Johanna Hynninen

The paper identifies conserved cell states in ovarian high-grade serous carcinoma that correlate with poor prognosis and presents actionable targets for improving platinum response.

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 analyzes subclonal gene dosage effects in cancer using DNTR-seq, finding additive and compensatory transcriptional impacts and identifying transient clonality in ovarian and soft tissue sarcomas.

The paper uses single-cell RNA sequencing to identify resistance signatures in ovarian cancer and develops a framework for combination therapy with carboplatin.

The paper identifies reduced IFN-I pathway activity as a driver of chemo-refractory ovarian cancer using multi-omics analysis in a clinical trial cohort.

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.

This study identifies BX-912 as a therapeutic agent for ovarian serous carcinoma, revealing HES1 as a target and showing synergistic effects with olaparib.

This study identifies molecular drivers of ctDNA variability in ovarian cancer by analyzing genomic and transcriptomic data from 118 patients, linking ctDNA levels to proliferation and immune activity.

This study shows chemotherapy causes myeloid-driven spatial T-cell exhaustion in ovarian cancer by altering immune cell states and interactions, suggesting new immunotherapy approaches.

GenomeSpy is a grammar-based visualization toolkit for genomic data analysis that enables interactive exploration of large datasets, demonstrated on ovarian cancer samples.

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.