Antti Häkkinen

This study maps the spatio-temporal DNA methylation patterns in ovarian cancer using longitudinal data from the DECIDER trial, showing early establishment of regulatory methylomes and their stability through treatment.

The paper identifies nine transcriptional programs driving metastasis in ovarian cancer using multi-modal data from 350 tumor samples, highlighting intra-patient heterogeneity and therapeutic implications.

The paper examines how changes in RNA polymerase concentration affect gene expression in E. coli, finding that mid-term responses correlate with transcription factor network topology.

The paper introduces an interactive workflow called Cyto for analyzing mass cytometry data, demonstrating its ability to capture immune cell sub-populations and tumor compositions in blood and ovarian cancer samples.

The paper identifies a stress-related transcriptional profile in ovarian cancer linked to chemotherapy resistance and poor survival, suggesting MEK1/2 inhibitors as a potential treatment.

PRISM recovers cell type specific expression profiles from composite RNA-seq data, improving patient response prediction compared to raw bulk data.

The paper presents a computational method to identify differentially methylated sites in heterogeneous tumor samples with varying cancer cell fractions, without needing normal controls or prior purity estimates.