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277 results on '"Hafner, Marc"'

2. heredERA Breast Cancer: a phase III, randomized, open-label study evaluating the efficacy and safety of giredestrant plus the fixed-dose combination of pertuzumab and trastuzumab for subcutaneous injection in patients with previously untreated HER2-positive, estrogen receptor-positive locally advanced or metastatic breast cancer

Author

Kuemmel, Sherko, Harper-Wynne, Catherine, Park, Yeon Hee, Franke, Fábio, de Laurentiis, Michelino, Schumacher-Wulf, Eva, Eiger, Daniel, Heeson, Sarah, Cardona, Andrés, Özyilkan, Özgür, Morales-Vàsquez, Flavia, Metcalfe, Ciara, Hafner, Marc, Restuccia, Eleonora, and O’Shaughnessy, Joyce

Published
2024
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4. Integration of Graph Neural Network and Neural-ODEs for Tumor Dynamic Prediction

Author

Bazgir, Omid, Wang, Zichen, Park, Ji Won, Hafner, Marc, and Lu, James

Subjects
Computer Science - Machine Learning
Abstract

In anti-cancer drug development, a major scientific challenge is disentangling the complex relationships between high-dimensional genomics data from patient tumor samples, the corresponding tumor's organ of origin, the drug targets associated with given treatments and the resulting treatment response. Furthermore, to realize the aspirations of precision medicine in identifying and adjusting treatments for patients depending on the therapeutic response, there is a need for building tumor dynamic models that can integrate both longitudinal tumor size as well as multimodal, high-content data. In this work, we take a step towards enhancing personalized tumor dynamic predictions by proposing a heterogeneous graph encoder that utilizes a bipartite Graph Convolutional Neural network (GCN) combined with Neural Ordinary Differential Equations (Neural-ODEs). We applied the methodology to a large collection of patient-derived xenograft (PDX) data, spanning a wide variety of treatments (as well as their combinations) on tumors that originated from a number of different organs. We first show that the methodology is able to discover a tumor dynamic model that significantly improves upon an empirical model which is in current use. Additionally, we show that the graph encoder is able to effectively utilize multimodal data to enhance tumor predictions. Our findings indicate that the methodology holds significant promise and offers potential applications in pre-clinical settings.

Published
2023

7. Evaluation of statistical approaches for association testing in noisy drug screening data

Author

Smirnov, Petr, Smith, Ian, Safikhani, Zhaleh, Ba-alawi, Wail, Khodakarami, Farnoosh, Lin, Eva, Yu, Yihong, Martin, Scott, Ortmann, Janosch, Aittokallio, Tero, Hafner, Marc, and Haibe-Kains, Benjamin

Subjects
Statistics - Applications
Abstract

dentifying associations among biological variables is a major challenge in modern quantitative biological research, particularly given the systemic and statistical noise endemic to biological systems. Drug sensitivity data has proven to be a particularly challenging field for identifying associations to inform patient treatment. To address this, we introduce two semi-parametric variations on the commonly used concordance index: the robust concordance index and the kernelized concordance index (rCI, kCI), which incorporate measurements about the noise distribution from the data. We demonstrate that common statistical tests applied to the concordance index and its variations fail to control for false positives, and introduce efficient implementations to compute p-values using adaptive permutation testing. We then evaluate the statistical power of these coefficients under simulation and compare with Pearson and Spearman correlation coefficients. Finally, we evaluate the various statistics in matching drugs across pharmacogenomic datasets. We observe that the rCI and kCI are better powered than the concordance index in simulation and show some improvement on real data. Surprisingly, we observe that the Pearson correlation was the most robust to measurement noise among the different metrics.

Published
2021

8. An allosteric pan-TEAD inhibitor blocks oncogenic YAP/TAZ signaling and overcomes KRAS G12C inhibitor resistance

Author

Hagenbeek, Thijs J., Zbieg, Jason R., Hafner, Marc, Mroue, Rana, Lacap, Jennifer A., Sodir, Nicole M., Noland, Cameron L., Afghani, Shervin, Kishore, Ayush, Bhat, Kamakoti P., Yao, Xiaosai, Schmidt, Stephen, Clausen, Saundra, Steffek, Micah, Lee, Wendy, Beroza, Paul, Martin, Scott, Lin, Eva, Fong, Rina, Di Lello, Paola, Kubala, Marta H., Yang, Michelle N.-Y., Lau, Jeffrey T., Chan, Emily, Arrazate, Alfonso, An, Le, Levy, Elizabeth, Lorenzo, Maria N., Lee, Ho-June, Pham, Trang H., Modrusan, Zora, Zang, Richard, Chen, Yi-Chen, Kabza, Michal, Ahmed, Musaddeque, Li, Jason, Chang, Matthew T., Maddalo, Danilo, Evangelista, Marie, Ye, Xin, Crawford, James J., and Dey, Anwesha

Published
2023
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10. Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients

Author

Ma, Jianzhu, Fong, Samson H, Luo, Yunan, Bakkenist, Christopher J, Shen, John Paul, Mourragui, Soufiane, Wessels, Lodewyk FA, Hafner, Marc, Sharan, Roded, Peng, Jian, and Ideker, Trey

Subjects
Clinical Research, 2.1 Biological and endogenous factors, Aetiology, Generic health relevance, DNA-Binding Proteins, Humans, Machine Learning, Neural Networks, Computer, Ubiquitin-Protein Ligases
Abstract

Cell-line screens create expansive datasets for learning predictive markers of drug response, but these models do not readily translate to the clinic with its diverse contexts and limited data. In the present study, we apply a recently developed technique, few-shot machine learning, to train a versatile neural network model in cell lines that can be tuned to new contexts using few additional samples. The model quickly adapts when switching among different tissue types and in moving from cell-line models to clinical contexts, including patient-derived tumor cells and patient-derived xenografts. It can also be interpreted to identify the molecular features most important to a drug response, highlighting critical roles for RB1 and SMAD4 in the response to CDK inhibition and RNF8 and CHD4 in the response to ATM inhibition. The few-shot learning framework provides a bridge from the many samples surveyed in high-throughput screens (n-of-many) to the distinctive contexts of individual patients (n-of-one).

Published
2021

14. The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations

Author

Keenan, Alexandra B, Jenkins, Sherry L, Jagodnik, Kathleen M, Koplev, Simon, He, Edward, Torre, Denis, Wang, Zichen, Dohlman, Anders B, Silverstein, Moshe C, Lachmann, Alexander, Kuleshov, Maxim V, Ma'ayan, Avi, Stathias, Vasileios, Terryn, Raymond, Cooper, Daniel, Forlin, Michele, Koleti, Amar, Vidovic, Dusica, Chung, Caty, Schürer, Stephan C, Vasiliauskas, Jouzas, Pilarczyk, Marcin, Shamsaei, Behrouz, Fazel, Mehdi, Ren, Yan, Niu, Wen, Clark, Nicholas A, White, Shana, Mahi, Naim, Zhang, Lixia, Kouril, Michal, Reichard, John F, Sivaganesan, Siva, Medvedovic, Mario, Meller, Jaroslaw, Koch, Rick J, Birtwistle, Marc R, Iyengar, Ravi, Sobie, Eric A, Azeloglu, Evren U, Kaye, Julia, Osterloh, Jeannette, Haston, Kelly, Kalra, Jaslin, Finkbiener, Steve, Li, Jonathan, Milani, Pamela, Adam, Miriam, Escalante-Chong, Renan, Sachs, Karen, Lenail, Alex, Ramamoorthy, Divya, Fraenkel, Ernest, Daigle, Gavin, Hussain, Uzma, Coye, Alyssa, Rothstein, Jeffrey, Sareen, Dhruv, Ornelas, Loren, Banuelos, Maria, Mandefro, Berhan, Ho, Ritchie, Svendsen, Clive N, Lim, Ryan G, Stocksdale, Jennifer, Casale, Malcolm S, Thompson, Terri G, Wu, Jie, Thompson, Leslie M, Dardov, Victoria, Venkatraman, Vidya, Matlock, Andrea, Van Eyk, Jennifer E, Jaffe, Jacob D, Papanastasiou, Malvina, Subramanian, Aravind, Golub, Todd R, Erickson, Sean D, Fallahi-Sichani, Mohammad, Hafner, Marc, Gray, Nathanael S, Lin, Jia-Ren, Mills, Caitlin E, Muhlich, Jeremy L, Niepel, Mario, Shamu, Caroline E, Williams, Elizabeth H, Wrobel, David, Sorger, Peter K, Heiser, Laura M, Gray, Joe W, Korkola, James E, Mills, Gordon B, LaBarge, Mark, Feiler, Heidi S, Dane, Mark A, Bucher, Elmar, Nederlof, Michel, Sudar, Damir, and Gross, Sean

Subjects
Bioengineering, Cancer, Genetics, Biotechnology, 2.1 Biological and endogenous factors, Aetiology, Generic health relevance, Good Health and Well Being, Cataloging, Computational Biology, Databases, Chemical, Gene Expression Profiling, Gene Library, Humans, Information Storage and Retrieval, National Health Programs, National Institutes of Health (U.S.), Systems Biology, Transcriptome, United States, BD2K, L1000, MCF10A, MEMA, P100, data integration, lincsprogram, lincsproject, systems biology, systems pharmacology, Biochemistry and Cell Biology
Abstract

The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability.

Published
2018

15. Common and cell-type specific responses to anti-cancer drugs revealed by high throughput transcript profiling.

Author

Niepel, Mario, Hafner, Marc, Duan, Qiaonan, Wang, Zichen, Paull, Evan O, Chung, Mirra, Lu, Xiaodong, Stuart, Joshua M, Golub, Todd R, Subramanian, Aravind, Ma'ayan, Avi, and Sorger, Peter K

Subjects
Cell Line, Tumor, Humans, Antineoplastic Agents, Gene Expression Profiling, Drug Synergism, High-Throughput Screening Assays, Genetics, Cancer, Pediatric Research Initiative, 5.1 Pharmaceuticals, 1.1 Normal biological development and functioning, Cell Line, Tumor, MD Multidisciplinary
Abstract

More effective use of targeted anti-cancer drugs depends on elucidating the connection between the molecular states induced by drug treatment and the cellular phenotypes controlled by these states, such as cytostasis and death. This is particularly true when mutation of a single gene is inadequate as a predictor of drug response. The current paper describes a data set of ~600 drug cell line pairs collected as part of the NIH LINCS Program ( http://www.lincsproject.org/ ) in which molecular data (reduced dimensionality transcript L1000 profiles) were recorded across dose and time in parallel with phenotypic data on cellular cytostasis and cytotoxicity. We report that transcriptional and phenotypic responses correlate with each other in general, but whereas inhibitors of chaperones and cell cycle kinases induce similar transcriptional changes across cell lines, changes induced by drugs that inhibit intra-cellular signaling kinases are cell-type specific. In some drug/cell line pairs significant changes in transcription are observed without a change in cell growth or survival; analysis of such pairs identifies drug equivalence classes and, in one case, synergistic drug interactions. In this case, synergy involves cell-type specific suppression of an adaptive drug response.

Published
2017

16. Systems Approaches to Cancer Biology

Author

Archer, Tenley C, Fertig, Elana J, Gosline, Sara JC, Hafner, Marc, Hughes, Shannon K, Joughin, Brian A, Meyer, Aaron S, Piccolo, Stephen R, and Shajahan-Haq, Ayesha N

Subjects
Biochemistry and Cell Biology, Biological Sciences, Cancer, Genetics, Good Health and Well Being, Humans, Neoplasms, Systems Biology, Oncology and Carcinogenesis, Oncology & Carcinogenesis, Biochemistry and cell biology, Oncology and carcinogenesis
Abstract

Cancer systems biology aims to understand cancer as an integrated system of genes, proteins, networks, and interactions rather than an entity of isolated molecular and cellular components. The inaugural Systems Approaches to Cancer Biology Conference, cosponsored by the Association of Early Career Cancer Systems Biologists and the National Cancer Institute of the NIH, focused on the interdisciplinary field of cancer systems biology and the challenging cancer questions that are best addressed through the combination of experimental and computational analyses. Attendees found that elucidating the many molecular features of cancer inevitably reveals new forms of complexity and concluded that ensuring the reproducibility and impact of cancer systems biology studies will require widespread method and data sharing and, ultimately, the translation of important findings to the clinic. Cancer Res; 76(23); 6774-7. ©2016 AACR.

Published
2016

17. Mapping behavioral specifications to model parameters in synthetic biology

Author

Koeppl, Heinz, Hafner, Marc, Lu, James, Koeppl, Heinz, Hafner, Marc, and Lu, James

Abstract

With recent improvements of protocols for the assembly of transcriptional parts, synthetic biological devices can now more reliably be assembled according to a given design. The standardization of parts open up the way for in silico design tools that improve the construct and optimize devices with respect to given formal design specifications. The simplest such optimization is the selection of kinetic parameters and protein abundances such that the specified design constraints are robustly satisfied. In this work we address the problem of determining parameter values that fulfill specifications expressed in terms of a functional on the trajectories of a dynamical model. We solve this inverse problem by linearizing the forward operator that maps parameter sets to specifications, and then inverting it locally. This approach has two advantages over brute-force random sampling. First, the linearization approach allows us to map back intervals instead of points and second, every obtained value in the parameter region is satisfying the specifications by construction. The method is general and can hence be incorporated in a pipeline for the rational forward design of arbitrary devices in synthetic biology.

Published
2024

18. Effect of Network Architecture on Synchronization and Entrainment Properties of the Circadian Oscillations in the Suprachiasmatic Nucleus

Author

Sporns, Olaf, Hafner, Marc, Koeppl, Heinz, Gonze, Didier, Sporns, Olaf, Hafner, Marc, Koeppl, Heinz, and Gonze, Didier

Abstract

In mammals, the suprachiasmatic nucleus (SCN) of the hypothalamus constitutes the central circadian pacemaker. The SCN receives light signals from the retina and controls peripheral circadian clocks (located in the cortex, the pineal gland, the liver, the kidney, the heart, etc.). This hierarchical organization of the circadian system ensures the proper timing of physiological processes. In each SCN neuron, interconnected transcriptional and translational feedback loops enable the circadian expression of the clock genes. Although all the neurons have the same genotype, the oscillations of individual cells are highly heterogeneous in dispersed cell culture: many cells present damped oscillations and the period of the oscillations varies from cell to cell. In addition, the neurotransmitters that ensure the intercellular coupling, and thereby the synchronization of the cellular rhythms, differ between the two main regions of the SCN. In this work, a mathematical model that accounts for this heterogeneous organization of the SCN is presented and used to study the implication of the SCN network topology on synchronization and entrainment properties. The results show that oscillations with larger amplitude can be obtained with scale-free networks, in contrast to random and local connections. Networks with the small-world property such as the scale-free networks used in this work can adapt faster to a delay or advance in the light/dark cycle (jet lag). Interestingly a certain level of cellular heterogeneity is not detrimental to synchronization performances, but on the contrary helps resynchronization after jet lag. When coupling two networks with different topologies that mimic the two regions of the SCN, efficient filtering of pulse-like perturbations in the entrainment pattern is observed. These results suggest that the complex and heterogeneous architecture of the SCN decreases the sensitivity of the network to short entrainment perturbations while, at the same time, i

Published
2024

19. ‘Glocal’ Robustness Analysis and Model Discrimination for Circadian Oscillators

Author

Papin, Jason A., Hafner, Marc, Koeppl, Heinz, Hasler, Martin, Wagner, Andreas, Papin, Jason A., Hafner, Marc, Koeppl, Heinz, Hasler, Martin, and Wagner, Andreas

Abstract

To characterize the behavior and robustness of cellular circuits with many unknown parameters is a major challenge for systems biology. Its difficulty rises exponentially with the number of circuit components. We here propose a novel analysis method to meet this challenge. Our method identifies the region of a high-dimensional parameter space where a circuit displays an experimentally observed behavior. It does so via a Monte Carlo approach guided by principal component analysis, in order to allow efficient sampling of this space. This ‘global’ analysis is then supplemented by a ‘local’ analysis, in which circuit robustness is determined for each of the thousands of parameter sets sampled in the global analysis. We apply this method to two prominent, recent models of the cyanobacterial circadian oscillator, an autocatalytic model, and a model centered on consecutive phosphorylation at two sites of the KaiC protein, a key circadian regulator. For these models, we find that the two-sites architecture is much more robust than the autocatalytic one, both globally and locally, based on five different quantifiers of robustness, including robustness to parameter perturbations and to molecular noise. Our ‘glocal’ combination of global and local analyses can also identify key causes of high or low robustness. In doing so, our approach helps to unravel the architectural origin of robust circuit behavior. Complementarily, identifying fragile aspects of system behavior can aid in designing perturbation experiments that may discriminate between competing mechanisms and different parameter sets.

Published
2024

20. Therapeutic Ligands Antagonize Estrogen Receptor Function by Impairing Its Mobility

Author

Guan, Jane, Zhou, Wei, Hafner, Marc, Blake, Robert A., Chalouni, Cecile, Chen, Irene P., De Bruyn, Tom, Giltnane, Jennifer M., Hartman, Steven J., Heidersbach, Amy, Houtman, Rene, Ingalla, Ellen, Kategaya, Lorn, Kleinheinz, Tracy, Li, Jun, Martin, Scott E., Modrusan, Zora, Nannini, Michelle, Oeh, Jason, Ubhayakar, Savita, Wang, Xiaojing, Wertz, Ingrid E., Young, Amy, Yu, Mamie, Sampath, Deepak, Hager, Jeffrey H., Friedman, Lori S., Daemen, Anneleen, and Metcalfe, Ciara

Published
2019
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21. A Multi-center Study on the Reproducibility of Drug-Response Assays in Mammalian Cell Lines

Author

Shamu, Caroline E., Jayaraman, Gomathi, Azeloglu, Evren U., Iyengar, Ravi, Sobie, Eric A., Mills, Gordon B., Liby, Tiera, Jaffe, Jacob D., Alimova, Maria, Davison, Desiree, Lu, Xiaodong, Golub, Todd R., Subramanian, Aravind, Shelley, Brandon, Svendsen, Clive N., Ma’ayan, Avi, Medvedovic, Mario, Feiler, Heidi S., Smith, Rebecca, Devlin, Kaylyn, Niepel, Mario, Hafner, Marc, Mills, Caitlin E., Subramanian, Kartik, Williams, Elizabeth H., Chung, Mirra, Gaudio, Benjamin, Barrette, Anne Marie, Stern, Alan D., Hu, Bin, Korkola, James E., Gray, Joe W., Birtwistle, Marc R., Heiser, Laura M., and Sorger, Peter K.

Published
2019
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23. Epiregulon: Inference of single-cell transcription factor activity to dissect mechanisms of lineage plasticity and drug response

Author

Wlodarczyk, Tomasz, primary, Lun, Aaron, additional, Wu, Diana, additional, Menon, Shreya, additional, Toneyan, Shushan, additional, Seidel, Kerstin, additional, Wang, Liang, additional, Tan, Jenille, additional, Chen, Shang-Yang, additional, Keyes, Timothy, additional, Chlebowski, Aleksander, additional, Guo, Yu, additional, Metcalfe, Ciara, additional, Hafner, Marc, additional, Siebel, Christian W., additional, Corces, M. Ryan, additional, Yauch, Robert, additional, Xie, Shiqi, additional, and Yao, Xiaosai, additional

Published
2023
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24. Data from RTK-Dependent Inducible Degradation of Mutant PI3Kα Drives GDC-0077 (Inavolisib) Efficacy

Author

Song, Kyung W., primary, Edgar, Kyle A., primary, Hanan, Emily J., primary, Hafner, Marc, primary, Oeh, Jason, primary, Merchant, Mark, primary, Sampath, Deepak, primary, Nannini, Michelle A., primary, Hong, Rebecca, primary, Phu, Lilian, primary, Forrest, William F., primary, Stawiski, Eric, primary, Schmidt, Stephen, primary, Endres, Nicholas, primary, Guan, Jane, primary, Wallin, Jeffrey J., primary, Cheong, Jonathan, primary, Plise, Emile G., primary, Lewis Phillips, Gail D., primary, Salphati, Laurent, primary, Heffron, Timothy P., primary, Olivero, Alan G., primary, Malek, Shiva, primary, Staben, Steven T., primary, Kirkpatrick, Donald S., primary, Dey, Anwesha, primary, and Friedman, Lori S., primary

Published
2023
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25. Figure S1 from RTK-Dependent Inducible Degradation of Mutant PI3Kα Drives GDC-0077 (Inavolisib) Efficacy

Author

Song, Kyung W., primary, Edgar, Kyle A., primary, Hanan, Emily J., primary, Hafner, Marc, primary, Oeh, Jason, primary, Merchant, Mark, primary, Sampath, Deepak, primary, Nannini, Michelle A., primary, Hong, Rebecca, primary, Phu, Lilian, primary, Forrest, William F., primary, Stawiski, Eric, primary, Schmidt, Stephen, primary, Endres, Nicholas, primary, Guan, Jane, primary, Wallin, Jeffrey J., primary, Cheong, Jonathan, primary, Plise, Emile G., primary, Lewis Phillips, Gail D., primary, Salphati, Laurent, primary, Heffron, Timothy P., primary, Olivero, Alan G., primary, Malek, Shiva, primary, Staben, Steven T., primary, Kirkpatrick, Donald S., primary, Dey, Anwesha, primary, and Friedman, Lori S., primary

Published
2023
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26. Abstract PD10-06: PD10-06 Clinical outcomes and exploratory gene expression analysis of OPPORTUNE: a phase II window-of-opportunity study to evaluate pictilisib+anastrozole versus anastrozole alone in ER-positive breast cancer

Author

Chibly, Alejandro Martinez, primary, Shia, Alice, additional, Johnson, Radia, additional, Hwang, Michael S., additional, Hafner, Marc, additional, Metcalfe, Ciara, additional, Shah, Kalpit, additional, Slyper, Michal, additional, Bolen, Chris, additional, Pinder, Sarah E., additional, Thompson, Alastair M., additional, Gendreau, Steven, additional, and Schmid, Peter, additional

Published
2023
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28. Abstract PD13-02: PD13-02 Exploratory gene expression analysis of coopERA Breast Cancer (BC): a study evaluating neoadjuvant giredestrant versus anastrozole alone and in combination with palbociclib in ER-positive, HER2-negative untreated early BC

Author

Chibly, Alejandro M., primary, Fernando, Tharu M., additional, Metcalfe, Ciara, additional, Hafner, Marc, additional, Owusu-Manu, Gilbert, additional, Hurvitz, Sara, additional, Bardia, Aditya, additional, Fasching, Peter A., additional, Park, Yeon H., additional, Quiroga, Vanesa, additional, Steinseifer, Jutta, additional, Perez-Moreno, Pablo, additional, and Moore, Heather M., additional

Published
2023
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30. Constitutive protein degradation induces acute cell death via proteolysis products

Author

Chen, Si-Han, primary, Prakash, Sumit, additional, Helgason, Elizabeth, additional, Gilchrist, Caroline L., additional, Kenner, Lillian R., additional, Srinivasan, Rajini, additional, Sterne-Weiler, Tim, additional, Hafner, Marc, additional, Piskol, Robert, additional, Dueber, Erin C., additional, Hamidi, Habib, additional, Endres, Nicholas, additional, Ye, Xin, additional, Fairbrother, Wayne J., additional, and Wertz, Ingrid E., additional

Published
2023
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34. Giredestrant reverses progesterone hypersensitivity driven by estrogen receptor mutations in breast cancer

Author

Liang, Jackson, primary, Ingalla, Ellen Rei, additional, Yao, Xiaosai, additional, Wang, Bu-Er, additional, Tai, Lisa, additional, Giltnane, Jennifer, additional, Liang, Yuxin, additional, Daemen, Anneleen, additional, Moore, Heather M., additional, Aimi, Junko, additional, Chang, Ching-Wei, additional, Gates, Mary R., additional, Eng-Wong, Jennifer, additional, Tam, Lucinda, additional, Bacarro, Natasha, additional, Roose-Girma, Merone, additional, Bellet, Meritxell, additional, Hafner, Marc, additional, and Metcalfe, Ciara, additional

Published
2022
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35. EHMT2 methyltransferase governs cell identity in the lung and is required for KRAS G12D tumor development and propagation

Author

Pribluda, Ariel, primary, Daemen, Anneleen, additional, Lima, Anthony Nelson, additional, Wang, Xi, additional, Hafner, Marc, additional, Poon, Chungkee, additional, Modrusan, Zora, additional, Katakam, Anand Kumar, additional, Foreman, Oded, additional, Eastham, Jefferey, additional, Hung, Jefferey, additional, Haley, Benjamin, additional, Garcia, Julia T, additional, Jackson, Erica L, additional, and Junttila, Melissa R, additional

Published
2022
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39. Author response: EHMT2 methyltransferase governs cell identity in the lung and is required for KRAS G12D tumor development and propagation

Author

Pribluda, Ariel, primary, Daemen, Anneleen, additional, Lima, Anthony Nelson, additional, Wang, Xi, additional, Hafner, Marc, additional, Poon, Chungkee, additional, Modrusan, Zora, additional, Katakam, Anand Kumar, additional, Foreman, Oded, additional, Eastham, Jefferey, additional, Hung, Jefferey, additional, Haley, Benjamin, additional, Garcia, Julia T, additional, Jackson, Erica L, additional, and Junttila, Melissa R, additional

Published
2022
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41. PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis

Author

Feizi, Nikta, primary, Nair, Sisira Kadambat, additional, Smirnov, Petr, additional, Beri, Gangesh, additional, Eeles, Christopher, additional, Esfahani, Parinaz Nasr, additional, Nakano, Minoru, additional, Tkachuk, Denis, additional, Mammoliti, Anthony, additional, Gorobets, Evgeniya, additional, Mer, Arvind Singh, additional, Lin, Eva, additional, Yu, Yihong, additional, Martin, Scott, additional, Hafner, Marc, additional, and Haibe-Kains, Benjamin, additional

Published
2021
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42. PharmacoDB 2.0 : Improving scalability and transparency ofin vitropharmacogenomics analysis

Author

Feizi, Nikta, primary, Nair, Sisira Kadambat, additional, Smirnov, Petr, additional, Beri, Gangesh, additional, Eeles, Christopher, additional, Esfahani, Parinaz Nasr, additional, Nakano, Minoru, additional, Tkachuk, Denis, additional, Mammoliti, Anthony, additional, Gorobets, Evgeniya, additional, Mer, Arvind Singh, additional, Lin, Eva, additional, Yu, Yihong, additional, Martin, Scott, additional, Hafner, Marc, additional, and Haibe-Kains, Benjamin, additional

Published
2021
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43. RTK-Dependent Inducible Degradation of Mutant PI3Kα Drives GDC-0077 (Inavolisib) Efficacy

Author

Song, Kyung W., primary, Edgar, Kyle A., additional, Hanan, Emily J., additional, Hafner, Marc, additional, Oeh, Jason, additional, Merchant, Mark, additional, Sampath, Deepak, additional, Nannini, Michelle A., additional, Hong, Rebecca, additional, Phu, Lilian, additional, Forrest, William F., additional, Stawiski, Eric, additional, Schmidt, Stephen, additional, Endres, Nicholas, additional, Guan, Jane, additional, Wallin, Jeffrey J., additional, Cheong, Jonathan, additional, Plise, Emile G., additional, Lewis Phillips, Gail D., additional, Salphati, Laurent, additional, Heffron, Timothy P., additional, Olivero, Alan G., additional, Malek, Shiva, additional, Staben, Steven T., additional, Kirkpatrick, Donald S., additional, Dey, Anwesha, additional, and Friedman, Lori S., additional

Published
2021
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