RNA-Seq of Tumor-Educated Platelets Enables Blood-Based Pan-Cancer, Multiclass, and Molecular Pathway Cancer Diagnostics

Myron G Best, Nik Sol, Irsan Kooi, Jihane Tannous, Bart A Westerman, François Rustenburg, Pepijn Schellen, Heleen Verschueren, Edward Post, Jan Koster, Bauke Ylstra, Najim Ameziane, Josephine Dorsman, Egbert F Smit, Henk M Verheul, David P Noske, Jaap C Reijneveld, R Jonas A Nilsson, Bakhos A Tannous, Pieter Wesseling, Thomas Wurdinger, Myron G Best, Nik Sol, Irsan Kooi, Jihane Tannous, Bart A Westerman, François Rustenburg, Pepijn Schellen, Heleen Verschueren, Edward Post, Jan Koster, Bauke Ylstra, Najim Ameziane, Josephine Dorsman, Egbert F Smit, Henk M Verheul, David P Noske, Jaap C Reijneveld, R Jonas A Nilsson, Bakhos A Tannous, Pieter Wesseling, Thomas Wurdinger

Abstract

Tumor-educated blood platelets (TEPs) are implicated as central players in the systemic and local responses to tumor growth, thereby altering their RNA profile. We determined the diagnostic potential of TEPs by mRNA sequencing of 283 platelet samples. We distinguished 228 patients with localized and metastasized tumors from 55 healthy individuals with 96% accuracy. Across six different tumor types, the location of the primary tumor was correctly identified with 71% accuracy. Also, MET or HER2-positive, and mutant KRAS, EGFR, or PIK3CA tumors were accurately distinguished using surrogate TEP mRNA profiles. Our results indicate that blood platelets provide a valuable platform for pan-cancer, multiclass cancer, and companion diagnostics, possibly enabling clinical advances in blood-based "liquid biopsies".

Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Figures

Graphical abstract
Graphical abstract
Figure 1
Figure 1
Tumor-Educated Platelet mRNA Profiling for Pan-Cancer Diagnostics (A) Schematic overview of tumor-educated platelets (TEPs) as biosource for liquid biopsies. (B) Number of platelet samples of healthy donors and patients with different types of cancer. (C) TEP mRNA sequencing (mRNA-seq) workflow, as starting from 6 ml EDTA-coated tubes, to platelet isolation, mRNA amplification, and sequencing. (D) Correlation plot of mRNAs detected in healthy donor (HD) platelets and cancer patients’ TEPs, including highlighted increased (red) and decreased (blue) TEP mRNAs. (E) Heatmap of unsupervised clustering of platelet mRNA profiles of healthy donors (red) and patients with cancer (gray). (F) Cross-table of pan-cancer SVM/LOOCV diagnostics of healthy donor subjects and patients with cancer in training cohort (n = 175). Indicated are sample numbers and detection rates in percentages. (G) Performance of pan-cancer SVM algorithm in validation cohort (n = 108). Indicated are sample numbers and detection rates in percentages. (H) ROC-curve of SVM diagnostics of training (red), validation (blue) cohort, and random classifiers, indicating the classification accuracies obtained by chance of the training and validation cohort (gray). (I) Total accuracy ratios of SVM classification in five subgroups, including corresponding predictive strengths. Genes, number of mRNAs included in training of the SVM algorithm. See also Figure S1 and Tables S1, S2, S3, and S4.
Figure 2
Figure 2
Tumor-Educated Platelet mRNA Profiles for Multiclass Cancer Diagnostics (A) Heatmaps of unsupervised clustering of platelet mRNA profiles of healthy donors (HD; n = 55) (red) and patients with non-small cell lung cancer (NSCLC; n = 60), colorectal cancer (CRC; n = 41), glioblastoma (GBM; n = 39), pancreatic cancer (PAAD, n = 35), breast cancer (BrCa; n = 39; female HD; n = 29), and hepatobiliary cancer (HBC; n = 14). (B) ROC-curve of SVM diagnostics of healthy donors and individual tumor classes in both training (left) and validation (right) cohort. Random classifiers, indicating the classification accuracies obtained by chance, are shown in gray. (C) Confusion matrix of multiclass SVM/LOOCV diagnostics of patients with CRC, PAAD, and HBC. Indicated are detection rates as compared to the actual classes in percentages. (D) Confusion matrix of multiclass SVM/LOOCV diagnostics of the training cohort consisting of healthy donors (healthy) and patients with GBM, NSCLC, PAAD, CRC, BrCa, and HBC. Indicated are detection rates as compared to the actual classes in percentages. (E) Confusion matrix of multiclass SVM algorithm in a validation cohort (n = 108). Indicated are sample numbers and detection rates in percentages. Genes, number of mRNAs included in training of the SVM algorithm. See also Figure S2 and Tables S4, S5, and S6.
Figure 3
Figure 3
Tumor-Educated Platelet mRNA Profiles for Molecular Pathway Diagnostics Cross tables of SVM/LOOCV diagnostics with the molecular markers KRAS in (A) CRC, (B) PAAD, and (C) NSCLC patients, (D) KRAS in the combined cohort of patients with either CRC, PAAD, NSCLC, or HBC, (E) EGFR and (F) MET in NSCLC patients, (G) PIK3CA mutations, (H) HER2-amplification, and (I) triple negative status in BrCa patients. Genes, number of mRNAs included in training of the SVM algorithm. See also Tables S4 and S7.
Figure 4
Figure 4
Organ-Focused TEP-Based Cancer Diagnostics SVM/LOOCV diagnostics of healthy donors (n = 55) and patients with primary or metastatic tumor burden in the lung (n = 99; totaling 154 tests), brain (n = 62; totaling 114 tests), or liver (n = 72; totaling 127 tests), to determine the presence or absence of cancer, with cancer subclassified as primary or metastatic tumor, in case of metastases the identified organ of origin, and the correctly identified molecular markers. Of note, at the exam level of mutational subtypes some samples were included in multiple classifiers (i.e., KRAS, EGFR, PIK3CA, HER2-amplification, MET-overexpression, or triple negative status), explaining the higher number in mutational tests than the total number of included samples. TP, true positive; FP, false positive; FN, false negative; TN, true negative. Indicated are sample numbers and detection rates in percentages.

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