Platelet RNA enables accurate detection of ovarian cancer: an intercontinental, biomarker identification study
Received date: 15 Aug 2022
Accepted date: 19 Oct 2022
Copyright
Platelets are reprogrammed by cancer via a process called education, which favors cancer development. The transcriptional profile of tumor-educated platelets (TEPs) is skewed and therefore practicable for cancer detection. This intercontinental, hospital-based, diagnostic study included 761 treatment-naïve inpatients with histologically confirmed adnexal masses and 167 healthy controls from nine medical centers (China, n = 3; Netherlands, n = 5; Poland, n = 1) between September 2016 and May 2019. The main out-comes were the performance of TEPs and their combination with CA125 in two Chinese (VC1 and VC2) and the European (VC3) validation cohorts collectively and independently. Exploratory outcome was the value of TEPs in public pan-cancer platelet transcriptome datasets. The AUCs for TEPs in the combined validation cohort, VC1, VC2, and VC3 were 0.918 (95% CI 0.889–0.948), 0.923 (0.855–0.990), 0.918 (0.872–0.963), and 0.887 (0.813–0.960), respectively. Combination of TEPs and CA125 demonstrated an AUC of 0.922 (0.889–0.955) in the combined validation cohort; 0.955 (0.912–0.997) in VC1; 0.939 (0.901–0.977) in VC2; 0.917 (0.824–1.000) in VC3. For subgroup analysis, TEPs exhibited an AUC of 0.858, 0.859, and 0.920 to detect early-stage, borderline, non-epithelial diseases and 0.899 to discriminate ovarian cancer from endometriosis. TEPs had robustness, compatibility, and universality for preoperative diagnosis of ovarian cancer since it withstood validations in populations of different ethnicities, heterogeneous histological subtypes, and early-stage ovarian cancer. However, these observations warrant prospective validations in a larger population before clinical utilities.
Yue Gao , Chun-Jie Liu , Hua-Yi Li , Xiao-Ming Xiong , Gui-Ling Li , Sjors G.J.G. In ”t Veld , Guang-Yao Cai , Gui-Yan Xie , Shao-Qing Zeng , Yuan Wu , Jian-Hua Chi , Jia-Hao Liu , Qiong Zhang , Xiao-Fei Jiao , Lin-Li Shi , Wan-Rong Lu , Wei-Guo Lv , Xing-Sheng Yang , Jurgen M.J. Piek , Cornelis D de Kroon , C.A.R. Lok , Anna Supernat , Sylwia Łapińska-Szumczyk , Anna Łojkowska , Anna J Żaczek , Jacek Jassem , Bakhos A. Tannous , Nik Sol , Edward Post , Myron G. Best , Bei-Hua Kong , Xing Xie , Ding Ma , Thomas Wurdinger , An-Yuan Guo , Qing-Lei Gao . Platelet RNA enables accurate detection of ovarian cancer: an intercontinental, biomarker identification study[J]. Protein & Cell, 2023 , 14(8) : 579 -590 . DOI: 10.1093/procel/pwac056
1 |
Anders S, Pyl P, Huber W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics (Oxford, England) 2015;31:166–169.
|
2 |
Best MG, Sol N, Vancura A et al. Swarm intelligence-enhanced detection of non-small-cell lung cancer using tumor-educated platelets. Cancer Cell 2017;32:238–252 e9.
|
3 |
Best MG, Sol N, Wurdinger T et al. RNA sequencing and swarm intelligence- enhanced classification algorithm development for blood-based disease diagnostics using spliced blood platelet RNA. Nat Protocols 2019;14:1206–1234.
|
4 |
Chen L, Wang K, Li L et al. Plasma exosomal miR-1260a, miR-7977 and miR-192-5p as diagnostic biomarkers in epithelial ovarian cancer. Future Oncol 2022;18:2919–2931.
|
5 |
Chiesa M, Colombo GI, Piacentini L. DaMiRseq-an R/Bioconductor package for data mining of RNA-Seq data: normalization, feature selection and classification. Bioinformatics 2018;34:1416–1418.
|
6 |
Cho MS, Bottsford-Miller J, Vasquez HG et al. Platelets increase the proliferation of ovarian cancer cells. Blood 2012;120:4869–4872.
|
7 |
Colombo N, Sessa C, du Bois A et al; ESMO-ESGO Ovarian Cancer Consensus Conference Working Group. ESMO-ESGO consensus conference recommendations on ovarian cancer: pathology and molecular biology, early and advanced stages, borderline tumours and recurrent disease. Ann Oncol 2019;30:672–705.
|
8 |
D’Ambrosi S, Nilsson RJ, Wurdinger T. Platelets and tumor-associated RNA transfer. Blood 2021;137:3181–3191.
|
9 |
Ding C, Peng H. Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol 2005;3:185–205.
|
10 |
Dobin A, Davis CA, Schlesinger F et al. STAR: ultrafast universal RNAseq aligner. Bioinformatics (Oxford, England) 2013;29:15–21.
|
11 |
Edelstein LC, Simon LM, Montoya RT et al. Racial differences in human platelet PAR4 reactivity reflect expression of PCTP and miR-376c. Nat Med 2013;19:1609–1616.
|
12 |
Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw 2010;33:1–22.
|
13 |
Giannakeas V, Narod SA. Incidence of cancer among adults with thrombocytosis in Ontario, Canada. JAMA Netw Open 2021;4:e2120633–e2120633.
|
14 |
Giudice LC. Clinical practice. Endometriosis. N Engl J Med 2010;362:2389–2398.
|
15 |
Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 2016;32:2847–2849.
|
16 |
Haemmerle M, Stone RL, Menter DG et al. The platelet lifeline to cancer: challenges and opportunities. Cancer Cell 2018;33:965–983.
|
17 |
Haemmerle M, Taylor ML, Gutschner T et al. Platelets reduce anoikis and promote metastasis by activating YAP1 signaling. Nat Commun 2017;8:310.
|
18 |
Henderson JT, Webber EM, Sawaya GF. Screening for ovarian cancer: updated evidence report and systematic review for the US preventive services task force. JAMA 2018;319:595–606.
|
19 |
In ‘t Veld S, Wurdinger T. Tumor-educated platelets. Blood 2019;133:2359–2364.
|
20 |
In ‘t Veld S, Arkani M, Post E et al. Detection and localization of early- and late-stage cancers using platelet RNA. Cancer Cell 2022;40:999–1009 e6.
|
21 |
Klement G, Yip TT, Cassiola F et al. Platelets actively sequester angiogenesis regulators. Blood 2009;113:2835–2842.
|
22 |
Kuhn M. Building predictive models in R using the caret package. J Stat Softw 2008;28:26.
|
23 |
Leek JT. svaseq: removing batch effects and other unwanted noise from sequencing data. Nucleic Acids Res 2014;42:e161.
|
24 |
Lheureux S, Braunstein M, Oza AM. Epithelial ovarian cancer: evolution of management in the era of precision medicine. CA Cancer J Clin 2019a;69:280–304.
|
25 |
Lheureux S, Gourley C, Vergote I et al. Epithelial ovarian cancer. The Lancet 2019b;393:1240–1253.
|
26 |
Longacre TA, Bell DA, Malpica A et al. Tumours of the ovary. WHO Classification of Tumours of Female Reproductive Organs. 2014: 11–86.
|
27 |
López JA. Introduction to a review series on platelets and cancer. Blood 2021;137:3151–3152.
|
28 |
Love M, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014;15:550.
|
29 |
Meng Q, Duan P, Li L et al. Expression of placenta growth factor is associated with unfavorable prognosis of advanced-stage serous ovarian cancer. Tohoku J Exp Med 2018;244:291–296.
|
30 |
Menon U, Gentry-Maharaj A, Burnell M et al. Ovarian cancer population screening and mortality after long-term follow-up in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS): a randomised controlled trial. The Lancet 2021;397:2182–2193.
|
31 |
Motohara T, Masuda K, Morotti M et al. An evolving story of the metastatic voyage of ovarian cancer cells: cellular and molecular orchestration of the adipose-rich metastatic microenvironment. Oncogene 2019;38:2885–2898.
|
32 |
Prat J, Mutch DG. Pathology of cancers of the female genital tract including molecular pathology. Int J Gynaecol Obstet 2018;143:93–108.
|
33 |
Prat J. Staging classification for cancer of the ovary, fallopian tube, and peritoneum. Int J Gynecol Obstet 2014;124:1–5.
|
34 |
Roweth HG, Battinelli EM. Lessons to learn from tumor-educated platelets. Blood 2021;137:3174–3180.
|
35 |
Sabatier R, Garnier S, Guille A et al. Whole-genome/exome analysis of circulating tumor DNA and comparison to tumor genomics from patients with heavily pre-treated ovarian cancer: subset analysis of the PERMED-01 trial. Front Oncol 2022;12:946257.
|
36 |
Siegel RL, Miller KD, Fuchs HE et al. Cancer statistics, 2021. Cancer J Clin 2021;71:7–33.
|
37 |
Stone RL, Nick AM, McNeish IA et al. Paraneoplastic thrombocytosis in ovarian cancer. N Engl J Med 2012;366:610–618.
|
38 |
Tibshirani R. Regression shrinkage and selection via the Lasso. J Royal Stat Soc B (Methodological) 1996;58:267–288.
|
39 |
Vaughan S, Coward JI, Bast sRC et al. Rethinking ovarian cancer: recommendations for improving outcomes. Nat Rev Cancer 2011;11:719–725.
|
40 |
Wang R, Stone RL, Kaelber JT et al. Electron cryotomography reveals ultrastructure alterations in platelets from patients with ovarian cancer. Proc Natl Acad Sci USA 2015;112:14266–14271.
|
41 |
Wang T, Gao Y, Wang X et al. Establishment of an optimized CTC detection model consisting of EpCAM, MUC1 and WT1 in epithelial ovarian cancer and its correlation with clinical characteristics. Chin J Cancer Res 2022;34:95–108.
|
42 |
Xu XR, Yousef GM, Ni H. Cancer and platelet crosstalk: opportunities and challenges for aspirin and other antiplatelet agents. Blood 2018;131:1777–1789.
|
43 |
Yates AD, Achuthan P, Akanni W et al. Ensembl 2020. Nucleic Acids Res 2020;48:D682–D688.
|
44 |
Yuan C, Liu X, Liu X et al. The GADD45A (1506T>C) polymorphism is associated with ovarian cancer susceptibility and prognosis. PLoS One 2015;10:e0138692.
|
45 |
Zhang M, Cheng S, Jin Y et al. Roles of CA125 in diagnosis, prediction, and oncogenesis of ovarian cancer. Biochim Biophys Acta Rev Cancer 2021;1875:188503.
|
46 |
Zhu A, Ibrahim JG, Love MI. Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics 2019;35:2084–2092.
|
47 |
Zhu JW, Charkhchi P, Akbari MR. Potential clinical utility of liquid biopsies in ovarian cancer. Mol Cancer 2022;21:114.
|
/
〈 | 〉 |