C, Wallraven G, Pruthi A Sci Rep. 2025; 15(1):8637.

PMID: 40082566 PMC: 11906592. DOI: 10.1038/s41598-025-90136-7.


Clinical outcomes and molecular characteristics of lung-only and liver-only metastatic pancreatic cancer: results from a real-world evidence database.

Levi A, Blais E, Davelaar J, Ebia M, Minasyan A, Nikravesh N Oncologist. 2025; 30(3).

PMID: 40079530 PMC: 11904785. DOI: 10.1093/oncolo/oyaf007.


AI Model for Predicting Anti-PD1 Response in Melanoma Using Multi-Omics Biomarkers.

Gschwind A, Ossowski S Cancers (Basel). 2025; 17(5).

PMID: 40075562 PMC: 11899402. DOI: 10.3390/cancers17050714.


Hallmarks of artificial intelligence contributions to precision oncology.

Chang T, Park S, Schaffer A, Jiang P, Ruppin E Nat Cancer. 2025; .

PMID: 40055572 DOI: 10.1038/s43018-025-00917-2.


Machine learning identifies clinical tumor mutation landscape pathways of resistance to checkpoint inhibitor therapy in NSCLC.

Fomin V, So W, Barbieri R, Hiller-Bittrolff K, Koletou E, Tu T J Immunother Cancer. 2025; 13(3).

PMID: 40032600 PMC: 11877243. DOI: 10.1136/jitc-2024-009092.


References
1.
Balachandran V, Luksza M, Zhao J, Makarov V, Moral J, Remark R . Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer. Nature. 2017; 551(7681):512-516. PMC: 6145146. DOI: 10.1038/nature24462. View

2.
Brash D . UV signature mutations. Photochem Photobiol. 2014; 91(1):15-26. PMC: 4294947. DOI: 10.1111/php.12377. View

3.
Mariathasan S, Turley S, Nickles D, Castiglioni A, Yuen K, Wang Y . TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature. 2018; 554(7693):544-548. PMC: 6028240. DOI: 10.1038/nature25501. View

4.
Favero F, Joshi T, Marquard A, Birkbak N, Krzystanek M, Li Q . Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data. Ann Oncol. 2014; 26(1):64-70. PMC: 4269342. DOI: 10.1093/annonc/mdu479. View

5.
Li H, Durbin R . Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009; 25(14):1754-60. PMC: 2705234. DOI: 10.1093/bioinformatics/btp324. View