Comparing the Diagnostic Yield of Germline Exome Versus Panel Sequencing in the Diverse Population of the Texas KidsCanSeq Pediatric Cancer Study.

PURPOSE: To evaluate the relative diagnostic yield of clinical germline genomic tests in a diverse pediatric cancer population. PATIENTS AND METHODS: The KidsCanSeq study enrolled pediatric cancer patients across six sites in Texas. Germline analysis included both exome sequencing and a therapy-focused pediatric cancer gene panel. The results were categorized by participants demographics, the presence of pathogenic or likely pathogenic (P/LP) variants, and variants of uncertain significance (VUS) in cancer predisposition genes (CPGs). Pediatric actionable CPGs were defined as those with cancer surveillance recommendations during childhood. RESULTS: Cancer P/LP variants were reported by at least one platform in 103 of 578 (17.8%) participants of which 76 were dominant cancer genes (13.1%) with no significant differences by self-described race or Hispanic ethnicity. However, the proportion of participants with VUS was greater in Asian and African American participants (P = .0029). Diagnostic yield was 16.6% for exome versus 8.5% for panel (P < .0001) with 42 participants with concordant germline results. Exome-only results included P/LP variants in 30 different CPGs in 54 participants, whereas panel-only results included seven participants with a copy number or structural P/LP variants in CPGs. There was no significant difference in diagnostic yield limited to pediatric actionable CPGs (P = .6171). CONCLUSION: Approximately 18% of a diverse pediatric cancer population had germline diagnostic findings with 50% of P/LP variants reported by only one platform because of CPGs not on the targeted panel and copy number variants (CNVs)/rearrangements not reported by exome. Although diagnostic yields were similar in this diverse population, increases in VUS results were observed in Asian and African American populations. Given the clinical significance of CNVs/rearrangements in this cohort, detection is critical to optimize germline analysis of pediatric cancer populations.

Evidence review and considerations for use of first line genome sequencing to diagnose rare genetic disorders.

Early use of genome sequencing (GS) in the diagnostic odyssey can reduce suffering and improve care, but questions remain about which patient populations are most amenable to GS as a first-line diagnostic test. To address this, the Medical Genome Initiative conducted a literature review to identify appropriate clinical indications for GS. Studies published from January 2011 to August 2022 that reported on the diagnostic yield (DY) or clinical utility of GS were included. An exploratory meta-analysis using a random effects model evaluated DY based on cohort size and diagnosed cases per cohort. Seventy-one studies met inclusion criteria, comprising over 13,000 patients who received GS in one of the following settings: hospitalized pediatric patients, pediatric outpatients, adult outpatients, or mixed. GS was the first-line test in 38% (27/71). The unweighted mean DY of first-line GS was 45% (12-73%), 33% (6-86%) in cohorts with prior genetic testing, and 33% (9-60%) in exome-negative cohorts. Clinical utility was reported in 81% of first-line GS studies in hospitalized pediatric patients. Changes in management varied by cohort and underlying molecular diagnosis (24-100%). To develop evidence-informed points to consider, the quality of all 71 studies was assessed using modified American College of Radiology (ACR) criteria, with five core points to consider developed, including recommendations for use of GS in the N/PICU, in lieu of sequential testing and when disorders with substantial allelic heterogeneity are suspected. Future large and controlled studies in the pediatric and adult populations may support further refinement of these recommendations.

Best practices for the interpretation and reporting of clinical whole genome sequencing.

Whole genome sequencing (WGS) shows promise as a first-tier diagnostic test for patients with rare genetic disorders. However, standards addressing the definition and deployment practice of a best-in-class test are lacking. To address these gaps, the Medical Genome Initiative, a consortium of leading health care and research organizations in the US and Canada, was formed to expand access to high quality clinical WGS by convening experts and publishing best practices. Here, we present best practice recommendations for the interpretation and reporting of clinical diagnostic WGS, including discussion of challenges and emerging approaches that will be critical to harness the full potential of this comprehensive test.

Standards for the classification of pathogenicity of somatic variants in cancer (oncogenicity): Joint recommendations of Clinical Genome Resource (ClinGen), Cancer Genomics Consortium (CGC), and Variant Interpretation for Cancer Consortium (VICC).

PURPOSE: Several professional societies have published guidelines for the clinical interpretation of somatic variants, which specifically address diagnostic, prognostic, and therapeutic implications. Although these guidelines for the clinical interpretation of variants include data types that may be used to determine the oncogenicity of a variant (eg, population frequency, functional, and in silico data or somatic frequency), they do not provide a direct, systematic, and comprehensive set of standards and rules to classify the oncogenicity of a somatic variant. This insufficient guidance leads to inconsistent classification of rare somatic variants in cancer, generates variability in their clinical interpretation, and, importantly, affects patient care. Therefore, it is essential to address this unmet need. METHODS: Clinical Genome Resource (ClinGen) Somatic Cancer Clinical Domain Working Group and ClinGen Germline/Somatic Variant Subcommittee, the Cancer Genomics Consortium, and the Variant Interpretation for Cancer Consortium used a consensus approach to develop a standard operating procedure (SOP) for the classification of oncogenicity of somatic variants. RESULTS: This comprehensive SOP has been developed to improve consistency in somatic variant classification and has been validated on 94 somatic variants in 10 common cancer-related genes. CONCLUSION: The comprehensive SOP is now available for classification of oncogenicity of somatic variants.

A phase I trial evaluating the effects of plerixafor, G-CSF, and azacitidine for the treatment of myelodysplastic syndromes.

Interactions between the bone marrow microenvironment and MDS tumor clones play a role in pathogenesis and response to treatment. We hypothesized G-CSF and plerixafor may enhance sensitivity to azacitidine in MDS. Twenty-eight patients with MDS were treated with plerixafor, G-CSF and azacitidine with a standard 3 + 3 design. Subjects received G-CSF 10 mcg/kg D1-D8, plerixafor D4-D8, and azacitidine 75 mg/m2 D4-D8, but the trial was amended to reduce G-CSF dose to 5 mcg/kg for 5 days after 2 patients had significant leukocytosis. Plerixafor was dose escalated to 560 mcg/kg/day without dose limiting toxicity. Two complete responses and 6 marrow responses were seen for an overall response rate (ORR) of 36% in evaluable patients, and ORR of 53% in patients receiving the triplet. Evidence of mobilization correlated with a higher ORR, 60% vs. 17%. Plerixafor, G-CSF and azacitidine appears tolerable when given over 5 days and has encouraging response rates.KEY POINTSPlerixafor and G-CSF can be safely combined with azacitidine for 5 days in patients with MDS.The overall response rate of 53% for evaluable patients with this regimen is higher than expected and more responses were seen in patients with blast mobilization.

Best practices for the analytical validation of clinical whole-genome sequencing intended for the diagnosis of germline disease.

Whole-genome sequencing (WGS) has shown promise in becoming a first-tier diagnostic test for patients with rare genetic disorders; however, standards addressing the definition and deployment practice of a best-in-class test are lacking. To address these gaps, the Medical Genome Initiative, a consortium of leading healthcare and research organizations in the US and Canada, was formed to expand access to high-quality clinical WGS by publishing best practices. Here, we present consensus recommendations on clinical WGS analytical validation for the diagnosis of individuals with suspected germline disease with a focus on test development, upfront considerations for test design, test validation practices, and metrics to monitor test performance. This work also provides insight into the current state of WGS testing at each member institution, including the utilization of reference and other standards across sites. Importantly, members of this initiative strongly believe that clinical WGS is an appropriate first-tier test for patients with rare genetic disorders, and at minimum is ready to replace chromosomal microarray analysis and whole-exome sequencing. The recommendations presented here should reduce the burden on laboratories introducing WGS into clinical practice, and support safe and effective WGS testing for diagnosis of germline disease.

The Medical Genome Initiative: moving whole-genome sequencing for rare disease diagnosis to the clinic.

Clinical whole-genome sequencing (WGS) offers clear diagnostic benefits for patients with rare disease. However, there are barriers to its widespread adoption, including a lack of standards for clinical practice. The Medical Genome Initiative consortium was formed to provide practical guidance and support the development of standards for the use of clinical WGS.

Utility of copy number variants in the classification of intracranial ependymoma.

Ependymomas are neuroepithelial tumors that differentiate along the ependymal cell lineage, a lining of the ventricles of the brain and the central canal of the spinal cord. They are rare in adults, but account for around 9% of brain tumors in children, where they usually have an aggressive course. Efficient stratification could lead to improved care but remains a challenge even in the genomic era. Recent studies proposed a multivariate classification system based on tumor location, age, and broad genomic findings like global patterns of methylation and copy number variants (CNVs). This system shows improved prognostic utility, but is relatively impractical in the routine clinical setting because it necessitates multiple diagnostic tests. We analyzed 13 intracranial grade II and III ependymoma specimens on a DNA microarray to identify discrete CNVs that could support the existing classification. The loss of chr22 and the gain of 5p15.31 were common throughout our cohort (6 and 11 cases, respectively). Other CNVs correlated well with the previously proposed classification system. For example, gains of chr20 were unique to PF-EPN-B tumors of the posterior fossa and may differentiate them from PF-EPN-A. Given the ease of detecting CNVs using multiple, clinically validated methods, these CNVs should be further studied to confirm their diagnostic and prognostic utility, for incorporation into clinical testing algorithms.

A case for expert curation: an overview of cancer curation in the Clinical Genome Resource (ClinGen).

We describe the Clinical Genome Resource (ClinGen) cancer-related curation activities and the importance of curation to the evolving state of variant interpretation in a clinical context for both pediatric and adult cancer patients. We highlight specific examples from the CDH1 and PTEN Variant Curation Expert Panels (VCEPs) of the FDA-recognized process by which ClinGen VCEPs specify the American College of Medical Genetics and Genomics/Association of Molecular Pathology evidence code to develop variant classifications. We also review gene curations performed within the Hereditary Cancer Clinical Domain. We describe the parallel efforts for curation of somatic cancer variants from the Somatic Cancer Working Group. The ClinGen Germline/Somatic Committee is working to improve incorporation of both hereditary and somatic variant data to aid clinical interpretation. These ClinGen efforts rely on broad data sharing and detailed phenotypic and molecular information from published case studies to provide expert-curated variant interpretation to the cancer community.

Publisher Correction: Non-invasive prenatal sequencing for multiple Mendelian monogenic disorders using circulating cell-free fetal DNA.

In the version of this article originally published, some cases that were presented in Fig. 3 should have been underlined but were not. The appropriate cases have now been underlined. The error has been corrected in the print, PDF and HTML versions of the article.

Non-invasive prenatal sequencing for multiple Mendelian monogenic disorders using circulating cell-free fetal DNA.

Current non-invasive prenatal screening is targeted toward the detection of chromosomal abnormalities in the fetus1,2. However, screening for many dominant monogenic disorders associated with de novo mutations is not available, despite their relatively high incidence3. Here we report on the development and validation of, and early clinical experience with, a new approach for non-invasive prenatal sequencing for a panel of causative genes for frequent dominant monogenic diseases. Cell-free DNA (cfDNA) extracted from maternal plasma was barcoded, enriched, and then analyzed by next-generation sequencing (NGS) for targeted regions. Low-level fetal variants were identified by a statistical analysis adjusted for NGS read count and fetal fraction. Pathogenic or likely pathogenic variants were confirmed by a secondary amplicon-based test on cfDNA. Clinical tests were performed on 422 pregnancies with or without abnormal ultrasound findings or family history. Follow-up studies on cases with available outcome results confirmed 20 true-positive, 127 true-negative, zero false-positive, and zero-false negative results. The initial clinical study demonstrated that this non-invasive test can provide valuable molecular information for the detection of a wide spectrum of dominant monogenic diseases, complementing current screening for aneuploidies or carrier screening for recessive disorders.

Integrating somatic variant data and biomarkers for germline variant classification in cancer predisposition genes.

In its landmark paper about Standards and Guidelines for the Interpretation of Sequence Variants, the American College of Medical Genetics and Genomics (ACMG), and Association for Molecular Pathology (AMP) did not address how to use tumor data when assessing the pathogenicity of germline variants. The Clinical Genome Resource (ClinGen) established a multidisciplinary working group, the Germline/Somatic Variant Subcommittee (GSVS) with this focus. The GSVS implemented a survey to determine current practices of integrating somatic data when classifying germline variants in cancer predisposition genes. The GSVS then reviewed and analyzed available resources of relevant somatic data, and performed integrative germline variant curation exercises. The committee determined that somatic hotspots could be systematically integrated into moderate evidence of pathogenicity (PM1). Tumor RNA sequencing data showing altered splicing may be considered as strong evidence in support of germline pathogenicity (PVS1) and tumor phenotypic features such as mutational signatures be considered supporting evidence of pathogenicity (PP4). However, at present, somatic data such as focal loss of heterozygosity and mutations occurring on the alternative allele are not recommended to be systematically integrated, instead, incorporation of this type of data should take place under the advisement of multidisciplinary cancer center tumor-normal sequencing boards.

Adapting crowdsourced clinical cancer curation in CIViC to the ClinGen minimum variant level data community-driven standards.

Harmonization of cancer variant representation, efficient communication, and free distribution of clinical variant-associated knowledge are central problems that arise with increased usage of clinical next-generation sequencing. The Clinical Genome Resource (ClinGen) Somatic Working Group (WG) developed a minimal variant level data (MVLD) representation of cancer variants, and has an ongoing collaboration with Clinical Interpretations of Variants in Cancer (CIViC), an open-source platform supporting crowdsourced and expert-moderated cancer variant curation. Harmonization between MVLD and CIViC variant formats was assessed by formal field-by-field analysis. Adjustments to the CIViC format were made to harmonize with MVLD and support ClinGen Somatic WG curation activities, including four new features in CIViC: (1) introduction of an assertions feature for clinical variant assessment following the Association of Molecular Pathologists (AMP) guidelines, (2) group-level curation tracking for organizations, enabling member transparency, and curation effort summaries, (3) introduction of ClinGen Allele Registry IDs to CIViC, and (4) mapping of CIViC assertions into ClinVar submission with automated submissions. A generalizable workflow utilizing MVLD and new CIViC features is outlined for use by ClinGen Somatic WG task teams for curation and submission to ClinVar, and provides a model for promoting harmonization of cancer variant representation and efficient distribution of this information.

Standardizing And Democratizing Access To Cancer Molecular Diagnostic Test Data From Patients To Drive Translational Research.

In the last 3-5 years, there has been a rapid increase in clinical use of next generation sequencing (NGS) based cancer molecular diagnostic (MolDx) testing to develop better treatment plans with targeted therapies. To truly achieve precision oncology, it is critical to catalog cancer sequence variants from MolDx testing for their clinical relevance along with treatment information and patient outcomes, and to do so in a way that supports large-scale data aggregation and new hypothesis generation. Through the NIH-funded Clinical Genome Resource (ClinGen), in collaboration with NLM's ClinVar database and >50 academic and industry based cancer research organizations, a Minimal Variant Level Data (MVLD) framework to standardize reporting and interpretation of drug associated alterations was developed. Methodological and technology development to standardize and map MolDx data to the MVLD standard are presented here. Also described is a novel community engagement effort through disease-focused taskforces to provide usecases for technology development.

Pathogenic Germline Variants in 10,389 Adult Cancers.

We conducted the largest investigation of predisposition variants in cancer to date, discovering 853 pathogenic or likely pathogenic variants in 8% of 10,389 cases from 33 cancer types. Twenty-one genes showed single or cross-cancer associations, including novel associations of SDHA in melanoma and PALB2 in stomach adenocarcinoma. The 659 predisposition variants and 18 additional large deletions in tumor suppressors, including ATM, BRCA1, and NF1, showed low gene expression and frequent (43%) loss of heterozygosity or biallelic two-hit events. We also discovered 33 such variants in oncogenes, including missenses in MET, RET, and PTPN11 associated with high gene expression. We nominated 47 additional predisposition variants from prioritized VUSs supported by multiple evidences involving case-control frequency, loss of heterozygosity, expression effect, and co-localization with mutations and modified residues. Our integrative approach links rare predisposition variants to functional consequences, informing future guidelines of variant classification and germline genetic testing in cancer.

Genomic heterogeneity of ALK fusion breakpoints in non-small-cell lung cancer.

In lung adenocarcinoma, canonical EML4-ALK inversion results in a fusion protein with a constitutively active ALK kinase domain. Evidence of ALK rearrangement occurs in a minority (2-7%) of lung adenocarcinoma, and only ~60% of these patients will respond to targeted ALK inhibition by drugs such as crizotinib and ceritinib. Clinically, targeted anti-ALK therapy is often initiated based on evidence of an ALK genomic rearrangement detected by fluorescence in situ hybridization (FISH) of interphase cells in formalin-fixed, paraffin-embedded tissue sections. At the genomic level, however, ALK rearrangements are heterogeneous, with multiple potential breakpoints in EML4, and alternate fusion partners. Using next-generation sequencing of DNA and RNA together with ALK immunohistochemistry, we comprehensively characterized genomic breakpoints in 33 FISH-positive lung adenocarcinomas. Of these 33 cases, 29 (88%) had detectable DNA level ALK rearrangements involving EML4, KIF5B, or non-canonical partners including ASXL2, ATP6V1B1, PRKAR1A, and SPDYA. A subset of 12 cases had material available for RNA-Seq. Of these, eight of eight (100%) cases with DNA rearrangements showed ALK fusion transcripts from RNA-Seq; three of four cases (75%) without detectable DNA rearrangements were similarly negative by RNA-Seq, and one case was positive by RNA-Seq but negative by DNA next-generation sequencing. By immunohistochemistry, 17 of 19 (89%) tested cases were clearly positive for ALK protein expression; the remaining cases had no detectable DNA level rearrangement or had a non-canonical rearrangement not predicted to form a fusion protein. Survival analysis of patients treated with targeted ALK inhibitors demonstrates a significant difference in mean survival between patients with next-generation sequencing confirmed EML4-ALK rearrangements, and those without (20.6 months vs 5.4 months, P<0.01). Together, these data demonstrate abundant genomic heterogeneity among ALK-rearranged lung adenocarcinoma, which may account for differences in treatment response with targeted ALK inhibitors.

ClinGen Cancer Somatic Working Group - standardizing and democratizing access to cancer molecular diagnostic data to drive translational research.

A growing number of academic and community clinics are conducting genomic testing to inform treatment decisions for cancer patients (1). In the last 3-5 years, there has been a rapid increase in clinical use of next generation sequencing (NGS) based cancer molecular diagnostic (MolDx) testing (2). The increasing availability and decreasing cost of tumor genomic profiling means that physicians can now make treatment decisions armed with patient-specific genetic information. Accumulating research in the cancer biology field indicates that there is significant potential to improve cancer patient outcomes by effectively leveraging this rich source of genomic data in treatment planning (3). To achieve truly personalized medicine in oncology, it is critical to catalog cancer sequence variants from MolDx testing for their clinical relevance along with treatment information and patient outcomes, and to do so in a way that supports large-scale data aggregation and new hypothesis generation. One critical challenge to encoding variant data is adopting a standard of annotation of those variants that are clinically actionable. Through the NIH-funded Clinical Genome Resource (ClinGen) (4), in collaboration with NLM's ClinVar database and >50 academic and industry based cancer research organizations, we developed the Minimal Variant Level Data (MVLD) framework to standardize reporting and interpretation of drug associated alterations (5). We are currently involved in collaborative efforts to align the MVLD framework with parallel, complementary sequence variants interpretation clinical guidelines from the Association of Molecular Pathologists (AMP) for clinical labs (6). In order to truly democratize access to MolDx data for care and research needs, these standards must be harmonized to support sharing of clinical cancer variants. Here we describe the processes and methods developed within the ClinGen's Somatic WG in collaboration with over 60 cancer care and research organizations as well as CLIA-certified, CAP-accredited clinical testing labs to develop standards for cancer variant interpretation and sharing.

Authors' Reply.

Authors' Reply to the Letter to the Editor by Montgomery et al (Identification of Germline Variants in Tumor Genomic Sequencing Analysis. J Mol Diagn 2017, 19:XXXX-XXXX).