Standards for Clinical Grade Genomic Databases

Analytical Validation of Clinical Whole-Genome and Transcriptome Sequencing of Patient-Derived Tumors for Reporting Targetable Variants in Cancer

We developed and validated a clinical whole-genome and transcriptome sequencing (WGTS) assay that provides a comprehensive genomic profile of a patient's tumor. The ability to fully capture the mappable genome with sufficient sequencing coverage to precisely call DNA somatic single nucleotide variants, insertions/deletions, copy number variants, structural variants, and RNA gene fusions was analyzed. New York State's Department of Health next-generation DNA sequencing guidelines were expanded for establishing performance validation applicable to whole-genome and transcriptome sequencing. Whole-genome sequencing laboratory protocols were validated for the Illumina HiSeq X Ten platform and RNA sequencing for Illumina HiSeq2500 platform for fresh or frozen and formalin-fixed, paraffin-embedded tumor samples. Various bioinformatics tools were also tested, and CIs for sensitivity and specificity thresholds in calling clinically significant somatic aberrations were determined. The validation was performed on a set of 125 tumor normal pairs. RNA sequencing was performed to call fusions and to confirm the DNA variants or exonic alterations. Here, we present our results and WGTS standards for variant allele frequency, reproducibility, analytical sensitivity, and present limit of detection analysis for single nucleotide variant calling, copy number identification, and structural variants. We show that The New York Genome Center WGTS clinical assay can provide a comprehensive patient variant discovery approach suitable for directed oncologic therapeutic applications.

Artificial Intelligence Approach for Variant Reporting

Purpose

Next-generation sequencing technologies are actively applied in clinical oncology. Bioinformatics pipeline analysis is an integral part of this process; however, humans cannot yet realize the full potential of the highly complex pipeline output. As a result, the decision to include a variant in the final report during routine clinical sign-out remains challenging.

Methods

We used an artificial intelligence approach to capture the collective clinical sign-out experience of six board-certified molecular pathologists to build and validate a decision support tool for variant reporting. We extracted all reviewed and reported variants from our clinical database and tested several machine learning models. We used 10-fold cross-validation for our variant call prediction model, which derives a contiguous prediction score from 0 to 1 (no to yes) for clinical reporting.

Results

For each of the 19,594 initial training variants, our pipeline generates approximately 500 features, which results in a matrix of > 9 million data points. From a comparison of naive Bayes, decision trees, random forests, and logistic regression models, we selected models that allow human interpretability of the prediction score. The logistic regression model demonstrated 1% false negativity and 2% false positivity. The final models' Youden indices were 0.87 and 0.77 for screening and confirmatory cutoffs, respectively. Retraining on a new assay and performance assessment in 16,123 independent variants validated our approach (Youden index, 0.93). We also derived individual pathologist-centric models (virtual consensus conference function), and a visual drill-down functionality allows assessment of how underlying features contributed to a particular score or decision branch for clinical implementation.

Conclusion

Our decision support tool for variant reporting is a practically relevant artificial intelligence approach to harness the next-generation sequencing bioinformatics pipeline output when the complexity of data interpretation exceeds human capabilities.

Review of Clinical Next-Generation Sequencing

CONTEXT

- Next-generation sequencing (NGS) is a technology being used by many laboratories to test for inherited disorders and tumor mutations. This technology is new for many practicing pathologists, who may not be familiar with the uses, methodology, and limitations of NGS.

OBJECTIVE

- To familiarize pathologists with several aspects of NGS, including current and expanding uses; methodology including wet bench aspects, bioinformatics, and interpretation; validation and proficiency; limitations; and issues related to the integration of NGS data into patient care.

DATA SOURCES

- The review is based on peer-reviewed literature and personal experience using NGS in a clinical setting at a major academic center.

CONCLUSIONS

- The clinical applications of NGS will increase as the technology, bioinformatics, and resources evolve to address the limitations and improve quality of results. The challenge for clinical laboratories is to ensure testing is clinically relevant, cost-effective, and can be integrated into clinical care.

Standards and Guidelines for the Interpretation and Reporting of Sequence Variants in Cancer: A Joint Consensus Recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists

Widespread clinical laboratory implementation of next-generation sequencing-based cancer testing has highlighted the importance and potential benefits of standardizing the interpretation and reporting of molecular results among laboratories. A multidisciplinary working group tasked to assess the current status of next-generation sequencing-based cancer testing and establish standardized consensus classification, annotation, interpretation, and reporting conventions for somatic sequence variants was convened by the Association for Molecular Pathology with liaison representation from the American College of Medical Genetics and Genomics, American Society of Clinical Oncology, and College of American Pathologists. On the basis of the results of professional surveys, literature review, and the Working Group's subject matter expert consensus, a four-tiered system to categorize somatic sequence variations based on their clinical significances is proposed: tier I, variants with strong clinical significance; tier II, variants with potential clinical significance; tier III, variants of unknown clinical significance; and tier IV, variants deemed benign or likely benign. Cancer genomics is a rapidly evolving field; therefore, the clinical significance of any variant in therapy, diagnosis, or prognosis should be reevaluated on an ongoing basis. Reporting of genomic variants should follow standard nomenclature, with testing method and limitations clearly described. Clinical recommendations should be concise and correlate with histological and clinical findings.