In cancer genomes, both large-scale CNVs (>100 kb) spanning chromosome arms, and smaller CNVs limited to a few genes are prevalent. While large CNVs are readily detected with existing methodologies (e.g. SNP array), gene-level CNVs require a higher resolution not routinely available in current tools, including next generation sequencing (NGS)-based clinical cancer mutation panels. We sought to leverage NGS data generated by one of these panels: the TCH Pediatric Solid Tumor panel (124 cancer genes), and built a clinical-grade analytic pipeline for detection of somatic CNVs.After reviewing literature, CNVkit was selected for its ability to perform unmatched (requiring no matched normal specimen) CNV analysis, customize segmentation algorithms, and provide superior visualization. CNVkit performs circular binary segmentation on the log2 difference of binned read depths (on- and off-target) from tumor and pooled-normal blood samples to identify CNVs. Although the panel captures only 124 genes (~1Mbp) at > 300X coverage, the low background coverage of 0.05X (off-target) due to imperfect hybridization capture allows us to detect chromosome-arm level changes. As a pilot study, we optimized CNVkit to detect gene-level and chromosome arm-level CNVs in a reference aneuploid colon cancer cell line (HT-29) characterized by aCGH and observed high correlation (r = 0.89) between the aCGH and CNVkit fold change. Through in-silico and bench experiments we performed limit of detection analysis, by diluting different proportions of CNV positive tumor samples with normal sample, to set thresholds for calling amplification and losses. We show the ability of the pipeline to detect shallow and deep deletions if they are present in at least 40% and 80% of the sample sequenced, respectively. During validation, we used 24 pediatric cancer samples with diverse histology to confirm and detect numerous CNVs of clinical significance, including deletions of SMARCB1, PTEN, BRCA2, RB1, and ARID1B, and amplifications of MYCN, MYC, CCND1 and KRAS.As the panel densely tiles tumor suppressors, we are also able to infer apparent intragenic deletions in BRCA1, BRCA2, ATRX, RB1 and PTEN, highlighting the theoretical resolution of this tool for detecting intragenic events over other methods. We have also developed a Python based interactive CNV Viewer for assessing the copy number analysis results from NGS data. The browser like interface allows the user to zoom, link out to UCSC Genome Browser, hover for information, take high-quality screenshots, etc. Hence, the CNV pipeline we have developed will allow more comprehensive evaluation of the existing integrated DNA and RNA analysis pipeline, increasing the diagnostic yield of mutation panel testing for childhood cancer patients.Citation Format: Raghu Chandramohan, Jacquelyn Reuther, Ilavarasi Gandhi, Horatiu Voicu, Karla R. Alvarez, Kevin E. Fisher, Dolores H. Lopez-Terrada, Donald W. Parsons, Angshumoy Roy. Improving the diagnostic yield of a 124 gene pediatric solid tumor panel through somatic copy number variation analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4233.
Chromosomal imbalances are commonly seen in cancer and inherited genetic diseases. These imbalances may assist in the diagnosis, prognosis, and/or therapeutic management of certain neoplasms. Several methods for detecting chromosomal imbalances, such as, fluorescent in situ hybridization, array comparative genomic hybridization, and single nucleotide polymorphism (SNP) arrays have proven useful in formalin-fixed paraffin-embedded (FFPE) tissues. Here, we report the performance and reproducibility of virtual karyotyping of FFPE tissues with Affymetrix SNP arrays.Virtual karyotypes from 442 FFPE tumor samples were generated using the Affymetrix GeneChip Mapping 10K Xba 2.0 and/or 250K Nsp SNP mapping arrays. Samples ranged from a few weeks to 17 years in archival storage. Virtual karyotypes were assessed for copy number changes, loss of heterozygosity, and acquired uniparental disomy.Overall, 75.3% of samples produced interpretable virtual karyotypes with the 10K arrays and 76.7% in the 250K arrays. Parameters for the selection of samples for hybridization were determined, which increased the success rate in both platforms to 81.3 and 92.6%, respectively. FFPE virtual karyotypes generated with both 10K Xba 2.0 and 250K Nsp arrays showed 100% concordance in intralaboratory and interlaboratory reproducibility studies. Samples older than 7 years showed decreased performance.SNP arrays are a reliable, reproducible, and robust platform for the virtual karyotyping of FFPE tumor tissues with performance characteristics adequate for clinical application. Parameters that most significantly affected sample performance were sample age and storage conditions.
Abstract Background Pediatric hepatocellular carcinoma (HCC) is a rare liver tumor in children with a poor prognosis. Comprehensive molecular profiling to understand the underlying genomic drivers of this tumor has not been completed, and it is unclear whether nonfibrolamellar pediatric HCC is more genomically similar to hepatoblastoma or adult HCC. Procedure To characterize the molecular landscape of these tumors, we analyzed a cohort of 15 pediatric non‐FL‐HCCs by sequencing a panel of cancer‐associated genes and conducting copy‐number and gene‐expression analyses. Results We detected multiple types of molecular alterations in Wnt signaling genes, including APC inversion, AMER1 somatic mutation, and most commonly CTNNB1 intragenic deletions. There were multiple alterations to the telomerase pathway via TERT activation or ATRX mutation. Therapeutically targetable activating mutations in MAPK/ERK signaling pathway genes, including MAPK1 and BRAF , were detected in 20% of tumors. TP53 mutations occurred far less frequently in our pediatric HCC cohort than reported in adult cohorts. Tumors arising in children with underlying liver disease were found to be molecularly distinct from the remainder and lacking detectable oncogenic drivers, as compared with those arising in patients without a history of underlying liver disease; the majority of both types were positive for glypican‐3, another potential therapeutic target. Conclusion Our study revealed pediatric HCC to be a molecularly heterogeneous group of tumors. Those non‐FL‐HCC tumors arising in the absence of underlying liver disease harbor genetic alterations affecting multiple cancer pathways, most notably Wnt signaling, and share some characteristics with adult HCC.
Oncocytoma, chromophobe renal cell carcinoma (chRCC), and the eosinophilic variant of clear cell RCC (ccRCC) are morphologically similar tumors with significantly different clinical courses. These renal tumor subtypes show characteristic structural genetic changes; however, the mRNA expression patterns of oncocytoma and chRCC are strikingly similar. MicroRNAs (miRNA) are small RNA molecules that regulate the expression of many genes and have been shown to be useful for tumor classification and identification. The miRNA expression was analyzed from formalin-fixed paraffin-embedded tissue in 5 cases each of oncocytoma, ccRCC, papillary RCC, chRCC, and 4 normal kidney tissues using microarrays. Affymetrix single-nucleotide polymorphism arrays were used to detect chromosomal imbalances in each of the tumors. Eighteen miRNAs were significantly different among the 4 tumor types. The microRNA miR-21, a known oncogenic miRNA, was found to be upregulated in papillary and clear cell carcinomas. Four miRNAs could differentiate oncocytomas from chRCCs and the 3 could differentiate papillary RCC from ccRCC, including miR-126, a known vasculogenic miRNA. Of the 18 differentially expressed miRNAs, only 2 correlated with copy number changes in the chromosomal region harboring these genes. One tumor, originally diagnosed as an oncocytoma by morphology, showed a virtual karyotype and miRNA expression pattern consistent with chromophobe carcinoma. Further investigation of the tumor showed vascular invasion. Our study suggests that miRNA expression can be used to differentiate the common subtypes of renal epithelial neoplasms but further validation is necessary. In addition, the lack of correlation between miRNA expression and virtual karyotype suggests a non-copy-number-related mechanism for miRNA gene expression regulation in renal neoplasia.
Abstract Context.—Renal epithelial neoplasms have characteristic chromosomal imbalances, and we have shown previously that virtual karyotypes derived from single-nucleotide polymorphism microarrays can be performed on formalin-fixed, paraffin-embedded tissue. Objective.—To perform a direct comparison of virtual and conventional karyotypes to evaluate concordance of results. Design.—Twenty archival formalin-fixed, paraffin-embedded tumor samples with preexisting, conventional cytogenetic results were analyzed with Affymetrix 10K 2.0 or 250K Nsp single-nucleotide polymorphism microarrays. Results.—Nineteen samples yielded adequate virtual karyotypes for interpretation. Eight samples showed complete agreement between the 2 techniques, and 8 samples showed partial agreement. The disease-defining lesions (eg, loss of 3p for clear cell carcinoma) were identified in all 19 cases by virtual karyotypes and in 15 cases by conventional karyotypes. Virtual and conventional karyotypic findings were concordant in the i...
Oncocytic features are a hallmark of renal oncocytoma (OC) but can be seen in other renal tumors such as clear cell renal cell carcinoma (ccRCC) with granular cells and eosinophilic variant of chromophobe RCC (chRCC). Up to 7% of renal neoplasms are ultimately diagnosed as unclassified RCC with oncocytic tumors accounting for a significant number of these. One of the common diagnostic challenges with renal oncocytic tumors is the differentiation between OC and eosinophilic variant of chRCC. The distinction between these 2 entities is critical due to the prognostic implications and patient management decisions. Immunohistochemistry is generally a useful tool for characterizing many renal tumors but is of limited utility in this situation, since staining patterns for these 2 neoplasms are quite similar. However, OC and chRCC have recurrent chromosomal abnormalities that are characteristic of each tumor type. The use of molecular chromosomal analyses as an ancillary tool for this diagnostic challenge has been reported. This case review focuses on the role of molecular analyses, such as virtual karyotyping with SNP arrays, in the diagnosis of unclassified oncocytic renal tumors.
