Exome sequencing and targeted gene panels: a simulated comparison of diagnostic yield using data from 158 patients with rare diseases

Next-generation sequencing (NGS) has altered clinical genetic testing by widening the access to molecular diagnosis of genetically determined rare diseases. However, physicians may face difficulties selecting the best diagnostic approach. Our goal is to estimate the rate of possible molecular diagnoses missed by different targeted gene panels using data from a cohort of patients with rare genetic diseases diagnosed with exome sequencing (ES). For this purpose, we simulated a comparison between different targeted gene panels and ES: the list of genes harboring clinically relevant variants from 158 patients was used to estimate the theoretical rate of diagnoses missed by NGS panels from 53 different NGS panels from eight different laboratories. Panels presented a mean rate of missed diagnoses of 64% (range 14%-100%) compared to ES, representing an average predicted sensitivity of 36%. Metabolic abnormalities represented the group with highest mean of missed diagnoses (86%), while seizure represented the group with lowest mean (46%). Focused gene panels are restricted in covering select sets of genes implicated in specific diseases and they may miss molecular diagnoses of rare diseases compared to ES. However, their role in genetic diagnosis remains important especially for well-known genetic diseases with established genetic locus heterogeneity.

Frequency of carriers for rare recessive Mendelian diseases in a Brazilian cohort of 320 patients

Several Mendelian disorders follow an autosomal recessive inheritance pattern. Epidemiological information on many inherited disorders may be useful to guide health policies for rare diseases, but it is often inadequate, particularly in developing countries. We aimed to calculate the carrier frequencies of rare autosomal recessive Mendelian diseases in a cohort of Brazilian patients using whole exome sequencing (WES). We reviewed the molecular findings of WES from 320 symptomatic patients who had carrier status for recessive diseases. Using the Hardy-Weinberg equation, we estimated recessive disease frequencies (q² ) considering the respective carrier frequencies (2pq) observed in our study. We calculated the sensitivity of carrier screening tests based on lists of genes from five different clinical laboratories that offer them in Brazil. A total of 425 occurrences of 351 rare variants were reported in 278 different genes from 230 patients (71.9%). Almost half (48.8%) were carriers of at least one heterozygous pathogenic/likely pathogenic variant for rare metabolic disorders, while 25.9% of epilepsy, 18.1% of intellectual disabilities, 15.6% of skeletal disorders, 10.9% immune disorders, and 9.1% of hearing loss. We estimated that an average of 67% of the variants would not have been detected by carrier screening panels. The combined frequencies of autosomal recessive diseases were estimated to be 26.39/10,000 (or ~0.26%). This study shows the potential research utility of WES to determine carrier status, which may be a possible strategy to evaluate the clinical and social burden of recessive diseases at the population level and guide the optimization of carrier screening panels.

Comprehensive analysis of advanced-stage solid tumors from TCGA reveal widespread variation of genomics evidence levels across cancer types

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Background: Improved scalability and affordability of next generation sequencing (NGS) has pivoted cancer care toward genomics-driven treatment decisions. Particularly in advanced-stage or refractory cancer, clinical insights gleaned from NGS have become an integral option as these patients have typically exhausted all lines of available therapy. As precision oncology evolves, NGS is expected to have a differential impact based on the cancer type. In this study, a comprehensive NGS panel was used to determine the strength of clinical evidence in various advanced stage tumor samples from The Cancer Genome Atlas (TCGA). Methods: A hybrid capture panel, Oncofoco, was developed to evaluate SNVs, INDELs, CNVs and TMB in 366 genes. The panel’s utility was validated by interrogating a broader cohort of 2847 TCGA samples (advanced tumors with T3 or T4; or N > = 1; or M > = 1). Watsonä for Genomics, an artificial intelligence offering, was used for variant interpretation and annotation of the 366 genes. A clinical evidence classification system that evaluated the strength of biomarker/drug response associations was used for annotation with level 1/R1 strongest and level 4 weakest from clinical literature, FDA drug labels and guidelines (PMID:28890946). Results: The highest level of evidence for the top nine frequently occurring advanced stage cancers in TCGA is shown in Table. Conclusions: Thyroid cancer and cutaneous melanoma have emerged as the cancer types with the most level 1 evidence (FDA approved drugs) owing to BRAF V600E mutations. Kidney and prostate cancers show no cases with level 1 evidence and also had the largest fraction of unactionable tumors. Over half of colorectal cancer cases had level R1 resistance evidence attributed to KRAS and NRAS mutations. The clinical utility of NGS in late-stage refractory cancer varies widely by tumor type. The presence of level 3 and level 4 evidence in all cancer types bodes well for the development of new targeted drugs. [Table: see text]

Performance and validation of a tumor mutation profiling, based on artificial intelligence annotation, to assist oncology decision making

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Background: Tumor mutation profiling has become a key component for orienteering the treatment of oncologic patients. A crucial step for this is the correct identification and classification of pathogenic and actionable variants. In the present work we aimed at the development and validation of a tumor mutation profiling panel, based on NGS, which uses artificial intelligence for variant annotation. Methods: We designed a hybrid capture panel, containing 366 genes to evaluate somatic SNVs, INDELs and CNVs, and to calculate TMB, using a customized bioinformatics pipeline. MSI status was determined by fragment analysis using capillary electrophoresis. Analytical performance was determined using reference cell lines. FFPE samples from 70 tumors were accessed and 53 were sequenced. Variant annotation was performed by IBM Watson for Genomics (WfG) platform. Assay performance on clinical samples was defined based on orthogonal assays using Agilent CGH+SNParray 400K (for CNVs only) and Foundation One test (Foundation Medicine) (F1). Results: Breast, colon and lung were the most common tumor origins. Fifty-three samples were successfully sequenced, while 41 of them could also be analyzed by F1 test. A summary of the assay performance is presented in Table 1. Our pipeline detected 1219 variants and 290 (23%) were classified as Pathogenic, Likely Pathogenic or Actionable, according to WfG. Thirty-five samples (66%) presented a variant that could drive the treatment, with 37.7% of samples being sensitive to targeted therapies, while 22.6% were resistant; additionally, 86% had an indication for a clinical trial. Conclusions: The developed assay presented a good overall sensitivity and allele frequency correlation, with TMB and MSI having the best rates. Comparisons with F1 had reduced values of concordance; however, SNVs and INDELs presented a similar frequency. Differences on CNVs identification may rely on distinct thresholds established by the different groups. The high percentage of samples that could benefit from mutational profiling highlights the importance of such approach in the clinical routine. Additionally, the high number of variants features the need for updated information for annotation. [Table: see text]

Abstract A25: Intragroup genomic, transcriptional, and epigenomic variability is higher in sporadic than in BRCA1-associated hereditary triple-negative breast tumors

Intra-tumor genetic heterogeneity is a possible cause of therapy resistance and has consequences in the disease progression and patient survival. Triple-negative breast cancer (TNBC) is associated with rapid growth, early metastasis, and worse prognosis than other breast cancer subtypes. Close association has been identified between TNBC and germline loss-of-function mutations in BRCA1 gene which trigger the tumorigenic process in hereditary TNBC. Although at genomic level, TNBC was previously characterized as more heterogeneous than other breast cancer subtypes, the difference in genomic, epigenomic, and transcriptional variability between two groups of TNBC—sporadic (BRCA1-proficent) and hereditary (BRCA1-associated)—remains unclear. Here, we investigated both sporadic and hereditary TNBC, by using data from copy-number alterations (CNAs) array, methylation array, and RNA-Seq. A total of 80 samples, 40 TNBC BRCA1-proficient (classified as sporadic), 18 TNBC with BRCA1 germline mutation (classifed as hereditary) and 22 normal breast tissues used as controls, were used. Data from methylation and CNA arrays were analyzed with the RnBeads and snapCGH softwares, respectively. RNA-seq data was analyzed mainly with the STAR aligner, Bioconductor-GenomicFeatures and DESeq2 softwares. To assess the intragroup genomic, transcriptional, and epigenomic variability we used the inter-quantile difference between patients of the same group. Our results clearly demonstrated that the TNBC BRCA1-proficient group has a higher intragroup heterogeneity than the BRCA1-mutated group regarding (i) global pattern of gene expression, (ii) number and intensity of CNAs (copy number alterations), and (iii) level of promoter methylation. Moreover, normal breast samples showed the lowest intra-group heterogeneity for the last two features. Then, our results show higher genomic, transcriptional, and epigenomic variability in sporadic than hereditary TNBC and motivates further molecular subtyping of the sporadic TNBCs, which may reveal new targets to improve TNBC treatment.

Citation Format: Kivvi Duarte de Mello Nakamura, Rodrigo Fernandes Ramalho, Rafael Canfield Brianese, Ana Krepischi, Bruna D. F. Barros, Dirce Maria Carraro. Intragroup genomic, transcriptional, and epigenomic variability is higher in sporadic than in BRCA1-associated hereditary triple-negative breast tumors [abstract]. In: Proceedings of the AACR International Conference held in cooperation with the Latin American Cooperative Oncology Group (LACOG) on Translational Cancer Medicine; May 4-6, 2017; São Paulo, Brazil. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(1_Suppl):Abstract nr A25.

Tempo and mode of ERV-K evolution in human and chimpanzee genomes

Several families of endogenous retrovirus (ERV) exist in copious numbers in the genomes of primate species. Therefore, we undertook a systematic search for endogenous retrovirus sequences from the ERV-K family, comparing across both human (Homo sapiens) and chimpanzee (Pan troglodytes) genomes. Using conserved motifs of the ERV-K as query we identified and characterized 76 complete ERV-K elements, 54 in human (HERV-K), 34 of which were described previously, and 21 in the chimpanzee (CERV-K). Phylogenetic analysis using coding regions and LTRs showed the existence of two main branches. Group I was the most heterogeneous and had an average integration time of 18.3 MYBP (million years before present), using rates ranging from 1.5 to 4.0 x 10(-9) s/s/y (substitution per site per year). Group O/N integrated around 19.4 MYBP and nested Group N integrated about 14 MYBP. We found evidence for strong positive selection on the gag, pol and env coding regions and for A/T hypermutation. Our data suggest that the endogenous elements were possibly involved in chromosomal rearrangements and retained a great deal of information from their active stage, most likely as a consequence of host interactions. This study also contributes to the annotation effort of both human and chimpanzee genomes.