IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures

Abstract

Recent advances in next-generation sequencing (NGS) technologies have triggered the rapid accumulation of publicly available multi-omics datasets. The application of integrated omics to explore robust signatures for clinical translation is increasingly emphasized, and this is attributed to the clinical success of immune checkpoint blockades in diverse malignancies. However, effective tools for comprehensively interpreting multi-omics data are still warranted to provide increased granularity into the intrinsic mechanism of oncogenesis and immunotherapeutic sensitivity. Therefore, we developed a computational tool for effective Immuno-Oncology Biological Research (IOBR), providing a comprehensive investigation of the estimation of reported or user-built signatures, TME deconvolution, and signature construction based on multi-omics data. Notably, IOBR offers batch analyses of these signatures and their correlations with clinical phenotypes, long non-coding RNA (lncRNA) profiling, genomic characteristics, and signatures generated from single-cell RNA sequencing (scRNA-seq) data in different cancer settings. Additionally, IOBR integrates multiple existing microenvironmental deconvolution methodologies and signature construction tools for convenient comparison and selection. Collectively, IOBR is a user-friendly tool for leveraging multi-omics data to facilitate immuno-oncology exploration and to unveil tumor-immune interactions and accelerating precision immunotherapy.

Novel Immune Infiltrating Cell Signature Based on Cell Pair Algorithm Is a Prognostic Marker in Cancer

“…Moreover, considering the differences in the reference genomes and gene signatures of immune cells used for quantifying RNA-sequencing data, multiple previous prognostic models may have limitations in the cross-validation of different transcriptional datasets or different cancer types. The measurements of cellular heterogeneity vary due to the frequent updated version of annotation for immune cells and reference genome, which may impede their extensive application and set back the prospect for clinical practice ( Figure S5 ) ( 35 , 36 ). To resolve this issue, we collected and integrated 65 immune cells to establish a robust and comprehensive prognostic signature with the concept of cell pair.…”

Tumor microenvironment evaluation promotes precise checkpoint immunotherapy of advanced gastric cancer

“…com/ IOBR/ IOBR). 28 Immun-eScore, Stromalscore, and tumor purity were assessed computationally in RNA-seq data using the ESTIMATE algorithm 29 that uses gene expression signatures to infer the fraction of stromal and immune cells in tumor samples. Other computational algorithms and tools used to estimate the microenvironment were detailed in the online supplemental methods.…”

“…40 Other prevalent gene signature scores with respect to the TME, tumor intrinsic pathway, and metabolism were calculated for each sample using the PCA algorithm by IOBR package. 28 Differentially methylated probes analysis Methylation data (β values of Illumina Infinium Human-Methylation450) of The Cancer Genome Atlas of Stomach Adenocarcinoma (TCGA-STAD) patients were obtained through TCGAbiolinks. 32 β values reported by the 450K Illumina platform for each probe were set as the methylation level measurement for the targeted CpG site.…”

A Gene Prognostic Index Associated With Epithelial-Mesenchymal Transition Predicting Biochemical Recurrence and Tumor Chemoresistance for Prostate Cancer

“…We used the quanTIseq and ESTIMATE algorithms to analyze the immune infiltration levels of cells in TME (26)(27)(28). Immune checkpoint analysis was conducted as well.…”