{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T18:02:08Z","timestamp":1777312928739,"version":"3.51.4"},"reference-count":99,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T00:00:00Z","timestamp":1777248000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/publicdomain\/zero\/1.0\/"}],"funder":[{"name":"NIH\/NIEHS","award":["1ZIAES103350-06"],"award-info":[{"award-number":["1ZIAES103350-06"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Identifying cancer driver genes (CDGs) remains a central challenge in cancer genomics, as frequency-based mutation approaches often miss rare but functionally important regulators. We present PICDGI, a computational framework that predicts driver-like regulatory genes by integrating dynamic gene-gene interaction modeling with single-cell RNA sequencing (scRNA-seq) data. Rather than relying on DNA mutation calls, PICDGI infers functional driver activity from time-resolved expression patterns and latent regulatory influence among genes during tumor progression. Methodologically, PICDGI employs a time-varying state-space model with variational Bayesian inference and Markov Chain Monte Carlo (MCMC) sampling to estimate evolving gene interaction effects. The posterior distributions capture both the magnitude and uncertainty of each gene\u2019s inferred regulatory influence. From these, PICDGI derives a driver coefficient that quantifies the strength and reliability of each gene\u2019s contribution to progression-associated expression dynamics, enabling the prioritization of impactful regulators over neutral passengers. Applied to lung adenocarcinoma (LUAD) scRNA-seq data, PICDGI recovered known oncogenes and tumor suppressors and nominated novel candidate drivers, including\n                    <jats:italic>JPH1<\/jats:italic>\n                    and\n                    <jats:italic>CHEK1<\/jats:italic>\n                    , which are implicated in calcium signaling, mitochondrial regulation, and DNA repair. These genes exhibit trajectory-aligned activity consistent with tumor evolution and immune-modulatory processes. Comparative analysis using Moran\u2019s I statistics in Monocle 3 showed that PICDGI-prioritized genes display stronger progression-associated dynamics than genes selected by spatial autocorrelation alone. We further validated PICDGI on an independent pediatric acute myeloid leukemia (AML) scRNA-seq cohort, where it consistently recovered known drivers and relapse-associated regulatory programs under fixed model parameters. By integrating interaction-informed dynamic modeling with single-cell resolution data, PICDGI provides a generalizable and biologically grounded framework for identifying rare and context-specific regulatory drivers of cancer progression, with broad applicability across tumor types.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1014143","type":"journal-article","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T17:30:50Z","timestamp":1777311050000},"page":"e1014143","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["PICDGI: A framework for predicting cancer driver genes through dynamic gene-gene interaction modeling of single-cell 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Med"},{"issue":"3","key":"pcbi.1014143.ref003","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1093\/jnci\/djae239","article-title":"Streamlining the conduct of cancer clinical trials: new standard data collection practices for National Cancer Institute late-phase clinical studies","volume":"117","author":"SA Prindiville","year":"2025","journal-title":"J Natl Cancer Inst"},{"issue":"9","key":"pcbi.1014143.ref004","doi-asserted-by":"crossref","first-page":"1570","DOI":"10.1101\/gr.092833.109","article-title":"Identification of rare cancer driver mutations by network reconstruction","volume":"19","author":"A Torkamani","year":"2009","journal-title":"Genome Res"},{"issue":"7457","key":"pcbi.1014143.ref005","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1038\/nature12213","article-title":"Mutational heterogeneity in cancer and the search for new cancer-associated genes","volume":"499","author":"MS Lawrence","year":"2013","journal-title":"Nature"},{"issue":"21","key":"pcbi.1014143.ref006","doi-asserted-by":"crossref","DOI":"10.1093\/nar\/gks743","article-title":"Functional impact bias reveals cancer drivers","volume":"40","author":"A Gonzalez-Perez","year":"2012","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"pcbi.1014143.ref007","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1186\/s13059-016-0994-0","article-title":"OncodriveFML: a general framework to identify coding and non-coding regions with cancer driver mutations","volume":"17","author":"L Mularoni","year":"2016","journal-title":"Genome Biol"},{"issue":"18","key":"pcbi.1014143.ref008","doi-asserted-by":"crossref","first-page":"2238","DOI":"10.1093\/bioinformatics\/btt395","article-title":"OncodriveCLUST: exploiting the positional clustering of somatic mutations to identify cancer genes","volume":"29","author":"D Tamborero","year":"2013","journal-title":"Bioinformatics"},{"key":"pcbi.1014143.ref009","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1186\/s12859-021-04377-0","article-title":"Identification of driver genes based on gene mutational effects and network centrality","volume":"22","author":"Y-Y Tang","year":"2021","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"pcbi.1014143.ref010","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s13755-024-00279-6","article-title":"Identification of cancer driver genes based on hierarchical weak consensus model","volume":"12","author":"G Li","year":"2024","journal-title":"Health Inf Sci Syst"},{"issue":"1","key":"pcbi.1014143.ref011","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1186\/s12957-021-02387-z","article-title":"Comprehensive analysis of the cancer driver genes in breast cancer demonstrates their roles in cancer prognosis and tumor microenvironment","volume":"19","author":"X-W Du","year":"2021","journal-title":"World J Surg Oncol"},{"issue":"1","key":"pcbi.1014143.ref012","article-title":"Towards simplified graph neural networks for identifying cancer driver genes in heterophilic networks","volume":"26","author":"X Li","year":"2024","journal-title":"Brief Bioinform"},{"issue":"7719","key":"pcbi.1014143.ref013","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1038\/s41586-018-0414-6","article-title":"RNA velocity of single cells","volume":"560","author":"G La Manno","year":"2018","journal-title":"Nature"},{"issue":"12","key":"pcbi.1014143.ref014","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1038\/s41587-020-0591-3","article-title":"Generalizing RNA velocity to transient cell states through dynamical modeling","volume":"38","author":"V Bergen","year":"2020","journal-title":"Nat Biotechnol"},{"issue":"4","key":"pcbi.1014143.ref015","doi-asserted-by":"crossref","DOI":"10.1016\/j.cell.2019.01.006","article-title":"Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming","volume":"176","author":"G Schiebinger","year":"2019","journal-title":"Cell"},{"issue":"12","key":"pcbi.1014143.ref016","doi-asserted-by":"crossref","first-page":"2159","DOI":"10.1093\/bioinformatics\/bty916","article-title":"GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks","volume":"35","author":"T Moerman","year":"2019","journal-title":"Bioinformatics"},{"issue":"15","key":"pcbi.1014143.ref017","doi-asserted-by":"crossref","first-page":"2314","DOI":"10.1093\/bioinformatics\/btx194","article-title":"SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation","volume":"33","author":"H Matsumoto","year":"2017","journal-title":"Bioinformatics"},{"issue":"1","key":"pcbi.1014143.ref018","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1038\/s41467-020-14766-3","article-title":"Trajectory-based differential 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Pham","year":"2021","journal-title":"Theranostics"},{"key":"pcbi.1014143.ref025","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1038\/msb.2012.68","article-title":"Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers","volume":"9","author":"J Reimand","year":"2013","journal-title":"Mol Syst Biol"},{"issue":"8","key":"pcbi.1014143.ref026","article-title":"IMI-driver: integrating multi-level gene networks and multi-omics for cancer driver gene identification","volume":"20","author":"P Shi","year":"2024","journal-title":"PLoS Comput Biol"},{"issue":"12","key":"pcbi.1014143.ref027","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1011450","article-title":"Unravelling cancer subtype-specific driver genes in single-cell transcriptomics data with CSDGI","volume":"19","author":"M Huang","year":"2023","journal-title":"PLoS Comput 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Anderson","year":"2020","journal-title":"Curr Biol"},{"issue":"7330","key":"pcbi.1014143.ref039","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1038\/nature09781","article-title":"Cells of origin in cancer","volume":"469","author":"JE Visvader","year":"2011","journal-title":"Nature"},{"issue":"6","key":"pcbi.1014143.ref040","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1038\/s41568-018-0006-7","article-title":"A history of exploring cancer in context","volume":"18","author":"S Maman","year":"2018","journal-title":"Nat Rev Cancer"},{"key":"pcbi.1014143.ref041","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.spl.2015.11.018","article-title":"Efficient simulation of non-Poisson non-stationary point processes to study queueing approximations","volume":"109","author":"N Ma","year":"2016","journal-title":"Statist Probabil Lett"},{"key":"pcbi.1014143.ref042","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1186\/1741-7007-9-41","article-title":"Stochastic dynamics and the evolution of mutations in stem cells","volume":"9","author":"D Dingli","year":"2011","journal-title":"BMC Biol"},{"issue":"4","key":"pcbi.1014143.ref043","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1109\/TASSP.1983.1164152","article-title":"Time-dependent ARMA modeling of nonstationary signals","volume":"31","author":"Y Grenier","year":"1983","journal-title":"IEEE Trans Acoust Speech Signal Process"},{"issue":"17","key":"pcbi.1014143.ref044","doi-asserted-by":"crossref","first-page":"3106","DOI":"10.1080\/00949655.2020.1797030","article-title":"Indirect inference for locally stationary ARMA processes with stable innovations","volume":"90","author":"SW Chou-Chen","year":"2020","journal-title":"J Stat Comput Simulat"},{"key":"pcbi.1014143.ref045","doi-asserted-by":"crossref","first-page":"012141","DOI":"10.1103\/PhysRevE.96.012141","article-title":"Consistency of detrended fluctuation analysis","volume":"96","author":"O L\u00f8vsletten","year":"2017","journal-title":"Phys Rev E"},{"key":"pcbi.1014143.ref046","doi-asserted-by":"crossref","first-page":"66","DOI":"10.3389\/fgene.2019.00066","article-title":"A stochastic phylogenetic algorithm for mitochondrial DNA analysis","volume":"10","author":"M Corona-Ruiz","year":"2019","journal-title":"Front Genet"},{"issue":"2","key":"pcbi.1014143.ref047","article-title":"Detrended fluctuation analysis of heart rate dynamics is an important prognostic factor in patients with end-stage renal disease receiving peritoneal dialysis","volume":"11","author":"J-Y Chiang","year":"2016","journal-title":"PLoS One"},{"issue":"24","key":"pcbi.1014143.ref048","doi-asserted-by":"crossref","first-page":"6057","DOI":"10.1016\/j.physa.2008.07.004","article-title":"Fractional brownian motion, fractional gaussian noise, and Tsallis permutation entropy","volume":"387","author":"L Zunino","year":"2008","journal-title":"Physica A: Stat Mech Appl"},{"key":"pcbi.1014143.ref049","article-title":"Stochastic variational inference","author":"MD Hoffman","year":"2013","journal-title":"J Machine Learn Res"},{"key":"pcbi.1014143.ref050","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1186\/1748-717X-5-101","article-title":"Carbonic anhydrase XII expression is associated with histologic grade of cervical cancer and superior radiotherapy outcome","volume":"5","author":"CW Yoo","year":"2010","journal-title":"Radiat Oncol"},{"issue":"1","key":"pcbi.1014143.ref051","doi-asserted-by":"crossref","first-page":"012148","DOI":"10.1103\/PhysRevE.91.012148","article-title":"Variational mean-field algorithm for efficient inference in large systems of stochastic differential equations","volume":"91","author":"MD Vrettas","year":"2015","journal-title":"Phys Rev E Stat Nonlin Soft Matter Phys"},{"key":"pcbi.1014143.ref052","doi-asserted-by":"crossref","first-page":"3569","DOI":"10.1016\/S1573-4412(01)05010-3","article-title":"Markov chain monte carlo methods: computation and inference.","author":"S Chib","year":"2001","journal-title":"Handbook of econometrics. Elsevier"},{"key":"pcbi.1014143.ref053","volume-title":"HDInterval: highest (posterior) density intervals","author":"M Meredith","year":"2016"},{"key":"pcbi.1014143.ref054","volume-title":"Bayesian Gaussian process latent variable model. Proceedings of the thirteenth international conference on artificial intelligence and statistics","author":"M Titsias","year":"2010"},{"issue":"4","key":"pcbi.1014143.ref055","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1158\/2159-8290.CD-23-1510","article-title":"Cell states in cancer: drivers, passengers, and trailers","volume":"14","author":"G Gargiulo","year":"2024","journal-title":"Cancer Discov"},{"issue":"1","key":"pcbi.1014143.ref056","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmet.2022.11.009","article-title":"Single-cell transcriptomic and spatial landscapes of the developing human pancreas","volume":"35","author":"OE Olaniru","year":"2023","journal-title":"Cell Metab"},{"issue":"2","key":"pcbi.1014143.ref057","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/S0304-4076(01)00064-1","article-title":"On the asymptotic distribution of the Moran I test statistic with applications","volume":"104","author":"H H. Kelejian","year":"2001","journal-title":"J Economet"},{"issue":"7745","key":"pcbi.1014143.ref058","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1038\/s41586-019-0969-x","article-title":"The single-cell transcriptional landscape of mammalian organogenesis","volume":"566","author":"J Cao","year":"2019","journal-title":"Nature"},{"issue":"5","key":"pcbi.1014143.ref059","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1038\/nbt.3192","article-title":"Spatial reconstruction of single-cell gene expression data","volume":"33","author":"R Satija","year":"2015","journal-title":"Nat Biotechnol"},{"issue":"3","key":"pcbi.1014143.ref060","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1007\/s10555-020-09898-3","article-title":"Expression and function of epithelial cell adhesion molecule EpCAM: where are we after 40 years?","volume":"39","author":"O Gires","year":"2020","journal-title":"Cancer and Metastasis Reviews"},{"issue":"1","key":"pcbi.1014143.ref061","first-page":"49","article-title":"Expression of epithelial cell adhesion molecule in carcinoma cells present in blood and primary and metastatic tumors","volume":"27","author":"CG Rao","year":"2005","journal-title":"Int J Oncol"},{"issue":"12","key":"pcbi.1014143.ref062","doi-asserted-by":"crossref","DOI":"10.1093\/gigascience\/giaa151","article-title":"SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data","volume":"9","author":"MD Young","year":"2020","journal-title":"Gigascience"},{"key":"pcbi.1014143.ref063","doi-asserted-by":"crossref","first-page":"1227","DOI":"10.1038\/1921227a0","article-title":"General nature of the genetic code for proteins","volume":"192","author":"FH Crick","year":"1961","journal-title":"Nature"},{"issue":"3","key":"pcbi.1014143.ref064","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1038\/scientificamerican0395-72","article-title":"The genetic basis of cancer","volume":"272","author":"WK Cavenee","year":"1995","journal-title":"Sci Am"},{"issue":"9","key":"pcbi.1014143.ref065","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1038\/cdd.2012.30","article-title":"TP53INP1, a tumor suppressor, interacts with LC3 and ATG8-family proteins through the LC3-interacting region (LIR) and promotes autophagy-dependent cell death","volume":"19","author":"M Seillier","year":"2012","journal-title":"Cell Death Differ"},{"issue":"8","key":"pcbi.1014143.ref066","doi-asserted-by":"crossref","first-page":"2065","DOI":"10.1002\/ijc.31607","article-title":"An inhibitory antibody targeting carbonic anhydrase XII abrogates chemoresistance and significantly reduces lung metastases in an orthotopic breast cancer model in vivo","volume":"143","author":"B von Neubeck","year":"2018","journal-title":"Int J Cancer"},{"issue":"3","key":"pcbi.1014143.ref067","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1158\/1055-9965.EPI-23-0613","article-title":"Lung cancer in ever- and never-smokers: findings from multi-population GWAS studies","volume":"33","author":"Y Li","year":"2024","journal-title":"Cancer Epidemiol Biomarkers Prev"},{"key":"pcbi.1014143.ref068","doi-asserted-by":"crossref","first-page":"1414487","DOI":"10.3389\/fgene.2024.1414487","article-title":"The genomic landscape of the immune system in lung cancer: present insights and continuing investigations","volume":"15","author":"M Roshan-Zamir","year":"2024","journal-title":"Front Genet"},{"issue":"1","key":"pcbi.1014143.ref069","first-page":"1","article-title":"Targeting PI3K signaling in lung cancer: advances, challenges and therapeutic opportunities","volume":"23","author":"B Zhang","year":"2025","journal-title":"J Transl Med"},{"issue":"1","key":"pcbi.1014143.ref070","first-page":"105","article-title":"Tumor heterogeneity: causes and consequences","volume":"1805","author":"A Marusyk","year":"2010","journal-title":"Biochim Biophys Acta"},{"issue":"10","key":"pcbi.1014143.ref071","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1038\/nrc.2017.77","article-title":"Tumour acidosis: from the passenger to the driver\u2019s seat","volume":"17","author":"C Corbet","year":"2017","journal-title":"Nat Rev Cancer"},{"issue":"6","key":"pcbi.1014143.ref072","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1038\/nmeth.4292","article-title":"Normalizing single-cell RNA sequencing data: challenges and opportunities","volume":"14","author":"CA Vallejos","year":"2017","journal-title":"Nat Methods"},{"issue":"4","key":"pcbi.1014143.ref073","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1038\/nmeth.4220","article-title":"Power analysis of single-cell RNA-sequencing experiments","volume":"14","author":"V Svensson","year":"2017","journal-title":"Nat 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