Th2 cells infiltrating high-grade serous ovarian cancer: a feature that may account for the poor prognosis

다국어 초록 (Multilingual Abstract)

Objective: We aimed to investigate the differences of transcriptome profile between 2 groups of high-grade serous ovarian cancer (HGSOC) patients with distinct outcomes and identify potential biomarkers for recurrence.
Methods: RNA sequencing was performed in 2 groups of HGSOC patients with similar demographic characteristics but exhibiting distinct progression-free survival (PFS). Transcriptome data of poor response (PR; PFS ≤6 months) and good response (GR; PFS ≥12 months) group were compared. We employed xCell to evaluate the abundance of 63 cells in tumor microenvironment. The predictive value of recurrence-related tumor infiltration cells was validated in cohort data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) dataset. The weighted correlation network analysis was performed to identify the genes related to cell infiltration.
Results: PR patients exhibited a distinct tumor infiltration immune cells-related transcriptional profile compared to GR patients, such as lower signatures of leukocyte differentiation, activation and chemotaxis. The fraction of T-helper 2 (Th2) cells infiltration was significantly higher in PR group than in GR group. High infiltration of Th2 was significantly associated with unfavorable prognosis in the GEO cohort (area under the curve=0.84 at 6 months recurrence) and TCGA cohort (p=0.008). Genes enriched to extracellular matrix organization and integrin binding were relevant to Th2 infiltration.
Conclusion: Patients with HGSOC having shorter PFS exhibited a distinct gene signature that related to tumor-infiltrating immune cells. The level of Th2 infiltration could facilitate patient recurrence risk stratification and may be a promising biomarker for prognosis prediction and immune-related treatment.

참고문헌 (Reference)

1 Aran D, "xCell : digitally portraying the tissue cellular heterogeneity landscape" 18 : 220-, 2017

2 Yu G, "clusterProfiler : an R package for comparing biological themes among gene clusters" 16 : 284-287, 2012

3 Langfelder P, "WGCNA : an R package for weighted correlation network analysis" 9 : 559-, 2008

4 Westergaard MC, "Tumour-reactive T cell subsets in the microenvironment of ovarian cancer" 120 : 424-434, 2019

5 Paijens ST, "Tumor-infiltrating lymphocytes in the immunotherapy era" 18 : 842-859, 2021

6 Chen K, "Tumor-infiltrating CD4+ lymphocytes predict a favorable survival in patients with operable esophageal squamous cell carcinoma" 23 : 4619-4632, 2017

7 Pignata S, "Treatment of recurrent ovarian cancer" 28 : viii51-6, 2017

8 Heagerty PJ, "Time-dependent ROC curves for censored survival data and a diagnostic marker" 56 : 337-344, 2000

9 Nappo G, "The immunosuppressive cytokine interleukin-4 increases the clonogenic potential of prostate stem-like cells by activation of STAT6 signalling" 6 : e342-, 2017

10 Colaprico A, "TCGAbiolinks : an R/Bioconductor package for integrative analysis of TCGA data" 44 : e71-, 2016

11 Silva TC, "TCGA Workflow : analyze cancer genomics and epigenomics data using Bioconductor packages" 5 : 1542-, 2016

12 Gaylo-Moynihan A, "Programming of distinct chemokine-dependent and-independent search strategies for Th1 and Th2 cells optimizes function at inflamed sites" 51 : 298-309, 2019

13 Yuan X, "Prognostic significance of tumor-associated macrophages in ovarian cancer : a meta-analysis" 147 : 181-187, 2017

14 Wu Y, "Prognostic implications of tumour-infiltrating lymphocytes for recurrence in epithelial ovarian cancer" 206 : 36-46, 2021

15 Hao J, "Prognostic impact of tumor-infiltrating lymphocytes in high grade serous ovarian cancer : a systematic review and meta-analysis" 12 : 1758835920967241-, 2020

16 Winter WE 3rd, "Prognostic factors for stage III epithelial ovarian cancer : a Gynecologic Oncology Group Study" 25 : 3621-3627, 2007

17 Hassan SOA, "Performance of seven criteria to assess CA125 increments among ovarian cancer patients monitored during first-line chemotherapy and the post-therapy follow-up period" 3 : FSO216-, 2017

18 Zuo S, "Pan-cancer analysis of immune cell infiltration identifies a prognostic immune-cell characteristic score(ICCS)in lung adenocarcinoma" 11 : 1218-, 2020

19 Morand S, "Ovarian cancer immunotherapy and personalized medicine" 22 : 6532-, 2021

20 Ray-Coquard I, "Olaparib plus bevacizumab as first-line maintenance in ovarian cancer" 381 : 2416-2428, 2019

21 González-Martín A, "Niraparib in patients with newly diagnosed advanced ovarian cancer" 381 : 2391-2402, 2019

22 Mounir M, "New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx" 15 : e1006701-, 2019

23 Yang H, "Neoantigens and the tumor microenvironment play important roles in the prognosis of high-grade serous ovarian cancer" 15 : 18-, 2022

24 Liu J, "Multi-omics analysis of tumor mutational burden combined with prognostic assessment in epithelial ovarian cancer based on TCGA database" 17 : 3200-3213, 2020

25 Ducie J, "Molecular analysis of high-grade serous ovarian carcinoma with and without associated serous tubal intra-epithelial carcinoma" 8 : 990-, 2017

26 Love MI, "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2" 15 : 550-, 2014

27 Gupta S, "Maintenance therapy for recurrent epithelial ovarian cancer : current therapies and future perspectives-a review" 12 : 103-, 2019

28 Badmann S, "M2 macrophages infiltrating epithelial ovarian cancer express MDR1 : a feature that may account for the poor prognosis" 9 : 1224-, 2020

29 Sato E, "Intraepithelial CD8+ tumor-infiltrating lymphocytes and a high CD8+/regulatory T cell ratio are associated with favorable prognosis in ovarian cancer" 102 : 18538-18543, 2005

30 Chen K, "Interluekin-17A(IL17A)" 614 : 8-14, 2017

31 Oja AE, "Functional heterogeneity of CD4+ tumor-infiltrating lymphocytes with a resident memory phenotype in NSCLC" 9 : 2654-, 2018

32 Boland JL, "Early disease progression and treatment discontinuation in patients with advanced ovarian cancer receiving immune checkpoint blockade" 152 : 251-258, 2019

33 Basu A, "Differentiation and regulation of TH cells : a balancing act for cancer immunotherapy" 12 : 669474-, 2021

34 Soneson C, "Differential analyses for RNA-seq : transcript-level estimates improve gene-level inferences" 4 : 1521-, 2015

35 Rustin GJ, "Definitions for response and progression in ovarian cancer clinical trials incorporating RECIST 1. 1 and CA 125 agreed by the Gynecological Cancer Intergroup(GCIG)" 21 : 419-423, 2011

36 Plesca I, "Characteristics of tumor-infiltrating lymphocytes prior to and during immune checkpoint inhibitor therapy" 11 : 364-, 2020

37 Doherty JA, "Challenges and opportunities in studying the epidemiology of ovarian cancer subtypes" 4 : 211-220, 2017

38 Siegel RL, "Cancer statistics, 2019" 69 : 7-34, 2019

39 Funingana IG, "Can integrative biomarker approaches improve prediction of platinum and PARP inhibitor response in ovarian cancer?" 77 : 67-82, 2021

40 Wu C, "Bioinformatic profiling identifies a platinum-resistant-related risk signature for ovarian cancer" 9 : 1242-1253, 2020