Browsing by Author "Borgia, Jeffrey A."

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    A pan-cancer organoid platform for precision medicine
    (Elsevier, 2021) Larsen, Brian M.; Kannan, Madhavi; Langer, Lee F.; Leibowitz, Benjamin D.; Bentaieb, Aicha; Cancino, Andrea; Dolgalev, Igor; Drummond, Bridgette E.; Dry, Jonathan R.; Ho, Chi-Sing; Khullar, Gaurav; Krantz, Benjamin A.; Mapes, Brandon; McKinnon, Kelly E.; Metti, Jessica; Perera, Jason F.; Rand, Tim A.; Sanchez-Freire, Veronica; Shaxted, Jenna M.; Stein, Michelle M.; Streit, Michael A.; Tan, Yi-Hung Carol; Zhang, Yilin; Zhao, Ende; Venkataraman, Jagadish; Stumpe, Martin C.; Borgia, Jeffrey A.; Masood, Ashiq; Catenacci, Daniel V. T.; Mathews, Jeremy V.; Gursel, Demirkan B.; Wei, Jian-Jun; Welling, Theodore H.; Simeone, Diane M.; White, Kevin P.; Khan, Aly A.; Igartua, Catherine; Salahudeen, Ameen A.; Medicine, School of Medicine
    Patient-derived tumor organoids (TOs) are emerging as high-fidelity models to study cancer biology and develop novel precision medicine therapeutics. However, utilizing TOs for systems-biology-based approaches has been limited by a lack of scalable and reproducible methods to develop and profile these models. We describe a robust pan-cancer TO platform with chemically defined media optimized on cultures acquired from over 1,000 patients. Crucially, we demonstrate tumor genetic and transcriptomic concordance utilizing this approach and further optimize defined minimal media for organoid initiation and propagation. Additionally, we demonstrate a neural-network-based high-throughput approach for label-free, light-microscopy-based drug assays capable of predicting patient-specific heterogeneity in drug responses with applicability across solid cancers. The pan-cancer platform, molecular data, and neural-network-based drug assay serve as resources to accelerate the broad implementation of organoid models in precision medicine research and personalized therapeutic profiling programs.
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    Refining colorectal cancer classification and clinical stratification through a single-cell atlas
    (Springer, 2022-05-11) Khaliq, Ateeq M.; Erdogan, Cihat; Kurt, Zeyneb; Turgut, Sultan Sevgi; Grunvald, Miles W.; Rand, Tim; Khare, Sonal; Borgia, Jeffrey A.; Hayden, Dana M.; Pappas, Sam G.; Govekar, Henry R.; Kam, Audrey E.; Reiser, Jochen; Turaga, Kiran; Radovich, Milan; Zang, Yong; Qiu, Yingjie; Liu, Yunlong; Fishel, Melissa L.; Turk, Anita; Gupta, Vineet; Al-Sabti, Ram; Subramanian, Janakiraman; Kuzel, Timothy M.; Sadanandam, Anguraj; Waldron, Levi; Hussain, Arif; Saleem, Mohammad; El-Rayes, Bassel; Salahudeen, Ameen A.; Masood, Ashiq; Medicine, School of Medicine
    Background Colorectal cancer (CRC) consensus molecular subtypes (CMS) have different immunological, stromal cell, and clinicopathological characteristics. Single-cell characterization of CMS subtype tumor microenvironments is required to elucidate mechanisms of tumor and stroma cell contributions to pathogenesis which may advance subtype-specific therapeutic development. We interrogate racially diverse human CRC samples and analyze multiple independent external cohorts for a total of 487,829 single cells enabling high-resolution depiction of the cellular diversity and heterogeneity within the tumor and microenvironmental cells. Results Tumor cells recapitulate individual CMS subgroups yet exhibit significant intratumoral CMS heterogeneity. Both CMS1 microsatellite instability (MSI-H) CRCs and microsatellite stable (MSS) CRC demonstrate similar pathway activations at the tumor epithelial level. However, CD8+ cytotoxic T cell phenotype infiltration in MSI-H CRCs may explain why these tumors respond to immune checkpoint inhibitors. Cellular transcriptomic profiles in CRC exist in a tumor immune stromal continuum in contrast to discrete subtypes proposed by studies utilizing bulk transcriptomics. We note a dichotomy in tumor microenvironments across CMS subgroups exists by which patients with high cancer-associated fibroblasts (CAFs) and C1Q+TAM content exhibit poor outcomes, providing a higher level of personalization and precision than would distinct subtypes. Additionally, we discover CAF subtypes known to be associated with immunotherapy resistance. Conclusions Distinct CAFs and C1Q+ TAMs are sufficient to explain CMS predictive ability and a simpler signature based on these cellular phenotypes could stratify CRC patient prognosis with greater precision. Therapeutically targeting specific CAF subtypes and C1Q + TAMs may promote immunotherapy responses in CRC patients.