Charles M. Perou

Jan 8, 2025 | nature.com | Akram Yazdani |Benjamin Vincent |Xueping Qu |Michael R. Kosorok |William F. Symmans |Scott Kopetz | +1 more

AbstractGene signatures derived from transcriptomic-causal networks offer potential for tailoring clinical care in cancer treatment by identifying predictive and prognostic biomarkers. This study aimed to uncover such signatures in metastatic colorectal cancer (CRC) patients to aid treatment decisions. We constructed transcriptomic-causal networks and integrated gene interconnectivity into overall survival (OS) analysis to control for confounding genes.

Aug 20, 2024 | nature.com | Daniel G. Stover |Roberto Salgado |Sherene Loi |W. Fraser Symmans |Charles M. Perou |Lisa Carey

AbstractAssociation of stromal tumor-infiltrating lymphocytes (sTILs) with survival outcomes among patients with metastatic breast cancer (MBC) remains unclear. The primary objective was to evaluate the association of sTILs with progression-free survival in randomized phase III trial CALGB 40502.

Nov 3, 2023 | nature.com | Mattia Rediti |Katherine A. Hoadley |Joel Parker |David Hillman |Martine Piccart |Serena Di Cosimo | +6 more

AbstractThe identification of prognostic markers in patients receiving neoadjuvant therapy is crucial for treatment optimization in HER2-positive breast cancer, with the immune microenvironment being a key factor. Here, we investigate the complexity of B and T cell receptor (BCR and TCR) repertoires in the context of two phase III trials, NeoALTTO and CALGB 40601, evaluating neoadjuvant paclitaxel with trastuzumab and/or lapatinib in women with HER2-positive breast cancer.

Apr 14, 2023 | nature.com | Frederick Howard |James M. Dolezal |Andrew Srisuwananukorn |Rita Nanda |Charles M. Perou |Olufunmilayo I. Olopade | +2 more

AbstractGene expression-based recurrence assays are strongly recommended to guide the use of chemotherapy in hormone receptor-positive, HER2-negative breast cancer, but such testing is expensive, can contribute to delays in care, and may not be available in low-resource settings. Here, we describe the training and independent validation of a deep learning model that predicts recurrence assay result and risk of recurrence using both digital histology and clinical risk factors.