Laboratory validation of a clinical metagenomic sequencing assay for pathogen detection in cerebrospinal fluid.

Metagenomic next-generation sequencing (mNGS) for pan-pathogen detection has been successfully tested in proof-of-concept case studies in patients with acute illness of unknown etiology but to date has been largely confined to research settings. Here, we developed and validated a clinical mNGS assay for diagnosis of infectious causes of meningitis and encephalitis from cerebrospinal fluid (CSF) in a licensed microbiology laboratory. A customized bioinformatics pipeline, SURPI+, was developed to rapidly analyze mNGS data, generate an automated summary of detected pathogens, and provide a graphical user interface for evaluating and interpreting results. We established quality metrics, threshold values, and limits of detection of 0.2-313 genomic copies or colony forming units per milliliter for each representative organism type. Gross hemolysis and excess host nucleic acid reduced assay sensitivity; however, spiked phages used as internal controls were reliable indicators of sensitivity loss. Diagnostic test accuracy was evaluated by blinded mNGS testing of 95 patient samples, revealing 73% sensitivity and 99% specificity compared to original clinical test results, and 81% positive percent agreement and 99% negative percent agreement after discrepancy analysis. Subsequent mNGS challenge testing of 20 positive CSF samples prospectively collected from a cohort of pediatric patients hospitalized with meningitis, encephalitis, and/or myelitis showed 92% sensitivity and 96% specificity relative to conventional microbiological testing of CSF in identifying the causative pathogen. These results demonstrate the analytic performance of a laboratory-validated mNGS assay for pan-pathogen detection, to be used clinically for diagnosis of neurological infections from CSF.

Differentiation of leukemic blasts is not completely blocked in acute myeloid leukemia.

Hematopoiesis, the formation of blood cells, involves the hierarchical differentiation of immature blast cells into mature, functional cell types and lineages of the immune system. Hematopoietic stem cells precisely regulate self-renewal versus differentiation to balance the production of blood cells and maintenance of the stem cell pool. The canonical view of acute myeloid leukemia (AML) is that it results from a combination of molecular events in a hematopoietic stem cell that block differentiation and drive proliferation. These events result in the accumulation of primitive hematopoietic blast cells in the blood and bone marrow. We used mathematical modeling to determine the impact of varying differentiation rates on myeloblastic accumulation. Our model shows that, instead of the commonly held belief that AML results from a complete block of differentiation of the hematopoietic stem cell, even a slight skewing of the fraction of cells that differentiate would produce an accumulation of blasts. We confirmed this model by interphase fluorescent in situ hybridization (FISH) and sequencing of purified cell populations from patients with AML, which showed that different leukemia-causing molecular abnormalities typically thought to block differentiation were consistently present in mature myeloid cells such as neutrophils and monocytes at similar levels to those in immature myeloid cells. These findings suggest reduced or skewed, rather than blocked, differentiation is responsible for the development of AML. Approaches that restore normal regulation of hematopoiesis could be effective treatment strategies.

Molecularly targeted drug combinations demonstrate selective effectiveness for myeloid- and lymphoid-derived hematologic malignancies.

Translating the genetic and epigenetic heterogeneity underlying human cancers into therapeutic strategies is an ongoing challenge. Large-scale sequencing efforts have uncovered a spectrum of mutations in many hematologic malignancies, including acute myeloid leukemia (AML), suggesting that combinations of agents will be required to treat these diseases effectively. Combinatorial approaches will also be critical for combating the emergence of genetically heterogeneous subclones, rescue signals in the microenvironment, and tumor-intrinsic feedback pathways that all contribute to disease relapse. To identify novel and effective drug combinations, we performed ex vivo sensitivity profiling of 122 primary patient samples from a variety of hematologic malignancies against a panel of 48 drug combinations. The combinations were designed as drug pairs that target nonoverlapping biological pathways and comprise drugs from different classes, preferably with Food and Drug Administration approval. A combination ratio (CR) was derived for each drug pair, and CRs were evaluated with respect to diagnostic categories as well as against genetic, cytogenetic, and cellular phenotypes of specimens from the two largest disease categories: AML and chronic lymphocytic leukemia (CLL). Nearly all tested combinations involving a BCL2 inhibitor showed additional benefit in patients with myeloid malignancies, whereas select combinations involving PI3K, CSF1R, or bromodomain inhibitors showed preferential benefit in lymphoid malignancies. Expanded analyses of patients with AML and CLL revealed specific patterns of ex vivo drug combination efficacy that were associated with select genetic, cytogenetic, and phenotypic disease subsets, warranting further evaluation. These findings highlight the heuristic value of an integrated functional genomic approach to the identification of novel treatment strategies for hematologic malignancies.