Biology subjects

Bolton, K.

Publications and source records attributed to Bolton, K..

3 recordsLinked to original sources

bioRxiv2022

Chemotherapy Signatures Map Evolution of Therapy-Related Myeloid Neoplasms

Patients treated with cytotoxic therapies, including autologous stem cell transplantation, are at risk for developing therapy-related myeloid neoplasms1, 2. Pre-leukemic clones (i.e., clonal hematopoiesis) are detectable years before the development of these aggressive malignancies3-5, though the genomic events leading to transformation and expansion are not well-defined. Here, leveraging distinctive chemotherapy-associated mutational signatures6-12 from whole-genome sequencing data and targeted sequencing of pre-chemotherapy samples, we reconstruct the evolutionary life-history of 39 therapy-related myeloid malignancies. A dichotomy is revealed, in which neoplasms with evidence of chemotherapy-induced mutagenesis from platinum and melphalan are relatively hypermutated and enriched for complex structural variants (i.e., chromothripsis), while neoplasms with alternative exposures bear a similar profile to de novo acute myeloid leukemia. Using chemotherapy-associated mutational signatures as a temporal barcode in each patients life, we estimate that several complex events and genomic drivers are acquired after chemotherapy exposure. In the case of treatment with high-dose melphalan and autologous stem cell transplantation, we demonstrate that the procedure allows clonal hematopoiesis to escape chemotherapy exposure entirely, and to be reinfused to expand to malignancy. This information reveals a novel mode of malignant progression for therapy-related malignancies that is not reliant on direct mutagenesis or even exposure to chemotherapy, itself, and prompts further investigation into leukemia-permissive effects of cytotoxic drugs.

cancer biology↗

bioRxiv2020

Single cell mutational profiling delineates clonal trajectories in myeloid malignancies

Myeloid malignancies, including acute myeloid leukemia (AML), arise from the proliferation and expansion of hematopoietic stem and progenitor cells which acquire somatic mutations. Bulk molecular profiling studies on patient samples have suggested that somatic mutations are obtained in a step-wise fashion, where mutant genes with high variant allele frequencies (VAFs) are proposed to occur early in disease development and mutations with lower VAFs are thought to be acquired later in disease progression1-3. Although bulk sequencing informs leukemia biology and prognostication, it cannot distinguish which mutations occur in the same clone(s), accurately measure clonal complexity and clone size, or offer definitive evidence of mutational order. To elucidate the clonal framework of myeloid malignancies, we performed single cell mutational profiling on 146 samples from 123 patients. We found AML is most commonly comprised of a small number of dominant clones, which in many cases harbor co-occurring mutations in epigenetic regulators. Conversely, mutations in signaling genes often occur more than once in distinct subclones consistent with increasing clonal diversity. We also used these data to map the clonal trajectory of each patient and found that specific mutation combinations (FLT3-ITD + NPM1c) synergize to promote clonal expansion and dominance. We combined cell surface protein expression with single cell mutational analysis to map somatic genotype and clonal architecture with immunophenotype. Our studies of clonal architecture at a single cell level provide novel insights into the pathogenesis of myeloid transformation and how clonal complexity contributes to disease progression.

cancer biology↗

bioRxiv2019

Analysis of Risk Factor Domains in Psychosis Patient Health Records

Readmission after discharge from a hospital is disruptive and costly, regardless of the reason. However, it can be particularly problematic for psychiatric patients, so predicting which patients may be readmitted is critically important but also very difficult. Clinical narratives in psychiatric electronic health records (EHRs) span a wide range of topics and vocabulary; therefore, a psychiatric readmission prediction model must begin with a robust and interpretable topic extraction component. We created a data pipeline for using document vector similarity metrics to perform topic extraction on psychiatric EHR data in service of our long-term goal of creating a readmission risk classifier. We show initial results for our topic extraction model and identify additional features we will be incorporating in the future.

bioinformatics↗