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1,040 results on '"Savona, Michael R."'

1. Risk Prediction for Clonal Cytopenia: Multicenter Real-World Evidence.

Author

Xie, Zhuoer, Komrokji, Rami S, Al-Ali, Najla, Regelson, Alexandra, Geyer, Susan, Patel, Anand A, Saygin, Caner, Zeidan, Amer M, Bewersdorf, Jan Philipp, Mendez, Lourdes M, Kishtagari, Ashwin, Zeidner, Joshua F, Coombs, Catherine C, Madanat, Yazan F, Chung, Stephen S, Badar, Talha, Foran, James M, Desai, Pinkal, Tsai, Charlton, Griffiths, Elizabeth A, Al Malki, Monzr M, Amanam, Idoroenyi, Lai, Catherine, Deeg, H Joachim, Ades, Lionel, Arana-Yi, Cecilia, Osman, Afaf Eg, Dinner, Shira Naomi, Abaza, Yasmin, Taylor, Justin, Chandhok, Namrata S, Soong, Deborah, Brunner, Andrew M, Carraway, Hetty E, Singh, Abhay, Elena, Chiara, Ferrari, Jacqueline, Galli, Anna, Pozzi, Sara, Padron, Eric, Patnaik, Mrinal M, Malcovati, Luca, Savona, Michael R, and Al-Kali, Aref

Subjects
Cardiorespiratory Medicine and Haematology, Clinical Sciences, Paediatrics and Reproductive Medicine, Immunology, Biochemistry and cell biology, Cardiovascular medicine and haematology, Paediatrics
Abstract

Clonal cytopenia of undetermined significance (CCUS) represents a distinct disease entity characterized by myeloid-related somatic mutations with a variant allele fraction of ≥2% in individuals with unexplained cytopenia(s) but without a myeloid neoplasm (MN). Notably, CCUS carries a risk of progressing to MN, particularly in cases featuring high-risk mutations. Understanding CCUS requires dedicated studies to elucidate its risk factors and natural history. Our analysis of 357 CCUS patients investigated the interplay between clonality, cytopenia, and prognosis. Multivariate analysis identified 3 key adverse prognostic factors: the presence of splicing mutation(s) (score = 2 points), platelet count

Published
2024

3. Molecular taxonomy of myelodysplastic syndromes and its clinical implications

Author

Bernard, Elsa, Hasserjian, Robert P., Greenberg, Peter L., Arango Ossa, Juan E., Creignou, Maria, Tuechler, Heinz, Gutierrez-Abril, Jesus, Domenico, Dylan, Medina-Martinez, Juan S., Levine, Max, Liosis, Konstantinos, Farnoud, Noushin, Sirenko, Maria, Jädersten, Martin, Germing, Ulrich, Sanz, Guillermo, van de Loosdrecht, Arjan A., Nannya, Yasuhito, Kosmider, Olivier, Follo, Matilde Y., Thol, Felicitas, Zamora, Lurdes, Pinheiro, Ronald F., Pellagatti, Andrea, Elias, Harold K., Haase, Detlef, Ganster, Christina, Ades, Lionel, Tobiasson, Magnus, Palomo, Laura, Della Porta, Matteo Giovanni, Fenaux, Pierre, Belickova, Monika, Savona, Michael R., Klimek, Virginia M., Santos, Fabio P. S., Boultwood, Jacqueline, Kotsianidis, Ioannis, Santini, Valeria, Solé, Francesc, Platzbecker, Uwe, Heuser, Michael, Valent, Peter, Finelli, Carlo, Voso, Maria Teresa, Shih, Lee-Yung, Fontenay, Michaela, Jansen, Joop H., Cervera, José, Gattermann, Norbert, Ebert, Benjamin L., Bejar, Rafael, Malcovati, Luca, Ogawa, Seishi, Cazzola, Mario, Hellström-Lindberg, Eva, and Papaemmanuil, Elli

Published
2024
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4. Ziftomenib in relapsed or refractory acute myeloid leukaemia (KOMET-001): a multicentre, open-label, multi-cohort, phase 1 trial

Author

Wang, Eunice S, Issa, Ghayas C, Erba, Harry P, Altman, Jessica K, Montesinos, Pau, DeBotton, Stephane, Walter, Roland B, Pettit, Kristen, Savona, Michael R, Shah, Mithun Vinod, Kremyanskaya, Marina, Baer, Maria R, Foran, James M, Schiller, Gary, Adès, Lionel, Heiblig, Mael, Berthon, Celine, Peterlin, Pierre, Rodríguez-Arbolí, Eduardo, Salamero, Olga, Patnaik, Mrinal M, Papayannidis, Cristina, Grembecka, Jolanta, Cierpicki, Tomasz, Clegg, Bradley, Ray, Joshua, Linhares, Brian M, Nie, Kun, Mitra, Amitava, Ahsan, Julie Mackey, Tabachri, Marilyn, Soifer, Harris S, Corum, Daniel, Leoni, Mollie, Dale, Stephen, and Fathi, Amir T

Published
2024
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6. Contemporary Approach to the Diagnosis and Classification of Myelodysplastic Neoplasms/Syndromes—Recommendations From the International Consortium for Myelodysplastic Neoplasms/Syndromes (MDS [icMDS])

Author

Aakash, Fnu, Gisriel, Savanah D., Zeidan, Amer M., Bennett, John M., Bejar, Rafael, Bewersdorf, Jan Philipp, Borate, Uma M., Boultwood, Jacqueline, Brunner, Andrew M., Buckstein, Rena, Carraway, Hetty E., Churpek, Jane E., Daver, Naval G., DeZern, Amy E., Efficace, Fabio, Fenaux, Pierre, Figueroa, Maria E., Garcia-Manero, Guillermo, Gore, Steven D., Greenberg, Peter L., Griffiths, Elizabeth A., Halene, Stephanie, Hourigan, Christopher S., Kim, Tae Kon, Kim, Nina, Komrokji, Rami S., Kutchroo, Vijay K., List, Alan F., Little, Richard F., Majeti, Ravindra, Nazha, Aziz, Nimer, Stephen D., Odenike, Olatoyosi, Padron, Eric, Patnaik, Mrinal M., Platzbecker, Uwe, Della Porta, Matteo G., Roboz, Gail J., Sallman, David A., Santini, Valeria, Sanz, Guillermo, Savona, Michael R., Sekeres, Mikkael A., Stahl, Maximilian, Starczynowski, Daniel T., Steensma, David P., Taylor, Justin, Abdel-Wahab, Omar, Wei, Andrew H., Xie, Zhuoer, Xu, Mina L., Hasserjian, Robert P., and Loghavi, Sanam

Published
2024
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7. Data-driven, harmonised classification system for myelodysplastic syndromes: a consensus paper from the International Consortium for Myelodysplastic Syndromes

Author

Komrokji, Rami S, Lanino, Luca, Ball, Somedeb, Bewersdorf, Jan P, Marchetti, Monia, Maggioni, Giulia, Travaglino, Erica, Al Ali, Najla H, Fenaux, Pierre, Platzbecker, Uwe, Santini, Valeria, Diez-Campelo, Maria, Singh, Avani, Jain, Akriti G, Aguirre, Luis E, Tinsley-Vance, Sarah M, Schwabkey, Zaker I, Chan, Onyee, Xie, Zhouer, Brunner, Andrew M, Kuykendall, Andrew T, Bennett, John M, Buckstein, Rena, Bejar, Rafael, Carraway, Hetty E, DeZern, Amy E, Griffiths, Elizabeth A, Halene, Stephanie, Hasserjian, Robert P, Lancet, Jeffrey, List, Alan F, Loghavi, Sanam, Odenike, Olatoyosi, Padron, Eric, Patnaik, Mrinal M, Roboz, Gail J, Stahl, Maximilian, Sekeres, Mikkael A, Steensma, David P, Savona, Michael R, Taylor, Justin, Xu, Mina L, Sweet, Kendra, Sallman, David A, Nimer, Stephen D, Hourigan, Christopher S, Wei, Andrew H, Sauta, Elisabetta, D’Amico, Saverio, Asti, Gianluca, Castellani, Gastone, Delleani, Mattia, Campagna, Alessia, Borate, Uma M, Sanz, Guillermo, Efficace, Fabio, Gore, Steven D, Kim, Tae Kon, Daver, Navel, Garcia-Manero, Guillermo, Rozman, Maria, Orfao, Alberto, Wang, Sa A, Foucar, M Kathryn, Germing, Ulrich, Haferlach, Torsten, Scheinberg, Phillip, Miyazaki, Yasushi, Iastrebner, Marcelo, Kulasekararaj, Austin, Cluzeau, Thomas, Kordasti, Shahram, van de Loosdrecht, Arjan A, Ades, Lionel, Zeidan, Amer M, and Della Porta, Matteo G

Published
2024
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9. Clonal hematopoiesis and inflammation in the vasculature: CHIVE, a prospective, longitudinal clonal hematopoiesis cohort and biorepository

Author

Shannon, Morgan L., Heimlich, J. Brett, Olson, Sydney, Debevec, Ariana, Copeland, Zachary, Kishtagari, Ashwin, Vlasschaert, Caitlyn, Snider, Christina, Silver, Alexander J., Brown, Donovan, Spaulding, Travis, Bhatta, Manasa, Pugh, Kelly, Stockton, Shannon S., Ulloa, Jessica, Xu, Yaomin, Baljevic, Muhamed, Moslehi, Javid, Jahangir, Eiman, Ferrell, P. Brent, Slosky, David, Bick, Alexander G., and Savona, Michael R.

Published
2024
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11. Venetoclax and hypomethylating agents in octogenarians and nonagenarians with acute myeloid leukemia

Author

Madarang, Ellen, Lykon, Jillian, Zhao, Wei, Sekeres, Mikkael A., Bradley, Terrence, Chandhok, Namrata S., Taylor, Justin, Venugopal, Sangeetha, Koru-Sengul, Tulay, Iyer, Sunil Girish, Gilbert, Jason S., Miller, Ryan M., Nanni, Jacopo, Zacheo, Irene, Mattei, Agnese, Al Ali, Najla, Kishtagari, Ashwin, Marconi, Giovanni, Sallman, David A., Pollyea, Daniel A., Savona, Michael R., Papayannidis, Cristina, Komrokji, Rami, and Watts, Justin

Published
2024
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12. Imetelstat in patients with lower-risk myelodysplastic syndromes who have relapsed or are refractory to erythropoiesis-stimulating agents (IMerge): a multinational, randomised, double-blind, placebo-controlled, phase 3 trial

Author

Platzbecker, Uwe, Santini, Valeria, Fenaux, Pierre, Sekeres, Mikkael A, Savona, Michael R, Madanat, Yazan F, Díez-Campelo, Maria, Valcárcel, David, Illmer, Thomas, Jonášová, Anna, Bělohlávková, Petra, Sherman, Laurie J, Berry, Tymara, Dougherty, Souria, Shah, Sheetal, Xia, Qi, Sun, Libo, Wan, Ying, Huang, Fei, Ikin, Annat, Navada, Shyamala, Feller, Faye, Komrokji, Rami S, and Zeidan, Amer M

Published
2024
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13. Oral decitabine–cedazuridine versus intravenous decitabine for myelodysplastic syndromes and chronic myelomonocytic leukaemia (ASCERTAIN): a registrational, randomised, crossover, pharmacokinetics, phase 3 study

Author

Garcia-Manero, Guillermo, McCloskey, James, Griffiths, Elizabeth A, Yee, Karen W L, Zeidan, Amer M, Al-Kali, Aref, Deeg, H Joachim, Patel, Prapti A, Sabloff, Mitchell, Keating, Mary-Margaret, Zhu, Nancy, Gabrail, Nashat Y, Fazal, Salman, Maly, Joseph, Odenike, Olatoyosi, Kantarjian, Hagop, DeZern, Amy E, O’Connell, Casey L, Roboz, Gail J, Busque, Lambert, Buckstein, Rena, Amin, Harshad, Randhawa, Jasleen, Leber, Brian, Shastri, Aditi, Dao, Kim-Hien, Oganesian, Aram, Hao, Yong, Keer, Harold N, Azab, Mohammad, and Savona, Michael R

Published
2024
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14. Outlier Guided Optimization of Abdominal Segmentation

Author

Xu, Yuchen, Tang, Olivia, Tang, Yucheng, Lee, Ho Hin, Chen, Yunqiang, Gao, Dashan, Han, Shizhong, Gao, Riqiang, Savona, Michael R., Abramson, Richard G., Huo, Yuankai, and Landman, Bennett A.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Abdominal multi-organ segmentation of computed tomography (CT) images has been the subject of extensive research interest. It presents a substantial challenge in medical image processing, as the shape and distribution of abdominal organs can vary greatly among the population and within an individual over time. While continuous integration of novel datasets into the training set provides potential for better segmentation performance, collection of data at scale is not only costly, but also impractical in some contexts. Moreover, it remains unclear what marginal value additional data have to offer. Herein, we propose a single-pass active learning method through human quality assurance (QA). We built on a pre-trained 3D U-Net model for abdominal multi-organ segmentation and augmented the dataset either with outlier data (e.g., exemplars for which the baseline algorithm failed) or inliers (e.g., exemplars for which the baseline algorithm worked). The new models were trained using the augmented datasets with 5-fold cross-validation (for outlier data) and withheld outlier samples (for inlier data). Manual labeling of outliers increased Dice scores with outliers by 0.130, compared to an increase of 0.067 with inliers (p<0.001, two-tailed paired t-test). By adding 5 to 37 inliers or outliers to training, we find that the marginal value of adding outliers is higher than that of adding inliers. In summary, improvement on single-organ performance was obtained without diminishing multi-organ performance or significantly increasing training time. Hence, identification and correction of baseline failure cases present an effective and efficient method of selecting training data to improve algorithm performance., Comment: SPIE2020 Medical Imaging

Published
2020

15. Validation and Optimization of Multi-Organ Segmentation on Clinical Imaging Archives

Author

Xu, Yuchen, Tang, Olivia, Tang, Yucheng, Lee, Ho Hin, Chen, Yunqiang, Gao, Dashan, Han, Shizhong, Gao, Riqiang, Savona, Michael R., Abramson, Richard G., Huo, Yuankai, and Landman, Bennett A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Segmentation of abdominal computed tomography(CT) provides spatial context, morphological properties, and a framework for tissue-specific radiomics to guide quantitative Radiological assessment. A 2015 MICCAI challenge spurred substantial innovation in multi-organ abdominal CT segmentation with both traditional and deep learning methods. Recent innovations in deep methods have driven performance toward levels for which clinical translation is appealing. However, continued cross-validation on open datasets presents the risk of indirect knowledge contamination and could result in circular reasoning. Moreover, 'real world' segmentations can be challenging due to the wide variability of abdomen physiology within patients. Herein, we perform two data retrievals to capture clinically acquired deidentified abdominal CT cohorts with respect to a recently published variation on 3D U-Net (baseline algorithm). First, we retrieved 2004 deidentified studies on 476 patients with diagnosis codes involving spleen abnormalities (cohort A). Second, we retrieved 4313 deidentified studies on 1754 patients without diagnosis codes involving spleen abnormalities (cohort B). We perform prospective evaluation of the existing algorithm on both cohorts, yielding 13% and 8% failure rate, respectively. Then, we identified 51 subjects in cohort A with segmentation failures and manually corrected the liver and gallbladder labels. We re-trained the model adding the manual labels, resulting in performance improvement of 9% and 6% failure rate for the A and B cohorts, respectively. In summary, the performance of the baseline on the prospective cohorts was similar to that on previously published datasets. Moreover, adding data from the first cohort substantively improved performance when evaluated on the second withheld validation cohort., Comment: SPIE2020 Medical Imaging

Published
2020

16. Contrast Phase Classification with a Generative Adversarial Network

Author

Tang, Yucheng, Lee, Ho Hin, Xu, Yuchen, Tang, Olivia, Chen, Yunqiang, Gao, Dashan, Han, Shizhong, Gao, Riqiang, Bermudez, Camilo, Savona, Michael R., Abramson, Richard G., Huo, Yuankai, and Landman, Bennett A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Dynamic contrast enhanced computed tomography (CT) is an imaging technique that provides critical information on the relationship of vascular structure and dynamics in the context of underlying anatomy. A key challenge for image processing with contrast enhanced CT is that phase discrepancies are latent in different tissues due to contrast protocols, vascular dynamics, and metabolism variance. Previous studies with deep learning frameworks have been proposed for classifying contrast enhancement with networks inspired by computer vision. Here, we revisit the challenge in the context of whole abdomen contrast enhanced CTs. To capture and compensate for the complex contrast changes, we propose a novel discriminator in the form of a multi-domain disentangled representation learning network. The goal of this network is to learn an intermediate representation that separates contrast enhancement from anatomy and enables classification of images with varying contrast time. Briefly, our unpaired contrast disentangling GAN(CD-GAN) Discriminator follows the ResNet architecture to classify a CT scan from different enhancement phases. To evaluate the approach, we trained the enhancement phase classifier on 21060 slices from two clinical cohorts of 230 subjects. Testing was performed on 9100 slices from 30 independent subjects who had been imaged with CT scans from all contrast phases. Performance was quantified in terms of the multi-class normalized confusion matrix. The proposed network significantly improved correspondence over baseline UNet, ResNet50 and StarGAN performance of accuracy scores 0.54. 0.55, 0.62 and 0.91, respectively. The proposed discriminator from the disentangled network presents a promising technique that may allow deeper modeling of dynamic imaging against patient specific anatomies., Comment: 8 pages, 4 figures

Published
2019

17. Semi-Supervised Multi-Organ Segmentation through Quality Assurance Supervision

Author

Lee, Ho Hin, Tang, Yucheng, Tang, Olivia, Xu, Yuchen, Chen, Yunqiang, Gao, Dashan, Han, Shizhong, Gao, Riqiang, Savona, Michael R., Abramson, Richard G., Huo, Yuankai, and Landman, Bennett A.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Human in-the-loop quality assurance (QA) is typically performed after medical image segmentation to ensure that the systems are performing as intended, as well as identifying and excluding outliers. By performing QA on large-scale, previously unlabeled testing data, categorical QA scores can be generatedIn this paper, we propose a semi-supervised multi-organ segmentation deep neural network consisting of a traditional segmentation model generator and a QA involved discriminator. A large-scale dataset of 2027 volumes are used to train the generator, whose 2-D montage images and segmentation mask with QA scores are used to train the discriminator. To generate the QA scores, the 2-D montage images were reviewed manually and coded 0 (success), 1 (errors consistent with published performance), and 2 (gross failure). Then, the ResNet-18 network was trained with 1623 montage images in equal distribution of all three code labels and achieved an accuracy 94% for classification predictions with 404 montage images withheld for the test cohort. To assess the performance of using the QA supervision, the discriminator was used as a loss function in a multi-organ segmentation pipeline. The inclusion of QA-loss function boosted performance on the unlabeled test dataset from 714 patients to 951 patients over the baseline model. Additionally, the number of failures decreased from 606 (29.90%) to 402 (19.83%). The contributions of the proposed method are threefold: We show that (1) the QA scores can be used as a loss function to perform semi-supervised learning for unlabeled data, (2) the well trained discriminator is learnt by QA score rather than traditional true/false, and (3) the performance of multi-organ segmentation on unlabeled datasets can be fine-tuned with more robust and higher accuracy than the original baseline method., Comment: 7 pages, 5 figures, Accepted by SPIE 2020: Medical Imaging

Published
2019

18. Classification, risk stratification and response assessment in myelodysplastic syndromes/neoplasms (MDS): A state-of-the-art report on behalf of the International Consortium for MDS (icMDS)

Author

Stahl, Maximilian, Bewersdorf, Jan Philipp, Xie, Zhuoer, Porta, Matteo Giovanni Della, Komrokji, Rami, Xu, Mina L., Abdel-Wahab, Omar, Taylor, Justin, Steensma, David P., Starczynowski, Daniel T., Sekeres, Mikkael A., Sanz, Guillermo, Sallman, David A., Roboz, Gail J., Platzbecker, Uwe, Patnaik, Mrinal M., Padron, Eric, Odenike, Olatoyosi, Nimer, Stephen D., Nazha, Aziz, Majeti, Ravi, Loghavi, Sanam, Little, Richard F., List, Alan F., Kim, Tae Kon, Hourigan, Christopher S., Hasserjian, Robert P., Halene, Stephanie, Griffiths, Elizabeth A., Gore, Steven D., Greenberg, Peter, Figueroa, Maria E., Fenaux, Pierre, Efficace, Fabio, DeZern, Amy E., Daver, Naval G., Churpek, Jane E., Carraway, Hetty E., Buckstein, Rena, Brunner, Andrew M., Boultwood, Jacqueline, Borate, Uma, Bejar, Rafael, Bennett, John M., Wei, Andrew H., Santini, Valeria, Savona, Michael R., and Zeidan, Amer M.

Published
2023
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19. LAIR-1 agonism as a therapy for acute myeloid leukemia

Author

Lovewell, Rustin R., Hong, Junshik, Kundu, Subhadip, Fielder, Carly M., Hu, Qianni, Kim, Kwang Woon, Ramsey, Haley E., Gorska, Agnieszka E., Fuller, Londa S., Tian, Linjie, Kothari, Priyanka, Paucarmayta, Ana, Mason, Emily F., Meza, Ingrid, Manzanarez, Yanira, Bosiacki, Jason, Maloveste, Karla, Mitchell, Ngan, Barbu, Emilia A., Morawski, Aaron, Maloveste, Sebastien, Cusumano, Zac, Patel, Shashank J., Savona, Michael R., Langermann, Solomon, Myint, Han, Flies, Dallas B., and Kim, Tae Kon

Subjects
STEMCELL Technologies Inc., Viral antibodies -- Health aspects -- Analysis, Collagen -- Analysis -- Health aspects, B cells -- Analysis -- Health aspects, Cancer -- Care and treatment, Diagnostic reagents industry -- Analysis -- Health aspects, Hematopoietic stem cells -- Health aspects -- Analysis, Apoptosis -- Analysis -- Health aspects, Antibodies -- Health aspects -- Analysis, Health care industry, Vanderbilt University. Medical Center
Abstract

Effective eradication of leukemic stem cells (LSCs) remains the greatest challenge in treating acute myeloid leukemia (AML). The immune receptor LAIR-1 has been shown to regulate LSC survival; however, the therapeutic potential of this pathway remains unexplored. We developed a therapeutic LAIR-1 agonist antibody, NC525, that induced cell death of LSCs, but not healthy hematopoietic stem cells in vitro, and killed LSCs and AML blasts in both cell- and patient-derived xenograft models. We showed that LAIR-1 agonism drives a unique apoptotic signaling program in leukemic cells that was enhanced in the presence of collagen. NC525 also significantly improved the activity of azacitidine and venetodaxto establish LAIR-1 targeting as a therapeutic strategy for AML that may synergize with standard-of-care therapies., Introduction Acute leukemias are characterized by the uncontrolled production of malignant hematopoietic progenitors. Acute myeloid leukemia (AML) is the most common adult acute leukemia (1). While extensive research has led [...]

Published
2023
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21. Finding consistency in classifications of myeloid neoplasms: a perspective on behalf of the International Workshop for Myelodysplastic Syndromes

Author

Zeidan, Amer M., Bewersdorf, Jan Philipp, Buckstein, Rena, Sekeres, Mikkael A., Steensma, David P., Platzbecker, Uwe, Loghavi, Sanam, Boultwood, Jacqueline, Bejar, Rafael, Bennett, John M., Borate, Uma, Brunner, Andrew M., Carraway, Hetty, Churpek, Jane E., Daver, Naval G., Della Porta, Matteo, DeZern, Amy E., Efficace, Fabio, Fenaux, Pierre, Figueroa, Maria E., Greenberg, Peter, Griffiths, Elizabeth A., Halene, Stephanie, Hasserjian, Robert P., Hourigan, Christopher S., Kim, Nina, Kim, Tae Kon, Komrokji, Rami S., Kutchroo, Vijay, List, Alan F., Little, Richard F., Majeti, Ravi, Nazha, Aziz, Nimer, Stephen D., Odenike, Olatoyosi, Padron, Eric, Patnaik, Mrinal M., Roboz, Gail J., Sallman, David A., Sanz, Guillermo, Stahl, Maximilian, Starczynowski, Daniel T., Taylor, Justin, Xie, Zhuoer, Xu, Mina, Savona, Michael R., Wei, Andrew H., Abdel-Wahab, Omar, and Santini, Valeria

Published
2022
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22. Current landscape of translational and clinical research in myelodysplastic syndromes/neoplasms (MDS): Proceedings from the 1st International Workshop on MDS (iwMDS) Of the International Consortium for MDS (icMDS)

Author

Bewersdorf, Jan Philipp, Xie, Zhuoer, Bejar, Rafael, Borate, Uma, Boultwood, Jacqueline, Brunner, Andrew M., Buckstein, Rena, Carraway, Hetty E., Churpek, Jane E., Daver, Naval G., Porta, Matteo Giovanni Della, DeZern, Amy E., Fenaux, Pierre, Figueroa, Maria E., Gore, Steven D., Griffiths, Elizabeth A., Halene, Stephanie, Hasserjian, Robert P., Hourigan, Christopher S., Kim, Tae Kon, Komrokji, Rami, Kuchroo, Vijay K., List, Alan F., Loghavi, Sanam, Majeti, Ravindra, Odenike, Olatoyosi, Patnaik, Mrinal M., Platzbecker, Uwe, Roboz, Gail J., Sallman, David A., Santini, Valeria, Sanz, Guillermo, Sekeres, Mikkael A., Stahl, Maximilian, Starczynowski, Daniel T., Steensma, David P., Taylor, Justin, Abdel-Wahab, Omar, Xu, Mina L., Savona, Michael R., Wei, Andrew H., and Zeidan, Amer M.

Published
2023
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23. Targeting RARA overexpression with tamibarotene, a potent and selective RARα agonist, is a novel approach in AML

Author

de Botton, Stéphane, Cluzeau, Thomas, Vigil, Carlos, Cook, Rachel J., Rousselot, Philippe, Rizzieri, David A., Liesveld, Jane L., Fenaux, Pierre, Braun, Thorsten, Banos, Anne, Jurcic, Joseph G., Sekeres, Mikkael A., Savona, Michael R., Roboz, Gail J., Bixby, Dale, Madigan, Kate, Volkert, Angela, Stephens, Kristin, Kang-Fortner, Qing, Baker, Kristen, Paul, Sofia, McKeown, Michael, Carulli, John, Eaton, Matthew, Hodgson, Graeme, Fiore, Christopher, Kelly, Michael J., Roth, David A., and Stein, Eytan M.

Published
2023
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24. Implications of TP53 allelic state for genome stability, clinical presentation and outcomes in myelodysplastic syndromes

Author

Bernard, Elsa, Nannya, Yasuhito, Hasserjian, Robert P, Devlin, Sean M, Tuechler, Heinz, Medina-Martinez, Juan S, Yoshizato, Tetsuichi, Shiozawa, Yusuke, Saiki, Ryunosuke, Malcovati, Luca, Levine, Max F, Arango, Juan E, Zhou, Yangyu, Solé, Francesc, Cargo, Catherine A, Haase, Detlef, Creignou, Maria, Germing, Ulrich, Zhang, Yanming, Gundem, Gunes, Sarian, Araxe, van de Loosdrecht, Arjan A, Jädersten, Martin, Tobiasson, Magnus, Kosmider, Olivier, Follo, Matilde Y, Thol, Felicitas, Pinheiro, Ronald F, Santini, Valeria, Kotsianidis, Ioannis, Boultwood, Jacqueline, Santos, Fabio PS, Schanz, Julie, Kasahara, Senji, Ishikawa, Takayuki, Tsurumi, Hisashi, Takaori-Kondo, Akifumi, Kiguchi, Toru, Polprasert, Chantana, Bennett, John M, Klimek, Virginia M, Savona, Michael R, Belickova, Monika, Ganster, Christina, Palomo, Laura, Sanz, Guillermo, Ades, Lionel, Della Porta, Matteo Giovanni, Elias, Harold K, Smith, Alexandra G, Werner, Yesenia, Patel, Minal, Viale, Agnès, Vanness, Katelynd, Neuberg, Donna S, Stevenson, Kristen E, Menghrajani, Kamal, Bolton, Kelly L, Fenaux, Pierre, Pellagatti, Andrea, Platzbecker, Uwe, Heuser, Michael, Valent, Peter, Chiba, Shigeru, Miyazaki, Yasushi, Finelli, Carlo, Voso, Maria Teresa, Shih, Lee-Yung, Fontenay, Michaela, Jansen, Joop H, Cervera, José, Atsuta, Yoshiko, Gattermann, Norbert, Ebert, Benjamin L, Bejar, Rafael, Greenberg, Peter L, Cazzola, Mario, Hellström-Lindberg, Eva, Ogawa, Seishi, and Papaemmanuil, Elli

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Hematology, Stem Cell Research, Cancer, Clinical Research, Rare Diseases, Genetics, 2.1 Biological and endogenous factors, Aetiology, Alleles, Cohort Studies, DNA Copy Number Variations, DNA Mutational Analysis, Female, Gene Frequency, Genomic Instability, Humans, Loss of Heterozygosity, Male, Mutation, Myelodysplastic Syndromes, Phenotype, Prognosis, Survival Analysis, Treatment Outcome, Tumor Suppressor Protein p53, Medical and Health Sciences, Immunology, Biomedical and clinical sciences, Health sciences
Abstract

Tumor protein p53 (TP53) is the most frequently mutated gene in cancer1,2. In patients with myelodysplastic syndromes (MDS), TP53 mutations are associated with high-risk disease3,4, rapid transformation to acute myeloid leukemia (AML)5, resistance to conventional therapies6-8 and dismal outcomes9. Consistent with the tumor-suppressive role of TP53, patients harbor both mono- and biallelic mutations10. However, the biological and clinical implications of TP53 allelic state have not been fully investigated in MDS or any other cancer type. We analyzed 3,324 patients with MDS for TP53 mutations and allelic imbalances and delineated two subsets of patients with distinct phenotypes and outcomes. One-third of TP53-mutated patients had monoallelic mutations whereas two-thirds had multiple hits (multi-hit) consistent with biallelic targeting. Established associations with complex karyotype, few co-occurring mutations, high-risk presentation and poor outcomes were specific to multi-hit patients only. TP53 multi-hit state predicted risk of death and leukemic transformation independently of the Revised International Prognostic Scoring System (IPSS-R)11. Surprisingly, monoallelic patients did not differ from TP53 wild-type patients in outcomes and response to therapy. This study shows that consideration of TP53 allelic state is critical for diagnostic and prognostic precision in MDS as well as in future correlative studies of treatment response.

Published
2020

25. Diagnostic and molecular testing patterns in patients with newly diagnosed acute myeloid leukemia in the Connect® MDS/AML Disease Registry

Author

Pollyea, Daniel A, George, Tracy I, Abedi, Mehrdad, Bejar, Rafael, Cogle, Christopher R, Foucar, Kathryn, Garcia‐Manero, Guillermo, Grinblatt, David L, Komrokji, Rami S, Maciejewski, Jaroslaw P, Revicki, Dennis A, Roboz, Gail J, Savona, Michael R, Scott, Bart L, Sekeres, Mikkael A, Thompson, Michael A, Kurtin, Sandra E, Louis, Chrystal U, Nifenecker, Melissa, Flick, E Dawn, Swern, Arlene S, Kiselev, Pavel, Steensma, David P, and Erba, Harry P

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Rare Diseases, Childhood Leukemia, Cancer, Hematology, Pediatric Cancer, Pediatric, Clinical Research, acute myeloid leukemia, diagnostic testing, leukemia diagnosis, leukemia therapy, molecular testing, registry, Genetics, Cardiovascular medicine and haematology
Abstract

Diagnostic and molecular genetic testing are key in advancing the treatment of acute myeloid leukemia (AML), yet little is known about testing patterns outside of clinical trials, especially in older patients. We analyzed diagnostic and molecular testing patterns over time in 565 patients aged ≥ 55 years with newly diagnosed AML enrolled in the Connect® MDS/AML Disease Registry (NCT01688011) in the United States. Diagnostic data were recorded at enrolment and compared with published guidelines. The percentage of bone marrow blasts was reported for 82.1% of patients, and cellularity was the most commonly reported bone marrow morphological feature. Flow cytometry, karyotyping, molecular testing, and fluorescence in situ hybridization were performed in 98.8%, 95.4%, 75.9%, and 75.7% of patients, respectively. Molecular testing was done more frequently at academic than community/government sites (84.3% vs 70.2%; P

Published
2020

26. Splenomegaly Segmentation on Multi-modal MRI using Deep Convolutional Networks

Author

Huo, Yuankai, Xu, Zhoubing, Bao, Shunxing, Bermudez, Camilo, Moon, Hyeonsoo, Parvathaneni, Prasanna, Moyo, Tamara K., Savona, Michael R., Assad, Albert, Abramson, Richard G., and Landman, Bennett A.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The findings of splenomegaly, abnormal enlargement of the spleen, is a non-invasive clinical biomarker for liver and spleen disease. Automated segmentation methods are essential to efficiently quantify splenomegaly from clinically acquired abdominal magnetic resonance imaging (MRI) scans. However, the task is challenging due to (1) large anatomical and spatial variations of splenomegaly, (2) large inter- and intra-scan intensity variations on multi-modal MRI, and (3) limited numbers of labeled splenomegaly scans. In this paper, we propose the Splenomegaly Segmentation Network (SS-Net) to introduce the deep convolutional neural network (DCNN) approaches in multi-modal MRI splenomegaly segmentation. Large convolutional kernel layers were used to address the spatial and anatomical variations, while the conditional generative adversarial networks (GAN) were employed to leverage the segmentation performance of SS-Net in an end-to-end manner. A clinically acquired cohort containing both T1-weighted (T1w) and T2-weighted (T2w) MRI splenomegaly scans was used to train and evaluate the performance of multi-atlas segmentation (MAS), 2D DCNN networks, and a 3D DCNN network. From the experimental results, the DCNN methods achieved superior performance to the state-of-the-art MAS method. The proposed SS-Net method achieved the highest median and mean Dice scores among investigated baseline DCNN methods., Comment: Accepted by IEEE Transactions on Medical Imaging

Published
2018

29. Toward a more patient‐centered drug development process in clinical trials for patients with myelodysplastic syndromes/neoplasms (MDS): Practical considerations from the International Consortium for MDS (icMDS)

Author

Efficace, Fabio, primary, Buckstein, Rena, additional, Abel, Gregory A., additional, Giesinger, Johannes M., additional, Fenaux, Pierre, additional, Bewersdorf, Jan Philipp, additional, Brunner, Andrew M., additional, Bejar, Rafael, additional, Borate, Uma, additional, DeZern, Amy E., additional, Greenberg, Peter, additional, Roboz, Gail J., additional, Savona, Michael R., additional, Sparano, Francesco, additional, Boultwood, Jacqueline, additional, Komrokji, Rami, additional, Sallman, David A., additional, Xie, Zhuoer, additional, Sanz, Guillermo, additional, Carraway, Hetty E., additional, Taylor, Justin, additional, Nimer, Stephen D., additional, Della Porta, Matteo Giovanni, additional, Santini, Valeria, additional, Stahl, Maximilian, additional, Platzbecker, Uwe, additional, Sekeres, Mikkael A., additional, and Zeidan, Amer M., additional

Published
2024
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30. Luspatercept for myelodysplastic syndromes/myeloproliferative neoplasm with ring sideroblasts and thrombocytosis

Author

Komrokji, Rami S., Platzbecker, Uwe, Fenaux, Pierre, Zeidan, Amer M., Garcia-Manero, Guillermo, Mufti, Ghulam J., Santini, Valeria, Díez-Campelo, María, Finelli, Carlo, Jurcic, Joseph G., Greenberg, Peter L., Sekeres, Mikkael A., DeZern, Amy E., Savona, Michael R., Shetty, Jeevan K., Ito, Rodrigo, Zhang, George, Ha, Xianwei, Backstrom, Jay T., and Verma, Amit

Published
2022
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31. SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth

Author

Huo, Yuankai, Xu, Zhoubing, Moon, Hyeonsoo, Bao, Shunxing, Assad, Albert, Moyo, Tamara K., Savona, Michael R., Abramson, Richard G., and Landman, Bennett A.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

A key limitation of deep convolutional neural networks (DCNN) based image segmentation methods is the lack of generalizability. Manually traced training images are typically required when segmenting organs in a new imaging modality or from distinct disease cohort. The manual efforts can be alleviated if the manually traced images in one imaging modality (e.g., MRI) are able to train a segmentation network for another imaging modality (e.g., CT). In this paper, we propose an end-to-end synthetic segmentation network (SynSeg-Net) to train a segmentation network for a target imaging modality without having manual labels. SynSeg-Net is trained by using (1) unpaired intensity images from source and target modalities, and (2) manual labels only from source modality. SynSeg-Net is enabled by the recent advances of cycle generative adversarial networks (CycleGAN) and DCNN. We evaluate the performance of the SynSeg-Net on two experiments: (1) MRI to CT splenomegaly synthetic segmentation for abdominal images, and (2) CT to MRI total intracranial volume synthetic segmentation (TICV) for brain images. The proposed end-to-end approach achieved superior performance to two stage methods. Moreover, the SynSeg-Net achieved comparable performance to the traditional segmentation network using target modality labels in certain scenarios. The source code of SynSeg-Net is publicly available (https://github.com/MASILab/SynSeg-Net)., Comment: IEEE Transactions on Medical Imaging (TMI)

Published
2018
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32. TP53 mutation status divides myelodysplastic syndromes with complex karyotypes into distinct prognostic subgroups.

Author

Haase, Detlef, Stevenson, Kristen E, Neuberg, Donna, Maciejewski, Jaroslaw P, Nazha, Aziz, Sekeres, Mikkael A, Ebert, Benjamin L, Garcia-Manero, Guillermo, Haferlach, Claudia, Haferlach, Torsten, Kern, Wolfgang, Ogawa, Seishi, Nagata, Yasunobu, Yoshida, Kenichi, Graubert, Timothy A, Walter, Matthew J, List, Alan F, Komrokji, Rami S, Padron, Eric, Sallman, David, Papaemmanuil, Elli, Campbell, Peter J, Savona, Michael R, Seegmiller, Adam, Adès, Lionel, Fenaux, Pierre, Shih, Lee-Yung, Bowen, David, Groves, Michael J, Tauro, Sudhir, Fontenay, Michaela, Kosmider, Olivier, Bar-Natan, Michal, Steensma, David, Stone, Richard, Heuser, Michael, Thol, Felicitas, Cazzola, Mario, Malcovati, Luca, Karsan, Aly, Ganster, Christina, Hellström-Lindberg, Eva, Boultwood, Jacqueline, Pellagatti, Andrea, Santini, Valeria, Quek, Lynn, Vyas, Paresh, Tüchler, Heinz, Greenberg, Peter L, Bejar, Rafael, and International Working Group for MDS Molecular Prognostic Committee

Subjects
International Working Group for MDS Molecular Prognostic Committee, Humans, Myelodysplastic Syndromes, Chromosome Aberrations, Prognosis, Combined Modality Therapy, Survival Rate, Follow-Up Studies, Karyotyping, Mutation, Aged, Aged, 80 and over, Middle Aged, Female, Male, Tumor Suppressor Protein p53, Biomarkers, Tumor, Hematology, Rare Diseases, Genetics, Clinical Sciences, Oncology and Carcinogenesis, Immunology
Abstract

Risk stratification is critical in the care of patients with myelodysplastic syndromes (MDS). Approximately 10% have a complex karyotype (CK), defined as more than two cytogenetic abnormalities, which is a highly adverse prognostic marker. However, CK-MDS can carry a wide range of chromosomal abnormalities and somatic mutations. To refine risk stratification of CK-MDS patients, we examined data from 359 CK-MDS patients shared by the International Working Group for MDS. Mutations were underrepresented with the exception of TP53 mutations, identified in 55% of patients. TP53 mutated patients had even fewer co-mutated genes but were enriched for the del(5q) chromosomal abnormality (p 10%), abnormal 3q, abnormal 9, and monosomy 7 as having the greatest survival risk. The poor risk associated with CK-MDS is driven by its association with prognostically adverse TP53 mutations and can be refined by considering clinical and karyotype features.

Published
2019

33. International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: integrating morphologic, clinical, and genomic data

Author

Arber, Daniel A., Orazi, Attilio, Hasserjian, Robert P., Borowitz, Michael J., Calvo, Katherine R., Kvasnicka, Hans-Michael, Wang, Sa A., Bagg, Adam, Barbui, Tiziano, Branford, Susan, Bueso-Ramos, Carlos E., Cortes, Jorge E., Dal Cin, Paola, DiNardo, Courtney D., Dombret, Hervé, Duncavage, Eric J., Ebert, Benjamin L., Estey, Elihu H., Facchetti, Fabio, Foucar, Kathryn, Gangat, Naseema, Gianelli, Umberto, Godley, Lucy A., Gökbuget, Nicola, Gotlib, Jason, Hellström-Lindberg, Eva, Hobbs, Gabriela S., Hoffman, Ronald, Jabbour, Elias J., Kiladjian, Jean-Jacques, Larson, Richard A., Le Beau, Michelle M., Loh, Mignon L.-C., Löwenberg, Bob, Macintyre, Elizabeth, Malcovati, Luca, Mullighan, Charles G., Niemeyer, Charlotte, Odenike, Olatoyosi M., Ogawa, Seishi, Orfao, Alberto, Papaemmanuil, Elli, Passamonti, Francesco, Porkka, Kimmo, Pui, Ching-Hon, Radich, Jerald P., Reiter, Andreas, Rozman, Maria, Rudelius, Martina, Savona, Michael R., Schiffer, Charles A., Schmitt-Graeff, Annette, Shimamura, Akiko, Sierra, Jorge, Stock, Wendy A., Stone, Richard M., Tallman, Martin S., Thiele, Jürgen, Tien, Hwei-Fang, Tzankov, Alexandar, Vannucchi, Alessandro M., Vyas, Paresh, Wei, Andrew H., Weinberg, Olga K., Wierzbowska, Agnieszka, Cazzola, Mario, Döhner, Hartmut, and Tefferi, Ayalew

Published
2022
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36. High burden of clonal hematopoiesis in first responders exposed to the World Trade Center disaster

Author

Jasra, Sakshi, Giricz, Orsi, Zeig-Owens, Rachel, Pradhan, Kith, Goldfarb, David G., Barreto-Galvez, Angelica, Silver, Alexander J., Chen, Jiahao, Sahu, Srabani, Gordon-Mitchell, Shanisha, Choudhary, Gaurav S., Aluri, Srinivas, Bhagat, Tushar D., Shastri, Aditi, Bejan, Cosmin A., Stockton, Shannon S., Spaulding, Travis P., Thiruthuvanathan, Victor, Goto, Hiroki, Gerhardt, Jeannine, Haider, Syed Hissam, Veerappan, Arul, Bartenstein, Matthias, Nwankwo, George, Landgren, Ola, Weiden, Michael D., Lekostaj, Jacqueline, Bender, Ryan, Fletcher, Frederick, Greenberger, Lee, Ebert, Benjamin L., Steidl, Ulrich, Will, Britta, Nolan, Anna, Madireddy, Advaitha, Savona, Michael R., Prezant, David J., and Verma, Amit

Published
2022
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37. The ABNL-MARRO 001 study: a phase 1–2 study of randomly allocated active myeloid target compound combinations in MDS/MPN overlap syndromes

Author

Moyo, Tamara K., Mendler, Jason H., Itzykson, Raphael, Kishtagari, Ashwin, Solary, Eric, Seegmiller, Adam C., Gerds, Aaron T., Ayers, Gregory D., Dezern, Amy E., Nazha, Aziz, Valent, Peter, van de Loosdrecht, Arjan A., Onida, Francesco, Pleyer, Lisa, Cirici, Blanca Xicoy, Tibes, Raoul, Geissler, Klaus, Komrokji, Rami S., Zhang, Jing, Germing, Ulrich, Steensma, David P., Wiseman, Daniel H., Pfeilstöecker, Michael, Elena, Chiara, Cross, Nicholas C. P., Kiladjian, Jean-Jacques, Luebbert, Michael, Mesa, Ruben A., Montalban-Bravo, Guillermo, Sanz, Guillermo F., Platzbecker, Uwe, Patnaik, Mrinal M., Padron, Eric, Santini, Valeria, Fenaux, Pierre, and Savona, Michael R.

Published
2022
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38. Expanded profiling of WD repeat domain 5 inhibitors reveals actionable strategies for the treatment of hematologic malignancies.

Author

Meyer, Christian T., Smith, Brianna N., Jing Wang, Teuscher, Kevin B., Grieb, Brian C., Howard, Gregory C., Silver, Alexander J., Lorey, Shelly L., Stott, Gordon M., Moore, William J., Taekyu Lee, Savona, Michael R., Weissmiller, April M., Qi Liu, Vito Quaranta, Fesik, Stephen W., and Tansey, William P.

Subjects
NUCLEAR proteins, RIBOSOMAL proteins, B cell lymphoma, HEMATOLOGIC malignancies, ACUTE myeloid leukemia
Abstract

WD40 Repeat Domain 5 (WDR5) is a highly conserved nuclear protein that recruits MYC oncoprotein transcription factors to chromatin to stimulate ribosomal protein gene expression. WDR5 is tethered to chromatin via an arginine-binding cavity known as the "WIN" site. Multiple pharmacological inhibitors of the WDR5-interaction site of WDR5 (WINi) have been described, including those with picomolar affinity and oral bioavailability in mice. Thus far, however, WINi have only been shown to be effective against a number of rare cancer types retaining wild-type p53. To explore the full potential of WINi for cancer therapy, we systematically profiled WINi across a panel of cancer cells, alone and in combination with other agents. We report that WINi are unexpectedly active against cells derived from both solid and blood-borne cancers, including those with mutant p53. Among hematologic malignancies, we find that WINi are effective as a single agent against leukemia and diffuse large B cell lymphoma xenograft models, and can be combined with the approved drug venetoclax to suppress disseminated acute myeloid leukemia in vivo. These studies reveal actionable strategies for the application of WINi to treat blood-borne cancers and forecast expanded utility of WINi against other cancer types. [ABSTRACT FROM AUTHOR]

Published
2024
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39. Molecular and clinical presentation of UBA1-mutated myelodysplastic syndromes

Author

Sirenko, Maria, primary, Bernard, Elsa, additional, Creignou, Maria, additional, Domenico, Dylan, additional, Farina, Andrea, additional, Arango Ossa, Juan Esteban, additional, Kosmider, Olivier, additional, Hasserjian, Robert P, additional, Jädersten, Martin, additional, Germing, Ulrich, additional, Sanz, Guillermo F., additional, van de Loosdrecht, Arjan A., additional, Gurnari, Carmelo, additional, Follo, Matilde Y, additional, Thol, Felicitas R., additional, Zamora, Lurdes, additional, Pellagatti, Andrea, additional, Elias, Harold Kunal, additional, Haase, Detlef Thomas, additional, Sander, Birgitta, additional, Orna, Elisa, additional, Zoldan, Katharina, additional, Eder, Lea Naomi, additional, Sperr, Wolfgang R, additional, Thalhammer, Renate, additional, Ganster, Christina, additional, Adès, Lionel, additional, Tobiasson, Magnus, additional, Palomo, Laura, additional, Della Porta, Matteo Giovanni, additional, Huberman, Kety H, additional, Fenaux, Pierre, additional, Belickova, Monika, additional, Savona, Michael R., additional, Klimek, Virginia, additional, Santos, Fabio P S, additional, Boultwood, Jacqueline, additional, Kotsianidis, Ioannis, additional, Santini, Valeria, additional, Sole, Francesc, additional, Platzbecker, Uwe, additional, Heuser, Michael, additional, Valent, Peter, additional, Finelli, Carlo, additional, Voso, Maria Teresa, additional, Shih, Lee Yung, additional, Ogawa, Seishi, additional, Fontenay, Michaela, additional, Jansen, Joop H, additional, Cervera, José, additional, Ebert, Benjamin L, additional, Bejar, Rafael, additional, Greenberg, Peter L., additional, Gattermann, Norbert, additional, Malcovati, Luca, additional, Cazzola, Mario, additional, Beck, David B., additional, Hellstrom-Lindberg, Eva S, additional, Papaemmanuil, Elli, additional, and Pinheiro, Ronald Feitosa, additional

Published
2024
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40. Author response: Ribosome subunit attrition and activation of the p53–MDM4 axis dominate the response of MLL-rearranged cancer cells to WDR5 WIN site inhibition

Author

Howard, Gregory Caleb, primary, Wang, Jing, additional, Rose, Kristie L, additional, Jones, Camden, additional, Patel, Purvi, additional, Tsui, Tina, additional, Florian, Andrea C, additional, Vlach, Logan, additional, Lorey, Shelly L, additional, Grieb, Brian C, additional, Smith, Brianna N, additional, Slota, Macey J, additional, Reynolds, Elizabeth M, additional, Goswami, Soumita, additional, Savona, Michael R, additional, Mason, Frank M, additional, Lee, Taekyu, additional, Fesik, Stephen, additional, Liu, Qi, additional, and Tansey, William P, additional

Published
2024
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41. Clonal Hematopoiesis and Inflammation in the VasculaturE (CHIVE): a prospective, longitudinal cohort and biorepository

Author

Shannon, Morgan L., primary, Heimlich, Jonathan Brett, additional, Olson, Sydney, additional, Debevec, Ariana, additional, Copeland, Zachary, additional, Kishtagari, Ashwin, additional, Vlasschaert, Caitlyn, additional, Snider, Christina A., additional, Silver, Alexander J, additional, Brown, Donovan, additional, Spaulding, Travis P., additional, Bhatta, Manasa Ram, additional, Pugh, Kelly, additional, Stockton, Shannon S., additional, Ulloa, Jessica, additional, Xu, Yaomin, additional, Baljevic, Muhamed, additional, Moslehi, Javid, additional, Jahangir, Eiman, additional, Ferrell, Paul Brent, additional, Slosky, David Alan, additional, Bick, Alexander G., additional, and Savona, Michael R., additional

Published
2024
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42. Ribosome subunit attrition and activation of the p53–MDM4 axis dominate the response of MLL-rearranged cancer cells to WDR5 WIN site inhibition

Author

Howard, Gregory C., primary, Wang, Jing, additional, Rose, Kristie Lindsey, additional, Jones, Camden, additional, Patel, Purvi, additional, Tsui, Tina, additional, Florian, Andrea C., additional, Vlach, Logan, additional, Lorey, Shelly L., additional, Grieb, Brian C., additional, Smith, Brianna N., additional, Slota, Macey J., additional, Reynolds, Elizabeth M., additional, Goswami, Soumita, additional, Savona, Michael R., additional, Mason, Frank M., additional, Lee, Taekyu, additional, Fesik, Stephen W., additional, Liu, Qi, additional, and Tansey, William P., additional

Published
2024
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43. Author response: Ribosome subunit attrition and activation of the p53–MDM4 axis dominate the response of MLL-rearranged cancer cells to WDR5 WIN site inhibition

Author

Howard, Gregory C., primary, Wang, Jing, additional, Rose, Kristie Lindsey, additional, Jones, Camden, additional, Patel, Purvi, additional, Tsui, Tina, additional, Florian, Andrea C., additional, Vlach, Logan, additional, Lorey, Shelly L., additional, Grieb, Brian C., additional, Smith, Brianna N., additional, Slota, Macey J., additional, Reynolds, Elizabeth M., additional, Goswami, Soumita, additional, Savona, Michael R., additional, Mason, Frank M., additional, Lee, Taekyu, additional, Fesik, Stephen W., additional, Liu, Qi, additional, and Tansey, William P., additional

Published
2024
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44. Table S2 from Lactate Utilization Enables Metabolic Escape to Confer Resistance to BET Inhibition in Acute Myeloid Leukemia

Author

Monteith, Andrew J., primary, Ramsey, Haley E., primary, Silver, Alexander J., primary, Brown, Donovan, primary, Greenwood, Dalton, primary, Smith, Brianna N., primary, Wise, Ashley D., primary, Liu, Juan, primary, Olmstead, Sarah D., primary, Watke, Jackson, primary, Arrate, Maria P., primary, Gorska, Agnieszka E., primary, Fuller, Londa, primary, Locasale, Jason W., primary, Stubbs, Matthew C., primary, Rathmell, Jeffrey C., primary, and Savona, Michael R., primary

Published
2024
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45. Figure S4 from Lactate Utilization Enables Metabolic Escape to Confer Resistance to BET Inhibition in Acute Myeloid Leukemia

Author

Monteith, Andrew J., primary, Ramsey, Haley E., primary, Silver, Alexander J., primary, Brown, Donovan, primary, Greenwood, Dalton, primary, Smith, Brianna N., primary, Wise, Ashley D., primary, Liu, Juan, primary, Olmstead, Sarah D., primary, Watke, Jackson, primary, Arrate, Maria P., primary, Gorska, Agnieszka E., primary, Fuller, Londa, primary, Locasale, Jason W., primary, Stubbs, Matthew C., primary, Rathmell, Jeffrey C., primary, and Savona, Michael R., primary

Published
2024
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46. Data from Lactate Utilization Enables Metabolic Escape to Confer Resistance to BET Inhibition in Acute Myeloid Leukemia

Author

Monteith, Andrew J., primary, Ramsey, Haley E., primary, Silver, Alexander J., primary, Brown, Donovan, primary, Greenwood, Dalton, primary, Smith, Brianna N., primary, Wise, Ashley D., primary, Liu, Juan, primary, Olmstead, Sarah D., primary, Watke, Jackson, primary, Arrate, Maria P., primary, Gorska, Agnieszka E., primary, Fuller, Londa, primary, Locasale, Jason W., primary, Stubbs, Matthew C., primary, Rathmell, Jeffrey C., primary, and Savona, Michael R., primary

Published
2024
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47. Interoperability of phenome-wide multimorbidity patterns: a comparative study of two large-scale EHR systems

Author

Strayer, Nick, primary, Vessels, Tess J., additional, Choi, Karmel W, additional, Zhang, Siwei, additional, Li, Yajing, additional, Sharber, Brian, additional, Hsi, Ryan S, additional, Bejan, Cosmin A, additional, Bick, Alexander G, additional, Balko, Justin M, additional, Johnson, Douglas B, additional, Wheless, Lee E, additional, Wells, Quinn S, additional, Shah, Ravi V, additional, Phillips, Elizabeth J, additional, Self, Wesley H., additional, Pulley, Jill M, additional, Wilkins, Consuelo H, additional, Chen, Qingxia, additional, Hartert, Tina, additional, Savona, Michael R, additional, Shyr, Yu, additional, Roden, Dan M, additional, Smoller, Jordan W, additional, Ruderfer, Douglas M, additional, and Xu, Yaomin, additional

Published
2024
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49. A geno-clinical decision model for the diagnosis of myelodysplastic syndromes

Author

Radakovich, Nathan, Meggendorfer, Manja, Malcovati, Luca, Hilton, C. Beau, Sekeres, Mikkael A., Shreve, Jacob, Rouphail, Yazan, Walter, Wencke, Hutter, Stephan, Galli, Anna, Pozzi, Sara, Elena, Chiara, Padron, Eric, Savona, Michael R., Gerds, Aaron T., Mukherjee, Sudipto, Nagata, Yasunobu, Komrokji, Rami S., Jha, Babal K., Haferlach, Claudia, Maciejewski, Jaroslaw P., Haferlach, Torsten, and Nazha, Aziz

Published
2021
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