Andrea Califano

The paper introduces a multi-omics framework to analyze protein activity in context-specific networks, demonstrating its utility in linking molecular profiles to phenotypic outcomes using HeLa and Kyoto cell lines.

DoFormer improves perturbation prediction by using a causal Transformer that distinguishes observational from interventional regimes without DAG assumptions.

The paper uses a forward genetic screen in a mouse model to identify Sirtuin1 as a driver of neuroendocrine prostate cancer, showing its role in promoting NEPC and potential as a therapeutic target.

The paper estimates splicing factor activity from exon inclusion changes, identifying cancer-related splicing programs and their association with patient survival and cancer hallmarks.

This study identifies TMPO as a driver of CTC heterogeneity and metastasis in prostate cancer by enhancing survival under metabolic stress.

This study identifies non-oncogene dependencies in imatinib-resistant GIST using network-based methods and validates six drugs that reverse MR activity in preclinical models.

This study identifies CD155 as a key regulator of tumor growth and immune evasion in diffuse midline glioma, showing that targeting CD155 or its downstream target FOXM1 can inhibit tumor progression.

The paper identifies ATR as a target for combination EZH2 epigenetic therapy in SMARCB1-deficient sarcomas, showing that EZH2 inhibition promotes DNA damage via PGBD5, and that ATR inhibition enhances therapeutic responses.

GREmLN is a foundation model that uses graph structure to improve gene embeddings in single-cell transcriptomics, outperforming baselines in cell type annotation and perturbation prediction.

The paper identifies Dclk1-expressing cells in the pancreas and reveals their role in inhibiting pancreatic tumor progression through a tuft cell-ILC2 axis.

The paper identifies master regulator modules in human airway epithelial cells reprogrammed by SARS-CoV-2 and finds 11 drugs that reverse these signatures.

The paper identifies six molecular subtypes of GIST using single nucleus RNA sequencing and correlates them with clinicopathologic features and clinical outcomes.

The paper identifies 132 master regulators of primed pluripotency in mouse epiblast stem cells and constructs a regulatory network revealing four functionally distinct modules.

pyVIPER is a Python package for fast, scalable rank-based enrichment analysis of scRNA-seq data, offering significant runtime improvements over VIPER for large datasets.

The paper uses single-cell transcriptomes to reverse engineer splicing-regulatory networks specific to and orthogonal of neuron types, identifying key RNA-binding proteins and regulatory modules.

This study challenges the traditional model of intestinal stem cells by showing that Lgr5+ cells are not the sole stem cell compartment and that stemness exists in the isthmus region, contributing to homeostasis and regeneration.

This study evaluates the therapeutic potential of RMC-7977, a broad-spectrum RAS inhibitor, in pancreatic ductal adenocarcinoma models, showing strong anti-tumor effects with minimal impact on normal tissues.

The paper identifies master regulator dependencies in coexisting diffuse midline glioma subpopulations and shows that combination therapy targeting these regulators improves survival in vivo.

The paper identifies treatment-resistant Cancer Stem-Like Cells in breast cancer using single-cell RNA sequencing and validates a drug combination to target them effectively.

The paper uses large-scale perturbation profiles to analyze drug mechanisms of action and polypharmacology across multiple tumor subtypes and tissues.

This study identifies NSD2 as a key driver of lineage plasticity and castration-resistance in neuroendocrine prostate cancer, showing its inhibition can restore sensitivity to AR inhibitors.

The paper functionally characterizes 577,866 non-recurrent mutations across TCGA cohorts by analyzing TF activity, identifying neomorphic and mimicry mutations, and validating them with knock-in assays.

The paper validates an RNA-based platform for predicting drug sensitivity in treatment-resistant cancers using patient-derived xenograft models and multi-protein therapies.

The paper introduces NaRnEA, a gene set analysis method that improves differential activity measurement of transcriptional regulatory proteins across cancer cohorts.

The paper uses phosphoproteomic time-series data and the VESPA algorithm to analyze drug resistance mechanisms in colon cancer cells, revealing adaptive signaling rewiring confirmed by CRISPRko assays.

The paper identifies six distinct basal cell subpopulations in the murine trachea, showing regional differences in their stemness and response to injury, with conservation in humans.

This study identifies molecular and immune features associated with response to anti-PD-(L)1 therapy in NSCLC using a cohort of 393 patients.

The paper uses CRISPRi and single-cell RNA-seq to reverse engineer gene regulatory networks in colorectal cancer cells, revealing autoregulatory and cooperative mechanisms.

The paper identifies widespread transcription initiation at microsatellites across species and develops deep learning models to predict STR-associated transcription and its impact on disease variants.

The paper introduces PISCES, a platform that improves single-cell analysis by estimating protein activity's role in cell state, validated with experimental assays and applied to human tissue data.