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1. Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review

Author

Mao, Lingchao, Wang, Hairong, Hu, Leland S., Tran, Nhan L, Canoll, Peter D, Swanson, Kristin R, and Li, Jing

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, 92B99
Abstract

Cancer remains one of the most challenging diseases to treat in the medical field. Machine learning has enabled in-depth analysis of rich multi-omics profiles and medical imaging for cancer diagnosis and prognosis. Despite these advancements, machine learning models face challenges stemming from limited labeled sample sizes, the intricate interplay of high-dimensionality data types, the inherent heterogeneity observed among patients and within tumors, and concerns about interpretability and consistency with existing biomedical knowledge. One approach to surmount these challenges is to integrate biomedical knowledge into data-driven models, which has proven potential to improve the accuracy, robustness, and interpretability of model results. Here, we review the state-of-the-art machine learning studies that adopted the fusion of biomedical knowledge and data, termed knowledge-informed machine learning, for cancer diagnosis and prognosis. Emphasizing the properties inherent in four primary data types including clinical, imaging, molecular, and treatment data, we highlight modeling considerations relevant to these contexts. We provide an overview of diverse forms of knowledge representation and current strategies of knowledge integration into machine learning pipelines with concrete examples. We conclude the review article by discussing future directions to advance cancer research through knowledge-informed machine learning., Comment: 41 pages, 4 figures, 2 tables

Published
2024

2. Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm

Author

Wang, Lujia, Wang, Hairong, D'Angelo, Fulvio, Curtin, Lee, Sereduk, Christopher P., De Leon, Gustavo, Singleton, Kyle W., Urcuyo, Javier, Hawkins-Daarud, Andrea, Jackson, Pamela R., Krishna, Chandan, Zimmerman, Richard S., Patra, Devi P., Bendok, Bernard R., Smith, Kris A., Nakaji, Peter, Donev, Kliment, Baxter, Leslie C., Mrugała, Maciej M., Ceccarelli, Michele, Iavarone, Antonio, Swanson, Kristin R., Tran, Nhan L., Hu, Leland S., and Li, Jing

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition, Mathematics - Optimization and Control
Abstract

Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models to quantify intra-tumoral genetic heterogeneity for each patient. This capability holds great promise for enabling better therapeutic selection to improve patient outcomes. We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI. WSO-SVM was applied to a unique dataset of 318 image-localized biopsies with spatially matched multiparametric MRI from 74 GBM patients. The model was trained to predict the regional genetic alteration of three GBM driver genes (EGFR, PDGFRA, and PTEN) based on features extracted from the corresponding region of five MRI contrast images. For comparison, a variety of existing ML algorithms were also applied. The classification accuracy of each gene was compared between the different algorithms. The SHapley Additive exPlanations (SHAP) method was further applied to compute contribution scores of different contrast images. Finally, the trained WSO-SVM was used to generate prediction maps within the tumoral area of each patient to help visualize the intra-tumoral genetic heterogeneity. This study demonstrated the feasibility of using MRI and WSO-SVM to enable non-invasive prediction of intra-tumoral regional genetic alteration for each GBM patient, which can inform future adaptive therapies for individualized oncology., Comment: 36 pages, 8 figures, 3 tables

Published
2023

4. Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures

Author

Hu, Leland S., D’Angelo, Fulvio, Weiskittel, Taylor M., Caruso, Francesca P., Fortin Ensign, Shannon P., Blomquist, Mylan R., Flick, Matthew J., Wang, Lujia, Sereduk, Christopher P., Meng-Lin, Kevin, De Leon, Gustavo, Nespodzany, Ashley, Urcuyo, Javier C., Gonzales, Ashlyn C, Curtin, Lee, Lewis, Erika M., Singleton, Kyle W., Dondlinger, Timothy, Anil, Aliya, Semmineh, Natenael B., Noviello, Teresa, Patel, Reyna A., Wang, Panwen, Wang, Junwen, Eschbacher, Jennifer M., Hawkins-Daarud, Andrea, Jackson, Pamela R., Grunfeld, Itamar S., Elrod, Christian, Mazza, Gina L., McGee, Sam C., Paulson, Lisa, Clark-Swanson, Kamala, Lassiter-Morris, Yvette, Smith, Kris A., Nakaji, Peter, Bendok, Bernard R., Zimmerman, Richard S., Krishna, Chandan, Patra, Devi P., Patel, Naresh P., Lyons, Mark, Neal, Matthew, Donev, Kliment, Mrugala, Maciej M., Porter, Alyx B., Beeman, Scott C., Jensen, Todd R., Schmainda, Kathleen M., Zhou, Yuxiang, Baxter, Leslie C., Plaisier, Christopher L., Li, Jing, Li, Hu, Lasorella, Anna, Quarles, C. Chad, Swanson, Kristin R., Ceccarelli, Michele, Iavarone, Antonio, and Tran, Nhan L.

Published
2023
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5. Temporospatial genomic profiling in glioblastoma identifies commonly altered core pathways underlying tumor progression

Author

Blomquist, Mylan R, Ensign, Shannon Fortin, D’Angelo, Fulvio, Phillips, Joanna J, Ceccarelli, Michele, Peng, Sen, Halperin, Rebecca F, Caruso, Francesca P, Garofano, Luciano, Byron, Sara A, Liang, Winnie S, Craig, David W, Carpten, John D, Prados, Michael D, Trent, Jeffrey M, Berens, Michael E, Iavarone, Antonio, Dhruv, Harshil, and Tran, Nhan L

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Clinical Research, Brain Cancer, Rare Diseases, Genetics, Human Genome, Cancer, Neurosciences, Brain Disorders, Aetiology, 2.1 Biological and endogenous factors, clonal evolution, contrast enhancement, glioblastoma, PI3K signaling, temporospatial heterogeneity
Abstract

BackgroundTumor heterogeneity underlies resistance and disease progression in glioblastoma (GBM), and tumors most commonly recur adjacent to the surgical resection margins in contrast non-enhancing (NE) regions. To date, no targeted therapies have meaningfully altered overall patient survival in the up-front setting. The aim of this study was to characterize intratumoral heterogeneity in recurrent GBM using bulk samples from primary resection and recurrent samples taken from contrast-enhancing (EN) and contrast NE regions.MethodsWhole exome and RNA sequencing were performed on matched bulk primary and multiple recurrent EN and NE tumor samples from 16 GBM patients who received standard of care treatment alone or in combination with investigational clinical trial regimens.ResultsPrivate mutations emerge across multi-region sampling in recurrent tumors. Genomic clonal analysis revealed increased enrichment in gene alterations regulating the G2M checkpoint, Kras signaling, Wnt signaling, and DNA repair in recurrent disease. Subsequent functional studies identified augmented PI3K/AKT transcriptional and protein activity throughout progression, validated by phospho-protein levels. Moreover, a mesenchymal transcriptional signature was observed in recurrent EN regions, which differed from the proneural signature in recurrent NE regions.ConclusionsSubclonal populations observed within bulk resected primary GBMs transcriptionally evolve across tumor recurrence (EN and NE regions) and exhibit aberrant gene expression of common signaling pathways that persist despite standard or targeted therapy. Our findings provide evidence that there are both adaptive and clonally mediated dependencies of GBM on key pathways, such as the PI3K/AKT axis, for survival across recurrences.

Published
2020

6. Defining the KRAS- and ERK-dependent transcriptome in KRAS-mutant cancers

Author

Klomp, Jeffrey A., primary, Klomp, Jennifer E., additional, Stalnecker, Clint A., additional, Bryant, Kirsten L., additional, Edwards, A. Cole, additional, Drizyte-Miller, Kristina, additional, Hibshman, Priya S., additional, Diehl, J. Nathaniel, additional, Lee, Ye S., additional, Morales, Alexis J., additional, Taylor, Khalilah E., additional, Peng, Sen, additional, Tran, Nhan L., additional, Herring, Laura E., additional, Prevatte, Alex W., additional, Barker, Natalie K., additional, Hover, Laura D., additional, Hallin, Jill, additional, Chowdhury, Saikat, additional, Coker, Oluwadara, additional, Lee, Hey Min, additional, Goodwin, Craig M., additional, Gautam, Prson, additional, Olson, Peter, additional, Christensen, James G., additional, Shen, John P., additional, Kopetz, Scott, additional, Graves, Lee M., additional, Lim, Kian-Huat, additional, Wang-Gillam, Andrea, additional, Wennerberg, Krister, additional, Cox, Adrienne D., additional, and Der, Channing J., additional

Published
2024
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7. EGFRvIII Confers Sensitivity to Saracatinib in a STAT5-Dependent Manner in Glioblastoma

Author

Blomquist, Mylan R., primary, Eghlimi, Ryan, additional, Beniwal, Angad, additional, Grief, Dustin, additional, Nascari, David G., additional, Inge, Landon, additional, Sereduk, Christopher P., additional, Tuncali, Serdar, additional, Roos, Alison, additional, Inforzato, Hannah, additional, Sharma, Ritin, additional, Pirrotte, Patrick, additional, Mehta, Shwetal, additional, Fortin Ensign, Shannon P., additional, Loftus, Joseph C., additional, and Tran, Nhan L., additional

Published
2024
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8. Evaluation of pre-analytical factors affecting plasma DNA analysis.

Author

Markus, Havell, Contente-Cuomo, Tania, Farooq, Maria, Liang, Winnie S, Borad, Mitesh J, Sivakumar, Shivan, Gollins, Simon, Tran, Nhan L, Dhruv, Harshil D, Berens, Michael E, Bryce, Alan, Sekulic, Aleksandar, Ribas, Antoni, Trent, Jeffrey M, LoRusso, Patricia M, and Murtaza, Muhammed

Subjects
Humans, Blood Specimen Collection, Polymerase Chain Reaction, Female, Male, Cell-Free Nucleic Acids
Abstract

Pre-analytical factors can significantly affect circulating cell-free DNA (cfDNA) analysis. However, there are few robust methods to rapidly assess sample quality and the impact of pre-analytical processing. To address this gap and to evaluate effects of DNA extraction methods and blood collection tubes on cfDNA yield and fragment size, we developed a multiplexed droplet digital PCR (ddPCR) assay with 5 short and 4 long amplicons targeting single copy genomic loci. Using this assay, we compared 7 cfDNA extraction kits and found cfDNA yield and fragment size vary significantly. We also compared 3 blood collection protocols using plasma samples from 23 healthy volunteers (EDTA tubes processed within 1 hour and Cell-free DNA Blood Collection Tubes processed within 24 and 72 hours) and found no significant differences in cfDNA yield, fragment size and background noise between these protocols. In 219 clinical samples, cfDNA fragments were shorter in plasma samples processed immediately after venipuncture compared to archived samples, suggesting contribution of background DNA by lysed peripheral blood cells. In summary, we have described a multiplexed ddPCR assay to assess quality of cfDNA samples prior to downstream molecular analyses and we have evaluated potential sources of pre-analytical variation in cfDNA studies.

Published
2018

9. Prospective Feasibility Trial for Genomics-Informed Treatment in Recurrent and Progressive Glioblastoma

Author

Byron, Sara A, Tran, Nhan L, Halperin, Rebecca F, Phillips, Joanna J, Kuhn, John G, de Groot, John F, Colman, Howard, Ligon, Keith L, Wen, Patrick Y, Cloughesy, Timothy F, Mellinghoff, Ingo K, Butowski, Nicholas A, Taylor, Jennie W, Clarke, Jennifer L, Chang, Susan M, Berger, Mitchel S, Molinaro, Annette M, Maggiora, Gerald M, Peng, Sen, Nasser, Sara, Liang, Winnie S, Trent, Jeffrey M, Berens, Michael E, Carpten, John D, Craig, David W, and Prados, Michael D

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Genetics, Biotechnology, Clinical Trials and Supportive Activities, Pediatric Research Initiative, Neurosciences, Clinical Research, Human Genome, Brain Disorders, Patient Safety, Cancer, Rare Diseases, Brain Cancer, Good Health and Well Being, Adult, Aged, Biomarkers, Tumor, Clinical Decision-Making, Combined Modality Therapy, Disease Management, Disease Progression, Female, Genetic Variation, Genome-Wide Association Study, Genomics, Glioblastoma, Humans, Immunohistochemistry, Male, Middle Aged, Recurrence, Treatment Outcome, Exome Sequencing, Oncology & Carcinogenesis, Clinical sciences, Oncology and carcinogenesis
Abstract

Purpose: Glioblastoma is an aggressive and molecularly heterogeneous cancer with few effective treatment options. We hypothesized that next-generation sequencing can be used to guide treatment recommendations within a clinically acceptable time frame following surgery for patients with recurrent glioblastoma.Experimental Design: We conducted a prospective genomics-informed feasibility trial in adults with recurrent and progressive glioblastoma. Following surgical resection, genome-wide tumor/normal exome sequencing and tumor RNA sequencing were performed to identify molecular targets for potential matched therapy. A multidisciplinary molecular tumor board issued treatment recommendations based on the genomic results, blood-brain barrier penetration of the indicated therapies, drug-drug interactions, and drug safety profiles. Feasibility of generating genomics-informed treatment recommendations within 35 days of surgery was assessed.Results: Of the 20 patients enrolled in the study, 16 patients had sufficient tumor tissue for analysis. Exome sequencing was completed for all patients, and RNA sequencing was completed for 14 patients. Treatment recommendations were provided within the study's feasibility time frame for 15 of 16 (94%) patients. Seven patients received treatment based on the tumor board recommendations. Two patients reached 12-month progression-free survival, both adhering to treatments based on the molecular profiling results. One patient remained on treatment and progression free 21 months after surgery, 3 times longer than the patient's previous time to progression. Analysis of matched nonenhancing tissue from 12 patients revealed overlapping as well as novel putatively actionable genomic alterations.Conclusions: Use of genome-wide molecular profiling is feasible and can be informative for guiding real-time, central nervous system-penetrant, genomics-informed treatment recommendations for patients with recurrent glioblastoma. Clin Cancer Res; 24(2); 295-305. ©2017 AACRSee related commentary by Wick and Kessler, p. 256.

Published
2018

10. Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm

Author

Wang, Lujia, primary, Wang, Hairong, additional, D’Angelo, Fulvio, additional, Curtin, Lee, additional, Sereduk, Christopher P., additional, Leon, Gustavo De, additional, Singleton, Kyle W., additional, Urcuyo, Javier, additional, Hawkins-Daarud, Andrea, additional, Jackson, Pamela R., additional, Krishna, Chandan, additional, Zimmerman, Richard S., additional, Patra, Devi P., additional, Bendok, Bernard R., additional, Smith, Kris A., additional, Nakaji, Peter, additional, Donev, Kliment, additional, Baxter, Leslie C., additional, Mrugała, Maciej M., additional, Ceccarelli, Michele, additional, Iavarone, Antonio, additional, Swanson, Kristin R., additional, Tran, Nhan L., additional, Hu, Leland S., additional, and Li, Jing, additional

Published
2024
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11. Weakly-Supervised Transfer Learning With Application in Precision Medicine

Author

Mao, Lingchao, Wang, Lujia, Hu, Leland S., Eschbacher, Jenny M., Leon, Gustavo De, Singleton, Kyle W., Curtin, Lee A., Urcuyo, Javier, Sereduk, Chris, Tran, Nhan L., Hawkins-Daarud, Andrea, Swanson, Kristin R., and Li, Jing

Abstract

Precision medicine aims to provide diagnosis and treatment accounting for individual differences. To develop machine learning models in support of precision medicine, personalized models are expected to have better performance than one-model-fits-all approaches. A significant challenge, however, is the limited number of labeled samples that can be collected from each individual due to practical constraints. Transfer Learning (TL) addresses this challenge by leveraging the information of other patients with the same disease (i.e., the source domain) when building a personalized model for each patient (i.e., the target domain). We propose Weakly-Supervised Transfer Learning (WS-TL) to tackle two challenges that existing TL algorithms do not address well: (i) the target domain has only a few or even no labeled samples; (ii) how to integrate domain knowledge into the TL design. We design a novel mathematical framework of WS-TL to learn a model for the target domain based on paired samples whose order relationships are inferred from domain knowledge, while at the same time integrating labeled samples in the source domain for transfer learning. Also, we propose an efficient active sampling strategy to select informative paired samples. Theoretical properties were investigated. Finally, we present a real-world application in precision medicine of brain cancer, where WS-TL is used to build personalized patient models to predict Tumor Cell Density (TCD) distribution across the brain based on MRI images. WS-TL has the highest accuracy compared to a variety of existing TL algorithms. The predicted TCD map for each patient can help facilitate individually optimized treatment. Note to Practitioners—This work was motivated by Precision Medicine applications that need to build personalized machine learning models to account for individual differences. Due to limited data from each person, Transfer Learning (TL) provides a promising approach, which can leverage the information of other patients with the same disease (i.e., the source domain) when building a personalized model for each patient (i.e., the target domain). The proposed WS-TL model addresses the application scenarios with two unique properties: (i) the target domain has a few and even no labeled samples, which is a challenging situation that most existing TL methods do not address well; (ii) there is domain knowledge to provide weak labels for a large number of unlabeled samples in the form of order relationships, which provides an opportunity to integrate the domain knowledge into the TL design. We demonstrate WS-TL in a Precision Medicine application for brain cancer and show promising results. WS-TL has the potential of addressing a broad range of other application areas in building personalized models.

Published
2024
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16. Uncertainty quantification in the radiogenomics modeling of EGFR amplification in glioblastoma

Author

Hu, Leland S., Wang, Lujia, Hawkins-Daarud, Andrea, Eschbacher, Jennifer M., Singleton, Kyle W., Jackson, Pamela R., Clark-Swanson, Kamala, Sereduk, Christopher P., Peng, Sen, Wang, Panwen, Wang, Junwen, Baxter, Leslie C., Smith, Kris A., Mazza, Gina L., Stokes, Ashley M., Bendok, Bernard R., Zimmerman, Richard S., Krishna, Chandan, Porter, Alyx B., Mrugala, Maciej M., Hoxworth, Joseph M., Wu, Teresa, Tran, Nhan L., Swanson, Kristin R., and Li, Jing

Published
2021
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17. Toward precision medicine in glioblastoma: the promise and the challenges

Author

Prados, Michael D, Byron, Sara A, Tran, Nhan L, Phillips, Joanna J, Molinaro, Annette M, Ligon, Keith L, Wen, Patrick Y, Kuhn, John G, Mellinghoff, Ingo K, de Groot, John F, Colman, Howard, Cloughesy, Timothy F, Chang, Susan M, Ryken, Timothy C, Tembe, Waibhav D, Kiefer, Jeffrey A, Berens, Michael E, Craig, David W, Carpten, John D, and Trent, Jeffrey M

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Neurosciences, Cancer, Brain Cancer, Rare Diseases, Genetics, Biotechnology, Brain Disorders, Human Genome, Good Health and Well Being, Antineoplastic Agents, Brain Neoplasms, Clinical Trials as Topic, Glioblastoma, Humans, Molecular Targeted Therapy, Mutation, Precision Medicine, clinical trial, genomics, glioblastoma, precision medicine, targeted therapy, Oncology & Carcinogenesis, Oncology and carcinogenesis
Abstract

Integrated sequencing strategies have provided a broader understanding of the genomic landscape and molecular classifications of multiple cancer types and have identified various therapeutic opportunities across cancer subsets. Despite pivotal advances in the characterization of genomic alterations in glioblastoma, targeted agents have shown minimal efficacy in clinical trials to date, and patient survival remains poor. In this review, we highlight potential reasons why targeting single alterations has yielded limited clinical efficacy in glioblastoma, focusing on issues of tumor heterogeneity and pharmacokinetic failure. We outline strategies to address these challenges in applying precision medicine to glioblastoma and the rationale for applying targeted combination therapy approaches that match genomic alterations with compounds accessible to the central nervous system.

Published
2015

18. BULK RNA-SEQ DECONVOLUTION OF IMAGE-LOCALIZED HIGGRADE GLIOMA BIOPSIES REVEALS MEANINGFUL CELLULAR STATES

Author

Curtin, Lee, primary, Bond, Kamila, additional, Velez, Sebastian, additional, Hawkins-Daarud, Andrea, additional, Urcuyo, Javier C, additional, De Leon, Gustavo, additional, Langworthy, Jazlynn, additional, Sereduk, Christopher, additional, Singleton, Kyle W, additional, Jackson, Pamela R, additional, Krishna, Chandan, additional, Zimmerman, Richard S, additional, Patra, Devi P, additional, Bendok, Bernard R, additional, Smith, Kris A, additional, Nakaji, Peter, additional, Donev, Kliment, additional, Baxter, Leslie C, additional, Mrugala, Maciej M, additional, Al-Dalahmah, Osama, additional, Hu, Leland S, additional, Tran, Nhan L, additional, Canoll, Peter, additional, and Swanson, Kristin R, additional

Published
2023
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20. Combination of ERK and autophagy inhibition as a treatment approach for pancreatic cancer

Author

Bryant, Kirsten L., Stalnecker, Clint A., Zeitouni, Daniel, Klomp, Jennifer E., Peng, Sen, Tikunov, Andrey P., Gunda, Venugopal, Pierobon, Mariaelena, Waters, Andrew M., George, Samuel D., Tomar, Garima, Papke, Björn, Hobbs, G. Aaron, Yan, Liang, Hayes, Tikvah K., Diehl, J. Nathaniel, Goode, Gennifer D., Chaika, Nina V., Wang, Yingxue, Zhang, Guo-Fang, Witkiewicz, Agnieszka K., Knudsen, Erik S., Petricoin, III, Emanuel F., Singh, Pankaj K., Macdonald, Jeffrey M., Tran, Nhan L., Lyssiotis, Costas A., Ying, Haoqiang, Kimmelman, Alec C., Cox, Adrienne D., and Der, Channing J.

Published
2019
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21. Image-based models of T-cell distribution identify a clinically meaningful response to a dendritic cell vaccine in patients with glioblastoma

Author

Bond, Kamila M., primary, Curtin, Lee, additional, Hawkins-Daarud, Andrea, additional, Urcuyo, Javier C., additional, De Leon, Gustavo, additional, Singleton, Kyle W., additional, Afshari, Ariana E., additional, Paulson, Lisa E., additional, Sereduk, Christopher P., additional, Smith, Kris A., additional, Nakaji, Peter, additional, Baxter, Leslie C., additional, Patra, Devi Prasad, additional, Gustafson, Michael P., additional, Dietz, Allan B., additional, Zimmerman, Richard S., additional, Bendok, Bernard R., additional, Tran, Nhan L., additional, Hu, Leland S., additional, Parney, Ian F., additional, Rubin, Joshua B., additional, and Swanson, Kristin R., additional

Published
2023
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24. WSD-0922, a novel brain-penetrant inhibitor of EGFR, promotes survival in glioblastoma mouse models

Author

Conage-Pough, Jason E, primary, Stopka, Sylwia A, additional, Oh, Ju-Hee, additional, Mladek, Ann C, additional, Burgenske, Danielle M, additional, Regan, Michael S, additional, Baquer, Gerard, additional, Decker, Paul A, additional, Carlson, Brett L, additional, Bakken, Katrina K, additional, Zhang, Jinqiang, additional, Liu, Lily, additional, Sun, Claire, additional, Mu, Zhihua, additional, Zhong, Wei, additional, Tran, Nhan L, additional, Elmquist, William F, additional, Agar, Nathalie Y R, additional, Sarkaria, Jann N, additional, and White, Forest M, additional

Published
2023
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25. The ATP-exporting channel Pannexin-1 promotes CD8+T cell effector and memory responses

Author

Vardam-Kaur, Trupti, primary, Banuelos, Alma, additional, Gabaldon-Parish, Maria, additional, Macedo, Bruna Gois, additional, Salgado, Caio Loureiro, additional, Wanhainen, Kelsey Marie, additional, Zhou, Maggie Hanqi, additional, van Dijk, Sarah, additional, Santiago-Carvalho, Igor, additional, Beniwal, Angad S., additional, Leff, Chloe L., additional, Peng, Changwei, additional, Tran, Nhan L., additional, Jameson, Stephen C., additional, and Borges da Silva, Henrique, additional

Published
2023
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26. Abstract 1507: Multiregional sampling of high grade glioma identifies regional biologic signatures

Author

Blomquist, Mylan R., primary, Hu, Leland S., additional, D'Angelo, Fulvio, additional, Weiskittel, Taylor M., additional, Caruso, Francesca P., additional, Ensign, Shannon P. Fortin, additional, Sereduk, Christopher, additional, De Leon, Gustavo, additional, Curtin, Lee, additional, Urcuyo, Javier, additional, Gonzalez, Ashlynn, additional, Nespodzany, Ashley, additional, Noviello, Teresa, additional, Eschbacher, Jennifer M., additional, Smith, Kris A., additional, Nakaji, Peter, additional, Bendok, Bernard R., additional, Zimmerman, Richard S., additional, Krishna, Chandan, additional, Patra, Devi, additional, Patel, Naresh, additional, Lyons, Mark, additional, Donev, Kliment, additional, Mrugala, Maciej, additional, Porter, Alyx, additional, Lasorella, Anna, additional, Swanson, Kristin R., additional, Ceccarelli, Michele, additional, Iavarone, Antonio, additional, and Tran, Nhan L., additional

Published
2023
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39. Data from The Fibroblast Growth Factor–Inducible 14 Receptor Is Highly Expressed in HER2-Positive Breast Tumors and Regulates Breast Cancer Cell Invasive Capacity

Author

Willis, Amanda L., primary, Tran, Nhan L., primary, Chatigny, Julie M., primary, Charlton, Nichole, primary, Vu, Hong, primary, Brown, Sharron A.N., primary, Black, Michael A., primary, McDonough, Wendy S., primary, Fortin, Shannon P., primary, Niska, Joshua R., primary, Winkles, Jeffrey A., primary, and Cunliffe, Heather E., primary

Published
2023
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45. Data from Development of Human Serine Protease-Based Therapeutics Targeting Fn14 and Identification of Fn14 as a New Target Overexpressed in TNBC

Author

Zhou, Hong, primary, Mohamedali, Khalid A., primary, Gonzalez-Angulo, Ana Maria, primary, Cao, Yu, primary, Migliorini, Mary, primary, Cheung, Lawrence H., primary, LoBello, Janine, primary, Lei, Xiudong, primary, Qi, Yuan, primary, Hittelman, Walter N., primary, Winkles, Jeffrey A., primary, Tran, Nhan L., primary, and Rosenblum, Michael G., primary

Published
2023
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