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Li Y, Qiu X, Wang X, Liu H, Geck RC, Tewari AK, Xiao T, Font-Tello A, Lim K, Jones KL, Morrow M, Vadhi R, Kao PL, Jaber A, Yerrum S, Xie Y, Chow KH, Cejas P, Nguyen QD, Long HW, Liu XS, Toker A, Brown M. FGFR-inhibitor-mediated dismissal of SWI/SNF complexes from YAP-dependent enhancers induces adaptive therapeutic resistance. Nat Cell Biol 2021; 23:1187-1198. [PMID: 34737445 DOI: 10.1038/s41556-021-00781-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 09/26/2021] [Indexed: 12/20/2022]
Abstract
How cancer cells adapt to evade the therapeutic effects of drugs targeting oncogenic drivers is poorly understood. Here we report an epigenetic mechanism leading to the adaptive resistance of triple-negative breast cancer (TNBC) to fibroblast growth factor receptor (FGFR) inhibitors. Prolonged FGFR inhibition suppresses the function of BRG1-dependent chromatin remodelling, leading to an epigenetic state that derepresses YAP-associated enhancers. These chromatin changes induce the expression of several amino acid transporters, resulting in increased intracellular levels of specific amino acids that reactivate mTORC1. Consistent with this mechanism, addition of mTORC1 or YAP inhibitors to FGFR blockade synergistically attenuated the growth of TNBC patient-derived xenograft models. Collectively, these findings reveal a feedback loop involving an epigenetic state transition and metabolic reprogramming that leads to adaptive therapeutic resistance and provides potential therapeutic strategies to overcome this mechanism of resistance.
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Affiliation(s)
- Yihao Li
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Xintao Qiu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Xiaoqing Wang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Hui Liu
- Department of Pathology, and Cancer Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Renee C Geck
- Department of Pathology, and Cancer Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Alok K Tewari
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Tengfei Xiao
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Alba Font-Tello
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Klothilda Lim
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kristen L Jones
- Lurie Family Imaging Center, Center for Biomedical Imaging in Oncology, Dana-Farber Cancer Institute, Boston, Boston, MA, USA
| | - Murry Morrow
- Lurie Family Imaging Center, Center for Biomedical Imaging in Oncology, Dana-Farber Cancer Institute, Boston, Boston, MA, USA
| | - Raga Vadhi
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Pei-Lun Kao
- Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Center for Patient Derived Models, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Aliya Jaber
- Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Center for Patient Derived Models, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Smitha Yerrum
- Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Center for Patient Derived Models, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Yingtian Xie
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kin-Hoe Chow
- Department of Oncologic Pathology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Center for Patient Derived Models, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Paloma Cejas
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Quang-Dé Nguyen
- Lurie Family Imaging Center, Center for Biomedical Imaging in Oncology, Dana-Farber Cancer Institute, Boston, Boston, MA, USA
| | - Henry W Long
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA
| | - X Shirley Liu
- Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Data Science, Dana-Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alex Toker
- Department of Pathology, and Cancer Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.,Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
| | - Myles Brown
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. .,Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA, USA. .,Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA.
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Zeng Z, Mao C, Vo A, Li X, Nugent JO, Khan SA, Clare SE, Luo Y. Deep learning for cancer type classification and driver gene identification. BMC Bioinformatics 2021; 22:491. [PMID: 34689757 PMCID: PMC8543824 DOI: 10.1186/s12859-021-04400-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 09/24/2021] [Indexed: 12/12/2022] Open
Abstract
Background Genetic information is becoming more readily available and is increasingly being used to predict patient cancer types as well as their subtypes. Most classification methods thus far utilize somatic mutations as independent features for classification and are limited by study power. We aim to develop a novel method to effectively explore the landscape of genetic variants, including germline variants, and small insertions and deletions for cancer type prediction.
Results We proposed DeepCues, a deep learning model that utilizes convolutional neural networks to unbiasedly derive features from raw cancer DNA sequencing data for disease classification and relevant gene discovery. Using raw whole-exome sequencing as features, germline variants and somatic mutations, including insertions and deletions, were interactively amalgamated for feature generation and cancer prediction. We applied DeepCues to a dataset from TCGA to classify seven different types of major cancers and obtained an overall accuracy of 77.6%. We compared DeepCues to conventional methods and demonstrated a significant overall improvement (p < 0.001). Strikingly, using DeepCues, the top 20 breast cancer relevant genes we have identified, had a 40% overlap with the top 20 known breast cancer driver genes. Conclusion Our results support DeepCues as a novel method to improve the representational resolution of DNA sequencings and its power in deriving features from raw sequences for cancer type prediction, as well as discovering new cancer relevant genes. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04400-4.
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Affiliation(s)
- Zexian Zeng
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 750 N Lake Shore Drive Room 11-189, Chicago, IL, 60611, USA.,Department of Data Sciences, Dana-Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Chengsheng Mao
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 750 N Lake Shore Drive Room 11-189, Chicago, IL, 60611, USA
| | - Andy Vo
- Committee on Developmental Biology and Regenerative Medicine, The University of Chicago, Chicago, IL, USA
| | | | - Janna Ore Nugent
- Research Computing Services, Northwestern University, Chicago, IL, USA
| | - Seema A Khan
- Department of Surgery, Feinberg School of Medicine, Northwestern University, NMH/Prentice Women's Hospital Room 4-420 250 E Superior, Chicago, IL, 60611, USA.
| | - Susan E Clare
- Department of Surgery, Feinberg School of Medicine, Northwestern University, Robert H Lurie Medical Research Center Room 4-113 250 E Superior, Chicago, IL, 60611, USA.
| | - Yuan Luo
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, 750 N Lake Shore Drive Room 11-189, Chicago, IL, 60611, USA.
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Covell DG. Bioinformatic analysis linking genomic defects to chemosensitivity and mechanism of action. PLoS One 2021; 16:e0243336. [PMID: 33909629 PMCID: PMC8081165 DOI: 10.1371/journal.pone.0243336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 03/16/2021] [Indexed: 11/18/2022] Open
Abstract
A joint analysis of the NCI60 small molecule screening data, their genetically defective genes, and mechanisms of action (MOA) of FDA approved cancer drugs screened in the NCI60 is proposed for identifying links between chemosensitivity, genomic defects and MOA. Self-Organizing-Maps (SOMs) are used to organize the chemosensitivity data. Student’s t-tests are used to identify SOM clusters with enhanced chemosensitivity for tumor cell lines with versus without genetically defective genes. Fisher’s exact and chi-square tests are used to reveal instances where defective gene to chemosensitivity associations have enriched MOAs. The results of this analysis find a relatively small set of defective genes, inclusive of ABL1, AXL, BRAF, CDC25A, CDKN2A, IGF1R, KRAS, MECOM, MMP1, MYC, NOTCH1, NRAS, PIK3CG, PTK2, RPTOR, SPTBN1, STAT2, TNKS and ZHX2, as possible candidates for roles in chemosensitivity for compound MOAs that target primarily, but not exclusively, kinases, nucleic acid synthesis, protein synthesis, apoptosis and tubulin. These results find exploitable instances of enhanced chemosensitivity of compound MOA’s for selected defective genes. Collectively these findings will advance the interpretation of pre-clinical screening data as well as contribute towards the goals of cancer drug discovery, development decision making, and explanation of drug mechanisms.
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Affiliation(s)
- David G Covell
- Information Technologies Branch, Developmental Therapeutics Program, National Cancer Institute, Frederick, MD, United States of America
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Zhang Y, Manjunath M, Kim Y, Heintz J, Song JS. SequencEnG: an interactive knowledge base of sequencing techniques. Bioinformatics 2020; 35:1438-1440. [PMID: 30202870 DOI: 10.1093/bioinformatics/bty794] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Revised: 07/09/2018] [Accepted: 09/06/2018] [Indexed: 12/14/2022] Open
Abstract
SUMMARY Next-generation sequencing (NGS) techniques are revolutionizing biomedical research by providing powerful methods for generating genomic and epigenomic profiles. The rapid progress is posing an acute challenge to students and researchers to stay acquainted with the numerous available methods. We have developed an interactive online educational resource called Sequencing Techniques Engine for Genomics (SequencEnG) to provide a tree-structured knowledge base of 66 different sequencing techniques and step-by-step NGS data analysis pipelines comparing popular tools. SequencEnG is designed to facilitate barrier-free learning of current NGS techniques and provides a user-friendly interface for searching through experimental and analysis methods. AVAILABILITY AND IMPLEMENTATION SequencEnG is part of the project Knowledge Engine for Genomics (KnowEnG) and is freely available at http://education.knoweng.org/sequenceng/.
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Affiliation(s)
- Yi Zhang
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.,Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Mohith Manjunath
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Yeonsung Kim
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Joerg Heintz
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Jun S Song
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.,Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL, USA
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