Tumor-educated platelet blood tests for Non-Small Cell Lung Cancer detection and management

Tumor-educated platelets in lung cancer

Non-invasive diagnostic monitoring techniques have become essential for treating lung cancer (LC), which continues to be the primary cause of cancer-related death worldwide. The new diagnostic biomarkers called tumour-educated platelets (TEPs) show strong prospects for providing vital information about tumor biology, tumor spread pathways, and treatment reaction patterns. Despite lacking a nucleus, platelets exhibit an active RNA profile that develops through interactions with tumor-derived compounds and the tumor microenvironments (TME). This review explains platelet-tumour interaction regulatory mechanisms while focusing on platelet contributions toward cancer development, immune system avoidance, and blood clot formation. The detection and classification of LC show promise through the analysis of RNA molecules extracted from platelets that encompass mRNAs and non-coding RNAs. RNA sequencing technology based on TEP demonstrates excellent diagnostic power by correctly identifying LC patients alongside their oncogenic alterations of EGFR, KRAS, and ALK. Treatment predictions have proven successful using platelet RNA profiles, specifically in immunotherapy and targeted therapy. Integrating next-generation sequencing with machine learning and artificial intelligence enhances TEP-based diagnostic tools, improving detection accuracy. Standardizing platelet extraction methods and vesicle purification from tumor material needs better development for effective and affordable clinical use. Future investigations should combine TEPs with circulating tumor DNA and exosomal RNA markers to enhance both earliest-stage LC diagnosis and patient-specific therapeutic approaches. TEPs introduce a groundbreaking technique in oncology since they can transform non-invasive medical diagnostics and therapeutic monitoring for cancer.

Persistent and novel changes in plasma microRNA profiles in patients with non-small cell lung cancer following tumour resection

Background

Non-small cell lung cancer (NSCLC) accounts for 80% of lung cancers, the leading cause of cancer mortality. microRNAs (miRNA, miR) have emerged as important components of carcinogenesis and promising biomarkers. We aimed to analyse global plasma miRs in NSCLC patients before and at least one year after tumour resection.

Methods

Plasma was collected from the peripheral blood of 24 donors without cancer and of NSCLC patients before surgery (n=36) and at least 1 year after surgery (n=12). Next-generation sequencing (NGS)-based miR profiling was performed. Patients were followed-up for 4 to 12 years after surgery to assess disease recurrence.

Results

Untreated NSCLC patients exhibited significant changes in plasma miR levels compared to cancer-free donors (48 up- and 17 down-regulated miRs). miR profiles in patients with adenocarcinoma (ADC) (n=18) and squamous cell carcinoma (SCC) significantly differed (16 and 86 miRs up-, and 15 and 16 miRs down-regulated, respectively). A subset of pre-surgery deregulated miRs was found to be associated with recurrence (49 miRs). Six miRs were shown to have independent prognostic value. After tumour resection, some pre-surgery miR alterations returned to control levels (18 miRs), some others persisted (27 miRs), while also novel plasma miR changes emerged (75 miRs) in patients with no clinical evidence of recurrence.

Conclusions

Untreated NSCLC patients present deregulated plasma miRs, some of which may have a potential of prognostic markers. After tumour excision plasma miR profiles change, some miR levels normalise, some changes persist and novel miR changes are observed despite no clinical symptoms of recurrence. Plasma miR profiles in NSCLC patients may suggest systemic abnormalities predisposing to lung cancer and/or reflect a systemic response to pre-cancer/dormant cancer cells.

Liquid Biopsies in the Early Diagnosis, Prognosis, and Tailored Treatment of Colorectal Cancer

INTRODUCTION

Liquid biopsies provide a less-invasive option to tissue biopsies for the early diagnosis, prognosis, and tailored therapy of colorectal cancer (CRC). CRC is a major cause of cancer-related death, and early identification is essential for improving patient outcomes.

REVIEW

Conventional diagnostic techniques, including colonoscopy and tissue biopsy, may be enhanced by liquid biopsies that examine circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), extracellular vesicles (EVs), and other indicators present in body fluids. These markers provide significant insights into tumor biology, heterogeneity, and therapeutic response. CTCs detected in early-stage CRC have prognostic significance for disease recurrence and survival, while ctDNA investigation may uncover genetic mutations, epigenetic alterations, and tumor development. The identification of ctDNA in minimal residual disease (MRD) postsurgery correlates with an elevated risk of recurrence and unfavorable prognosis, underscoring its use in assessing treatment effectiveness. Furthermore, non-coding RNAs (ncRNAs) contained inside EVs provide potential prospective biomarkers and therapeutic targets, facilitating diagnosis and treatment assessment. Notwithstanding the potential of liquid biopsies, obstacles persist in assay standardization, sensitivity enhancement, and the management of tumor heterogeneity. Additional extensive research is required to determine their function in clinical practice.

CONCLUSION

Overall, liquid biopsies serve as a potential instrument for real-time monitoring, evaluating therapy responses, and directing individualized therapeutic strategies in CRC patients.

Deciphering the role of TMEM164 in autophagy-mediated ferroptosis and immune modulation in non-small cell lung cancer

BACKGROUND

Non-small cell lung cancer (NSCLC) remains one of the most prevalent and deadly malignancies. Despite advancements in molecular therapies and diagnostic methods, the 5-year survival rate for lung adenocarcinoma patients remains unacceptably low, highlighting the urgent need for novel therapeutic strategies. Ferroptosis, a distinct form of regulated cell death, has emerged as a promising target in cancer treatment. This study investigates the role of TMEM164, a membrane protein, in promoting ferroptosis and modulating anti-tumor immunity in NSCLC, aiming to elucidate its therapeutic potential.

METHODS

Using publicly available datasets, we performed bioinformatics analyses to identify TMEM164-regulated genes involved in ferroptosis. In addition, in vitro and in vivo assays were conducted to assess the impact of TMEM164 on cellular functions in NSCLC.

RESULTS

Functional assays demonstrated that TMEM164 overexpression significantly inhibited invasion, migration, and cell proliferation in both in vitro and in vivo models. TMEM164 was also found to induce ferroptosis in NSCLC cells by promoting autophagy. Specifically, we identified a mechanism whereby TMEM164 mediates ATG5-dependent autophagosome formation, leading to the degradation of ferritin, GPX4, and lipid droplets. This degradation facilitated iron accumulation and lipid peroxidation, which triggered iron-dependent cell death. Notably, co-administration of TMEM164 upregulation and anti-PD-1 antibodies exhibited synergistic anti-tumor effects in a mouse model.

CONCLUSION

These findings suggest that targeting TMEM164 to enhance ferroptosis and stimulate anti-tumor immunity may inhibit NSCLC progression. Consequently, TMEM164 holds promise as a new therapeutic target for NSCLC treatment.

Development of an accurate breast cancer detection classifier based on platelet RNA

Platelets possess cancer-induced reprogramming properties, thereby contributing to RNA profile alterations and further cancer progression, while the former is considered a promising biosource for cancer detection. Hence, tumor-educated platelets (TEP) are considered a prospective novel method for early breast cancer (BC) screening. Our study integrated the data from 276 patients with untreated BC, 95 with benign disease controls, 214 healthy controls, and 2 who underwent mastectomy in Chinese and European cohorts to develop a 10-biomarker diagnostic model. The model demonstrated high diagnostic performance for BC in an independent test set (n = 177) with an area under the curve of 0.957. The sensitivity for BC diagnosis was 89.2%, with 100% specificity in asymptomatic controls, while that for the symptomatic group, including benign tumors and inflammatory diseases, was 62.1%. The model demonstrated substantial accuracy for stages 0-III BC (80% for stage 0 [n = 5], 83.3% for stage I [n = 12], 94.6% for stage II [n = 37], and 88.9% for stage III [n = 9]) and precisely helped determine residual cancer in two patients who underwent mastectomy. Moreover, our developed classifiers distinguish different BC subtypes properly. In summary, we created and tested a new TEP-RNA-based BC diagnostic model that was confirmed valid and demonstrated high efficiency in detecting early-stage BC and heterogeneous subtypes, including recurrent tumors. However, these results warrant more validation in larger population-based prospective studies before clinical implementation.

Clinical application of liquid biopsy in colorectal cancer: detection, prediction, and treatment monitoring

Colorectal cancer (CRC) is one of the most prevalent malignancies affecting the gastrointestinal tract and is ranked third among cancers with the highest incidence and second-highest mortality rate worldwide. CRC exhibits a slow progression providing a wide treatment window. The currently employed CRC screening methods have shown great potential to prevent CRC and reduce CRC-related morbidity and mortality. The diagnosis of CRC is achieved by colonoscopy and tissue biopsy, with studies showing that liquid biopsy is more effective in detecting and diagnosing early CRC patients. Increasing number of studies have shown that the tumor components shed into circulating blood can be detected in liquid form, and can be applied in the clinical management of CRC. Analysis of circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), or tumor-associated platelets (TEPs) in the blood can be used for early screening and diagnosis of CRC, aid tumor staging, treatment response monitoring, and prediction of CRC recurrence and metastasis in a minimally invasive manner. This chapter provides an updated review of CTCs, ctDNA, and TEPs as novel biomarkers for CRC, highlighting their strengths and limitations.

Immunological Signatures for Early Detection of Human Head and Neck Squamous Cell Carcinoma through RNA Transcriptome Analysis of Blood Platelets

Although there has been a reduction in head and neck squamous cell carcinoma occurrence, it continues to be a serious global health concern. The lack of precise early diagnostic biomarkers and postponed diagnosis in the later stages are notable constraints that contribute to poor survival rates and emphasize the need for innovative diagnostic methods. In this study, we employed machine learning alongside weighted gene co-expression network analysis (WGCNA) and network biology to investigate the gene expression patterns of blood platelets, identifying transcriptomic markers for HNSCC diagnosis. Our comprehensive examination of publicly available gene expression datasets revealed nine genes with significantly elevated expression in samples from individuals diagnosed with HNSCC. These potential diagnostic markers were further assessed using TCGA and GTEx datasets, demonstrating high accuracy in distinguishing between HNSCC and non-cancerous samples. The findings indicate that these gene signatures could revolutionize early HNSCC identification. Additionally, the study highlights the significance of tumor-educated platelets (TEPs), which carry RNA signatures indicative of tumor-derived material, offering a non-invasive source for early-detection biomarkers. Despite using platelet and tumor samples from different individuals, our results suggest that TEPs reflect the transcriptomic and epigenetic landscape of tumors. Future research should aim to directly correlate tumor and platelet samples from the same patients to further elucidate this relationship. This study underscores the potential of these biomarkers in transforming early diagnosis and personalized treatment strategies for HNSCC, advocating for further research to validate their predictive and therapeutic potential.

Assessing the complementary information from an increased number of biologically relevant features in liquid biopsy-derived RNA-Seq data

Liquid biopsy-derived RNA sequencing (lbRNA-seq) exhibits significant promise for clinic-oriented cancer diagnostics due to its non-invasiveness and ease of repeatability. Despite substantial advancements, obstacles like technical artefacts and process standardisation impede seamless clinical integration. Alongside addressing technical aspects such as normalising fluctuating low-input material and establishing a standardised clinical workflow, the lack of result validation using independent datasets remains a critical factor contributing to the often low reproducibility of liquid biopsy-detected biomarkers. Considering the outlined drawbacks, our objective was to establish a workflow/methodology characterised by: 1. Harness the rich diversity of biological features accessible through lbRNA-seq data, encompassing a holistic range of molecular and functional attributes. These components are seamlessly integrated via a Machine Learning-based Ensemble Classification framework, enabling a unified and comprehensive analysis of the intricate information encoded within the data. 2. Implementing and rigorously benchmarking intra-sample normalisation methods to heighten their relevance within clinical settings. 3. Thoroughly assessing its efficacy across independent test sets to ascertain its robustness and potential utility. Using ten datasets from several studies comprising three different sources of biological material, we first show that while the best-performing normalisation methods depend strongly on the dataset and coupled Machine Learning method, the rather simple Counts Per Million method is generally very robust, showing comparable performance to cross-sample methods. Subsequently, we demonstrate that the innovative biofeature types introduced in this study, such as the Fraction of Canonical Transcript, harbour complementary information. Consequently, their inclusion consistently enhances prediction power compared to models relying solely on gene expression-based biofeatures. Finally, we demonstrate that the workflow is robust on completely independent datasets, generally from different labs and/or different protocols. Taken together, the workflow presented here outperforms generally employed methods in prediction accuracy and may hold potential for clinical diagnostics application due to its specific design.