Academic literature on the topic 'Variant calling, whole exome sequencing, database'

Abstract Introduction Multiple myeloma (MM) is a largely incurable plasma cell malignancy characterised by marked genomic heterogeneity, in which chromosome 1q21 amplification (amp1q21) associates with poor prognosis. Genomic analysis using next generation sequencing has identified recurrent mutations, but no universal acquired somatic mutation(s) have emerged in MM, suggesting that understanding pathways of survival will require analysis of individual tumours in distinct disease subsets. To compound complexity of the problem, intraclonal variation (ICV), known as a major driver mechanism in cancer plasticity, in which clonal competitor cells undergo selection during disease evolution and progression by Darwinian principles, will need to be fully mapped at the genome level. Identifying the true level of ICV in a tumour will thus require analysis at the level of whole exome sequencing (WES) in single cells (SCs). In this study, we sought to establish WES methodology able to identify ICV in SCs in an index case of amp1q21 MM. Methods Cell selection and sequencing CD138+ tumour cells and CD3+ T-cells were isolated from a presentation case of amp1q21 MM as bulk populations to high purity (>97%). Single MM cells and normal T cells were individually isolated and used for single cell (SC) whole exome sequencing (WES). Whole genome amplification (WGA) was performed by multiple displacement amplification (Qiagen REPLI-g Mini kit), and exome capture was performed using Agilent SureSelect. Libraries were then 90 bp paired end sequenced on an Illumina HiSeq2000 (BGI, China). Data analysis Data was produced for bulk (1000 cells) MM and bulk germline T cells, twenty MM SCs and five T cell SCs. Raw data was aligned to hg19 reference sequence using NovoAlignMPI (v3.02.03). Variant calling was performed using SAMtools (v1.2.1) and VarScan (v2.3.6) and variants were annotated using ANNOVAR. High confidence variants were called in the bulk tumour WES by pairwise comparison with bulk germline WES. Variant lists were also cross-searched against various variant databases (CG46, 1000 genomes, dbSNP, esp650 and in-house database) in order to exclude variants that occur in the general population. Multiple quality control measures were employed to reduce the number of false positive calls. Results and Discussion Data and bioinformatics pipelines are of a high quality SC WES generated raw data reads that were similar to bulk WES of 1000 cells, with comparable mapping to Agilent SureSelect target exome (69-76% SC vs. 70% bulk) and mean fold coverage (56.8-59.1x vs. 59.7x bulk). On average, 82% of the exome was covered sufficiently for somatic variant (SV) calling (often considered as ≥ 5x), which was higher than seminal published SC WES studies (70-80%) (Hou et al., Cell, 2012; Xu et al., Cell, 2012). We identified 33 potentially deleterious SVs in the bulk tumour exome with high confidence bioinformatics, 21 of which were also identified in one or more SC exomes. The variants identified include suspected deleterious mutations in genes involved in MAPK pathway, plasma cell differentiation, and those with known roles in B cell malignancies. To confirm SV calls, randomly selected variants were validated by conventional Sanger sequencing, and of 15/15 variants in the bulk WES and of 55/55 variants in SCs, to obtain 100% concordance. Intraclonal variation in MM Significantly, ICV was apparent from the SC exome variant data. Total variant counts varied considerably among SCs and most variant positions had at least several cells where no evidence of the variant existed. Bulk WES lacks crucial information We identified an additional 23 variants that were present in 2+ SC exomes, but absent in the bulk MM tumour exomes. Of these, 30% (7 variants) were examined for validation, and were amplifiable in at least one cell to deliver 100% concordance with variant calls. These variants are of significant interest as they reveal a marked occurrence of subclonal mutations in the MM tumour population that are not identified by bulk exome sequencing. They indicate that the mutational status of the MM genome may be substantially underestimated by analysis at the bulk tumour population level. Conclusion In this work we establish the feasibility of SC WES as a method for defining intraclonal genetic variation in MM. Disclosures No relevant conflicts of interest to declare.

Introduction: Whole Exome Sequencing (WES) involves sequencing, analysis of protein coding regions in genome. In present investigation, the potential gene variants were identified in human breast cancer genome using WES data analysis. Materials and Method: The NGS data samples with accession numbers (SRR1274896_1, SRR1274896_2) and (SRR1275000_1, SRR1275000_2) were collected from ENA database. The quality of the samples was assessed by using FastQC tool and followed by aligning samples with reference genome sequence hg38 using Bowtie2 tool. The results were retrieved in SAM format and converted to BAM format and then to sorted bam file using SAM tools, then duplicates were removed using Picard tool. Finally, Variant Calling format file was generated using BCF tools which projected the possible gene variants in the samples. Results: The results showed variant types out of them MUC3A1 showed an average of 53 mutations, highlighting its importance as a potential gene variant observed in breast cancer. Out of nonsynonymous mutations of samples, common gene variants in samples that possess 5 and more mutations were selected. The study was carried out on pathway analysis, domain analysis, gene involvement in biological processes and gene function. Conclusion: Majority of gene variants were involved in DNA Biosynthesis and Protein Biosynthesis and also resulted in tissue specific location. The location of these genes showed mutated genes in cytoplasm and in nucleus indicating the impact of gene variation on intracellular process.

The genomic landscape of breast cancer (BC) is complex. The purpose of this study was to decipher the mutational profiles of Taiwanese patients with BC using next-generation sequencing. We performed whole-exome sequencing on DNA from 24 tumor tissue specimens from BC patients. Sanger sequencing was used to validate the identified variants. Sanger sequencing was also performed on paired adjacent nontumor tissues. After genotype calling and algorithmic annotations, we identified 49 deleterious variants in canonical cancer-related genes in our BC cohort. The most frequently mutated genes were PIK3CA (16.67%), FKBP9 (12.5%), TP53 (12.5%), ATM (8.33%), CHEK2 (8.33%), FOXO3 (8.33%), NTRK1 (8.33%), and NUTM2B (8.33%). Seven mutated variants (ATR p.V1581fs, CSF1R p.R579Q, GATA3 p.T356delinsTMKS, LRP5 p.W389*, MAP3K1 p.T918fs, MET p.K1161fs, and MTR p.P1178S) were novel variants that are not present in any gene mutation database. After grouping the samples according to molecular subtype, we found that the cell cycle, MAPK, and chemokine signaling pathways in the luminal A subtype of BC; the focal adhesion, axon guidance, and endocytosis pathways in the luminal B subtype; and amyotrophic lateral sclerosis in the basal-like subtype were exclusively altered. Survival curve analysis showed that the presence of the MAPK signaling pathway and endocytosis mutations were correlated with a poor prognosis. These survival data were consistent with cBioPortal analyses of 2,051 BC cases. We discovered novel mutations in patients with BC. These results have implications for developing strategic, adjuvant, and gene-targeted therapies.

Background.Previously we established two cell lines (SNU_MM1393_BM and SNU_MM1393_SC) from different tissues (bone marrow and subcutis) of mice which were injected with single patient’s myeloma sample. We tried to define genetic changes specific for each cell line using whole exome sequencing (WES).Materials and Methods.We extracted DNA from SNU_MM1393_BM and SNU_MM1393_SC and performed WES. For single nucleotide variants (SNV) calling, we used Varscan2. Annotation of mutation was performed using ANNOVAR.Results.When calling of somatic mutations was performed, 68 genes were nonsynonymously mutated only in SNU_MM1393_SC, while 136 genes were nonsynonymously mutated only in SNU_MM1393_BM.KIAA1199, FRY, AP3B2,andOPTCwere representative genes specifically mutated in SNU_MM1393_SC. When comparison analysis was performed using TCGA data, mutational pattern of SNU_MM1393_SC resembled that of melanoma mostly. Pathway analysis using KEGG database showed that mutated genes specific of SNU_MM1393_BM were related to differentiation, while those of SNU_MM1393_SC were related to tumorigenesis.Conclusion.We found out genetic changes that underlie tropism of myeloma cells using WES. Genetic signature of cutaneous plasmacytoma shares that of melanoma implying common mechanism for skin tropism.KIAA1199, FRY, AP3B2,andOPTCare candidate genes for skin tropism of cancers.

Abstract Introduction: Transplant-associated thrombotic microangiopathy (TA-TMA) is an increasingly recognized hematologic complication after allogeneic hematopoietic cell transplantation (HCT). While few studies have reported germline association with rare variants in complement genes using targeted next generation sequencing (NGS) method, they were limited by small sample size (≤40 TMA cases) and lack of analysis of non-complement genes (PMIDs 26603840, 32131130). In the present study, we employed whole exome sequencing (WES) to assess rare variant contribution to the development of TMA in a hypothesis-driven pathway-specific approach. Methods: In the current case-control genetic association study conducted at Fred Hutchinson Cancer Research Center, we selected 100 patients with a diagnosis of TMA and pre-transplant DNA samples (case definition described previously in PMID 30940363, 33836868). We then performed incidence density sampling to randomly select 100 non-TMA controls after allogeneic HCT matching by age, sex, race, and year of HCT. WES (germline variant detection 40x) was conducted using Illumina NovaSeq. Sequence reads were mapped to hg38 reference genome followed by deduplication and base quality score recalibration. Joint-genotyping was performed to call single nucleotide polymorphism (SNPs) and insertion/deletion (indels) using the GATK v3.3 and Atlas2. Variants were filtered during quality control (QC) and variant quality score recalibration (VQSR) and annotated using ANNOVAR and Ensembl VEP. To optimize signal detection by reducing neutral background variation, we defined qualifying variants as those meeting all 3 criteria: 1) novel or rare variants with a minor allele frequency (MAF) <1% in the reference database gnomAD; 2) functional variants with missense, frameshift, indel, splice region/acceptor/donor, start/stop gained/lost, coding sequence, and protein altering in VEP; and 3) missense variants previously reported to be likely pathogenic from the ClinVar database or predicted to be deleterious from 4/6 in-silico prediction tools (SIFT, Polyphen-2, MutationTaster, MutationAssessor, FATHMM, and FATHMM-MKL) (Figure 1). We then focused on the exome profiles of 5 a priori selected genetic pathways: complement regulation (17 genes), VWF and coagulation (7 genes), VWF clearance (10 genes), ADAMTS13 mimics or interacting proteins (10 genes), and angiopoietin family and endothelial activation (7 genes). Pathway-based and gene-based collapsing association tests were performed using the Optimized Sequence Kernel Association (SKAT-O) test as an optimal test combining burden test and SKAT. Results: After joint variant calling, 91 TMA cases and 93 non-TMA controls passed all QC filters (Table 1). Among 1,485 variants detected in the 5 pathways after QC, 60 variants (73 total mutations) were considered as qualifying variants with MAF <1%, functional coding, and in-silico pathogenic prediction (Figure 1). From pathway-based analysis, a significant association was observed in the VWF clearance pathway (p=0.041) but not in the complement regulation pathway (p=0.308) or the other 3 pathways (Table 2). From gene-based analysis, the significant association in the VWF clearance pathway appeared to be driven by rare variants within the LRP1 gene (Figure 2), which encodes a member of the low-density lipoprotein receptor family of proteins that contributes to the clearance of VWF (PMID 22234691). Sensitivity analyses performed including all rare variants without in-silico pathogenicity prediction resulted in similar findings. Conclusion: Contrary to the initial hypothesis, we did not observe pathogenic germline rare variants in the complement regulation pathway in patients with TA-TMA. Instead, we found a significant association in the VWF clearance pathway, particularly that of the LRP1 gene. In recent years, researchers have shown that VWF can bind to and activate complement proteins. Impaired VWF clearance could lead to the higher predisposition for complement activation observed in patients with TA-TMA. Future functional studies are needed to determine the impact of VWF clearance on the pathogenesis of the disease. Figure 1 Figure 1. Disclosures Sartain: Alexon Pharamaceuticals: Membership on an entity's Board of Directors or advisory committees. Lee: Incyte: Research Funding; Janssen: Other; Takeda: Research Funding; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Research Funding; Kadmon: Research Funding; National Marrow Donor Program: Membership on an entity's Board of Directors or advisory committees; Syndax: Research Funding; AstraZeneca: Research Funding; Amgen: Research Funding.

Abstract Background: The National Cancer Institute's Patient-Derived Models Repository (NCI PDMR; pdmr.cancer.gov) is developing a variety of patient-derived xenograft (PDX) models for pre-clinical drug studies. All NCI PDMR models undergo quality control (QC) processes. Two unique QC challenges are: a) to assess genomic stability across PDX model passages; and b) to confirm the suitability of PDX-derived cancer associated fibroblasts (CAFs) as germline surrogates when blood is not available. Multiple bioinformatics QC assessments have been developed to measure the genomic fidelity in these PDX models using low-pass whole genome sequencing (LP-WGS) and in CAFs using whole exome sequencing (WES). Methods: LP-WGS was performed on 502 PDX samples from 38 models of rare cancer across passages 2 through 9 and WES was performed on 92 CAFs from 32 different histologies. In the QC workflow for estimating the genomic stability of passages within models, BBSplit was used for the assessment of human/mouse DNA content. CNVkit was utilized for copy number (CN) detection. The fraction of genome changed was calculated by comparing the copy numbers of each passage sample to the original patient sample. To evaluate purity of CAFs, three QC steps were constructed: a) plot of SNP variant allele frequency (ideogram); b) variant annotation using OncoKB (www.oncokb.org); c) percentage of genomic loss of heterozygosity (LOH), based on a set of ~800,000 heterozygous SNPs from a population-level genomic database (gnomAD) based on WES data. Results: PDX models showed genomic stability in CN profile when measured by LP-WGS. Human tumor DNA content remains stable ranging from 75-85% across different tiers of PDX passages from Donor +1 to Donor +6 and more. No models showed statistically significant evolution in CN profile, given the average 5 samples per model in each tier of passages. The QC workflow for CAFs generated five categories based on SNP ideograms, the presence/absence of oncogenic variants and LOH. Following observations were made: a) 72.5% CAFs were confirmed as matched diploid CAFs (category 1); b) 6.6% of CAFs were diploid and had >= 1 germline oncogenic variant - classified as category 2. CAFs in category 1&2 were suitable as germline surrogates; c) 12% of CAFs (category 3) showed putative polyploidy on SNP ideograms with no oncogenic variants and suitable for somatic variant calling; d) 8.8% of CAFs (category 4) had polyploidy and oncogenic variants present; e) LOH high CAF (category 5) - we identified a CAF with 42% LOH, later confirmed to be a tumor cell line by immunohistochemistry (IHC). Other CAFs (n=91) showed little variance, ranging from 0.6%-1.7% LOH. Conclusions: We developed standard QC workflows to evaluate genomic stability of PDX models during passaging and qualify CAFs as germline surrogates for pre-clinical study. Citation Format: Ting-Chia Chang, Li Chen, Biswajit Das, Yvonne A. Evrard, Chris A. Karlovich, Tomas Vilimas, Alyssa Chapman, Nikitha Nair, Luis Romero, Anna J. Lee Fong, Amanda Peach, Brandie Fullmer, Lindsay Dutko, Kelly Benauer, Gloryvee Rivera, Erin Cantu, Shahanawaz Jiwani, Nastaran Neishaboori, Tomas Forbes, Corinne Camalier, Luke Stockwin, Michael Mullendore, Michelle A. Eugeni, Dianne Newton, Melinda G. Hollingshead, Mickey P. Williams, James H. Doroshow. Quality control workflows developed for the NCI Patient-Derived Models Repository using low pass whole genome sequencing and whole exome sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1913.

Abstract Introduction RBC alloimmunization is common in patients with sickle cell disease (SCD). Despite serological matching RBCs for major Rh antigens, Rh alloimmunization remains problematic. The Rh blood group is encoded by two genes RHD and RHCE, which exhibit extensive nucleotide polymorphism and chromosome structural changes, resulting in the formation of Rh variant antigens. Rh variants can result in loss of protein epitopes or expression of neo-epitopes, and are common in SCD patients. Hence SCD patients harboring Rh variants can be predisposed to Rh alloimmunization. Given the limitation of traditional serologic antigen typing for detection of Rh variants, molecular genotyping has become required. A DNA microarray-based platform, BioArray RHCE and RHD BeadChip (Immuncor) is available for RH genotyping. However, it detects the most common, but not all, variants. Whole exome sequence data have been used for prediction of Rh variants (Chou, et. al, Blood Adv., 2017), offer some advantages, including detection of rare variants, structural rearrangements and copy number variation. However, whole genome sequence (WGS) analysis of RHD/RHCE is challenging due to difficulties in mapping next generation sequencing (NGS) reads to this duplicated gene family. We developed a computational algorithm to identify RH variants using WGS data. Methods The pipeline included three major components, RH allele database construction, RH variant calling, and classification of Rh blood group according the identified variants. The RH allele database was built based on NCBI Blood Group Antigen Gene Mutation (BGMUT) and International Society of Blood Transfusion (ISBT) database. Since the alleles in the BGMUT and ISBT databases were specified according to conventional RH genes (RHD, L08429; RHCE, DQ322275) that are different from those on reference human genome, we first called the variations based on the reference human genome. The positions of the identified variations were subsequently corrected to match with the BGMUT and ISBT annotation system. Next, the NGS reads with low base quality and/or mapping quality were discarded during the variation calling step. Synonymous and non-synonymous amino acid changes were characterized for each polymorphism. Haplotypes were constructed for the segments with NGS read support. Gene sequencing coverage was calculated to determine gene deletions or amplifications. Lastly, we implemented an algorithm to predict RH genotypes based on a selection of candidate alleles by read-mapping profile which considers both sequence variations and sequence consistency followed by a likelihood-based ranking of all pairwise combinations of the selected alleles. The allele combination with the highest likelihood is considered the most likely pair of alleles at a given locus. Patient specimens used in this study were from participants of the Sickle Cell Clinical Research and Intervention Program (SCCRIP, Hankins et al. Pediatr Blood Cancer. 2018). Results We validated our method in a cohort of 58 SCD patients whose RH genotypes had been determined by BioArray RhCE and RhD BeadChip and supplementary molecular tests that identify the most common variants among individuals of African descent. In this validation cohort including a total of 11 RHD and 13 RHCE alleles, our approach achieved a concordance rate of 85.85% (91 of 106 alleles) for RHD and 83.02% (88 of 106 alleles) for RHCE genotyping. WGS was highly sensitive in distinguishing homozygosity from heterozygosity of genes. By comparing the numbers of NGS reads on RH regions and whole genome average coverage, heterozygous deletion can be determined. Since WGS provides comprehensive genotyping, our analysis identified single nucleotide polymorphisms that were not identified by the BeadChip and supplemental molecular testing. The final source of discordance was likely due to the short read length of NGS such that haplotype phases cannot be correctly predicted if the variations are separated by thousands of base pairs, for which long read DNA sequencing or RNA/cDNA sequencing are required. Evaluation of the identified discrepancies is ongoing. Conclusions We developed and validated a diagnostic method for RH genotyping that leveraged the accuracy and flexibility of RH genotyping based on WGS data. With further optimization of our method, this may be useful for RBC genotype matching sickle cell patients to blood donors in the future. Disclosures Hankins: Novartis: Research Funding; Global Blood Therapeutics: Research Funding; NCQA: Consultancy; bluebird bio: Consultancy.

Abstract Multiple Myeloma (MM) is a cancer of plasma cells with poor prognosis. Although, MM has been shown to be highly heritable in genealogy studies, no inherited risk-alleles have been identified. We hypothesize MM heritability is in part due to rare, germline variation that can be discovered in high-risk pedigrees. High-risk MM pedigrees were identified using the Utah Population Database which contains both genealogical and state cancer records. High-risk pedigrees were defined to have statistical excess of MM (p < 0.05). In this study we whole-exome sequenced germline DNA from 42 MM cases from high-risk pedigrees. Best practice variant calling, joint genotyping, and quality control were performed on the cases, a set of background controls from the 1000 Genome Project (1000G), and a small set of local controls (for technical artifacts), and resulted in 607,908 variants. We prioritized variants that were: 1) shared by at least 3 related MM cases, 2) absent in local controls, and 3) rare (frequency ≤ 0.01 in 1000G). This prioritization resulted in 116 variants of interest. Of the 116 variants, 3 MM cases in one high-risk pedigree shared multiple variants on chromosome 1p36.11-35.1. To formally assess whether these variants were inherited from a common founder we performed shared genome segment analysis using high-density SNP genotyping. We identified a region at 1p36.11-35.1, 8.9 Mb in length (p = 6.0 × 10-4; 22,000 simulations), providing positive evidence for segregation from a common founder. The segregating variants identified from exome sequencing in this region are in the genes CNKSR1, ARID1A, and SDC3. All three variants are individually predicted to be moderately deleterious. The variant in CNKSR1 falls in a splice region and has minor allele frequency of 0.004 in the 1000G Europeans. This variant was also observed in 3 additional cases in our sequencing set, indicating a strong enrichment of this variant in our high-risk MM cases (6/42 = 0.143). CNKSR1 is involved in the RAS1 and NOTCH signaling pathways and is a known target for cancer therapy. The variants in ARID1A and SDC3 both result in non-synonymous codon changes. ARID1A is commonly mutated across cancers and has been associated with accelerated tumor growth in hepatocellular carcinomas. SDC3 also interacts with NOTCH signaling, a pathway involved in MM growth (especially in patients with MAF translocations) and osteoclastogenesis. The variants in ARID1A and SDC3 are not carried by other cases in our sequencing set, which may indicate the combination of these variants is important to confer risk in the pedigree, or merely that the variants are hitchhikers on the segregating chromosome. Germline risk-alleles will shed light on the genetic factors involved in MM susceptibility and ultimately may provide new avenues for screening, diagnosis and treatment. Here we have identified evidence for segregating variants in a high-risk pedigree that implicate the RAS1 and NOTCH signaling pathways as involved in MM risk. Future work includes confirmation sequencing in the pedigree and a broader set of MM cases, and functional follow-up of the variants and their role in disruption of the genes and pathways. Disclosures No relevant conflicts of interest to declare.

e13155 Background: Next generation sequencing (NGS) technology is transforming the diagnosis and treatment of cancer. However, the massive scale of data has overwhelmed pathologists who need streamlined tools to process this data, automate report generation and minimize human errors. Methods: We developed the Variant interpretation station for ONCology, VONC, as an end-to-end solution for moving from NGS whole exome and transcriptome data to actionable clinical reports that support cancer diagnosis, prognosis, and personalized treatment strategies for solid and hematologic malignancies. Results: VONC integrates all steps for moving from raw NGS data, variant calling and LIMS, to comprehensive annotation of variants. The main functional feature of VONC is a transparent process that effectively combines automated and expert curation to identify clinically relevant and actionable driver variants. VONC also enables efficient management of multi-group, -role, -system and -site curation processes. In contrast to current tools, VONC handles all somatic and constitutional genomic alterations including SNV, indel, CNV, fusion, splicing, and gene expression. Key data sources include 1) 350,000 variants for 50 tumor types across 57,000 sequenced cancer patients; 2) variant frequencies estimated from 1.5M cancer patients; 3) expert curated literature evidence from 16,818 papers covering 26,496 alterations spanning 2,448 cancer driver genes; and 4) curated database of FDA-approved drugs and recruiting clinical trials. VONC presents a prioritized list of variants in oncogenes and tumor suppressors through functional (literature-based) and structure-based (hotspots) algorithms. This is coupled to all supporting information necessary for clinical decision making. Curators can quickly screen variant type, QC metrics, and frequency in sequencing cohorts of cancer patients as well as healthy subjects. Within minutes, variants can be triaged and annotated with FDA approved, NCCN guidelines reported, or literature supported therapeutics, including resistance and contraindicated. Conclusions: VONC is a clinically-ready tool with an intuitive end-user interface tailored for the rapid assessment of variants in cancer patients, to facilitate personalized cancer medicine in a high-throughput laboratory.

287 Background: The TAPUR Study is a phase II multi‐basket study that evaluates the anti‐tumor activity of commercially available targeted agents in pts with advanced cancers with genomic alterations known to be drug targets. Results in two cohorts of PC and GBC pts each with CDKN2A loss or mutation were reported at ASCO 2018. The conclusion was that monotherapy with palbocicilib is not associated with clinical activity in these patients. This may be a false conclusion if the genomic targets were absent in these patients. Methods: A total of 158 GI pts (P = 123, GB = 20, Bile Duct = 15) with deep whole exome sequencing (WES) of tumor and blood samples, and whole transcriptomic sequencing (RNA-Seq) (∼200x106 reads per tumor) were available for this analysis from a commercial database. Variant calling was performed through joint probabilistic analysis of tumor and normal DNA reads, with germline status of variants being determined by heterozygous or homozygous alternate allele fraction in the germline sample. Results: 26 somatic variants and 12 germline variants were detected, with one sample overlapping with a germline and a somatic variant (p.A148T and p.A76Rfs∗44). Counting all 11 discrete germline variants as false positives, a total 37 of 158 samples would be positive for CDKN2A mutant status, a rate of 23% (17%-31% CI). However, if the 8 common germline variants are excluded, the call rate is 29/158 = 18% (12%-25% CI). The false positive rate is 4/158 = 14% (4%-31% CI). By RNAseq, true somatic CDKN2A variants had significantly higher TPM counts than germline variants (T-test p = 0.0002). RB expression was not significantly different between the two groups. Conclusions: The failure of palbociclib to show benefit in CDKN2A mutated PC and GBC patients in the 20 patient cohort of the TAPUR study could possibly be explained by patient selection rather than solely drug failure. It is unlikely related to RB loss.

Identifying high quality variants in whole exome sequencing (WES) analysis can be very complex due to the different modifications that can be made in the sample sequencing preparation protocol. This can adversely affect bioinformatics analysis in the identification of variants. The evaluation and correlation of the quality parameters of each analysis stage could help to obtain a better accuracy and precision in the identification of the variants. Furthermore, after identifying high quality variants, the use of reference databases where the clinical significance and frequency of the variants can be consulted, allows for a more accurate diagnosis. During laboratory and bioinformatics analysis, it is possible to calculate many metrics to evaluate the quality of the data being processed. All this data is usually looked at separately and their history is lost over time. Besides, the process of comparing a new workflow to existing ones can be very time-consuming when done manually. In addition, for a significant diagnosis of rare variants, it is important to consider the variant frequency in the sample population. For this reason, a database that incorporates all quality metrics from the entire WES analysis over time and collects population-specific variants for accurate clinical variant identification, is needed. This thesis aims to optimise the evaluation of quality metrics and the classification of variants in the Italian population through the creation of a Structured Query Language (SQL) database directly linked to a website for more intuitive use. The thesis sets out the structure of the database and the configuration of the web page created. Furthermore, during the writing of the thesis, approximately 2,500 exomes were analysed and all quality control parameters derived from both laboratory and bioinformatics analyses were collected. All the data obtained were uploaded to the database in order to verify the usefulness of the application in monitoring data quality trends over time and in identifying possible problems. Two examples of problems identified by the implemented application and subsequently solved by modifications to the laboratory protocol are presented. Moreover, the potential of the database to simplify comparisons between existing and new laboratory protocols storing quality control parameters, is shown. All variants identified in the analysed samples were uploaded to create an accessible reference of genetic variation in Italians. The correct classification of the Italian variants is shown in relation to renowned databases that only report a broader view of the European population. This approach enables researchers to classify variants that are not observed in the most widely used databases (gnomAD Exomes, ExAC, 1KgPhase3). It also allows the identification of rare variants that are generally classified as common and might represent a disease predisposition in the Italian population. In addition, it is possible to recognize common and non-damaging variants in the Italian population that are classified as rare in the European population. In conclusion, the reported results and examples have shown how the new application (extended database with its own website) simplifies and facilitates the identification of problems in clinical WES analysis. It also makes the comparison between the various laboratory protocols easier, allowing for more precision in exome analysis aimed at identifying variants. Finally, the specific investigation of the Italian variants could improve diagnostic accuracy in the specific population.