AI and Cancer Research ── Cancer biology ── 2026-10-27
Cancer molecular biology and AI
- Abstract
Raman spectroscopy is widely used for its ability to provide rich molecular-level information from biological samples. However, the high sensitivity of Raman spectra can also unintentionally encode extraneous information, such as patient identity, instrument-specific signatures, environmental conditions, and sample handling protocols. This hidden structure, referred to as the batch effect, poses increasing challenges in machine learning applications, where it can lead to model overfitting, poor generalization, and even privacy risks when datasets are shared. In this work, we investigate the presence and impact of the batch effect in a Raman spectroscopy dataset derived from glioma tumor samples. We show that deep learning can accurately predict patient identity from individual spectra, revealing strong bias patterns in the data. To mitigate this issue, we explore several approaches and introduce an algorithmic framework based on adversarial deep learning. Our method effectively reduces the influence of confounding sample-specific signals while preserving information relevant to tumor classification. These findings highlight the need for careful bias control in Raman-based AI pipelines and provide tools to support more robust applications in biomedical spectroscopy.
Journal IF-equivalent: 5.2 (OpenAlex 2-year mean citedness, value as of 2026-10-04, retrieved 2026-10-05; not the official Clarivate IF)Reference: Sjöberg J, Lita A, Larion M, Petre I. Deep learning mitigates batch effects in Raman spectroscopy. Sci Rep. 2026 Oct 26;16(1):34019. doi:10.1038/s41598-026-62086-1.Checked: Abstract only - Abstract
T-cell receptor (TCR) recognition of antigenic peptides presented by human leukocyte antigen (HLA) molecules underpins adaptive immunity and T cell-based immunotherapy. However, the scarcity of complete interaction data and TCR cross-reactivity challenge robust prediction. Here, we present UpTCR, a unified progressive knowledge-transfer foundation model that learns from incomplete data to predict TCR-antigen-HLA binding. UpTCR progressively transfers knowledge from dimeric and trimeric interactions to tetrameric complexes and uses soft contrastive learning to mitigate false negatives. It outperforms existing methods in predicting TCR binding specificity and antigen-HLA binding affinity, particularly for neoantigens, and reveals pairwise residue-level interactions across the tetramer. UpTCR also transfers effectively to breast cancer cohorts with limited data. Prospective validation against melanoma antigen variants identifies eight immunogenic peptides that elicit T cell responses and one variant associated with immune escape. These findings establish UpTCR as a generalizable and interpretable tool for studying antigen recognition and advancing TCR-based immunotherapies.
Journal IF-equivalent: 18.1 (OpenAlex 2-year mean citedness, value as of 2026-10-06, retrieved 2026-10-07; not the official Clarivate IF)Reference: Lv T, Xiao Y, Chen L, He B, Zhong M, Feng Z, et al. UpTCR: a unified progressive knowledge transfer foundation model for robust T-cell receptor-antigen binding recognition. Nat Commun. 2026 Sep 24;17(1):11086. doi:10.1038/s41467-026-78075-x. PMID: 42928063.Checked: Abstract only