Background: Liquid biopsy has gained significant attention as a non-invasive method for cancer detection and monitoring. IsomiRs and tRNA-derived fragments (tRFs) are small non-coding RNAs that arise from non-canonical microRNA (miRNAs) processing and the cleavage of tRNAs, respectively. These small non-coding RNAs have emerged as pro-mising cancer biomarkers, and their distinct expression patterns highlight the need for further exploration of their roles in cancer research. Methods: In this study, we investigated the differential expression profiles of miRNAs, isomiRs, and tRFs in plasma extracellular vesicles (EVs) from colorectal and prostate cancer patients compared to healthy controls. Subsequently, a combinatorial analysis using the CombiROC package was performed to identify a panel of biomarkers with optimal diagnostic accuracy. Results: Our results demonstrate that a combination of miRNAs, isomiRs, and tRFs can effectively di- stinguish cancer patients from healthy controls, achieving accuracy and an area under the curve (AUC) of approximately 80%. Conclusions: These findings highlight the potential of a combinatorial approach to small RNA analysis in liquid biopsies for improved cancer diagnosis and management.

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.

RNA modifications, including N-7-methylguanosine (m7G), are pivotal in governing RNA stability and gene expression regulation. The accurate detection of internal m7G modifications is of paramount significance, given recent associations between altered m7G deposition and elevated expression of the methyltransferase METTL1 in various human cancers. The development of robust m7G detection techniques has posed a significant challenge in the field of epitranscriptomics. In this study, we introduce two methodologies for the global and accurate identification of m7G modifications in human RNA. We introduce borohydride reduction sequencing (Bo-Seq), which provides base resolution mapping of m7G modifications. Bo-Seq achieves exceptional performance through the optimization of RNA depurination and scission, involving the strategic use of high concentrations of NaBH4, neutral pH and the addition of 7-methylguanosine monophosphate (m7GMP) during the reducing reaction. Notably, compared to NaBH4-based methods, Bo-Seq enhances the m7G detection performance, and simplifies the detection process, eliminating the necessity for intricate chemical steps and reducing the protocol duration. In addition, we present an antibody-based approach, which enables the assessment of m7G relative levels across RNA molecules and biological samples, however it should be used with caution due to limitations associated with variations in antibody quality between batches. In summary, our novel approaches address the pressing need for reliable and accessible methods to detect RNA m7G methylation in human cells. These advancements hold the potential to catalyse future investigations in the critical field of epitranscriptomics, shedding light on the complex regulatory roles of m7G in gene expression and its implications in cancer biology.

Introduction: Multiple myeloma (MM) is a complex hematologic malignancy characterized by genetic instability, variable survival rates, and diverse treatment responses. SKY92 gene expression profiling (GEP) is a valuable tool in risk stratifying patients into high-risk and standard-risk groups for progression and survival. The PRospective Observational Multiple Myeloma Impact Study (PROMMIS; NCT02911571) trial, a prospective US multicenter study, was designed to validate the prognostic performance of SKY92 through real-world data. Methods: The GEP of 251 newly diagnosed MM patients was assessed using the SKY92 assay (SkylineDx, San Diego), which involved analyzing RNA extracted from CD138-positive plasma cells (≥80% purity) isolated through immunomagnetic separation. The test was performed at the enrolling institute, or by sending the samples into a central lab. The standard method for risk stratification by R-ISS stage was integrated with SKY92 into three distinct groups: Low-Risk (LR), defined by SKY92 standard-risk (SR) combined with R-ISS I; Intermediate Risk (IR), as SKY92 SR with R-ISS II/III, or SKY92 high-risk with R-ISS I; and High-Risk as SKY92 high-risk with R-ISS II/III. Progression-free survival (PFS) and overall survival (OS) were available for 221 patients, measured from the date of diagnosis of the patients. The median follow-up at the time of analysis is 38 months. Survival analyses were performed using Cox proportional hazards model. Results: Upon analysis, 179 patients (71.3%) were classified as SKY92 standard-risk, and 72 patients (28.7%) as SKY92 high-risk. Notably, the percentage of high-risk patients in this study was slightly higher than that observed in previous retrospective analyses (15-25%). Furthermore, the survival analysis revealed significant differences in both PFS (HR: 2.1, 95%CI 1.4-3.1, p<0.001) and OS (HR: 3.7, 95%CI 1.8-7.6, p<0.001) between the two risk categories. The median PFS from the time of the diagnosis for SKY92 standard-risk patients was 50 months, whereas for SKY92 high-risk patients, it was 26 months. The median OS was not reached within the 60-month timeframe for both the standard- and high-risk groups. R-ISS could be determined for 204 patients (81%), of which 29.9% (n=61) were classified as stage I, 59.3% (n=121) as stage II, and 10.8% (n=22) as stage III. We integrated R-ISS staging with SKY92 classification for these patients. The combination of these two classification systems yielded the following stratification ( Figure 1): LR n=53 (27%), IR n=96 (47%), and High-Risk n=55 (26%). Consistent with previous studies, R-ISS and SKY92 hold independent prognostic value: in particular, we found that this combined classification method identified a higher number of patients as high-risk compared to the R-ISS annotation (55 [27%] vs. 22 [11%]). The high-risk group exhibited significantly shorter OS and PFS compared to the LR group (OS: HR:18.7, 95% CI 2.5-140, p=0.004 and PFS: HR:2.6, 95% CI 1.4-4.7, p=0.002). Conclusions: This study represents the first US-based multicenter prospective evaluation of the GEP SKY92 in a real-world clinical setting. The results obtained align with the retrospective validations of SKY92, further confirming its reliability as a prognostic marker for MM patients. In addition, we observed that the SKY92 is able to identify a higher number of high-risk patients, with significantly shorter PFS and OS, compared to the conventional R-ISS staging system. Figure 1: PFS and OS based on SKY92+R-ISS risk classification

Background: Platelets are active players in hemostasis, coagulation and also tumorigenesis. The cross-talk between platelets and circulating tumor cells (CTCs) may have various pro-cancer effects, including promoting tumor growth, epithelial-mesenchymal transition (EMT), metastatic cell survival, adhesion, arrest and also pre-metastatic niche and metastasis formation. Interaction with CTCs might alter the platelet transcriptome. However, as CTCs are rare events, the cross-talk between CTCs and platelets is poorly understood. Here, we used our established colon CTC lines to investigate the colon CTC-platelet cross-talk in vitro and its impact on the behavior/phenotype of both cell types. Methods: We exposed platelets isolated from healthy donors to thrombin (positive control) or to conditioned medium from three CTC lines from one patient with colon cancer and then we monitored the morphological and protein expression changes by microscopy and flow cytometry. We then analyzed the transcriptome by RNA-sequencing of platelets indirectly (presence of a Transwell insert) co-cultured with the three CTC lines. We also quantified by reverse transcription-quantitative PCR the expression of genes related to EMT and cancer development in CTCs after direct co-culture (no Transwell insert) with platelets. Results: We observed morphological and transcriptomic changes in platelets upon exposure to CTC conditioned medium and indirect co-culture (secretome). Moreover, the expression levels of genes involved in EMT (p < 0.05) were decreased in CTCs co-cultured with platelets, but not of genes encoding mesenchymal markers (FN1 and SNAI2). The expression levels of genes involved in cancer invasiveness (MYC, VEGFB, IL33, PTGS2, and PTGER2) were increased. Conclusion: For the first time, we studied the CTC-platelet cross-talk using our unique colon CTC lines. Incubation with CTC conditioned medium led to platelet aggregation and activation, supporting the hypothesis that their interaction may contribute to preserve CTC integrity during their journey in the bloodstream. Moreover, co-culture with platelets influenced the expression of several genes involved in invasiveness and EMT maintenance in CTCs.

Introduction:Despite the advancements in therapeutic approaches for multiple myeloma (MM), which have led to improve the survival rates, a subset of high-risk patients still faces limited benefits from current treatments. Therefore, it is crucial to accurately identify specific patient groups who would benefit from tailored therapies through risk stratification. In this study, we aim to assess the risk classification of patients based on conventional risk markers currently used in clinical practices and with the gene expression profiling (GEP)-based SKY92 risk classifier (SkylineDx, San Diego). This approach could provide further insights for optimizing risk-adapted treatment compared to the standard guidelines used in clinical practice. Methods: The PRospective Observational Multiple Myeloma Impact Study (PROMMIS; NCT02911571) trial, a prospective US multicenter study, enrolled a total of 251 newly diagnosed MM patients (NDMM). The patients underwent risk stratification using two distinct approaches. Firstly, physicians assessed their risk of progression at the time of diagnosis according to their routine practice guidelines. Then, the patients were classified into risk groups using the SKY92 molecular test. The analysis involved the processing of RNA extracted from CD138-positive plasma cells (≥80% purity), which were isolated through immunomagnetic separation. SKY92 test was carried out either at the enrolling institute or by sending samples to a central laboratory. For 221 patients, progression-free survival (PFS) and overall survival (OS) data were available, with measurements taken from date of diagnosis; the median follow-up period was 38 months. Survival analysis was performed utilizing Cox proportional hazards model. Results: Based on conventional risk classification, 123 (49%) NDMM were classified as standard-risk and 128 (51%) patients as high-risk. However, SKY92 yielded a noticeable difference in classification, with 179 (71%) patients being categorized as standard-risk and 72 (29%) patients as high-risk. In specific 23 standard-risk and 79 high-risk (n=102, 41%) patients accordingly with clinical practice assessment were classified differently by the SKY92 test. Standard-risk and high-risk groups identified by current clinical practice classification showed a significant difference in PFS (HR: 1.6, 95%CI 1.0-2.4, p=0.031), but not in OS (HR: 1.9, 95%CI 0.9-4.0, p=0.10). With SKY92 risk stratification, instead, a significant difference was observed between the two risk groups in both PFS (HR: 2.1, 95%CI 1.4-3.1, p<0.001) and OS (HR: 3.7, 95%CI 1.8-7.6, p<0.001); furthermore, the median PFS for standard-risk and high-risk patients was, respectively, 50 and 26 months. The median OS was not reached within the 60-month timeframe for both groups. For the n=221 MM patients with survival data, we observed that most patients classified as standard-risk based on clinical practice classification were also categorized as standard-risk according to SKY92 analysis (83 out of 105, 79%). However, among those initially identified as high-risk by conventional risk classification, 71 out of 116 (61%) patients exhibit no significant difference in PFS (HR: 1.1, 95%CI 0.6-1.8, p=0.8) and OS (HR: 0.6, 95%CI 0.2-1.9, p=0.4) when compared to standard-risk patients, as determined by both risk classification methods ( Figure 1). Conversely, only 22 out of 72 (30%) patients initially identified as high-risk by SKY92 and as standard-risk by clinical practice show no significance difference for PFS (HR: 1.1, 95%CI 0.5-2.4, p=0.8) and OS (HR: 0.9, 95%CI 0.2-4.1, p=0.9) compared with standard-risk patients determined by both tests. Forty-five patients identified as HR by both risk classification methods show highly significant differences for PSF (HR: 2.9, 95%CI 1.7-4.9, p<0.001) and OS (HR: 4.1, 95%CI 1.8-9.6, p<0.001) compare with standard-risk group. Conclusions: These findings based on prospective real-world clinical data, collected in a prospective US multicenter trial confirm the effectiveness of the GEP SKY92 on risk stratification both for PFS and OS. These results support the added value of integrating SKY92 into clinical practice for precise risk assessment of patients. Figure1: PFS and OS based on the combination of the clinical risk assessment (CRA) and the SKY92 risk.

Newly growing evidence highlights the essential role that epitranscriptomic marks play in the development of many cancers; however, little is known about the role and implications of altered epitranscriptome deposition in prostate cancer. Here, we show that the transfer RNA N7-methylguanosine (m7G) transferase METTL1 is highly expressed in primary and advanced prostate tumours. Mechanistically, we find that METTL1 depletion causes the loss of m7G tRNA methylation and promotes the biogenesis of a novel class of small non-coding RNAs derived from 5'tRNA fragments. 5'tRNA-derived small RNAs steer translation control to favour the synthesis of key regulators of tumour growth suppression, interferon pathway, and immune effectors. Knockdown of Mettl1 in prostate cancer preclinical models increases intratumoural infiltration of pro-inflammatory immune cells and enhances responses to immunotherapy. Collectively, our findings reveal a therapeutically actionable role of METTL1-directed m7G tRNA methylation in cancer cell translation control and tumour biology.

Liquid biopsy approaches offer a promising technology for early and minimally invasive cancer detection. Tumor-educated platelets (TEPs) have emerged as a promising liquid biopsy biosource for the detection of various cancer types. In this study, we processed and analyzed the TEPs collected from 466 Non-small Cell Lung Carcinoma (NSCLC) patients and 410 asymptomatic individuals (controls) using the previously established thromboSeq protocol. We developed a novel particle-swarm optimization machine learning algorithm which enabled the selection of an 881 RNA biomarker panel (AUC 0.88). Herein we propose and validate in an independent cohort of samples (n = 558) two approaches for blood samples testing: one with high sensitivity (95% NSCLC detected) and another with high specificity (94% controls detected). Our data explain how TEP-derived spliced RNAs may serve as a biomarker for minimally-invasive clinical blood tests, complement existing imaging tests, and assist the detection and management of lung cancer patients.

Despite the diversity of liquid biopsy transcriptomic repertoire, numerous studies often exploit only a single RNA type signature for diagnostic biomarker potential. This frequently results in insufficient sensitivity and specificity necessary to reach diagnostic utility. Combinatorial biomarker approaches may offer a more reliable diagnosis. Here, we investigated the synergistic contributions of circRNA and mRNA signatures derived from blood platelets as biomarkers for lung cancer detection. We developed a comprehensive bioinformatics pipeline permitting an analysis of platelet-circRNA and mRNA derived from non-cancer individuals and lung cancer patients. An optimal selected signature is then used to generate the predictive classification model using machine learning algorithm. Using an individual signature of 21 circRNA and 28 mRNA, the predictive models reached an area under the curve (AUC) of 0.88 and 0.81, respectively. Importantly, combinatorial analysis including both types of RNAs resulted in an 8-target signature (6 mRNA and 2 circRNA), enhancing the differentiation of lung cancer from controls (AUC of 0.92). Additionally, we identified five biomarkers potentially specific for early-stage detection of lung cancer. Our proof-of-concept study presents the first multi-analyte-based approach for the analysis of platelets-derived biomarkers, providing a potential combinatorial diagnostic signature for lung cancer detection.

Additional file 15: Supplementary Table S6. Differentially expressed proteins in WT and METTL1 KO PC3 cells.

Cancer patients benefit from early tumor detection since treatment outcomes are more favorable for less advanced cancers. Platelets are involved in cancer progression and are considered a promising biosource for cancer detection, as they alter their RNA content upon local and systemic cues. We show that tumor-educated platelet (TEP) RNA-based blood tests enable the detection of 18 cancer types. With 99% specificity in asymptomatic controls, thromboSeq correctly detected the presence of cancer in two-thirds of 1,096 blood samples from stage I-IV cancer patients and in half of 352 stage I-III tumors. Symptomatic controls, including inflammatory and cardiovascular diseases, and benign tumors had increased false-positive test results with an average specificity of 78%. Moreover, thromboSeq determined the tumor site of origin in five different tumor types correctly in over 80% of the cancer patients. These results highlight the potential properties of TEP-derived RNA panels to supplement current approaches for blood-based cancer screening.

Tumor-educated Platelets (TEPs) have emerged as rich biosources of cancer-related RNA profiles in liquid biopsies applicable for cancer detection. Although human blood platelets have been found to be enriched in circular RNA (circRNA), no studies have investigated the potential of circRNA as platelet-derived biomarkers for cancer. In this proof-of-concept study, we examine whether the circRNA signature of blood platelets can be used as a liquid biopsy biomarker for the detection of non-small cell lung cancer (NSCLC). We analyzed the total RNA, extracted from the platelet samples collected from NSCLC patients and asymptomatic individuals, using RNA sequencing (RNA-Seq). Identification and quantification of known and novel circRNAs were performed using the accurate CircRNA finder suite (ACFS), followed by the differential transcript expression analysis using a modified version of our thromboSeq software. Out of 4732 detected circRNAs, we identified 411 circRNAs that are significantly (p-value < 0.05) differentially expressed between asymptomatic individuals and NSCLC patients. Using the false discovery rate (FDR) of 0.05 as cutoff, we selected the nuclear receptor-interacting protein 1 (NRIP1) circRNA (circNRIP1) as a potential biomarker candidate for further validation by reverse transcription–quantitative PCR (RT-qPCR). This analysis was performed on an independent cohort of platelet samples. The RT-qPCR results confirmed the RNA-Seq data analysis, with significant downregulation of circNRIP1 in platelets derived from NSCLC patients. Our findings suggest that circRNAs found in blood platelets may hold diagnostic biomarkers potential for the detection of NSCLC using liquid biopsies.

Until recently, the nucleic acid content of platelets was considered to be fully determined by their progenitor megakaryocyte. However, it is now well understood that additional mediators (eg, cancer cells) can intervene, thereby influencing the RNA repertoire of platelets. Platelets are highly dynamic cells that are able to communicate and influence their environment. For instance, platelets have been involved in various steps of cancer development and progression by supporting tumor growth, survival, and dissemination. Cancer cells can directly and/or indirectly influence platelet RNA content, resulting in tumor-mediated "education" of platelets. Alterations in the tumor-educated platelet RNA profile have been described as a novel source of potential biomarkers. Individual platelet RNA biomarkers as well as complex RNA signatures may be used for early detection of cancer and treatment monitoring. Here, we review the RNA transfer occurring between cancer cells and platelets. We explore the potential use of platelet RNA biomarkers as a liquid biopsy biosource and discuss methods to evaluate the transcriptomic content of platelets.