The Sloan-Kettering Institute for Cancer Research, which draws on Memorial Sloan Kettering Cancer Center clinical and biospecimen resources, has received a USD 1.5 million FDA U01 award to develop a genome-scale cell-surface proteogenomics framework for identifying therapeutic targets in ultra-rare sarcomas driven by transcriptional and epigenetic dysregulation, which the project summary describes as presenting a barrier to conventional precision oncology.
The funded work targets sclerosing epithelioid fibrosarcoma (SEF), atypical teratoid/rhabdoid tumors (ATRT/RTs), malignant rhabdoid tumors, and related sarcomas — cancers driven by oncogenic fusion transcription factors or chromatin-remodeling defects rather than recurrent actionable mutations, leaving patients with limited treatment options and frequent therapy resistance. These tumor types have historically been excluded from biomarker-driven trial designs that depend on mutational hotspots, making systematic target discovery particularly difficult.
Principal investigators Alex Kentsis and Andrew William McPherson will integrate live-cell surface labeling, plasma-membrane enrichment, long-read and single-cell RNA sequencing, high-resolution multidimensional mass spectrometry, and computational proteogenomics using patient tumor specimens from MSK's clinical and biospecimen resources. The approach is designed to capture non-canonical, tumor-specific proteoforms — including those arising from aberrant splicing, structural variation, and post-translational modifications (PTMs) — that current target-discovery methods are said to miss. By anchoring the analysis in live-cell surface labeling and patient-derived specimens rather than cell lines or reference proteomes, the platform is intended to reflect the actual surface landscape of primary tumors.
Validated targets would be intended to support downstream development of antibody-drug conjugates, engineered cellular therapies, bispecific molecules, and radiopharmaceuticals. The project will also produce a transparent target-prioritization framework and NIH Cancer Data Science Initiative-aligned regulatory-science tools intended as shared resources for the rare sarcoma field.