Enhanced detection of circulating tumor DNA by fragment size analysis.

Existing methods to improve detection of circulating tumor DNA (ctDNA) have focused on genomic alterations but have rarely considered the biological properties of plasma cell-free DNA (cfDNA). We hypothesized that differences in fragment lengths of circulating DNA could be exploited to enhance sensitivity for detecting the presence of ctDNA and for noninvasive genomic analysis of cancer. We surveyed ctDNA fragment sizes in 344 plasma samples from 200 patients with cancer using low-pass whole-genome sequencing (0.4×). To establish the size distribution of mutant ctDNA, tumor-guided personalized deep sequencing was performed in 19 patients. We detected enrichment of ctDNA in fragment sizes between 90 and 150 bp and developed methods for in vitro and in silico size selection of these fragments. Selecting fragments between 90 and 150 bp improved detection of tumor DNA, with more than twofold median enrichment in >95% of cases and more than fourfold enrichment in >10% of cases. Analysis of size-selected cfDNA identified clinically actionable mutations and copy number alterations that were otherwise not detected. Identification of plasma samples from patients with advanced cancer was improved by predictive models integrating fragment length and copy number analysis of cfDNA, with area under the curve (AUC) >0.99 compared to AUC 0.91 compared to AUC < 0.5 without fragmentation features. Fragment size analysis and selective sequencing of specific fragment sizes can boost ctDNA detection and could complement or provide an alternative to deeper sequencing of cfDNA.We would like to acknowledge the support of The University of Cambridge, Cancer Research UK and the EPSRC (CRUK grant numbers A11906 (NR), A20240 (NR), A22905 (JDB), A15601 (JDB), A25177 (CRUK Cancer Centre Cambridge), A17242 (KMB), A16465 (CRUK-EPSRC Imaging Centre in Cambridge and Manchester)). The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 337905. The research was supported by the National Institute for Health Research Cambridge, National Cancer Research Network, Cambridge Experimental Cancer Medicine Centre and Hutchison Whampoa Limited. This research is also supported by Target Ovarian Cancer and the Medical Research Council through their Joint Clinical Research Training Fellowship for Dr Moore. The CALIBRATE study was supported by funding from AstraZeneca

Copy number signatures and mutational processes in ovarian carcinoma.

The genomic complexity of profound copy number aberrations has prevented effective molecular stratification of ovarian cancers. Here, to decode this complexity, we derived copy number signatures from shallow whole-genome sequencing of 117 high-grade serous ovarian cancer (HGSOC) cases, which were validated on 527 independent cases. We show that HGSOC comprises a continuum of genomes shaped by multiple mutational processes that result in known patterns of genomic aberration. Copy number signature exposures at diagnosis predict both overall survival and the probability of platinum-resistant relapse. Measurement of signature exposures provides a rational framework to choose combination treatments that target multiple mutational processes.NIHR, Ovarian Cancer Action, Cancer Research UK Cambridge Centre, Cambridge Experimental Cancer Medicine Centr

Development and Validation of the Gene Expression Predictor of High-grade Serous Ovarian Carcinoma Molecular SubTYPE (PrOTYPE).

PURPOSE: Gene expression-based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by nonstandardized methods, which are not applicable in a clinical setting. We sought to generate a clinical grade minimal gene set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features. EXPERIMENTAL DESIGN: Adopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene expression data from 1,650 tumors. We applied resulting models to NanoString data on 3,829 HGSOCs from the Ovarian Tumor Tissue Analysis consortium. We further developed, confirmed, and validated a reduced, minimal gene set predictor, with methods suitable for a single-patient setting. RESULTS: Gene expression data were used to derive the predictor of high-grade serous ovarian carcinoma molecular subtype (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor-infiltrating lymphocytes, and outcome. The locked-down clinical grade PrOTYPE test includes a model with 55 genes that predicted gene expression subtype with >95% accuracy that was maintained in all analytic and biological validations. CONCLUSIONS: We validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications.See related commentary by McMullen et al., p. 5271.Core funding for this project was provided by the National Institutes of Health (R01-CA172404, PI: S.J. Ramus; and R01-CA168758, PIs: J.A. Doherty and M.A.Rossing), the Canadian Institutes for Health Research (Proof-of-Principle I program, PIs: D.G.Huntsman and M.S. Anglesio), the United States Department of Defense Ovarian Cancer Research Program (OC110433, PI: D.D. Bowtell). A. Talhouk is funded through a Michael Smith Foundation for Health Research Scholar Award. M.S. Anglesio is funded through a Michael Smith Foundation for Health Research Scholar Award and the Janet D. Cottrelle Foundation Scholars program managed by the BC Cancer Foundation. J. George was partially supported by the NIH/National Cancer Institute award number P30CA034196. C. Wang was a Career Enhancement Awardee of the Mayo Clinic SPORE in Ovarian Cancer (P50 CA136393). D.G. Huntsman receives support from the Dr. Chew Wei Memorial Professorship in Gynecologic Oncology, and the Canada Research Chairs program (Research Chair in Molecular and Genomic Pathology). M. Widschwendter receives funding from the European Union’s Horizon 2020 European Research Council Programme, H2020 BRCA-ERC under Grant Agreement No. 742432 as well as the charity, The Eve Appeal (https://eveappeal.org.uk/), and support of the National Institute for Health Research (NIHR) and the University College London Hospitals (UCLH) Biomedical Research Centre. G.E. Konecny is supported by the Miriam and Sheldon Adelson Medical Research Foundation. B.Y. Karlan is funded by the American Cancer Society Early Detection Professorship (SIOP-06-258-01-COUN) and the National Center for Advancing Translational Sciences (NCATS), Grant UL1TR000124. H.R. Harris is 20 supported by the NIH/National Cancer Institute award number K22 CA193860. OVCARE (including the VAN study) receives support through the BC Cancer Foundation and The VGH+UBC Hospital Foundation (authors AT, BG, DGH, and MSA). The AOV study is supported by the Canadian Institutes of Health Research (MOP86727). The Gynaecological Oncology Biobank at Westmead, a member of the Australasian Biospecimen Network-Oncology group, was funded by the National Health and Medical Research Council Enabling Grants ID 310670 & ID 628903 and the Cancer Institute NSW Grants ID 12/RIG/1-17 & 15/RIG/1-16. The Australian Ovarian Cancer Study Group was supported by the U.S. Army Medical Research and Materiel Command under DAMD17-01-1-0729, The Cancer Council Victoria, Queensland Cancer Fund, The Cancer Council New South Wales, The Cancer Council South Australia, The Cancer Council Tasmania and The Cancer Foundation of Western Australia (Multi-State Applications 191, 211 and 182) and the National Health and Medical Research Council of Australia (NHMRC; ID199600; ID400413 and ID400281). BriTROC-1 was funded by Ovarian Cancer Action (to IAM and JDB, grant number 006) and supported by Cancer Research UK (grant numbers A15973, A15601, A18072, A17197, A19274 and A19694) and the National Institute for Health Research Cambridge and Imperial Biomedical Research Centres. Samples from the Mayo Clinic were collected and provided with support of P50 CA136393 (E.L.G., G.L.K, S.H.K, M.E.S.)

Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer.

To identify common alleles associated with different histotypes of epithelial ovarian cancer (EOC), we pooled data from multiple genome-wide genotyping projects totaling 25,509 EOC cases and 40,941 controls. We identified nine new susceptibility loci for different EOC histotypes: six for serous EOC histotypes (3q28, 4q32.3, 8q21.11, 10q24.33, 18q11.2 and 22q12.1), two for mucinous EOC (3q22.3 and 9q31.1) and one for endometrioid EOC (5q12.3). We then performed meta-analysis on the results for high-grade serous ovarian cancer with the results from analysis of 31,448 BRCA1 and BRCA2 mutation carriers, including 3,887 mutation carriers with EOC. This identified three additional susceptibility loci at 2q13, 8q24.1 and 12q24.31. Integrated analyses of genes and regulatory biofeatures at each locus predicted candidate susceptibility genes, including OBFC1, a new candidate susceptibility gene for low-grade and borderline serous EOC

Prognostic gene expression signature for high-grade serous ovarian cancer

Background: Median overall survival (OS) for women with high-grade serous ovarian cancer (HGSOC) is ∼4 years, yet survival varies widely between patients. There are no well-established, gene expression signatures associated with prognosis. The aim of this study was to develop a robust prognostic signature for OS in patients with HGSOC. Patients and methods: Expression of 513 genes, selected from a meta-analysis of 1455 tumours and other candidates, was measured using NanoString technology from formalin-fixed paraffin-embedded tumour tissue collected from 3769 women with HGSOC from multiple studies. Elastic net regularization for survival analysis was applied to develop a prognostic model for 5-year OS, trained on 2702 tumours from 15 studies and evaluated on an independent set of 1067 tumours from six studies. Results: Expression levels of 276 genes were associated with OS (false discovery rate < 0.05) in covariate-adjusted single-gene analyses. The top five genes were TAP1, ZFHX4, CXCL9, FBN1 and PTGER3 (P < 0.001). The best performing prognostic signature included 101 genes enriched in pathways with treatment implications. Each gain of one standard deviation in the gene expression score conferred a greater than twofold increase in risk of death [hazard ratio (HR) 2.35, 95% confidence interval (CI) 2.02–2.71; P < 0.001]. Median survival [HR (95% CI)] by gene expression score quintile was 9.5 (8.3 to –), 5.4 (4.6–7.0), 3.8 (3.3–4.6), 3.2 (2.9–3.7) and 2.3 (2.1–2.6) years. Conclusion: The OTTA-SPOT (Ovarian Tumor Tissue Analysis consortium - Stratified Prognosis of Ovarian Tumours) gene expression signature may improve risk stratification in clinical trials by identifying patients who are least likely to achieve 5-year survival. The identified novel genes associated with the outcome may also yield opportunities for the development of targeted therapeutic approaches

Enhanced detection of circulating tumor DNA by fragment size analysis

Existing methods to improve detection of circulating tumor DNA (ctDNA) have focused on genomic alterations but have rarely considered the biological properties of plasma cell-free DNA (cfDNA). We hypothesized that differences in fragment lengths of circulating DNA could be exploited to enhance sensitivity for detecting the presence of ctDNA and for noninvasive genomic analysis of cancer. We surveyed ctDNA fragment sizes in 344 plasma samples from 200 patients with cancer using low-pass whole-genome sequencing (0.4×). To establish the size distribution of mutant ctDNA, tumor-guided personalized deep sequencing was performed in 19 patients. We detected enrichment of ctDNA in fragment sizes between 90 and 150 bp and developed methods for in vitro and in silico size selection of these fragments. Selecting fragments between 90 and 150 bp improved detection of tumor DNA, with more than twofold median enrichment in >95% of cases and more than fourfold enrichment in >10% of cases. Analysis of size-selected cfDNA identified clinically actionable mutations and copy number alterations that were otherwise not detected. Identification of plasma samples from patients with advanced cancer was improved by predictive models integrating fragment length and copy number analysis of cfDNA, with area under the curve (AUC) >0.99 compared to AUC 0.91 compared to AUC < 0.5 without fragmentation features. Fragment size analysis and selective sequencing of specific fragment sizes can boost ctDNA detection and could complement or provide an alternative to deeper sequencing of cfDNA

Development and Validation of the Gene Expression Predictor of High-grade Serous Ovarian Carcinoma Molecular SubTYPE (PrOTYPE).

PurposeGene expression-based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by nonstandardized methods, which are not applicable in a clinical setting. We sought to generate a clinical grade minimal gene set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features.Experimental designAdopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene expression data from 1,650 tumors. We applied resulting models to NanoString data on 3,829 HGSOCs from the Ovarian Tumor Tissue Analysis consortium. We further developed, confirmed, and validated a reduced, minimal gene set predictor, with methods suitable for a single-patient setting.ResultsGene expression data were used to derive the predictor of high-grade serous ovarian carcinoma molecular subtype (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor-infiltrating lymphocytes, and outcome. The locked-down clinical grade PrOTYPE test includes a model with 55 genes that predicted gene expression subtype with &gt;95% accuracy that was maintained in all analytic and biological validations.ConclusionsWe validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications.See related commentary by McMullen et al., p. 5271

Development and validation of the gene-expression predictor of high-grade-serous ovarian carcinoma molecular subTYPE (PrOTYPE)

Purpose: Gene expression–based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by nonstandardized methods, which are not applicable in a clinical setting. We sought to generate a clinical grade minimal gene set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features. Experimental Design: Adopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene expression data from 1,650 tumors. We applied resulting models to NanoString data on 3,829 HGSOCs from the Ovarian Tumor Tissue Analysis consortium. We further developed, confirmed, and validated a reduced, minimal gene set predictor, with methods suitable for a single-patient setting. Results: Gene expression data were used to derive the predictor of high-grade serous ovarian carcinoma molecular subtype (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor-infiltrating lymphocytes, and outcome. The locked-down clinical grade PrOTYPE test includes a model with 55 genes that predicted gene expression subtype with &gt;95% accuracy that was maintained in all analytic and biological validations. Conclusions: We validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications