Challenges in bioinformatics approaches to tumor mutation burden analysis.

Several immune checkpoint inhibitors (ICIs) have already been introduced into clinical practice or are in advanced phases of clinical experimentation. Extensive efforts are being made to identify robust biomarkers to select patients who may benefit from treatment with ICIs. Tumor mutation burden (TMB) may be a relevant biomarker of response to ICIs in different tumor types; however, its clinical use is challenged by the analytical methods required for its evaluation. The possibility of using targeted next-generation sequencing panels has been investigated as an alternative to the standard whole exome sequencing approach. However, no standardization exists in terms of genes covered, types of mutations included in the estimation of TMB, bioinformatics pipelines for data analysis, and cut-offs used to discriminate samples with high, intermediate or low TMB. Bioinformatics serve a relevant role in the analysis of targeted sequencing data and its standardization is essential to deliver a reliable test in clinical practice. In the present study, cultured and formalin-fixed, paraffin-embedded cell lines were analyzed using a commercial panel for TMB testing; the results were compared with data from the literature and public databases, demonstrating a good correlation. Additionally, the correlation between high tumor mutation burden and microsatellite instability was confirmed. The bioinformatics analyses were conducted using two different pipelines to highlight the challenges associated with the development of an appropriate analytical workflow.

Validation of a Targeted Next-Generation Sequencing Panel for Tumor Mutation Burden Analysis: Results from the Onconetwork Immuno-Oncology Consortium.

Tumor mutation burden (TMB) is evaluated as a biomarker of response to immunotherapy. We present the efforts of the Onconetwork Immuno-Oncology Consortium to validate a commercial targeted sequencing test for TMB calculation. A three-phase study was designed to validate the Oncomine Tumor Mutational Load (OTML) assay at nine European laboratories. Phase 1 evaluated reproducibility and accuracy on seven control samples. In phase 2, six formalin-fixed, paraffin-embedded samples tested with FoundationOne were reanalyzed with the OTML panel to evaluate concordance and reproducibility. Phase 3 involved analysis of 90 colorectal cancer samples with known microsatellite instability (MSI) status to evaluate TMB and MSI association. High reproducibility of TMB was demonstrated among the sites in the first and second phases. Strong correlation was also detected between mean and expected TMB in phase 1 (r2 = 0.998) and phase 2 (r2 = 0.96). Detection of actionable mutations was also confirmed. In colorectal cancer samples, the expected pattern of MSI-high/high-TMB and microsatellite stability/low-TMB was present, and gene signatures produced by the panel suggested the presence of a POLE mutation in two samples. The OTML panel demonstrated robustness and reproducibility for TMB evaluation. Results also suggest the possibility of using the panel for mutational signatures and variant detection. Collaborative efforts between academia and companies are crucial to accelerate the translation of new biomarkers into clinical research.

The employment gap: the relationship between medical student career choices and the future needs of the New Zealand medical workforce.

AIMS: To determine the career decision intentions of graduating doctors, and the relationship between these intentions and the predicted medical workforce needs in New Zealand in 10 years' time. METHODS: A workforce forecasting model developed by the Ministry of Health (MOH) has been used to predict the proportion of doctors required in each medical specialty in 2028 in New Zealand. The future work intentions of recently graduated doctors at the Universities of Auckland and Otago were collected from the Medical Student Outcomes Data (MSOD), and compared with these predicted needs. RESULTS: Between 2013 and 2017, 2,292 doctors graduated in New Zealand, of whom 1,583 completed the MSOD preferences section (response rate 69%). Of these only 50.1% had decided on a future medical specialty. The most popular were surgical specialties (26.2%), general practice (20.7%), and internal medicine (11.0%). Compared to the MOH workforce forecast model there appears to be insufficient interest in general practice at the time of graduation. CONCLUSIONS: To shape the medical workforce to meet forecast needs, multiple stakeholders will need to collaborate, with a special focus on the early postgraduate years, as many doctors have yet to decide on specialisation.

Developing New Zealand's medical workforce: realising the potential of longitudinal career tracking.

For over a decade, the Medical Schools Outcomes Database and Longitudinal Tracking Project (MSOD) has collected data from medical students in Australia and New Zealand. This project aims to explore how individual student background or attributes might interact with curriculum or early postgraduate training to affect eventual career choice and location. In New Zealand, over 4,000 students have voluntarily provided information at various time points, and the project is at a stage where some firm conclusions are starting to be drawn. This paper presents the background to the project along with some early results and future directions.

User involvement in digital health: Working together to design smart home health technology.

BACKGROUND: Public involvement adds value to numerous aspects of health research, yet few studies have attempted to evaluate its impact on research. Evidence of such impact is needed to develop recommendations for best practice and ensure adequate resourcing. AIM: To evaluate public involvement within a large interdisciplinary Science, Technology, Engineering and Mathematics (STEM) research project that focused on digital health. METHODS: The evaluation was conducted with members of the project's Public Advisory Groups (PAG) and with researchers who had participated in involvement activities. Two questionnaires were designed based on a public involvement value systems and clusters framework. RESULTS: Responses from members of the PAG (n = 10) were mostly positive towards normative values, which include moral, ethical and political aspects of involvement in research, and towards values concerning the conduct of public involvement and best practices. Researchers' responses (n = 16) indicated they felt that involvement was generally effective and increased the quality, relevance and generalizability of their work. However, their responses about the validity of involvement in research were varied. They also highlighted several challenges including how well public involvement impacted on research, how decisions made in the research might differ from the views generated from public involvement, and barriers to researchers' participation. DISCUSSION AND CONCLUSION: Our evaluation suggests that members of the public and the researchers value involvement. However, there is a need to consider how to embed public involvement to an even greater extent in STEM contexts and a need to address any barriers for researchers' own involvement.

A scalable solution for tumor mutational burden from formalin-fixed, paraffin-embedded samples using the Oncomine Tumor Mutation Load Assay.

BACKGROUND: Tumor mutational burden (TMB) is an increasingly important biomarker for immune checkpoint inhibitors. Recent publications have described strong association between high TMB and objective response to mono- and combination immunotherapies in several cancer types. Existing methods to estimate TMB require large amount of input DNA, which may not always be available. METHODS: In this study, we develop a method to estimate TMB using the Oncomine Tumor Mutation Load (TML) Assay with 20 ng of DNA, and we characterize the performance of this method on various formalin-fixed, paraffin-embedded (FFPE) research samples of several cancer types. We measure the analytical performance of TML workflow through comparison with control samples with known truth, and we compare performance with an orthogonal method which uses matched normal sample to remove germline variants. We perform whole exome sequencing (WES) on a batch of FFPE samples and compare the WES TMB values with TMB estimates by the TML assay. RESULTS: In-silico analyses demonstrated the Oncomine TML panel has sufficient genomic coverage to estimate somatic mutations with a strong correlation (r2=0.986) to WES. Further, in silico prediction using WES data from three separate cohorts and comparing with a subset of the WES overlapping with the TML panel, confirmed the ability to stratify responders and non-responders to immune checkpoint inhibitors with high statistical significance. We found the rate of somatic mutations with the TML assay on cell lines and control samples were similar to the known truth. We verified the performance of germline filtering using only a tumor sample in comparison to a matched tumor-normal experimental design to remove germline variants. We compared TMB estimates by the TML assay with that from WES on a batch of FFPE research samples and found high correlation (r2=0.83). We found biologically interesting tumorigenesis signatures on FFPE research samples of colorectal cancer (CRC), lung, and melanoma origin. Further, we assessed TMB on a cohort of FFPE research samples including lung, colon, and melanoma tumors to discover the biologically relevant range of TMB values. CONCLUSIONS: These results show that the TML assay targeting a 1.7-Mb genomic footprint can accurately predict TMB values that are comparable to the WES. The TML assay workflow incorporates a simple workflow using the Ion GeneStudio S5 System. Further, the AmpliSeq chemistry allows the use of low input DNA to estimate mutational burden from FFPE samples. This TMB assay enables scalable, robust research into immuno-oncology biomarkers with scarce samples.

Analytical Validation of a Next-Generation Sequencing Assay to Monitor Immune Responses in Solid Tumors.

We have developed a next-generation sequencing assay to quantify biomarkers of the host immune response in formalin-fixed, paraffin-embedded (FFPE) tumor specimens. This assay aims to provide clinicians with a comprehensive characterization of the immunologic tumor microenvironment as a guide for therapeutic decisions on patients with solid tumors. The assay relies on RNA-sequencing (seq) to semiquantitatively measure the levels of 43 transcripts related to anticancer immune responses and 11 transcripts that reflect the relative abundance of tumor-infiltrating lymphocytes, as well as on DNA-seq to estimate mutational burden. The assay has a clinically relevant 5-day turnaround time and can be conducted on as little as 2.5 ng of RNA and 1.8 ng of genomic DNA extracted from three to five standard FFPE sections. The standardized next-generation sequencing workflow produced sequencing reads adequate for clinical testing of matched RNA and DNA from several samples in a single run. Assay performance for gene-specific sensitivity, linearity, dynamic range, and detection threshold was estimated across a wide range of actual and artificial FFPE samples selected or generated to address preanalytical variability linked to specimen features (eg, tumor-infiltrating lymphocyte abundance, percentage of necrosis), and analytical variability linked to assay features (eg, batch size, run, day, operator). Analytical precision studies demonstrated that the assay is highly reproducible and accurate compared with established orthogonal approaches.

Principles and Recommendations for Standardizing the Use of the Next-Generation Sequencing Variant File in Clinical Settings.

A national workgroup convened by the Centers for Disease Control and Prevention identified principles and made recommendations for standardizing the description of sequence data contained within the variant file generated during the course of clinical next-generation sequence analysis for diagnosing human heritable conditions. The specifications for variant files were initially developed to be flexible with regard to content representation to support a variety of research applications. This flexibility permits variation with regard to how sequence findings are described and this depends, in part, on the conventions used. For clinical laboratory testing, this poses a problem because these differences can compromise the capability to compare sequence findings among laboratories to confirm results and to query databases to identify clinically relevant variants. To provide for a more consistent representation of sequence findings described within variant files, the workgroup made several recommendations that considered alignment to a common reference sequence, variant caller settings, use of genomic coordinates, and gene and variant naming conventions. These recommendations were considered with regard to the existing variant file specifications presently used in the clinical setting. Adoption of these recommendations is anticipated to reduce the potential for ambiguity in describing sequence findings and facilitate the sharing of genomic data among clinical laboratories and other entities.

Extensive sequencing of seven human genomes to characterize benchmark reference materials.

The Genome in a Bottle Consortium, hosted by the National Institute of Standards and Technology (NIST) is creating reference materials and data for human genome sequencing, as well as methods for genome comparison and benchmarking. Here, we describe a large, diverse set of sequencing data for seven human genomes; five are current or candidate NIST Reference Materials. The pilot genome, NA12878, has been released as NIST RM 8398. We also describe data from two Personal Genome Project trios, one of Ashkenazim Jewish ancestry and one of Chinese ancestry. The data come from 12 technologies: BioNano Genomics, Complete Genomics paired-end and LFR, Ion Proton exome, Oxford Nanopore, Pacific Biosciences, SOLiD, 10X Genomics GemCode WGS, and Illumina exome and WGS paired-end, mate-pair, and synthetic long reads. Cell lines, DNA, and data from these individuals are publicly available. Therefore, we expect these data to be useful for revealing novel information about the human genome and improving sequencing technologies, SNP, indel, and structural variant calling, and de novo assembly.

Development and validation of a scalable next-generation sequencing system for assessing relevant somatic variants in solid tumors.

Next-generation sequencing (NGS) has enabled genome-wide personalized oncology efforts at centers and companies with the specialty expertise and infrastructure required to identify and prioritize actionable variants. Such approaches are not scalable, preventing widespread adoption. Likewise, most targeted NGS approaches fail to assess key relevant genomic alteration classes. To address these challenges, we predefined the catalog of relevant solid tumor somatic genome variants (gain-of-function or loss-of-function mutations, high-level copy number alterations, and gene fusions) through comprehensive bioinformatics analysis of >700,000 samples. To detect these variants, we developed the Oncomine Comprehensive Panel (OCP), an integrative NGS-based assay [compatible with <20 ng of DNA/RNA from formalin-fixed paraffin-embedded (FFPE) tissues], coupled with an informatics pipeline to specifically identify relevant predefined variants and created a knowledge base of related potential treatments, current practice guidelines, and open clinical trials. We validated OCP using molecular standards and more than 300 FFPE tumor samples, achieving >95% accuracy for KRAS, epidermal growth factor receptor, and BRAF mutation detection as well as for ALK and TMPRSS2:ERG gene fusions. Associating positive variants with potential targeted treatments demonstrated that 6% to 42% of profiled samples (depending on cancer type) harbored alterations beyond routine molecular testing that were associated with approved or guideline-referenced therapies. As a translational research tool, OCP identified adaptive CTNNB1 amplifications/mutations in treated prostate cancers. Through predefining somatic variants in solid tumors and compiling associated potential treatment strategies, OCP represents a simplified, broadly applicable targeted NGS system with the potential to advance precision oncology efforts.

Characterizing the heterogeneity of triple-negative breast cancers using microdissected normal ductal epithelium and RNA-sequencing.

Triple-negative breast cancers (TNBCs) are a heterogeneous set of tumors defined by an absence of actionable therapeutic targets (ER, PR, and HER-2). Microdissected normal ductal epithelium from healthy volunteers represents a novel comparator to reveal insights into TNBC heterogeneity and to inform drug development. Using RNA-sequencing data from our institution and The Cancer Genome Atlas (TCGA) we compared the transcriptomes of 94 TNBCs, 20 microdissected normal breast tissues from healthy volunteers from the Susan G. Komen for the Cure Tissue Bank, and 10 histologically normal tissues adjacent to tumor. Pathway analysis comparing TNBCs to optimized normal controls of microdissected normal epithelium versus classic controls composed of adjacent normal tissue revealed distinct molecular signatures. Differential gene expression of TNBC compared with normal comparators demonstrated important findings for TNBC-specific clinical trials testing targeted agents; lack of over-expression for negative studies and over-expression in studies with drug activity. Next, by comparing each individual TNBC to the set of microdissected normals, we demonstrate that TNBC heterogeneity is attributable to transcriptional chaos, is associated with non-silent DNA mutational load, and explains transcriptional heterogeneity in addition to known molecular subtypes. Finally, chaos analysis identified 146 core genes dysregulated in >90 % of TNBCs revealing an over-expressed central network. In conclusion, use of microdissected normal ductal epithelium from healthy volunteers enables an optimized approach for studying TNBC and uncovers biological heterogeneity mediated by transcriptional chaos.

RNA-Seq mapping and detection of gene fusions with a suffix array algorithm.

High-throughput RNA sequencing enables quantification of transcripts (both known and novel), exon/exon junctions and fusions of exons from different genes. Discovery of gene fusions-particularly those expressed with low abundance- is a challenge with short- and medium-length sequencing reads. To address this challenge, we implemented an RNA-Seq mapping pipeline within the LifeScope software. We introduced new features including filter and junction mapping, annotation-aided pairing rescue and accurate mapping quality values. We combined this pipeline with a Suffix Array Spliced Read (SASR) aligner to detect chimeric transcripts. Performing paired-end RNA-Seq of the breast cancer cell line MCF-7 using the SOLiD system, we called 40 gene fusions among over 120,000 splicing junctions. We validated 36 of these 40 fusions with TaqMan assays, of which 25 were expressed in MCF-7 but not the Human Brain Reference. An intra-chromosomal gene fusion involving the estrogen receptor alpha gene ESR1, and another involving the RPS6KB1 (Ribosomal protein S6 kinase beta-1) were recurrently expressed in a number of breast tumor cell lines and a clinical tumor sample.

SNPs for a universal individual identification panel.

An efficient method to uniquely identify every individual would have value in quality control and sample tracking of large collections of cell lines or DNA as is now often the case with whole genome association studies. Such a method would also be useful in forensics. SNPs represent the best markers for such purposes. We have developed a globally applicable resource of 92 SNPs for individual identification (IISNPs) with extremely low probabilities of any two unrelated individuals from anywhere in the world having identical genotypes. The SNPs were identified by screening over 500 likely/candidate SNPs on samples of 44 populations representing the major regions of the world. All 92 IISNPs have an average heterozygosity [0.4 and the F(st) values are all\0.06 on our 44 populations making these a universally applicable panel irrespective of ethnicity or ancestry. No significant linkage disequilibrium (LD) occurs for all unique pairings of 86 of the 92 IISNPs (median LD = 0.011) in all of the 44 populations. The remaining 6 IISNPs show strong LD in most of the 44 populations for a small subset (7) of the unique pairings in which they occur due to close linkage. 45 of the 86 SNPs are spread across the 22 human autosomes and show very loose or no genetic linkage with each other. These 45 IISNPs constitute an excellent panel for individual identification including paternity testing with associated probabilities of individual genotypes less than 10(-15), smaller than achieved with the current panels of forensic markers. This panel also improves on an interim panel of 40 IISNPs previously identified using 40 population samples. The unlinked status of the subset of 45 SNPs we have identified also makes them useful for situations involving close biological relationships. Comparisons with random sets of SNPs illustrate the greater discriminating power, efficiency, and more universal applicability of this IISNP panel to populations around the world. The full set of 86 IISNPs that do not show LD can be used to provide even smaller genotype match probabilities in the range of 10(-31)-10(-35) based on the 44 population samples studied.

Sequence and structural variation in a human genome uncovered by short-read, massively parallel ligation sequencing using two-base encoding.

We describe the genome sequencing of an anonymous individual of African origin using a novel ligation-based sequencing assay that enables a unique form of error correction that improves the raw accuracy of the aligned reads to >99.9%, allowing us to accurately call SNPs with as few as two reads per allele. We collected several billion mate-paired reads yielding approximately 18x haploid coverage of aligned sequence and close to 300x clone coverage. Over 98% of the reference genome is covered with at least one uniquely placed read, and 99.65% is spanned by at least one uniquely placed mate-paired clone. We identify over 3.8 million SNPs, 19% of which are novel. Mate-paired data are used to physically resolve haplotype phases of nearly two-thirds of the genotypes obtained and produce phased segments of up to 215 kb. We detect 226,529 intra-read indels, 5590 indels between mate-paired reads, 91 inversions, and four gene fusions. We use a novel approach for detecting indels between mate-paired reads that are smaller than the standard deviation of the insert size of the library and discover deletions in common with those detected with our intra-read approach. Dozens of mutations previously described in OMIM and hundreds of nonsynonymous single-nucleotide and structural variants in genes previously implicated in disease are identified in this individual. There is more genetic variation in the human genome still to be uncovered, and we provide guidance for future surveys in populations and cancer biopsies.

Validation of the performance of a comprehensive genotyping assay panel of single nucleotide polymorphisms in drug metabolism enzyme genes.

A class of genes, known as drug metabolism enzymes (DMEs) are responsible for the metabolism and transport of drugs and other xenobiotics. Variation in DME genes most likely accounts for a proportion of the variability in drug response in humans, and may contribute to complex diseases such as cancer (Nebert DW, Dieter MZ. Pharmacology 2000;61:124-135). To date, assessing the extent of this variation has proven difficult, especially because of sequence paralogy issues that cause difficulty when attempting to genotype polymorphisms in very closely-related gene families (Murphy MP. Pharmacogenomics 2000;1:115-123; Ingelman-Sundberg M. Drug Metab Rev 1999;31:449-459). We have developed and genotyped a panel of N=2,325 individual TaqMan genotyping assays for polymorphisms in >200 DME genes; many of the variants in the panel are single nucleotide polymorphisms (SNPs) that are of known or putative function (e.g., missense, nonsense or frameshift). Using these assays, we have examined genetic variation among several groups of populations, including: 1) the two SNP500 Cancer population panels (http://snp500cancer.nci.nih.gov; last accessed: 11 December 2007); and 2) the panel used in the International HapMap Project panel (www.hapmap.org; last accessed: 11 December 2007). We have developed a comprehensive validation strategy to ensure reproducibility and accuracy of the assays and estimated minor allele frequencies. Here, we present the results of these analyses, which strongly suggest that this panel of DME assays are of extremely high quality and produce robust, accurate, and reproducible results.

Patterns of linkage disequilibrium between SNPs in a Sardinian population isolate and the selection of markers for association studies.

OBJECTIVE: In isolated populations, 'background' linkage disequilibrium (LD) has been shown to extend over large genetic distances. This and their reduced environmental and genetic heterogeneity has stimulated interest in their potential for association mapping. We compared LD unit map distances with pair-wise measurements of LD in a dense single nucleotide polymorphism (SNP) set. METHODS: We genotyped 771 SNPs in an 8 Mb segment of chromosome 22 on 101 individuals from the isolated village of Talana, Sardinia, and compared with outbred European populations. RESULTS: Heterozygosity was remarkably similar in both populations. In contrast, the extent of LD observed was quite different. The decay of LD with distance is slower in the isolate. The differences in LD map lengths suggest that useful LD extends up to three times farther in the Sardinian population; smaller differences are seen with pairwise LD metrics. While LD map length slightly decreases with average relatedness, cryptic relatedness does not explain the decrease in LD map length. Haplotypes, block boundaries, and patterns of LD are similar in both populations, suggesting a shared distribution of recombination hotspots. CONCLUSIONS: About 15% fewer haplotype tagging SNPs need to be genotyped in the isolate, and possibly 70% fewer if selecting SNPs evenly spaced on the metric LD map.

A second-generation combined linkage physical map of the human genome.

We have completed a second-generation linkage map that incorporates sequence-based positional information. This new map, the Rutgers Map v.2, includes 28,121 polymorphic markers with physical positions corroborated by recombination-based data. Sex-averaged and sex-specific linkage map distances, along with confidence intervals, have been estimated for all map intervals. In addition, a regression-based smoothed map is provided that facilitates interpolation of positions of unmapped markers on this map. With nearly twice as many markers as our first-generation map, the Rutgers Map continues to be a unique and comprehensive resource for obtaining genetic map information for large sets of polymorphic markers.

Large scale real-time PCR validation on gene expression measurements from two commercial long-oligonucleotide microarrays.

BACKGROUND: DNA microarrays are rapidly becoming a fundamental tool in discovery-based genomic and biomedical research. However, the reliability of the microarray results is being challenged due to the existence of different technologies and non-standard methods of data analysis and interpretation. In the absence of a "gold standard"/"reference method" for the gene expression measurements, studies evaluating and comparing the performance of various microarray platforms have often yielded subjective and conflicting conclusions. To address this issue we have conducted a large scale TaqMan Gene Expression Assay based real-time PCR experiment and used this data set as the reference to evaluate the performance of two representative commercial microarray platforms. RESULTS: In this study, we analyzed the gene expression profiles of three human tissues: brain, lung, liver and one universal human reference sample (UHR) using two representative commercial long-oligonucleotide microarray platforms: (1) Applied Biosystems Human Genome Survey Microarrays (based on single-color detection); (2) Agilent Whole Human Genome Oligo Microarrays (based on two-color detection). 1,375 genes represented by both microarray platforms and spanning a wide dynamic range in gene expression levels, were selected for TaqMan Gene Expression Assay based real-time PCR validation. For each platform, four technical replicates were performed on the same total RNA samples according to each manufacturer's standard protocols. For Agilent arrays, comparative hybridization was performed using incorporation of Cy5 for brain/lung/liver RNA and Cy3 for UHR RNA (common reference). Using the TaqMan Gene Expression Assay based real-time PCR data set as the reference set, the performance of the two microarray platforms was evaluated focusing on the following criteria: (1) Sensitivity and accuracy in detection of expression; (2) Fold change correlation with real-time PCR data in pair-wise tissues as well as in gene expression profiles determined across all tissues; (3) Sensitivity and accuracy in detection of differential expression. CONCLUSION: Our study provides one of the largest "reference" data set of gene expression measurements using TaqMan Gene Expression Assay based real-time PCR technology. This data set allowed us to use an alternative gene expression technology to evaluate the performance of different microarray platforms. We conclude that microarrays are indeed invaluable discovery tools with acceptable reliability for genome-wide gene expression screening, though validation of putative changes in gene expression remains advisable. Our study also characterizes the limitations of microarrays; understanding these limitations will enable researchers to more effectively evaluate microarray results in a more cautious and appropriate manner.