Diagnostic utility of whole genome sequencing in adults with B-other acute lymphoblastic leukemia

Genomic profiling during the diagnosis of B-cell precursor acute lymphoblastic leukemia (BCP-ALL) in adults is used to guide disease classification, risk stratification, and treatment decisions. Patients for whom diagnostic screening fails to identify disease-defining or risk-stratifying lesions are classified as having B-other ALL. We screened a cohort of 652 BCP-ALL cases enrolled in UKALL14 to identify and perform whole genome sequencing (WGS) of paired tumor-normal samples. For 52 patients with B-other, we compared the WGS findings with data from clinical and research cytogenetics. WGS identified a cancer-associated event in 51 of 52 patients, including an established subtype defining genetic alterations that were previously missed with standard-of-care (SoC) genetics in 5 of them. Of the 47 true B-other ALL, we identified a recurrent driver in 87% (41). A complex karyotype via cytogenetics emerges as a heterogeneous group, including distinct genetic alterations associated with either favorable (DUX4-r) or poor outcomes (MEF2D-r and IGK::BCL2). For a subset of 31 cases, we integrated the findings from RNA sequencing (RNA-seq) analysis to include fusion gene detection and classification based on gene expression. Compared with RNA-seq, WGS was sufficient to detect and resolve recurrent genetic subtypes; however, RNA-seq can provide orthogonal validation of findings. In conclusion, we demonstrated that WGS can identify clinically relevant genetic abnormalities missed with SoC testing as well as identify leukemia driver events in virtually all cases of B-other ALL.

Clonal evolution during metastatic spread in high-risk neuroblastoma

Patients with high-risk neuroblastoma generally present with widely metastatic disease and often relapse despite intensive therapy. As most studies to date focused on diagnosis-relapse pairs, our understanding of the genetic and clonal dynamics of metastatic spread and disease progression remain limited. Here, using genomic profiling of 470 sequential and spatially separated samples from 283 patients, we characterize subtype-specific genetic evolutionary trajectories from diagnosis through progression and end-stage metastatic disease. Clonal tracing timed disease initiation to embryogenesis. Continuous acquisition of structural variants at disease-defining loci (MYCN, TERT, MDM2-CDK4) followed by convergent evolution of mutations targeting shared pathways emerged as the predominant feature of progression. At diagnosis metastatic clones were already established at distant sites where they could stay dormant, only to cause relapses years later and spread via metastasis-to-metastasis and polyclonal seeding after therapy.

Unified classification and risk-stratification in Acute Myeloid Leukemia

Clinical recommendations for Acute Myeloid Leukemia (AML) classification and risk-stratification remain heavily reliant on cytogenetic findings at diagnosis, which are present in <50% of patients. Using comprehensive molecular profiling data from 3,653 patients we characterize and validate 16 molecular classes describing 100% of AML patients. Each class represents diverse biological AML subgroups, and is associated with distinct clinical presentation, likelihood of response to induction chemotherapy, risk of relapse and death over time. Secondary AML-2, emerges as the second largest class (24%), associates with high-risk disease, poor prognosis irrespective of flow Minimal Residual Disease (MRD) negativity, and derives significant benefit from transplantation. Guided by class membership we derive a 3-tier risk-stratification score that re-stratifies 26% of patients as compared to standard of care. This results in a unified framework for disease classification and risk-stratification in AML that relies on information from cytogenetics and 32 genes. Last, we develop an open-access patient-tailored clinical decision support tool.

Feasibility of whole genome and transcriptome profiling in pediatric and young adult cancers

The utility of cancer whole genome and transcriptome sequencing (cWGTS) in oncology is increasingly recognized. However, implementation of cWGTS is challenged by the need to deliver results within clinically relevant timeframes, concerns about assay sensitivity, reporting and prioritization of findings. In a prospective research study we develop a workflow that reports comprehensive cWGTS results in 9 days. Comparison of cWGTS to diagnostic panel assays demonstrates the potential of cWGTS to capture all clinically reported mutations with comparable sensitivity in a single workflow. Benchmarking identifies a minimum of 80× as optimal depth for clinical WGS sequencing. Integration of germline, somatic DNA and RNA-seq data enable data-driven variant prioritization and reporting, with oncogenic findings reported in 54% more patients than standard of care. These results establish key technical considerations for the implementation of cWGTS as an integrated test in clinical oncology.

Cancer whole-genome and transcriptome sequencing (cWGTS) has been challenging to implement in clinical settings. Here, the authors develop a workflow to deliver robust cWGTS analyses and reports within clinically-relevant timeframes for paediatric, adolescent and young adult solid tumour patients.

Isabl Platform, a digital biobank for processing multimodal patient data

BACKGROUND

The widespread adoption of high throughput technologies has democratized data generation. However, data processing in accordance with best practices remains challenging and the data capital often becomes siloed. This presents an opportunity to consolidate data assets into digital biobanks-ecosystems of readily accessible, structured, and annotated datasets that can be dynamically queried and analysed.

RESULTS

We present Isabl, a customizable plug-and-play platform for the processing of multimodal patient-centric data. Isabl's architecture consists of a relational database (Isabl DB), a command line client (Isabl CLI), a RESTful API (Isabl API) and a frontend web application (Isabl Web). Isabl supports automated deployment of user-validated pipelines across the entire data capital. A full audit trail is maintained to secure data provenance, governance and ensuring reproducibility of findings.

CONCLUSIONS

As a digital biobank, Isabl supports continuous data utilization and automated meta analyses at scale, and serves as a catalyst for research innovation, new discoveries, and clinical translation.

Abstract 5105: A plug-and-play infrastructure for scalable bioinformatics operations

Genome profiling represents a critical pillar for clinical, translational, and basic research studies. Hospitals, core facilities, and research enterprises invest significant resources to generate genomic data sets. Yet, data management and analysis is frequently manual, which demands significant operator time and often results in siloed resources rendering them as single-use assets. Centralization of the genomic capital in a framework that enables automated processing, metadata integration and continuous interrogation maximizes return for investment and serves as the critical catalyst for research innovation, clinical translation and reproducible research. We developed Isabl, a plug-and-play infrastructure for scalable bioinformatics operations. Isabl provides solutions for databasing, assets management, tracking, automated and reproducible data processing. Dynamic reporting and meta-analysis across data assets is enabled.

Isabl is built on four main components. First, an individual-centric and extensible relational database with tracking support for samples (temporal, spatial, aliquot), experimental data (assays, platforms, sequencing runs), cohorts (clinical trials, research projects) and versioned bioinformatics applications (assembly aware, tools, results). Second, the database is exposed through a fully featured RESTful API that enables horizontal integration with information systems such as sequencing cores LIMS, variant visualization platforms like cBioPortal, and where applicable, clinical and biospecimen institutional databases. Third, a Software Development Kit (SDK) built for Next Generation Sequencing assets management. The SDK enables automated execution of data import and language-agnostic bioinformatics applications (alignment, variant calling, post-processing) with support for cohort and individual level reporting features. Furthermore, the SDK facilitates dynamic retrieval of results using vertical and horizontal queries (individual and cohort level, respectively). Lastly, Isabl comes with a Single Page Web Application that fosters user interaction with multidisciplinary teams (i.e. researchers, project coordinators, engineers, clinicians) facilitating tracking of analyses, results visualization, and dynamic query processing.

Isabl is currently supporting the Memorial Sloan Kettering Genome Pediatrics Precision Medicine Initiative, a prototype platform that delivers integrated, real-time automated reporting of clinical targeted gene re-sequencing, research whole genome and transcriptome profiling data; as well as linked data from pre-clinical models (i.e. PDX) and single cells studies. As an open-source tool, Isabl democratizes access to a purpose built, automated, scalable and fully integrable bioinformatics architecture. Isabl will be available at https://github.com/isabl-io.

Citation Format: Juan S. Medina-Martínez, Juan E. Arango-Ossa, Gunes Gundem, Max F. Levine, Minal Patel, Noushin R. Farnoud, Venkata D. Yellapantula, Gao Teng, Joseph G. Mccarter, Elsa Bernard, Franck Rapaport, Dominik Glodzik, Ross L. Levine, Andrew Kung, Elli Papaemmanuil. A plug-and-play infrastructure for scalable bioinformatics operations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 5105.