Abstract IA02: Cancer-specific foundation models: Friend or foe in healthcare AI?
Bibliographic record
Abstract
Abstract Foundation models have become a powerful tool in single-cell transcriptomics, enabling broad generalization across tasks such as cell type annotation, data integration, and drug response prediction. Yet, most current models are trained predominantly on healthy cells, with a strong bias toward peripheral blood mononuclear cells. This raises an important question: how well do these models generalize to cancer-specific contexts? In this talk, I will explore whether training a foundation model exclusively on malignant cells—across diverse cancer types—can improve performance on tasks relevant to cancer biology and treatment. I will introduce CancerFoundation, a single-cell foundation model trained on malignant cells from over 40 tumor types. The model incorporates strategies for addressing tissue imbalance and technical variation, including domain-invariant training and tailored sampling. Through this work, I aim to address whether disease-specific pretraining can better capture the molecular features of cancer and improve the utility of foundation models in oncology applications such as batch integration and drug response prediction. Citation Format: Alexander Theus, Florian Barkmann, David Wissel, Tobias Scheithauer, Maria Brbic, Valentina Boeva. Cancer-specific foundation models: Friend or foe in healthcare AI? [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr IA02.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".