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class="c-facetbox"><summary class="c-facetbox__summary"><span id="facetbox6">Journal</span></summary><fieldset aria-labelledby="facetbox6"><ul class="c-checkbox"></ul></fieldset></details><details class="c-facetbox"><summary class="c-facetbox__summary"><span id="facetbox7">Discipline</span></summary><fieldset aria-labelledby="facetbox7"><ul class="c-checkbox"></ul></fieldset></details><details class="c-facetbox"><summary class="c-facetbox__summary"><span id="facetbox8">Reuse License</span></summary><fieldset aria-labelledby="facetbox8"><ul class="c-checkbox"><li class="c-checkbox__attrib-cc-by"><input type="checkbox" id="rights-CC_BY" class="c-checkbox__input" name="rights" value="CC BY"/><label for="rights-CC_BY" class="c-checkbox__label">BY - Attribution required (1)</label></li></ul></fieldset></details></div></div><button type="submit" id="facet-form-submit" style="display:none">Search</button></div></aside><main id="maincontent"><section class="o-columnbox1"><header><h2 class="o-columnbox1__heading" aria-live="polite">Scholarly Works (3 results)</h2></header><div class="c-sortpagination"><div class="c-sort"><div class="o-input__droplist1"><label for="c-sort1">Sort By:</label><select name="sort" id="c-sort1" form="facetForm"><option selected="" value="rel">Relevance</option><option value="a-title">A-Z By Title</option><option value="z-title">Z-A By Title</option><option value="a-author">A-Z By Author</option><option value="z-author">Z-A By Author</option><option value="asc">Date Ascending</option><option value="desc">Date Descending</option></select></div></div><input type="hidden" name="start" form="facetForm" value="0"/></div><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/80s7v9kh"><div class="c-clientmarkup">Multiparametric MRI Texture Analysis in Prediction of Glioma Biomarker Status: Added Value of MR Diffusion</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AKihira%2C%20Shingo">Kihira, Shingo</a>; </li><li><a href="/search/?q=author%3ATsankova%2C%20Nadejda">Tsankova, Nadejda</a>; </li><li><a href="/search/?q=author%3ABauer%2C%20Adam">Bauer, Adam</a>; </li><li><a href="/search/?q=author%3ASakai%2C%20Yu">Sakai, Yu</a>; </li><li><a href="/search/?q=author%3AMahmoudi%2C%20Keon">Mahmoudi, Keon</a>; </li><li><a href="/search/?q=author%3AZubizarreta%2C%20Nicole">Zubizarreta, Nicole</a>; </li><li><a href="/search/?q=author%3AHouldsworth%2C%20Jane">Houldsworth, Jane</a>; </li><li><a href="/search/?q=author%3AKhan%2C%20Fahad">Khan, Fahad</a>; </li><li><a href="/search/?q=author%3ASalamon%2C%20Noriko">Salamon, Noriko</a>; </li><li><a href="/search/?q=author%3AHormigo%2C%20Adilia">Hormigo, Adilia</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3ANael%2C%20Kambiz">Nael, Kambiz</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucla_postprints">UCLA Previously Published Works</a> (2021)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup"><h3>Background</h3>Early identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma.<h3>Methods</h3>In this retrospective study, patients were included if they (1) had diagnosis of gliomas with known <i>IDH1</i>, <i>EGFR</i>, <i>MGMT</i>, <i>ATRX</i>, <i>TP53</i>, and <i>PTEN</i> status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiver-operating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed.<h3>Results</h3>From a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for <i>IDH1</i>, 0.99/80% for <i>ATRX</i>, 0.79/67% for <i>MGMT</i>, and 0.77/66% for <i>EGFR</i>. The addition of diffusion data to conventional MRI features significantly (<i>P</i> < .05) increased predictive performance for <i>IDH1</i>, <i>MGMT</i>, and <i>ATRX</i>. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (<i>IDH1</i>), 70% (<i>ATRX</i>), 70% (<i>MGMT</i>), and 75% (<i>EGFR</i>).<h3>Conclusion</h3>Addition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/80s7v9kh"><img src="/cms-assets/b23628ad5854dae279b2838ac0f928e0755645fc15f27641a47ff9f029b1d00f" alt="Cover page: Multiparametric MRI Texture Analysis in Prediction of Glioma Biomarker Status: Added Value of MR Diffusion"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/2s21b7zx"><div class="c-clientmarkup">Development and Validation of a Gene-Based Model for Outcome Prediction in Germ Cell Tumors Using a Combined Genomic and Expression Profiling Approach</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AKorkola%2C%20James%20E">Korkola, James E</a>; </li><li><a href="/search/?q=author%3AHeck%2C%20Sandy">Heck, Sandy</a>; </li><li><a href="/search/?q=author%3AOlshen%2C%20Adam%20B">Olshen, Adam B</a>; </li><li><a href="/search/?q=author%3AFeldman%2C%20Darren%20R">Feldman, Darren R</a>; </li><li><a href="/search/?q=author%3AReuter%2C%20Victor%20E">Reuter, Victor E</a>; </li><li><a href="/search/?q=author%3AHouldsworth%2C%20Jane">Houldsworth, Jane</a>; </li><li><a href="/search/?q=author%3ABosl%2C%20George%20J">Bosl, George J</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3AChaganti%2C%20RSK">Chaganti, RSK</a> </li><li class="c-authorlist__begin"><span class="c-authorlist__heading">Editor(s):</span> <a href="/search/?q=author%3AKerr%2C%20Candace">Kerr, Candace</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucsf_postprints">UC San Francisco Previously Published Works</a> (2015)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">Germ Cell Tumors (GCT) have a high cure rate, but we currently lack the ability to accurately identify the small subset of patients who will die from their disease. We used a combined genomic and expression profiling approach to identify genomic regions and underlying genes that are predictive of outcome in GCT patients. We performed array-based comparative genomic hybridization (CGH) on 53 non-seminomatous GCTs (NSGCTs) treated with cisplatin based chemotherapy and defined altered genomic regions using Circular Binary Segmentation. We identified 14 regions associated with two year disease-free survival (2yDFS) and 16 regions associated with five year disease-specific survival (5yDSS). From corresponding expression data, we identified 101 probe sets that showed significant changes in expression. We built several models based on these differentially expressed genes, then tested them in an independent validation set of 54 NSGCTs. These predictive models correctly classified outcome in 64-79.6% of patients in the validation set, depending on the endpoint utilized. Survival analysis demonstrated a significant separation of patients with good versus poor predicted outcome when using a combined gene set model. Multivariate analysis using clinical risk classification with the combined gene model indicated that they were independent prognostic markers. This novel set of predictive genes from altered genomic regions is almost entirely independent of our previously identified set of predictive genes for patients with NSGCTs. These genes may aid in the identification of the small subset of patients who are at high risk of poor outcome.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/2s21b7zx"><img src="/cms-assets/9575f8be2b71e9cb4b84220510a0b79e3daa8ebb05799b57c6aec037c6d29ecd" alt="Cover page: Development and Validation of a Gene-Based Model for Outcome Prediction in Germ Cell Tumors Using a Combined Genomic and Expression Profiling Approach"/></a><a href="https://creativecommons.org/licenses/by/4.0/" class="c-scholworks__license"><img class="c-lazyimage" data-src="/images/cc-by-small.svg" alt="Creative Commons 'BY' version 4.0 license"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/6bx172w6"><div class="c-clientmarkup">Multiple Myeloma Risk and Outcomes Are Associated with Pathogenic Germline Variants in DNA Repair Genes.</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AThibaud%2C%20Santiago">Thibaud, Santiago</a>; </li><li><a href="/search/?q=author%3ASubaran%2C%20Ryan">Subaran, Ryan</a>; </li><li><a href="/search/?q=author%3ANewman%2C%20Scott">Newman, Scott</a>; </li><li><a href="/search/?q=author%3ALagana%2C%20Alessandro">Lagana, Alessandro</a>; </li><li><a href="/search/?q=author%3AMelnekoff%2C%20David">Melnekoff, David</a>; </li><li><a href="/search/?q=author%3ABodnar%2C%20Saoirse">Bodnar, Saoirse</a>; </li><li><a href="/search/?q=author%3ARam%2C%20Meghana">Ram, Meghana</a>; </li><li><a href="/search/?q=author%3ASoens%2C%20Zachry">Soens, Zachry</a>; </li><li><a href="/search/?q=author%3AGenthe%2C%20William">Genthe, William</a>; </li><li><a href="/search/?q=author%3ABrander%2C%20Tehilla">Brander, Tehilla</a>; </li><li><a href="/search/?q=author%3AMouhieddine%2C%20Tarek">Mouhieddine, Tarek</a>; </li><li><a href="/search/?q=author%3AVan%20Oekelen%2C%20Oliver">Van Oekelen, Oliver</a>; </li><li><a href="/search/?q=author%3AHouldsworth%2C%20Jane">Houldsworth, Jane</a>; </li><li><a href="/search/?q=author%3ACho%2C%20Hearn">Cho, Hearn</a>; </li><li><a href="/search/?q=author%3ARichard%2C%20Shambavi">Richard, Shambavi</a>; </li><li><a href="/search/?q=author%3ARichter%2C%20Joshua">Richter, Joshua</a>; </li><li><a href="/search/?q=author%3ARodriguez%2C%20Cesar">Rodriguez, Cesar</a>; </li><li><a href="/search/?q=author%3ARossi%2C%20Adriana">Rossi, Adriana</a>; </li><li><a href="/search/?q=author%3ASanchez%2C%20Larysa">Sanchez, Larysa</a>; </li><li><a href="/search/?q=author%3AChari%2C%20Ajai">Chari, Ajai</a>; </li><li><a href="/search/?q=author%3AMoshier%2C%20Erin">Moshier, Erin</a>; </li><li><a href="/search/?q=author%3AJagannath%2C%20Sundar">Jagannath, Sundar</a>; </li><li><a href="/search/?q=author%3AParekh%2C%20Samir">Parekh, Samir</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3AOnel%2C%20Kenan">Onel, Kenan</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucsf_postprints">UC San Francisco Previously Published Works</a> (2024)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">First-degree relatives of patients with multiple myeloma are at increased risk for the disease, but the contribution of pathogenic germline variants (PGV) in hereditary cancer genes to multiple myeloma risk and outcomes is not well characterized. To address this, we analyzed germline exomes in two independent cohorts of 895 and 786 patients with multiple myeloma. PGVs were identified in 8.6% of the Discovery cohort and 11.5% of the Replication cohort, with a notable presence of high- or moderate-penetrance PGVs (associated with autosomal dominant cancer predisposition) in DNA repair genes (3.6% and 4.1%, respectively). PGVs in BRCA1 (OR = 3.9, FDR < 0.01) and BRCA2 (OR = 7.0, FDR < 0.001) were significantly enriched in patients with multiple myeloma when compared with 134,187 healthy controls. Five of the eight BRCA2 PGV carriers exhibited tumor-specific copy number loss in BRCA2, suggesting somatic loss of heterozygosity. PGVs associated with autosomal dominant cancer predisposition were associated with younger age at diagnosis, personal or familial cancer history, and longer progression-free survival after upfront high-dose melphalan and autologous stem-cell transplantation (P < 0.01). Significance: Our findings suggest up to 10% of patients with multiple myeloma may have an unsuspected cancer predisposition syndrome. Given familial implications and favorable outcomes with high-dose melphalan and autologous stem-cell transplantation in high-penetrance PGV carriers, genetic testing should be considered for young or newly diagnosed patients with a personal or family cancer history. See related commentary by Walker, p. 375.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/6bx172w6"><img src="/cms-assets/c97edb5c63b671e2ca6e8d3cfa6655e0a38f2cb11fab917ceba787d561fd8be9" alt="Cover page: Multiple Myeloma Risk and Outcomes Are Associated with Pathogenic Germline Variants in DNA Repair Genes."/></a></div></section></section></main></form></div><div><div class="c-toplink"><a href="javascript:window.scrollTo(0, 0)">Top</a></div><footer class="c-footer"><nav class="c-footer__nav"><ul><li><a href="/">Home</a></li><li><a href="/aboutEschol">About eScholarship</a></li><li><a href="/campuses">Campus Sites</a></li><li><a href="/ucoapolicies">UC Open Access Policy</a></li><li><a href="/publishing">eScholarship Publishing</a></li><li><a href="https://www.cdlib.org/about/accessibility.html">Accessibility</a></li><li><a href="/privacypolicy">Privacy 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President","type":"link"},{"id":24,"url":"/uc/lbnl","name":"Lawrence Berkeley National Laboratory","type":"link"},{"id":25,"url":"/uc/anrcs","name":"UC Agriculture & Natural Resources","type":"link"}]},{"id":10,"name":"UC Open Access Policies","slug":"ucoapolicies","type":"page","url":"/ucoapolicies"},{"id":12,"name":"eScholarship Publishing","slug":"publishing","type":"page","url":"/publishing"}],"social":{"facebook":null,"twitter":null,"rss":"/rss/unit/root"},"breadcrumb":[{"name":"eScholarship","id":"root","url":"/"}]},"campuses":[{"id":"","name":"eScholarship at..."},{"id":"ucb","name":"UC Berkeley"},{"id":"ucd","name":"UC Davis"},{"id":"uci","name":"UC Irvine"},{"id":"ucla","name":"UCLA"},{"id":"ucm","name":"UC Merced"},{"id":"ucr","name":"UC Riverside"},{"id":"ucsd","name":"UC San Diego"},{"id":"ucsf","name":"UCSF"},{"id":"ucsb","name":"UC Santa Barbara"},{"id":"ucsc","name":"UC Santa Cruz"},{"id":"ucop","name":"UC Office of the President"},{"id":"lbnl","name":"Lawrence Berkeley National Laboratory"},{"id":"anrcs","name":"UC Agriculture & Natural Resources"}],"query":{"q":"author:Houldsworth, Jane","sort":"rel","rows":"10","info_start":"0","start":"0","filters":{}},"count":3,"info_count":0,"infoResults":[],"searchResults":[{"id":"qt80s7v9kh","title":"Multiparametric MRI Texture Analysis in Prediction of Glioma Biomarker Status: Added Value of MR Diffusion","abstract":"<h4>Background</h4>Early identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma.<h4>Methods</h4>In this retrospective study, patients were included if they (1) had diagnosis of gliomas with known <i>IDH1</i>, <i>EGFR</i>, <i>MGMT</i>, <i>ATRX</i>, <i>TP53</i>, and <i>PTEN</i> status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiver-operating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed.<h4>Results</h4>From a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for <i>IDH1</i>, 0.99/80% for <i>ATRX</i>, 0.79/67% for <i>MGMT</i>, and 0.77/66% for <i>EGFR</i>. The addition of diffusion data to conventional MRI features significantly (<i>P</i> < .05) increased predictive performance for <i>IDH1</i>, <i>MGMT</i>, and <i>ATRX</i>. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (<i>IDH1</i>), 70% (<i>ATRX</i>), 70% (<i>MGMT</i>), and 75% (<i>EGFR</i>).<h4>Conclusion</h4>Addition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma.","content_type":"application/pdf","author_hide":null,"authors":[{"name":"Kihira, Shingo","fname":"Shingo","lname":"Kihira"},{"name":"Tsankova, Nadejda","fname":"Nadejda","lname":"Tsankova"},{"name":"Bauer, Adam","fname":"Adam","lname":"Bauer"},{"name":"Sakai, Yu","fname":"Yu","lname":"Sakai"},{"name":"Mahmoudi, Keon","fname":"Keon","lname":"Mahmoudi"},{"name":"Zubizarreta, Nicole","fname":"Nicole","lname":"Zubizarreta"},{"name":"Houldsworth, Jane","fname":"Jane","lname":"Houldsworth"},{"name":"Khan, Fahad","fname":"Fahad","lname":"Khan"},{"name":"Salamon, Noriko","email":"nsalamon@mednet.ucla.edu","fname":"Noriko","lname":"Salamon","ORCID_id":"0000-0002-3520-9467"},{"name":"Hormigo, Adilia","fname":"Adilia","lname":"Hormigo"},{"name":"Nael, Kambiz","email":"kanael@mednet.ucla.edu","fname":"Kambiz","lname":"Nael","ORCID_id":"0000-0002-4194-9488"}],"supp_files":[{"type":"pdf","count":0},{"type":"image","count":0},{"type":"video","count":0},{"type":"audio","count":0},{"type":"zip","count":0},{"type":"other","count":0}],"thumbnail":{"width":121,"height":156,"asset_id":"b23628ad5854dae279b2838ac0f928e0755645fc15f27641a47ff9f029b1d00f","timestamp":1632144030,"image_type":"jpeg"},"pub_year":2021,"genre":"article","rights":null,"peerReviewed":true,"unitInfo":{"displayName":"UCLA Previously Published Works","link_path":"ucla_postprints"}},{"id":"qt2s21b7zx","title":"Development and Validation of a Gene-Based Model for Outcome Prediction in Germ Cell Tumors Using a Combined Genomic and Expression Profiling Approach","abstract":"Germ Cell Tumors (GCT) have a high cure rate, but we currently lack the ability to accurately identify the small subset of patients who will die from their disease. We used a combined genomic and expression profiling approach to identify genomic regions and underlying genes that are predictive of outcome in GCT patients. We performed array-based comparative genomic hybridization (CGH) on 53 non-seminomatous GCTs (NSGCTs) treated with cisplatin based chemotherapy and defined altered genomic regions using Circular Binary Segmentation. We identified 14 regions associated with two year disease-free survival (2yDFS) and 16 regions associated with five year disease-specific survival (5yDSS). From corresponding expression data, we identified 101 probe sets that showed significant changes in expression. We built several models based on these differentially expressed genes, then tested them in an independent validation set of 54 NSGCTs. These predictive models correctly classified outcome in 64-79.6% of patients in the validation set, depending on the endpoint utilized. Survival analysis demonstrated a significant separation of patients with good versus poor predicted outcome when using a combined gene set model. Multivariate analysis using clinical risk classification with the combined gene model indicated that they were independent prognostic markers. This novel set of predictive genes from altered genomic regions is almost entirely independent of our previously identified set of predictive genes for patients with NSGCTs. These genes may aid in the identification of the small subset of patients who are at high risk of poor outcome.","content_type":"application/pdf","author_hide":null,"authors":[{"name":"Korkola, James E","fname":"James E","lname":"Korkola"},{"name":"Heck, Sandy","fname":"Sandy","lname":"Heck"},{"name":"Olshen, Adam B","email":"adam.olshen@ucsf.edu","fname":"Adam B","lname":"Olshen"},{"name":"Feldman, Darren R","fname":"Darren R","lname":"Feldman"},{"name":"Reuter, Victor E","fname":"Victor E","lname":"Reuter"},{"name":"Houldsworth, Jane","fname":"Jane","lname":"Houldsworth"},{"name":"Bosl, George J","fname":"George J","lname":"Bosl"},{"name":"Chaganti, RSK","fname":"RSK","lname":"Chaganti"}],"editors":[{"name":"Kerr, Candace","fname":"Candace","lname":"Kerr"}],"supp_files":[{"type":"pdf","count":0},{"type":"image","count":0},{"type":"video","count":0},{"type":"audio","count":0},{"type":"zip","count":0},{"type":"other","count":0}],"thumbnail":{"width":121,"height":163,"asset_id":"9575f8be2b71e9cb4b84220510a0b79e3daa8ebb05799b57c6aec037c6d29ecd","timestamp":1615814271,"image_type":"png"},"pub_year":2015,"genre":"article","rights":"https://creativecommons.org/licenses/by/4.0/","peerReviewed":true,"unitInfo":{"displayName":"UC San Francisco Previously Published Works","link_path":"ucsf_postprints"}},{"id":"qt6bx172w6","title":"Multiple Myeloma Risk and Outcomes Are Associated with Pathogenic Germline Variants in DNA Repair Genes.","abstract":"First-degree relatives of patients with multiple myeloma are at increased risk for the disease, but the contribution of pathogenic germline variants (PGV) in hereditary cancer genes to multiple myeloma risk and outcomes is not well characterized. To address this, we analyzed germline exomes in two independent cohorts of 895 and 786 patients with multiple myeloma. PGVs were identified in 8.6% of the Discovery cohort and 11.5% of the Replication cohort, with a notable presence of high- or moderate-penetrance PGVs (associated with autosomal dominant cancer predisposition) in DNA repair genes (3.6% and 4.1%, respectively). PGVs in BRCA1 (OR = 3.9, FDR < 0.01) and BRCA2 (OR = 7.0, FDR < 0.001) were significantly enriched in patients with multiple myeloma when compared with 134,187 healthy controls. Five of the eight BRCA2 PGV carriers exhibited tumor-specific copy number loss in BRCA2, suggesting somatic loss of heterozygosity. PGVs associated with autosomal dominant cancer predisposition were associated with younger age at diagnosis, personal or familial cancer history, and longer progression-free survival after upfront high-dose melphalan and autologous stem-cell transplantation (P < 0.01). Significance: Our findings suggest up to 10% of patients with multiple myeloma may have an unsuspected cancer predisposition syndrome. Given familial implications and favorable outcomes with high-dose melphalan and autologous stem-cell transplantation in high-penetrance PGV carriers, genetic testing should be considered for young or newly diagnosed patients with a personal or family cancer history. See related commentary by Walker, p. 375.","content_type":"application/pdf","author_hide":null,"authors":[{"name":"Thibaud, Santiago","fname":"Santiago","lname":"Thibaud"},{"name":"Subaran, Ryan","fname":"Ryan","lname":"Subaran"},{"name":"Newman, Scott","fname":"Scott","lname":"Newman"},{"name":"Lagana, Alessandro","fname":"Alessandro","lname":"Lagana"},{"name":"Melnekoff, David","fname":"David","lname":"Melnekoff"},{"name":"Bodnar, Saoirse","fname":"Saoirse","lname":"Bodnar"},{"name":"Ram, Meghana","fname":"Meghana","lname":"Ram"},{"name":"Soens, Zachry","fname":"Zachry","lname":"Soens"},{"name":"Genthe, William","fname":"William","lname":"Genthe"},{"name":"Brander, Tehilla","fname":"Tehilla","lname":"Brander"},{"name":"Mouhieddine, Tarek","fname":"Tarek","lname":"Mouhieddine"},{"name":"Van Oekelen, Oliver","fname":"Oliver","lname":"Van Oekelen"},{"name":"Houldsworth, Jane","fname":"Jane","lname":"Houldsworth"},{"name":"Cho, Hearn","fname":"Hearn","lname":"Cho"},{"name":"Richard, Shambavi","fname":"Shambavi","lname":"Richard"},{"name":"Richter, Joshua","fname":"Joshua","lname":"Richter"},{"name":"Rodriguez, Cesar","fname":"Cesar","lname":"Rodriguez"},{"name":"Rossi, Adriana","fname":"Adriana","lname":"Rossi"},{"name":"Sanchez, Larysa","fname":"Larysa","lname":"Sanchez"},{"name":"Chari, Ajai","email":"ajai.chari@ucsf.edu","fname":"Ajai","lname":"Chari"},{"name":"Moshier, Erin","fname":"Erin","lname":"Moshier"},{"name":"Jagannath, Sundar","fname":"Sundar","lname":"Jagannath"},{"name":"Parekh, Samir","fname":"Samir","lname":"Parekh"},{"name":"Onel, Kenan","fname":"Kenan","lname":"Onel"}],"supp_files":[{"type":"pdf","count":0},{"type":"image","count":0},{"type":"video","count":0},{"type":"audio","count":0},{"type":"zip","count":0},{"type":"other","count":0}],"thumbnail":{"width":121,"height":156,"asset_id":"c97edb5c63b671e2ca6e8d3cfa6655e0a38f2cb11fab917ceba787d561fd8be9","timestamp":1731104255,"image_type":"jpeg"},"pub_year":2024,"genre":"article","rights":null,"peerReviewed":true,"unitInfo":{"displayName":"UC San Francisco Previously Published Works","link_path":"ucsf_postprints"}}],"facets":[{"display":"Type of Work","fieldName":"type_of_work","facets":[{"value":"article","count":3,"displayName":"Article"},{"value":"monograph","count":0,"displayName":"Book"},{"value":"dissertation","count":0,"displayName":"Theses"},{"value":"multimedia","count":0,"displayName":"Multimedia"}]},{"display":"Peer Review","fieldName":"peer_reviewed","facets":[{"value":"1","count":3,"displayName":"Peer-reviewed only"}]},{"display":"Supplemental Material","fieldName":"supp_file_types","facets":[{"value":"video","count":0,"displayName":"Video"},{"value":"audio","count":0,"displayName":"Audio"},{"value":"images","count":0,"displayName":"Images"},{"value":"zip","count":0,"displayName":"Zip"},{"value":"other files","count":0,"displayName":"Other files"}]},{"display":"Publication Year","fieldName":"pub_year","range":{"pub_year_start":null,"pub_year_end":null}},{"display":"Campus","fieldName":"campuses","facets":[{"value":"ucb","count":0,"displayName":"UC Berkeley"},{"value":"ucd","count":0,"displayName":"UC Davis"},{"value":"uci","count":0,"displayName":"UC Irvine"},{"value":"ucla","count":1,"displayName":"UCLA"},{"value":"ucm","count":0,"displayName":"UC Merced"},{"value":"ucr","count":0,"displayName":"UC Riverside"},{"value":"ucsd","count":0,"displayName":"UC San Diego"},{"value":"ucsf","count":2,"displayName":"UCSF"},{"value":"ucsb","count":0,"displayName":"UC Santa Barbara"},{"value":"ucsc","count":0,"displayName":"UC Santa Cruz"},{"value":"ucop","count":0,"displayName":"UC Office of the President"},{"value":"lbnl","count":0,"displayName":"Lawrence Berkeley National Laboratory"},{"value":"anrcs","count":0,"displayName":"UC Agriculture & Natural Resources"}]},{"display":"Department","fieldName":"departments","facets":[{"value":"deb","count":1,"displayName":"Department of Epidemiology and Biostatistics"}]},{"display":"Journal","fieldName":"journals","facets":[]},{"display":"Discipline","fieldName":"disciplines","facets":[]},{"display":"Reuse License","fieldName":"rights","facets":[{"value":"CC BY","count":1,"displayName":"BY - Attribution required"}]}]};</script> <script src="/js/vendors~app-bundle-7424603c338d723fd773.js"></script> <script src="/js/app-bundle-8362e6d7829414ab4baa.js"></script> </body> </html>