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Purpose: BRAF V600E mutations portend poor prognosis in metastatic colorectal cancer (mCRC); however, the true prevalence and prognosis are unknown, as unwell patients may not undergo BRAF sequencing. Experimental Design: We reviewed a population-based cohort of 1,898 patients with colorectal cancer that underwent reflexive IHC mismatch repair (MMR) and BRAFV600E testing. Outcomes among IHC-detected BRAFV600E mCRC (BRAFIHC) were compared with patients with next-generation sequencing (NGS)–identified BRAFV600E-mutated mCRC from two institutions (BRAFNGS) with patients spanning from 2004 to 2018. Results: All-stage population prevalence of BRAFV600E was 12.5% (238/1,898) and did not differ between early and metastatic stages (P = 0.094). Prevalence among mCRC was 10.6% (61/575), of whom 51 (83.6%) were referred to oncology and 26 (42.6%) had NGS testing. BRAFIHC had worse median overall survival (mOS) than BRAFNGS [5.5 vs. 20.4 months; HR, 2.90; 95% confidence interval (CI), 1.89–4.45; P < 0.0001], which persisted in multivariate analysis (P < 0.0001). Across a combined NGS and IHC cohort, BRAFV600E tumors with deficient MMR showed worse mOS compared with MMR proficient tumors (8.9 vs. 17.2 months; HR, 1.46; 95% CI, 0.96–2.27; P = 0.043). In this combined cohort, first-line progression-free survival was 5.9 months, with minimal differences between regimens. Within the population-based cohort, attrition between treatment lines was high with only 60.7% receiving first-line chemotherapy and 26.2% receiving second line. Conclusions: Patients with BRAFV600E-mutated mCRC have a worse prognosis than previously suggested, potentially arising from referral bias for testing. High attrition between lines of therapy suggests efficacious therapies need to be prioritized early for patients to benefit.

As more clinically-relevant genomic features of myeloid malignancies are revealed, it has become clear that targeted clinical genetic testing is inadequate for risk stratification. Here, we develop and validate a clinical transcriptome-based assay for stratification of acute myeloid leukemia (AML). Comparison of ribonucleic acid sequencing (RNA-Seq) to whole genome and exome sequencing reveals that a standalone RNA-Seq assay offers the greatest diagnostic return, enabling identification of expressed gene fusions, single nucleotide and short insertion/deletion variants, and whole-transcriptome expression information. Expression data from 154 AML patients are used to develop a novel AML prognostic score, which is strongly associated with patient outcomes across 620 patients from three independent cohorts, and 42 patients from a prospective cohort. When combined with molecular risk guidelines, the risk score allows for the re-stratification of 22.1 to 25.3% of AML patients from three independent cohorts into correct risk groups. Within the adverse-risk subgroup, we identify a subset of patients characterized by dysregulated integrin signaling and RUNX1 or TP53 mutation. We show that these patients may benefit from therapy with inhibitors of focal adhesion kinase, encoded by PTK2, demonstrating additional utility of transcriptome-based testing for therapy selection in myeloid malignancy., Several genomic features have been found for acute myeloid leukaemia (AML) but targeted clinical genetic testing fails to predict prognosis. Here, the authors generate an AML prognostic score from RNA-seq data of patients, which successfully stratifies AML patients and which may provide guidance for therapeutic strategies.

Next-generation sequencing assays are capable of identifying cancer patients eligible for targeted therapies and can also detect germline variants associated with increased cancer susceptibility. However, these capabilities have yet to be routinely harmonized in a single assay because of challenges with accurately identifying germline variants from tumor-only data. We have developed the Oncology and Hereditary Cancer Program targeted capture panel, which uses tumor tissue to simultaneously screen for both clinically actionable solid tumor variants and germline variants across 45 genes. Validation using 14 tumor specimens, composed of patient samples and cell lines analyzed in triplicate, demonstrated high coverage with sensitive and specific identification of single-nucleotide variants and small insertions and deletions. Average coverage across all targets remained >2000× in 198 additional patient tumor samples. Analysis of 55 formalin-fixed, paraffin-embedded tumor samples for the detection of known germline variants within a subset of cancer-predisposition genes, including one multiexon deletion, yielded a 100% detection rate, demonstrating that germline variants can be reliably detected in tumor samples using a single panel. Combining targetable somatic and actionable germline variants into a single tumor tissue assay represents a streamlined approach that can inform treatment for patients with advanced cancers as well as identify those with potential germline variants who are eligible for confirmatory testing, but would not otherwise have been identified.

Non-invasive prenatal aneuploidy testing (NIPT) by next-generation sequencing of circulating cell-free DNA in maternal plasma relies on chromosomal ratio (chrratio) measurements to detect aneuploid values that depart from euploid ratios. Diagnostic performances are known to depend on the fraction of fetal DNA (FF) present in maternal plasma, although how this translates into specific quantitative changes in specificity/positive predictive values and which other variables might also be important is not well understood.To explore this issue, theoretical relationships between FF and various measures of diagnostic performances were assessed for a range of parameter values. Empirical data from three NIPT assays were then used to validate theoretical calculations.For a given positivity threshold, dramatic changes in specificity and positive predictive values (PPV) as a function of both FF and the coefficient of variation (CV) of the chrratio measurement were observed. Theoretically predicted and observed chrratio z-scores agreed closely, confirming the determinant impact of small changes in both FF and chrratio CV.Evaluation of NIPT assay performances therefore requires knowledge of the FF distribution in the population in which the test is intended to be used and, in particular, of the precise value of the assay chrratio CV for each chromosome or genomic region of interest. Laboratories offering NIPT testing should carefully measure these parameters to ensure test reliability and clinical usefulness in interpreting individual patients' results.

Clinical reporting of solid tumor sequencing requires reliable assessment of the accuracy and reproducibility of each assay. Somatic mutation variant allele fractions may be below 10% in many samples due to sample heterogeneity, tumor clonality, and/or sample degradation in fixatives such as formalin. The toolkits available to the clinical sequencing community for correlating assay design parameters with assay sensitivity remain limited, and large-scale empirical assessments are often relied upon due to the lack of clear theoretical grounding. To address this uncertainty, a theoretical model was developed for predicting the expected variant calling sensitivity for a given library complexity and sequencing depth. Binomial models were found to be appropriate when assay sensitivity was only limited by library complexity or sequencing depth, but functional scaling for library complexity was necessary when both library complexity and sequencing depth were co-limiting. This model was empirically validated with sequencing experiments by using a series of DNA input amounts and sequencing depths. Based on these findings, a workflow is proposed for determining the limiting factors to sensitivity in different assay designs, and the formulas for these scenarios are presented. The approach described here provides designers of clinical assays with the methods to theoretically predict assay design outcomes a priori, potentially reducing burden in clinical tumor assay design and validation efforts.

Sample tracking and identity are essential when processing multiple samples in parallel. Sequencing applications often involve high sample numbers, and the data are frequently used in a clinical setting. As such, a simple and accurate intrinsic sample tracking process through a sequencing pipeline is essential. Various solutions have been implemented to verify sample identity, including variant detection at the start and end of the pipeline using arrays or genotyping, bioinformatic comparisons, and optical barcoding of samples. None of these approaches are optimal. To establish a more effective approach using genetic barcoding, we developed a panel of unique DNA sequences cloned into a common vector. A unique DNA sequence is added to the sample when it is first received and can be detected by PCR and/or sequencing at any stage of the process. The control sequences are approximately 200 bases long with low identity to any sequence in the National Center for Biotechnology Information nonredundant database (30 bases) and contain no long homopolymer (7) stretches. When a spiked next-generation sequencing library is sequenced, sequence reads derived from this control sequence are generated along with the standard sequencing run and are used to confirm sample identity and determine cross-contamination levels. This approach is used in our targeted clinical diagnostic whole-genome and RNA-sequencing pipelines and is an inexpensive, flexible, and platform-agnostic solution.

Formalin fixation is the standard method for the preservation of tissue for diagnostic purposes, including pathologic review and molecular assays. However, this method is known to cause artifacts that can affect the accuracy of molecular genetic test results. We assessed the applicability of alternative fixatives to determine whether these perform significantly better on next-generation sequencing assays, and whether adequate morphology is retained for primary diagnosis, in a prospective study using a clinical-grade, laboratory-developed targeted resequencing assay. Several parameters relating to sequencing quality and variant calling were examined and quantified in tumor and normal colon epithelial tissues. We identified an alternative fixative that suppresses many formalin-related artifacts while retaining adequate morphology for pathologic review.

Background: Chimeric transcripts, including partial and internal tandem duplications (PTDs, ITDs) and gene fusions, are important in the detection, prognosis, and treatment of human cancers. Results: We describe Barnacle, a production-grade analysis tool that detects such chimeras in de novo assemblies of RNA-seq data, and supports prioritizing them for review and validation by reporting the relative coverage of co-occurring chimeric and wild-type transcripts. We demonstrate applications in large-scale disease studies, by identifying PTDs in MLL, ITDs in FLT3, and reciprocal fusions between PML and RARA, in two deeply sequenced acute myeloid leukemia (AML) RNA-seq datasets. Conclusions: Our analyses of real and simulated data sets show that, with appropriate filter settings, Barnacle makes highly specific predictions for three types of chimeric transcripts that are important in a range of cancers: PTDs, ITDs, and fusions. High specificity makes manual review and validation efficient, which is necessary in large-scale disease studies. Characterizing an extended range of chimera types will help generate insights into progression, treatment, and outcomes for complex diseases., Simon Fraser University. Bioinformatics for Combating Infectious Disease Project, Simon Fraser University (Graduate Fellowship), Pacific Century Institute (Graduate Scholarship), Genome Canada (Firm), Canadian Institutes of Health Research, Genome British Columbia (Firm) (Grant #121AML), Provincial Health Services Authority (British Columbia, Canada), BC Cancer Foundation

Motivation: Computational identification of genomic structural variants via high-throughput sequencing is an important problem for which a number of highly sophisticated solutions have been recently developed. With the advent of high-throughput transcriptome sequencing (RNA-Seq), the problem of identifying structural alterations in the transcriptome is now attracting significant attention. In this article, we introduce two novel algorithmic formulations for identifying transcriptomic structural variants through aligning transcripts to the reference genome under the consideration of such variation. The first formulation is based on a nucleotide-level alignment model; a second, potentially faster formulation is based on chaining fragments shared between each transcript and the reference genome. Based on these formulations, we introduce a novel transcriptome-to-genome alignment tool, Dissect (DIScovery of Structural Alteration Event Containing Transcripts), which can identify and characterize transcriptomic events such as duplications, inversions, rearrangements and fusions. Dissect is suitable for whole transcriptome structural variation discovery problems involving sufficiently long reads or accurately assembled contigs. Results: We tested Dissect on simulated transcripts altered via structural events, as well as assembled RNA-Seq contigs from human prostate cancer cell line C4-2. Our results indicate that Dissect has high sensitivity and specificity in identifying structural alteration events in simulated transcripts as well as uncovering novel structural alterations in cancer transcriptomes., Pacific Institute for the Mathematical Sciences (Fellowship)