Cancer Therapy Response and Resistance

CIDE (Cancer Immunology Data Engine)

We developed the Cancer Immunology Data Engine (CIDE, https://cide.ccr.cancer.gov), which incorporates 90 omics datasets spanning 8,575 tumor profiles with immunotherapy outcomes from 17 solid tumor types (Gong, ..., Jiang. Cell, 2025). CIDE systematically identifies all genes associated with immunotherapy outcomes. Then, we focused on secreted proteins prioritized by CIDE without known cancer roles and validated regulatory effects on immune checkpoint blockade for AOAH, CR1L, COLQ, and ADAMTS7 in mouse models. The top hit, acyloxyacyl hydrolase (AOAH), potentiates immunotherapies in multiple tumor models by sensitizing T cell receptors to weak antigens and protecting dendritic cells through depleting immunosuppressive arachidonoyl phosphatidylcholines and oxidized derivatives.

TRES (Tumor-Resilient T cell)

Despite breakthroughs in cancer immunotherapy, most T cells reactive to tumor targets cannot persist in immunosuppressive solid tumors. Identifying molecular programs of T cells sustaining effective antitumor immunity is the center of cancer research. We developed a computational framework named the tumor-resilient T cell (Tres) model (Zhang, ..., Jiang. Nature Medicine, 2022). Tres utilizes single-cell transcriptomic data from solid tumors to identify signatures of T cells that are resilient to immunosuppressive signals, including TGF-beta, TRAIL, and PGE2. Analyzing single-cell data cohorts, the Tres model can predict the clinical efficacies of T cells in immune checkpoint blockade and adoptive cell transfer.

TIDE (Tumor Immune Dysfunction and Exclusion)

TIDE is an infrastructure with several modules to assist cancer immunotherapy applications and research (Jiang et al., Nature Medicine, 2018). The first component is a gene expression biomarker to predict the clinical response to immune checkpoint blockade. The input is a gene expression profile of a cancer sample measured by RNA-Seq on genome-scale or Nano-String on a gene panel. The output is a likelihood score of therapy response or resistance. The second component provides gene query functions for the gene activity associations with T-cell dysfunction and immunotherapy response. The input is a gene name. The output is the associations between gene activity and cancer immune evasion potentials computed from a vast amount of datasets from human clinical studies or pre-clinical models.

CARE (Computational Analysis of REsistance)

CARE is a software developed to identify genome-scale biomarkers of targeted therapy response using compound screen data (Jiang et al., Cell Systems 2018). For each drug, its CARE score vector can serve as a pattern of good responder. Patients will be predicted as responders or non-responders depending on the Pearson correlation between the gene expression profile of cancer samples and CARE score vector. For each gene, the CARE score indicates the association between its molecular alteration and drug efficacy. A positive score indicates a higher expression value (or presence of mutation) to be associated with drug response, while a negative score indicates drug resistance. You can search the results on CCLE, CTRP and CTRP datasets here. Please use the auto-completed name when available.

Spatial Genomics

SpaCET (Spatial Cellular Estimator for Tumors)

SpaCET is an R package for analyzing cancer spatial transcriptomics (ST) datasets to estimate cell lineage and intercellular interactions in tumor microenvironment (Ru et al., Nature Communications 2023). Briefly, SpaCET first estimates cancer cell abundance by integrating a gene pattern dictionary of common malignancies. SpaCET then uses a constrained regression model to calibrate local tissue densities and determine stromal and immune cell lineage fraction. Further, SpaCET can reveal putative cell-cell interactions in tumor microenvironment. Additionally, although SpaCET does not require any input cell reference profile to process tumor ST data, SpaCET can still accept a matched scRNA-seq dataset as customized references to carry out cell type deconvolution.

Large-scale Data Integration

FDC (Framework for Data Curation)

The Framework for Data Curation (FDC, Jiang et al., Nature Methods 2021) aims to enable researchers to annotate the meta information of datasets in the GEO and ArrayExpress databases to enable automatic algorithmic analysis. Focusing on a research topic, users can input a query result, composing a list of dataset IDs, downloaded from the GEO and ArrayExpress databases. The server will download the meta information of uploaded dataset IDs. Then, curators will annotate the meta-information based on a set of pre-defined schemes. The annotated sample information will be combined with the processed data matrices from GEO and ArrayExpress databases to enable algorithmic analysis.

Biological Network Analysis

CytoSig (Cytokine Signaling Analyzer)

The Cytokine Signaling analyzer (CytoSig, Jiang et al., Nature Methods 2021) platform aims to help biologists to study the cellular response to cytokine signaling molecules (e.g., cytokines, chemokines, and growth factors), leveraging the public expression data from treatment experiments deposited in the NCBI GEO and ArrayExpress databases. You can query cell signals and analyze genes induced or repressed (SEARCH module). You can also input a gene expression profile, and analyze the enriched signals, leveraging the treatment response profiles collected (RUN module).

NEST (Network Essentiality Scoring Tool)

NEST is designed to predict the gene essentiality based on protein interaction network and gene expression or epigenetic profiles (Jiang et al., Genome Bio 2015). NEST can also be used to enhance the quality of CRISPR or shRNA screen results.

RABIT (Regression Analysis with Background InTegration)

RABIT is a very efficient feature selection algorithm (Jiang et al., PNAS 2015). We applied RABIT to find gene expression regulators in shaping tumor-specific gene expression patterns. The gene expression regulator could be a transcription factor or an RNA binding protein. Besides our application here, you can use RABIT as a general algorithm for feature selection.

SPICi (Speed and Performance In ClusterIng)

SPICi is a fast local network clustering algorithm (Jiang et al., Bioinformatics 2010). SPICi runs in time O(Vlog V +E) and space O(E), where V and E are the numbers of vertices and edges in the network. It also has a state-of-the-art performance with respect to the quality of the clusters it uncovers.

Combinatorial Regulation

CCAT (Combinatorial Code Analysis Tool)

CCAT is a software package for predicting genome-wide co-binding between biological regulators such as transcription factors (TF) (Jiang et al., Nucleic Acids Res 2014) or RNA binding proteins (RBP) (Jiang et al., PLoS Comput Biol 2013). The CCAT package also includes accompanying tools to cluster similar Position weight matrix (PWM) of different TFs or RBPs into clusters, and search PWMs on multiple genome alignments for conserved motif instances.