Dataset Information

TIminer: NGS data mining pipeline for cancer immunology and immunotherapy.

ABSTRACT:

Summary

Recently, a number of powerful computational tools for dissecting tumor-immune cell interactions from next-generation sequencing data have been developed. However, the assembly of analytical pipelines and execution of multi-step workflows are laborious and involve a large number of intermediate steps with many dependencies and parameter settings. Here we present TIminer, an easy-to-use computational pipeline for mining tumor-immune cell interactions from next-generation sequencing data. TIminer enables integrative immunogenomic analyses, including: human leukocyte antigens typing, neoantigen prediction, characterization of immune infiltrates and quantification of tumor immunogenicity.

Availability and implementation

TIminer is freely available at http://icbi.i-med.ac.at/software/timiner/timiner.shtml.

Contact

zlatko.trajanoski@i-med.ac.at.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Tappeiner E

PROVIDER: S-EPMC5870678 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Publications

TIminer: NGS data mining pipeline for cancer immunology and immunotherapy.

Tappeiner Elias E Finotello Francesca F Charoentong Pornpimol P Mayer Clemens C Rieder Dietmar D Trajanoski Zlatko Z

Bioinformatics (Oxford, England) 20171001 19

<h4>Summary</h4>Recently, a number of powerful computational tools for dissecting tumor-immune cell interactions from next-generation sequencing data have been developed. However, the assembly of analytical pipelines and execution of multi-step workflows are laborious and involve a large number of intermediate steps with many dependencies and parameter settings. Here we present TIminer, an easy-to-use computational pipeline for mining tumor-immune cell interactions from next-generation sequencin ...[more]

PMID: 28633385

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Project description:Historically, non-small-cell lung cancer (NSCLC) has been regarded as a nonimmunogenic tumor; however, recent studies have shown that NSCLCs are among the most responsive cancers to monoclonal antibody immune checkpoint inhibitors (ICIs). ICIs have dramatically improved clinical outcomes for a subset of patients (∼20%) with locally advanced and metastatic NSCLC, and they have also demonstrated promise as neoadjuvant therapy for early-stage resectable disease. Nevertheless, the majority of patients with NSCLC are refractory to ICIs for reasons that are poorly understood. Thus, major questions are: how do we initially identify the patients most likely to derive significant clinical benefit from these therapies; how can we increase the number of patients benefiting; what are the mechanisms of primary and acquired resistance to immune-based therapies; are there additional immune checkpoints besides PD-1/PD-L1 and CTLA-4 that can be targeted to provide greater clinical benefit to patients; and how do we best combine ICI therapy with surgery, radiotherapy, chemotherapy, and targeted therapy? To answer these questions, we need to deploy the latest technologies to study tumors and their microenvironment and how they interact with components of the innate and adaptive immune systems. There is also a need for new preclinical model systems to investigate the molecular mechanisms of resistance to treatment and identify novel therapeutic targets. Recent advances in technology are beginning to shed new light on the immune landscape of NSCLC that may uncover biomarkers of response and maximize the clinical benefit of immune-based therapies. Identification of the mechanisms of resistance should lead to the identification of novel targets and the generation of new therapeutic strategies that improve outcomes for a greater number of patients. In the sections below, we discuss the results of studies examining the immune microenvironment in NSCLC, summarize the clinical experience with immunotherapy for NSCLC, and review candidate biomarkers of response to these agents in NSCLC.

Project description:Tumor-specific antigens or neoantigens are peptides that are expressed only in cancer cells and not in healthy cells. Some of these molecules can induce an immune response, and therefore, their use in immunotherapeutic strategies based on cancer vaccines has been extensively explored. Studies based on these approaches have been triggered by the current high-throughput DNA sequencing technologies. However, there is no universal nor straightforward bioinformatic protocol to discover neoantigens using DNA sequencing data. Thus, we propose a bioinformatic protocol to detect tumor-specific antigens associated with single nucleotide variants (SNVs) or "mutations" in tumoral tissues. For this purpose, we used publicly available data to build our model, including exome sequencing data from colorectal cancer and healthy cells obtained from a single case, as well as frequent human leukocyte antigen (HLA) class I alleles in a specific population. HLA data from Costa Rican Central Valley population was selected as an example. The strategy included three main steps: (1) pre-processing of sequencing data; (2) variant calling analysis to detect tumor-specific SNVs in comparison with healthy tissue; and (3) prediction and characterization of peptides (protein fragments, the tumor-specific antigens) derived from the variants, in the context of their affinity with frequent alleles of the selected population. In our model data, we found 28 non-silent SNVs, present in 17 genes in chromosome one. The protocol yielded 23 strong binders peptides derived from the SNVs for frequent HLA class I alleles for the Costa Rican population. Although the analyses were performed as an example to implement the pipeline, to our knowledge, this is the first study of an in silico cancer vaccine using DNA sequencing data in the context of the HLA alleles. It is concluded that the standardized protocol was not only able to identify neoantigens in a specific but also provides a complete pipeline for the eventual design of cancer vaccines using the best bioinformatic practices.Supplementary informationThe online version contains supplementary material available at 10.1007/s43657-022-00084-9.

Dataset Information

TIminer: NGS data mining pipeline for cancer immunology and immunotherapy.

Summary

Availability and implementation

Contact

Supplementary information

Publications

TIminer: NGS data mining pipeline for cancer immunology and immunotherapy.

Similar Datasets

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