C. Sander

Data-modeling the interplay between single cell shape, single cell protein expression, and tissue state

The paper investigates the relationship between cell shape, protein expression, and tissue state using spatial imaging and machine learning, finding a bidirectional link and identifying p53-positive tumor cells.

Identification of tumor-intrinsic drivers of immune exclusion in acral melanoma

This study identifies 11 oncogenic pathways associated with immune exclusion and ICI resistance in acral melanoma, suggesting therapeutic targets for immunotherapy combinations.

Machine learning prediction of enzyme optimum pH

This study develops and evaluates machine learning models to predict enzyme optimum pH, finding that language model embeddings significantly improve prediction accuracy, leading to the release of the EpHod model.

Learning from pre-pandemic data to forecast viral escape

This paper develops EVEscape, a framework predicting viral escape mutations using historical data and structural information, showing it can forecast SARS-CoV-2 variants as effectively as experimental methods.

Molecular Response to PARP1 Inhibition in Ovarian Cancer Cells as Determined by Mass Spectrometry Based Proteomics

This study uses proteomics to analyze how ovarian cancer cells respond to PARP1 inhibition, identifying adaptive resistance mechanisms and potential therapeutic targets.

Precision combination therapies based on recurrent oncogenic co-alterations

The paper develops REFLECT, a method that maps recurrent co-alteration signatures to drug combinations using multi-omic data, validating its effectiveness across 201 cohorts and 10,392 patients.

BET inhibition induces an anti-apoptotic adaptive response and therapeutic vulnerability to MCL1 inhibitors in breast cancer

This study shows BET inhibition in breast cancer triggers an anti-apoptotic response via MCL1 upregulation, and co-targeting BET and MCL1 is synergistic and effective in basal-like breast cancers.

Protein structure from experimental evolution

This study uses laboratory evolution to infer protein structures by analyzing residue interactions from evolved sequences.