Citations 2
Enriched Adaptive Immunity and Reduced Inflammatory Response in Plasma Cell Disorders of Self-Identified Black Patients
Pilcher, William , Khanfar, Mariam , Sharma, Neeraj , Lehman, Stacey J , Bai, Mahek, Cook, Joselle M , Abdallah, Nadine H , Bolarinwa, Abiola B , Anderson, Emilie I., Kourelis, Taxiarchis , Kumar, Shaji K , Weivoda, Megan , Gomez, Felicia
Blood Journal | October 5, 2026 | Volume: No volume available | Issue: No issue available | Pages: No page available
The immune tumor microenvironment (iTME) plays a crucial role in disease biology of plasma cell (PC) dyscrasias, but remains poorly characterized in self-identified Black individuals, a population at increased risk. Mass cytometry and scRNA-seq of 128 bone marrow aspirates revealed significant changes in immune cell composition across the disease spectrum, including shifts from naïve to effector T cells with declines in B cells, MAIT, pDCs, and increases in monocytes with disease progression. Self-identified Black patients had higher relative proportions of terminally differentiated T cells and lower monocytes in newly diagnosed and treated multiple myeloma (MM). ScRNA-seq revealed genes overexpressed in self-identified Black individuals were enriched for E2F targets and G2-M checkpoints, while reduced genes were enriched for TNF-α/NF-kB, interferon and inflammatory responses pathways. These findings were supported by genetic ancestry and validated in an independent dataset of 286 patients with MM from the MMRF Immune Atlas. Self-identified race and African genetic similarity are significantly associated with remodeling the MM iTME, with potential implications for disease biology and treatment response.
CIViCdb 2022: evolution of an open-access cancer variant interpretation knowledgebase
Krysiak, Kilannin , Danos, Arpad , McMichael, Joshua F. , Coffman, Adam , Kiwala, Susanna , Sheta, Lana , Kujan, Lynzey , Pema, Shahil , Ridd, Sarah , Spies, Nicholas C. , Andric, Veronica , Chiorean, Andreea , Rieke, Damian , Clark, Kaitlin A., Reisle, Caralyn , Venigalla, Ajay C, Jani, Payal , Takahashi, Hideaki , Suda, Avila, Horak, Peter , Ritter, Deborah , DeLong, Sean , Lamping, Mario , Shen, Haolin , Marr, Alexander , Hoang, My , Singhal, Kartik , Khanfar, Mariam , Li, Brian V , Lin, Wan‐Hsin , Terraf, Panieh, Corson, Laura , Salama, Yasser, Farncombe, Kirsten M. , Ji, Jianling , Zhao, Xiaonan, Xu, Xinjie , Kanagal‐Shamanna, Rashmi , Walker, Jason , Zhang, Jinghui , Patel, Ronak Y. , Kim, Raymond H. , Jones, Steven J.M. , Raca, Gordana , Madhavan, Subha , Griffith, Malachi , Griffith, Obi L. , Coffman, Adam C , Kiwala, Susanna , Zhou, Xin , Campbell, Katie M , Ji, Jianling , Cotto, Kelsy C , Schriml, Lynn M , Wagner, Alex H , Griffith, Malachi , Griffith, Obi L , Krysiak, Kilannin , Kanagal-Shamanna, Rashmi , Kim, Raymond H , Saliba, Jason , Barnell, Erica K. , Grisdale, Cameron J. , Lever, Jake , Venigalla, Ajay , Evans, Mark G. , Ainscough, Benjamin J. , Kesserwan, Chimene A. , Zhao, Xiaonan, King, Ian F. G. , Skidmore, Zachary L. , Milosavljevic, Aleksandar , Giles, Rachel H. , Mardis, Elaine R. , Rao, Shruti , Wagner, Alex H. , Khanfar, Mariam
Nucleic Acids Research | October 15, 2022 | Volume: 51 | Issue: D1 | Pages: D1230-D1241
Abstract CIViC (Clinical Interpretation of Variants in Cancer; civicdb.org) is a crowd-sourced, public domain knowledgebase composed of literature-derived evidence characterizing the clinical utility of cancer variants. As clinical sequencing becomes more prevalent in cancer management, the need for cancer variant interpretation has grown beyond the capability of any single institution. CIViC contains peer-reviewed, published literature curated and expertly-moderated into structured data units (Evidence Items) that can be accessed globally and in real time, reducing barriers to clinical variant knowledge sharing. We have extended CIViC’s functionality to support emergent variant interpretation guidelines, increase interoperability with other variant resources, and promote widespread dissemination of structured curated data. To support the full breadth of variant interpretation from basic to translational, including integration of somatic and germline variant knowledge and inference of drug response, we have enabled curation of three new Evidence Types (Predisposing, Oncogenic and Functional). The growing CIViC knowledgebase has over 300 contributors and distributes clinically-relevant cancer variant data currently representing >3200 variants in >470 genes from >3100 publications.