N. Beerenwinkel

The paper identifies novel subgroups in myeloid malignancies using a network-based clustering approach that integrates genomic and clinical features, improving prognostic accuracy.

This study shows that RNA virus evolution under host immune pressure leads to parallel mutations in a specific viral protein, increasing virulence and replication efficiency across different host genotypes.

This study uses long-read single-cell RNA sequencing to detect isoforms and genomic alterations in ovarian cancer, revealing novel isoforms, cell type-specific expression patterns, and gene fusions.

This study uses single-cell DNA sequencing to test for neutral evolution in cancer and healthy tissues, finding deviations from a clock-like model in many datasets.

This paper introduces TreeMHN, a probabilistic framework for jointly inferring exclusivity patterns and recurrent trajectories from tumor mutation trees, showing improved performance over existing methods.

SCATrEx maps single-cell transcriptomes to copy number evolutionary trees, revealing clone-specific gene expression heterogeneity in tumors.

This paper introduces a statistical test, GeneAccord, to identify clonally exclusive gene pairs in tumours and applies it to AML data, finding 6 exclusive and 2 co-occurring pairs.

The paper analyzes intra-tumour heterogeneity and subclonal mutations in clear cell renal cell carcinoma using multi-regional sequencing, finding that most tumours harbor private mutations and clonally exclusive pathway pairs.

This study tracks genomic and transcriptomic changes in melanoma progression using a mouse model, identifying key genes and pathways involved in metastasis.

This study uses single-cell DNA sequencing to analyze clonal diversity in AML, revealing mutation co-occurrence, mutual exclusivity, and evolutionary patterns linked to treatment resistance and poor prognosis.

This paper shows that aggressive drug treatments can increase resistance evolution by altering mutation rates, and proposes an optimal treatment strategy that minimizes resistance risk.

This paper compares mutational pathways to lopinavir resistance in HIV-1 subtypes B and C using a probabilistic model, finding distinct initial mutations and diverse evolutionary pathways.

This study identifies GATA3 and MDM2 as synthetic lethal in ER-positive breast cancers, showing MDM2 inhibition can target GATA3-deficient tumors via the PI3K/Akt/mTOR pathway.

This paper develops SCICoNE, a statistical model for single-cell copy number analysis from low-depth sequencing data, showing its accuracy on simulated data and application to breast cancer samples.

The paper shows that tumour architecture determines evolutionary patterns, identifying four onco-evolutionary modes based on spatial structure and cell dispersal.

This study uses a Bayesian network model to analyze mutational interactions across 8,198 cancer samples, identifying 22 novel clusters that improve survival prediction and suggest drug targets.

The paper develops a stochastic model of metastatic bottleneck to predict patient outcomes and therapy response, showing that interventions targeting the bottleneck are more effective than reducing overall tumor burden.

This paper introduces an efficient method to detect genetic interactions from partial fitness orders using geometric and graph-theoretic approaches, improving upon previous methods exponentially.

The paper identifies mRNA subsets associated with P-bodies under stress and shows how Puf5p regulates their decay or storage depending on cellular conditions.

This paper develops quantitative tools to infer genetic interactions from fitness rank orders, demonstrating their application across multiple genetic systems.

This study tests the infinite sites assumption in tumor evolution using single-cell sequencing data and finds recurrent mutations in multiple datasets.