Luca Zanella

A compendium of next-generation patient-derived models for diverse cancers

Dina ElHarouni, Mushriq Al‐Jazrawe, Seongmin Choi, Merve Dede et autres

Abstract The development of new therapeutics and the validation of pathogenetic cancer mechanisms require representative laboratory models 1,2 . However, existing collections represent only a fraction of the diversity observed in human cancer 2–4 . Recent technologies have enabled efficient in vitro …

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pyVIPER: a fast and scalable Python package for protein activity estimation and master regulator analysis of single-cell RNA sequencing data

Alexander L.E. Wang, Luca Zanella, Zizhao Lin, Filippo Riva et autres

Single-cell sequencing has revolutionized biomedical research by offering insights into cellular heterogeneity at unprecedented resolution. Yet, the low signal-to-noise ratio characteristic of single-cell RNA sequencing (scRNA-seq) challenges quantitative analyses. Gene regulatory network (GRN) analysis can help overcome this obstacle, enabling the mechanistic …

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Abstract 6858: Single-cell elucidation of molecularly distinct states and therapeutic vulnerabilities in IDH-mutant glioma.

Luca Zanella, Patrick Kerwin, Mikko Turunen, Peter Sims et autres

Abstract IDH-mutant gliomas, including oligodendrogliomas (IDH-O) and astrocytomas (IDH-A), are a molecularly defined class of fatal primary brain tumors characterized by mutations in the IDH1 and IDH2 genes. Despite advances in standard-of-care approaches, prognosis remains poor with median survival rates of 5-15 …

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Abstract 6759: High-throughput drug screening and single-cell network analysis identify rational combination therapies in IDH-mutant glioma

Patrick Kerwin, Luca Zanella, Andrea Califano

Abstract IDH-mutant gliomas are lethal brain tumors marked by multiple coexisting malignant cell states, likely to elicit heterogeneous drug sensitivities thus limiting the effectiveness of monotherapy. To address this challenge, we integrated multimodal single-cell analysis and functional high-throughput drug screening to identify …

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HCMI_snRNAseq_data_metadata

Luca Zanella

Collection of data and metadata for running snRNA-seq demo codes at Califano Lab Github: HCMI Demo Codes. (Directories in bold). demultiplexed_samples_best_gt_thresh-filtered-01/: Directory containing .h5ad files for demultiplexed samples.metadata/: Metadata tables for each demultiplexed sample, including standard gene expression analyses and infercnv outputs.clustering/: …

HCMI_snRNAseq_data_metadata

Luca Zanella

Collection of data and metadata for running snRNA-seq demo codes at Califano Lab Github: HCMI Demo Codes. (Directories in bold). demultiplexed_samples_best_gt_thresh-filtered-01/: Directory containing .h5ad files for demultiplexed samples.metadata/: Metadata tables for each demultiplexed sample, including standard gene expression analyses and infercnv outputs.clustering/: …

Table S7 from OncoLoop: A Network-Based Precision Cancer Medicine Framework

Alessandro Vasciaveo, Juan Martín Arriaga, Francisca Nunes de Almeida, Min Zou et autres

Supplementary Table 7. Transcriptomic analyses of the PDX models A. PDX interactome B. OncoTreat for drugs to PDX using DU145 cell perturbation data

Table S5 from OncoLoop: A Network-Based Precision Cancer Medicine Framework

Alessandro Vasciaveo, Juan Martín Arriaga, Francisca Nunes de Almeida, Min Zou et autres

Supplementary Table 5. Drug perturbation and OncoTreat analysis A. Summary of drugs and concentrations used B. Drug perturbation data for DU145 C. OncoTreat for drugs to SU2C patients using DU145 D. OncoTreat for drugs to GEMMs using DU145

Supplementary Figures S1-S11 from OncoLoop: A Network-Based Precision Cancer Medicine Framework

Alessandro Vasciaveo, Juan Martín Arriaga, Francisca Nunes de Almeida, Min Zou et autres

Figure S1: Genomic alterations in prostate cancer represented in the GEMMs (related to Fig. 2). Figure S2: Additional phenotypic analyses of the GEMMs (related to Fig. 2). Figure S3: Phenotypic analysis of allograft and organoid models (related to Fig. 2). Figure S4: …

Table S4 from OncoLoop: A Network-Based Precision Cancer Medicine Framework

Alessandro Vasciaveo, Juan Martín Arriaga, Francisca Nunes de Almeida, Min Zou et autres

Supplementary Table 4: OncoMatch A. OncoMatch TCGA B. OncoMatch SU2C

Table S2 from OncoLoop: A Network-Based Precision Cancer Medicine Framework

Alessandro Vasciaveo, Juan Martín Arriaga, Francisca Nunes de Almeida, Min Zou et autres

Supplementary Table 2: Transcriptomic analyses of the GEMMs A. GEMMs Interactome B. Protein activity C. Cluster analyses D. Pathway analyses

Table S3 from OncoLoop: A Network-Based Precision Cancer Medicine Framework

Alessandro Vasciaveo, Juan Martín Arriaga, Francisca Nunes de Almeida, Min Zou et autres

Supplementary Table 3: Transcriptomic analyses of the human patient samples A. TCGA Interactome B. SU2C Interactome C. Protein activity TCGA D. Protein Activity SU2C