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April 8, 20260 citationsOpen Access

The Immune Barrier Atlas: Cancer-Type-Specific Resistance Mechanisms Across 33 Tumor Types and Their Matched Drug Classes

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RKRaimo van der Klein

Key Points

  • The research aims to identify cancer-type-specific immune resistance mechanisms to enhance treatment strategies using immune checkpoint inhibitors.
  • Analyzed immune cell fractions from 11,373 tumors across 33 TCGA cancer types using CIBERSORTx.
  • Classified immune barriers into four categories: activation, detection, drain, and suppression.
  • Mapped each barrier to associated drug classes for tailored therapeutic approaches.
  • Validated the classifications with four independent single-cell RNA-seq datasets.
  • Identified 13 cancer types as suppression-dominant and 8 each as detection-dominant and activation-dominant.
  • Cross-validation demonstrated a consistent identification of dominant barricade in independent datasets.
  • Exhausted T cell fractions were found to be higher in responders than non-responders, indicating immune engagement.

Abstract

Immune checkpoint inhibitors fail in most patients, yet the dominant mechanism of resistance differs by cancer type. Using CIBERSORTx immune cell fractions from 11,373 tumors across 33 TCGA cancer types, we classified each sample by its primary immune barrier from four structurally derived categories corresponding to the four stages of the anti-tumor immune cycle: activation (effector T cells not primed or mobilised against the tumor), detection (insufficient antigen presentation to direct the response), drain (inflammatory myeloid cells overwhelming the effector response), and suppression (regulatory T cells and M2 macrophages actively inhibiting effectors). The classification reveals that 13 cancer types are suppression-dominant, 8 detection-dominant, 8 activation-dominant, and 4 drain-dominant. Each barrier maps to a specific drug class: CTLA-4 + PD-1 or IL-2/IL-15 for activation, cancer vaccines or STING agonists for detection, anti-VEGF + ICI for drain, and anti-CCR8 or Treg depletion for suppression. Cross-validation against four independent single-cell RNA-seq datasets (NSCLC n=242, melanoma n=37, breast cancer n=29, basal cell carcinoma n=11) confirms the classification, with each dataset identifying a different dominant barrier — one per stage of the cycle. The NSCLC scRNA-seq classification independently converges on Suppression at 42%, matching the TCGA bulk estimate of 41.8%. A consistent cross-cancer finding is that T cell exhaustion marks immune engagement rather than failure: exhausted T cell fractions are higher in responders than non-responders across all four scRNA-seq datasets (breast cancer 11-fold, p = 0.0003). The atlas provides a resource for matching drug class to resistance mechanism at the cancer-type level.

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Cite This Study

Raimo van der Klein (2026) studied this question.

synapsesocial.com/papers/69d5f13674eaea4b11a7ab85https://doi.org/10.5281/zenodo.19436598
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