Anna Vähärautio

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.

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.

Cyclic immunofluorescence of 265 high-grade serous ovarian carcinomas and random-forest feature ranking identify cancer-cell MHC-II as an adjusted predictor of longer survival marking immune-activated tumor-stroma hotspots.

This study uses ReSisTrace to identify pre-sensitizing agents for FLT3 inhibitors in FLT3-ITD-positive AML by analyzing transcriptional signatures and validating compounds that enhance treatment efficacy.

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.

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

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.

miQC is a probabilistic framework that jointly models mitochondrial DNA and gene detection metrics to improve single-cell RNA-seq quality control.

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.