Instance-based transfer learning enables cross-cohort early detection of colorectal cancer.

Colorectal cancer (CRC) continues to be a major global public health challenge. Extensive research has underscored the critical role of the gut microbiome for diagnostics of CRC. However, early-stage prediction of CRC, particularly at the precancerous adenomas (ADA) stage, remains challenging due to the instability of microbial features across cohorts. In this study, we conducted a systematic analysis of 2053 gut metagenomes from 14 globally-sampled public cohorts and a newly recruited cohort. Despite substantial regional and cohort-level heterogeneity in microbiome composition, we elucidated that the consistent differences between groups in microbial signatures provide the fundamental basis for CRC detection. These patterns enabled robust performance in both inter-cohort and independent validations using an optimized bioinformatics framework. In contrast, such basis was lacking in ADA-associated microbial markers, limiting the generalizability of early detection models. To address this, we developed an instance-based transfer learning approach, Meta-iTL, which effectively leveraged knowledge from existing datasets to detect CRC risk at the ADA stage in the newly recruited cohort. Thus, Meta-iTL overcomes challenges posed by cohort-specific variability and limited data availability and advances the application of non-invasive approaches for the early screening and prevention of CRC.