{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T22:16:35Z","timestamp":1789769795752,"version":"4.0.1"},"reference-count":27,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2020,9,21]],"date-time":"2020-09-21T00:00:00Z","timestamp":1600646400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Biometrics"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>It is increasingly common clinically for cancer specimens to be examined using techniques that identify somatic mutations. In principle, these mutational profiles can be used to diagnose the tissue of origin, a critical task for the 3% to 5% of tumors that have an unknown primary site. Diagnosis of primary site is also critical for screening tests that employ circulating DNA. However, most mutations observed in any new tumor are very rarely occurring mutations, and indeed the preponderance of these may never have been observed in any previous recorded tumor. To create a viable diagnostic tool we need to harness the information content in this \u201chidden genome\u201d of variants for which no direct information is available. To accomplish this we propose a multilevel meta\u2010feature regression to extract the critical information from rare variants in the training data in a way that permits us to also extract diagnostic information from any previously unobserved variants in the new tumor sample. A scalable implementation of the model is obtained by combining a high\u2010dimensional feature screening approach with a group\u2010lasso penalized maximum likelihood approach based on an equivalent mixed\u2010effect representation of the multilevel model. We apply the method to the Cancer Genome Atlas whole\u2010exome sequencing data set including 3702 tumor samples across seven common cancer sites. Results show that our multilevel approach can harness substantial diagnostic information from the hidden genome.<\/jats:p>","DOI":"10.1111\/biom.13367","type":"journal-article","created":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T08:36:39Z","timestamp":1600763799000},"page":"1445-1455","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Using the \u201cHidden\u201d genome to improve classification of cancer types"],"prefix":"10.1093","volume":"77","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3121-9174","authenticated-orcid":false,"given":"Saptarshi","family":"Chakraborty","sequence":"first","affiliation":[{"name":"Department of Epidemiology &amp; Biostatistics Memorial Sloan\u2010Kettering Cancer Center New York New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Colin B.","family":"Begg","sequence":"additional","affiliation":[{"name":"Department of Epidemiology &amp; Biostatistics Memorial Sloan\u2010Kettering Cancer Center New York New York"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronglai","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Epidemiology &amp; Biostatistics Memorial Sloan\u2010Kettering Cancer Center New York New York"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,9,21]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature12477"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2018.02.060"},{"key":"e_1_2_7_4_1","first-page":"27","article-title":"Conditional likelihood maximisation: a unifying framework for information theoretic feature selection","volume":"13","author":"Brown G.","year":"2012","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1214\/13-BA816"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-020-1969-6"},{"key":"e_1_2_7_7_1","first-page":"73","volume-title":"Artificial Intelligence and Statistics: Proceedings of Machine Learning Research","author":"Carvalho C.M.","year":"2009"},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-12884-1"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1200\/PO.17.00011"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41416-018-0332-2"},{"key":"e_1_2_7_11_1","volume-title":"Elements of Information Theory","author":"Cover T.M.","year":"2012"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2012.45.2011"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1097\/PAS.0000000000000749"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2008.00674.x"},{"key":"e_1_2_7_15_1","first-page":"1","volume-title":"Wiley StatsRef: Statistics Reference Online","author":"Fan J.","year":"2017"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaw3472"},{"key":"e_1_2_7_17_1","first-page":"369","article-title":"Penalized regression, standard errors, and Bayesian lassos","author":"Kyung M.","year":"2010","journal-title":"Bayesian Analysis"},{"key":"e_1_2_7_18_1","volume-title":"R: A Language and Environment for Statistical Computing","author":"R Core Team","year":"2018"},{"key":"e_1_2_7_19_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btw570"},{"key":"e_1_2_7_20_1","doi-asserted-by":"crossref","unstructured":"Shi Y.andKnoblock C.A.(2017)Learning with previously unseen features. 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