D. Gulhan

The paper introduces a method to time copy-number amplification events in cancer genomes, revealing that whole-genome duplication (WGD) enables bursts of oncogene amplification.

Cornet improves mutational signature discovery by modeling correlations, identifying new signatures in cancer genomes.

The paper identifies oxidative protein states as drivers of metastatic fitness in lung cancer by linking ROS-regulated protein modifications to mitochondrial function and metastasis.

The paper introduces MuTopia, a framework that identifies topographic archetypes of mutagenesis in cancer genomes, revealing that mutational processes are constrained by replication timing and chromatin state.

The paper infers enhancer activity from cfDNA fragmentation patterns and proposes a chromatin fiber model to explain these signatures.

The paper estimates the timing of homologous recombination deficiency (HRD) onset in breast cancers using HRDTimer, finding HRD occurs years before diagnosis in different subtypes.

This study identifies two immune archetypes in HRD tumors—immune-inflamed and immune-evasive—linked to chromosomal instability and lineage, informing biomarker-driven therapies.

The paper identifies HECTD4 as a tumor and metastasis suppressor that targets COX-2 for degradation, with COX-2 stabilization driving pro-tumorigenic effects in breast cancer.

The paper identifies immune phenotypes and myeloid cell networks in recurrent ovarian cancer that influence prognosis and immune environment reinstatement.

MuSiCal improves mutational signature analysis by outperforming existing methods in discovery and assignment, revealing new signatures and resolving ambiguities in cancer genomes.

The paper identifies mutational signatures SBS26 and SBS54 in MMRD tumors that correlate with immune checkpoint blockade response and survival, suggesting their use as prognostic biomarkers.

DeepMosaic detects mosaic single nucleotide variants without control samples using deep convolutional neural networks, achieving high accuracy and uncovering many variants missed by existing methods.

The paper evaluates brain somatic mosaicism in the adult human to identify rules governing cellular distribution during embryogenesis.

The paper introduces a machine learning method to detect somatic L1 insertions in human brain tissue, revealing their widespread distribution in neurons and glia and potential role in neuropsychiatric diseases.

The paper analyzes somatic mutations in autistic and control brains, finding higher mutation rates in ASD brains, particularly in enhancer regions.

The paper introduces LiRA, a method to distinguish true mutations from artifacts in single-cell DNA sequencing data by phasing somatic variants with germline variants.