Literature DB >> 33753096

Cross-platform comparison of immune-related gene expression to assess intratumor immune responses following cancer immunotherapy.

Li Zhang1, Jason Cham2, James Cooley3, Tao He4, Katsunobu Hagihara5, Hai Yang6, Frances Fan7, Alexander Cheung7, Debrah Thompson3, B J Kerns3, Lawrence Fong8.   

Abstract

Neoadjuvant immunotherapy can induce immune responses within the tumor microenvironment. Gene expression can be used to assess responses with limited amounts of conventionally-fixed patient-derived samples. We aim to assess the cross-platform concordance of immune-related gene expression data. We performed comparisons across three panels in two platforms: Nanostring nCounter® PanCancer Immune Profiling Panel (nS), HTG EdgeSeq Oncology Biomarker Panel (HTG OBP) and Precision Immuno-Oncology Panel (HTG PIP). All tissue samples of 14 neoadjuvant GM-CSF treated, 14 neoadjuvant Provenge treated, and 12 untreated prostate cancer patients were radical prostatectomy (RP) tissues, while 6 prostatitis patients and 6 non-prostatitis subjects were biopsies. For all 52 patients, more than 90% of the common genes were significantly correlated (p < 0.05) and more than 76% of the common genes were highly correlated (r > 0.5) between any two panels. Co-inertia analysis also demonstrated high overall dataset structure similarity (correlation>0.84). Although both dimensionality reduction visualization analysis and unsupervised hierarchical cluster analysis for highly correlated common genes (r > 0.9) suggested a high-level of consistency across the panels, there were subsets of genes that were differentially expressed across the panels. In addition, while the effect size of the differential testing for neoadjuvant treated vs. untreated localized prostate cancer patients across the panels were significantly correlated, some genes were only differentially expressed in the HTG panels. Finally, the HTG PIP panel had the best classification performance among the 3 panels. These differences detected may be a result of the different panels or platforms due to their technical setting and focus. Thus, researchers should be aware of those potential differences when deciding which platform and panel to use. Published by Elsevier B.V.

Entities:  

Keywords:  Classification performance; Gene expression profiling; HTG EdgeSeq; Nanostring platform; Neoadjuvant immunotherapy; Prostate cancer

Year:  2021        PMID: 33753096     DOI: 10.1016/j.jim.2021.113041

Source DB:  PubMed          Journal:  J Immunol Methods        ISSN: 0022-1759            Impact factor:   2.303


  3 in total

1.  Avelumab maintenance in advanced urothelial carcinoma: biomarker analysis of the phase 3 JAVELIN Bladder 100 trial.

Authors:  Thomas Powles; Srikala S Sridhar; Yohann Loriot; Joaquim Bellmunt; Xinmeng Jasmine Mu; Keith A Ching; Jie Pu; Cora N Sternberg; Daniel P Petrylak; Rosa Tambaro; Louis M Dourthe; Carlos Alvarez-Fernandez; Maureen Aarts; Alessandra di Pietro; Petros Grivas; Craig B Davis
Journal:  Nat Med       Date:  2021-12-10       Impact factor: 53.440

2.  A Decentralized Kidney Transplant Biopsy Classifier for Transplant Rejection Developed Using Genes of the Banff-Human Organ Transplant Panel.

Authors:  Myrthe van Baardwijk; Iacopo Cristoferi; Jie Ju; Hilal Varol; Robert C Minnee; Marlies E J Reinders; Yunlei Li; Andrew P Stubbs; Marian C Clahsen-van Groningen
Journal:  Front Immunol       Date:  2022-05-10       Impact factor: 8.786

3.  Transcriptome and unique cytokine microenvironment of Castleman disease.

Authors:  Anna Wing; Jason Xu; Wenzhao Meng; Aaron M Rosenfeld; Elizabeth Y Li; Gerald Wertheim; Michele Paessler; Adam Bagg; Dale Frank; Kai Tan; David T Teachey; Megan S Lim; Eline Luning Prak; David C Fajgenbaum; Vinodh Pillai
Journal:  Mod Pathol       Date:  2021-10-22       Impact factor: 8.209

  3 in total

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