Novel Sequencing Based Diagnostics for Leukemia in Low Resource Settings

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Project Summary Most cases of cancer worldwide are diagnosed in low and middle income countries (LMIC), where access to diagnostic technologies is limited and survival rates are low. Diagnostic resources such as flow cytometry, cytogenetics, and molecular panels are inconsistently accessible or wholly unavailable. Specifically, pathologists and clinicians cannot reliably differentiate lymphoid- from myeloid leukemia, or stratify biologic risk, leading to inaccurate or incomplete diagnosis and inappropriate treatment selection, which contributes to lower survival rates. Through development of new technical and computational approaches and feasibility testing in Malawi, we propose to advance a novel cost-effective sequencing approach to improve comprehensive leukemia diagnosis in LMICs. Our approach, using unbiased Oxford Nanopore RNA sequencing, requires low capital and per specimen costs. We have performed nanopore RNA sequencing for gene expression analysis on 124 cases of acute leukemia, demonstrating high quality RNA transcripts across a range of input conditions. We developed a pipeline that classifies leukemia lineage and core genomic subtypes. We hypothesize that locally implemented genomic sequencing, with minimal capital investment and limited training, accompanied by appropriate computational algorithms, can overcome diagnostic deficiencies for acute leukemia. In the proposed project, we will demonstrate feasibility of unbiased nanopore RNA sequencing as a diagnostic tool for acute leukemia in a low resource setting through technical and computational implementation. In Malawi, we will train laboratory personal to generate nanopore RNA sequencing from sixty diagnostic acute leukemia specimens. We will develop protocols and regulatory approvals for deposition of data into a secure cloud base system for analysis, which will allow iterative improvement of classification algorithms through ongoing collection of long-read RNA sequencing data. In parallel, we will significantly expand our cohort of nanopore RNA sequencing cases at the University of North Carolina, improving computational algorithms to classify genomic subtypes based upon nanopore gene expression profiling, validated with pathologic diagnosis and short read (eg. Illumina) sequencing data. We will develop computational pipelines and explore sequencing depth required using unbiased nanopore RNA sequencing to directly identify genomic alterations, such as fusion transcripts, aneuploidy, and FLT3 internal tandem duplications, which could allow for precision medicine approaches. Throughout this research period, we will plan for a future implementation study with a multidisciplinary group of domestic and international collaborators with expertise in clinical oncology, pathology, genomics, health economics, and implementation. Our innovative diagnostic approach could provide lineage and genotype leukemia classification on a single cost-effective platform, leading to transformational change in the diagnostic accuracy and subsequent clinical management of patients with acute leukemia in LMICs.