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Louissaint, Abner

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Louissaint

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Abner

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Louissaint, Abner

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Now showing 1 - 3 of 3
  • Publication

    Hypercalcemia Associated with Isolated Bone Marrow Sarcoidosis in a Patient with Underlying Monoclonal Gammopathy of Undetermined Significance: Case Report and Review of Literature

    (BioMed Central, 2016) Gubatan, John Mark B; Wang, Xiaohui; Louissaint, Abner; Mahindra, Anuj; Vanderpool, John

    Bone marrow sarcoidosis is extremely rare. The association between sarcoidosis and lymphoproliferative disorders has been previously speculated, although the diagnosis of sarcoidosis often precedes any hematological derangements. Here, we report for the first time, a case of a 57 year old woman with a previous diagnosis of monoclonal gammopathy of undetermined significance (MGUS) developing hypercalcemia and renal failure with work notable for isolated bone marrow sarcoidosis and not multiple myeloma as expected. The patient was successfully managed with prednisone taper therapy with resolution of her hypercalcemia and repeat bone marrow biopsies demonstrating resolving granulomas. Our case illustrates the diagnostic challenges associated with bone marrow sarcoidosis and suggest that chronic immune stimulation in the bone marrow in the setting of MGUS may be a risk factor for the development of localized sarcoidosis. The long term consequences of steroid therapy targeting sarcoidosis in this patient with underlying MGUS remain unknown. Close followup is planned in light of the increased risk of malignant transformation of MGUS into multiple myeloma in the setting of bone marrow sarcoidosis.

  • Publication

    Nuclear IHC enumeration: A digital phantom to evaluate the performance of automated algorithms in digital pathology

    (Public Library of Science, 2018) Niazi, Muhammad Khalid Khan; Abas, Fazly Salleh; Senaras, Caglar; Pennell, Michael; Sahiner, Berkman; Chen, Weijie; Opfer, John; Hasserjian, Robert; Louissaint, Abner; Shana'ah, Arwa; Lozanski, Gerard; Gurcan, Metin N.

    Automatic and accurate detection of positive and negative nuclei from images of immunostained tissue biopsies is critical to the success of digital pathology. The evaluation of most nuclei detection algorithms relies on manually generated ground truth prepared by pathologists, which is unfortunately time-consuming and suffers from inter-pathologist variability. In this work, we developed a digital immunohistochemistry (IHC) phantom that can be used for evaluating computer algorithms for enumeration of IHC positive cells. Our phantom development consists of two main steps, 1) extraction of the individual as well as nuclei clumps of both positive and negative nuclei from real WSI images, and 2) systematic placement of the extracted nuclei clumps on an image canvas. The resulting images are visually similar to the original tissue images. We created a set of 42 images with different concentrations of positive and negative nuclei. These images were evaluated by four board certified pathologists in the task of estimating the ratio of positive to total number of nuclei. The resulting concordance correlation coefficients (CCC) between the pathologist and the true ratio range from 0.86 to 0.95 (point estimates). The same ratio was also computed by an automated computer algorithm, which yielded a CCC value of 0.99. Reading the phantom data with known ground truth, the human readers show substantial variability and lower average performance than the computer algorithm in terms of CCC. This shows the limitation of using a human reader panel to establish a reference standard for the evaluation of computer algorithms, thereby highlighting the usefulness of the phantom developed in this work. Using our phantom images, we further developed a function that can approximate the true ratio from the area of the positive and negative nuclei, hence avoiding the need to detect individual nuclei. The predicted ratios of 10 held-out images using the function (trained on 32 images) are within ±2.68% of the true ratio. Moreover, we also report the evaluation of a computerized image analysis method on the synthetic tissue dataset.

  • Publication

    Targetable vulnerabilities in T- and NK-cell lymphomas identified through preclinical models

    (Nature Publishing Group UK, 2018) Ng, Samuel Y.; Yoshida, Noriaki; Christie, Amanda L.; Ghandi, Mahmoud; Dharia, Neekesh; Dempster, Joshua; Murakami, Mark; Shigemori, Kay; Morrow, Sara N.; Van Scoyk, Alexandria; Cordero, Nicolas A.; Stevenson, Kristen E.; Puligandla, Maneka; Haas, Brian; Lo, Christopher; Meyers, Robin; Gao, Galen; Cherniack, Andrew; Louissaint, Abner; Nardi, Valentina; Thorner, Aaron R.; Long, Henry; Qiu, Xintao; Morgan, Elizabeth; Dorfman, David; Fiore, Danilo; Jang, Julie; Epstein, Alan L.; Dogan, Ahmet; Zhang, Yanming; Horwitz, Steven M.; Jacobsen, Eric; Santiago, Solimar; Ren, Jian-Guo; Guerlavais, Vincent; Annis, D. Allen; Aivado, Manuel; Saleh, Mansoor N.; Mehta, Amitkumar; Tsherniak, Aviad; Root, David; Vazquez, Francisca; Hahn, William; Inghirami, Giorgio; Aster, Jon; Weinstock, David; Koch, Raphael

    T- and NK-cell lymphomas (TCL) are a heterogenous group of lymphoid malignancies with poor prognosis. In contrast to B-cell and myeloid malignancies, there are few preclinical models of TCLs, which has hampered the development of effective therapeutics. Here we establish and characterize preclinical models of TCL. We identify multiple vulnerabilities that are targetable with currently available agents (e.g., inhibitors of JAK2 or IKZF1) and demonstrate proof-of-principle for biomarker-driven therapies using patient-derived xenografts (PDXs). We show that MDM2 and MDMX are targetable vulnerabilities within TP53-wild-type TCLs. ALRN-6924, a stapled peptide that blocks interactions between p53 and both MDM2 and MDMX has potent in vitro activity and superior in vivo activity across 8 different PDX models compared to the standard-of-care agent romidepsin. ALRN-6924 induced a complete remission in a patient with TP53-wild-type angioimmunoblastic T-cell lymphoma, demonstrating the potential for rapid translation of discoveries from subtype-specific preclinical models.