Oscar M. Rueda

This study infers somatic copy number aberrations from spatial transcriptomics data to identify spatially coherent SCNA-defined populations in early cancer development.

Serval is a modular framework for decoding spatial transcriptomics data that introduces Cosine, a method improving transcript recovery and correlation with expression references.

This study shows chemotherapy reshapes clonal architecture in breast cancer xenografts by selecting for chemotherapy-resistant clones and stress response programs.

This paper introduces a method that uses causal signals from genetic screens to improve the robustness and interpretability of AI models for cancer medicine, showing effective generalization across data and biological contexts.

The paper shows that inducing epithelial traits in breast cancer cells increases autophagy and DNA damage, potentially contributing to tumor dormancy and reactivation.

The paper identifies Arrdc4's role in suppressing pyroptosis in biphenotypic EMT cells, linking its downregulation to increased metastatic potential and poor prognosis in breast cancer.

This study identifies immune phenotype-driven clustering in HR+/HER2- breast cancer from an Asian cohort, deviating from the luminal A-luminal B classification seen in European populations.

CopyClust improves breast cancer subtype classification using DNA copy number data alone, achieving higher accuracy than existing methods.

This study uses ctDNA from metastatic breast cancer patients and a Bayesian model to predict treatment response and progression-free survival, outperforming alternative methods.

The paper uses single-cell proteomics and perturbation experiments to improve drug sensitivity predictions in breast cancer and identify genomic features associated with treatment response.

ExSeq enables multiplexed RNA mapping at nanoscale resolution by expanding biological specimens for in situ sequencing.

The paper identifies copy number motifs in breast cancer genomes that predict disease outcome and driver events, outperforming existing clinical and genomic markers.

This study demonstrates that shallow whole genome sequencing (sWGS) can robustly profile copy number alterations in FFPE breast cancer tissues, even with low-quality DNA.