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• Matching of actionable mutations with therapies in cancer patients: comparison of three commercial decision support platforms

  Purpose: In oncology, precision medicine centers on tailoring the most appropriate therapy to a patient according to their tumor molecular profile. We addressed the question whether there is a consensus in the classification of actionability of somatic variants and their alignment to treatment recommendations. To this end, we evaluated three commercial clinical decision support tools to assess their variant classification and drug matching strategies.Methods: In this two centers study 48 patients with metastatic breast (n=12), colorectal (n=17) or non-small cell lung cancer (n=19) were recruited based on circulating tumor DNA levels in peripheral blood. In individual patients’ plasma samples, we established somatic copy number alterations and somatic mutations across a 77 cancer-associated gene panel and exposed the obtained molecular profiles to the decision support packages NAVIFY Mutation Profiler (NMP; Roche), Qiagen Clinical Insight (QCI) Interpret (Qiagen) and CureMatch Bionov (CureMatch).Results: In the plasma samples, altogether 492 somatic alterations were assessed. Each decision support platform varied in their format of data input, mode of variant classification and strategies for identifying druggable targets and clinical trials, which resulted in discrepancies in tier classification as well as designations of actionability. The frequency of concordant actionable events for tier I-A or tier I-B classifications, i.e. those with the strongest clinical evidence, was only 4.3%, 9.5% and 28.4% when comparing NMP with QCI, NMP with CureMatch, and CureMatch with QCI, respectively. As a consequence, the obtained treatment recommendations differed drastically, and alignment of treatment recommendations was limited to established predictive markers.Conclusions: The lack of standards for the extraction of treatment recommendations from comprehensive tumor molecular profiling data challenge the promising concepts of precision oncology.

  Study EGAS00001004383

• Analysis of RAD51C promoter methylation using targeted bisulfite sequencing (amplicon sequencing) in ovarian cancer pre-clinical models and patient samples.

  In high-grade serous ovarian carcinoma (HGSC), deleterious mutations in the DNA repair gene RAD51C are established drivers of defective homologous recombination and are emerging biomarkers of PARP inhibitor (PARPi) sensitivity. RAD51C promoter methylation (meRAD51C) is detected at similar frequencies to mutations, yet its effects on PARPi responses remain unresolved. In this study, three HGSC patient-derived xenograft (PDX) models with methylation at most or all examined CpG sites in the RAD51C promoter show responses to PARPi. Both complete and heterogeneous methylation patterns were associated with RAD51C gene silencing and homologous recombination deficiency (HRD). PDX models lost meRAD51C following treatment with PARPi rucaparib or niraparib, where a single unmethylated copy of RAD51C was sufficient to drive PARPi resistance. Genomic copy number profiling of one of the PDX models using SNP arrays revealed that this resistance was acquired independently in two genetically distinct lineages. In a cohort of 11 patients with RAD51C-methylated HGSC, various patterns of meRAD51C were associated with genomic 'scarring', indicative of HRD history, but exhibited no clear correlations with clinical outcome. Differences in methylation stability under treatment pressure were also observed between patients, where one HGSC was found to maintain meRAD51C after 6 lines of therapy (4 platinum-based), whilst another HGSC sample was found to have heterozygous meRAD51C and elevated RAD51C gene expression (relative to homozygous meRAD51C controls) after only neo-adjuvant chemotherapy. As meRAD51C loss in a single gene copy was sufficient to cause PARPi resistance in PDX, methylation zygosity should be carefully assessed in previously treated patients when considering PARPi therapy.

  Study EGAS00001005395

• Functional Mapping of AKT Signaling and Biomarkers of Response From the FAIRLANE Trial of Neoadjuvant Ipatasertib Plus Paclitaxel for Triple-Negative Breast Cancer

  Purpose: Despite extensive genomic and transcriptomic profiling, it remains unknown how signaling pathways are differentially activated and how tumors are differentially sensitized to certain perturbations. Here, we aim to characterize AKT signaling activity and its association with other genomic or immunohistochemistry-based PI3K/AKT pathway biomarkers as well as the clinical activity of ipatasertib (AKT inhibitor) in the FAIRLANE trial. Experimental Design: In FAIRLANE, 151 patients with early triple-negative breast cancer were randomized 1:1 to receive paclitaxel with ipatasertib or placebo for 12 weeks prior to surgery. Adding ipatasertib did not increase pathologic complete response rate and numerically improved overall response rate by magnetic resonance imaging (MRI). We used reverse-phase protein microarrays (RPPA) to examine the total level and/or phosphorylation states of over 100 proteins in various signaling or cell processes including PI3K/AKT and mTOR signaling. 125 baseline and 127 on-treatment samples were evaluable by RPPA, with 110 paired samples at both time points. Results: Tumors with genomic/protein alterations in PIK3CA/AKT1/PTEN were associated with higher levels of AKT phosphorylation. In addition, phosphorylated(p)AKT levels exhibited a significant association with enriched clinical benefit of ipatasertib, and identified patients who received benefit in the absence of PIK3CA/AKT1/PTEN alterations. Ipatasertib treatment led to a down-regulation of AKT/mTORC1 signaling, which was more pronounced among the tumors with PIK3CA/AKT1/PTEN alterations or among the responders to the treatment. Conclusions: We showed that the high baseline pAKT levels are associated with the alterations of PI3K/AKT pathway components and enriched benefit of ipatasertib in TNBC.

  Study EGAS00001005892

• Analysis of error profiles in deep next-generation sequencing data

  BackgroundSequencing errors are key confounding factors for detecting low-frequency genetic variants that are important for cancer molecular diagnosis, treatment, and surveillance using deep next-generation sequencing (NGS). However, there is a lack of comprehensive understanding of errors introduced at various steps of a conventional NGS workflow, such as sample handling, library preparation, PCR enrichment, and sequencing. In this study, we use current NGS technology to systematically investigate these questions.ResultsBy evaluating read-specific error distributions, we discover that the substitution error rate can be computationally suppressed to 10-5 to 10-4, which is 10- to 100-fold lower than generally considered achievable (10-3) in the current literature. We then quantify substitution errors attributable to sample handling, library preparation, enrichment PCR, and sequencing by using multiple deep sequencing datasets. We find that error rates differ by nucleotide substitution types, ranging from 10-5 for A>C/T>G, C>A/G>T, and C>G/G>C changes to 10-4 for A>G/T>C changes. Furthermore, C>T/G>A errors exhibit strong sequence context dependency, sample-specific effects dominate elevated C>A/G>T errors, and target enrichment PCR led to aprox 6-fold increase of overall error rate. We also find that more than 70% of hotspot variants can be detected at 0.1% - 0.01% frequency with the current NGS technology by applying in-silico error suppression.ConclusionsWe present the first comprehensive analysis of sequencing error sources in conventional NGS workflows. The error profiles revealed by our study highlight new directions for further improving NGS analysis accuracy both experimentally and computationally, ultimately enhancing the precision of deep sequencing.

  Study EGAS00001003444

• Exome sequencing from cfDNA blood samples. 159 samples at 2x101bp Illumina reads in Fastq format.

  Differential presence of exons (DPE) by next generation sequencing (NGS) is a method of interpretation of whole exome sequencing. This method has been proposed to design a predictive and diagnostic algorithm with clinical value in plasma from patients bearing colorectal cancer (CRC). The aim of the present study was to determine a common exonic signature to discriminate between different clinical pictures, such as non-metastatic, metastatic and non-disease (healthy), using a sustainable and novel technology in liquid biopsy. Through DPE analysis, we determined the differences in DNA exon levels circulating in plasma between patients bearing CRC vs. healthy, patients bearing CRC metastasis vs. non-metastatic and patients bearing CRC metastasis vs. healthy comparisons. We identified a set of 510 exons (469 up and 41 down) whose differential presence in plasma allowed us to group and classify between the three cohorts. Random forest classification (machine learning) was performed and an estimated out-of-bag (OOB) error rate of 35.9% was obtained and the predictive model had an accuracy of 75% with a confidence interval (CI) of 56.6-88.5. In conclusion, the DPE analysis allowed us to discriminate between different patho-physiological status such as metastatic, non-metastatic and healthy donors. In addition, this analysis allowed us to obtain very significant values with respect to previous published results, since we increased the number of samples in our study. These results suggest that circulating DNA in patient’s plasma may be actively released by cells and may be involved in intercellular communication and, therefore, may play a pivotal role in malignant transformation (genometastasis).

  Study EGAS00001006656

• Analysis of T-cell receptor clonotypes in tumor micro-environment identifies shared cancer type-specific signatures

  Despite the conventional view that a truly random V(D)J recombination process should generate a highly diverse immune repertoire, emerging reports suggest that there is a certain bias towards the generation of shared/public immune receptor chains. These studies were performed in viral diseases where public T-cell receptors (TCR) appear to confer better protective responses. Selective pressures generating common TCR clonotypes are currently not well understood but it is believed that they confer a growth advantage. As very little is known about public TCR clonotypes in cancer, here we set out to determine the extent of shared TCR clonotypes in the intra-tumor microenvironments of virus- and non-virus- driven head and neck cancers using TCR sequencing. We report that tumor-infiltrating T-cell clonotypes were indeed shared across individuals with the same cancer type, where the majority of shared sequences were specific to the cancer type (ie viral versus non-viral). These shared clonotypes were not particularly enriched in EBV-associated nasopharynx cancer, but in both cancers, exhibited distinct characteristics, namely shorter CDR3 lengths, restricted V- and J-gene usages and also demonstrated convergent V(D)J recombination. Many of these shared TCRs were expressed in patients with a shared HLA background. Pattern recognition of CDR3 amino acid sequences revealed strong convergence to specific motifs that were unique to each cancer type, suggesting they may be enriched for specificity to common antigens found in the tumor microenvironment. The identification of shared TCRs in infiltrating tumor T-cells not only adds to our understanding of the tumor adaptive immune recognition but could also serve as disease-specific biomarkers and guide the development of future immunotherapies.

  Study EGAS00001005480

• Whole genome sequencing of 108 epileptic patients from CENet cohort

  Epileptic patients have been sequenced as part of the CENet cohort which is composed of patients with Genetic Generalized Epilepsy (GGE) or Non-Acquired Focal Epilepsy (NAFE) collected in CHUM Research Center in Montreal. Patients were diagnosed by epileptologists. The clinical epilepsy phenotype was classified according to the current classification by the International League against Epilepsy (ILAE). Libraries preparation and whole-genome sequencing: gDNA was cleaned up using ZR-96 DNA Clean & ConcentratorTM-5 Kit (Zymo) prior to being quantified using the Quant-iTTM PicoGreen dsDNA Assay Kit (Life Technologies) and its integrity assessed on agarose gels. Libraries were generated using the TruSeq DNA PCR-Free Library Preparation Kit (Illumina) according to the manufacturer’s recommendations. Libraries were quantified using the Quant-iTTM PicoGreen dsDNA Assay Kit (Life Technologies) and the Kapa Illumina GA with Revised Primers-SYBR Fast Universal kit (Kapa Biosystems). Average size fragment was determined using a LabChip GX (PerkinElmer) instrument. The libraries were denatured in 0.05N NaOH and diluted to 8pM using HT1 buffer. The clustering was done on a Illumina cBot and the flowcell was ran on a HiSeq 2500 for 2x125 cycles (paired-end mode) using v4 chemistry and following the manufacturer's instructions. A phiX library was used as a control and mixed with libraries at 0.01 level. Bioinformatics: The Illumina control software was HCS 2.2.58, the real-time analysis program was RTA v. 1.18.64. Program bcl2fastq v1.8.4 was used to demultiplex samples and generate fastq reads. The filtered reads were aligned to reference Homo_sapiens assembly b37. Each readset was aligned to create a .cram file.

  Study EGAS00001007507

• Whole genome sequencing of tumor samples from advanced pre-treated NSCLC patients recruited in a phase I/II single-arm trial utilising CVA21, an oncolytic coxsackie virus, in combination with pembrolizumab.

  Acquired or de novo resistance to immune checkpoint inhibitors occurs in the majority of advanced non-small cell lung cancers (NSCLC). There is an unmet need to improve outcomes for patients with this condition. Oncolytic viruses represent an attractive treatment approach due to their dual activity in inducing tumor cell lysis directly and potentially augmenting anti-tumor immunity. Here, we present the safety, efficacy and translational findings from a phase I/II single-arm trial utilising CVA21, an oncolytic coxsackie virus, in combination with pembrolizumab in patients with advanced pre-treated NSCLC. We performed paired biopsies pre- and post-treatment in 10 patients who received intravenous CVA21 and pembrolizumab, 8 of whom had prior treatment with immune checkpoint inhibitor (ICI) therapy. Whole genome sequencing and spatial proteomics were performed to comprehensively characterise the response to CVA21. Combination CVA21/pembrolizumab (anti PD-1) therapy was well tolerated with no serious treatment-related adverse events. Partial responses were seen in two patients with prior acquired anti-PD-1 resistance and disease stabilisation in 6 patients, giving a clinical benefit rate of 80%. High baseline tumor mutational burden and PD-L1 expression were observed in patients with better response to treatment. Interestingly, an increase in antigen presentation and CD8+ T cell infiltration was observed on treatment compared with baseline in patients with better progression-free survival. This study demonstrates the potential of CVA21 to modulate the immunogenicity of tumor cells and remodelling the tumor microenvironment, providing novel insights for patient selection for trials involving novel immunotherapeutic approaches.

  Study EGAS00001008258

• H3Africa - Genomic Characterization and Surveillance of Microbial Threats in West Africa

  Viral diseases are remarkably heterogeneous in their presentation and clinical course. Although diseases such as Lassa Fever and Ebola Virus Disease are thought to be severe and rapidly fatal, they can often present with mild symptoms or remain asymptomatic. Host genetic variability is likely an important mediator of this heterogeneity. Complete characterization of the clinical, laboratory, and host genetic features of these illnesses is not only imperative to rapid diagnosis and effective clinical care, but also important for further scientific study. In this project, we propose to address this need through deep characterization of the clinical and laboratory features of viral disease and through study of genetic mediators of resistance to Lassa Fever and Ebola Virus Disease. Will carry out deep clinical characterization and laboratory assessment of viral febrile cases at two West African Collaborative Centers, and will use this data to develop prognostic models using cutting-edge tools in exploratory data visualization and machine learning. Investigate host genetic factors of Lassa Fever and Ebola Virus Disease through genome-wide association studies. We will then develop field-deployable genotyping assays for the top-associated variants, and apply them in new cohorts to replicate our findings and investigate patterns of genetic resistance in clinical subgroups. This project can shed light on the clinical manifestations of viral illness and their host genetic mediators, leading to potential new insights in disease biology and pathophysiology. We will also create a rich resource of clinical, laboratory, and genetic data for these illnesses in two countries in West Africa, which will serve as a foundation for advancing the clinical care and scientific study of viral disease in the region.

  Study EGAS00001007250

• WGSPD Project 3 UCLA DAC

  Data Access Committee to review applications to access Whole Genome Sequencing in Psychiatric Disorders (WGSPD) Project 3 data (WGS) from Dutch cohorts.

  Dac EGAC50000000442