Large-scale Next-generation Sequencing Research Articles

SwissGenVar: A Platform for Clinical-Grade Interpretation of Genetic Variants to Foster Personalized Healthcare in Switzerland

Large-scale next-generation sequencing (NGS) germline testing is technically feasible today, but variant interpretation represents a major bottleneck in analysis workflows. This includes extensive variant prioritization, annotation, and time-consuming evidence curation. The scale of the interpretation problem is massive, and variants of uncertain significance (VUSs) are a challenge to personalized medicine. This challenge is further compounded by the complexity and heterogeneity of the standards used to describe genetic variants and the associated phenotypes when searching for relevant information to support clinical decision making. To address this, all five Swiss academic institutions for Medical Genetics joined forces with the Swiss Institute of Bioinformatics (SIB) to create SwissGenVar as a user-friendly nationwide repository and sharing platform for genetic variant data generated during routine diagnostic procedures and research sequencing projects. Its aim is to provide a protected environment for expert evidence sharing about individual variants to harmonize and upscale their significance interpretation at the clinical grade according to international standards. To corroborate the clinical assessment, the variant-related data will be combined with consented high-quality clinical information. Broader visibility will be achieved by interfacing with international databases, thus supporting global initiatives in personalized healthcare.

Comparative analysis of actionable gene reporting in targeted panels versus comprehensive NGS testing for solid tumor samples.

11185 Background: The landscape of actionable genes significantly influences clinical decisions in cancer diagnosis, prognosis, and treatment planning. In a clinical context, the selection of NGS panels hinges on striking a balance among informative data, insurance coverage, and the preferences of patients and healthcare providers. Molecular pathology reference laboratories commonly employ large-scale next-generation sequencing (NGS) testing, curating smaller, disease-specific targeted panels. This study investigates the prevalence of unreported actionable genes in real-world clinical samples subjected to disease-specific panels. Methods: We analyzed SNV/InDel DNA variants in 795 clinical solid tumor samples using the Illumina TSO 500 platform for comprehensive NGS panel testing of 517 genes. Comprehensive NGS panel-tested samples were intersected with disease-specific targeted NGS panels for breast (54 genes), brain (62 genes), colorectal (36 genes), and lung (44 genes). An artificial filter was applied to align targeted panels with patient disease. Variants were classified based on pathogenicity, and benign variants were filtered out. Detected variants were assessed for actionable mutations as defined by the FDA recognized OncoKB actionable gene database criteria. Additionally, 1484 lung patient samples tested with a lung-specific targeted panel were de-identified and unmasked to the comprehensive NGS tumor profiling to investigate the prevalence of actionable alteration beyond those covered by the disease-specific panel. Results: The study revealed that 3.2% of breast, 0% of brain, 66.2% of colorectal, and 2.7% of lung samples had additional actionable mutations undisclosed by the targeted panels. The number of actionable genes beyond those included in the targeted panels were 14 for breast, 2 for brain, 25 for lung, and 41 for colorectal cancer. This underscores the importance of comprehensive testing to fully capture each patient tumor’s actionable mutation profile. Conclusions: Our findings detect masked actionable mutations in down-sampled targeted panels and highlight the need for comprehensive testing to accurately identify actionable mutations in various cancer types. Reporting actionable mutations can significantly influence clinical decision-making, emphasizing the importance of NGS test selection in routine molecular diagnostics as well as the impact of integrating comprehensive NGS testing.

Molecular Mechanisms of Prostate Cancer Development in the Precision Medicine Era: A Comprehensive Review

The progression of prostate cancer (PCa) relies on the activation of the androgen receptor (AR) by androgens. Despite efforts to block this pathway through androgen deprivation therapy, resistance can occur through several mechanisms, including the abnormal activation of AR, resulting in castration-resistant PCa following the introduction of treatment. Mutations, amplifications, and splicing variants in AR-related genes have garnered attention in this regard. Furthermore, recent large-scale next-generation sequencing analysis has revealed the critical roles of AR and AR-related genes, as well as the DNA repair, PI3K, and cell cycle pathways, in the onset and progression of PCa. Moreover, research on epigenomics and microRNA has increasingly become popular; however, it has not translated into the development of effective therapeutic strategies. Additionally, treatments targeting homologous recombination repair mutations and the PI3K/Akt pathway have been developed and are increasingly accessible, and multiple clinical trials have investigated the efficacy of immune checkpoint inhibitors. In this comprehensive review, we outline the status of PCa research in genomics and briefly explore potential future developments in the field of epigenetic modifications and microRNAs.

Integrative NGS testing reveals clonal dynamics of adverse genomic defects contributing to a natural progression in treatment-naïve CLL patients.

Large-scale next-generation sequencing (NGS) studies revealed extensive genetic heterogeneity, driving a highly variable clinical course of chronic lymphocytic leukaemia (CLL). The evolution of subclonal populations contributes to diverse therapy responses and disease refractoriness. Besides, the dynamics and impact of subpopulations before therapy initiation are not well understood. We examined changes in genomic defects in serial samples of 100 untreated CLL patients, spanning from indolent to aggressive disease. A comprehensive NGS panel LYNX, which provides targeted mutational analysis and genome-wide chromosomal defect assessment, was employed. We observed dynamic changes in the composition and/or proportion of genomic aberrations in most patients (62%). Clonal evolution of gene variants prevailed over the chromosomal alterations. Unsupervised clustering based on aberration dynamics revealed four groups of patients with different clinical behaviour. An adverse cluster was associated with fast progression and early therapy need, characterized by the expansion of TP53 defects, ATM mutations, and 18p- alongside dynamic SF3B1 mutations. Our results show that clonal evolution is active even without therapy pressure and that repeated genetic testing can be clinically relevant during long-term patient monitoring. Moreover, integrative NGS testing contributes to the consolidated evaluation of results and accurate assessment of individual patient prognosis.

Distinct genomic landscapes in radiation-associated angiosarcoma compared with other radiation-associated sarcoma histologies.

MYC amplifications have been frequently detected in radiation (RT)-associated angiosarcomas (ASs) by low-resolution molecular methods. However, large-scale next-generation sequencing (NGS) studies to investigate the genomic landscape of RT-AS are scarce, particularly compared with other RT-associated sarcomas. We performed a detailed comparative genomic investigation of RT-AS versus other RT-associated histotypes, as well as sporadic sarcomas with similar histologies. Our institutional targeted DNA-NGS assay database was searched for RT-associated sarcomas. Clinical outcome data, pathologic diagnosis, and the types and frequencies of genomic alterations, including single nucleotide variants (SNVs) and copy number alterations (CNAs), were analyzed. The cohort consisted of 82 patients, 68 (83%) females and 14 (17%) males, aged 37-88 (mean 64) years. Forty-four RT-ASs (38 from breast) and 38 RT sarcomas of other histologies, including 12 malignant peripheral nerve sheath tumors (RT-MPNSTs), 14 undifferentiated pleomorphic sarcomas (RT-UPSs), and 12 osteosarcomas (RT-OSs), were included. Median time intervals from radiation to initial diagnosis in RT-AS (8.0 years) were significantly lower than those in RT-MPNST and RT-UPS (12.5 and 18.5 years), respectively. Each RT-sarcoma histotype harbored distinct mutations and CNAs. RT-associated AS had more frequent MYC, FLT4, CRKL, HRAS, and KMT2D alterations than sporadic AS (enriched in TP53, KDR, ATM, ATRX), whereas the mutational landscapes of MPNST, UPS, and OS were similar in both RT and non-RT settings. CDKN2A/B deletions and TP53 alterations were infrequent in RT-AS compared with other RT sarcomas. Among RT sarcomas, RT-AS harbored the lowest fraction of genome altered (FGA), while RT-MPNST showed the highest FGA. RT-AS had the lowest insertion:SNV and deletion:SNV ratios, while RT-UPS had the highest. The predominant mutational signatures were associated with errors in DNA repair and replication. In conclusion, RT-AS has a distinct genomic landscape compared with other RT sarcomas and sporadic AS. Potential molecular targets for precision medicine may be histotype-dependent. © 2023 The Pathological Society of Great Britain and Ireland.

Large-scale antibody immune response mapping of splenic B cells and bone marrow plasma cells in a transgenic mouse model.

Molecular characterization of antibody immunity and human antibody discovery is mainly carried out using peripheral memory B cells, and occasionally plasmablasts, that express B cell receptors (BCRs) on their cell surface. Despite the importance of plasma cells (PCs) as the dominant source of circulating antibodies in serum, PCs are rarely utilized because they do not express surface BCRs and cannot be analyzed using antigen-based fluorescence-activated cell sorting. Here, we studied the antibodies encoded by the entire mature B cell populations, including PCs, and compared the antibody repertoires of bone marrow and spleen compartments elicited by immunization in a human immunoglobulin transgenic mouse strain. To circumvent prior technical limitations for analysis of plasma cells, we applied single-cell antibody heavy and light chain gene capture from the entire mature B cell repertoires followed by yeast display functional analysis using a cytokine as a model immunogen. We performed affinity-based sorting of antibody yeast display libraries and large-scale next-generation sequencing analyses to follow antibody lineage performance, with experimental validation of 76 monoclonal antibodies against the cytokine antigen that identified three antibodies with exquisite double-digit picomolar binding affinity. We observed that spleen B cell populations generated higher affinity antibodies compared to bone marrow PCs and that antigen-specific splenic B cells had higher average levels of somatic hypermutation. A degree of clonal overlap was also observed between bone marrow and spleen antibody repertoires, indicating common origins of certain clones across lymphoid compartments. These data demonstrate a new capacity to functionally analyze antigen-specific B cell populations of different lymphoid organs, including PCs, for high-affinity antibody discovery and detailed fundamental studies of antibody immunity.

Hereditary cancer syndromes.

Hereditary cancer syndromes (HCSs) are arguably the most frequent category of Mendelian genetic diseases, as at least 2% of presumably healthy subjects carry highly-penetrant tumor-predisposing pathogenic variants (PVs). Hereditary breast-ovarian cancer and Lynch syndrome make the highest contribution to cancer morbidity; in addition, there are several dozen less frequent types of familial tumors. The development of the majority albeit not all hereditary malignancies involves two-hit mechanism, i.e. the somatic inactivation of the remaining copy of the affected gene. Earlier studies on cancer families suggested nearly fatal penetrance for the majority of HCS genes; however, population-based investigations and especially large-scale next-generation sequencing data sets demonstrate that the presence of some highly-penetrant PVs is often compatible with healthy status. Hereditary cancer research initially focused mainly on cancer detection and prevention. Recent studies identified multiple HCS-specific drug vulnerabilities, which translated into the development of highly efficient therapeutic options.

Genomic Profiling With Large-Scale Next-Generation Sequencing Panels Distinguishes Separate Primary Lung Adenocarcinomas From Intrapulmonary Metastases

The distinction between different separate primary lung cancers (SPLCs) and intrapulmonary metastases (IPMs) is a challenging but clinically significant issue. Histopathology-based classification is the current practice; however, it is subjective and affected by interobserver variability. Recently, next-generation sequencing (NGS) panels have been used in lung cancer diagnostics. This study aimed to investigate the value of large-scale NGS panels for distinguishing between SPLCs and IPMs. A total of 32 patients with 69 lung adenocarcinomas were included. Comprehensive histopathologic assessments of multiple pulmonary adenocarcinomas were performed independently by 3 pathologists. The consensus of histopathologic classification was determined by a majority vote. Genomic analysis was performed using an amplicon-based large-scale NGS panel, targeting single-nucleotide variants and short insertions and deletions in 409 genes. Tumor pairs were classified as SPLCs or IPMs according to a predefined molecular classification algorithm. Using NGS and our molecular classification algorithm, 97.6% of the tumor pairs can be unambiguously classified as SPLCs or IPMs. The molecular classification was predictive of postoperative clinical outcomes in terms of overall survival (P = .015) and recurrence-free interval (P = .0012). There was a moderate interobserver agreement regarding histopathologic classification (κ = 0.524 at the tumor pair level). The concordance between histopathologic and molecular classification was 100% in cases where pathologists reached a complete agreement but only 53.3% where they did not. This study showed that large-scale NGS panels are a powerful modality that can help distinguish SPLCs from IPMs in patients with multiple lung adenocarcinomas and objectively provide accurate risk stratification.

From the patient to the population: Use of genomics for population screening.

Genomic medicine is expanding from a focus on diagnosis at the patient level to prevention at the population level given the ongoing under-ascertainment of high-risk and actionable genetic conditions using current strategies, particularly hereditary breast and ovarian cancer (HBOC), Lynch Syndrome (LS) and familial hypercholesterolemia (FH). The availability of large-scale next-generation sequencing strategies and preventive options for these conditions makes it increasingly feasible to screen pre-symptomatic individuals through public health-based approaches, rather than restricting testing to high-risk groups. This raises anew, and with urgency, questions about the limits of screening as well as the moral authority and capacity to screen for genetic conditions at a population level. We aimed to answer some of these critical questions by using the WHO Wilson and Jungner criteria to guide a synthesis of current evidence on population genomic screening for HBOC, LS, and FH.

Abstract 1900: Rapid variant detection and annotations from next generation sequencing data using a GPU accelerated framework

Abstract Introduction Next Generation Sequencing (NGS) played a crucial role in revolutionizing the field of human genetics to become an integral part of research and clinical management. Some examples are diagnosis and treatment of cancer patients, identifying syndromes in the NICU (Neonatal Intensive Care Units), insights into neuro-degenerative disease to name a few. NGS integrated with multi-omics data provides greater insight to underlying molecular mechanisms and helps to identify alterations specific to the disease condition. Accurate and robust detection of somatic and germline genomic alterations from whole genome sequencing (WGS) data is complex where the datasets tend to be very large, and they require highly compute intensive operations that often take days to weeks to complete. By leveraging the Graphical Processing Unit (GPU) accelerated framework, it is possible to analyze the WGS data and identify clinically actionable variants with fast turnaround times without compromising on the accuracy. In this study, we implemented a GPU accelerated framework for most used variant callers ie. Mutect2, MuSE, Somatic Sniper and LoFreq with significantly faster run times in comparison to their Central Processing Unit (CPU) counterpart. Methods Commonly used CPU based somatic variant calling algorithms Mutect2, MuSE, Somatic Sniper and LoFreq, were selected and subsequently accelerated to run on GPUs. The goal of this work is to generate results that are equivalent to the CPU version of these algorithms, but at much faster run times. As a control data set, we utilize the SEQC2 consortium WGS dataset consisting of ~50x tumor and normal samples to assess the performance of individual CPU variant callers to their GPU equivalents. Results The GPU accelerated framework overall provides 10-66x faster run times compared to their CPU-based counterparts. Individually, CPU vs GPU comparisons are, Mutect2 achieved a maximum acceleration of 66x, followed by MuSE, LoFreq both 11x and Somatic Sniper 10x acceleration respectively. The results from accelerated MuSE, LoFreq, Somatic sniper are identical in comparison to the CPU-based somatic variant callers, For Mutect2 we achieve 99.9% sensitivity and 99.85% precision. Discussion Recently, machine-learning based variant detection algorithms exhibited better performance compared to the statistics based heuristic approaches. Here, we have presented the results for an ensemble of four variant callers to be used as the starting point for an ongoing effort to develop a deep learning-based approach for somatic variant detection and smart filtering algorithms. The goal is to enable improved variant calling and provide a GPU computing framework for faster, better, and reproducible workflows to analyze large scale NGS datasets. Citation Format: Pankaj Vats, Ankit Sethia, Mehrzad Samadi, Timothy T. Harkins. Rapid variant detection and annotations from next generation sequencing data using a GPU accelerated framework [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 1900.

PhenGenVar: A User-Friendly Genetic Variant Detection and Visualization Tool for Precision Medicine.

Precision medicine has been revolutionized by the advent of high-throughput next-generation sequencing (NGS) technology and development of various bioinformatic analysis tools for large-scale NGS big data. At the population level, biomedical studies have identified human diseases and phenotype-associated genetic variations using NGS technology, such as whole-genome sequencing, exome sequencing, and gene panel sequencing. Furthermore, patients’ genetic variations related to a specific phenotype can also be identified by analyzing their genomic information. These breakthroughs paved the way for the clinical diagnosis and precise treatment of patients’ diseases. Although many bioinformatics tools have been developed to analyze the genetic variations from the individual patient’s NGS data, it is still challenging to develop user-friendly programs for clinical physicians who do not have bioinformatics programing skills to diagnose a patient’s disease using the genomic data. In response to this demand, we developed a Phenotype to Genotype Variation program (PhenGenVar), which is a user-friendly interface for monitoring the variations in a gene of interest for molecular diagnosis. This allows for flexible filtering and browsing of variants of the disease and phenotype-associated genes. To test this program, we analyzed the whole-genome sequencing data of an anonymous person from the 1000 human genome project data. As a result, we were able to identify several genomic variations, including single-nucleotide polymorphism, insertions, and deletions in specific gene regions. Therefore, PhenGenVar can be used to diagnose a patient’s disease. PhenGenVar is freely accessible and is available at our website.

Clinical pharmacogenetic analysis in 5,001 individuals with diagnostic Exome Sequencing data

Exome sequencing is utilized in routine clinical genetic diagnosis. The technical robustness of repurposing large-scale next-generation sequencing data for pharmacogenetics has been demonstrated, supporting the implementation of preemptive pharmacogenetic strategies based on adding clinical pharmacogenetic interpretation to exomes. However, a comprehensive study analyzing all actionable pharmacogenetic alleles contained in international guidelines and applied to diagnostic exome data has not been performed. Here, we carried out a systematic analysis based on 5001 Spanish or Latin American individuals with diagnostic exome data, either Whole Exome Sequencing (80%), or the so-called Clinical Exome Sequencing (20%) (60 Mb and 17 Mb, respectively), to provide with global and gene-specific clinical pharmacogenetic utility data. 788 pharmacogenetic alleles, distributed through 19 genes included in Clinical Pharmacogenetics Implementation Consortium guidelines were analyzed. We established that Whole Exome and Clinical Exome Sequencing performed similarly, and 280 alleles in 11 genes (CACNA1S, CYP2B6, CYP2C9, CYP4F2, DPYD, G6PD, NUDT15, RYR1, SLCO1B1, TPMT, and UGT1A1) could be used to inform of pharmacogenetic phenotypes that change drug prescription. Each individual carried in average 2.2 alleles and overall 95% (n = 4646) of the cohort could be informed of at least one actionable pharmacogenetic phenotype. Differences in variant allele frequency were observed among the populations studied and the corresponding gnomAD population for 7.9% of the variants. In addition, in the 11 selected genes we uncovered 197 novel variants, among which 27 were loss-of-function. In conclusion, we provide with the landscape of actionable pharmacogenetic information contained in diagnostic exomes, that can be used preemptively in the clinics.

An Integrated Bioinformatics and Functional Approach for miRNA Validation.

MicroRNAs (miRNAs) are small (20-24 nucleotides) non-coding ribo-regulatory molecules with significant roles in regulating target mRNA and long non-coding RNAs at transcriptional and post-transcriptional levels. Rapid advancement in the small RNA sequencing methods with integration of degradome sequencing has accelerated the understanding of miRNA-mediated regulatory hubs in plants and yielded extensive annotation of miRNAs and corresponding targets. However, it is becoming clear that large numbers of such annotations are questionable. Therefore, it is imperative to adopt reliable and strict bioinformatics pipelines for miRNA identification. Furthermore, sensitive methods are needed for validation and functional characterization of miRNA and its target(s). In this chapter, we have provided a comprehensive and streamlined methodology for miRNA identification and its functional validation in plants. This includes a combination of various in silico and experimental methodologies. To identify miRNA compendium from large-scale Next-Generation Sequencing (NGS) small RNA datasets, the miR-PREFeR (miRNA PREdiction From small RNA-Seq data) bioinformatics tool has been described. Also, a homology-based search protocol for finding members of a specific miRNA family has been discussed. The chapter also includes techniques to ascertain miRNA:target pair specificity using in silico target prediction from degradome NGS libraries using CleaveLand pipeline, miRNA:target validation by in planta transient assays, 5' RLM-RACE and expression analysis as well as functional techniques like miRNA overexpression, short tandem target mimic and resistant target approaches. The proposed strategy offers a reliable and sensitive way for miRNA:target identification and validation. Additionally, we strongly promulgate the use of multiple methodologies to validate a miRNA as well as its target.

Rapid screening and identification of viral pathogens in metagenomic data

BackgroundVirus screening and viral genome reconstruction are urgent and crucial for the rapid identification of viral pathogens, i.e., tracing the source and understanding the pathogenesis when a viral outbreak occurs. Next-generation sequencing (NGS) provides an efficient and unbiased way to identify viral pathogens in host-associated and environmental samples without prior knowledge. Despite the availability of software, data analysis still requires human operations. A mature pipeline is urgently needed when thousands of viral pathogen and viral genome reconstruction samples need to be rapidly identified.ResultsIn this paper, we present a rapid and accurate workflow to screen metagenomics sequencing data for viral pathogens and other compositions, as well as enable a reference-based assembler to reconstruct viral genomes. Moreover, we tested our workflow on several metagenomics datasets, including a SARS-CoV-2 patient sample with NGS data, pangolins tissues with NGS data, Middle East Respiratory Syndrome (MERS)-infected cells with NGS data, etc. Our workflow demonstrated high accuracy and efficiency when identifying target viruses from large scale NGS metagenomics data. Our workflow was flexible when working with a broad range of NGS datasets from small (kb) to large (100 Gb). This took from a few minutes to a few hours to complete each task. At the same time, our workflow automatically generates reports that incorporate visualized feedback (e.g., metagenomics data quality statistics, host and viral sequence compositions, details about each of the identified viral pathogens and their coverages, and reassembled viral pathogen sequences based on their closest references).ConclusionsOverall, our system enabled the rapid screening and identification of viral pathogens from metagenomics data, providing an important piece to support viral pathogen research during a pandemic. The visualized report contains information from raw sequence quality to a reconstructed viral sequence, which allows non-professional people to screen their samples for viruses by themselves (Additional file 1).

Targeting cyclin-dependent kinase 4/6 as a therapeutic approach for mucosal melanoma.

Mucosal melanoma is a rare but devastating subtype of melanoma which typically has a worse prognosis than other melanoma subtypes. Large-scale next-generation sequencing studies, including our recent research, have also proved that the molecular landscape and potential oncogenic drivers of mucosal melanoma remain distinct from that of cutaneous melanoma. Recently, a number of selective cyclin-dependent kinase 4 (CDK4)/6 inhibitors have been approved for clinical application in breast cancer or entered phase III clinical trial in other solid tumors. Additionally, we have revealed that the dysregulation of cell cycle progression, caused by CDK4 amplification, is a key genetic feature in half of mucosal melanoma and targeting of CDK4 in selected mucosal melanoma patients is a potentially promising direction for precision cancer treatment by using molecular-characterized mucosal melanoma patient-derived-xenograft models. This review summarizes the current literature regarding CDK4/6 dysregulation in mucosal melanoma, preclinical and clinical studies of CDK4/6 inhibitors and potential combinational strategies in treating mucosal melanoma.

Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence.

Simple SummaryIn recent years both research areas of next-generation sequencing and artificial intelligence have grown remarkably. Their intersection simultaneously gave rise to a panacea of different algorithms and applications. This article delineates tailored machine learning and systems biology approaches and combinations thereof that tackle the various challenges that arise in the face of big data. Moreover, it provides an overview of the numerous applications of artificial intelligence aiding the analysis and interpretation of next-generation sequencing data.The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question.

Clinical and Biological Impact of TP53 Alterations in Del(11q) Chronic Lymphocytic Leukemia

Large-scale next-generation sequencing (NGS) studies have suggested common patterns of co-occurrence or mutual exclusivity between genetic alterations in chronic lymphocytic leukemia (CLL). However, little is known about how most of these alterations cooperate to drive CLL pathogenesis, as well as the impact of these concurrencies in clinical outcome. In this regard, we investigated the clinical and biological impact of the co-occurrence of high-risk lesions such as del(11q)/ATM mutation and del(17p)/TP53 mutation by integrating NGS and CRISPR/Cas9 approaches. To address these questions, we first analyzed the mutational profile of 271 CLLs (17.3% del(11q); 10.7% del(17p)). The most frequently mutated genes were NOTCH1 (20%), TP53 (14%), SF3B1 (11%) and ATM (10%). Within del(11q), 32% showed TP53 alterations (53% biallelic; 47% monoallelic). Interestingly, patients harboring combined del(11q) and TP53 alterations by either mutation or deletion (del(11q) TP53ALT) exhibited significantly shorter overall survival (OS) than del(11q) CLLs without TP53 alterations (del(11q) TP53WT) and those TP53 altered without del(11q) (no del(11q) TP53ALT) (median 17 vs. 88, 36 months; P=0.0004, P=0.02). Conversely, we observed a significant lack of ATM mutations in CLLs with biallelic TP53 alterations (P=0.002) and a mutual exclusivity between biallelic TP53 and biallelic ATM losses (P=0.03)(Fig 1A). Based on the NGS results, we next used the CRISPR/Cas9 system to model monoallelic and biallelic ATM and TP53 loss in vitro. We generated isogenic HG3-Cas9 CLL-derived cell lines harboring monoallelic del(11q) (targeting 11q22.1/11q23.3 regions) and further loss-of-function mutations in ATM and/or TP53 to mimic all the possible combinations observed in our CLL cohort. By proliferation assays, we noted that the introduction of TP53 mutations increased the proliferation rates in both HG3WT and HG3-del(11q) cells. In contrast, the introduction of an ATM truncating mutation on the remaining allele of the HG3-del(11q) TP53MUT clone, suppressed this proliferative advantage, with growth rates comparable to those of HG3-del(11q). Accordingly, DNA content analysis by propidium iodide revealed that cells harboring biallelic ATM and TP53 loss also showed mitotic and cell cycle defects. To further evaluate the implications of these alterations in the clonal dynamics of CLL in vivo, we performed fluorescence-based clonal competition experiments by injecting these edited cell lines intravenously into NGS mice. First, we observed that HG3-TP53MUT cells outgrew HG3WT cells in spleen of xenotransplanted mice 14 days after injection (P<0.001). In a second experiment, HG3-del(11q), HG3-del(11q) TP53MUT and HG3-del(11q) ATMMUTTP53MUT cells were injected. Strikingly, HG3-del(11q) TP53MUT cells were able to outcompete HG3-del(11q) cells in spleen and bone marrow (P<0.001; P<0.001). By contrast, HG3-del(11q) ATMMUTTP53MUT cells failed to engraft neither in spleen nor bone marrow, being outcompeted by the other injected cells (P<0.001), providing biological insights on the mutual exclusivity of these genetic events in CLL (Fig 1B). We next assessed whether these cell models could predict responses of these combined abnormalities to BTK and PI3K inhibitors. We observed that HG3-del(11q) TP53MUT and HG3-TP53MUT cells showed partial response to ibrutinib and idelalisib, although the IC50 values were still higher than the ones observed in HG3WT clones, especially with idelalisib (27.4 and 20.6 vs. 1.8 uM, respectively). Nonetheless, we found that HG3-del(11q) TP53MUT cells were highly sensitive to novel preclinical drugs that have been shown to be effective in TP53 deficient cells such ATR inhibitors, with an IC50 value comparable to HG3WT cells (mean IC50 0.55 vs. 0.67 uM) (Fig 1C). In summary, we show that mutations in TP53 can appear in a subset of monoallelic del(11q) CLL cases, conferring a synergistic clonal advantage in vivo, and therefore a dismal clinical impact on the OS of this CLL subgroup. In addition, the biological basis of mutual exclusivity of biallelic ATM and TP53 alterations in CLL was assessed, underscoring the importance of the number of alleles affected by these alterations, and establishing novel pre-clinical models for the study of the biology and therapeutic response of concurrent genetic abnormalities in the disease. Funding: PI18/01500 FI19/00191 CD19/00222 *MQA CPC equal contr Disclosures No relevant conflicts of interest to declare.

Epidemiological data analysis of viral quasispecies in the next-generation sequencing era.

The unprecedented coverage offered by next-generation sequencing (NGS) technology has facilitated the assessment of the population complexity of intra-host RNA viral populations at an unprecedented level of detail. Consequently, analysis of NGS datasets could be used to extract and infer crucial epidemiological and biomedical information on the levels of both infected individuals and susceptible populations, thus enabling the development of more effective prevention strategies and antiviral therapeutics. Such information includes drug resistance, infection stage, transmission clusters and structures of transmission networks. However, NGS data require sophisticated analysis dealing with millions of error-prone short reads per patient. Prior to the NGS era, epidemiological and phylogenetic analyses were geared toward Sanger sequencing technology; now, they must be redesigned to handle the large-scale NGS datasets and properly model the evolution of heterogeneous rapidly mutating viral populations. Additionally, dedicated epidemiological surveillance systems require big data analytics to handle millions of reads obtained from thousands of patients for rapid outbreak investigation and management. We survey bioinformatics tools analyzing NGS data for (i) characterization of intra-host viral population complexity including single nucleotide variant and haplotype calling; (ii) downstream epidemiological analysis and inference of drug-resistant mutations, age of infection and linkage between patients; and (iii) data collection and analytics in surveillance systems for fast response and control of outbreaks.

The Application of Deep Learning in Cancer Prognosis Prediction.

Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, doctor assistance, etc. Cancer prognosis is to estimate the fate of cancer, probabilities of cancer recurrence and progression, and to provide survival estimation to the patients. The accuracy of cancer prognosis prediction will greatly benefit clinical management of cancer patients. The improvement of biomedical translational research and the application of advanced statistical analysis and machine learning methods are the driving forces to improve cancer prognosis prediction. Recent years, there is a significant increase of computational power and rapid advancement in the technology of artificial intelligence, particularly in deep learning. In addition, the cost reduction in large scale next-generation sequencing, and the availability of such data through open source databases (e.g., TCGA and GEO databases) offer us opportunities to possibly build more powerful and accurate models to predict cancer prognosis more accurately. In this review, we reviewed the most recent published works that used deep learning to build models for cancer prognosis prediction. Deep learning has been suggested to be a more generic model, requires less data engineering, and achieves more accurate prediction when working with large amounts of data. The application of deep learning in cancer prognosis has been shown to be equivalent or better than current approaches, such as Cox-PH. With the burst of multi-omics data, including genomics data, transcriptomics data and clinical information in cancer studies, we believe that deep learning would potentially improve cancer prognosis.

Abstract P4-07-07: Establishing whole-exome sequencing for breast cancer patient care

Abstract Background. Next-generation sequencing (NGS) technology enables profiling of individual tumours and to measure the increasing number of biomarkers relevant to the management of breast cancer patients. The Australian Translational Genomics Centre (ATGC) is a collaboration between the healthcare sector (Metro South Hospital and Health Service), higher education sector (Queensland University of Technology) and a government-run pathology service (Pathology Queensland) to provide genomic profiling of breast cancer patients at the Princess Alexandra Hospital. The program was developed to integrate the ordering, processing and interpretation of large-scale NGS into clinical practice. Methods. Patients were consented for somatic testing and ordering was integrated into the hospital’s electronic ordering system. Samples were derived from fresh tissue biopsies after surgeries (47%) or from FFPE histology sections. No selection criteria were applied during the initial phase and the cohort was representative of newly presenting patients at the hospitals breast cancer clinic. Patients were sequenced using a NATA-accredited ISO15189 program using whole-exome sequencing (WES) combined with a high-coverage spike-in panel of known cancer genes. Clinical reports included the calculation of tumour purity, tumour mutational burden (TMB), the assessment of copy number events and somatic mutations down to 3% allele frequency. Standard molecular testing in Australia includes ER, PR and HER2 status, and additional testing included testing of Tier 1-2 somatic variants in the genes ABCC3, AKT1, CCND1, CCNE1, CDKN2A, ERBB2, ESR1, FGF3, FGFR1, FGFR2, MTOR, NCOA3, NF2, PIK3CA, PIK3R1, PTEN, RB1, RSF1, SF3B1, TP53. Results. Seventy-one patients were tested by WES/panel, and an average of 1.5 clinically significant Tier 1- 2 mutations were detected per patient. In 77% of cases, the molecular profiling could stratify patients to those with either PI3K/Akt/mTOR pathway activation (by PIK3CA activating mutations, AKT, MTOR mutations or PTEN loss) or CDK4/6 activation (by CCND1 expansion or CDKN2A loss). The most frequently observed mutations were PIK3CA activation (40%) and CCND1 copy number expansions (24%). A small proportion (n=3) were found to have mTOR or TCS1/2 mutations reported to have association with a durable response to mTOR inhibitors. Additionally, 11 patients (15%) had a high TMB (TMB, >6.8 mutations/megabase) with 6 having >10 mutations/Mb. Within this high-TMB cohort, 3 were found to be ER-PR-HER-, however, the majority (n=7) were ER+PR+HER2- patients. Calculation of the tumour purity indicated that, despite expert resection of the biopsies to isolate the most tumour dense regions, tumour purity was not significantly enriched. In 58% of cases, the tumour purity was less than 50%, and in 13% of samples, it was less than 25%, indicating that in clinical practice the method of sequencing must be robust, as many samples have significant amounts of contaminating stroma. Conclusion. We demonstrate that integration of WES/panel testing into clinical practice is practical and provides multiplexed testing of current and emerging biomarkers in a significant number of tested patients. While a high proportion of patients had mutations that could stratify them to targeted therapies in the event of metastatic disease, the somatic molecular profiles did not modify first line therapy decision-making. Training of clinical staff for patient consent and the dissemination of findings, the development of a dedicated molecular tumour board, and decision protocols to identify patients of metastatic risk were identified as key developments in this clinical program. The program identified previously unidentified subsets of patients including a subset of ER+ patients with high TMB for which for there are no effective treatments option in Australia. Citation Format: Kate Roberts, Paul J Leo, Jeremy Khoo, Alice Febery, Jonathan Ellis, Mhairi Clout, Lawrie Wheeler, Lisa Anderson, Matthew Brown, Ian Bennett. Establishing whole-exome sequencing for breast cancer patient care [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P4-07-07.