Publications
Underline = past/current group member , * = authors contributed equally, ✉ = corresponding author
2026 -
Multi-omic deconvolution and DNA-RNA dynamics in cancer
Montierth MD*, Yan H*, Xie L*, Nemeth K, Pan X, Li R,
Ercan C, Yang P, Sinjab A, Zhou T, Peng F, Singh M, Wang L, Kopetz S, Kadara H, Yuan Y,
Calin GA, Wang W✉. Deconvolution of sparse-count RNA sequencing data for tumor
cells using embedded negative binomial distributions. under review
[Preprint]
[Software: DeMixT2.0] *authors contributed equally
Dai Y, Pan X, Guo S, Ji S, Cao S, Montierth MD,
Jiang Y, Shi L, Shalapour S, Echeverria GV, Yates L, Staaf J, Chang JT, Lim B,
Yuan Y, Wang W✉. Tumor Microenvironment Transcriptional Activity Enables Robust Stratification of Chemotherapy Response in Triple-Negative Breast Cancer. Cell Reports Medicine. accepted.
Jiang Y*, Montierth M*, Yu K* , et al., Wang W✉.
Subclonal mutation load predicts survival and response to immunotherapy in cancers with low to moderate tumor mutation burden.
under revision [Preprint][Github:CliPP]
Risk prediction with machine learning and Bayesian semi-parametric models
Liu X, Yan, H, Shi H, Montellier E, Chi E, Hainaut P, Wang
W✉. Transfer learning for survival-based
clustering of predictors with an application
to TP53 mutation annotation. Journal of the American Statistics Association, under revision. 2025 [Preprint] [GitHub Repo: TL-SCP] Proceedings of The Inaugural Workshop on Frontiers in Statistical Machine Learning (FSML) travel award
Corredor JL*, Ruonan Li*, Dodd-Eaton EB, Woodman-Ross J, Nguyen NH, Peng
G, Green S, Gutierrez AM, Arun BK✉, Wang W✉. Performance of LFSPRO
TP53 germline carrier risk predictions compared to standard genetic counseling
practice on prospectively collected probands. American Journal of Human Genetics, under revision [Preprint]
Collaboration Papers
Li S et al. PRECISE: A prognostic thyrocyte-derived gene signature for papillary thyroid carcinoma. submitted
Duan Y, Guo S, Yan H, Wang W, Mueller P. Spatially aligned random
partition models on spatially resolved transcriptomics data. Bayesian Analysis, under 2nd review. [Preprint]
Risk prediction with machine learning and Bayesian semi-parametric models
Collaboration Papers
2025 -
Multi-omic deconvolution and DNA-RNA dynamics in cancer
Dai Y, Guo S, Pan Y, Castignani C, Montierth MD, Van Loo P✉,
Wang W✉. A guide to transcriptomic deconvolution in cancer. Nature Reviews Cancer. 2025, https://doi.org/10.1038/s41568-025-00886-9. [Full text]
Guo S*, Liu X*, Cheng X*, Jiang Y, Ji S, Liang Q, Koval A, Li Y,
Owen LA, Kim IK, Aparicio A, Lee S, Sood AK, Kopetz S, Shen JP, Weinstein JN, Deangelis
MM, Chen R, Wang W✉. A deconvolution framework that uses single-cell
sequencing plus a small benchmark data set for accurate analysis of cell type ratios in
complex tissue samples. Genome Res. Published in Advance January 6, 2025,
doi:10.1101/gr.279605.124 [abstract]
[GitHub repository: DeMixSC]
Risk prediction with machine learning and Bayesian semi-parametric models
Nguyen NH*, Shin SJ*, Dodd-Eaton EB, Ning J, Wang W✉.
Personalized Risk Prediction for Cancer Survivors: A Bayesian Semi-parametric Recurrent
Event Model with Competing Outcomes.
Annals of Applied Statistics. 19(4): 3091-3112 (December 2025). Doi: 10.1214/25-AOAS2083 [full text] *author contributed equally
Stats Up AI
Zheng et al. Statistics and AI - A Fireside Conversation. Harvard Data Science
Review. 2025 Apr 30;7(2). [Full text]
Collaboration Papers
Foutunno C, Fone MN, Mester J, et al. A quantitative, Bayesian-informed approach to
gene-specific variant classification: Updated Expert Panel recommendations improve
analyses of TP53 germline variants for Li-Fraumeni syndrome. Genome Medicine. 2025 Oct 22;17(1):128. doi: 10.1186/s13073-025-01536-3. [Full text]
Bhinder B et al. Pan-cancer immune and stromal deconvolution predicts clinical outcomes and mutation profiles.
Scientific Reports. 2025 Jul 4;15(1):23921. doi: 10.1038/s41598-025-09075-y.
[Full text]
Lin C et al. Cholesterol metabolism regulated by CAMKK2-CREB signaling promotes
castration-resistant prostate cancer. Cell Reports. 2025 Jun 24;44(6):115792. doi: 10.1016/j.celrep.2025.115792.
[Full text]
Chowdhury S, Ferri-Borgogno S, Yang P, Wang W, Peng J, Mok S, Wang P.
Learning directed acyclic graphs for ligands and receptors based on spatially resolved
transcriptomic analysis of ovarian cancer. Briefings in Bioinformatics. 2025 Mar
4;26(2):bbaf085. doi: 10.1093/bib/bbaf085. [Full
text]
Ito I et al. Development and Characterization of orthotopic patient-derived xenograft
models of mucinous appendiceal adenocarcinoma. ESMO Gastrointestinal Oncology.
2025 Mar 1;7:100133. [Full text]
Duan, Y, Guo, S, Wang, W, Mueller, P. Immune Profiling among Colorectal
Cancer Subtypes using Dependent Mixture Models. JASA Published online: 25 Feb
2025. [Abstract]
Risk prediction with machine learning and Bayesian semi-parametric models
Stats Up AI
Collaboration Papers
2024 -
Multi-omic deconvolution and DNA-RNA dynamics in cancer
Ji S, Zhu T, Sethia A, Wang W. Accelerated somatic mutation calling
for whole-genome and whole-exome sequencing data from heterogenous tumor samples.
Genome Res. 2024 May 3;. doi: 10.1101/gr.278456.123. [Epub ahead of print] [Full
text]
Risk prediction with machine learning and Bayesian semi-parametric models
Nguyen NH, Dodd-Eaton EB, Corredor JL, Woodman-Ross J, Green S,
Gutierrez AM, Arun BK, Wang W. Validating Risk Prediction Models for Multiple
Primaries and Competing Cancer Outcomes in Families With Li-Fraumeni Syndrome Using
Clinically Ascertained Data. J Clin Oncol. 2024 Apr 3;:JCO2301926. doi:
10.1200/JCO.23.01926. [Epub ahead of print] [Full
text]
Nguyen NH, Dodd-Eaton EB, Peng G, Corredor JL, Jiao W,
Woodman-Ross J, Arun BK, Wang W✉. LFSPROShiny: an interactive
R/Shiny app for prediction and visualization of cancer risks in families with
deleterious germline TP53 mutations. JCO Clinical Cancer
Informatics. 2024 Feb 12. Volume 8. doi: 10.1200/CCI.23.00167. [Full text] [Github:LFSPROShiny]
Collaboration Papers
Yuan, Dongbang, Zhang, Yunfeng, Guo, Shuai, Wang, Wenyi, Gaynanova,
Irina. Exponential canonical correlation analysis with orthogonal variation. [preprint]
Yousef M, Yousef A, Chowdury S, Fanaeian M, Knafl M, Peterson J, Zeineddine M,
Alfaro K, Zeineddine F, Godstein D, Horstein N, Dasari A, Huey R, Johnson B, Higbie
V, Bent A, Kee B, Lee M, Morelli MP, Morris VK, Halperin D, Overman MJ, Parseghian
C, Villar E, Wolff R, Raghav KP, White MG, Uppal A, Sun R, Wang W, Kopetz SK,
Willis J, Shen JP. Molecular, Socioeconomic, and Clinical Factors Affecting Racial
and Ethnic Disparities in Colorectal Cancer Survival. JAMA Oncol. 2024 Sep
12. doi: 10.1001/jamaoncol.2024.3666. Online ahead of print. [Full
text]
Wang JR, Zafereo ME, Cabanillas ME, Wu CC, Xu L, Dai Y, Wang W, Lai
SY, Henderson Y, Erasmus L, Williams MD, Joshu C, Ray D. The association between
thyroid differentiation score and survival outcomes in papillary thyroid carcinoma.
J Clin Endocrinol Metab. 2024 Aug 1:dgae532. doi: 10.1210/clinem/dgae532.
Online ahead of print. [Full
text]
Aparicio AM, Tidwell RSS, Yadav SS, Chen JS, Zhang M, Liu J, Guo S, Pilie
PG, Yu Y, Song X, Vundavilli H, Jindal S, Zhu K, Viscuse PV, Lebenthal JM,
Hahn AW, Soundararajan R, Corn PG, Zurita AJ, Subudhi SK, Zhang J, Wang W,
Huff C, Troncoso P, Allison JP, Sharma P, Logothetis CJ. A Modular Trial of Androgen
Signaling Inhibitor Combinations Testing a Risk-Adapted Strategy in Patients with
Metastatic Castration-Resistant Prostate Cancer. Clin Cancer Res. 2024 Apr
29;. doi: 10.1158/1078-0432.CCR-23-3740. [Epub ahead of print] [Full
text]
Bahrambeigi V, Lee JJ, Branchi V, Rajapakshe KI, Xu Z, Kui N, Henry JT, Wang K,
Stephens BM, Dhebat S, Hurd MW, Sun R, Yang P, Ruppin E, Wang W,
Kopetz S, Maitra A, Guerrero PA. Transcriptomic Profiling of Plasma Extracellular
Vesicles Enables Reliable Annotation of the Cancer-specific Transcriptome and
Molecular Subtype. Cancer Res. 2024 Mar 7;. doi:
10.1158/0008-5472.CAN-23-4070. [Epub ahead of print][Full
text]
Risk prediction with machine learning and Bayesian semi-parametric models
Collaboration Papers
2023 -
2022 -
Multi-omic deconvolution and DNA-RNA dynamics in cancer
Ji S, Montierth M, Wang W✉. MuSE: A Novel Approach to
Mutation Calling with Sample-Specific Error Modeling. Methods Molecular
Biology. 2022; 2493:21-27. doi: 10.1007/978-1-0716-2293-3_2.[Full
text]
Cao S, et al., Wang W✉ Estimation of tumor cell total
mRNA expression in 15 cancer types predicts disease progression. Nature
Biotechnology Published online June 13 2022. doi
10.1038/s41587-022-01342-x. [Abstract]
Collaboration Papers
Wang JR, Montierth M, Li X, Goswami M, Zhao X, Cote G, Wang
W, Iyer P, Dadu R, Busaidy NL, Lai SY, Grosss ND, Ferrarotto R, Lu
C, Gunn GB, Williams MD, Routbort M, Zafereo ME, Cabanillas ME. Impact of
Somatic Mutations on Survival Outcomes in Anaplastic Thyroid Carcinoma
Patients. JCO Precision Oncology no. 6 (2022) e2100504. Published
online August 17, 2022. DOI: 10.1200/PO.21.00504 [abstract][full
text]
Bondaruk J et al., Wang W, McConkey D, Wei P, Kimmel M, Czerniak B.
The origin of bladder cancer from mucosal field effects. iScience
2022 Jun 7; 25(7):104551. doi: 10.1016/j.isci.2022.104551. eCollection 2022
Jul 15 [Full
text]
Wang Z, Baladandayuthapani V, Kaseb AO, Amin HM, Hassan MM, Wang
W, Morris JS. Bayesian Edge
Regression in Undirected Graphical Models to Characterize
Interpatient Heterogeneity in Cancer. Journal of the American Statistical
Association. Pages 533-546. Published online Jan 5 2022. [Full
text]
Collaboration Papers
2021 -
2020 -
*authors contributed equally
2019 -
2020 Jan 15;80(2):354-360. doi: 10.1158/0008-5472.CAN-19-0728. Epub 2019 Nov 12.
[Abstract] [Software: LFSPRO2.0.0] for risk assessment of specific cancer type.
[Preprint] [Software: LFSPRO2.0.0] for risk assessment of multiple primary cancers.
2018 -
2017 -
2016 -
2015 -
2014 -
2013 -
2012 -
2011 -
2010 -
2008 -
2007 -
2006 -
2004 -