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Biology subjects

Becker, P. S.

Publications and source records attributed to Becker, P. S..

2 recordsLinked to original sources

Elucidation and Pharmacologic Targeting of Master Regulator Dependencies in Coexisting Diffuse Midline Glioma Subpopulations

Diffuse Midline Gliomas (DMGs) are universally fatal, primarily pediatric malignancies affecting the midline structures of the central nervous system. Despite decades of clinical trials, treatment remains limited to palliative radiation therapy. A major challenge is the coexistence of molecularly distinct malignant cell states with potentially orthogonal drug sensitivities. To address this challenge, we leveraged established network-based methodologies to elucidate Master Regulator (MR) proteins representing mechanistic, non-oncogene dependencies of seven coexisting subpopulations identified by single-cell analysis--whose enrichment in essential genes was validated by pooled CRISPR/Cas9 screens. Perturbational profiles of 372 clinically relevant drugs helped identify those able to invert the activity of subpopulation-specific MRs for follow-up in vivo validation. While individual drugs predicted to target individual subpopulations--including avapritinib, larotrectinib, and ruxolitinib--produced only modest tumor growth reduction in orthotopic models, systemic co-administration induced significant survival extension, making this approach a valuable contribution to the rational design of combination therapy.

systems biology↗

Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is an aggressive malignancy of myeloid progenitor cells characterized by successive acquisition of genetic alterations. This inherent heterogeneity poses challenges in the development of precise and effective therapies. To gain insights into the genetic influence on drug response and optimize treatment selection, we performed targeted sequencing, ex vivo drug screening, and single-cell genomic profiling on leukemia cell samples derived from AML patients. We detected genetic signatures associated with sensitivity or resistance to specific agents. By integrating large public datasets, we discovered statistical patterns of co-occurring and mutually exclusive mutations in AML. The application of single-cell genomic sequencing unveiled the co-occurrence of variants at the individual cell level, highlighting the presence of distinct sub- clones within AML patients. Machine learning models were built to predict ex vivo drug sensitivity using the genetic variants. Notably, these models demonstrated high accuracy in predicting sensitivity to some drugs, such as MEK inhibitors. Our study provides valuable resources for characterizing AML patients and predicting drug sensitivity, emphasizing the significance of considering subclonal distribution in drug response prediction. These findings provide a foundation for advancing precision medicine in AML. By tailoring treatment based on individual genetic profiles and functional testing, as well as accounting for the presence of subclones, we envision a future of improved therapeutic strategies for AML patients. One Sentence SummaryIntegrative computational and experimental analysis of mutation patterns and drug responses provide biologic insight and therapeutic guidance for patients with adult AML.

cell biology↗