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B-cell acute lymphoblastic leukemia (B-ALL) is the most common childhood cancer. Subtypes within B-ALL are distinguished by characteristic structural variants and mutations, which in some instances strongly correlate with responses to treatment. The World Health Organisation (WHO) recognises seven distinct classifications, or subtypes, as of 2016. However, recent studies have demonstrated that B-ALL can be segmented into 23 subtypes based on a combination of genomic features and gene expression profiles. A method to identify a patient's subtype would have clear utility. Despite this, no publically available classification methods using RNA-Seq exist for this purpose. Here we present ALLSorts: a publicly available method that uses RNA-Seq data to classify B-ALL samples to 18 known subtypes and five meta-subtypes. ALLSorts is the result of a hierarchical supervised machine learning algorithm applied to a training set of 1223 B-ALL samples aggregated from multiple cohorts. Validation revealed that ALLSorts can accurately attribute samples to subtypes and can attribute multiple subtypes to a sample. Furthermore, when applied to both paediatric and adult cohorts, ALLSorts was able to classify previously undefined samples into subtypes. ALLSorts is available and documented on GitHub (https://github.com/Oshlack/AllSorts/).

In cancer, fusions are important diagnostic markers and targets for therapy. Long-read transcriptome sequencing allows the discovery of fusions with their full-length isoform structure. However, due to higher sequencing error rates, fusion finding algorithms designed for short reads do not work. Here we present JAFFAL, to identify fusions from long-read transcriptome sequencing. We validate JAFFAL using simulations, cell lines, and patient data from Nanopore and PacBio. We apply JAFFAL to single-cell data and find fusions spanning three genes demonstrating transcripts detected from complex rearrangements. JAFFAL is available at https://github.com/Oshlack/JAFFA/wiki .

Transcriptome sequencing has identified multiple subtypes of B-progenitor acute lymphoblastic leukemia (B-ALL) of prognostic significance, but a minority of cases lack a known genetic driver. Here, we used integrated whole-genome (WGS) and -transcriptome sequencing (RNA-seq), enhancer mapping, and chromatin topology analysis to identify previously unrecognized genomic drivers in B-ALL. Newly diagnosed (n = 3221) and relapsed (n = 177) B-ALL cases with tumor RNA-seq were studied. WGS was performed to detect mutations, structural variants, and copy number alterations. Integrated analysis of histone 3 lysine 27 acetylation and chromatin looping was performed using HiChIP. We identified a subset of 17 newly diagnosed and 5 relapsed B-ALL cases with a distinct gene expression profile and 2 universal and unique genomic alterations resulting from aberrant recombination-activating gene activation: a focal deletion downstream of PAN3 at 13q12.2 resulting in CDX2 deregulation by the PAN3 enhancer and a focal deletion of exons 18-21 of UBTF at 17q21.31 resulting in a chimeric fusion, UBTF::ATXN7L3. A subset of cases also had rearrangement and increased expression of the PAX5 gene, which is otherwise uncommon in B-ALL. Patients were more commonly female and young adult with median age 35 (range,12-70 years). The immunophenotype was characterized by CD10 negativity and immunoglobulin M positivity. Among 16 patients with known clinical response, 9 (56.3%) had high-risk features including relapse (n = 4) or minimal residual disease >1% at the end of remission induction (n = 5). CDX2-deregulated, UBTF::ATXN7L3 rearranged (CDX2/UBTF) B-ALL is a high-risk subtype of leukemia in young adults for which novel therapeutic approaches are required.

Bone marrow failure (BMF) related to hypoplasia of hematopoietic elements in the bone marrow is a heterogeneous clinical entity with a broad differential diagnosis including both inherited and acquired causes. Accurate diagnostic categorization is critical to optimal patient care and detection of genomic variants in these patients may provide this important diagnostic and prognostic information. We performed real-time, accredited (ISO15189) comprehensive genomic characterization including targeted sequencing and whole exome sequencing in 115 patients with BMF syndrome (median age 24 years, range 3 months - 81 years). In patients with clinical diagnoses of inherited BMF syndromes, acquired BMF syndromes or clinically unclassifiable BMF we detected variants in 52% (12/23), 53% (25/47) and 56% (25/45) respectively. Genomic characterization resulted in a change of diagnosis in 30/115 (26%) including the identification of germline causes for 3/47 and 16/45 cases with pre-test diagnoses of acquired and clinically unclassifiable BMF respectively. The observed clinical impact of accurate diagnostic categorization included choice to perform allogeneic stem cell transplantation, disease-specific targeted treatments, identification of at-risk family members and influence of sibling allogeneic stem cell donor choice. Multiple novel pathogenic variants and copy number changes were identified in our cohort including in TERT, FANCA, RPS7 and SAMD9. Whole exome sequence analysis facilitated the identification of variants in two genes not typically associated with a primary clinical manifestation of BMF but also demonstrated reduced sensitivity for detecting low level acquired variants. In conclusion, genomic characterization can improve diagnostic categorization of patients presenting with hypoplastic BMF syndromes and should be routinely performed in this group of patients.

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.

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. A server running CNspector loaded with the figures from this paper can be accessed at https://shiny.wehi.edu.au/jmarkham/CNspector/index.html.

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.

Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is a rare form of T-cell lymphoma that occurs after implantation of breast prostheses. We performed comprehensive next generation sequencing based genomic characterization of 11 cases of BIA-ALCL including sequence variant detection on 180 genes frequently mutated in haematological malignancy, genome-wide copy number assessment, structural variant detection involving the T-cell receptor loci and TRB deep-sequencing. We observed sequence variants leading to JAK/STAT activation in 10 out of 11 patients. We also observed germline TP53 mutations in two cases. In addition we detected a recurrent copy number loss involving RPL5 as well as copy number amplifications involving TNFRSF11A [RANK] (in 2 cases), MYC, P2RX7, TMEM119 and PDGFRA. In summary, our comprehensive genomic characterisation of 11 cases of BIA-ALCL has provided insight into potential pathobiological mechanisms (JAK/STAT, MYC and TP53) as well as identifying targets for future therapeutic intervention (TNFRSF11A, PDGFRA) in this rare entity.

ALK, ROS1 and RET gene fusions are important predictive biomarkers for tyrosine kinase inhibitors in lung cancer. Currently, the gold standard method for gene fusion detection is Fluorescence In Situ Hybridization (FISH) and while highly sensitive and specific, it is also labour intensive, subjective in analysis, and unable to screen a large numbers of gene fusions. Recent developments in high-throughput transcriptome-based methods may provide a suitable alternative to FISH as they are compatible with multiplexing and diagnostic workflows. However, the concordance between these different methods compared with FISH has not been evaluated. In this study we compared the results from three transcriptome-based platforms (Nanostring Elements, Agena LungFusion panel and ThermoFisher NGS fusion panel) to those obtained from ALK, ROS1 and RET FISH on 51 clinical specimens. Overall agreement of results ranged from 86-96% depending on the platform used. While all platforms were highly sensitive, both the Agena panel and Thermo Fisher NGS fusion panel reported minor fusions that were not detectable by FISH. Our proof-of-principle study illustrates that transcriptome-based analyses are sensitive and robust methods for detecting actionable gene fusions in lung cancer and could provide a robust alternative to FISH testing in the diagnostic setting.

BACKGROUND: Mucinous ovarian tumors are an unusual group of rare neoplasms with an apparently clear progression from benign to borderline to carcinoma, yet with a controversial cell of origin in the ovarian surface epithelium. They are thought to be molecularly distinct from other ovarian tumors but there have been no exome-level sequencing studies performed to date. METHODS: To understand the genetic etiology of mucinous ovarian tumors and assess the presence of novel therapeutic targets or pathways, we undertook exome sequencing of 24 tumors encompassing benign (5), borderline (8) and carcinoma (11) histologies and also assessed a validation cohort of 58 tumors for specific gene regions including exons 4-9 of TP53. RESULTS: The predominant mutational signature was of C>T transitions in a NpCpG context, indicative of deamination of methyl-cytosines. As well as mutations in known drivers (KRAS, BRAF and CDKN2A), we identified a high percentage of carcinomas with TP53 mutations (52 %), and recurrent mutations in RNF43, ELF3, GNAS, ERBB3 and KLF5. CONCLUSIONS: The diversity of mutational targets suggests multiple routes to tumorigenesis in this heterogeneous group of tumors that is generally distinct from other ovarian subtypes.

Low grade serous ovarian tumours are a rare and under-characterised histological subtype of epithelial ovarian tumours, with little known of the molecular drivers and facilitators of tumorigenesis beyond classic oncogenic RAS/RAF mutations. With a move towards targeted therapies due to the chemoresistant nature of this subtype, it is pertinent to more fully characterise the genetic events driving this tumour type, some of which may influence response to therapy and/or development of drug resistance. We performed genome-wide high-resolution genomic copy number analysis (Affymetrix SNP6.0) and mutation hotspot screening (KRAS, BRAF, NRAS, HRAS, ERBB2 and TP53) to compare a large cohort of ovarian serous borderline tumours (SBTs, n = 57) with low grade serous carcinomas (LGSCs, n = 19). Whole exome sequencing was performed for 13 SBTs, nine LGSCs and one mixed low/high grade carcinoma. Copy number aberrations were detected in 61% (35/57) of SBTs, compared to 100% (19/19) of LGSCs. Oncogenic RAS/RAF/ERBB2 mutations were detected in 82.5% (47/57) of SBTs compared to 63% (12/19) of LGSCs, with NRAS mutations detected only in LGSC. Some copy number aberrations appeared to be enriched in LGSC, most significantly loss of 9p and homozygous deletions of the CDKN2A/2B locus. Exome sequencing identified BRAF, KRAS, NRAS, USP9X and EIF1AX as the most frequently mutated genes. We have identified markers of progression from borderline to LGSC and novel drivers of LGSC. USP9X and EIF1AX have both been linked to regulation of mTOR, suggesting that mTOR inhibitors may be a key companion treatment for targeted therapy trials of MEK and RAF inhibitors.

BACKGROUND: Loss of heterozygosity (LOH) is a common genetic event in cancer development, and is known to be involved in the somatic loss of wild-type alleles in many inherited cancer syndromes. The wider involvement of LOH in cancer is assumed to relate to unmasking a somatically mutated tumour suppressor gene through loss of the wild type allele. METHODS: We analysed 86 ovarian carcinomas for mutations in 980 genes selected on the basis of their location in common regions of LOH. RESULTS: We identified 36 significantly mutated genes, but these could only partly account for the quanta of LOH in the samples. Using our own and TCGA data we then evaluated five possible models to explain the selection for non-random accumulation of LOH in ovarian cancer genomes: 1. Classic two-hit hypothesis: high frequency biallelic genetic inactivation of tumour suppressor genes. 2. Epigenetic two-hit hypothesis: biallelic inactivation through methylation and LOH. 3. Multiple alternate-gene biallelic inactivation: low frequency gene disruption. 4. Haplo-insufficiency: Single copy gene disruption. 5. Modified two-hit hypothesis: reduction to homozygosity of low penetrance germline predisposition alleles. We determined that while high-frequency biallelic gene inactivation under model 1 is rare, regions of LOH (particularly copy-number neutral LOH) are enriched for deleterious mutations and increased promoter methylation, while copy-number loss LOH regions are likely to contain under-expressed genes suggestive of haploinsufficiency. Reduction to homozygosity of cancer predisposition SNPs may also play a minor role. CONCLUSION: It is likely that selection for regions of LOH depends on its effect on multiple genes. Selection for copy number neutral LOH may better fit the classic two-hit model whereas selection for copy number loss may be attributed to its effect on multi-gene haploinsufficiency. LOH mapping alone is unlikely to be successful in identifying novel tumour suppressor genes; a

Targeted resequencing by massively parallel sequencing has become an effective and affordable way to survey small to large portions of the genome for genetic variation. Despite the rapid development in open source software for analysis of such data, the practical implementation of these tools through construction of sequencing analysis pipelines still remains a challenging and laborious activity, and a major hurdle for many small research and clinical laboratories. We developed TREVA (Targeted REsequencing Virtual Appliance), making pre-built pipelines immediately available as a virtual appliance. Based on virtual machine technologies, TREVA is a solution for rapid and efficient deployment of complex bioinformatics pipelines to laboratories of all sizes, enabling reproducible results. The analyses that are supported in TREVA include: somatic and germline single-nucleotide and insertion/deletion variant calling, copy number analysis, and cohort-based analyses such as pathway and significantly mutated genes analyses. TREVA is flexible and easy to use, and can be customised by Linux-based extensions if required. TREVA can also be deployed on the cloud (cloud computing), enabling instant access without investment overheads for additional hardware. TREVA is available at http://bioinformatics.petermac.org/treva/.

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/.

Intraepithelial carcinomas of the fallopian tube are putative precursors to high-grade serous carcinomas of the ovary and peritoneum. Molecular characterization of these early precursors is limited but could be the key to identifying tumor biomarkers for early detection. This study presents a genome-wide copy number analysis of occult fallopian tube carcinomas identified through risk-reducing prophylactic oophorectomy from three women with germline BRCA1 mutations, demonstrating that extensive genomic aberrations are already established at this early stage. We found no indication of a difference in the level of genomic aberration observed in fallopian tube carcinomas compared with high-grade serous ovarian carcinomas. These findings suggest that spread to the peritoneal cavity may require no or very little further tumor evolution, which raises the question of what is the real window of opportunity to detect high-grade serous peritoneal carcinoma arising from the fallopian tube before it spreads. Nonetheless, the similarity of the genomic aberrations to those observed in high-grade serous ovarian carcinomas suggests that genetic biomarkers identified in late-stage disease may be relevant for early detection.

Differentiating true somatic mutations from artifacts in massively parallel sequencing data is an immense challenge. To develop methods for optimal somatic mutation detection and to identify factors influencing somatic mutation prediction accuracy, we validated predictions from three somatic mutation detection algorithms, MuTect, JointSNVMix2 and SomaticSniper, by Sanger sequencing. Full consensus predictions had a validation rate of >98%, but some partial consensus predictions validated too. In cases of partial consensus, read depth and mapping quality data, along with additional prediction methods, aided in removing inaccurate predictions. Our consensus approach is fast, flexible and provides a high-confidence list of putative somatic mutations.

MicroRNAs are key regulators of gene expression and have been shown to have altered expression in a variety of cancer types, including epithelial ovarian cancer. MiRNA function is most often achieved through binding to the 3'-untranslated region of the target protein coding gene. Mutation screening using massively-parallel sequencing of 712 miRNA genes in 86 ovarian cancer cases identified only 5 mutated miRNA genes, each in a different case. One mutation was located in the mature miRNA, and three mutations were predicted to alter the secondary structure of the miRNA transcript. Screening of the 3'-untranslated region of 18 candidate cancer genes identified one mutation in each of AKT2, EGFR, ERRB2 and CTNNB1. The functional effect of these mutations is unclear, as expression data available for AKT2 and EGFR showed no increase in gene transcript. Mutations in miRNA genes and 3'-untranslated regions are thus uncommon in ovarian cancer.

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.

Despite intensive efforts using linkage and candidate gene approaches, the genetic etiology for the majority of families with a multi-generational breast cancer predisposition is unknown. In this study, we used whole-exome sequencing of thirty-three individuals from 15 breast cancer families to identify potential predisposing genes. Our analysis identified families with heterozygous, deleterious mutations in the DNA repair genes FANCC and BLM, which are responsible for the autosomal recessive disorders Fanconi Anemia and Bloom syndrome. In total, screening of all exons in these genes in 438 breast cancer families identified three with truncating mutations in FANCC and two with truncating mutations in BLM. Additional screening of FANCC mutation hotspot exons identified one pathogenic mutation among an additional 957 breast cancer families. Importantly, none of the deleterious mutations were identified among 464 healthy controls and are not reported in the 1,000 Genomes data. Given the rarity of Fanconi Anemia and Bloom syndrome disorders among Caucasian populations, the finding of multiple deleterious mutations in these critical DNA repair genes among high-risk breast cancer families is intriguing and suggestive of a predisposing role. Our data demonstrate the utility of intra-family exome-sequencing approaches to uncover cancer predisposition genes, but highlight the major challenge of definitively validating candidates where the incidence of sporadic disease is high, germline mutations are not fully penetrant, and individual predisposition genes may only account for a tiny proportion of breast cancer families.

BACKGROUND: MAP2K4 is a putative tumor and metastasis suppressor gene frequently found to be deleted in various cancer types. We aimed to conduct a comprehensive analysis of this gene to assess its involvement in ovarian cancer. METHODS: We screened for mutations in MAP2K4 using High Resolution Melt analysis of 149 primary ovarian tumors and methylation at the promoter using Methylation-Specific Single-Stranded Conformation Polymorphism analysis of 39 tumors. We also considered the clinical impact of changes in MAP2K4 using publicly available expression and copy number array data. Finally, we used siRNA to measure the effect of reducing MAP2K4 expression in cell lines. RESULTS: In addition to 4 previously detected homozygous deletions, we identified a homozygous 16 bp truncating deletion and a heterozygous 4 bp deletion, each in one ovarian tumor. No promoter methylation was detected. The frequency of MAP2K4 homozygous inactivation was 5.6% overall, and 9.8% in high-grade serous cases. Hemizygous deletion of MAP2K4 was observed in 38% of samples. There were significant correlations of copy number and expression in three microarray data sets. There was a significant correlation between MAP2K4 expression and overall survival in one expression array data set, but this was not confirmed in an independent set. Treatment of JAM and HOSE6.3 cell lines with MAP2K4 siRNA showed some reduction in proliferation. CONCLUSIONS: MAP2K4 is targeted by genetic inactivation in ovarian cancer and restricted to high grade serous and endometrioid carcinomas in our cohort.