Xiaotu Ma

The paper introduces CNR, a framework to quantify how protein contact networks respond to stabilizing interaction changes, using human AlphaFold models to analyze 22,167 structures.

The paper develops ALTitude, a machine learning model to predict ALT status from whole genome sequencing data, identifies SMARCAL1 as a therapeutic target in ALT-driven cancers.

The paper identifies non-coding somatic variants in TAL1 regulatory regions that reactivate enhancers normally active in hematopoietic stem cells in T-lineage acute lymphoblastic leukemia.

This study analyzes structural variants in 1,616 pediatric cancer genomes, revealing unique SV hotspots and patterns distinct from adult cancers.

CanID is a machine learning classifier for pediatric cancer subtypes using RNAseq data, achieving 99% accuracy for solid tumors and 92-93% for hematologic malignancies.

This study analyzes genetic changes in pediatric AML relapse using whole-genome sequencing and finds that relapse is driven by the selection of pre-existing clones rather than new mutations.

Seq2Karyotype (S2K) enables in-silico karyotyping from whole-genome sequencing data, revealing intratumoral heterogeneity and evolutionary trajectories in cancer samples.

This study shows TP53 mutations cause resistance to revumenib in AML and suggests combining MCL-1 inhibitors with menin inhibitors could be effective.

bamSliceR enables rapid, cross-cohort variant analysis by extracting and analyzing aligned reads from GDC data, identifying clinically relevant variants in leukemia cohorts.

This study creates two mouse models with conditional c-MYC activation in specific cell types, leading to distinct neuroendocrine tumors resembling human neuroblastoma and somatostatinoma.