{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T07:58:55Z","timestamp":1769587135629,"version":"3.49.0"},"reference-count":12,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T00:00:00Z","timestamp":1615939200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T00:00:00Z","timestamp":1615939200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100017239","name":"BeiGene","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100017239","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>An increasing number of\u00a0clinical trials require biomarker-driven patient stratification, especially for revolutionary immune checkpoint blockade therapy. Due to the complicated interaction between a tumor and its microenvironment, single biomarkers, such as PDL1 protein level, tumor mutational burden (TMB), single gene mutation and expression, are far from satisfactory for response prediction or patient stratification. Recently, combinatorial biomarkers were reported to be more precise and powerful for predicting therapy response and identifying potential target populations with superior survival. However, there is a lack of dedicated tools for such combinatorial biomarker analysis.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Here, we present <jats:italic>dualmarker<\/jats:italic>, an R package designed to facilitate the data exploration for dual biomarker combinations. Given two biomarkers, <jats:italic>dualmarker<\/jats:italic> comprehensively visualizes their association with drug response and patient survival through 14 types of plots, such as boxplots, scatterplots, ROCs, and Kaplan\u2013Meier plots. Using logistic regression and Cox regression models, <jats:italic>dualmarker<\/jats:italic> evaluated the superiority of dual markers over single markers by comparing the data fitness of dual-marker versus single-marker models, which was utilized for de novo searching for new biomarker pairs. We demonstrated this straightforward workflow and comprehensive capability by using public biomarker data from one bladder cancer patient cohort (IMvigor210 study); we confirmed the previously reported biomarker pair TMB\/TGF-beta signature and CXCL13 expression\/ARID1A mutation for response and survival analyses, respectively. In addition, <jats:italic>dualmarker<\/jats:italic> de novo identified new biomarker partners, for example, in overall survival modelling, the model with combination of HMGB1 expression and ARID1A mutation had statistically better goodness-of-fit than the model with either HMGB1 or ARID1A as single marker.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The <jats:italic>dualmarker<\/jats:italic> package is an\u00a0open-source tool for the\u00a0visualization and\u00a0identification of\u00a0combinatorial dual biomarkers. It streamlines the dual marker analysis flow into user-friendly functions and can be used for data exploration and hypothesis generation. Its code is freely available at GitHub at\u00a0<jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/maxiaopeng\/dualmarker\">https:\/\/github.com\/maxiaopeng\/dualmarker<\/jats:ext-link> under MIT license.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-021-04050-6","type":"journal-article","created":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T15:02:46Z","timestamp":1615993366000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Dualmarker: a flexible toolset for exploratory analysis of combinatorial dual biomarkers for clinical efficacy"],"prefix":"10.1186","volume":"22","author":[{"given":"Xiaopeng","family":"Ma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruiqi","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xikun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,17]]},"reference":[{"key":"4050_CR1","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1016\/j.cell.2011.02.013","volume":"144","author":"D Hanahan","year":"2011","unstructured":"Hanahan D, Weinberg RA. Hallmarks of cancer: The next generation. Cell. 2011;144:646\u201374. https:\/\/doi.org\/10.1016\/j.cell.2011.02.013.","journal-title":"Cell"},{"key":"4050_CR2","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1038\/s41568-019-0116-x","volume":"19","author":"JJ Havel","year":"2019","unstructured":"Havel JJ, Chowell D, Chan TA. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat Rev Cancer. 2019;19:133\u201350. https:\/\/doi.org\/10.1038\/s41568-019-0116-x.","journal-title":"Nat Rev Cancer"},{"key":"4050_CR3","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1038\/s41571-019-0173-9","volume":"16","author":"DR Camidge","year":"2019","unstructured":"Camidge DR, Doebele RC, Kerr KM. Comparing and contrasting predictive biomarkers for immunotherapy and targeted therapy of NSCLC. Nat Rev Clin Oncol. 2019;16:341\u201355. https:\/\/doi.org\/10.1038\/s41571-019-0173-9.","journal-title":"Nat Rev Clin Oncol"},{"key":"4050_CR4","doi-asserted-by":"publisher","first-page":"eaar3593","DOI":"10.1126\/science.aar3593","volume":"362","author":"R Cristescu","year":"2018","unstructured":"Cristescu R, Mogg R, Ayers M, Albright A, Murphy E, Yearley J, et al. Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science. 2018;362:eaar3593.","journal-title":"Science"},{"key":"4050_CR5","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1038\/nature25501","volume":"554","author":"S Mariathasan","year":"2018","unstructured":"Mariathasan S, Turley SJ, Nickles D, Castiglioni A, Yuen K, Wang Y, et al. TGF\u03b2 attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature. 2018;554:544\u20138. https:\/\/doi.org\/10.1038\/nature25501.","journal-title":"Nature"},{"key":"4050_CR6","doi-asserted-by":"publisher","first-page":"eabc4220","DOI":"10.1126\/scitranslmed.abc4220","volume":"12","author":"S Goswami","year":"2020","unstructured":"Goswami S, Chen Y, Anandhan S, Szabo PM, Basu S, Blando JM, et al. ARID1A mutation plus CXCL13 expression act as combinatorial biomarkers to predict responses to immune checkpoint therapy in mUCC. Sci Transl Med. 2020;12:eabc4220.","journal-title":"Sci Transl Med."},{"key":"4050_CR7","unstructured":"Alboukadel Kassambara, Marcin Kosinski PB. survminer: Drawing Survival Curves using \u201cggplot2.\u201d 2020. https:\/\/cran.r-project.org\/package=survminer."},{"key":"4050_CR8","unstructured":"Ning Leng, Alexey Pronin, Jemma Fan, Doug Kelkhoff, Christina Rabe KO. gClinBiomarker. 2020. https:\/\/github.com\/lengning\/gClinBiomarker."},{"key":"4050_CR9","unstructured":"Xiao N. ggsci: Scientific Journal and Sci-Fi Themed Color Palettes for \u201cggplot2.\u201d 2018. https:\/\/cran.r-project.org\/package=ggsci."},{"key":"4050_CR10","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1186\/1471-2105-12-77","volume":"12","author":"X Robin","year":"2011","unstructured":"Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. 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CPE: concordance probability estimates in survival analysis. 2018. https:\/\/cran.r-project.org\/package=CPE."},{"key":"4050_CR12","unstructured":"TME related genes from HTG EdgeSeq Precision Immuno-Oncology Panel. https:\/\/www.htgmolecular.com\/assays\/pip."}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-04050-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-021-04050-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-04050-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,17]],"date-time":"2021-03-17T15:04:35Z","timestamp":1615993475000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-021-04050-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,17]]},"references-count":12,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["4050"],"URL":"https:\/\/doi.org\/10.1186\/s12859-021-04050-6","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,17]]},"assertion":[{"value":"7 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 February 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The data used herein was previously published (Creative Commons-BY-3.0 license), with ethics approval in the original publication (PMC6028240).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Authors are current BeiGene employees and shareholders. There are no other conflicts of interest to disclose.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"127"}}