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TIRA: Tumor Immune Representation Adaptation for Zero-Shot Cross-Cancer MSI and TMB Prediction — arXiv:2610.09441

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.09441
License (as archived): CC0-1.0
Archived: 2026-10-08T12:47:18.711Z
SHA-256 of the archived text: 021107a34354fa1bca3a16960d42e67658bbe662f3c97884426cd45850c12fbb
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How to cite

TIRA: Tumor Immune Representation Adaptation for Zero-Shot Cross-Cancer MSI and TMB Prediction — arXiv:2610.09441 — arXiv (en), https://arxiv.org/abs/2610.09441 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_02e79ea76d38 (Metraipolis archive dc_02e79ea76d38, sha256 021107a34354fa1bca3a16960d42e67658bbe662f3c97884426cd45850c12fbb)

BibTeX

@misc{dc02e79ea76d38,
  title = {TIRA: Tumor Immune Representation Adaptation for Zero-Shot Cross-Cancer MSI and TMB Prediction — arXiv:2610.09441},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.09441},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_02e79ea76d38; sha256 021107a34354fa1bca3a16960d42e67658bbe662f3c97884426cd45850c12fbb},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.09441. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Microsatellite instability-high (MSI-H) and high tumor mutational burden (TMB-H) are clinically relevant biomarkers, yet their histopathological prediction remains challenging when models are transferred across morphologically distinct cancer types. Immune-associated spatial patterns can persist across cancers despite these morphological differences, but foundation-model-based predictors trained on a single cancer do not explicitly use this information, limiting cross-cancer generalization. To address this limitation, we propose TIRA (Tumor Immune Representation Adaptation), a target-free framework that refines frozen foundation-model representations using spatial immune topology, without requiring target-domain data during model development or test-time adaptation. TIRA uses a topology-supervised biology representation to condition tile-level attention while pooling only morphological features for joint MSI and TMB prediction. We train TIRA on TCGA-COAD+READ and evaluate it zero-shot on CPTAC-COAD, TCGA-STAD, TCGA-UCEC, and CPTAC-UCEC, covering cross-site, cross-cancer, and combined cross-cancer-site distribution shifts under UNI2, CONCH, and Virchow2. With UNI2, TIRA improved zero-shot AUROC on TCGA-STAD from 0.633 to 0.766 for MSI and from 0.651 to 0.772 for TMB. Source-derived spatial immune topology improved the cross-cancer robustness of frozen pathology foundation-model representations.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv cs.CV, nuovi lavori del giorno).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2610.09441 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T12:18:15.364Z dalla pagina https://arxiv.org/list/cs.CV/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).