Blood-based liquid biopsies, including tumor-educated blood platelets (TEPs), have emerged as promising biomarker sources for non-invasive detection of cancer. Here we demonstrate that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels from platelet RNA-sequencing libraries (n = 779). This resulted in accurate TEP-based detection of early- and late-stage non-small-cell lung cancer (n = 518 late-stage validation cohort, accuracy, 88%; AUC, 0.94; 95% CI, 0.92-0.96; p < 0.001; n = 106 early-stage validation cohort, accuracy, 81%; AUC, 0.89; 95% CI, 0.83-0.95; p < 0.001), independent of age of the individuals, smoking habits, whole-blood storage time, and various inflammatory conditions. PSO enabled selection of gene panels to diagnose cancer from TEPs, suggesting that swarm intelligence may also benefit the optimization of diagnostics readout of other liquid biopsy biosources.
Summary. Platelets are multifunctional cell fragments, circulating in blood in high abundance. Platelets assist in thrombus formation, sensing of pathogens entering the blood stream, signaling to immune cells, releasing vascular remodeling factors, and, negatively, enhancing cancer metastasis. Platelets are 'educated' by their environment, including in patients with cancer. Cancer cells appear to initiate intraplatelet signaling, resulting in splicing of platelet pre-mRNAs, and enhance secretion of cytokines. Platelets can induce leukocyte and endothelial cell modeling factors, for example, through adenine nucleotides (ATP), thereby facilitating extravasation of cancer cells. Besides releasing factors, platelets can also sequester RNAs and proteins released by cancer cells. Thus, platelets actively respond to queues from local and systemic conditions, thereby altering their transcriptome and molecular content. Platelets contain a rich repertoire of RNA species, including mRNAs, small non-coding RNAs and circular RNAs; although studies regarding the functionality of the various platelet RNA species require more attention. Recent advances in high-throughput characterization of platelet mRNAs revealed 10 to > 1000 altered mRNAs in platelets in the presence of disease. Hence, platelet RNA appears to be dynamically affected by pathological conditions, thus possibly providing opportunities to use platelet RNA as diagnostic, prognostic, predictive, or monitoring biomarkers. In this review, we cover the literature regarding the platelet RNA families, processing of platelet RNAs, and the potential application of platelet RNA as disease biomarkers.
Summary Tumor-educated platelets (TEPs) are potential biomarkers for cancer diagnostics. We employ TEP-derived RNA panels, determined by swarm intelligence, to detect and monitor glioblastoma . We assessed specificity by comparing the spliced RNA profile of TEPs from glioblastoma patients with multiple sclerosis and brain metastasis patients (validation series, n = 157; accuracy, 80%; AUC, 0.81 [95% CI, 0.74–0.89; p < 0.001]). Second, analysis of patients with glioblastoma versus asymptomatic healthy controls in an independent validation series (n = 347) provided a detection accuracy of 95% and AUC of 0.97 (95% CI, 0.95–0.99; p < 0.001). Finally, we developed the digitalSWARM algorithm to improve monitoring of glioblastoma progression and demonstrate that the TEP tumor scores of individual glioblastoma patients represent tumor behavior and could be used to distinguish false positive progression from true progression (validation series, n = 20; accuracy, 85%; AUC, 0.86 [95% CI, 0.70–1.00; p < 0.012]). In conclusion, TEPs have potential as a minimally invasive biosource for blood-based diagnostics and monitoring of glioblastoma patients.
Transcription factors (TFs) orchestrate the gene expression programs that define each cell's identity. The canonical TF accomplishes this with two domains, one that binds specific DNA sequences and the other that binds protein coactivators or corepressors. We find that at least half of TFs also bind RNA, doing so through a previously unrecognized domain with sequence and functional features analogous to the arginine-rich motif of the HIV transcriptional activator Tat. RNA-binding contributes to TF function by promoting the dynamic association between DNA, RNA and TF on chromatin. TF-RNA interactions are a conserved feature essential for vertebrate development and disrupted in disease. We propose that the ability to bind DNA, RNA and protein is a general property of many TFs and is fundamental to their gene regulatory function.
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