TESSERA is a foundation model for the cancer genome that uses self-supervised learning to predict variant pathogenicity, tumour typing, and treatment response, revealing interpretable biomarkers for chemotherapy selection.

bioRxiv · Jun 1, 2026 · PreprintBioinformatics

Pan-Cancer PDOs Preserve Tumor Heterogeneity and Uncover Therapeutic Vulnerabilities

Hui–Hsuan Kuo, Bhavneet Bhinder, Hamza Numan Gokozan, Kathryn Gorski, Pooja Chandra, Jyothi Manohar, Daniela E. Guevara, John R. Otilano, Jenna E. Moyer, Marvel Tranquille, Sarah E. Ackermann, Jared A. Capuano, Cynthia Cheung, Thomas Anthony Caiazza, Phoebe L. Reuben, , M. Laura Martín

This study develops a pan-cancer PDO platform to preserve tumor heterogeneity and identify therapeutic vulnerabilities, finding that 58% of PDOs respond to Talazoparib and combination screens reveal resistance-overcoming drugs.

bioRxiv · Apr 10, 2025 · PreprintCancer Biology

Whole Genome Landscape Analysis of Homologous Recombination Deficiency in a Pan-Cancer Cohort

Majd Al Assaad, Kevin Hadi, Max Fine Levine, Daniela E. Guevara, Minal A. Patel, Marvel Tranquille, Abigail S. King, John R. Otilano, Alissa Semaan, Gunes Gundem, Juan Santiago Medina-Martinez, Michael Sigouros, Jyothi Manohar, Hui–Hsuan Kuo, David C. Wilkes, , Juan Miguel Mosquera

The paper analyzes whole-genome sequencing data from a pan-cancer cohort to define HRD, showing it extends beyond BRCA1/2 mutations and highlights the benefits of WGS over commercial assays for HRD detection.

medRxiv · Jun 28, 2024 · PreprintGenetic & Genomic Medicine