Carlos Caldas

This paper introduces a 3D imaging pipeline to detect functionally perfused vasculogenic mimicry in mouse tumor models, providing evidence of its presence and functionality.

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

Path2Space predicts spatial gene expression and tumor microenvironment from histopathology slides, identifying new breast cancer subgroups and improving biomarker prediction.

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

This paper develops DECODEM to predict chemotherapy response by integrating deconvolved expression patterns of immune, stromal, and tumor cells in breast cancer, finding that cell-type-specific expression is predictive of response.

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.

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 identifies risk alleles at the 17q22 locus associated with breast cancer by analyzing allelic expression ratios in case-control studies.

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.

A personalized ctDNA test achieves high accuracy for residual disease detection after neoadjuvant therapy in breast cancer.

NG-TAS is a cost-effective method for profiling ctDNA mutations in cancer patients, showing consistent detection of mutations at >1% allele fraction.

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.