Biology subjects

Lesokhin, A.

Publications and source records attributed to Lesokhin, A..

3 recordsLinked to original sources

bioRxiv2025

Knowledge-informed multimodal cfDNA analysis improves sensitivity and generalization in cancer detection

Liquid biopsy offers a minimally invasive opportunity to detect and monitor cancers through analysis of cell-free DNA (cfDNA). However, current approaches face challenges of limited sensitivity at low tumor fractions, technical variability, and poor generalization across cohorts. Tumor-informed targeted methods offer high specificity but suffer from low sensitivity due to random sampling, tumor evolution and adaptation (including resistance mechanisms), and other sources of heterogeneity. Conversely, tumor-naive genome-wide methods can increase sensitivity but often sacrifice specificity, particularly at low tumor fractions. We developed Fragmentomics Analysis for Tumor Evaluation with AI (Fate-AI), a multimodal framework that integrates fragmentomic and methylation-derived features from low-pass whole-genome sequencing (LPWGS) and cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq). It employs a knowledge-informed strategy to select recurrently altered genomic regions and tissue-specific methylation loci to combine the advantages of tumor-naive approaches with the specificity of tumor-informed approaches. This approach derives robust per-sample normalized features that mitigate batch effects and enhance cross-cohort reproducibility. We evaluated Fate-AI on a total of 1,219 plasma samples spanning ten cancer types and healthy controls from multiple laboratories and sequencing centers, including 432 newly profiled cases (280 with both cfMeDIP-seq and LPWGS) together with 787 samples from four independent public datasets. Fate-AI achieved superior sensitivity and specificity compared to state-of-the-art methods, detecting tumor-derived signals at fractions as low as 10-5 in experimental dilutions. Fate-AI scores correlated with disease stage and tracked longitudinal progression, anticipating relapse months before clinical progression. Furthermore, Fate-AI enabled tissue-of-origin classification, with AUCs ranging from 0.84 to 0.97 across six cancer types. Collectively, our results demonstrate that Fate-AI provides a sensitive, generalizable, and clinically actionable platform for early detection, minimal residual disease monitoring, and tissue-of-origin classification, supporting its potential as a liquid biopsy framework in precision oncology.

cancer biology↗

bioRxiv2024

Temporal Genomic Dynamics Shape Clinical Trajectory in Multiple Myeloma

To comprehensively unravel the temporal relationship between initiating and driver events and its impact on clinical outcomes, we analyzed 421 whole-genome sequencing profiles from 382 patients. Using clock-like mutational signatures, we estimated a time lag of 2-4 decades between initiating events and diagnosis. In patients with hyperdiploidy, we demonstrate that trisomies of odd-numbered chromosomes can be acquired simultaneously with other chromosomal gains, such as 1q gain. We provide evidence that hyperdiploidy is acquired after canonical IGH translocation when both events are present. Finally, patients with early 1q gain had adverse outcomes similar to those with 1q amplification (>1 extra-copies), but faring worse than those with late 1q gain. This underscores that the prognostic impact of 1q gain/amp depends more on the timing of acquisition than on the number of extra copies gained. Overall, this study contributes to a better understanding of the life history of MM and may have prognostic implications.

cancer biology↗

bioRxiv2022

Chemotherapy Signatures Map Evolution of Therapy-Related Myeloid Neoplasms

Patients treated with cytotoxic therapies, including autologous stem cell transplantation, are at risk for developing therapy-related myeloid neoplasms1, 2. Pre-leukemic clones (i.e., clonal hematopoiesis) are detectable years before the development of these aggressive malignancies3-5, though the genomic events leading to transformation and expansion are not well-defined. Here, leveraging distinctive chemotherapy-associated mutational signatures6-12 from whole-genome sequencing data and targeted sequencing of pre-chemotherapy samples, we reconstruct the evolutionary life-history of 39 therapy-related myeloid malignancies. A dichotomy is revealed, in which neoplasms with evidence of chemotherapy-induced mutagenesis from platinum and melphalan are relatively hypermutated and enriched for complex structural variants (i.e., chromothripsis), while neoplasms with alternative exposures bear a similar profile to de novo acute myeloid leukemia. Using chemotherapy-associated mutational signatures as a temporal barcode in each patients life, we estimate that several complex events and genomic drivers are acquired after chemotherapy exposure. In the case of treatment with high-dose melphalan and autologous stem cell transplantation, we demonstrate that the procedure allows clonal hematopoiesis to escape chemotherapy exposure entirely, and to be reinfused to expand to malignancy. This information reveals a novel mode of malignant progression for therapy-related malignancies that is not reliant on direct mutagenesis or even exposure to chemotherapy, itself, and prompts further investigation into leukemia-permissive effects of cytotoxic drugs.

cancer biology↗