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Cruz A, Dias EM, Scoton MLD, Branco BHM. Health 4.0 in the medical sector: a narrative review. REVISTA DA ASSOCIACAO MEDICA BRASILEIRA (1992) 2024; 70:e20231149. [PMID: 38511760 PMCID: PMC10941873 DOI: 10.1590/1806-9282.20231149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Accepted: 09/24/2023] [Indexed: 03/22/2024]
Affiliation(s)
- Antônio Cruz
- Universidade de Sao Paulo, Institute of Radiology, Clinical Hospital, Faculty of Medicine – São Paulo (SP), Brazil
| | - Eduardo Mário Dias
- Universidade de Sao Paulo, Polytechnic School, Department of Electrical Energy Engineering and Automation – São Paulo (SP), Brazil
| | - Maria Lídia Dias Scoton
- Universidade de Sao Paulo, Electrical Automation in Industrial Systems Group – São Paulo (SP), Brazil
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2
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Dias-Santagata D, Heist RS, Bard AZ, da Silva AFL, Dagogo-Jack I, Nardi V, Ritterhouse LL, Spring LM, Jessop N, Farahani AA, Mino-Kenudson M, Allen J, Goyal L, Parikh A, Misdraji J, Shankar G, Jordan JT, Martinez-Lage M, Frosch M, Graubert T, Fathi AT, Hobbs GS, Hasserjian RP, Raje N, Abramson J, Schwartz JH, Sullivan RJ, Miller D, Hoang MP, Isakoff S, Ly A, Bouberhan S, Watkins J, Oliva E, Wirth L, Sadow PM, Faquin W, Cote GM, Hung YP, Gao X, Wu CL, Garg S, Rivera M, Le LP, John Iafrate A, Juric D, Hochberg EP, Clark J, Bardia A, Lennerz JK. Implementation and Clinical Adoption of Precision Oncology Workflows Across a Healthcare Network. Oncologist 2022; 27:930-939. [PMID: 35852437 PMCID: PMC9632318 DOI: 10.1093/oncolo/oyac134] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 06/17/2022] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Precision oncology relies on molecular diagnostics, and the value-proposition of modern healthcare networks promises a higher standard of care across partner sites. We present the results of a clinical pilot to standardize precision oncology workflows. METHODS Workflows are defined as the development, roll-out, and updating of disease-specific molecular order sets. We tracked the timeline, composition, and effort of consensus meetings to define the combination of molecular tests. To assess clinical impact, we examined order set adoption over a two-year period (before and after roll-out) across all gastrointestinal and hepatopancreatobiliary (GI) malignancies, and by provider location within the network. RESULTS Development of 12 disease center-specific order sets took ~9 months, and the average number of tests per indication changed from 2.9 to 2.8 (P = .74). After roll-out, we identified significant increases in requests for GI patients (17%; P < .001), compliance with testing recommendations (9%; P < .001), and the fraction of "abnormal" results (6%; P < .001). Of 1088 GI patients, only 3 received targeted agents based on findings derived from non-recommended orders (1 before and 2 after roll-out); indicating that our practice did not negatively affect patient treatments. Preliminary analysis showed 99% compliance by providers in network sites, confirming the adoption of the order sets across the network. CONCLUSION Our study details the effort of establishing precision oncology workflows, the adoption pattern, and the absence of harm from the reduction of non-recommended orders. Establishing a modifiable communication tool for molecular testing is an essential component to optimize patient care via precision oncology.
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Affiliation(s)
- Dora Dias-Santagata
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Rebecca S Heist
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Adam Z Bard
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Ibiayi Dagogo-Jack
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Valentina Nardi
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Lauren L Ritterhouse
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Laura M Spring
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Nicholas Jessop
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexander A Farahani
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mari Mino-Kenudson
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jill Allen
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Lipika Goyal
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Aparna Parikh
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Joseph Misdraji
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Present affiliation: Department of Pathology, Yale University, New Haven, CT, USA
| | - Ganesh Shankar
- Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Justin T Jordan
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Maria Martinez-Lage
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Matthew Frosch
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Timothy Graubert
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Amir T Fathi
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Gabriela S Hobbs
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Robert P Hasserjian
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Noopur Raje
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Jeremy Abramson
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Joel H Schwartz
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Ryan J Sullivan
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - David Miller
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Mai P Hoang
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Steven Isakoff
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Amy Ly
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Sara Bouberhan
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Jaclyn Watkins
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Esther Oliva
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Lori Wirth
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Peter M Sadow
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - William Faquin
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Gregory M Cote
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Yin P Hung
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Xin Gao
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Chin-Lee Wu
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Salil Garg
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Miguel Rivera
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Long P Le
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - A John Iafrate
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Dejan Juric
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Ephraim P Hochberg
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Jeffrey Clark
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Aditya Bardia
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, USA
| | - Jochen K Lennerz
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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3
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Lau LMS, Mayoh C, Xie J, Barahona P, MacKenzie KL, Wong M, Kamili A, Tsoli M, Failes TW, Kumar A, Mould EVA, Gifford A, Chow S, Pinese M, Fletcher JI, Arndt GM, Khuong‐Quang D, Wadham C, Batey D, Eden G, Trebilcock P, Joshi S, Alfred S, Gopalakrishnan A, Khan A, Grebert Wade D, Strong PA, Manouvrier E, Morgan LT, Span M, Lim JY, Cadiz R, Ung C, Thomas DM, Tucker KM, Warby M, McCowage GB, Dalla‐Pozza L, Byrne JA, Saletta F, Fellowes A, Fox SB, Norris MD, Tyrrell V, Trahair TN, Lock RB, Cowley MJ, Ekert PG, Haber M, Ziegler DS, Marshall GM. In vitro and in vivo drug screens of tumor cells identify novel therapies for high-risk child cancer. EMBO Mol Med 2022; 14:e14608. [PMID: 34927798 PMCID: PMC8988207 DOI: 10.15252/emmm.202114608] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 11/25/2021] [Accepted: 12/01/2021] [Indexed: 01/08/2023] Open
Abstract
Biomarkers which better match anticancer drugs with cancer driver genes hold the promise of improved clinical responses and cure rates. We developed a precision medicine platform of rapid high-throughput drug screening (HTS) and patient-derived xenografting (PDX) of primary tumor tissue, and evaluated its potential for treatment identification among 56 consecutively enrolled high-risk pediatric cancer patients, compared with conventional molecular genomics and transcriptomics. Drug hits were seen in the majority of HTS and PDX screens, which identified therapeutic options for 10 patients for whom no targetable molecular lesions could be found. Screens also provided orthogonal proof of drug efficacy suggested by molecular analyses and negative results for some molecular findings. We identified treatment options across the whole testing platform for 70% of patients. Only molecular therapeutic recommendations were provided to treating oncologists and led to a change in therapy in 53% of patients, of whom 29% had clinical benefit. These data indicate that in vitro and in vivo drug screening of tumor cells could increase therapeutic options and improve clinical outcomes for high-risk pediatric cancer patients.
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Affiliation(s)
- Loretta M S Lau
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
- School of Women’s and Children’s HealthUNSW SydneyKensingtonNSWAustralia
- Kids Cancer CentreSydney Children’s HospitalRandwickNSWAustralia
| | - Chelsea Mayoh
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
- School of Women’s and Children’s HealthUNSW SydneyKensingtonNSWAustralia
| | - Jinhan Xie
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | - Paulette Barahona
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | - Karen L MacKenzie
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | - Marie Wong
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
- School of Women’s and Children’s HealthUNSW SydneyKensingtonNSWAustralia
| | - Alvin Kamili
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
- School of Women’s and Children’s HealthUNSW SydneyKensingtonNSWAustralia
| | - Maria Tsoli
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | - Tim W Failes
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
- ACRF Drug Discovery Centre for Childhood CancerChildren’s Cancer InstituteLowy Cancer Research CentreUNSW SydneySydneyNSWAustralia
| | - Amit Kumar
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
- Children’s Cancer CentreRoyal Children’s HospitalParkvilleVic.Australia
| | - Emily V A Mould
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | - Andrew Gifford
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
- School of Women’s and Children’s HealthUNSW SydneyKensingtonNSWAustralia
| | - Shu‐Oi Chow
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
- ACRF Drug Discovery Centre for Childhood CancerChildren’s Cancer InstituteLowy Cancer Research CentreUNSW SydneySydneyNSWAustralia
| | - Mark Pinese
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
- School of Women’s and Children’s HealthUNSW SydneyKensingtonNSWAustralia
| | - Jamie I Fletcher
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
- School of Women’s and Children’s HealthUNSW SydneyKensingtonNSWAustralia
| | - Greg M Arndt
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
- ACRF Drug Discovery Centre for Childhood CancerChildren’s Cancer InstituteLowy Cancer Research CentreUNSW SydneySydneyNSWAustralia
| | - Dong‐Anh Khuong‐Quang
- Children’s Cancer CentreRoyal Children’s HospitalParkvilleVic.Australia
- Murdoch Children’s Research InstituteRoyal Children’s HospitalParkvilleVic.Australia
| | - Carol Wadham
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | - Daniel Batey
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | - Georgina Eden
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | - Peter Trebilcock
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | - Swapna Joshi
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | - Stephanie Alfred
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | | | - Aaminah Khan
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | - Dylan Grebert Wade
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | - Patrick A Strong
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | - Elodie Manouvrier
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | - Lisa T Morgan
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | - Miriam Span
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | - Jin Yi Lim
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | - Roxanne Cadiz
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | - Caitlin Ung
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | - David M Thomas
- Kinghorn Cancer CentreGarvan Institute of Medical ResearchDarlinghurstNSWAustralia
- Faculty of MedicineSt Vincent’s Clinical SchoolUNSW SydneyKensingtonNSWAustralia
| | - Katherine M Tucker
- Hereditary Cancer CentrePrince of Wales HospitalRandwickNSWAustralia
- Prince of Wales Hospital Clinical SchoolUNSW SydneyRandwickNSWAustralia
| | - Meera Warby
- Hereditary Cancer CentrePrince of Wales HospitalRandwickNSWAustralia
| | - Geoffrey B McCowage
- Cancer Centre for ChildrenThe Children’s Hospital at WestmeadWestmeadNSWAustralia
| | - Luciano Dalla‐Pozza
- Cancer Centre for ChildrenThe Children’s Hospital at WestmeadWestmeadNSWAustralia
| | - Jennifer A Byrne
- Children’s Cancer Research UnitKids ResearchWestmeadNSWAustralia
- Faculty of Medicine and HealthThe University of SydneyNSWAustralia
| | - Federica Saletta
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | | | - Stephen B Fox
- Peter MacCallum Cancer CentreMelbourneVic.Australia
- Department of Medical OncologyUniversity of MelbourneMelbourneVic.Australia
| | - Murray D Norris
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
- School of Women’s and Children’s HealthUNSW SydneyKensingtonNSWAustralia
- University of New South Wales Centre for Childhood Cancer ResearchUNSW SydneyKensingtonVic.Australia
| | - Vanessa Tyrrell
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
| | - Toby N Trahair
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
- School of Women’s and Children’s HealthUNSW SydneyKensingtonNSWAustralia
- Kids Cancer CentreSydney Children’s HospitalRandwickNSWAustralia
| | - Richard B Lock
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
- School of Women’s and Children’s HealthUNSW SydneyKensingtonNSWAustralia
| | - Mark J Cowley
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
- School of Women’s and Children’s HealthUNSW SydneyKensingtonNSWAustralia
- Kinghorn Centre for Clinical GenomicsGarvan Institute of Medical ResearchDarlinghurstNSWAustralia
| | - Paul G Ekert
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
- School of Women’s and Children’s HealthUNSW SydneyKensingtonNSWAustralia
- Peter MacCallum Cancer CentreMelbourneVic.Australia
| | - Michelle Haber
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
- School of Women’s and Children’s HealthUNSW SydneyKensingtonNSWAustralia
| | - David S Ziegler
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
- School of Women’s and Children’s HealthUNSW SydneyKensingtonNSWAustralia
- Kids Cancer CentreSydney Children’s HospitalRandwickNSWAustralia
| | - Glenn M Marshall
- Children’s Cancer Institute, Lowy Cancer CentreUNSW SydneyKensingtonNSWAustralia
- School of Women’s and Children’s HealthUNSW SydneyKensingtonNSWAustralia
- Kids Cancer CentreSydney Children’s HospitalRandwickNSWAustralia
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4
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Doig KD, Love CG, Conway T, Seleznev A, Ma D, Fellowes A, Blombery P, Fox SB. Findings from precision oncology in the clinic: rare, novel variants are a significant contributor to scaling molecular diagnostics. BMC Med Genomics 2022; 15:70. [PMID: 35346197 PMCID: PMC8962530 DOI: 10.1186/s12920-022-01214-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 03/15/2022] [Indexed: 11/24/2022] Open
Abstract
Background Next generation sequencing for oncology patient management is now routine in clinical pathology laboratories. Although wet lab, sequencing and pipeline tasks are largely automated, the analysis of variants for clinical reporting remains largely a manual task. The increasing volume of sequencing data and the limited availability of genetic experts to analyse and report on variants in the data is a key scalability limit for molecular diagnostics. Method To determine the impact and size of the issue, we examined the longitudinally compiled genetic variants from 48,036 cancer patients over a six year period in a large cancer hospital from ten targeted cancer panel tests in germline, solid tumour and haematology contexts using hybridization capture and amplicon assays. This testing generated 24,168,398 sequenced variants of which 23,255 (8214 unique) were clinically reported. Results Of the reported variants, 17,240 (74.1%) were identified in more than one assay which allowed curated variant data to be reused in later reports. The remainder, 6015 (25.9%) were not subsequently seen in later assays and did not provide any reuse benefit. The number of new variants requiring curation has significantly increased over time from 1.72 to 3.73 variants per sample (292 curated variants per month). Analysis of the 23,255 variants reported, showed 28.6% (n = 2356) were not present in common public variant resources and therefore required de novo curation. These in-house only variants were enriched for indels, tumour suppressor genes and from solid tumour assays. Conclusion This analysis highlights the significant percentage of variants not present within common public variant resources and the level of non-recurrent variants that consequently require greater curation effort. Many of these variants are unique to a single patient and unlikely to appear in other patients reflecting the personalised nature of cancer genomics. This study depicts the real-world situation for pathology laboratories faced with curating increasing numbers of low-recurrence variants while needing to expedite the process of manual variant curation. In the absence of suitably accurate automated methods, new approaches are needed to scale oncology diagnostics for future genetic testing volumes. Supplementary Information The online version contains supplementary material available at 10.1186/s12920-022-01214-y.
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5
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Delahunty R, Nguyen L, Craig S, Creighton B, Ariyaratne D, Garsed DW, Christie E, Fereday S, Andrews L, Lewis A, Limb S, Pandey A, Hendley J, Traficante N, Carvajal N, Spurdle AB, Thompson B, Parsons MT, Beshay V, Volcheck M, Semple T, Lupat R, Doig K, Yu J, Chen XQ, Marsh A, Love C, Bilic S, Beilin M, Nichols CB, Greer C, Lee YC, Gerty S, Gill L, Newton E, Howard J, Williams R, Norris C, Stephens AN, Tutty E, Smyth C, O'Connell S, Jobling T, Stewart CJR, Tan A, Fox SB, Pachter N, Li J, Ellul J, Mir Arnau G, Young MA, Gordon L, Forrest L, Harris M, Livingstone K, Hill J, Chenevix-Trench G, Cohen PA, Webb PM, Friedlander M, James P, Bowtell D, Alsop K. TRACEBACK: Testing of Historical Tubo-Ovarian Cancer Patients for Hereditary Risk Genes as a Cancer Prevention Strategy in Family Members. J Clin Oncol 2022; 40:2036-2047. [PMID: 35263119 PMCID: PMC9197360 DOI: 10.1200/jco.21.02108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
PURPOSE Tubo-ovarian cancer (TOC) is a sentinel cancer for BRCA1 and BRCA2 pathogenic variants (PVs). Identification of a PV in the first member of a family at increased genetic risk (the proband) provides opportunities for cancer prevention in other at-risk family members. Although Australian testing rates are now high, PVs in patients with TOC whose diagnosis predated revised testing guidelines might have been missed. We assessed the feasibility of detecting PVs in this population to enable genetic risk reduction in relatives. PATIENTS AND METHODS In this pilot study, deceased probands were ascertained from research cohort studies, identification by a relative, and gynecologic oncology clinics. DNA was extracted from archival tissue or stored blood for panel sequencing of 10 risk-associated genes. Testing of deceased probands ascertained through clinic records was performed with a consent waiver. RESULTS We identified 85 PVs in 84 of 787 (11%) probands. Familial contacts of 39 of 60 (65%) deceased probands with an identified recipient (60 of 84; 71%) have received a written notification of results, with follow-up verbal contact made in 85% (33 of 39). A minority of families (n = 4) were already aware of the PV. For many (29 of 33; 88%), the genetic result provided new information and referral to a genetic service was accepted in most cases (66%; 19 of 29). Those who declined referral (4 of 29) were all male next of kin whose family member had died more than 10 years before. CONCLUSION We overcame ethical and logistic challenges to demonstrate that retrospective genetic testing to identify PVs in previously untested deceased probands with TOC is feasible. Understanding reasons for a family member's decision to accept or decline a referral will be important for guiding future TRACEBACK projects. Genetic testing of deceased patients allows identification of at-risk families for cancer prevention![]()
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Affiliation(s)
- Rachel Delahunty
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia
| | - Linh Nguyen
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia
| | - Stuart Craig
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia
| | | | | | - Dale W Garsed
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia
| | - Elizabeth Christie
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia
| | - Sian Fereday
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia
| | - Lesley Andrews
- Prince of Wales Hospital, Randwick, New South Wales, Australia.,Royal Hospital for Women, Randwick, New South Wales Australia
| | - Alexandra Lewis
- Parkville Familial Cancer Centre, Peter MacCallum Cancer Centre and Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Sharne Limb
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia.,Parkville Familial Cancer Centre, Peter MacCallum Cancer Centre and Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Ahwan Pandey
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia
| | - Joy Hendley
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia
| | - Nadia Traficante
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia
| | | | - Amanda B Spurdle
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Bryony Thompson
- Department of Pathology, Royal Melbourne Hospital, Parkville, Victoria, Australia.,Department of Clinical Pathology, University of Melbourne, Parkville, Victoria, Australia
| | - Michael T Parsons
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Victoria Beshay
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia
| | - Mila Volcheck
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia.,The Royal Women's Hospital, Melbourne Victoria, Australia.,The Royal Children's Hospital, Melbourne Victoria, Australia
| | - Timothy Semple
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia
| | - Richard Lupat
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia
| | - Kenneth Doig
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia
| | - Jiaan Yu
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia
| | - Xiao Qing Chen
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Anna Marsh
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | | | - Sanela Bilic
- Department of Gynecological Oncology, St John of God Hospital, Subiaco, Western Australia, Australia
| | - Maria Beilin
- Department of Gynecological Oncology, St John of God Hospital, Subiaco, Western Australia, Australia
| | | | - Christina Greer
- Genetic Services of Western Australia, Perth, Western Australia, Australia
| | - Yeh Chen Lee
- Prince of Wales Hospital, Randwick, New South Wales, Australia.,Royal Hospital for Women, Randwick, New South Wales Australia.,Prince of Wales Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Susan Gerty
- Prince of Wales Hospital, Randwick, New South Wales, Australia
| | - Lynette Gill
- Prince of Wales Hospital, Randwick, New South Wales, Australia
| | - Emma Newton
- Prince of Wales Hospital, Randwick, New South Wales, Australia
| | - Julie Howard
- Prince of Wales Hospital, Randwick, New South Wales, Australia
| | - Rachel Williams
- Prince of Wales Clinical School, Faculty of Medicine, University of New South Wales, Sydney, Australia.,Prince of Wales Hereditary Cancer Centre, Prince of Wales Hospital Randwick, New South Wales, Australia
| | - Christie Norris
- Prince of Wales Hospital, Randwick, New South Wales, Australia
| | - Andrew N Stephens
- Hudson Institute of Medical Research, Clayton, Victoria, Australia.,Department of Molecular and Translational Sciences, Monash University, Clayton, Victoria, Australia.,School of Health and Biomedical Sciences, RMIT University, Melbourne, Australia
| | - Erin Tutty
- Parkville Familial Cancer Centre, Peter MacCallum Cancer Centre and Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Courtney Smyth
- Monash Health Familial Cancer Centre, Clayton, Victoria, Australia
| | - Shona O'Connell
- Monash Health Familial Cancer Centre, Clayton, Victoria, Australia
| | | | - Colin J R Stewart
- PathWest, King Edward Memorial Hospital, Subiaco, Western Australia, Australia
| | - Adeline Tan
- Clinipath Pathology, Osborne Park, Western Australia, Australia
| | - Stephen B Fox
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia
| | - Nicholas Pachter
- Genetic Services of Western Australia, Perth, Western Australia, Australia.,King Edward Memorial Hospital, Perth, Western Australia, Australia.,Faculty of Health and Medical Sciences, University of Western Australia, Perth Australia
| | - Jason Li
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia
| | - Jason Ellul
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia
| | - Gisela Mir Arnau
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia
| | - Mary-Anne Young
- Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research Darlinghurst, New South Wales, Australia
| | - Louisa Gordon
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,School of Nursing, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Laura Forrest
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia
| | | | | | - Jane Hill
- Ovarian Cancer Australia, Melbourne, Victoria, Australia
| | | | - Paul A Cohen
- Department of Gynecological Oncology, St John of God Hospital, Subiaco, Western Australia, Australia.,Division of Obstetrics and Gynaecology, Faculty of Health and Medical Sciences, University of Western Australia, Perth, Australia
| | - Penelope M Webb
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Michael Friedlander
- Prince of Wales Hospital, Randwick, New South Wales, Australia.,Royal Hospital for Women, Randwick, New South Wales Australia.,Genetic Services of Western Australia, Perth, Western Australia, Australia
| | - Paul James
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia
| | - David Bowtell
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia
| | - Kathryn Alsop
- Peter MacCallum Cancer Centre, Parkville, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia
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6
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Borchert F, Mock A, Tomczak A, Hügel J, Alkarkoukly S, Knurr A, Volckmar AL, Stenzinger A, Schirmacher P, Debus J, Jäger D, Longerich T, Fröhling S, Eils R, Bougatf N, Sax U, Schapranow MP. Knowledge bases and software support for variant interpretation in precision oncology. Brief Bioinform 2021; 22:bbab134. [PMID: 33971666 PMCID: PMC8574624 DOI: 10.1093/bib/bbab134] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 03/10/2021] [Accepted: 03/30/2021] [Indexed: 12/12/2022] Open
Abstract
Precision oncology is a rapidly evolving interdisciplinary medical specialty. Comprehensive cancer panels are becoming increasingly available at pathology departments worldwide, creating the urgent need for scalable cancer variant annotation and molecularly informed treatment recommendations. A wealth of mainly academia-driven knowledge bases calls for software tools supporting the multi-step diagnostic process. We derive a comprehensive list of knowledge bases relevant for variant interpretation by a review of existing literature followed by a survey among medical experts from university hospitals in Germany. In addition, we review cancer variant interpretation tools, which integrate multiple knowledge bases. We categorize the knowledge bases along the diagnostic process in precision oncology and analyze programmatic access options as well as the integration of knowledge bases into software tools. The most commonly used knowledge bases provide good programmatic access options and have been integrated into a range of software tools. For the wider set of knowledge bases, access options vary across different parts of the diagnostic process. Programmatic access is limited for information regarding clinical classifications of variants and for therapy recommendations. The main issue for databases used for biological classification of pathogenic variants and pathway context information is the lack of standardized interfaces. There is no single cancer variant interpretation tool that integrates all identified knowledge bases. Specialized tools are available and need to be further developed for different steps in the diagnostic process.
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Affiliation(s)
- Florian Borchert
- Digital Health Center, Hasso Plattner Institute (HPI), University of Potsdam, Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam, Germany
| | - Andreas Mock
- Department of Translational Medical Oncology (TMO), National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
- Department of Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
| | - Aurelie Tomczak
- Institute of Pathology Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany
- Liver Cancer Center Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
| | - Jonas Hügel
- Department of Medical Informatics, University Medical Center Göttingen, Von-Siebold-Str. 3, 37099 Göttingen, Germany
- Campus Institute Data Science, Göttingen, Germany
| | - Samer Alkarkoukly
- CECAD, Faculty of Medicine and University Hospital Cologne, University of Cologne, Joseph-Stelzmann-Straße 26, 50931 Cologne
| | - Alexander Knurr
- Division of Medical Informatics for Translational Oncology, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Anna-Lena Volckmar
- Institute of Pathology Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany
| | - Albrecht Stenzinger
- Institute of Pathology Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany
| | - Peter Schirmacher
- Institute of Pathology Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany
- Liver Cancer Center Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Center (HIT), Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 450, 69120 Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Dirk Jäger
- Department of Medical Oncology, National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
- Clinical Coorporation Unit Applied Tumor-Immunity, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Thomas Longerich
- Institute of Pathology Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany
- Liver Cancer Center Heidelberg, Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
| | - Stefan Fröhling
- Department of Translational Medical Oncology (TMO), National Center for Tumor Diseases (NCT) Heidelberg, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
- German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
| | - Roland Eils
- Health Data Science Unit, Heidelberg University Hospital, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany
- Center for Digital Health, Berlin Institute of Health and Charité Universitötsmedizin Berlin, Kapelle-Ufer 2, 10117 Berlin, Germany
| | - Nina Bougatf
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Heidelberg University Hospital, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ) Heidelberg, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
- Heidelberg Ion-Beam Therapy Center (HIT), Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 450, 69120 Heidelberg, Germany
- Heidelberg Institute of Radiation Oncology (HIRO), Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Ulrich Sax
- Department of Medical Informatics, University Medical Center Göttingen, Von-Siebold-Str. 3, 37099 Göttingen, Germany
- Campus Institute Data Science, Göttingen, Germany
| | - Matthieu-P Schapranow
- Digital Health Center, Hasso Plattner Institute (HPI), University of Potsdam, Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam, Germany
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7
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HIV is associated with an increased risk of age-related clonal hematopoiesis among older adults. Nat Med 2021; 27:1006-1011. [PMID: 34099923 DOI: 10.1038/s41591-021-01357-y] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 04/19/2021] [Indexed: 12/13/2022]
Abstract
People with human immunodeficiency virus (HIV) have higher rates of certain comorbidities, particularly cardiovascular disease and cancer, than people without HIV1-5. In view of observations that somatic mutations associated with age-related clonal hematopoiesis (CH) are linked to similar comorbidities in the general population6-10, we hypothesized that CH may be more prevalent in people with HIV. To address this issue, we established a prospective cohort study, the ARCHIVE study (NCT04641013), in which 220 HIV-positive and 226 HIV-negative participants aged 55 years or older were recruited in Australia. Demographic characteristics, clinical data and peripheral blood were collected to assess the presence of CH mutations and to identify potential risk factors for and clinical sequelae of CH. In total, 135 CH mutations were identified in 100 (22.4%) of 446 participants. CH was more prevalent in HIV-positive participants than in HIV-negative participants (28.2% versus 16.8%, P = 0.004), overall and across all age groups; the adjusted odds ratio for having CH in those with HIV was 2.16 (95% confidence interval 1.34-3.48, P = 0.002). The most common genes mutated overall were DNMT3A (47.4%), TET2 (20.0%) and ASXL1 (13.3%). CH and HIV infection were independently associated with increases in blood parameters and biomarkers associated with inflammation. These data suggest a selective advantage for the emergence of CH in the context of chronic infection and inflammation related to HIV infection.
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8
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A new tool CovReport generates easy-to-understand sequencing coverage summary for diagnostic reports. Sci Rep 2020; 10:6247. [PMID: 32277129 PMCID: PMC7148332 DOI: 10.1038/s41598-020-63079-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 03/20/2020] [Indexed: 11/24/2022] Open
Abstract
In order to properly interpret the results of a diagnostic gene panel sequencing test, gene coverage needs to be taken into consideration. If coverage is too low, an additional re-sequencing test is needed to make sure that a pathogenic variant is not missed. To facilitate the interpretation of coverage data, we designed CovReport, a novel easy-to-use visualization tool. CovReport generates a concise coverage summary that allows one-glance assessment of the sequencing test performance. Both gene-level and exon-level coverage can be immediately appreciated and taken into consideration for further medical decisions. CovReport does not require complex installation and can thus be easily implemented in any diagnostic laboratory setting. A user-friendly interface generates a graphic summary of coverage that can be directly included in the diagnostic report. In addition to a stand-alone version, we also provide a command line version of CovReport that can be integrated into any bioinformatics pipeline. This flexible tool is now part of routine sequencing analysis at the Department of Medical Genetics at La Timone Hospital (Marseille, France). CovReport is available at http://jdotsoft.com/CovReport.php. It is implemented in Java and supported on Windows, Mac OS X and Linux.
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9
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Ivanov M, Ivanov M, Kasianov A, Rozhavskaya E, Musienko S, Baranova A, Mileyko V. Novel bioinformatics quality control metric for next-generation sequencing experiments in the clinical context. Nucleic Acids Res 2019; 47:e135. [PMID: 31511888 PMCID: PMC6868350 DOI: 10.1093/nar/gkz775] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 07/22/2019] [Accepted: 08/29/2019] [Indexed: 11/23/2022] Open
Abstract
As the use of next-generation sequencing (NGS) for the Mendelian diseases diagnosis is expanding, the performance of this method has to be improved in order to achieve higher quality. Typically, performance measures are considered to be designed in the context of each application and, therefore, account for a spectrum of clinically relevant variants. We present EphaGen, a new computational methodology for bioinformatics quality control (QC). Given a single NGS dataset in BAM format and a pre-compiled VCF-file of targeted clinically relevant variants it associates this dataset with a single arbiter parameter. Intrinsically, EphaGen estimates the probability to miss any variant from the defined spectrum within a particular NGS dataset. Such performance measure virtually resembles the diagnostic sensitivity of given NGS dataset. Here we present case studies of the use of EphaGen in context of BRCA1/2 and CFTR sequencing in a series of 14 runs across 43 blood samples and 504 publically available NGS datasets. EphaGen is superior to conventional bioinformatics metrics such as coverage depth and coverage uniformity. We recommend using this software as a QC step in NGS studies in the clinical context. Availability: https://github.com/m4merg/EphaGen or https://hub.docker.com/r/m4merg/ephagen.
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Affiliation(s)
- Maxim Ivanov
- Department of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny 141700, Russian Federation
| | - Mikhail Ivanov
- Department of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny 141700, Russian Federation
| | - Artem Kasianov
- Vavilov Institute of General Genetics, Moscow, Russian Federation
| | - Ekaterina Rozhavskaya
- Vavilov Institute of General Genetics, Moscow, Russian Federation.,Atlas Oncology Diagnostics, Ltd, Moscow, Russian Federation
| | | | - Ancha Baranova
- Department of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny 141700, Russian Federation.,Atlas Oncology Diagnostics, Ltd, Moscow, Russian Federation.,Research Centre for Medical Genetics, Moscow, Russian Federation.,School of Systems Biology, George Mason University, Fairfax, VA 22030, USA
| | - Vladislav Mileyko
- Department of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudny 141700, Russian Federation.,Atlas Oncology Diagnostics, Ltd, Moscow, Russian Federation
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10
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Iravani A, Solomon B, Pattison DA, Jackson P, Ravi Kumar A, Kong G, Hofman MS, Akhurst T, Hicks RJ. Mitogen-Activated Protein Kinase Pathway Inhibition for Redifferentiation of Radioiodine Refractory Differentiated Thyroid Cancer: An Evolving Protocol. Thyroid 2019; 29:1634-1645. [PMID: 31637953 DOI: 10.1089/thy.2019.0143] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background: Some patients with metastatic differentiated thyroid cancer (DTC) lack iodine avidity and are therefore unsuitable for radioactive iodine (RAI) therapy. Limited experience suggests that single-agent selective mitogen-activated protein kinase (MAPK) pathway inhibitors can restore expression of the sodium-iodide symporter rendering RAI refractory (RAIR) DTC patients amenable to RAI therapy. The aim of this study was to assess the feasibility of mutation-guided MAPK-pathway blockade combined with thyroid hormone withdrawal (THW) for redifferentiation. Methods: This is a retrospective review of metastatic RAIR DTC and driver mutation in MAPK pathway, treated on a redifferentiation protocol. All patients had metastatic disease that had never been RAI-avid and/or imaging and biochemical progression despite treatment with RAI within the past 12 months. Patients with tumors harboring an NRAS mutation were treated with an MEK inhibitor (trametinib), and tumors with a BRAFV600E mutation with combined BRAF and MEK inhibition (dabrafenib and trametinib; or vemurafenib and cobimetinib) for four weeks. Thyrotropin stimulation was performed by THW for four weeks. Restoration of RAI uptake was determined by 124I positron emission tomography/computed tomography imaging. The response was assessed at least three months post-RAI. Results: From 2015 to 2017, six patients (age 45-70, four females) received redifferentiation therapy. Three patients had an NRAS mutation; two with follicular thyroid carcinoma (FTC) and one with a poorly differentiated thyroid carcinoma (PDTC); and three patients had a BRAFV600E mutation and papillary thyroid carcinoma (PTC). One NRAS and all BRAFV600E mutation cases demonstrated restoration of RAI uptake and proceeded to RAI therapy with a median follow-up of 16.6 months (range 13.5-42.3 months). The patient with an NRAS mutation and two of three patients with a BRAFV600E demonstrated partial imaging response beyond a three-month follow-up. Grade 3 adverse events (acneiform rash) were observed in two patients with NRAS mutations. Conclusions: Mutation-guided MAPK pathway inhibition with MEK inhibitor or a combination of BRAF inhibitor and MEK inhibitor under concurrent THW is a feasible and a promising strategy to redifferentiate RAIR DTC, thereby rendering them suitable for RAI therapy with satisfactory retention following treatment.
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Affiliation(s)
- Amir Iravani
- Department of Cancer Imaging and Peter MacCallum Cancer Centre, Melbourne, Australia
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Benjamin Solomon
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Australia
| | - David A Pattison
- Department of Nuclear Medicine and Specialised PET Services, Royal Brisbane & Women's Hospital, Brisbane, Australia
- School of Medicine, University of Queensland, St Lucia, Australia
| | - Price Jackson
- Department of Cancer Imaging and Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Aravind Ravi Kumar
- Department of Cancer Imaging and Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Grace Kong
- Department of Cancer Imaging and Peter MacCallum Cancer Centre, Melbourne, Australia
| | - Michael S Hofman
- Department of Cancer Imaging and Peter MacCallum Cancer Centre, Melbourne, Australia
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Tim Akhurst
- Department of Cancer Imaging and Peter MacCallum Cancer Centre, Melbourne, Australia
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Rodney J Hicks
- Department of Cancer Imaging and Peter MacCallum Cancer Centre, Melbourne, Australia
- The Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
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11
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Benton MC, Smith RA, Haupt LM, Sutherland HG, Dunn PJ, Albury CL, Maksemous N, Lea R, Griffiths L. Variant Call Format-Diagnostic Annotation and Reporting Tool: A Customizable Analysis Pipeline for Identification of Clinically Relevant Genetic Variants in Next-Generation Sequencing Data. J Mol Diagn 2019; 21:951-960. [PMID: 31442673 DOI: 10.1016/j.jmoldx.2019.07.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 06/28/2019] [Accepted: 07/17/2019] [Indexed: 11/19/2022] Open
Abstract
In this article, we introduce the variant call format-diagnostic annotation and reporting tool (VCF-DART), a customized analysis pipeline tool for the rapid annotation of variants from exome or genome sequencing and the generation of reports for genetic diagnostics. VCF-DART uses custom gene lists to categorize variants into specific analysis tiers and to subcategorize them on the basis of standard parameters to facilitate the rapid interrogation of potentially pathogenic variants by human operators. The tool uses publicly available databases to identify a range of data to assist with variant classification and curation processes and includes robust logging of parameters and database versions to allow comparison of analyses performed at different times. VCF-DART-an online analysis pipeline for next-generation sequencing data-is a platform agnostic tool that leverages the use of publicly available databases to improve a laboratory's calling ability and reduce analysis times long-term. It also runs highly efficiently and scales from desk and laptop machines to servers. Overall, VCF-DART provides a simple, customizable, and entirely open-source method to identify genetic variants that may be of clinical importance in a variety of genetically important conditions.
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Affiliation(s)
- Miles C Benton
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Robert A Smith
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Larisa M Haupt
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Heidi G Sutherland
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Paul J Dunn
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Cassie L Albury
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Neven Maksemous
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Rodney Lea
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Lyn Griffiths
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Science, Queensland University of Technology, Brisbane, Queensland, Australia.
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12
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Ševa J, Wiegandt DL, Götze J, Lamping M, Rieke D, Schäfer R, Jähnichen P, Kittner M, Pallarz S, Starlinger J, Keilholz U, Leser U. VIST - a Variant-Information Search Tool for precision oncology. BMC Bioinformatics 2019; 20:429. [PMID: 31419935 PMCID: PMC6697931 DOI: 10.1186/s12859-019-2958-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 06/18/2019] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Diagnosis and treatment decisions in cancer increasingly depend on a detailed analysis of the mutational status of a patient's genome. This analysis relies on previously published information regarding the association of variations to disease progression and possible interventions. Clinicians to a large degree use biomedical search engines to obtain such information; however, the vast majority of scientific publications focus on basic science and have no direct clinical impact. We develop the Variant-Information Search Tool (VIST), a search engine designed for the targeted search of clinically relevant publications given an oncological mutation profile. RESULTS VIST indexes all PubMed abstracts and content from ClinicalTrials.gov. It applies advanced text mining to identify mentions of genes, variants and drugs and uses machine learning based scoring to judge the clinical relevance of indexed abstracts. Its functionality is available through a fast and intuitive web interface. We perform several evaluations, showing that VIST's ranking is superior to that of PubMed or a pure vector space model with regard to the clinical relevance of a document's content. CONCLUSION Different user groups search repositories of scientific publications with different intentions. This diversity is not adequately reflected in the standard search engines, often leading to poor performance in specialized settings. We develop a search engine for the specific case of finding documents that are clinically relevant in the course of cancer treatment. We believe that the architecture of our engine, heavily relying on machine learning algorithms, can also act as a blueprint for search engines in other, equally specific domains. VIST is freely available at https://vist.informatik.hu-berlin.de/.
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Affiliation(s)
- Jurica Ševa
- Knowledge Management in Bioinformatics, Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, Berlin, 12489, Germany
| | - David Luis Wiegandt
- Knowledge Management in Bioinformatics, Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, Berlin, 12489, Germany
| | - Julian Götze
- University Hospital Tübingen, Hoppe-Seyler-Straße 3, Tübingen, 72076, Germany
| | - Mario Lamping
- Charité Comprehensive Cancer Center, Charitéplatz 1, Berlin, 10117, Germany
| | - Damian Rieke
- Charité Comprehensive Cancer Center, Charitéplatz 1, Berlin, 10117, Germany
- Department of Hematology and Medical Oncology, Campus Benjamin Franklin, Charité Unviersitätsmedizin Berlin, Hindenburgdamm 30, Berlin, 12203, Germany
- Berlin Institute of Health, Kapelle-Ufer 2, Berlin, 10117, Germany
| | - Reinhold Schäfer
- Charité Comprehensive Cancer Center, Charitéplatz 1, Berlin, 10117, Germany
- German Cancer Consortium (DKTK), DKFZ Heidelberg, Im Neuenheimer Feld 280, Heidelberg, 69120, Germany
| | - Patrick Jähnichen
- Knowledge Management in Bioinformatics, Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, Berlin, 12489, Germany
| | - Madeleine Kittner
- Knowledge Management in Bioinformatics, Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, Berlin, 12489, Germany
| | - Steffen Pallarz
- Knowledge Management in Bioinformatics, Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, Berlin, 12489, Germany
| | - Johannes Starlinger
- Knowledge Management in Bioinformatics, Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, Berlin, 12489, Germany
| | - Ulrich Keilholz
- Charité Comprehensive Cancer Center, Charitéplatz 1, Berlin, 10117, Germany
| | - Ulf Leser
- Knowledge Management in Bioinformatics, Department of Computer Science, Humboldt-Universität zu Berlin, Rudower Chaussee 25, Berlin, 12489, Germany.
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13
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Merkelbach-Bruse S, Rehker J, Siemanowski J, Klauschen F. [Detection and interpretation of somatic variants in molecular pathology]. DER PATHOLOGE 2019; 40:243-249. [PMID: 31037375 DOI: 10.1007/s00292-019-0603-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
BACKGROUND Due to the increasing amount of data and sources of information, data evaluation is a crucial step in parallel sequencing. OBJECTIVES Illustration of pitfalls in evaluating the variant list of parallel sequencing and recommendations regarding software tools and databases. METHODS Description of filtering steps used, demonstration of criteria and recommendations for annotation by examples from everyday work, comparative analysis of databases with somatic variants, description of the installation of an individualized database. RESULTS Variant filtering is a multistep process using information from different databases. The plausibility of variant calling should be verified using the Integrative Genomics Viewer and variants should be described according to the Human Genome Variation Society (HGVS) recommendations. Different databases, which all show advantages and disadvantages, are available for variant interpretation. An individualized database can be built up with the open-source tool cBioPortal. CONCLUSIONS Different tools and databases might be used for the analysis of parallel sequencing data. The application depends on, amongst other things, the local situation and has to be extensively validated.
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Affiliation(s)
- S Merkelbach-Bruse
- Institut für Pathologie, Universitätsklinikum Köln, Kerpener Str. 62, Gebäude 8e, 50937, Köln, Deutschland.
| | - J Rehker
- Institut für Pathologie, Universitätsklinikum Köln, Kerpener Str. 62, Gebäude 8e, 50937, Köln, Deutschland
| | - J Siemanowski
- Institut für Pathologie, Universitätsklinikum Köln, Kerpener Str. 62, Gebäude 8e, 50937, Köln, Deutschland
| | - F Klauschen
- Institut für Pathologie, Charité Universitätsmedizin Berlin, Berlin, Deutschland.,Standort Berlin, Deutsches Konsortium für Translationale Krebsforschung (DKTK), Berlin, Deutschland.,Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Deutschland
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14
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CNspector: a web-based tool for visualisation and clinical diagnosis of copy number variation from next generation sequencing. Sci Rep 2019; 9:6426. [PMID: 31015508 PMCID: PMC6478945 DOI: 10.1038/s41598-019-42858-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Accepted: 04/08/2019] [Indexed: 11/23/2022] Open
Abstract
Next Generation Sequencing is now routinely used in the practice of diagnostic pathology to detect clinically relevant somatic and germline sequence variations in patient samples. However, clinical assessment of copy number variations (CNVs) and large-scale structural variations (SVs) is still challenging. While tools exist to estimate both, their results are typically presented separately in tables or static plots which can be difficult to read and are unable to show the context needed for clinical interpretation and reporting. We have addressed this problem with CNspector, a multi-scale interactive browser that shows CNVs in the context of other relevant genomic features to enable fast and effective clinical reporting. We illustrate the utility of CNspector at different genomic scales across a variety of sample types in a range of case studies. We show how CNspector can be used for diagnosis and reporting of exon-level deletions, focal gene-level amplifications, chromosome and chromosome arm level amplifications/deletions and in complex genomic rearrangements. CNspector is a web-based clinical variant browser tailored to the clinical application of next generation sequencing for CNV assessment. We have demonstrated the utility of this interactive software in typical applications across a range of tissue types and disease contexts encountered in the context of diagnostic pathology. CNspector is written in R and the source code is available for download under the GPL3 Licence from https://github.com/PapenfussLab/CNspector.
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15
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Agarwal R, Chan YC, Tam CS, Hunter T, Vassiliadis D, Teh CE, Thijssen R, Yeh P, Wong SQ, Ftouni S, Lam EYN, Anderson MA, Pott C, Gilan O, Bell CC, Knezevic K, Blombery P, Rayeroux K, Zordan A, Li J, Huang DCS, Wall M, Seymour JF, Gray DHD, Roberts AW, Dawson MA, Dawson SJ. Dynamic molecular monitoring reveals that SWI–SNF mutations mediate resistance to ibrutinib plus venetoclax in mantle cell lymphoma. Nat Med 2018; 25:119-129. [DOI: 10.1038/s41591-018-0243-z] [Citation(s) in RCA: 108] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Accepted: 09/21/2018] [Indexed: 11/09/2022]
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16
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Lee B, Tran B, Hsu AL, Taylor GR, Fox SB, Fellowes A, Marquis R, Mooi J, Desai J, Doig K, Ekert P, Gaff C, Herath D, Hamilton A, James P, Roberts A, Snyder R, Waring P, McArthur G. Exploring the feasibility and utility of exome-scale tumour sequencing in a clinical setting. Intern Med J 2018; 48:786-794. [PMID: 29607586 DOI: 10.1111/imj.13806] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Accepted: 03/20/2018] [Indexed: 11/29/2022]
Abstract
BACKGROUND Technology has progressed from single gene panel to large-scale genomic sequencing. This is raising expectations from clinicians and patients alike. The utility and performance of this technology in a clinical setting needs to be evaluated. AIM This pilot study investigated the feasibility of using exome-scale sequencing (ESS) to identify molecular drivers within cancers in real-time for Precision Oncology in the clinic. METHODS Between March 2014 and March 2015, the Victorian Comprehensive Cancer Centre Alliance explored the feasibility and utility of ESS in a pilot study. DNA extracted from the tumour specimens underwent both ESS and targeted 'hotspot' sequencing (TS). Blood was taken for germline analysis. A multi-disciplinary molecular tumour board determined the clinical relevance of identified mutations; in particular, whether they were 'actionable' and/or 'druggable'. RESULTS Of 23 patients screened, 15 (65%) met the tissue requirements for genomic analysis. TS and ESS were successful in all cases. ESS identified pathogenic somatic variants in 73% (11/15 cases) versus 53% (8/15 cases) using TS. Clinically focused ESS identified 63 variants, consisting of 30 somatic variants (including all 13 identified by TS) and 33 germline variants. Overall, there were 48 unique variants. ESS had a clinical impact in 53% (8/15 cases); 47% (7/15 cases) were referred to the familial cancer clinic, and 'druggable' targets were identified in 53% (8/15 cases). CONCLUSION ESS of tumour DNA impacted clinical decision-making in 53%, with 20% more pathogenic variants identified through ESS than TS. The identification of germline variants in 47% was an unexpected finding.
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Affiliation(s)
- Belinda Lee
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Division of Systems Biology and Personalized Medicine, Walter and Eliza Hall Institute, Melbourne, Victoria, Australia
| | - Ben Tran
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Division of Systems Biology and Personalized Medicine, Walter and Eliza Hall Institute, Melbourne, Victoria, Australia
| | - Arthur L Hsu
- Centre for Translational Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Graham R Taylor
- Centre for Translational Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Stephen B Fox
- Department of Molecular Pathology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Andrew Fellowes
- Department of Molecular Pathology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Renata Marquis
- Centre for Translational Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Jennifer Mooi
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Jayesh Desai
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Division of Systems Biology and Personalized Medicine, Walter and Eliza Hall Institute, Melbourne, Victoria, Australia
| | - Ken Doig
- Department of Molecular Pathology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Paul Ekert
- Murdoch Children's Research Institute, The Royal Children's Hospital, Melbourne, Victoria, Australia
| | - Clara Gaff
- Melbourne Genomics Health Alliance, Melbourne, Victoria, Australia
| | - Dishan Herath
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Department of Medical Oncology, Western Health, Melbourne, Victoria, Australia
| | - Anne Hamilton
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Department of Gynaecological Oncology, The Royal Women's Hospital, Melbourne, Victoria, Australia
| | - Paul James
- Familial Cancer Centre, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Andrew Roberts
- Clinical Translation Centre, Walter and Eliza Hall Institute, Melbourne, Victoria, Australia.,Department of Clinical Haematology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Victorian Comprehensive Cancer Centre, St Vincent's Hospital, Melbourne, Victoria, Australia
| | - Ray Snyder
- Department of Medical Oncology, St Vincent's Hospital, Melbourne, Victoria, Australia
| | - Paul Waring
- Centre for Translational Pathology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Grant McArthur
- Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Victorian Comprehensive Cancer Centre, St Vincent's Hospital, Melbourne, Victoria, Australia
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17
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Tawari NR, Seow JJW, Perumal D, Ow JL, Ang S, Devasia AG, Ng PC. ChronQC: a quality control monitoring system for clinical next generation sequencing. Bioinformatics 2018; 34:1799-1800. [PMID: 29300845 DOI: 10.1093/bioinformatics/btx843] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Accepted: 12/27/2017] [Indexed: 11/12/2022] Open
Abstract
Summary ChronQC is a quality control (QC) tracking system for clinical implementation of next-generation sequencing (NGS). ChronQC generates time series plots for various QC metrics to allow comparison of current runs to historical runs. ChronQC has multiple features for tracking QC data including Westgard rules for clinical validity, laboratory-defined thresholds and historical observations within a specified time period. Users can record their notes and corrective actions directly onto the plots for long-term recordkeeping. ChronQC facilitates regular monitoring of clinical NGS to enable adherence to high quality clinical standards. Availability and implementation ChronQC is freely available on GitHub (https://github.com/nilesh-tawari/ChronQC), Docker (https://hub.docker.com/r/nileshtawari/chronqc/) and the Python Package Index. ChronQC is implemented in Python and runs on all common operating systems (Windows, Linux and Mac OS X). Contact tawari.nilesh@gmail.com or pauline.c.ng@gmail.com. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Nilesh R Tawari
- Computational and Systems Biology, Genome Institute of Singapore, Genome, #02-01, Singapore 138672, Singapore
| | - Justine Jia Wen Seow
- Computational and Systems Biology, Genome Institute of Singapore, Genome, #02-01, Singapore 138672, Singapore
| | - Dharuman Perumal
- Computational and Systems Biology, Genome Institute of Singapore, Genome, #02-01, Singapore 138672, Singapore
| | - Jack L Ow
- Computational and Systems Biology, Genome Institute of Singapore, Genome, #02-01, Singapore 138672, Singapore
| | - Shimin Ang
- Computational and Systems Biology, Genome Institute of Singapore, Genome, #02-01, Singapore 138672, Singapore
| | - Arun George Devasia
- Computational and Systems Biology, Genome Institute of Singapore, Genome, #02-01, Singapore 138672, Singapore
| | - Pauline C Ng
- Computational and Systems Biology, Genome Institute of Singapore, Genome, #02-01, Singapore 138672, Singapore
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18
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Jang Y, Choi T, Kim J, Park J, Seo J, Kim S, Kwon Y, Lee S, Lee S. An integrated clinical and genomic information system for cancer precision medicine. BMC Med Genomics 2018; 11:34. [PMID: 29697362 PMCID: PMC5918454 DOI: 10.1186/s12920-018-0347-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Increasing affordability of next-generation sequencing (NGS) has created an opportunity for realizing genomically-informed personalized cancer therapy as a path to precision oncology. However, the complex nature of genomic information presents a huge challenge for clinicians in interpreting the patient's genomic alterations and selecting the optimum approved or investigational therapy. An elaborate and practical information system is urgently needed to support clinical decision as well as to test clinical hypotheses quickly. RESULTS Here, we present an integrated clinical and genomic information system (CGIS) based on NGS data analyses. Major components include modules for handling clinical data, NGS data processing, variant annotation and prioritization, drug-target-pathway analysis, and population cohort explorer. We built a comprehensive knowledgebase of genes, variants, drugs by collecting annotated information from public and in-house resources. Structured reports for molecular pathology are generated using standardized terminology in order to help clinicians interpret genomic variants and utilize them for targeted cancer therapy. We also implemented many features useful for testing hypotheses to develop prognostic markers from mutation and gene expression data. CONCLUSIONS Our CGIS software is an attempt to provide useful information for both clinicians and scientists who want to explore genomic information for precision oncology.
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Affiliation(s)
- Yeongjun Jang
- Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul, Korea.,Interdisciplinary Program in Bioinformatics, College of Natural Science, Seoul National University, Seoul, Korea
| | | | | | | | - Jihae Seo
- Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul, Korea
| | - Sangok Kim
- Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul, Korea
| | - Yeajee Kwon
- Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul, Korea
| | | | - Sanghyuk Lee
- Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul, Korea.
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19
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E-Health und die Realität – was sehen wir heute schon in der Klinik? Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 2018; 61:252-262. [DOI: 10.1007/s00103-018-2690-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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20
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Doig KD, Ellul J, Fellowes A, Thompson ER, Ryland G, Blombery P, Papenfuss AT, Fox SB. Canary: an atomic pipeline for clinical amplicon assays. BMC Bioinformatics 2017; 18:555. [PMID: 29246107 PMCID: PMC5732437 DOI: 10.1186/s12859-017-1950-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 11/22/2017] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND High throughput sequencing requires bioinformatics pipelines to process large volumes of data into meaningful variants that can be translated into a clinical report. These pipelines often suffer from a number of shortcomings: they lack robustness and have many components written in multiple languages, each with a variety of resource requirements. Pipeline components must be linked together with a workflow system to achieve the processing of FASTQ files through to a VCF file of variants. Crafting these pipelines requires considerable bioinformatics and IT skills beyond the reach of many clinical laboratories. RESULTS Here we present Canary, a single program that can be run on a laptop, which takes FASTQ files from amplicon assays through to an annotated VCF file ready for clinical analysis. Canary can be installed and run with a single command using Docker containerization or run as a single JAR file on a wide range of platforms. Although it is a single utility, Canary performs all the functions present in more complex and unwieldy pipelines. All variants identified by Canary are 3' shifted and represented in their most parsimonious form to provide a consistent nomenclature, irrespective of sequencing variation. Further, proximate in-phase variants are represented as a single HGVS 'delins' variant. This allows for correct nomenclature and consequences to be ascribed to complex multi-nucleotide polymorphisms (MNPs), which are otherwise difficult to represent and interpret. Variants can also be annotated with hundreds of attributes sourced from MyVariant.info to give up to date details on pathogenicity, population statistics and in-silico predictors. CONCLUSIONS Canary has been used at the Peter MacCallum Cancer Centre in Melbourne for the last 2 years for the processing of clinical sequencing data. By encapsulating clinical features in a single, easily installed executable, Canary makes sequencing more accessible to all pathology laboratories. Canary is available for download as source or a Docker image at https://github.com/PapenfussLab/Canary under a GPL-3.0 License.
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Affiliation(s)
- Kenneth D Doig
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia. .,Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia. .,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia.
| | - Jason Ellul
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Andrew Fellowes
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Ella R Thompson
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia
| | - Georgina Ryland
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Piers Blombery
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia
| | - Anthony T Papenfuss
- Research Division, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia.,Department of Medical Biology, University of Melbourne, Melbourne, Australia.,Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
| | - Stephen B Fox
- Department of Pathology, Peter MacCallum Cancer Centre, East Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia.,Department of Pathology, University of Melbourne, Melbourne, Australia
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21
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Eilbeck K, Quinlan A, Yandell M. Settling the score: variant prioritization and Mendelian disease. Nat Rev Genet 2017; 18:599-612. [PMID: 28804138 PMCID: PMC5935497 DOI: 10.1038/nrg.2017.52] [Citation(s) in RCA: 152] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
When investigating Mendelian disease using exome or genome sequencing, distinguishing disease-causing genetic variants from the multitude of candidate variants is a complex, multidimensional task. Many prioritization tools and online interpretation resources exist, and professional organizations have offered clinical guidelines for review and return of prioritization results. In this Review, we describe the strengths and weaknesses of widely used computational approaches, explain their roles in the diagnostic and discovery process and discuss how they can inform (and misinform) expert reviewers. We place variant prioritization in the wider context of gene prioritization, burden testing and genotype-phenotype association, and we discuss opportunities and challenges introduced by whole-genome sequencing.
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Affiliation(s)
- Karen Eilbeck
- Department of Biomedical Informatics, School of Medicine, University of Utah, 421 Wakara Way, Suite 120, Salt Lake City, Utah 84108, USA
| | - Aaron Quinlan
- Department of Biomedical Informatics, School of Medicine, University of Utah, 421 Wakara Way, Suite 120, Salt Lake City, Utah 84108, USA
- Department of Human Genetics, Eccles Institute of Human Genetics, School of Medicine, University of Utah, 15 S 2030 E, Salt Lake City, Utah 84112, USA
| | - Mark Yandell
- Department of Human Genetics, Eccles Institute of Human Genetics, School of Medicine, University of Utah, 15 S 2030 E, Salt Lake City, Utah 84112, USA
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Abstract
Molecular tumor profiling is now a routine part of patient care, revealing targetable genomic alterations and molecularly distinct tumor subtypes with therapeutic and prognostic implications. The widespread adoption of next-generation sequencing technologies has greatly facilitated clinical implementation of genomic data and opened the door for high-throughput multigene-targeted sequencing. Herein, we discuss the variability of cancer genetic profiling currently offered by clinical laboratories, the challenges of applying rapidly evolving medical knowledge to individual patients, and the need for more standardized population-based molecular profiling.
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Affiliation(s)
- Doreen Nguyen
- The Department of Pathology, Johns Hopkins University, Baltimore, MD, 21287, USA
| | - Christopher D Gocke
- The Department of Pathology, Johns Hopkins University, Baltimore, MD, 21287, USA. .,The Department of Oncology, Johns Hopkins University, Baltimore, MD, 21287, USA. .,Johns Hopkins Genomics, Johns Hopkins University School of Medicine, 1812 Ashland Ave, Suite 200, Baltimore, MD, 21205, USA.
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