INTRODUCTION:

The diagnosis and management of cancer have undergone remarkable transformation over the past two decades, driven by advances in molecular profiling technologies and a deepening understanding of tumor biology. Tissue biopsy has long served as the cornerstone of oncological diagnosis, providing histological and molecular information essential for treatment planning. However, tissue-based approaches are constrained by invasiveness, sampling limitations, tumor heterogeneity, and the practical challenges of serial sampling to monitor disease evolution over time. These limitations have catalysed intense interest in liquid biopsy, a paradigm that exploits circulating biomarkers in blood and other body fluids to detect cancer, characterize its molecular landscape, and track therapeutic response and resistance mechanisms.

The liquid biopsy ecosystem encompasses multiple analyte classes, including circulating tumor cells (CTCs), cell-free DNA (cfDNA), extracellular vesicles (EVs), and circulating RNA species. Each modality offers distinct advantages and limitations regarding abundance, stability, isolation methodology, and the type of molecular information conveyed. Circulating tumor DNA has achieved the greatest clinical traction, with applications in genotyping, minimal residual disease detection, and resistance monitoring. However, cfDNA-based assays face challenges in early-stage cancers where mutant allele fractions may be exceedingly low, and they require a prior knowledge of tumor-specific mutations for certain applications.

Within this context, tumor-educated platelets have emerged as a compelling alternative or complementary biosource for liquid biopsy. Platelets are anucleate cellular fragments derived from megakaryocytes in the bone marrow, with a primary physiological role in hemostasis and wound healing. Despite lacking nuclei, platelets retain a repertoire of megakaryocyte-derived messenger RNA (mRNA) and pre-mRNA. The concept of tumor education refers to the observation that platelets can sequester tumor-derived RNA from the circulation and through direct interactions with tumor cells or tumor-derived microvesicles¹. In a seminal discovery, Nilsson and colleagues demonstrated that platelets isolated from glioma patients contain EGFRvIII mutant RNA, and platelets from prostate cancer patients contain PCA3 transcripts, providing direct evidence that tumor-associated RNA biomarkers are detectable in circulating platelets¹. Gene expression profiling further revealed distinct RNA signatures in platelets from cancer patients compared to healthy controls¹.

Building on this foundational discovery, subsequent studies demonstrated that TEP mRNA profiles, captured through RNA sequencing and analysed with machine learning classifiers, can distinguish cancer patients from healthy individuals with high accuracy and identify the primary site of tumor origin². This observation established TEPs as a promising platform for blood-based pan-cancer diagnostics. Over the past decade, a substantial body of evidence has accumulated supporting the diagnostic and monitoring potential of TEP RNA profiling across multiple cancer types. Seminal pan-cancer studies have demonstrated high accuracy for cancer detection and primary site identification^2,3, while disease-specific investigations have validated performance in non-small cell lung cancer (NSCLC)⁴, glioblastoma⁵, colorectal cancer (CRC)⁶, ovarian cancer^7,8, pancreatic cancer⁹, sarcoma¹⁰, and other malignancies^11,12. Importantly, TEP signatures exhibit dynamic changes during treatment, opening avenues for real-time monitoring of early detection and therapeutic monitoring of disease progression^4,5.

Despite these encouraging findings, the translation of TEP-based diagnostics to routine clinical practice faces important obstacles. Studies have highlighted marked protocol sensitivity and pre-analytical variability, with hospital-of-origin effects accounting for substantial gene expression variance in multicenter settings¹³. A negative prospective study in patients with unprovoked venous thromboembolism found poor accuracy for occult cancer detection, raising questions about performance in real-world screening-like scenarios¹⁴. The study aimed in evaluation of platelets’ circRNA signature to serve as reliable biomarkers for the detection, prognosis and monitoring of cancer progression¹⁵. The integration of multiple analytes-based approach for the analysis of platelets-derived biomarkers, provides evidence for combinatorial signature for lung cancer screening¹⁶. Furthermore, the precise mechanisms governing platelet education and the contribution of specific platelet subpopulations remain incompletely elucidated. The broadening of the biomarkers indicates that the oxidative stress biomarkers such as superoxide dismutase, glutathione peroxidase, glutathione reductase and reduced levels of glutathione and sulfhydryl groups play an important role on the breast cancer’s pathogenesis and progression¹⁷. The genetic biomarkers across breast, ovarian and cervical cancers serves as essential tools for the prognosis, monitoring and metastatic potential, utilizing them facilitates for earlier and more reliable detection critical for the improvement of therapeutic outcomes in gynaecological cancers¹⁸. Although endoscopy remains the gold standard for diagnosing colorectal cancer, its limited accessibility in developing nations highlights the critical need for non-invasive biomarkers, whereas the integrated panel of Tissue Polypeptide Specific antigen and hematopoietic growth factor offers a viable and early diagnostic approach for CRC in resource limited settings¹⁹.

This narrative review aims to provide a comprehensive, critical synthesis of the current evidence on tumor-educated platelet RNA profiling for cancer detection and treatment monitoring. Drawing upon verified primary research studies, we examine the biological rationale underpinning platelet tumor-RNA sequestration, review diagnostic performance across multiple cancer types, discuss technical platforms and computational approaches, address limitations and negative findings, compare TEPs with other liquid biopsy modalities, and outline future directions necessary to realize the clinical potential of this promising approach.

METHODOLOGY:

A comprehensive literature search was conducted on peer-reviewed research studies focusing between 2011-2025 that investigated on Tumor-Educated Platelet RNA profiling for cancer detection and monitoring the treatment. The search strategy included databases such as PubMed, Web of Science, Scopus and Google Scholar by utilizing keywords like “Tumor-Educated Platelet”, “RNA profiling”, “gene expression”, “cancer”, “oncology”, “tumor”, “detection”, “diagnosis”, “screening”, “treatment”, “therapy” and “monitoring”. Additionally, records were identified through manual search of reference lists from included studies and relevant review articles. Over 80 search results were screened by title, abstracts and approximately 20 full-text articles were reviewed indepth. The inclusion criteria were: (1) investigations on Tumor-Educated Platelets RNA profiling in relation with oncology (cancer detection or assessment, treatment etc.); (2) reported diagnostic and treatment metrics; (3) were published in peer-reviewed journals in English; (4) involved human participants. Exclusion criteria were editorials and articles without original data. Across these studies, a narrative synthesis approach was adopted to summarize the findings. Table 1. Summarizes the diagnostic performance of Tumor-Educated Platelet RNA profiling for some cancer types.

DISCUSSION:

Biological Foundations of Platelet RNA and Tumor Education:

Platelet RNA biology:

Platelets are derived from megakaryocytes through a regulated process of thrombopoiesis in the bone marrow. The isolation process of platelets considered to be a simple and standard method in blood banks as well as hemotological laboratories¹. Despite their anucleate state, platelets retain a rich RNA repertoire. Studies have demonstrated that the Tumor-Educated Platelets have potential on diagnostic applications^2,4. Beyond mRNA, the platelet RNA repertoire includes small nucleolar RNA (snoRNA), circular RNA (circRNA), and other non-coding RNA species^11,15,16, further expanding the analyte space for biomarker discovery.

Evidence for Tumor-Derived RNA Sequestration by Platelets:

The seminal evidence that platelets can contain tumor-derived RNA biomarkers was provided by Nilsson and colleagues in 2011¹. Using RT-PCR analysis of platelets isolated from cancer patients, this study demonstrated that: (1) platelets from glioma patients contain EGFRvIII mutant RNA, a tumor-specific transcript not found in normal tissues; (2) platelets from prostate cancer patients contain PCA3 transcripts, a well-established prostate cancer biomarker; and (3) gene expression profiling reveals distinct RNA signatures in platelets from glioma patients compared to healthy controls¹. The study further provided evidence that tumor-derived RNA can be transferred into platelets via tumor cell microvesicles, establishing a mechanistic basis for the sequestration phenomenon¹. This discovery established the foundational principle that platelets serve as repositories of tumor-associated RNA that can be exploited for diagnostic purposes.

TEP RNA Profiles Reflect Cancer Status:

Building on the discovery of tumor-RNA sequestration, Best and colleagues demonstrated in 2015 that comprehensive TEP mRNA profiles, captured through RNA sequencing, can distinguish cancer patients from healthy donors with approximately 96% accuracy and identify the tumor site of origin with 71% accuracy across six tumor types². This landmark study established that the aggregate platelet transcriptome carries sufficient information to serve as a pan-cancer diagnostic platform. The classifier could also distinguish tumors harboring specific oncogenic alterations (KRAS, EGFR, PIK3CA, MET amplification or HER2-positive status), demonstrating the capacity for molecular pathway inference². While this study focused on demonstrating diagnostic performance rather than elucidating mechanisms, it confirmed that TEP RNA profiles reflect cancer presence and characteristics in a manner amenable to machine learning-based classification.

2RNA Species in Tumor-Educated Platelets:

The TEP transcriptome encompasses multiple RNA species with diagnostic potential. Most studies have focused on mRNA profiles captured through conventional RNA sequencing^2-4,6,7,10. However, emerging evidence supports the diagnostic value of other RNA classes. Circular RNAs have been identified in platelets and implicated in cancer diagnosis. D'Ambrosi and colleagues demonstrated that circNRIP1 is downregulated in platelets from NSCLC patients, while combinatorial mRNA and circRNA signatures achieved improved diagnostic performance compared to either modality alone, with an eight-biomarker signature achieving AUC of 0.92^15,16. Small nucleolar RNAs, including SNORA58, SNORA68, and SNORD93, have shown promise as TEP-based biomarkers for esophageal cancer detection with AUC of 0.846 for all stages and 0.857 for early-stage disease¹¹. These findings indicate that multi-analyte approaches leveraging diverse RNA species may enhance the sensitivity and specificity of TEP-based diagnostics.

Pan-Cancer Detection and Site-of-Origin Identification

Seminal Pan-Cancer Studies:

The diagnostic potential of TEP RNA profiling was established through landmark pan-cancer studies. In 2015, Best and colleagues analyzed TEP RNA profiles from 228 cancer patients across six tumor types (non-small cell lung cancer, colorectal cancer, glioblastoma, pancreatic cancer, breast cancer, and hepatobiliary cancer) and 55 healthy controls². Using support vector machine (SVM) classifiers applied to RNA sequencing data, they achieved 96% accuracy for discriminating cancer patients from healthy individuals. The classifier identified the primary tumor site with 71% accuracy across the six tumor types. Furthermore, TEP profiles could distinguish tumors harboring specific oncogenic mutations, including KRAS, EGFR, PIK3CA, MET amplification or HER2-positive status².

A subsequent large-scale study published in 2022 by In 't Veld and colleagues substantially expanded the scope of TEP-based cancer detection³. Using the thromboSeq platform, which employs particle swarm optimization (PSO) for feature selection and SVM-based classification, the investigators analyzed TEP profiles from approximately 2,400 individuals in European and North American populations, including patients with stage I-IV cancers across 18 tumor types. The study achieved 99% specificity in asymptomatic controls and detected cancer in two-thirds of 1,096 patients from stage I-IV and half of 352 patients from stage I-III. Detection rates varied by stage: 46% for stage I, 47% for stage II, 54% for stage III, and 72% for stage IV disease. Site-of-origin prediction for five tumor types was over 80% of the cancer patients³. Notably, specificity declined to approximately 78% in symptomatic controls with inflammatory conditions, cardiovascular disease, and benign tumors, indicating that non-malignant conditions can produce platelet RNA changes that may confound cancer-specific classification³.

Implications for Multi-Cancer Early Detection:

The concept of multi-cancer early detection (MCED) has gained considerable momentum. TEP-based pan-cancer screening offers several attractive features for MCED applications. First, platelet isolation from routine blood draws is straightforward and scalable. Second, platelet RNA isolation and evaluation took place for detection of quality and quantity². Third, the pan-tumor nature of TEP profiles may enable detection across diverse cancer types without requiring tumor-specific assay design. However, the observation that specificity declined in symptomatic controls highlights the challenge of distinguishing cancer-related changes from general pathophysiological perturbations³. Integration of TEP profiles with other liquid biopsy markers and clinical risk factors may be necessary to achieve the performance thresholds required for population-based screening.

Disease-Specific Diagnostic Applications:

Non-Small Cell Lung Cancer:

Non-small cell lung cancer has served as a primary model system for TEP-based diagnostics. In a seminal study, Best and colleagues applied swarm intelligence-enhanced classification to TEP RNA profiles NSCLC patients and controls across 779 platelet RNA-seq libraries⁴. The study achieved area under the receiver operating characteristic curve (AUC) values of 0.94 (95% CI: 0.92-0.96) for late-stage disease and 0.89 (95% CI: 0.83-0.95) for locally advanced NSCLC validation studies independently, with corresponding accuracies of 88% and 81%, respectively. Importantly, diagnostic performance was independent of age, smoking status, whole-blood storage time and several inflammatory conditions⁴. The particle swarm optimization algorithm identified compact gene panels with robust diagnostic performance, demonstrating the feasibility of reducing RNA signature complexity for clinical translation.

Subsequent studies explored alternative RNA species for NSCLC detection. D'Ambrosi and colleagues characterized the platelet-derived circRNA repertoire, identifying 4,732 circRNAs with 411 significantly differentially expressed, and circNRIP1 as significantly downregulated in NSCLC patients compared to controls¹⁵. A follow-up study combining mRNA and circRNA targets achieved an AUC of 0.92 using an eight-biomarker combinatorial signature (6 mRNA + 2 circRNA), with 77% sensitivity, 87% specificity, and 81% accuracy¹⁶. An early-stage-specific model achieved AUC of 0.96. These findings demonstrate that multi-analyte approaches integrating different RNA species may enhance diagnostic accuracy in lung cancer¹⁶.

Glioblastoma:

Glioblastoma presents unique diagnostic challenges due to its central nervous system location, making non-invasive monitoring particularly valuable. Sol and colleagues applied TEP RNA profiling to glioblastoma detection and monitoring in a study comprising 89 glioblastoma patients at first tumor resection, 151 follow-up samples, and 353 healthy controls⁵. The study achieved an AUC of 0.97 (95% CI: 0.95-0.99) for distinguishing glioblastoma patients from healthy controls with 95% accuracy in an independent validation study of 347 samples. The digitalSWARM classifier achieved an AUC of 0.86, in validation series to distinguish progressors from non-progressors⁵. Perhaps most notably, the study demonstrated that serial TEP "tumor scores" (using the digitalSWARM algorithm) tracked tumor behavior longitudinally and discriminated true tumor progression from treatment-related false positive progression with 85% accuracy⁵, addressing a critical clinical challenge in neuro-oncology.

Colorectal Cancer:

Colorectal cancer screening represents another high-impact application for TEP-based diagnostics. Xu and colleagues evaluated TEP RNA profiling for CRC detection in a retrospective cohort study comparing 132 patients with CRC (early and late stages), 190 controls of healthy donors and including patients with ulcerative disease, Crohn's disease, polyps, and adenomas, by using binary particle swarm optimization with 921 contributive genes, the classifier achieved AUROC values of 0.928 in the training set, 0.92 in internal validation set⁶. CRC Stage prediction achieved remarkable AUROC values of 0.984 and 1.000 on training set and internal validation set respectively. The TEP-based approach outperformed or complemented conventional serum markers such as CEA and CA19-9⁶.

Ovarian Cancer:

Ovarian cancer is typically diagnosed at advanced stages due to the absence of effective screening strategies, making early detection a critical unmet need. Gao and colleagues conducted an intercontinental, biomarker identification study evaluating TEP RNA profiling across 761 treatment-naïve inpatients with histologically confirmed adnexal masses and 167 healthy controls participants from nine centres in China, the Netherlands, and Poland⁷. The AUC values of TEPOC achieved 0.923 and 0.918 in the two Chinese cohorts and 0.887 across European cohorts, demonstrating robust performance across different populations. When combined with the conventional biomarker CA125, the integrated model achieved an AUC of up to 0.922, highlighting the complementary value of TEP and serum protein markers. Early-stage detection of ovarian cancer achieved an AUC of 0.858 exhibited by TEPOC. This approach showed consistent performance across histological subtypes, including borderline and non-epithelial tumors⁷.

Building on this work, Ahn and colleagues developed a qPCR-based algorithm using a ten-marker platelet RNA panel for ovarian cancer detection in a proof-of-concept study with 90 participants (19 ovarian cancer, 37 benign tumor patients and 34 asymptomatic controls)⁸. This approach which included classification algorithm achieved sensitivity of 94.1% and specificity of 94.4% among training and test datasets, and AUC of 0.933⁸. The development of targeted qPCR panels represents an important step toward clinical implementation by reducing complexity and cost compared to full transcriptome sequencing, though the small sample size necessitates validation in larger cohorts.

Pancreatic Cancer:

Pancreatic cancer is characterized by late diagnosis, rapid progression, and poor prognosis, creating an urgent need for effective early detection strategies. Ji and colleagues performed transcriptomic profiling of blood platelets across 673 samples from multicentre cohorts to identify a diagnostic signature for pancreatic cancer⁹. The investigators developed a two-RNA signature designated PLA2Sig (comprising SCN1B and MAGOHB), which achieved AUC values ranging from 0.808, 0.900, 0.783 and 0.830 across different cohorts. The signature maintained diagnostic performance in resectable stage I-II disease, achieving an AUC of 0.812 for early-stage detection. The PLA2Sig signature outperformed conventional serum markers carcinoembryonic antigen and CA19-9 to differentiate between pancreatic cancer and healthy controls⁹.

Sarcoma and Other Malignancies:

The diagnostic utility of TEP RNA profiling has been extended to additional tumor types. Heinhuis and colleagues evaluated TEP RNA sequencing for sarcoma diagnosis in a study comprising 57 sarcoma patients with active disease, 38 former patients who were cancer-free for at least three years, and 65 healthy donors¹⁰. The study identified 2,647 differentially expressed RNAs where the SVM algorithm achieved an AUC of 0.93 (95% CI: 0.86-1) with 87% diagnostic accuracy in the validation set (n=53)¹⁰.

Zhang and colleagues identified three small nucleolar RNAs (SNORA58, SNORA68, and SNORD93) as novel TEP-based diagnostic biomarkers for esophageal cancer, with these snoRNAs significantly upregulated in platelets from esophageal cancer patients¹¹. The study reported AUC values of 0.846 for all-stage disease and 0.857 for early-stage disease¹¹. Additional investigations have explored TEP applications in head and neck squamous cell carcinoma, where Gill and colleagues identified differentially expressed RNA signatures in platelets from HNSCC patients, though specific performance metrics from this 2024 study require further verification as it is very recently published¹².

Treatment Monitoring and Longitudinal Disease Dynamics:

Evidence from Glioblastoma Monitoring Studies:

Beyond primary diagnosis, the dynamic nature of TEP RNA profiles positions them as candidates for longitudinal treatment monitoring. The most compelling evidence comes from glioblastoma, where distinguishing true tumor progression from treatment-related pseudoprogression presents a formidable clinical challenge⁵. Pseudoprogression refers to imaging findings that mimic tumor growth but actually represent treatment effects such as radiation necrosis or inflammatory responses.

Sol and colleagues addressed this challenge by developing the digitalSWARM algorithm to generate patient-specific TEP tumor scores that could be tracked longitudinally⁵. Serial TEP sampling of 151 follow-up samples demonstrated that tumor scores tracked tumor behavior over time, increasing during disease progression and decreasing during response to therapy. Critically, the approach discriminated true progression from pseudoprogression with 85% accuracy (AUC 0.86), providing a minimal invasive adjunct to imaging for clinical decision-making⁵. These findings established proof-of-concept for TEP-based monitoring.

Observations on TEP Profile Changes with Treatment:

Pan-cancer analyses have provided broader observations that TEP profiles change following treatment². In the seminal study by Best and colleagues, platelet RNA signatures were observed to change after therapy, conceptually supporting the potential for TEP-based recurrence monitoring through follow-up sampling². While formal prospective validation of recurrence monitoring remains to be conducted, these observations establish the biological plausibility of using serial TEP measurements to track disease status. Such an approach would be particularly valuable in malignancies with high recurrence rates where early detection of relapse could enable curative salvage therapy.

Technical Platforms and Computational Methodologies:

Platelet Isolation and RNA Sequencing:

The technical workflow for TEP mRNA sequencing begins with EDTA anticoagulant tubes to platelet isolation along with mRNA amplification and sequencing². According to the prediction, the post-sequencing of polyadenylated RNA marked an important enrichment of platelet that reads within exonic regions⁴. Platelets are separated from whole blood components through serial centrifugation steps and extraction of total RNA was performed⁴.

RNA sequencing of platelet isolates captures the spliced mRNA transcriptome, which forms the foundation for most published classifiers. Sequencing data undergo standard quality control, alignment, and quantification pipelines to generate gene expression matrices suitable for downstream analysis. The highly expressed genes from deep thromboSeq datasets were considered and extracted the matching read counts from the shallow sequencing library⁴. Beyond mRNA, specialized protocols can capture circular RNA^15,16and small non-coding RNA species¹¹, expanding the analyte space for diagnostic marker discovery.

Machine Learning Classification Approaches:

The analytical backbone of TEP-based diagnostics comprises machine learning algorithms trained to distinguish diagnostic categories based on gene expression patterns. The thromboSeq platform represents the most extensively validated computational framework for TEP classification^3,4,6,10. ThromboSeq employs particle swarm optimization (PSO), a nature-inspired metaheuristic algorithm, for feature selection to identify compact gene panels with optimal discriminatory power. Selected features are then used to train support vector machine classifiers, which assign probability scores for cancer versus non-cancer status or for specific tumor type assignment^3,4.

The digitalSWARM algorithm extends the PSO framework to generate patient-specific longitudinal scores for treatment monitoring applications⁵. By tracking changes in classifier probability scores over time, digitalSWARM enables visualization of disease trajectories and detection of transitions between clinical states⁵. Other machine learning approaches, including weighted gene co-expression network analysis (WGCNA) have also been explored for identification of diagnostic biomarkers targets¹².

Development of Targeted Assays for Clinical Implementation:

While RNA sequencing provides comprehensive transcriptome coverage and has been invaluable for biomarker discovery, the complexity and cost of sequencing present barriers to routine clinical implementation. Several groups have pursued the development of targeted assays that measure expression of selected biomarker panels using quantitative polymerase chain reaction (qPCR) or similar platforms^8,9. Such targeted panels offer advantages of lower cost, faster turnaround, and greater standardization potential, facilitating integration into clinical laboratory workflows. The translation from discovery-phase RNA-seq to implementation-ready targeted assays represents a critical step in the clinical development pathway for TEP-based diagnostics.

Limitations, Negative Findings, and Methodological Considerations

Pre-analytical Variability and Protocol Sensitivity:

A critical challenge for TEP-based diagnostics, illuminated by recent studies, is the sensitivity of platelet RNA profiles to pre-analytical variables and processing protocols. Liefaard and colleagues conducted an independent multicenter study of TEP-based breast cancer detection involving 266 breast cancer female patients (stages I-IV) and 212 controls from six hospitals, and classifier performance was carried out by independent validation of 37 cases and 36 controls¹³. The investigators observed that classifiers performing well in internal validation (AUC 0.85) for PSO-SVM and Elastic net-based classifier (EN) and shown poor reproducibility upon independent validation set¹³. Strikingly, approximately 19% of gene expression variance was attributable to hospital-of-origin, with platelet activity-related genes were differentially expressed between the hospitals¹³. These findings highlight that differences in blood collection procedures, handling times, centrifugation protocols, and storage conditions across sites can introduce confounding that overwhelms disease-related signals.

The protocol sensitivity of TEP profiling necessitates rigorous standardization of pre-analytical procedures for clinical implementation. Harmonization of platelet isolation protocols, RNA handling, and sequencing methodologies across laboratories will be essential to achieve reproducible performance in multicenter and real-world settings^3,13. The development of quality control metrics and reference materials may further support assay standardization.

Negative Prospective Study in Occult Cancer Detection:

An important cautionary finding emerged from a prospective cohort study evaluating TEP RNA sequencing for occult cancer detection in patients with unprovoked venous thromboembolism (VTE), a population at elevated risk for underlying malignancy. Mulder and colleagues conducted a study involving patients with ≥40 years of age with unprovoked VTE at 13 centers, enrolled 476 participants with 25 (5.3%) diagnosed with cancer during 12-month follow-up¹⁴. Application of platelet RNA sequencing to this clinically relevant population yielded poor accuracy for cancer detection, with an AUROC of only 0.54 (95% CI: 0.41-0.66)¹⁴. At a primary positivity threshold, 100% of 25 cases resulted in a positive test result with no negative test results. The sensitivity observed was (~68%) in platelet RNA sequencing¹⁴. These results underscore that performance observed in case-control studies comparing clearly defined cancer patients with healthy controls may not translate to more challenging clinical scenarios.

Study Design Limitations:

The majority of published TEP studies are retrospective, case-control designs with clear-cut contrasts between cancer patients and healthy individuals^2-4,6-12. While such designs are appropriate for biomarker discovery and proof-of-concept, they typically overestimate diagnostic performance compared to prospective studies in realistic clinical populations. The use of complex machine learning models with many features introduces risks of overfitting, particularly when sample sizes are modest relative to feature space dimensionality. Prospective validation and independent validation cohorts cannot be validated in intended-use populations^6,13. Future clinical development of TEP-based diagnostics will require large, prospective, multicenter studies in clinically relevant populations.

Comparison with Other Liquid Biopsy Modalities:

Circulating tumor DNA:

Circulating tumor DNA represents the most clinically mature liquid biopsy modality. In addition to improving the detection sensitivity for early-stage cancer, platelet RNA profiles reflect tumor-induced changes from local and systemic cues as opposed to requiring detectable DNA molecules from a tumor.

The gene expression through RNA-sequencing via thromboSeq offered analysis of RNA molecules present in platelets⁴. The RNA quality of platelets was evaluated by usage of bioanalyzer⁴.

TEPs encounter obstacles of a technical nature that restrict their advantages over traditional cfDNA assessment. The various liquid biopsy sources such as blood platelets and exosomes are used at present in detection of cancer despite of circRNA¹⁵. In addition, the various RNA types obtained from same biosource serves as an important biomarker in early-stage lung cancer detection by utilizing both mRNA and circRNA¹⁶.

Multi-Analyte Liquid Biopsy Strategies:

The recognition that no single liquid biopsy modality achieves optimal performance across all clinical applications has motivated the development of multi-analyte approaches that combine complementary biosources. Several authors have envisioned TEPs as components of integrated liquid biopsy panels rather than stand-alone tests^3,15. TEP RNA profiles in combination with protein biomarkers such as CA125 enables the detection of ovarian cancer⁷, and clinical risk factors could enhance both sensitivity and specificity compared to individual modalities. The complementary information provided by different analytes may enable more comprehensive disease characterization than any single modality alone.

Specificity Challenges in Symptomatic Populations:

Platelet RNA changes extend beyond cancer to encompass other pathophysiological conditions, creating potential specificity challenges when TEP-based tests are applied to real-world populations. In the large pan-cancer study by in 't Veld and colleagues, specificity remained high (99%) in asymptomatic healthy controls but declined to average specificity of 78% in symptomatic controls with inflammatory conditions, cardiovascular disease, and benign tumors³. This observation suggests that false-positive test results ability have not been examined yet in depth from non-malignant conditions.

The pharmacological processes driving anticancer resistance including drug efflux, modified targets and the tumor microenvironment highlights the importance of developing efficient strategies to overcome these obstacles and emphasizes future directions with personalized medicine, combinational therapies and novel delivery systems in order to improve long-term cancer treatment outcomes²⁰.

Technological Frontiers in Various Cancer Detection:

The illustration of this study involves the utilization of Matlab toolbox that have been used to improve the quality of the image essential for lung cancer detection which includes image enhancement and segmentation, feature extraction to overcome barriers such as noise and low-quality of images ultimately aiming to optimize accurate results²¹.The employment of Deep Convolutional Network and transfer learning categorizes 10,000 dermoscopic images into healthy skin lesion types such as eczema, acne, malignant and benign skin lesions and highlights the accurate lesion segmentation and high-prediction accuracy via end-to-end training and fine tuning, and designing a model having the capacity of efficient performance on mobile devices²².A novel approach utilizes Discrete Wavelet and Wavelet Packet Transforms to analyze biomedical signals for the automated detection of iris tumor. By image processing from specialized cancer centers, the malignant and normal tissues can be differentiated by providing robust diagnostic foundation for the practical clinical uses²³.

Applications of Nanotechnology in Oncology Treatment:

By using lipid-polymer hybrid nanoparticles, breast cancer treatment can be enhanced which has drug stability with precise, controlled-release delivery to tumor sites, while these minimize the side effects and has the significant therapeutic potential in terms of effect and safety profile²⁴.The CRISPR/Cas9 is a major biotechnology method which breakthrough due to its high accuracy and versatility in micro-genome editing, this technology kill cancer cells by modifying single DNA letters or inserting new segments²⁵. Nanotechnology is the upcoming field with the vast applications in medicine which provide an essential advancement in prevention and treatment of cancer²⁶.

Future Directions and Clinical Translation Priorities:

The foremost priority for advancing TEP-based diagnostics toward clinical implementation is the conduct of large, prospective, multicenter validation studies in clinically relevant populations^3,7,9,13,14. Such studies should be designed to evaluate performance in intended-use settings, including symptomatic patients undergoing diagnostic evaluation and asymptomatic high-risk populations undergoing screening. Protocol harmonization across RNA processing pathways and platelet isolation, to reduce technical bias is essential^4,13. Further mechanistic studies dissecting tumor-platelet communication would refine biomarker selection and improve biological interpretability. Development of cost-effective, clinically deployable assays (qPCR or targeted panels) and integration with other liquid biopsy markers and clinical variables into multimodal risk models represent important translational steps^3,7,8,9,16. The advancements and future directions of cancer treatment gives a promising hope by the integration of precision medicine, combinational therapies, novel drug delivery systems and immunotherapeutic techniques²⁷.

CONCLUSION:

Tumor-educated platelet RNA profiling has emerged as a promising liquid biopsy approach for cancer detection and treatment monitoring. The foundational discovery that platelets can sequester tumor-derived RNA, demonstrated by detection of EGFRvIII in glioma patients and PCA3 in prostate cancer patients¹, established the biological basis for this technology. Subsequent studies demonstrated that comprehensive TEP based classifier can distinguish cancer patients from healthy individuals with high accuracy (up to 96%)² and identify primary tumor sites through thromboSeq classifier achieved 85% accuracy via cross-validation studies³.

Longitudinal studies have established proof-of-concept for treatment monitoring, with TEP signatures tracking disease dynamics and distinguishing between true and false progression with 85% accuracy in glioblastoma⁵. Nevertheless, critical challenges remain that must be addressed before TEP-based diagnostics can achieve widespread clinical adoption. Protocol sensitivity and pre-analytical variability, illuminated by multicenter validation failures where internal validation AUC dropped from 0.85 to 0.55 in independent validation reproducibility for the PSO-SVM classifier, necessitate rigorous standardization efforts¹³. A negative prospective study demonstrating AUC of only 0.54 for platelet RNA sequencing in unprovoked VTE patients highlights the gap between case-control discovery studies and real-world clinical performance¹⁴. The integration of mRNA with circRNA signatures has demonstrated enhanced diagnostic performance^15,16. Prospective validation in intended-use populations, technical harmonization, and development of cost-effective clinical assays constitute the key priorities for clinical translation.

In conclusion, tumor-educated platelet RNA profiling represents a maturing field with a robust biological foundation and encouraging empirical results. While substantial work remains to translate this technology into routine clinical practice, the potential impact on cancer detection and management justifies continued investment in research and development. As technical challenges are addressed and prospective evidence accumulates, TEP-based diagnostics may emerge as an important component of multi-analyte liquid biopsy strategies, contributing to the broader goal of improving cancer outcomes through earlier detection and more precise disease monitoring.

CONFLICT OF INTEREST:

The authors have no conflicts of interest regarding this investigation.

ACKNOWLEDGEMENT:

The funds were not received towards the study.

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Received on 02.02.2026 Revised on 04.03.2026

Accepted on 30.03.2026 Published on 10.07.2026

Available online from July 14, 2026

Res.J. Pharmacology and Pharmacodynamics.2026;18(3):219-228.

DOI: 10.52711/2321-5836.2026.00030

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.

Cancer Type	Sample Size	Performance Highlights	Ref
Pan-cancer (6 types)	283	Accuracy-96% and 71% in tumor-of-origin	[2]
NSCLC (mRNA)	779	AUC: 0.89 (early); 0.94 (late); 81-88% accuracy	[4]
Colorectal cancer	322	AUC 0.984-training set, 1.000-internal validation set	[6]
Ovarian cancer (qPCR)	90	AUC 0.933; 94.1% sensitivity/ 94.4% specificity	[8]
Pancreatic cancer	673	Stage I-II AUC 0.812	[9]
Sarcoma	160	AUC 0.93; 87% diagnostic accuracy	[10]
Esophageal cancer	NS	AUC 0.846 (all); 0.857 (early)	[11]
Breast cancer*	551	Internal validation AUC 0.85; Independent validation AUC 0.55 for PSO-SVM & 0.54 for EN classifier	[13]
VTE screening*	476	AUC 0.54 (negative result)	[14]
NSCLC (combined)	60	AUC 0.96; early-stage sensitivity-85%; specificity-86%	[16]