Exploring the feasibility and utility of exome-scale tumour sequencing in a clinical setting.

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.

AmpliVar: mutation detection in high-throughput sequence from amplicon-based libraries.

Conventional means of identifying variants in high-throughput sequencing align each read against a reference sequence, and then call variants at each position. Here, we demonstrate an orthogonal means of identifying sequence variation by grouping the reads as amplicons prior to any alignment. We used AmpliVar to make key-value hashes of sequence reads and group reads as individual amplicons using a table of flanking sequences. Low-abundance reads were removed according to a selectable threshold, and reads above this threshold were aligned as groups, rather than as individual reads, permitting the use of sensitive alignment tools. We show that this approach is more sensitive, more specific, and more computationally efficient than comparable methods for the analysis of amplicon-based high-throughput sequencing data. The method can be extended to enable alignment-free confirmation of variants seen in hybridization capture target-enrichment data.

Transcriptomic analysis supports similar functional roles for the two thymuses of the tammar wallaby.

BACKGROUND: The thymus plays a critical role in the development and maturation of T-cells. Humans have a single thoracic thymus and presence of a second thymus is considered an anomaly. However, many vertebrates have multiple thymuses. The tammar wallaby has two thymuses: a thoracic thymus (typically found in all mammals) and a dominant cervical thymus. Researchers have known about the presence of the two wallaby thymuses since the 1800s, but no genome-wide research has been carried out into possible functional differences between the two thymic tissues. Here, we used pyrosequencing to compare the transcriptomes of a cervical and thoracic thymus from a single 178 day old tammar wallaby. RESULTS: We show that both the tammar thoracic and the cervical thymuses displayed gene expression profiles consistent with roles in T-cell development. Both thymuses expressed genes that mediate distinct phases of T-cells differentiation, including the initial commitment of blood stem cells to the T-lineage, the generation of T-cell receptor diversity and development of thymic epithelial cells. Crucial immune genes, such as chemokines were also present. Comparable patterns of expression of non-coding RNAs were seen. 67 genes differentially expressed between the two thymuses were detected, and the possible significance of these results are discussed. CONCLUSION: This is the first study comparing the transcriptomes of two thymuses from a single individual. Our finding supports that both thymuses are functionally equivalent and drive T-cell development. These results are an important first step in the understanding of the genetic processes that govern marsupial immunity, and also allow us to begin to trace the evolution of the mammalian immune system.

Clinical Utility of Real-Time Targeted Molecular Profiling in the Clinical Management of Ovarian Cancer: The ALLOCATE Study.

PURPOSE: The ALLOCATE study was designed as a pilot to demonstrate the feasibility and clinical utility of real-time targeted molecular profiling of patients with recurrent or advanced ovarian cancer for identification of potential targeted therapies. PATIENTS AND METHODS: A total of 113 patients with ovarian cancer of varying histologies were recruited from two tertiary hospitals, with 99 patient cases suitable for prospective analysis. Targeted molecular and methylation profiling of fresh biopsy and archived tumor samples were performed by screening for mutations or copy-number variations in 44 genes and for promoter methylation of BRCA1 and RAD51C. RESULTS: Somatic genomic or methylation events were identified in 85% of all patient cases, with potentially actionable events with defined targeted therapies (including four resistance events) detected in 60% of all patient cases. On the basis of these findings, six patients received molecularly guided therapy, three patients had unsuspected germline cancer-associated BRCA1/2 mutations and were referred for genetic counseling, and two intermediate differentiated (grade 2) serous ovarian carcinomas were reclassified as low grade, leading to changes in clinical management. Additionally, secondary reversion mutations in BRCA1/2 were identified in fresh biopsy samples of two patients, consistent with clinical platinum/poly (ADP-ribose) polymerase inhibitor resistance. Timely reporting of results if molecular testing is done at disease recurrence, as well as early referral for patients with platinum-resistant cancers, were identified as factors that could improve the clinical utility of molecular profiling. CONCLUSION: ALLOCATE molecular profiling identified known genomic and methylation alterations of the different ovarian cancer subtypes and was deemed feasible and useful in routine clinical practice. Better patient selection and access to a wider range of targeted therapies or clinical trials will further enhance the clinical utility of molecular profiling.

Binning sequences using very sparse labels within a metagenome.

BACKGROUND: In metagenomic studies, a process called binning is necessary to assign contigs that belong to multiple species to their respective phylogenetic groups. Most of the current methods of binning, such as BLAST, k-mer and PhyloPythia, involve assigning sequence fragments by comparing sequence similarity or sequence composition with already-sequenced genomes that are still far from comprehensive. We propose a semi-supervised seeding method for binning that does not depend on knowledge of completed genomes. Instead, it extracts the flanking sequences of highly conserved 16S rRNA from the metagenome and uses them as seeds (labels) to assign other reads based on their compositional similarity. RESULTS: The proposed seeding method is implemented on an unsupervised Growing Self-Organising Map (GSOM), and called Seeded GSOM (S-GSOM). We compared it with four well-known semi-supervised learning methods in a preliminary test, separating random-length prokaryotic sequence fragments sampled from the NCBI genome database. We identified the flanking sequences of the highly conserved 16S rRNA as suitable seeds that could be used to group the sequence fragments according to their species. S-GSOM showed superior performance compared to the semi-supervised methods tested. Additionally, S-GSOM may also be used to visually identify some species that do not have seeds. The proposed method was then applied to simulated metagenomic datasets using two different confidence threshold settings and compared with PhyloPythia, k-mer and BLAST. At the reference taxonomic level Order, S-GSOM outperformed all k-mer and BLAST results and showed comparable results with PhyloPythia for each of the corresponding confidence settings, where S-GSOM performed better than PhyloPythia in the >/= 10 reads datasets and comparable in the > or = 8 kb benchmark tests. CONCLUSION: In the task of binning using semi-supervised learning methods, results indicate S-GSOM to be the best of the methods tested. Most importantly, the proposed method does not require knowledge from known genomes and uses only very few labels (one per species is sufficient in most cases), which are extracted from the metagenome itself. These advantages make it a very attractive binning method. S-GSOM outperformed the binning methods that depend on already-sequenced genomes, and compares well to the current most advanced binning method, PhyloPythia.

Using growing self-organising maps to improve the binning process in environmental whole-genome shotgun sequencing.

Metagenomic projects using whole-genome shotgun (WGS) sequencing produces many unassembled DNA sequences and small contigs. The step of clustering these sequences, based on biological and molecular features, is called binning. A reported strategy for binning that combines oligonucleotide frequency and self-organising maps (SOM) shows high potential. We improve this strategy by identifying suitable training features, implementing a better clustering algorithm, and defining quantitative measures for assessing results. We investigated the suitability of each of di-, tri-, tetra-, and pentanucleotide frequencies. The results show that dinucleotide frequency is not a sufficiently strong signature for binning 10 kb long DNA sequences, compared to the other three. Furthermore, we observed that increased order of oligonucleotide frequency may deteriorate the assignment result in some cases, which indicates the possible existence of optimal species-specific oligonucleotide frequency. We replaced SOM with growing self-organising map (GSOM) where comparable results are obtained while gaining 7%-15% speed improvement.

High-Throughput Amplicon-Based Copy Number Detection of 11 Genes in Formalin-Fixed Paraffin-Embedded Ovarian Tumour Samples by MLPA-Seq.

Whilst next generation sequencing can report point mutations in fixed tissue tumour samples reliably, the accurate determination of copy number is more challenging. The conventional Multiplex Ligation-dependent Probe Amplification (MLPA) assay is an effective tool for measurement of gene dosage, but is restricted to around 50 targets due to size resolution of the MLPA probes. By switching from a size-resolved format, to a sequence-resolved format we developed a scalable, high-throughput, quantitative assay. MLPA-seq is capable of detecting deletions, duplications, and amplifications in as little as 5ng of genomic DNA, including from formalin-fixed paraffin-embedded (FFPE) tumour samples. We show that this method can detect BRCA1, BRCA2, ERBB2 and CCNE1 copy number changes in DNA extracted from snap-frozen and FFPE tumour tissue, with 100% sensitivity and >99.5% specificity.

Novel venom gene discovery in the platypus.

BACKGROUND: To date, few peptides in the complex mixture of platypus venom have been identified and sequenced, in part due to the limited amounts of platypus venom available to study. We have constructed and sequenced a cDNA library from an active platypus venom gland to identify the remaining components. RESULTS: We identified 83 novel putative platypus venom genes from 13 toxin families, which are homologous to known toxins from a wide range of vertebrates (fish, reptiles, insectivores) and invertebrates (spiders, sea anemones, starfish). A number of these are expressed in tissues other than the venom gland, and at least three of these families (those with homology to toxins from distant invertebrates) may play non-toxin roles. Thus, further functional testing is required to confirm venom activity. However, the presence of similar putative toxins in such widely divergent species provides further evidence for the hypothesis that there are certain protein families that are selected preferentially during evolution to become venom peptides. We have also used homology with known proteins to speculate on the contributions of each venom component to the symptoms of platypus envenomation. CONCLUSIONS: This study represents a step towards fully characterizing the first mammal venom transcriptome. We have found similarities between putative platypus toxins and those of a number of unrelated species, providing insight into the evolution of mammalian venom.

An unsupervised hierarchical dynamic self-organizing approach to cancer class discovery and marker gene identification in microarray data.

MOTIVATION: Current Self-Organizing Maps (SOMs) approaches to gene expression pattern clustering require the user to predefine the number of clusters likely to be expected. Hierarchical clustering methods used in this area do not provide unique partitioning of data. We describe an unsupervised dynamic hierarchical self-organizing approach, which suggests an appropriate number of clusters, to perform class discovery and marker gene identification in microarray data. In the process of class discovery, the proposed algorithm identifies corresponding sets of predictor genes that best distinguish one class from other classes. The approach integrates merits of hierarchical clustering with robustness against noise known from self-organizing approaches. RESULTS: The proposed algorithm applied to DNA microarray data sets of two types of cancers has demonstrated its ability to produce the most suitable number of clusters. Further, the corresponding marker genes identified through the unsupervised algorithm also have a strong biological relationship to the specific cancer class. The algorithm tested on leukemia microarray data, which contains three leukemia types, was able to determine three major and one minor cluster. Prediction models built for the four clusters indicate that the prediction strength for the smaller cluster is generally low, therefore labelled as uncertain cluster. Further analysis shows that the uncertain cluster can be subdivided further, and the subdivisions are related to two of the original clusters. Another test performed using colon cancer microarray data has automatically derived two clusters, which is consistent with the number of classes in data (cancerous and normal). AVAILABILITY: JAVA software of dynamic SOM tree algorithm is available upon request for academic use. SUPPLEMENTARY INFORMATION: A comparison of rectangular and hexagonal topologies for GSOM is available from http://www.mame.mu.oz.au/mechatronics/journalinfo/Hsu2003supp.pdf