Contact

Additional Info

  • Mail Code: 5080

All Publications

  • Evolutionary Pressures Shape Undifferentiated Pleomorphic Sarcoma Development and Radiotherapy Response. Cancer research Blomain, E., Soudi, S., Wang, Z., Somani, A., Subramanian, A., Nouth, S. C., Oladipo, E., New, C., Kenney, D. E., Nemat-Gorgani, N., Kindler, T., Avedian, R. S., Steffner, R. J., Mohler, D. G., Hiniker, S. M., Chin, A. L., Kalbasi, A., Binkley, M. S., Fried, M., Gaida, M. M., van de Rijn, M., Moding, E. J. 2025

    Abstract

    Radiotherapy is an integral component in the treatment of many types of cancer, with approximately half of cancer patients receiving radiotherapy. Systemic therapy applies pressure that can select for resistant tumor subpopulations, underscoring the importance of understanding how radiation impacts tumor evolution to improve treatment outcomes. We integrated temporal genomic profiling of 120 spatially distinct tumor regions from 20 patients with undifferentiated pleomorphic sarcomas (UPS), longitudinal circulating tumor DNA (ctDNA) analysis, and evolutionary biology computational pipelines to study UPS evolution during tumorigenesis and in response to radiotherapy. Most unirradiated UPS displayed initial linear evolution followed by subsequent branching evolution with distinct mutational processes during early and late development. Metrics of genetic divergence between regions provided evidence of strong selection pressures during UPS development that further increased during radiotherapy. Subclone abundance changed following radiotherapy with subclone contraction tied to alterations in calcium signaling, and inhibiting calcium transporters radiosensitized sarcoma cells. Finally, ctDNA analysis accurately measured subclone abundance and enabled non-invasive monitoring of subclonal changes. These results demonstrate that radiation exerts selective pressures on UPS and suggest that targeting radioresistant subclonal populations could improve outcomes after radiotherapy.

    View details for DOI 10.1158/0008-5472.CAN-24-3281

    View details for PubMedID 39808162

  • Sarcoma microenvironment cell states and ecosystems are associated with prognosis and predict response to immunotherapy. Nature cancer Subramanian, A., Nemat-Gorgani, N., Ellis-Caleo, T. J., van IJzendoorn, D. G., Sears, T. J., Somani, A., Luca, B. A., Zhou, M. Y., Bradic, M., Torres, I. A., Oladipo, E., New, C., Kenney, D. E., Avedian, R. S., Steffner, R. J., Binkley, M. S., Mohler, D. G., Tap, W. D., D'Angelo, S. P., van de Rijn, M., Ganjoo, K. N., Bui, N. Q., Charville, G. W., Newman, A. M., Moding, E. J. 2024

    Abstract

    Characterization of the diverse malignant and stromal cell states that make up soft tissue sarcomas and their correlation with patient outcomes has proven difficult using fixed clinical specimens. Here, we employed EcoTyper, a machine-learning framework, to identify the fundamental cell states and cellular ecosystems that make up sarcomas on a large scale using bulk transcriptomes with clinical annotations. We identified and validated 23 sarcoma-specific, transcriptionally defined cell states, many of which were highly prognostic of patient outcomes across independent datasets. We discovered three conserved cellular communities or ecotypes associated with underlying genomic alterations and distinct clinical outcomes. We show that one ecotype defined by tumor-associated macrophages and epithelial-like malignant cells predicts response to immune-checkpoint inhibition but not chemotherapy and validate our findings in an independent cohort. Our results may enable identification of patients with soft tissue sarcomas who could benefit from immunotherapy and help develop new therapeutic strategies.

    View details for DOI 10.1038/s43018-024-00743-y

    View details for PubMedID 38429415

    View details for PubMedCentralID 4486342