Caroline Akemi Heckman

The paper identifies aldehyde metabolism as a therapeutic target in Fanconi anemia using a multi-omics approach integrating CRISPR data and transcriptomics.

This paper introduces a Transformer-derived transcriptomic score to identify drug-tolerant persister-like states in AML, showing external association with ex-vivo drug response.

This paper introduces CoPISA, a proteomics workflow that identifies combination-specific protein targets in AML, revealing conjunctional targeting mechanisms beyond classical drug synergy.

This study uses scRNA-seq to analyze AML patient samples across disease stages, identifying immune dysregulation and potential therapeutic targets.

This study evaluates two drug combinations for AML treatment, showing improved efficacy over venetoclax-based therapy in specific mutation profiles.

This study identifies DEK::NUP214 as an XPO1-dependent transcriptional activator of leukemia genes, showing selective response to XPO1 inhibitors in t(6;9) AML cases.

The paper compares whole exome sequencing and single-cell DNA sequencing in AML to assess clonal heterogeneity and evolution, finding both methods have strengths and limitations in variant detection and clonal analysis.

The paper evaluates germline variants in hematological malignancies using a computational pipeline, identifying pathogenic alleles and enriched variants in CHEK2 and other genes.

The paper develops a machine learning model to identify patient-specific multi-targeting therapies using single-cell transcriptomic data for hematological cancers and solid tumors.

This study identifies molecular mechanisms of carfilzomib resistance in multiple myeloma using multi-omics data from cell lines and patient samples, revealing potential drug combinations to overcome resistance.

The paper develops Blood Cell Painting (BCP) to profile immune cell morphology in healthy donors, identifying genetic and clinical associations with immune cell features.

This study identifies ADRA2A as a major risk locus for Raynaud's syndrome through genome-wide association analysis and functional validation in vascular smooth muscle cells.

The paper identifies two distinct subgroups of KMT2A-rearranged AML using phosphoproteomics and shows different drug response profiles.

The paper identifies genetic risk factors for tuberculosis, including HLA alleles and INPP5A variants, and shows smoking as a lifestyle risk factor.

This study uses multipartite network models to predict effective drug combinations and patient subclasses in acute myeloid leukemia using drug response data.

This paper develops a computational-experimental approach to design patient-specific drug combinations for AML that are synergistic and minimally toxic.

The paper improves gene essentiality consistency between RNAi and CRISPR screens by integrating molecular features, resulting in a more accurate combined essentiality score.