Find research datasets worth reusing

opencc-by-4.0Oct 2023View details →

Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer

Data supporting the findings of "<a href="https://doi.org/10.1158/2326-6066.CIR-23-0873" target="_blank" rel="noopener">Machine learning links T cell function and spatial localization to neoadjuvant immunotherapy and clinical outcome in pancreatic cancer</a>" publication. Files include patient and tissue region metadata (in metadata folder) and output of multiplex immunohistochemistry computational image processing workflow for each tissue region (in mIHC_files folder). The code used to produce the results of this study is available at: <a href="https://github.com/kblise/PDAC_mIHC_paper">https://github.com/kblise/PDAC_mIHC_paper</a>.

opencc-by-4.0Jul 2022View details →

Multiplexed imaging mass cytometry reveals distinct tumor-immune microenvironments linked to immunotherapy responses in melanoma

- melanoma_IMC_data.zip The zip file contains the raw IMC images (in the raw_tiff folder) and corresponding single cell masks (in the mask folder) associated with the paper "Multiplexed imaging mass cytometry reveals distinct tumor-immune microenvironments linked to immunotherapy responses in melanoma". The MCD files by CyTOF IMC were exported to a multi-channel TIFF file including 41 channels, and the order of the channel was provided in the Melanoma_panel.csv.  - Melanoma_code_data.zip The zip file contains the 4 folders described as follows:  <ul> <li>Folder ”data“: the processed data for the result shown in paper - Folder "input":      - sc_data.csv: the single cell protein expression data;     - Folder "abundance": the cell type abundance files;     - Folder "clidata": the response and survival data for 4 melanoma datasets used in the paper;      - Folder "hc_result": TME archetypes annotation for each sample/ROI from hierarchical clustering;     - Folder "ICB": data for ICB analysis (presented in FigS3);     - Folder "meta": panel file for clustering;     - Folder "RNAseq_data": the RNAseq data for 4 melanoma datasets used in the paper;     - Folder "RNAseq_deconv": the result of cell type deconvolution from bulk RNAseq;      - Folder "spatial": data for neighbourhood analysis (presented in Fig3, FigS4). - Folder "output": intermediate result for analysis.</li> <li>Folder "Rscript": R scripts for reproducing results in the paper. - generate_Figs.Rmd: ploting figures presented in the paper; - functions.R: functions used for analysis; - Clustering.Rmd: determining cell types based on marker intensities; - Spatial_analysis.Rmd: neighbourhood analysis to get significant interction/avoidance cell relationships.</li> <li>Folder "Figs": figures presented  in paper.</li> <li>Folder "HE_figs": the H&E image and the ROIs distribution for each sample.</li> </ul>

opencc-by-4.0Feb 2024View details →

Human lung cancer harbors spatially-organized stem-immunity hubs that associate with response to immunotherapy

Data associated with Chen, Nieman, Spurrell et al "Human lung cancer harbors spatially-organized stem-immunity hubs that associate with response to immunotherapy" Nature Immunology, 2024.  (1) Multiplex RNA scope. Processed cell-level data from 3 datasets, separated into manually selected "Stem Immunity" regions and "Tumor" regions, in separate files.  (2) GeoMx. Unprocessed GeoMex data, with standard fields provided by Nanostring software.  (3) MERFISH. Transcript-level information, including CellPose and Baysor segmentation.  (4) Co-registered multispectral RNA and protein images. Processed cell-level data from 46 patients. Patient-level meta data information is available in supplementary table 1, tab B_Patient_Metadata. Fluorophore channel information is Figure 1a.  Paper: <a title="https://secure-web.cisco.com/1OQRHTBMPf1SngM0FLdNuKQOPPx7fLOR87kGiirhnF_e0M74duym9zratwJMs0WJXqHJPAlkZoWXBoHafhJ72n9s0edU76bj8nCUblNWpOnoK6c8KGEQgcNuk8OuBqXAooW9yoh6etJ0bq6VGDmoFd95MrgMob6is1GxcOWx-XK996d_j8QDVu1rptbeyI8n08UfqmHA3n0A0P37k_-DYUWMh7VFNSmbLGzVqGG2n-czoFN3vL5JrMDPRvo0LDGJs96rzB6zYiaLdk6UgYXXc6eUV6FI64MTxmuXCbEmVRqONrjLdyG6HJULOrJLmKURM/https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41590-024-01792-2" href="https://secure-web.cisco.com/1OQRHTBMPf1SngM0FLdNuKQOPPx7fLOR87kGiirhnF_e0M74duym9zratwJMs0WJXqHJPAlkZoWXBoHafhJ72n9s0edU76bj8nCUblNWpOnoK6c8KGEQgcNuk8OuBqXAooW9yoh6etJ0bq6VGDmoFd95MrgMob6is1GxcOWx-XK996d_j8QDVu1rptbeyI8n08UfqmHA3n0A0P37k_-DYUWMh7VFNSmbLGzVqGG2n-czoFN3vL5JrMDPRvo0LDGJs96rzB6zYiaLdk6UgYXXc6eUV6FI64MTxmuXCbEmVRqONrjLdyG6HJULOrJLmKURM/https%3A%2F%2Fwww.nature.com%2Farticles%2Fs41590-024-01792-2">https://www.nature.com/articles/s41590-024-01792-2</a>

opengpl-3.0-or-laterMay 2024View details →

Immunotherapy datasets associated with TimiGP-Response pan-cancer TIME landscape

This version includes preprocessed single-cell RNA-seq data in the TimiGP-Response study. Please refer to https://github.com/CSkylarL/MSofTimiGP-Response for more information. Please cite our paper if you use this data: Li, C. et al. TimiGP-Response: the pan-cancer immune landscape associated with response to immunotherapy. bioRxiv, 2024.2006.2021.600089, doi:10.1101/2024.06.21.600089 (2024).

ClinicalTrials.gov40/100

Feasibility Study of Intra-Tumoral Lipopolysaccharide Immunotherapy for Intra-Abdominal

ClinicalTrials.gov study NCT05751837. IPD Sharing: NO. Countries: 1. Publications: 19.

closedIPD-NOFeb 2026View details →

opencc-by-4.0Jan 2021View details →

Response Rates to Anti–PD-1 Immunotherapy in Microsatellite-Stable Solid Tumors With 10 or More Mutations per Megabase

This dataset has been used to analyze the association between tumor mutational burden and response to treatment in immunotherapy-treated patients with microsatellite-stable solid tumors. The dataset contains clinical and genomic data for 1,678 patients with 16 cancer types.

opencc-zeroNov 2023View details →

Identification of a small molecule Tim-3 inhibitor to potentiate T cell-mediated antitumor immunotherapy in preclinical models

T cell immunoglobulin and mucin-containing molecule 3 (Tim-3), expressed in dysfunctional and exhausted T cells, has been widely acknowledged as a promising immune checkpoint target for tumor immunotherapy. Here, using a strategy combining virtual and functional screening, we identified a compound named ML-T7 that targets the FG-CC' cleft of Tim-3, a highly conserved binding site of phosphatidylserine (PtdSer) and carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1). ML-T7 enhanced the survival and antitumor activity of primary CD8+ cytotoxic T lymphocytes (CTLs) and human chimeric antigen receptor (CAR) T cells and reduced their exhaustion in vitro and in vivo. In addition, ML-T7 promoted NK cells' killing activity and DC antigen-presenting capacity, consistent with the reported activity of Tim-3. Notably, ML-T7 strengthened DCs' functions through both Tim-3 and Tim-4, consistent with the hypothesis that Tim-4 contains a similar FG-CC' loop. Intraperitoneal dosing of ML-T7 showed comparable tumor inhibitory effects to Tim-3 blocking antibody. ML-T7 reduced syngeneic tumor progression in both wildtype and Tim-3 humanized mice and alleviated the immunosuppressive microenvironment. Furthermore, combined ML-T7 and anti-PD-1 therapy had greater therapeutic efficacy than monotherapy in mice, supporting further development of ML-T7 for tumor immunotherapy. Our study demonstrates a potential small molecule for selectively blocking Tim-3 and warrants further study.

opengpl-3.0-or-laterNov 2023View details →

Multiomics analyses reveal a Type III-associated immune response in immunotherapy-induced toxicity in melanoma and identifies potential new therapeutic targets

Immune checkpoint inhibitors (ICIs) are standard-of-care for the treatment of advanced melanoma, but their use is limited by immune-related adverse events (irAEs). Proteomic analyses and multiplex cytokine/chemokine assays from serum at baseline and at irAEs onset in melanoma patients indicated aberrant T-cell activity with differential expression of Type I and III immune signatures. This was in line with an increase in the proportions of monocytes and decrease of IL-17A-producing CD4+ T-cells in the peripheral blood using single cell RNA sequencing. Multiplex immunohistochemistry and spatial transcriptomics on ICI-induced skin rash and colitis showed an increase in the proportion of CD4+ T-cells with IL-17A expression.   Anti-IL-17A mAbs were administered in two patients with myocarditis, colitis and skin rash with resolution of the irAE. This study highlights the potential role of Type III CD4+ T-cells in irAEs development and provides proof-of-principle evidence for a corresponding clinical trial using anti-IL17A for treating irAEs. Code to generate the figures are located at https://github.com/pcheng84/AE_analysis/

opencc-zeroDec 2023View details →

CODEX multiplexed imaging of immunotherapy in human and mouse melanomas

Our research used CODEX (Co-Detection by Indexing) multiplexed imaging to gain insights into melanoma tumors in both murine models and human samples. CODEX imaging involves an iterative process of annealing and stripping fluorophore-labeled oligonucleotide barcodes, complementing the barcodes attached to over 40 antibodies used for tissue staining. Subsequently, images underwent standard CODEX image processing (tile stitching, drift compensation, cycle concatenation, background subtraction, deconvolution, and determination of best focal plane), single cell segmentation, and column marker z-normalization by tissue. Our datasets comprise individual cells as rows, each characterized by 40+ antibody fluorescence values quantified from various markers evaluated for each study. These markers correspond to the antibodies targeting specific proteins within the tissue, quantified at the single-cell level. The values represent per-cell/area-averaged fluorescent intensities, z-normalized along each column. Each cell is mapped with its cell type and cellular neighborhood, defined by x and y coordinates representing pixel locations in the original image. Refer to the table in the "Usage Notes" section below for further details. The CODEX multiplexed imaging data is organized into three distinct files, each representing key aspects of our research and the studies detailed in our manuscript. We then used this data investigate the major cellular organization of the tumor sections we imaged, with downstream spatial statistics and analyses like cellular neighborhood analysis and cell-cell interaction analysis. These data could be used to understand the cellular interactions, composition, and structure of anti-tumor melanoma responses induced by antigen-specific immunotherapy either with adoptive T cell transfer for checkpoint blockade immunotherapy. These datasets offer valuable insights for researchers interested in anti-tumor microenvironments, immune responses, and therapeutic interventions such as T cell therapies. 1. Time-course of tumor microenvironment following antigen-specific T cell therapy in mice We investigate the dynamic interplay between immune responses, antigen-specific T cell interactions, and tumor progression in a murine melanoma model. We activated PMEL CD8+ T cells with cognate antigen gp100 and IL-2 for 10 days ex vivo and transferred into mice with established B16-F10 tumors. Tumors were harvested and imaged with CODEX imaging at 0-, 1-, 3-, 5-, and 12-days post-treatment (n=3-7 per time point). Our 42-plex CODEX antibody panel characterizes immune cell types, T cell phenotypes, stromal cell types, and tumor cell phenotypes, resulting in a rich dataset of 1,052,125 cells across 42 marker channels. 2. Tumor microenvironment following antigen-specific T cell therapies with different phenotypes in mice We delve deeper into the modulation of the tumor microenvironment by manipulating T cell phenotypes. By comparing activated T cells stimulated with and without 2-hydroxycitrate (2HC), a metabolic inhibitor of acetyl CoA production, we explore the impact of phenotype on tumor progression. Our datasets from mice treated with 2HC T cells or T cells provide insights into the role of T cell phenotype manipulation in the tumor microenvironment (n=4-7 per group). 3. Tumor microenvironment before and after checkpoint blockade in human melanoma patients of both responders and non-responders Our research extends to human melanoma patients with advanced, metastatic, stage IV tumors. We examine 12 FFPE tumor samples from six patients, each with samples taken before and after checkpoint inhibitor therapy. Our CODEX multiplexed imaging, using a panel of 58 antibodies, reveals changes in immune, stromal, and tumor compartments. We segmented 5,019,159 individual cells from the 12 CODEX images, facilitating unsupervised clustering to identify 39 major cell types based on their expression profiles. Our accompanying donor metadata table links donor IDs to essential clinical information, including treatment response, demographics, and sample details.

opencc-zeroMar 2024View details →

Data from: Gastrointestinal gd T cells reveal upregulated T-cell transcripts and signaling pathways during peanut oral immunotherapy

Oral immunotherapy (OIT) has been successful in desensitizing patients to offending food allergens, although identification of tissue-resident T cell subsets and cognate pathways leading to desensitization has been challenging. The T cells are a major T-cell subset of mucosal intraepithelial lymphocytes (IELs) and play a significant role in tissue homeostasis and repair. Studies in mouse models suggested a regulatory role of gd T cells in food allergy (FA). Also, peripheral gd T cells from patients analyzed over 24 weeks of peanut OIT were shown to undergo dynamic changes in expression profiles, implicating pathways involved in immune homeostasis. To our knowledge, the role of gd T cells in the intestinal mucosa of FA patients during immunotherapy has not been examined. To this end, we investigated whether gd T cells in the gastrointestinal (GI) tract are modulated during peanut OIT. We hypothesized that GI-resident gd T cells in FA patients would increase during the course of peanut OIT and reveal transcripts and pathways relevant to the mechanisms of peanut desensitization.

opencc-by-4.0Nov 2024View details →

Chromosomal instability degrades developmental phenotypes essential for anti-GD2 immunotherapy outcomes in high-risk neuroblastoma

Childhood Cancer Data Initiative (CCDI) dbGaP Study Accession: phs002431

opencc-zeroJul 2022View details →

Machine learning on syngeneic mouse tumor profiles to model clinical immunotherapy response

Most cancer patients are refractory to immune checkpoint blockade (ICB) therapy, and proper patient stratification remains an open question. Primary patient data suffer from high heterogeneity, low accessibility, and lack of proper controls. In contrast, syngeneic mouse tumor models enable controlled experiments with ICB treatments. Using transcriptomic and experimental variables from >700 ICB-treated/control syngeneic mouse tumors, we developed a novel machine learning framework to model tumor immunity and identify factors influencing ICB response. Projected on human immunotherapy trial data, we found that the model can predict clinical ICB response. We further applied the model to predicting ICB-responsive/resistant cancer types in TCGA, which agreed well with existing clinical reports. Finally, feature analysis implicated factors associated with ICB response. In summary, our novel computational framework based on mouse tumor data reliably stratified patients regarding ICB response, informed resistance mechanisms, and has the potential for wide applications in disease treatment studies.

opencc-by-4.0Aug 2022View details →

Distinct mechanisms of mismatch repair deficiency delineate two modes of response to PD-1 immunotherapy in endometrial carcinoma

Responses to immune checkpoint blockade (ICB) are variable even among mismatch repair deficient (MMRd) cancers. We completed a phase 2 clinical trial of the PD-1 inhibitor pembrolizumab in 24 patients with MMRd endometrial cancer (NCT02899793). Patients with mutational MMRd tumors (6 patients) had higher response rates and longer survival than those with epigenetic MMRd tumors (18 patients). Mutation burden was higher in tumors with mutational MMRd compared to epigenetic MMRd; however, within each category of MMRd, mutation burden was not associated with ICB response. Notably, JAK1 mutations did not confer resistance to pembrolizumab. Longitudinal single-cell RNA-seq of circulating immune cells revealed contrasting modes of anti-tumor immunity against mutational and epigenetic MMRd tumors. Whereas effector CD8+ T cell responses correlated with mutational MMRd, highly active CD16+ NK cells were associated with epigenetic MMRd tumors responsive to ICB. These data highlight factors beyond neoantigen burden that influence ICB response.

opencc-zeroOct 2022View details →

CD8+ T cell differentiation status correlates with the feasibility of sustained unresponsiveness following oral immunotherapy

While food allergy oral immunotherapy (OIT) can provide safe and effective desensitization (DS), the immune mechanisms underlying development of sustained unresponsiveness (SU) following a period of avoidance are largely unknown. We compared high dimensional immunophenotypes of innate and adaptive immune cell subsets of participants in a phase 2 randomized, controlled, peanut OIT trial who achieved SU vs. DS (no vs. with allergic reactions upon food challenge after a withdrawal period; n=21 vs. 30 respectively among total 120 intent-to-treat participants). Lower frequencies of naïve CD8+ T cells and terminally differentiated CD57+ CD8+ T cell subsets at baseline (pre-OIT) were highly associated with SU. Frequency of naïve CD8+ T cells showed a significant positive correlation with peanut- and Ara h 2-specific IgE at baseline. Higher frequencies of IL-4+ and IFNg+ CD8+ T cells post-OIT were negatively correlated with SU. Our findings provide compelling evidence that an immune signature consisting of certain CD8+ T cell subset frequencies is predictive of SU following OIT.

opencc-by-4.0Apr 2024View details →

Imaging mass cytometry analysis of brain tissues with CNS immune-related adverse events during anti-PD-1 cancer immunotherapy

Dataset accompanying the manuscript "Anti-PD-1 cancer immunotherapy induces CNS immune-related adverse events by Spleen tyrosine kinase activation in microglia". The Metadata.xls file includes the metadata, the raw image data is saved as .txt file, the segmented cellular expression data is available as csv files.

opengpl-3.0-or-laterFeb 2024View details →

LORIS: a logistic regression-based immunotherapy-response score

This is a repository of input data and code for reproducing the paper titled "LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic, and genomic features" by Chang et al. (Nature Cancer 2024). Briefly, in this work, Chang et al. developed a new clinical score called the LOgistic Regression-based Immunotherapy-response Score (LORIS) using a transparent and concise 6-feature logistic regression model. LORIS outperforms previous signatures in ICB response prediction and can identify responsive patients, even those with low tumor mutational burden or tumor PD-L1 expression. Importantly, LORIS consistently predicts both objective responses and short-term and long-term survival across multiple cancer types. Moreover, LORIS showcases a near-monotonic relationship with ICB response probability and patient survival, enabling more precise patient stratification across the board. As the method is accurate, interpretable, and only utilizes a few readily measurable features, it could help improve clinical decision-making practices in precision medicine to maximize patient benefit.

opencc-by-4.0May 2023View details →

HLApollo: Towards designing improved cancer immunotherapy targets with a superior peptide-MHC-I presentation model

Based on the success of cancer immunotherapy, personalized cancer vaccines have recently emerged as the vanguard of oncology treatment. Because antigen presentation on MHC class I (MHC-I) is key to the adaptive immune response to cancerous cells, it is critical to have highly predictive computational methods to model which peptides are presented on MHC-I. Here, we introduce HLApollo, a transformer-based model with end-to-end treatment of MHC-I sequence, deconvolution of multi-allelic data, and ligand-flanking sequences. We develop negative-set switching, a novel training strategy that greatly reduces overfitting, which is key to HLApollo’s performance, leading to increases of 20.19% and 4.1% in average precision (AP) vs. next best model on MHC-I presentation and immunogenicity, respectively. Incorporating protein features derived from protein language models yielded further gains and reduced the need for gene expression measurements. We achieve excellent pan-allelic generalization, and create a framework for estimating performance on untrained alleles. This guides the clinical use of HLApollo, where rare alleles may be observed – particularly for individuals from underrepresented ancestries. Our work uses all facets of available MHC-I data to develop a highly accurate MHC-I presentation predictor that meaningfully improves immunogenicity prediction and allelic coverage, important for clinical applications of personalized neoantigen vaccines.