Grant Details
| Grant Number: | 2R01CA204120-09A1 Interpret this number |
| Primary Investigator: | Ma, Shuangge |
| Organization: | Yale University |
| Project Title: | Novel Methods for Identifying Genetic Interactions for Cancer Prognosis |
| Fiscal Year: | 2026 |
Abstract
Project Summary Gene-environment (G-E) interactions have critical implications for better understanding and modeling cancer prognosis. For over a decade, our group has been continuously supported by NIH and other funding agencies and has taken a leading role in developing cutting-edge methods for G-E interaction analysis. In the first funding period, we developed a series of regularized regression methods. In the current funding period, we have been the first to develop statistically principled deep learning (SDL) techniques tailored to G-E interaction analysis. We have extensively analyzed groundbreaking prognosis studies on lung cancer, skin cancer, lymphoma, thyroid cancer, and others. Our research has led to the identification of important G-E interactions otherwise missed, advanced understanding of disease etiology and prognosis, suggested new targets for therapeutic development, and assisted in constructing more accurate disease models. In a series of recent studies, we have shown that deep neural network (DNN)-based G-E interaction analysis significantly outperforms regression-based, more flexibly accommodating unspecified nonlinear relationships and more accurately modeling/predicting prognosis. However, such advantages are often accompanied by inferior stability and interpretability, which critically hinders practical applications. To directly tackle such limitations and facilitate broader utilization, in this renewal, we will be the first to develop a comprehensive set of novel G-E interaction analysis strategies and methods, integrate outside information to assist local data analysis, and fundamentally improve stability and interpretability. Specifically, we will: (Aim 1) Develop SDL techniques to integrate information in pathological imaging data; (Aim 2) Develop SDL techniques to integrate information in published literature; and (Aim 3) Develop SDL techniques to integrate information in raw data of independent studies. These three sources of information are likely the most effective and cost- effective and complement each other. The proposed DNN techniques will be tailored to the unique characteristics of each type of information, while enjoying methodological coherence. We will develop effective computational algorithms and portable software, conduct extensive simulations and benchmarking, and establish statistical properties, which will lead to foundational contributions to cancer analytics. Equally importantly, we will (Aim 4) Analyze the Yale SPORE, Veteran Affairs (VA) Million Veteran Program (MVP), and TCGA data on lung and skin cancers. Such data are among the most powerful. Analysis will be comprehensively conducted on multiple prognosis outcomes and molecular measurements. Results will be thoroughly and rigorously evaluated, benchmarked, and validated. This effort will lead to a high and direct translational and clinical impact.
Publications
Efficient estimation for deep generalized accelerated hazards models with interval-censored data.
Authors: Wu Q. , Du M. , Ma S. , Zhao X. .
Source: Biometrics, 2026-07-01 00:00:00.0; 82(3), .
PMID: 42574000
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Robust Heterogeneity Adjustment for Gaussian Graphical Model With Latent Variables.
Authors: Li L. , Li R. , Ma S. , Zhang Q. .
Source: Statistics In Medicine, 2026 May; 45(10-12), p. e70571.
PMID: 42053355
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Integrating Omics and Pathological Imaging Data for Cancer Prognosis via a Deep Neural Network-Based Cox Model.
Authors: Li J. , Ma S. .
Source: Statistics In Medicine, 2026 Feb; 45(3-5), p. e70435.
PMID: 41641685
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DNN-based semiparametric AFT model for integrating genomic and pathological imaging data in cancer prognosis.
Authors: Li J. , Zhang Q. , Ma S. .
Source: Biometrics, 2026-01-06 00:00:00.0; 82(1), .
PMID: 41837305
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Hierarchical structure-guided high-dimensional multi-view clustering.
Authors: Jiang J. , Fang K. , Ma S. , Zhang Q. .
Source: Journal Of Multivariate Analysis, 2026 Jan; 211, .
EPub date: 2025-09-25 00:00:00.0.
PMID: 41551979
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Robust sparse Bayesian regression for longitudinal gene-environment interactions.
Authors: Fan K. , Jiang Y. , Ma S. , Wang W. , Wu C. .
Source: Journal Of The Royal Statistical Society. Series C, Applied Statistics, 2025 Dec; 74(5), p. 1372-1394.
EPub date: 2025-04-08 00:00:00.0.
PMID: 41245172
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JOINT IDENTIFICATION OF SPATIALLY VARIABLE GENES VIA A NETWORK-ASSISTED BAYESIAN REGULARIZATION APPROACH.
Authors: Wu M. , Li Y. , Ma S. , Wu M. .
Source: The Annals Of Applied Statistics, 2025 Dec; 19(4), p. 2705-2723.
EPub date: 2025-12-05 00:00:00.0.
PMID: 42136619
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Ordinal Sparse Neural Networks for Modeling Gene- and Imaging-Environment Interactions.
Authors: Xue J. , Xu Y. , Li J. , Ma S. , Fang K. .
Source: Statistics In Medicine, 2025 Oct; 44(23-24), p. e70302.
PMID: 41105049
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Subgroup Analysis of Differential Networks with Latent Variables.
Authors: Li L. , Ma S. , Zhang Q. .
Source: Statistics And Computing, 2025 Oct; 35(5), .
EPub date: 2025-07-02 00:00:00.0.
PMID: 42130826
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NETWORK-BASED MODELING OF EMOTIONAL EXPRESSIONS FOR MULTIPLE CANCERS VIA A LINGUISTIC ANALYSIS OF AN ONLINE HEALTH COMMUNITY.
Authors: Fan X. , Liu M. , Ma S. .
Source: The Annals Of Applied Statistics, 2025 Sep; 19(3), p. 2218-2236.
EPub date: 2025-08-28 00:00:00.0.
PMID: 41104371
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Local Clustering for Functional Data.
Authors: Chen Y. , Zhang Q. , Ma S. .
Source: Journal Of Computational And Graphical Statistics : A Joint Publication Of American Statistical Association, Institute Of Mathematical Statistics, Interface Foundation Of North America, 2025 Sep; 34(3), p. 1075-1090.
EPub date: 2025-02-10 00:00:00.0.
PMID: 41200425
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Joint modeling of mixed outcomes using a rank-based sparse neural network.
Authors: Xue J. , Xu Y. , Li J. , Ma S. , Fang K. .
Source: Journal Of Biomedical Informatics, 2025-07-05 00:00:00.0; 169, p. 104870.
EPub date: 2025-07-05 00:00:00.0.
PMID: 40623577
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Robust Transfer Learning for High-Dimensional GLM Using
Authors: Xu F. , Ma S. , Zhang Q. , Xu Y. .
Source: Statistics In Medicine, 2025 Jul; 44(15-17), p. e70170.
PMID: 40662636
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Subgroup Testing in the Change-Plane Cox Model.
Authors: Zhang X. , Ren P. , Shi X. , Ma S. , Liu X. .
Source: Statistics In Medicine, 2025 Jul; 44(15-17), p. e70179.
PMID: 40662752
Related Citations
High-Dimensional Gene-Environment Interaction Analysis.
Authors: Wu M. , Li Y. , Ma S. .
Source: Annual Review Of Statistics And Its Application, 2025 Mar; 12, .
EPub date: 2024-09-11 00:00:00.0.
PMID: 40881670
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Bayesian Modeling of Cancer Outcomes Using Genetic Variables Assisted by Pathological Imaging Data.
Authors: Im Y. , Li R. , Ma S. .
Source: Statistics In Medicine, 2025-02-10 00:00:00.0; 44(3-4), p. e10350.
PMID: 39840672
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Hierarchical Multi-Label Classification With Gene-Environment Interactions in Disease Modeling.
Authors: Li J. , Zhang Q. , Ma S. , Fang K. , Xu Y. .
Source: Statistics In Medicine, 2025-02-10 00:00:00.0; 44(3-4), p. e10330.
PMID: 39865593
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Integrative rank-based regression for multi-source high-dimensional data with multi-type responses.
Authors: Xu F. , Ma S. , Zhang Q. .
Source: Journal Of Applied Statistics, 2025; 52(11), p. 2011-2030.
EPub date: 2025-01-16 00:00:00.0.
PMID: 40904949
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Incorporating prior information in gene expression network-based cancer heterogeneity analysis.
Authors: Li R. , Xu S. , Li Y. , Tang Z. , Feng D. , Cai J. , Ma S. .
Source: Biostatistics (oxford, England), 2024-12-31 00:00:00.0; 26(1), .
PMID: 39074174
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The spike-and-slab quantile LASSO for robust variable selection in cancer genomics studies.
Authors: Liu Y. , Ren J. , Ma S. , Wu C. .
Source: Statistics In Medicine, 2024-09-11 00:00:00.0; , .
EPub date: 2024-09-11 00:00:00.0.
PMID: 39260448
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Estimation of multiple networks with common structures in heterogeneous subgroups.
Authors: Qin X. , Hu J. , Ma S. , Wu M. .
Source: Journal Of Multivariate Analysis, 2024 Jul; 202, .
EPub date: 2024-02-13 00:00:00.0.
PMID: 38433779
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Hierarchical False Discovery Rate Control for High-dimensional Survival Analysis with Interactions.
Authors: Liang W. , Zhang Q. , Ma S. .
Source: Computational Statistics & Data Analysis, 2024 Apr; 192, .
EPub date: 2023-12-05 00:00:00.0.
PMID: 38098875
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Information-incorporated sparse hierarchical cancer heterogeneity analysis.
Authors: Han W. , Zhang S. , Ma S. , Ren M. .
Source: Statistics In Medicine, 2024-03-30 00:00:00.0; , .
EPub date: 2024-03-30 00:00:00.0.
PMID: 38553996
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Prediction Consistency Regularization for Learning with Noise Labels Based on Contrastive Clustering.
Authors: Sun X. , Zhang S. , Ma S. .
Source: Entropy (basel, Switzerland), 2024-03-30 00:00:00.0; 26(4), .
EPub date: 2024-03-30 00:00:00.0.
PMID: 38667864
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Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis.
Authors: Chen Y. , Zhang Q. , Ma S. , Fang K. .
Source: Journal Of Machine Learning Research : Jmlr, 2024; 25, .
PMID: 42306232
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FunctanSNP: an R package for functional analysis of dense SNP data (with interactions).
Authors: Ren R. , Fang K. , Zhang Q. , Ma S. .
Source: Bioinformatics (oxford, England), 2023-12-01 00:00:00.0; 39(12), .
PMID: 38060266
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Gene-environment interaction analysis under the Cox model.
Authors: Fang K. , Li J. , Xu Y. , Ma S. , Zhang Q. .
Source: Annals Of The Institute Of Statistical Mathematics, 2023 Dec; 75(6), p. 931-948.
EPub date: 2023-04-10 00:00:00.0.
PMID: 39990259
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The Bayesian Regularized Quantile Varying Coefficient Model.
Authors: Zhou F. , Ren J. , Ma S. , Wu C. .
Source: Computational Statistics & Data Analysis, 2023 Nov; 187, .
EPub date: 2023-06-23 00:00:00.0.
PMID: 38746689
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Locally sparse quantile estimation for a partially functional interaction model.
Authors: Liang W. , Zhang Q. , Ma S. .
Source: Computational Statistics & Data Analysis, 2023 Oct; 186, .
EPub date: 2023-05-25 00:00:00.0.
PMID: 39555004
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Aligned deep neural network for integrative analysis with high-dimensional input.
Authors: Zhang S. , Zhang S. , Yi H. , Ma S. .
Source: Journal Of Biomedical Informatics, 2023 Aug; 144, p. 104434.
EPub date: 2023-06-28 00:00:00.0.
PMID: 37391115
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Pathological imaging-assisted cancer gene-environment interaction analysis.
Authors: Fang K. , Li J. , Zhang Q. , Xu Y. , Ma S. .
Source: Biometrics, 2023-05-03 00:00:00.0; , .
EPub date: 2023-05-03 00:00:00.0.
PMID: 37132273
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Bi-level structured functional analysis for genome-wide association studies.
Authors: Wu M. , Wang F. , Ge Y. , Ma S. , Li Y. .
Source: Biometrics, 2023-04-26 00:00:00.0; , .
EPub date: 2023-04-26 00:00:00.0.
PMID: 37098961
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Bayesian finite mixture of regression analysis for cancer based on histopathological imaging-environment interactions.
Authors: Im Y. , Huang Y. , Tan A. , Ma S. .
Source: Biostatistics (oxford, England), 2023-04-14 00:00:00.0; 24(2), p. 425-442.
PMID: 37057611
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Gene-environment interaction analysis via deep learning.
Authors: Wu S. , Xu Y. , Zhang Q. , Ma S. .
Source: Genetic Epidemiology, 2023 Apr; 47(3), p. 261-286.
EPub date: 2023-02-19 00:00:00.0.
PMID: 36807383
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Unified model-free interaction screening via CV-entropy filter.
Authors: Xiong W. , Chen Y. , Ma S. .
Source: Computational Statistics & Data Analysis, 2023 Apr; 180, .
EPub date: 2022-12-28 00:00:00.0.
PMID: 36910335
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HETEROGENEITY ANALYSIS VIA INTEGRATING MULTI-SOURCES HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO CANCER STUDIES.
Authors: Zhong T. , Zhang Q. , Huang J. , Wu M. , Ma S. .
Source: Statistica Sinica, 2023 Apr; 33(2), p. 729-758.
PMID: 38037567
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Spatio-temporally smoothed deep survival neural network.
Authors: Li Y. , Liang D. , Ma S. , Ma C. .
Source: Journal Of Biomedical Informatics, 2023 Jan; 137, p. 104255.
EPub date: 2022-12-01 00:00:00.0.
PMID: 36462600
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A General Framework for Identifying Hierarchical Interactions and Its Application to Genomics Data.
Authors: Xiao Z. , Xingjie S. , Yiming L. , Xu L. , Ma S. .
Source: Journal Of Computational And Graphical Statistics : A Joint Publication Of American Statistical Association, Institute Of Mathematical Statistics, Interface Foundation Of North America, 2023; 32(3), p. 873-883.
EPub date: 2023-02-06 00:00:00.0.
PMID: 38009111
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Rank-Based Greedy Model Averaging for High-Dimensional Survival Data.
Authors: He B. , Ma S. , Zhang X. , Zhu L.X. .
Source: Journal Of The American Statistical Association, 2023; 118(544), p. 2658-2670.
EPub date: 2022-07-07 00:00:00.0.
PMID: 39552724
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Two-level Bayesian interaction analysis for survival data incorporating pathway information.
Authors: Qin X. , Ma S. , Wu M. .
Source: Biometrics, 2022-12-16 00:00:00.0; , .
EPub date: 2022-12-16 00:00:00.0.
PMID: 36524727
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A tree-based gene-environment interaction analysis with rare features.
Authors: Liu M. , Zhang Q. , Ma S. .
Source: Statistical Analysis And Data Mining, 2022 Oct; 15(5), p. 648-674.
EPub date: 2022-03-01 00:00:00.0.
PMID: 38046814
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Survival Analysis with High-Dimensional Omics Data Using a Threshold Gradient Descent Regularization-Based Neural Network Approach.
Authors: Fan Y. , Zhang S. , Ma S. .
Source: Genes, 2022-09-19 00:00:00.0; 13(9), .
EPub date: 2022-09-19 00:00:00.0.
PMID: 36140842
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Sparse group variable selection for gene-environment interactions in the longitudinal study.
Authors: Zhou F. , Lu X. , Ren J. , Fan K. , Ma S. , Wu C. .
Source: Genetic Epidemiology, 2022-06-29 00:00:00.0; , .
EPub date: 2022-06-29 00:00:00.0.
PMID: 35766061
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Network-based cancer heterogeneity analysis incorporating multi-view of prior information.
Authors: Li Y. , Xu S. , Ma S. , Wu M. .
Source: Bioinformatics (oxford, England), 2022-05-13 00:00:00.0; 38(10), p. 2855-2862.
PMID: 35561185
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Network-based cancer heterogeneity analysis incorporating multi-view of prior information.
Authors: Li Y. , Xu S. , Ma S. , Wu M. .
Source: Bioinformatics (oxford, England), 2022-05-13 00:00:00.0; 38(10), p. 2855-2862.
PMID: 35561185
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Biclustering analysis of functionals via penalized fusion.
Authors: Fang K. , Chen Y. , Ma S. , Zhang Q. .
Source: Journal Of Multivariate Analysis, 2022 May; 189, .
EPub date: 2021-10-29 00:00:00.0.
PMID: 36817965
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GEInfo: an R package for gene-environment interaction analysis incorporating prior information.
Authors: Wang X. , Liu H. , Ma S. .
Source: Bioinformatics (oxford, England), 2022-04-29 00:00:00.0; , .
EPub date: 2022-04-29 00:00:00.0.
PMID: 35485739
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iSFun: an R package for integrative dimension reduction analysis.
Authors: Fang K. , Ren R. , Zhang Q. , Ma S. .
Source: Bioinformatics (oxford, England), 2022-04-20 00:00:00.0; , .
EPub date: 2022-04-20 00:00:00.0.
PMID: 35441661
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Integrative functional linear model for genome-wide association studies with multiple traits.
Authors: Li Y. , Wang F. , Wu M. , Ma S. .
Source: Biostatistics (oxford, England), 2022-04-13 00:00:00.0; 23(2), p. 574-590.
PMID: 33040145
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Integrative functional linear model for genome-wide association studies with multiple traits.
Authors: Li Y. , Wang F. , Wu M. , Ma S. .
Source: Biostatistics (oxford, England), 2022-04-13 00:00:00.0; 23(2), p. 574-590.
PMID: 33040145
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