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595 results on '"Kalari, Krishna R."'

1. A Short Survey on Set-Based Aggregation Techniques for Single-Vector WSI Representation in Digital Pathology

Author

Hemati, S., Kalari, Krishna R., and Tizhoosh, H. R.

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

Digital pathology is revolutionizing the field of pathology by enabling the digitization, storage, and analysis of tissue samples as whole slide images (WSIs). WSIs are gigapixel files that capture the intricate details of tissue samples, providing a rich source of information for diagnostic and research purposes. However, due to their enormous size, representing these images as one compact vector is essential for many computational pathology tasks, such as search and retrieval, to ensure efficiency and scalability. Most current methods are "patch-oriented," meaning they divide WSIs into smaller patches for processing, which prevents a holistic analysis of the entire slide. Additionally, the necessity for compact representation is driven by the expensive high-performance storage required for WSIs. Not all hospitals have access to such extensive storage solutions, leading to potential disparities in healthcare quality and accessibility. This paper provides an overview of existing set-based approaches to single-vector WSI representation, highlighting the innovations that allow for more efficient and effective use of these complex images in digital pathology, thus addressing both computational challenges and storage limitations.

Published
2024

2. Sweetwater: An interpretable and adaptive autoencoder for efficient tissue deconvolution

Author

de la Fuente, Jesus, Legarra, Naroa, Serrano, Guillermo, Marin-Goni, Irene, Diaz-Mazkiaran, Aintzane, Sendin, Markel Benito, Osta, Ana Garcia, Kalari, Krishna R., Fernandez-Granda, Carlos, Ochoa, Idoia, and Hernaez, Mikel

Subjects
Quantitative Biology - Genomics, Quantitative Biology - Quantitative Methods
Abstract

Single-cell RNA-sequencing (scRNA-seq) stands as a powerful tool for deciphering cellular heterogeneity and exploring gene expression profiles at high resolution. However, its high cost renders it impractical for extensive sample cohorts within routine clinical care, hindering its broader applicability. Hence, many methodologies have recently arised to estimate cell type proportions from bulk RNA-seq samples (known as deconvolution methods). However, they have several limitations: Many depend on selecting a robust scRNA-seq reference dataset, which is often challenging. Secondly, building reliable pseudobulk samples requires determining the optimal number of genes or cells involved in the simulated data generation process, which has not been studied in depth. Moreover, pseudobulk and bulk RNA-seq samples often exhibit distribution shifts. Finally, most modern deconvolution approaches behave as a black box, and the underlying mechanisms of the deconvolution task are still unknown, which can compromise the reliability of the results. In this work, we present Sweetwater, an adaptive and interpretable autoencoder able to efficiently deconvolve bulk RNA-seq and microarray samples leveraging multiple classes of reference data, such as scRNA-seq and single-nuclei RNA-seq. Moreover, it can be trained on a mixture of FACS-sorted FASTQ files, which we newly propose to use as this reduces platform-specific biases and may potentially outperform single-cell-based references. Also, we demonstrate that Sweetwater effectively uncovers biologically meaningful patterns during the training process, increasing the reliability of the results. Sweetwater is available at https://github.com/ubioinformat/Sweetwater, and we anticipate will facilitate and expedite the accurate examination of high-throughput clinical data across diverse applications.

Published
2023

4. Endoxifen downregulates AKT phosphorylation through protein kinase C beta 1 inhibition in ERα+ breast cancer

Author

Jayaraman, Swaathi, Wu, Xinyan, Kalari, Krishna R., Tang, Xiaojia, Kuffel, Mary J., Bruinsma, Elizabeth S., Jalali, Shahrzad, Peterson, Kevin L., Correia, Cristina, Kudgus, Rachel A., Kaufmann, Scott H., Renuse, Santosh, Ingle, James N., Reid, Joel M., Ames, Matthew M., Fields, Alan P., Schellenberg, Matthew J., Hawse, John R., Pandey, Akhilesh, and Goetz, Matthew P.

Published
2023
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5. Distinct spatial immune microlandscapes are independently associated with outcomes in triple-negative breast cancer

Author

Carter, Jodi M., Chumsri, Saranya, Hinerfeld, Douglas A., Ma, Yaohua, Wang, Xue, Zahrieh, David, Hillman, David W., Tenner, Kathleen S., Kachergus, Jennifer M., Brauer, Heather Ann, Warren, Sarah E., Henderson, David, Shi, Ji, Liu, Yi, Joensuu, Heikki, Lindman, Henrik, Leon-Ferre, Roberto A., Boughey, Judy C., Liu, Minetta C., Ingle, James N., Kalari, Krishna R., Couch, Fergus J., Knutson, Keith L., Goetz, Matthew P., Perez, Edith A., and Thompson, E. Aubrey

Published
2023
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8. Implementation of preemptive DNA sequence–based pharmacogenomics testing across a large academic medical center: The Mayo-Baylor RIGHT 10K Study

Author

Wang, Liewei, Scherer, Steven E., Bielinski, Suzette J., Muzny, Donna M., Jones, Leila A., Black, John Logan, III, Moyer, Ann M., Giri, Jyothsna, Sharp, Richard R., Matey, Eric T., Wright, Jessica A., Oyen, Lance J., Nicholson, Wayne T., Wiepert, Mathieu, Sullard, Terri, Curry, Timothy B., Rohrer Vitek, Carolyn R., McAllister, Tammy M., St. Sauver, Jennifer L., Caraballo, Pedro J., Lazaridis, Konstantinos N., Venner, Eric, Qin, Xiang, Hu, Jianhong, Kovar, Christie L., Korchina, Viktoriya, Walker, Kimberly, Doddapaneni, HarshaVardhan, Wu, Tsung-Jung, Raj, Ritika, Denson, Shawn, Liu, Wen, Chandanavelli, Gauthami, Zhang, Lan, Wang, Qiaoyan, Kalra, Divya, Karow, Mary Beth, Harris, Kimberley J., Sicotte, Hugues, Peterson, Sandra E., Barthel, Amy E., Moore, Brenda E., Skierka, Jennifer M., Kluge, Michelle L., Kotzer, Katrina E., Kloke, Karen, Vander Pol, Jessica M., Marker, Heather, Sutton, Joseph A., Kekic, Adrijana, Ebenhoh, Ashley, Bierle, Dennis M., Schuh, Michael J., Grilli, Christopher, Erickson, Sara, Umbreit, Audrey, Ward, Leah, Crosby, Sheena, Nelson, Eric A., Levey, Sharon, Elliott, Michelle, Peters, Steve G., Pereira, Naveen, Frye, Mark, Shamoun, Fadi, Goetz, Matthew P., Kullo, Iftikhar J., Wermers, Robert, Anderson, Jan A., Formea, Christine M., El Melik, Razan M., Zeuli, John D., Herges, Joseph R., Krieger, Carrie A., Hoel, Robert W., Taraba, Jodi L., St. Thomas, Scott R., Absah, Imad, Bernard, Matthew E., Fink, Stephanie R., Gossard, Andrea, Grubbs, Pamela L., Jacobson, Therese M., Takahashi, Paul, Zehe, Sharon C., Buckles, Susan, Bumgardner, Michelle, Gallagher, Colette, Fee-Schroeder, Kelliann, Nicholas, Nichole R., Powers, Melody L., Ragab, Ahmed K., Richardson, Darcy M., Stai, Anthony, Wilson, Jaymi, Pacyna, Joel E., Olson, Janet E., Sutton, Erica J., Beck, Annika T., Horrow, Caroline, Kalari, Krishna R., Larson, Nicholas B., Liu, Hongfang, Wang, Liwei, Lopes, Guilherme S., Borah, Bijan J., Freimuth, Robert R., Zhu, Ye, Jacobson, Debra J., Hathcock, Matthew A., Armasu, Sebastian M., McGree, Michaela E., Jiang, Ruoxiang, Koep, Tyler H., Ross, Jason L., Hilden, Matthew G., Bosse, Kathleen, Ramey, Bronwyn, Searcy, Isabelle, Boerwinkle, Eric, Gibbs, Richard A., and Weinshilboum, Richard M.

Published
2022
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10. Estrogen receptor beta repurposes EZH2 to suppress oncogenic NFκB/p65 signaling in triple negative breast cancer

Author

Aspros, Kirsten G. M., Carter, Jodi M., Hoskin, Tanya L., Suman, Vera J., Subramaniam, Malayannan, Emch, Michael J., Ye, Zhenqing, Sun, Zhifu, Sinnwell, Jason P., Thompson, Kevin J., Tang, Xiaojia, Rodman, Esther P. B., Wang, Xiyin, Nelson, Adam W., Chernukhin, Igor, Hamdan, Feda H., Bruinsma, Elizabeth S., Carroll, Jason S., Fernandez-Zapico, Martin E., Johnsen, Steven A., Kalari, Krishna R., Huang, Haojie, Leon-Ferre, Roberto A., Couch, Fergus J., Ingle, James N., Goetz, Matthew P., and Hawse, John R.

Published
2022
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13. OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks

Author

Tang, Xiaojia, primary, Prodduturi, Naresh, additional, Thompson, Kevin J., additional, Weinshilboum, Richard, additional, O’Sullivan, Ciara C., additional, Boughey, Judy C., additional, Tizhoosh, Hamid R., additional, Klee, Eric W., additional, Wang, Liewei, additional, Goetz, Matthew P., additional, Suman, Vera, additional, and Kalari, Krishna R., additional

Published
2024
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15. Toward Individualized Prediction of Response to Methotrexate in Early Rheumatoid Arthritis: A Pharmacogenomics‐Driven Machine Learning Approach

Author

Myasoedova, Elena, Athreya, Arjun P., Crowson, Cynthia S., Davis, John M., III, Warrington, Kenneth J., Walchak, Robert C., Carlson, Erin, Kalari, Krishna R., Bongartz, Tim, Tak, Paul P., van Vollenhoven, Ronald F., Padyukov, Leonid, Emery, Paul, Morgan, Ann, Wang, Liewei, Weinshilboum, Richard M., and Matteson, Eric L.

Published
2022
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16. Correction to: Establishing and characterizing patient-derived xenografts using pre-chemotherapy percutaneous biopsy and post-chemotherapy surgical samples from a prospective neoadjuvant breast cancer study

Author

Yu, Jia, Qin, Bo, Moyer, Ann M., Sinnwell, Jason P., Thompson, Kevin J., Copland, III, John A., Marlow, Laura A., Miller, James L., Yin, Ping, Gao, Bowen, Minter-Dykhouse, Katherine, Tang, Xiaojia, McLaughlin, Sarah A., Moreno-Aspitia, Alvaro, Schweitzer, Anthony, Lu, Yan, Hubbard, Jason, Northfelt, Donald W., Gray, Richard J., Hunt, Katie, Conners, Amy L., Suman, Vera J., Kalari, Krishna R., Ingle, James N., Lou, Zhenkun, Visscher, Daniel W., Weinshilboum, Richard, Boughey, Judy C., Goetz, Matthew P., and Wang, Liewei

Published
2021
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18. ReCorDE: a framework for identifying drug classes targeting shared vulnerabilities with applications to synergistic drug discovery.

Author

John, August J., Ghose, Emily T., Huanyao Gao, Luck, Meagan, Jeong, Dabin, Kalari, Krishna R., and Liewei Wang

Subjects
DRUG discovery, AURORA kinases, CYTOTOXINS, KINASE inhibitors, DRUG addiction
Abstract

Cancer is typically treated with combinatorial therapy, and such combinations may be synergistic. However, discovery of these combinations has proven difficult as brute force combinatorial screening approaches are both logistically complex and resource-intensive. Therefore, computational approaches to augment synergistic drug discovery are of interest, but current approaches are limited by their dependencies on combinatorial drug screening training data or molecular profiling data. These dataset dependencies can limit the number and diversity of drugs for which these approaches can make inferences. Herein, we describe a novel computational framework, ReCorDE (Recurrent Correlation of Drugs with Enrichment), that uses publicly-available cell line-derived monotherapy cytotoxicity datasets to identify drug classes targeting shared vulnerabilities across multiple cancer lineages; and we show how these inferences can be used to augment synergistic drug combination discovery. Additionally, we demonstrate in preclinical models that a drug class combination predicted by ReCorDE to target shared vulnerabilities (PARP inhibitors and Aurora kinase inhibitors) exhibits class-class synergy across lineages. ReCorDE functions independently of combinatorial drug screening and molecular profiling data, using only extensive monotherapy cytotoxicity datasets as its input. This allows ReCorDE to make robust inferences for a large, diverse array of drugs. In conclusion, we have described a novel framework for the identification of drug classes targeting shared vulnerabilities using monotherapy cytotoxicity datasets, and we showed how these inferences can be used to aid discovery of novel synergistic drug combinations. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Table S2 from Characteristics and Spatially Defined Immune (micro)landscapes of Early-stage PD-L1–positive Triple-negative Breast Cancer

Author

Carter, Jodi M., primary, Polley, Mei-Yin C., primary, Leon-Ferre, Roberto A., primary, Sinnwell, Jason, primary, Thompson, Kevin J., primary, Wang, Xue, primary, Ma, Yaohua, primary, Zahrieh, David, primary, Kachergus, Jennifer M., primary, Solanki, Malvika, primary, Boughey, Judy C., primary, Liu, Minetta C., primary, Ingle, James N., primary, Kalari, Krishna R., primary, Couch, Fergus J., primary, Thompson, E. Aubrey, primary, and Goetz, Matthew P., primary

Published
2023
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20. Vitamin D Levels, Vitamin D Receptor Polymorphisms, and Inflammatory Cytokines in Aromatase Inhibitor-Induced Arthralgias: An Analysis of CCTG MA.27

Author

Niravath, Polly, Chen, Bingshu, Chapman, Judy-Anne W., Agarwal, Sandeep K., Welschhans, Robert L., Bongartz, Tim, Kalari, Krishna R., Shepherd, Lois E., Bartlett, John, Pritchard, Kathleen, Gelmon, Karen, Hilsenbeck, Susan G., Rimawi, Mothaffar F., Osborne, C. Kent, Goss, Paul E., and Ingle, James N.

Published
2018
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22. Folate receptor alpha expression associates with improved disease-free survival in triple negative breast cancer patients

Author

Norton, Nadine, Youssef, Bahaaeldin, Hillman, David W., Nassar, Aziza, Geiger, Xochiquetzal J., Necela, Brian M., Liu, Heshan, Ruddy, Kathryn J., Polley, Mei-Yin C., Ingle, James N., Couch, Fergus J., Perez, Edith A., Liu, Minetta C., Carter, Jodi M., Leon-Ferre, Roberto A., Boughey, Judy C., Somers, Elizabeth B., Kalari, Krishna R., Visscher, Daniel W., Goetz, Matthew P., and Knutson, Keith L.

Published
2020
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23. DNA methyltransferase expression in triple-negative breast cancer predicts sensitivity to decitabine

Author

Yu, Jia, Qin, Bo, Moyer, Ann M., Nowsheen, Somaira, Liu, Tongzheng, Qin, Sisi, Zhuang, Yongxian, Liu, Duan, Lu, Shijia W., Kalari, Krishna R., Visscher, Daniel W., Copland, John A., McLaughlin, Sarah A., Moreno-Aspitia, Alvaro, Gray, Donald W. Northfel Richard J., Lou, Zhenkun, Suman, Vera J., Weinshilboum, Richard, Boughey, Judy C., Goetz, Matthew P., and Wang, Liewei

Subjects
Epigenetic inheritance -- Health aspects, Triple negative breast cancer -- Drug therapy -- Prognosis, Methyltransferases -- Physiological aspects -- Health aspects, Decitabine -- Dosage and administration, Health care industry
Abstract

Triple-negative breast cancer (TNBC) is a heterogeneous disease with poor prognosis that lacks targeted therapies, especially in patients with chemotherapy-resistant disease. Since DNA methylation-induced silencing of tumor suppressors is common in cancer, reversal of promoter DNA hypermethylation by 5-aza-2'-deoxycytidine (decitabine), an FDA-approved DNA methyltransferase (DNMT) inhibitor, has proven effective in treating hematological neoplasms. However, its antitumor effect varies in solid tumors, stressing the importance of identifying biomarkers predictive of therapeutic response. Here, we focused on the identification of biomarkers to select decitabine-sensitive TNBC through increasing our understanding of the mechanism of decitabine action. We showed that protein levels of DNMTs correlated with response to decitabine in patient-derived xenograft (PDX) organoids originating from chemotherapy-sensitive and -resistant TNBCs, suggesting DNMT levels as potential biomarkers of response. Furthermore, all 3 methytransferases, DNMT1, DNMT3A, and DNMT3B, were degraded following low-concentration, long-term decitabine treatment both in vitro and in vivo. The DNMT proteins could be ubiquitinated by the E3 ligase, TNF receptor-associated factor 6 (TRAF6), leading to lysosome-dependent protein degradation. Depletion of TRAF6 blocked decitabine-induced DNMT degradation, conferring resistance to decitabine. Our study suggests a potential mechanism of regulating DNMT protein degradation and DNMT levels as response biomarkers for DNMT inhibitors in TNBCs., Introduction Breast cancer is the second leading cause of cancer death in women, with 40,610 women predicted to die from breast cancer in 2017 (1). Triple-negative breast cancer (TNBC), characterized [...]

Published
2018
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24. FOXA1 overexpression suppresses interferon signaling and immune response in cancer

Author

He, Yundong, Wang, Liguo, Wei, Ting, Xiao, Yu-Tian, Sheng, Haoyue, Su, Hengchuan, Hollern, Daniel P., Zhang, Xiaoling, Ma, Jian, Wen, Simeng, Xie, Hongyan, Yan, Yuqian, Pan, Yunqian, Hou, Xiaonan, Tang, Xiaojia, Suman, Vera J., Carter, Jodi M., Weinshilboum, Richard, Wang, Liewei, Kalari, Krishna R., Weroha, Saravut J., Bryce, Alan H., Boughey, Judy C., Dong, Haidong, Perou, Charles M., Ye, Dingwei, Goetz, Matthew P., Ren, Shancheng, and Huang, Haojie

Subjects
Interferon -- Genetic aspects -- Health aspects, Oncology, Experimental, Gene expression -- Research, Prostate cancer -- Genetic aspects -- Development and progression, Immune response -- Genetic aspects -- Health aspects, Cellular signal transduction -- Research, Breast cancer -- Genetic aspects -- Development and progression, Cancer -- Research, Health care industry
Abstract

Androgen receptor-positive prostate cancer (PCa) and estrogen receptor-positive luminal breast cancer (BCa) are generally less responsive to immunotherapy compared with certain tumor types such as melanoma. However, the underlying mechanisms are not fully elucidated. In this study, we found that FOXA1 overexpression inversely correlated with interferon (IFN) signature and antigen presentation gene expression in PCa and BCa patients. FOXA1 bound the STAT2 DNA- binding domain and suppressed STAT2 DNA-binding activity, IFN signaling gene expression, and cancer immune response independently of the transactivation activity of FOXA1 and its mutations detected in PCa and BCa. Increased FOXA1 expression promoted cancer immuno- and chemotherapy resistance in mice and PCa and BCa patients. These findings were also validated in bladder cancer expressing high levels of FOXA1. FOXA1 overexpression could be a prognostic factor to predict therapy resistance and a viable target to sensitize luminal PCa, BCa, and bladder cancer to immuno- and chemotherapy., Introduction Patients with certain cancer types such as melanoma and non-small cell lung cancer have significantly benefited from immunotherapies such as immune checkpoint inhibitors (ICIs) (1). However, ICIs including the [...]

Published
2021
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25. Supplementary File 3 from Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer

Author

Blatti, Charles, primary, de la Fuente, Jesús, primary, Gao, Huanyao, primary, Marín-Goñi, Irene, primary, Chen, Zikun, primary, Zhao, Sihai D., primary, Tan, Winston, primary, Weinshilboum, Richard, primary, Kalari, Krishna R., primary, Wang, Liewei, primary, and Hernaez, Mikel, primary

Published
2023
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26. Supplementary File 2 from Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer

Author

Blatti, Charles, primary, de la Fuente, Jesús, primary, Gao, Huanyao, primary, Marín-Goñi, Irene, primary, Chen, Zikun, primary, Zhao, Sihai D., primary, Tan, Winston, primary, Weinshilboum, Richard, primary, Kalari, Krishna R., primary, Wang, Liewei, primary, and Hernaez, Mikel, primary

Published
2023
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27. Supplementary File 1 from Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer

Author

Blatti, Charles, primary, de la Fuente, Jesús, primary, Gao, Huanyao, primary, Marín-Goñi, Irene, primary, Chen, Zikun, primary, Zhao, Sihai D., primary, Tan, Winston, primary, Weinshilboum, Richard, primary, Kalari, Krishna R., primary, Wang, Liewei, primary, and Hernaez, Mikel, primary

Published
2023
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29. Supplementary File 4 from Bayesian Machine Learning Enables Identification of Transcriptional Network Disruptions Associated with Drug-Resistant Prostate Cancer

Author

Blatti, Charles, primary, de la Fuente, Jesús, primary, Gao, Huanyao, primary, Marín-Goñi, Irene, primary, Chen, Zikun, primary, Zhao, Sihai D., primary, Tan, Winston, primary, Weinshilboum, Richard, primary, Kalari, Krishna R., primary, Wang, Liewei, primary, and Hernaez, Mikel, primary

Published
2023
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33. Supplementary Figure from Molecular Profile Changes in Patients with Castrate-Resistant Prostate Cancer Pre- and Post-Abiraterone/Prednisone Treatment

Author

Sicotte, Hugues, primary, Kalari, Krishna R., primary, Qin, Sisi, primary, Dehm, Scott M., primary, Bhargava, Vipul, primary, Gormley, Michael, primary, Tan, Winston, primary, Sinnwell, Jason P., primary, Hillman, David W., primary, Li, Ying, primary, Vedell, Peter T., primary, Carlson, Rachel E., primary, Bryce, Alan H., primary, Jimenez, Raphael E., primary, Weinshilboum, Richard M., primary, Kohli, Manish, primary, and Wang, Liewei, primary

Published
2023
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40. Data from Molecular Profile Changes in Patients with Castrate-Resistant Prostate Cancer Pre- and Post-Abiraterone/Prednisone Treatment

Author

Sicotte, Hugues, primary, Kalari, Krishna R., primary, Qin, Sisi, primary, Dehm, Scott M., primary, Bhargava, Vipul, primary, Gormley, Michael, primary, Tan, Winston, primary, Sinnwell, Jason P., primary, Hillman, David W., primary, Li, Ying, primary, Vedell, Peter T., primary, Carlson, Rachel E., primary, Bryce, Alan H., primary, Jimenez, Raphael E., primary, Weinshilboum, Richard M., primary, Kohli, Manish, primary, and Wang, Liewei, primary

Published
2023
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42. Supplementary Data from Molecular Profile Changes in Patients with Castrate-Resistant Prostate Cancer Pre- and Post-Abiraterone/Prednisone Treatment

Author

Sicotte, Hugues, primary, Kalari, Krishna R., primary, Qin, Sisi, primary, Dehm, Scott M., primary, Bhargava, Vipul, primary, Gormley, Michael, primary, Tan, Winston, primary, Sinnwell, Jason P., primary, Hillman, David W., primary, Li, Ying, primary, Vedell, Peter T., primary, Carlson, Rachel E., primary, Bryce, Alan H., primary, Jimenez, Raphael E., primary, Weinshilboum, Richard M., primary, Kohli, Manish, primary, and Wang, Liewei, primary

Published
2023
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45. Deep sequencing across germline genome-wide association study signals relating to breast cancer events in women receiving aromatase inhibitors for adjuvant therapy of early breast cancer

Author

Ingle, James N., Kalari, Krishna R., Momozawa, Yukihide, Kubo, Michiaki, Furukawa, Yoichi, Shepherd, Lois E., Ellis, Matthew J., Goss, Paul E., Barman, Poulami, Carlson, Erin E., Sinnwell, Jason P., Tang, Xiaojia, Goetz, Matthew P., Chen, Bingshu E., Cairns, Junmei, Weinshilboum, Richard M., and Wang, Liewei

Published
2019
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48. Supplementary Tables from A Transcriptionally Definable Subgroup of Triple-Negative Breast and Ovarian Cancer Samples Shows Sensitivity to HSP90 Inhibition

Author

Shee, Kevin, primary, Wells, Jason D., primary, Ung, Matthew, primary, Hampsch, Riley A., primary, Traphagen, Nicole A., primary, Yang, Wei, primary, Liu, Stephanie C., primary, Zeldenrust, Megan A., primary, Wang, Liewei, primary, Kalari, Krishna R., primary, Yu, Jia, primary, Boughey, Judy C., primary, Demidenko, Eugene, primary, Kettenbach, Arminja N., primary, Cheng, Chao, primary, Goetz, Matthew P., primary, and Miller, Todd W., primary

Published
2023
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49. Supplementary Data from A Transcriptionally Definable Subgroup of Triple-Negative Breast and Ovarian Cancer Samples Shows Sensitivity to HSP90 Inhibition

Author

Shee, Kevin, primary, Wells, Jason D., primary, Ung, Matthew, primary, Hampsch, Riley A., primary, Traphagen, Nicole A., primary, Yang, Wei, primary, Liu, Stephanie C., primary, Zeldenrust, Megan A., primary, Wang, Liewei, primary, Kalari, Krishna R., primary, Yu, Jia, primary, Boughey, Judy C., primary, Demidenko, Eugene, primary, Kettenbach, Arminja N., primary, Cheng, Chao, primary, Goetz, Matthew P., primary, and Miller, Todd W., primary

Published
2023
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50. Supplementary Figure S2 from Genetic Polymorphisms in the Long Noncoding RNA MIR2052HG Offer a Pharmacogenomic Basis for the Response of Breast Cancer Patients to Aromatase Inhibitor Therapy

Author

Ingle, James N., primary, Xie, Fang, primary, Ellis, Matthew J., primary, Goss, Paul E., primary, Shepherd, Lois E., primary, Chapman, Judith-Anne W., primary, Chen, Bingshu E., primary, Kubo, Michiaki, primary, Furukawa, Yoichi, primary, Momozawa, Yukihide, primary, Stearns, Vered, primary, Pritchard, Kathleen I., primary, Barman, Poulami, primary, Carlson, Erin E., primary, Goetz, Matthew P., primary, Weinshilboum, Richard M., primary, Kalari, Krishna R., primary, and Wang, Liewei, primary

Published
2023
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