Detection and localization of early- and late-stage cancers using platelet RNA

Veld, Sjors G J G In ‘t; Arkani, Mohammad; Post, Edward P.; Antunes-Ferreira, Mafalda; D’Ambrosi, Silvia; Vessies, Daan C.L.; Vermunt, Lisa; Vancura, Adrienne; Muller, Mirte; Niemeijer, Anna-Larissa N.; Tannous, Jihane; Meijer, Laura L.; Large, Tessa Y.S. Le; Mantini, G.; Wondergem, Niels E.; Heinhuis, Kimberley M.; Wilpe, Sandra van; Smits, Josien; Drees, Esther E.E.; Roos, Eva; Leurs, Cyra E.; Fat, Lee-Ann Tjon Kon; Lelij, Ewoud J. van der; Dwarshuis, Govert; Kamphuis, Maarten J.; Visser, Lisanne E.; Harting, Romée; Gregory, Annemijn; Schweiger, Markus; Wedekind, Laurine E.; Ramaker, Jip; Zwaan, Kenn; Verschueren, Heleen; Bahce, Idris; Langen, Adrianus J. de; Smit, Egbert F.; Heuvel, Michel M. van den; Hartemink, Koen J.; Kuijpers, Marijke J. E.; Egbrink, Mirjam G.A. oude; Griffioen, Arjan W.; Rossel, Rafael; Hiltermann, T Jeroen N; Lee-Lewandrowski, Elizabeth; Lewandrowski, Kent; Hamer, Philip C. De Witt; Kouwenhoven, Mathilde C.M.; Reijneveld, Jaap C.; Leenders, William P.; Hoeben, Ann; Leeuw, Irma M. Verdonck‐de; Leemans, C. René; Jong, Robert J. Baatenburg de; Terhaard, Chris H.J.; Takes, Robert P.; Langendijk, Johannes A.; Jager, Saskia C.A. de; Kraaijeveld, Adriaan O.; Pasterkamp, Gérard; Smits, Minke; Schalken, Jack A.; Łapińska‐Szumczyk, Sylwia; Łojkowska, Anna; Żaczek, Anna; Lokhorst, Henk M.; Donk, Niels W.C.J. van de; Nijhof, Inger S.; Prins, Henk-Jan; Zijlstra, Josée M.; Idema, Sander; Baayen, Johannes C.; Teunissen, Charlotte E.; Killestein, Joep; Besselink, Marc G.; Brammen, Lindsay; Bachleitner‐Hofmann, Thomas; Mateen, Farrah J.; Plukker, John; Heger, Michal; Mast, Quirijn de; Lisman, Ton; Pegtel, D. Michiel; Bogaard, Harm-Jan; Jassem, Jacek; Supernat, Anna; Mehra, Niven; Gerritsen, Winald R.; Kroon, Cornelis D. de; Lok, Christianne; Piek, Jurgen M.J.; Steeghs, Neeltje; Houdt, Winan J. van; Brakenhoff, Ruud H.; Sonke, Gabe S.; Verheul, H.; Giovannetti, Elisa; Kazemier, Geert; Sabrkhany, Siamack; Schuuring, Ed; Sistermans, Erik A.; Wolthuis, Rob M. F.; Meijers-Heijboer, Hanne; Dorsman, Josephine C.; Oudejans, Cees B.M.; Ylstra, Bauke; Westerman, Bart A.; Broek, Daan van den; Koppers-Lalić, Danijela; Wesseling, Pieter; Nilsson, R. Jonas A.; Vandertop, W. Peter; Noske, David P.; Tannous, Bakhos A.; Sol, Nik; Best, Myron G.; Würdinger, Thomas

doi:10.1016/j.ccell.2022.08.006

Cited by 38 publications

(25 citation statements)

References 41 publications

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“…Non-small cell lung cancer Best [7] 575 genes RNA-seq 24 16 Se = 95.8%, Sp = 100%, AUC = 0.977 Best [101] 800 genes RNA-seq 90 40 AUC = 0.95 1000 genes RNA-seq 44 44 AUC = 0.93 Goswami [102] 11 genes qRT-PCR 10 7 AUC = 0.972 Luo [77] MAGI2-AS3, ZF AS1 qRT-PCR 101 60 For MAGI2-AS3: Se = 78.2%, Sp = 81.7%; for ZF AS1: Se = 81.2%, Sp = 43.3%; for their combination: Se = 87.1%, Sp = 86.7% Xing [103] ITGA2B For ITGA2B, Se = 93.2%, Sp = 95.6%, AUC = 0.967; For SELP, Se = 90.0%, Sp = 100%, AUC = 0.956 Dong [104] SNORD55 qRT-PCR 290 189 Se = 79.3%, Sp = 68.3%, AUC = 0.803 D'Ambrosi [99] circNRIP1 qRT-PCR 23 24 circNRIP1 in NSCLC downregulated (P = .0302) In 't Veld [105] 493 genes RNA-seq 522 723 AUC = 0.94 Lung cancer Dong [98] snRNA U1, U2, U5 qRT-PCR 382 361 AUC for U1, U2, U5 and their combination: 0.769, 0840, 0.809, and 0.840 Li [78] linc-GTF2H2-1, RP3-466P17.2, lnc-ST8SIA4-12…”

Section: Patients Controlsmentioning

confidence: 99%

“…Liu [106] MAX, MTURN, HLA-B qPCR 127 82 Se = 60.6%, Sp = 81.7%, AUC = 0.734 Breast cancer Best [7] 192 genes RNA-seq 16 10 Se = 100%, Sp = 100%, Ac = 100% Liefaard [15] NA RNA-seq 77 62 Internal validation set: AUC of 0.86 for PSO-SVM; AUC of 0.87 for elastic net algorithm 37 36 Single-center, independent, blinded set: AUC of 0.57 for the PSO-SVM, AUC of 0.60 for elastic net algorithm Yao [107] TPM3 qRT-PCR 504 109 AUC of 0.9705 for diagnosis, AUC of 0.8404 for metastatic cancer In 't Veld [105] 493 genes RNA-seq 93 723 AUC = 0.81 Lips [108] NA RNA-seq NA NA Validation set: Ac = 86%, AUC = 0.93. For stage I/II patients: Ac = 91%.…”

Section: Patients Controlsmentioning

confidence: 99%

“…In 't Veld [105] 493 genes RNA-seq 23 723 AUC = 0.96 Zhu [110] miR-495-3p qRT-PCR 25 25 AUC = 0.76 miR-1293 qRT-PCR 25 25 AUC = 0.78 Colorectal cancer Best [7] 1048 genes RNA-seq 16 16 Se = 100%; Sp = 100%; Ac = 100% Xu [111] 921 genes RNA-seq 68 52 Se = 88.5%; Sp = 86.8%; Ac = 87.5%; AUC = 0.920 Ye [79] LNCAROD, SNHG20, LINC00534, and TSPOAP-AS1…”

Section: Patients Controlsmentioning

confidence: 99%

“…In 't Veld [105] 493 genes RNA-seq 35 723 AUC = 0.98 Sarcoma Heinhuis [120] 2647 genes RNA-seq 17 36 Se = 88%, Sp = 86%, AUC = 0.93 In 't Veld [105] 493 genes RNA-seq 53 723 AUC = 0.96 Renal cell carcinoma Xiao [121] 68 genes RNA-seq 24 25 Se = 91.7%, Sp = 100%, Ac = 95.9%, AUC = 0.988 In 't Veld [105] 493 genes RNA-seq 28 723 AUC = 0.87 Esophageal carcinoma…”

Section: Myeloproliferative Neoplasmsmentioning

confidence: 99%

“…In 't Veld [105] 493 genes RNA-seq 15 723 AUC = 0.8 Melanoma In 't Veld [105] 493 genes RNA-seq 68 723 AUC = 0.9 Multiple myeloma In 't Veld [105] 493 genes RNA-seq 31 723 AUC = 0.99 Lymphoma In 't Veld [105] 493 genes RNA-seq 20 723 AUC = 0.92 Ovarian cancer…”

Section: Myeloproliferative Neoplasmsmentioning

confidence: 99%

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A narrative review for platelets and their RNAs in cancers: New concepts and clinical perspectives

Xiang

Xiang²,

Zhang

et al. 2022

Medicine

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Recent years have witnessed a growing body of evidence suggesting that platelets are involved in several stages of the metastatic process via direct or indirect interactions with cancer cells, contributing to the progression of neoplastic malignancies. Cancer cells can dynamically exchange components with platelets in and out of blood vessels, and directly phagocytose platelets to hijack their proteome, transcriptome, and secretome, or be remotely regulated by metabolites or microparticles released by platelets, resulting in phenotypic, genetic, and functional modifications. Moreover, platelet interactions with stromal and immune cells in the tumor microenvironment lead to alterations in their components, including the ribonucleic acid (RNA) profile, and complicate the impact of platelets on cancers. A deeper understanding of the roles of platelets and their RNAs in cancer will contribute to the development of anticancer strategies and the optimization of clinical management. Encouragingly, advances in high-throughput sequencing, bioinformatics data analysis, and machine learning have allowed scientists to explore the potential of platelet RNAs for cancer diagnosis, prognosis, and guiding treatment. However, the clinical application of this technique remains controversial and requires larger, multicenter studies with standardized protocols. Here, we integrate the latest evidence to provide a broader insight into the role of platelets in cancer progression and management, and propose standardized recommendations for the clinical utility of platelet RNAs to facilitate translation and benefit patients.

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Section: Patients Controlsmentioning

confidence: 99%

Section: Patients Controlsmentioning

confidence: 99%

Section: Patients Controlsmentioning

confidence: 99%

Section: Myeloproliferative Neoplasmsmentioning

confidence: 99%

Section: Myeloproliferative Neoplasmsmentioning

confidence: 99%

See 3 more Smart Citations

A narrative review for platelets and their RNAs in cancers: New concepts and clinical perspectives

Xiang

Xiang²,

Zhang

et al. 2022

Medicine

View full text Add to dashboard Cite

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Superresolution Fluorescence Microscopy of Platelet Subcellular Structures as a Potential Tumor Liquid Biopsy

Deng

Hong

et al. 2023

Small Methods

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Blood‐based tumor liquid biopsies are promising as an alternative or complement to tissue biopsies due to their noninvasiveness, convenience, and safety, and there is still a great demand for the discovery of new biomarkers for these biopsies. Here, nanoscale distribution patterns of subcellular structures in platelets, as imaged by structured illumination superresolution fluorescence microscopy, as a new type of potential biomarker for tumor liquid biopsies are presented. A standardized protocol for platelet sample preparation and developed an automated high‐throughput image analysis workflow is established. The diagnostic capability based on the statistical analysis of 280 000 superresolution images of individual platelets from a variety of tumor patients, benign mass patients, and healthy volunteers (n = 206) is explored. These results suggest that the nanoscale distribution patterns of α‐granules in platelets have the potential to be biomarkers for several cancers, including glioma and cervical, endometrial, and ovarian cancers, facilitating not only diagnosis but also therapeutic monitoring. This study provides a promising novel type of platelet parameter for tumor liquid biopsies at the subcellular level rather than the existing cellular or molecular level and opens up a new avenue for clinical applications of superresolution imaging techniques.

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TEP RNA: a new frontier for early diagnosis of NSCLC

Wang,

Dong,

Jin

et al. 2024

J Cancer Res Clin Oncol

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Background Non-small cell lung cancer (NSCLC) is the most common type of lung cancer (LC), which is the leading cause of tumor mortality. In recent years, compared with tissue biopsy, which is the diagnostic gold standard for tumor diagnosis, Liquid biopsy (LB) is considered to be a more minimally invasive, sensitive, and safer alternative or auxiliary diagnostic method. However, the current value of LB in early diagnosis of LC is not ideal, so it is particularly important to study the changes in blood composition during the process of tumorigenesis and find more sensitive biomarkers. Purpose Platelets are a type of abundant blood cells that carry a large amount of RNA. In the LC regulatory network, activated platelets play an important role in the process of tumorigenesis, development, and metastasis. In order to identify predictive liquid biopsy biomarkers for the diagnosis of NSCLC, we summarized the development and function of platelets, the interaction between platelets and tumors, the value of TEP RNA in diagnosis, prognosis, and treatment of NSCLC, and the method for detecting TEP RNA of NSCLC in this article. Conclusion The application of platelets in the diagnosis and treatment of NSCLC remains at a nascent stage. In addition to the drawbacks of low platelet count and complex experimental processes, the diagnostic accuracy of TEP RNA-seq for cancer in different populations still needs to be improved and validated. At present, a large number of studies have confirmed significant differences in the expression of TEP RNA in platelets between NSCLC patients and healthy individuals. Continuous exploration of the diagnostic value of TEP RNA in NSCLC is of utmost importance. The integration of NSCLC platelet-related markers with other NSCLC markers can improve current tumor diagnosis and prognostic evaluation systems, providing broad prospects in tumor screening, disease monitoring, and prognosis assessment.

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Detection and localization of early- and late-stage cancers using platelet RNA

Cited by 38 publications

References 41 publications

A narrative review for platelets and their RNAs in cancers: New concepts and clinical perspectives

A narrative review for platelets and their RNAs in cancers: New concepts and clinical perspectives

Superresolution Fluorescence Microscopy of Platelet Subcellular Structures as a Potential Tumor Liquid Biopsy

TEP RNA: a new frontier for early diagnosis of NSCLC

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