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Rosenberger G, Li W, Turunen M, He J, Subramaniam PS, Pampou S, Griffin AT, Karan C, Kerwin P, Murray D, Honig B, Liu Y, Califano A. Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis. Nat Commun 2024; 15:3909. [PMID: 38724493 PMCID: PMC11082183 DOI: 10.1038/s41467-024-47957-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 04/16/2024] [Indexed: 05/12/2024] Open
Abstract
Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.
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Affiliation(s)
- George Rosenberger
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Wenxue Li
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Mikko Turunen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Jing He
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Prem S Subramaniam
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Sergey Pampou
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Aaron T Griffin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Medical Scientist Training Program, Columbia University Irving Medical Center, New York, NY, USA
| | - Charles Karan
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Patrick Kerwin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Diana Murray
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Barry Honig
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Yansheng Liu
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA.
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA.
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA.
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
- Chan Zuckerberg Biohub New York, New York, NY, USA.
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2
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Smirnov P, Smith I, Safikhani Z, Ba-Alawi W, Khodakarami F, Lin E, Yu Y, Martin S, Ortmann J, Aittokallio T, Hafner M, Haibe-Kains B. Evaluation of statistical approaches for association testing in noisy drug screening data. BMC Bioinformatics 2022; 23:188. [PMID: 35585485 PMCID: PMC9118710 DOI: 10.1186/s12859-022-04693-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 04/15/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Identifying 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. RESULTS 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. CONCLUSIONS 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.
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Affiliation(s)
- Petr Smirnov
- Department of Medical Biophysics, University of Toronto, Toronto, Canada.,Princess Margaret Cancer Center, University Health Network, Toronto, Canada
| | - Ian Smith
- Department of Medical Biophysics, University of Toronto, Toronto, Canada.,Princess Margaret Cancer Center, University Health Network, Toronto, Canada
| | - Zhaleh Safikhani
- Princess Margaret Cancer Center, University Health Network, Toronto, Canada
| | - Wail Ba-Alawi
- Princess Margaret Cancer Center, University Health Network, Toronto, Canada
| | | | - Eva Lin
- Department of Discovery Oncology, Genentech Inc., South San Francisco, USA
| | - Yihong Yu
- Department of Discovery Oncology, Genentech Inc., South San Francisco, USA
| | - Scott Martin
- Department of Discovery Oncology, Genentech Inc., South San Francisco, USA
| | - Janosch Ortmann
- Département d'analytique, opérations et technologies de l'information, École des sciences de la gestion, Université du Québec à Montréal, Montréal, Canada
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland.,Institute for Cancer Research, Oslo University Hospital, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway.,iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland
| | - Marc Hafner
- Department of Oncology Bioinformatics, Genentech Inc., South San Francisco, USA
| | - Benjamin Haibe-Kains
- Department of Medical Biophysics, University of Toronto, Toronto, Canada. .,Princess Margaret Cancer Center, University Health Network, Toronto, Canada. .,Vector Institute, Toronto, Canada.
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Carroll MJ, Parent CR, Page D, Kreeger PK. Tumor cell sensitivity to vemurafenib can be predicted from protein expression in a BRAF-V600E basket trial setting. BMC Cancer 2019; 19:1025. [PMID: 31672130 PMCID: PMC6822426 DOI: 10.1186/s12885-019-6175-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Accepted: 09/20/2019] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Genetics-based basket trials have emerged to test targeted therapeutics across multiple cancer types. However, while vemurafenib is FDA-approved for BRAF-V600E melanomas, the non-melanoma basket trial was unsuccessful, suggesting mutation status is insufficient to predict response. We hypothesized that proteomic data would complement mutation status to identify vemurafenib-sensitive tumors and effective co-treatments for BRAF-V600E tumors with inherent resistance. METHODS Reverse Phase Proteomic Array (RPPA, MD Anderson Cell Lines Project), RNAseq (Cancer Cell Line Encyclopedia) and vemurafenib sensitivity (Cancer Therapeutic Response Portal) data for BRAF-V600E cancer cell lines were curated. Linear and nonlinear regression models using RPPA protein or RNAseq were evaluated and compared based on their ability to predict BRAF-V600E cell line sensitivity (area under the dose response curve). Accuracies of all models were evaluated using hold-out testing. CausalPath software was used to identify protein-protein interaction networks that could explain differential protein expression in resistant cells. Human examination of features employed by the model, the identified protein interaction networks, and model simulation suggested anti-ErbB co-therapy would counter intrinsic resistance to vemurafenib. To validate this potential co-therapy, cell lines were treated with vemurafenib and dacomitinib (a pan-ErbB inhibitor) and the number of viable cells was measured. RESULTS Orthogonal partial least squares (O-PLS) predicted vemurafenib sensitivity with greater accuracy in both melanoma and non-melanoma BRAF-V600E cell lines than other leading machine learning methods, specifically Random Forests, Support Vector Regression (linear and quadratic kernels) and LASSO-penalized regression. Additionally, use of transcriptomic in place of proteomic data weakened model performance. Model analysis revealed that resistant lines had elevated expression and activation of ErbB receptors, suggesting ErbB inhibition could improve vemurafenib response. As predicted, experimental evaluation of vemurafenib plus dacomitinb demonstrated improved efficacy relative to monotherapies. CONCLUSIONS Combined, our results support that inclusion of proteomics can predict drug response and identify co-therapies in a basket setting.
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Affiliation(s)
- Molly J Carroll
- Department of Biomedical Engineering, University of Wisconsin-Madison 1111 Highland Ave, WIMR 4553, Madison, WI, 53705, USA
| | - Carl R Parent
- Department of Biomedical Engineering, University of Wisconsin-Madison 1111 Highland Ave, WIMR 4553, Madison, WI, 53705, USA
| | - David Page
- Department of Biostatistics and Bioinformatics, Duke University, Box 2721, Durham, NC, 27710, USA.
- University of Wisconsin Carbone Cancer Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
| | - Pamela K Kreeger
- Department of Biomedical Engineering, University of Wisconsin-Madison 1111 Highland Ave, WIMR 4553, Madison, WI, 53705, USA.
- Department of Obstetrics and Gynecology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
- Department of Cell and Regenerative Biology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
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Robert BM, Brindha GR, Santhi B, Kanimozhi G, Prasad NR. Computational models for predicting anticancer drug efficacy: A multi linear regression analysis based on molecular, cellular and clinical data of oral squamous cell carcinoma cohort. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 178:105-112. [PMID: 31416538 DOI: 10.1016/j.cmpb.2019.06.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 04/15/2019] [Accepted: 06/11/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVES The computational prediction of drug responses based on the analysis of multiple clinical features of the tumor will be a novel strategy for accomplishing the long-term goal of precision medicine in oncology. The cancer patients will be benefitted if we computationally account all the tumor characteristics (data) for the selection of most effective and precise therapeutic drug. In this study, we developed and validated few computational models to predict anticancer drug efficacy based on molecular, cellular and clinical features of 31 oral squamous cell carcinoma (OSCC) cohort using computational methods. METHODS We developed drug efficacy prediction models using multiple tumor features by employing the statistical methods like multi linear regression (MLR), modified MLR-weighted least square (MLR-WLS) and enhanced MLR-WLS. All the three developed drug efficacy prediction models were then validated using the data of actual OSCC samples (train-test ratio 31: 31) and actual Vs hypothetical samples (train-test ratio 31: 30). The selected best statistical model i.e. enhanced MLR-WLS has then been cross-validated (CV) using 341 theoretical tumor data. Finally, the performances of the models were assessed by the level of learning confidence, significance, accuracy and error terms. RESULTS The train-test process for the real tumor samples of MLR-WLS method revealed the drug efficacy prediction enhancement and we observed that there was very less priming difference between actual and predicted. Furthermore, we found there was a less difference between actual apoptotic priming and predicted apoptotic priming for the tumors 6, 8, 21 and 30 whereas, for the remaining tumors there were no differences between predicted and actual priming data. The error terms (Actual Vs Predicted) also revealed the reliability of enhanced MLR-WLS model for drug efficacy prediction. CONCLUSION We developed effective computational prediction models using MLR analysis for anticancer drug efficacy which will be useful in the field of precision medicine to choose the choice of drug in a personalized manner. We observed that the enhanced MLR-WLS model was the best fit to predict anticancer drug efficacy which may have translational applications.
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Affiliation(s)
- Beaulah Mary Robert
- Department of Biochemistry and Biotechnology, Annamalai University, Annamalainagar 608 002, Tamilnadu, India
| | - G R Brindha
- School of Computing, SASTRA Deemed to be University, Tirumalaisamudram, Thanjavur 613401, Tamilnadu, India.
| | - B Santhi
- School of Computing, SASTRA Deemed to be University, Tirumalaisamudram, Thanjavur 613401, Tamilnadu, India
| | - G Kanimozhi
- Department of Biochemistry, Dharmapuramn Gnanambigai Government Arts and Science College for Women, Mayiladuthurai, Tamilnadu, India
| | - Nagarajan Rajendra Prasad
- Department of Biochemistry and Biotechnology, Annamalai University, Annamalainagar 608 002, Tamilnadu, India.
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Wass MN, Ray L, Michaelis M. Understanding of researcher behavior is required to improve data reliability. Gigascience 2019; 8:giz017. [PMID: 30715291 PMCID: PMC6528747 DOI: 10.1093/gigascience/giz017] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Revised: 01/20/2019] [Accepted: 01/25/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND A lack of data reproducibility ("reproducibility crisis") has been extensively debated across many academic disciplines. RESULTS Although a reproducibility crisis is widely perceived, conclusive data on the scale of the problem and the underlying reasons are largely lacking. The debate is primarily focused on methodological issues. However, examples such as the use of misidentified cell lines illustrate that the availability of reliable methods does not guarantee good practice. Moreover, research is often characterized by a lack of established methods. Despite the crucial importance of researcher conduct, research and conclusive data on the determinants of researcher behavior are widely missing. CONCLUSION Meta-research that establishes an understanding of the factors that determine researcher behavior is urgently needed. This knowledge can then be used to implement and iteratively improve measures that incentivize researchers to apply the highest standards, resulting in high-quality data.
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Affiliation(s)
- Mark N Wass
- Industrial Biotechnology Centre and School of Biosciences, University of Kent, Canterbury, CT2 7NJ, UK
| | - Larry Ray
- School of Social Policy, Sociology and Social Research, University of Kent, Canterbury, CT2 7NJ, UK
| | - Martin Michaelis
- Industrial Biotechnology Centre and School of Biosciences, University of Kent, Canterbury, CT2 7NJ, UK
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