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Background: Muscle wasting (Sarcopenia) is associated with poor outcomes in cancer patients. Early identification of sarcopenia can facilitate nutritional and exercise intervention. Cross-sectional skeletal muscle (SM) area at the third lumbar vertebra (L3) slice of a computed tomography (CT) image is increasingly used to assess body composition and calculate SM index (SMI), a validated surrogate marker for sarcopenia in cancer. Manual segmentation of SM requires multiple steps, which limits use in routine clinical practice. This project aims to develop an automatic method to segment L3 muscle in CT scans., Methods: Attenuation correction CTs from full body PET-CT scans from patients enrolled in two prospective trials were used. The training set consisted of 66 non-small cell lung cancer (NSCLC) patients who underwent curative intent radiotherapy. An additional 42 NSCLC patients prescribed curative intent chemo-radiotherapy from a second trial were used for testing. Each patient had multiple CT scans taken at different time points prior to and post- treatment (147 CTs in the training and validation set and 116 CTs in the independent testing set). Skeletal muscle at L3 vertebra was manually segmented by two observers, according to the Alberta protocol to serve as ground truth labels. This included 40 images segmented by both observers to measure inter-observer variation. An ensemble of 2.5D fully convolutional neural networks (U-Nets) was used to perform the segmentation. The final layer of U-Net produced the binary classification of the pixels into muscle and non-muscle area. The model performance was calculated using Dice score and absolute percentage error (APE) in skeletal muscle area between manual and automated contours., Results: We trained five 2.5D U-Nets using 5-fold cross validation and used them to predict the contours in the testing set. The model achieved a mean Dice score of 0.92 and an APE of 3.1% on the independent testing set. This was similar to inter-observer variation of 0.96 and 2.9% for mean Dice and APE respectively. We further quantified the performance of sarcopenia classification using computer generated skeletal muscle area. To meet a clinical diagnosis of sarcopenia based on Alberta protocol the model achieved a sensitivity of 84% and a specificity of 95%., Conclusions: This work demonstrates an automated method for accurate and reproducible segmentation of skeletal muscle area at L3. This is an efficient tool for large scale or routine computation of skeletal muscle area in cancer patients which may have applications on low quality CTs acquired as part of PET/CT studies for staging and surveillance of patients with cancer., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Amarasinghe, Lopes, Beraldo, Kiss, Bucknell, Everitt, Jackson, Litchfield, Denehy, Blyth, Siva, MacManus, Ball, Li and Hardcastle.)

Objective: Mucinous ovarian carcinoma (MOC) is an uncommon ovarian cancer histotype that responds poorly to conventional chemotherapy regimens. Although long overall survival outcomes can occur with early detection and optimal surgical resection, recurrent and advanced disease are associated with extremely poor survival. There are no current guidelines specifically for the systemic management of recurrent MOC. We analyzed data from a large cohort of women with MOC to evaluate the potential for clinical utility from a range of systemic agents., Methods: We analyzed gene copy number (n = 191) and DNA sequencing data (n = 184) from primary MOC to evaluate signatures of mismatch repair deficiency and homologous recombination deficiency, and other genetic events. Immunohistochemistry data were collated for ER, CK7, CK20, CDX2, HER2, PAX8 and p16 (n = 117-166)., Results: Molecular aberrations noted in MOC that suggest a match with current targeted therapies include amplification of ERBB2 (26.7%) and BRAF mutation (9%). Observed genetic events that suggest potential efficacy for agents currently in clinical trials include: KRAS/NRAS mutations (66%), TP53 missense mutation (49%), RNF43 mutation (11%), ARID1A mutation (10%), and PIK3CA/PTEN mutation (9%). Therapies exploiting homologous recombination deficiency (HRD) may not be effective in MOC, as only 1/191 had a high HRD score. Mismatch repair deficiency was similarly rare (1/184)., Conclusions: Although genetically diverse, MOC has several potential therapeutic targets. Importantly, the lack of response to platinum-based therapy observed clinically corresponds to the lack of a genomic signature associated with HRD, and MOC are thus also unlikely to respond to PARP inhibition., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest. ADF, NT and DDLB have received research grant funding from AstraZeneca, unrelated to the contents on this manuscript. DDLB also reports funding from Roche-Genentech and BeiGene, also unrelated. CG reports funding from AstraZeneca, Roche, Clovis, Tesaro, Foundation One, Nucana, Aprea, Novartis, Chugai, and MSD, all outside the submitted work. CLS reports non-financial support and/or other support from Clovis Oncology, Roche, Eisai Australia, Beigene, Sierra Oncology, and AstraZeneca, all outside the submitted work., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Mucinous ovarian carcinoma (MOC) is a unique subtype of ovarian cancer with an uncertain etiology, including whether it genuinely arises at the ovary or is metastatic disease from other organs. In addition, the molecular drivers of invasive progression, high-grade and metastatic disease are poorly defined. We perform genetic analysis of MOC across all histological grades, including benign and borderline mucinous ovarian tumors, and compare these to tumors from other potential extra-ovarian sites of origin. Here we show that MOC is distinct from tumors from other sites and supports a progressive model of evolution from borderline precursors to high-grade invasive MOC. Key drivers of progression identified are TP53 mutation and copy number aberrations, including a notable amplicon on 9p13. High copy number aberration burden is associated with worse prognosis in MOC. Our data conclusively demonstrate that MOC arise from benign and borderline precursors at the ovary and are not extra-ovarian metastases.

Purpose: The identification of carriers of hereditary breast and ovarian cancer (HBOC) gene variants through family cancer history alone is suboptimal, and most population-based genetic testing studies have been limited to founder mutations in high-risk populations. Here, we determine the clinical utility of identifying actionable variants in a healthy cohort of women., Methods: Germline DNA from a subset of healthy Australian women participating in the lifepool project was screened using an 11-gene custom sequencing panel. Women with clinically actionable results were invited to attend a familial cancer clinic (FCC) for post-test genetic counseling and confirmatory testing. Outcomes measured included the prevalence of pathogenic variants, and the uptake rate of genetic counseling, risk reduction surgery, and cascade testing., Results: Thirty-eight of 5908 women (0.64%) carried a clinically actionable pathogenic variant. Forty-two percent of pathogenic variant carriers did not have a first-degree relative with breast or ovarian cancer and 89% pursued referral to an FCC. Forty-six percent (6/13) of eligible women pursued risk reduction surgery, and the uptake rate of cascade testing averaged 3.3 family members per index case., Conclusion: Within our cohort, HBOC genetic testing was well accepted, and the majority of high-risk gene carriers identified would not meet eligibility criteria for genetic testing based on their existing family history.

Adult granulosa cell tumor (aGCT), the most common malignant ovarian sex cord-stromal tumor, is characterized by the forkhead transcription factor FOXL2 p.C134W somatic mutation. Late recurrences are relatively common but the molecular mechanisms of relapse or aggressive behavior are not known. The mutational landscape of FOXL2 p.C134W mutation-positive tumors ( n = 22) was determined using whole-exome sequencing (WES). An average of 64 coding and essential splice-site variants were identified per tumor. As the TERT promoter region is poorly covered by the WES, targeted sequencing identified the TERT -124C>T promoter mutation as the only recurrent mutation (∼40% of cases). Pathway analysis suggested an association with DNA replication/repair and the EGFR family canonical pathways. Copy number analysis confirmed that gains of chromosomes 12 and 14 occur in approximately 30% of aGCT and loss of chromosome 22 occurs in approximately 40% of cases. In summary, exome-wide analysis of the mutational landscape of aGCT revealed that, except for the TERT promoter mutation, recurrence and/or aggressive behavior is not defined by activation or loss of specific genes. IMPLICATIONS: This study found that although aGCTs are defined by the presence of a common FOXL2 gene mutation, recurrence and/or aggressive behavior cannot be attributed to subsequent mutation of specific gene(s) or pathways; however, there is a high frequency of the TERT -124C>T promoter mutation, which is associated with more aggressive disease., (©2018 American Association for Cancer Research.)

Background: Genome-wide association studies (GWASs) have identified numerous single-nucleotide polymorphisms (SNPs) associated with small increases in breast cancer risk. Studies to date suggest that some SNPs alter the expression of the associated genes, which potentially mediates risk modification. On this basis, we hypothesised that some of these genes may be enriched for rare coding variants associated with a higher breast cancer risk., Methods: The coding regions and exon-intron boundaries of 56 genes that have either been proposed by GWASs to be the regulatory targets of the SNPs and/or located < 500 kb from the risk SNPs were sequenced in index cases from 1043 familial breast cancer families that previously had negative test results for BRCA1 and BRCA2 mutations and 944 population-matched cancer-free control participants from an Australian population. Rare (minor allele frequency ≤ 0.001 in the Exome Aggregation Consortium and Exome Variant Server databases) loss-of-function (LoF) and missense variants were studied., Results: LoF variants were rare in both the cases and control participants across all the candidate genes, with only 38 different LoF variants observed in a total of 39 carriers. For the majority of genes (n = 36), no LoF variants were detected in either the case or control cohorts. No individual gene showed a significant excess of LoF or missense variants in the cases compared with control participants. Among all candidate genes as a group, the total number of carriers with LoF variants was higher in the cases than in the control participants (26 cases and 13 control participants), as was the total number of carriers with missense variants (406 versus 353), but neither reached statistical significance (p = 0.077 and p = 0.512, respectively). The genes contributing most of the excess of LoF variants in the cases included TET2, NRIP1, RAD51B and SNX32 (12 cases versus 2 control participants), whereas ZNF283 and CASP8 contributed largely to the excess of missense variants (25 cases versus 8 control participants)., Conclusions: Our data suggest that rare LoF and missense variants in genes associated with low-penetrance breast cancer risk SNPs may contribute some additional risk, but as a group these genes are unlikely to be major contributors to breast cancer heritability.

Background: Using whole exome sequencing to predict aberrations in tumours is a cost effective alternative to whole genome sequencing, however is predominantly used for variant detection and infrequently utilised for detection of somatic copy number variation., Results: We propose a new method to infer copy number and genotypes using whole exome data from paired tumour/normal samples. Our algorithm uses two Hidden Markov Models to predict copy number and genotypes and computationally resolves polyploidy/aneuploidy, normal cell contamination and signal baseline shift. Our method makes explicit detection on chromosome arm level events, which are commonly found in tumour samples. The methods are combined into a package named ADTEx (Aberration Detection in Tumour Exome). We applied our algorithm to a cohort of 17 in-house generated and 18 TCGA paired ovarian cancer/normal exomes and evaluated the performance by comparing against the copy number variations and genotypes predicted using Affymetrix SNP 6.0 data of the same samples. Further, we carried out a comparison study to show that ADTEx outperformed its competitors in terms of precision and F-measure., Conclusions: Our proposed method, ADTEx, uses both depth of coverage ratios and B allele frequencies calculated from whole exome sequencing data, to predict copy number variations along with their genotypes. ADTEx is implemented as a user friendly software package using Python and R statistical language. Source code and sample data are freely available under GNU license (GPLv3) at http://adtex.sourceforge.net/.

Background: One of the main types of genetic variations in cancer is Copy Number Variations (CNV). Whole exome sequencing (WES) is a popular alternative to whole genome sequencing (WGS) to study disease specific genomic variations. However, finding CNV in Cancer samples using WES data has not been fully explored., Results: We present a new method, called CoNVEX, to estimate copy number variation in whole exome sequencing data. It uses ratio of tumour and matched normal average read depths at each exonic region, to predict the copy gain or loss. The useful signal produced by WES data will be hindered by the intrinsic noise present in the data itself. This limits its capacity to be used as a highly reliable CNV detection source. Here, we propose a method that consists of discrete wavelet transform (DWT) to reduce noise. The identification of copy number gains/losses of each targeted region is performed by a Hidden Markov Model (HMM)., Conclusion: HMM is frequently used to identify CNV in data produced by various technologies including Array Comparative Genomic Hybridization (aCGH) and WGS. Here, we propose an HMM to detect CNV in cancer exome data. We used modified data from 1000 Genomes project to evaluate the performance of the proposed method. Using these data we have shown that CoNVEX outperforms the existing methods significantly in terms of precision. Overall, CoNVEX achieved a sensitivity of more than 92% and a precision of more than 50%.

Motivation: In light of the increasing adoption of targeted resequencing (TR) as a cost-effective strategy to identify disease-causing variants, a robust method for copy number variation (CNV) analysis is needed to maximize the value of this promising technology., Results: We present a method for CNV detection for TR data, including whole-exome capture data. Our method calls copy number gains and losses for each target region based on normalized depth of coverage. Our key strategies include the use of base-level log-ratios to remove GC-content bias, correction for an imbalanced library size effect on log-ratios, and the estimation of log-ratio variations via binning and interpolation. Our methods are made available via CONTRA (COpy Number Targeted Resequencing Analysis), a software package that takes standard alignment formats (BAM/SAM) and outputs in variant call format (VCF4.0), for easy integration with other next-generation sequencing analysis packages. We assessed our methods using samples from seven different target enrichment assays, and evaluated our results using simulated data and real germline data with known CNV genotypes.