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Clinical cancer research : an official journal of the American Association for Cancer Research

October 1, 2026 · via Europe PMC

Intratumoral Immune Heterogeneity Drives Divergent Outcomes to PD-(L)1 Blockade in Lung Cancer.

Cipriani L, Mascolo D, Mascolo D, +25 more, Maugeri-Saccà M.

Journal ArticleAbstract only — full text under license

AI summary — for doctors, generated from the abstract only

Lung tumors that are uniformly immune-enriched had much longer survival on PD-(L)1 blockade than other tumors, regardless of PD-L1 status.

What was studied

Spatial immune subtyping in the TRACERx421 multiregion cohort to define intratumoral immune heterogeneity, a computational model to infer it from single samples, and validation in three independent cohorts totaling 1,085 advanced NSCLC patients on checkpoint inhibitors.

Key findings

  • Homogeneously immune-enriched tumors had median overall survival of 34.6 vs 12.1-16.6 months (cohort A), 21.9 vs 8.4-13.3 (B) and 22.6 vs 11.3-13.4 (C)
  • The benefit held regardless of PD-L1 status, including PD-L1-low or negative tumors
  • The adverse effect of KEAP1/STK11 mutations persisted in the immune-enriched background

Clinical relevance

It suggests a biomarker beyond PD-L1 that identifies long-term benefit from immunotherapy.

Who should care

Medical Oncology

Limitations

Retrospective cohorts with inferred heterogeneity from single samples, needing prospective validation.

Not yet available clinically; it may augment PD-L1 testing in future.

This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.

Abstract

Purpose

Clinical outcomes with immune checkpoint inhibitors (ICI) in non-small cell lung cancer (NSCLC) range from durable disease control to rapid progression. Current biomarkers have a limited ability to predict these divergent outcomes.

Experimental design

Spatially resolved immune subtyping in the TRACERx421 multiregion cohort was used to assess intratumoral immune heterogeneity (ITIH), defined as coexisting immune-enriched and immune-depleted regions. A computational model was used to infer ITIH from single samples, classifying tumors as homogenously immune-enriched (Hom-IE), heterogeneously immune-enriched, heterogeneously immune-depleted, and homogenously immune-depleted. ITIH was validated by digital pathology and evaluated in three independent cohorts (n = 1,085) of patients with advanced NSCLC treated with ICIs.

Results

In TRACERx421, ITIH was associated with differences in immune programs among tumors otherwise classified within the same immune subtype. Single-sample ITIH inference recapitulated these patterns, as supported by gradients of tumor-infiltrating lymphocytes detected via digital pathology. Across cohorts, Hom-IE tumors demonstrated longer overall survival (OS) compared with other groups (34.6 vs. 12.1-16.6 months in cohort A; 21.9 vs. 8.4-13.3 months in cohort B; and 22.6 vs. 11.3-13.4 months in cohort C). Hom-IE tumors were associated with longer OS regardless of PD-L1 status. Consistently, among PD-L1-low/negative tumors, Hom-IE tumors showed significantly longer OS compared with their non-Hom-IE counterparts. Conversely, the detrimental effects of KEAP1/STK11 mutations persisted in the Hom-IE background.

Conclusions

These findings demonstrate that ITIH can be assessed from individual tumor samples, identify a microenvironment state associated with long-term benefit from immunotherapy, and provide an additional layer of resolution to established and emerging biomarkers.

DOI

10.1158/1078-0432.ccr-26-1466

PMID

42456046

Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.

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