DOI: 10.5281/zenodo.15012968
title: Cancer immunology data engine reveals secreted AOAH as a potential immunotherapy

description: This dataset includes raw data, source data tables, and result files to generate the figures and tables reported in the manuscript. We deposited newly generated RNA-seq data separately to the NCBI GEO.

Figures_Source_Data.zip: Source data of all figures

AOAH Flow Data Organized-20250626T140353Z-1-001.zip: raw FCS files of all flow analysis

data_open.zip: Processed omics data for all immunotherapy cohorts that are openly accessible. For private cohorts requiring data access applications, please email the lead contact (Peng Jiang) to get the processed data after you acquire approval from the original authors of each study.

gene_scores.zip: Statistical analysis results using immunotherapy cohorts. Each tested variable is a separate row. Results from bulk and single-cell data are shown in two separate folders. Column names are explained below:



coef: Regression coefficients

se(coef): Standard error

z: coef /se, for Cox-PH, logistic regressions, and rank-sum test.

t: coef/se, for the ordinary least squares (OLS) regression

p: two-sided p-value. Wald test for Cox-PH or logistic regressions; Student t-test for the OLS regressions; Rank-sum test for datasets with only binary outcomes and no other clinical covariates.

thres.opt (only for visualization purposes): To visualize each value’s result, we selected an optimal cutoff to maximize the difference between positive and negative groups by optimizing the z-score in the Cox-PH regression using the binary value after the cutoff.

z.opt (only for visualization purposes): The best z-score achieved through the procedure described above.

mean.value: mean value across all samples

N: number of samples

FDR: false discovery rate from the p-value through the Benjamini-Hochberg correction.


Untargeted_Lipidomics.zip: Untargeted Lipidomics analysis for the cell medium and cell pellets from mouse CD8 T cells treated with human recombinant AOAH protein and buffer control.

HE_TCR-T_tissues.zip: H&E SVS files for spleen, bone marrow, and lymph nodes of mice treated with adoptive transfer of TCR-T cells armored with Aoah over-expression and vector control over-expression.

MultiIF_B16.zip: Multiplexed immunofluorescence imaging of B16F10 mouse tumors with Aoah versus vector over-expression.

MultiIF_LucOS.zip: Multiplexed immunofluorescence imaging of spontaneous hepatocellular carcinoma mouse tumors triggered by MYC β-catenin Luc with or without OS antigens, with Aoah versus vector over-expression. The image format MRXS can NOT be opened with regular software such as QuPath. We used CaseViewer on Windows or HALO to view these images. If you need help, please contact the lead corresponding author (Peng Jiang).

HE_LucOS.zip: Matched H&E staining for all images in the MultiIF_LucOS.zip, for identifying tumor nodules.

Edu_staining.zip: Edu staining of DNA synthesis for cancer cell lines in vitro growth.

Western_blot.zip: uncropped raw images of western blot experiments.

rhAOAH_quality_controls.zip: quality control reports for AOAH protein productions.



WBP70579_1&4 Analytical Testing Report_20240312: Quality control metrics for the final large-scale protein production with two-step purifications. We used the proteins manufactured in this batch for experiments in the paper.

WBP71420_1 Analytical Testing Report_20240611: Additional quality control for His-tag protein by Liquid chromatography-mass spectrometry (LC-MS) on the denatured and deglycosylated protein.

WBP71306_1 Analytical Testing Report_20240514: Quality control metrics for the production of AOAH protein with the S263A mutation.


SC_4genes_comb.pdf: single-cell RNA-seq profiles for four target genes validated in the study. Each dataset is a t-SNE plot. Each scRNA-seq dataset takes one row with cell labels on the left and gene expression heat maps on the right.

ELISA_qPCR.xlsx: raw data from qRT-PCR and ELISA to validate the candidate gene overexpression.
description: Secreted proteins are central mediators of intercellular communications and can serve as therapeutic targets in diverse diseases. The ~1903 human genes encoding secreted proteins are difficult to study through common genetic approaches. To address this hurdle and, more generally, to discover cancer therapeutics, we developed the Cancer Immunology Data Engine (CIDE, https://cide.ccr.cancer.gov), which incorporates 90 omics datasets spanning 8575 tumor profiles with immunotherapy outcomes from 17 solid tumor types. 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, AOAH (Acyloxyacyl Hydrolase), 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.