Serum RNA Profiling in the 10-Years Period Prior to Diagnosis of Testicular Germ Cell Tumor

Blood-Based mRNA Tests as Emerging Diagnostic Tools for Personalised Medicine in Breast Cancer

Molecular diagnostic tests help clinicians understand the underlying biological mechanisms of their patients' breast cancer (BC) and facilitate clinical management. Several tissue-based mRNA tests are used routinely in clinical practice, particularly for assessing the BC recurrence risk, which can guide treatment decisions. However, blood-based mRNA assays have only recently started to emerge. This review explores the commercially available blood mRNA diagnostic assays for BC. These tests enable differentiation of BC from non-BC subjects (Syantra DX, BCtect), detection of small tumours <10 mm (early BC detection) (Syantra DX), detection of different cancers (including BC) from a single blood sample (multi-cancer blood test Aristotle), detection of BC in premenopausal and postmenopausal women and those with high breast density (Syantra DX), and improvement of diagnostic outcomes of DNA testing (variant interpretation) (+RNAinsight). The review also evaluates ongoing transcriptomic research on exciting possibilities for future assays, including blood transcriptome analyses aimed at differentiating lymph node positive and negative BC, distinguishing BC and benign breast disease, detecting ductal carcinoma in situ, and improving early detection further (expression changes can be detected in blood up to eight years before diagnosing BC using conventional approaches, while future metastatic and non-metastatic BC can be distinguished two years before BC diagnosis).

Integrated Microarray-Based Data Analysis of miRNA Expression Profiles: Identification of Novel Biomarkers of Cisplatin-Resistance in Testicular Germ Cell Tumours

Testicular germ cell tumours (TGCTs) are the most common solid malignancy among young men, and their incidence is still increasing. Despite good curability with cisplatin (CDDP)-based chemotherapy, about 10% of TGCTs are non-responsive and show a chemoresistant phenotype. To further increase TGCT curability, better prediction of risk of relapse and early detection of refractory cases is needed. Therefore, to diagnose this malignancy more precisely, stratify patients more accurately and improve decision-making on treatment modality, new biomarkers are still required. Numerous studies showed association of differential expressions of microRNAs (miRNAs) with cancer. Using microarray analysis followed by RT-qPCR validation, we identified specific miRNA expression patterns that discriminate chemoresistant phenotypes in TGCTs. Comparing CDDP-resistant vs. -sensitive TGCT cell lines, we identified miR-218-5p, miR-31-5p, miR-125b-5p, miR-27b-3p, miR-199a-5p, miR-214-3p, let-7a and miR-517a-3p as significantly up-regulated and miR-374b-5p, miR-378a-3p, miR-20b-5p and miR-30e-3p as significantly down-regulated. In patient tumour samples, we observed the highest median values of relative expression of miR-218-5p, miR-31-5p, miR-375-5p and miR-517a-3p, but also miR-20b-5p and miR-378a-3p, in metastatic tumour samples when compared with primary tumour or control samples. In TGCT patient plasma samples, we detected increased expression of miR-218-5p, miR-31-5p, miR-517a-3p and miR-375-5p when compared to healthy individuals. We propose that miR-218-5p, miR-31-5p, miR-375-5p, miR-517-3p, miR-20b-5p and miR-378a-3p represent a new panel of biomarkers for better prediction of chemoresistance and more aggressive phenotypes potentially underlying metastatic spread in non-seminomatous TGCTs. In addition, we provide predictions of the targets and functional and regulatory networks of selected miRNAs.

Dynamic and static circulating cancer microRNA biomarkers - a validation study

For cancers and other pathologies, early diagnosis remains the most promising path to survival. Profiling of longitudinal cohorts facilitates insights into trajectories of biomarkers. We measured microRNA expression in 240 serum samples from patients with colon, lung, and breast cancer and from cancer-free controls. Each patient provided at least two serum samples, one prior to diagnosis and one following diagnosis. The median time interval between the samples was 11.6 years. Using computational models, we evaluated the circulating profiles of 21 microRNAs. The analysis yielded two sets of biomarkers, static ones that show an absolute difference between certain cancer types and controls and dynamic ones where the level over time provided higher diagnostic information content. In the first group, miR-99a-5p stands out for all three cancer types. In the second group, miR-155-5p allows to predict lung cancers and colon cancers. Classification in samples from cancer and non-cancer patients using gradient boosted trees reached an average accuracy of 79.9%. The results suggest that individual change over time or an absolute value at one time point may predict a disease with high specificity and sensitivity.

A Role of the TEX101 Interactome in the Common Aetiology Behind Male Subfertility and Testicular Germ Cell Tumor

Patients who develop testicular germ cell tumours (TGCT) are at higher risk to be subfertile than the general population. The conditions are believed to originate during foetal life, however, the mechanisms behind a common aetiology of TGCT and male subfertility remains unknown. Testis-expressed 101 (TEX101) is a glycoprotein that is related to male fertility, and downregulation of the TEX101 gene was shown in pre-diagnostic TGCT patients. In this review, we summarize the current knowledge of TEX101 and its interactome related to fertility and TGCT development. We searched literature and compilation of data from curated databases. There are studies from both human and animals showing that disruption of TEX101 result in abnormal semen parameters and sperm function. Members of the TEX101 interactome, like SPATA19, Ly6k, PICK1, and ODF genes are important for normal sperm function. We found only two studies of TEX101 related to TGCT, however, several genes in its interactome may be associated with TGCT development, such as PLAUR, PRSS21, CD109, and ALP1. Some of the interactome members are related to both fertility and cancer. Of special interest is the presence of the glycosylphosphatidylinositol anchored proteins TEX101 and PRSS21 in basophils that may be coupled to the immune response preventing further development of TGCT precursor cells. The findings of this review indicate that members of the TEX101 interactome could be a part of the link between TGCT and male subfertility.

Serum RNAs can predict lung cancer up to 10 years prior to diagnosis

Lung cancer (LC) prognosis is closely linked to the stage of disease when diagnosed. We investigated the biomarker potential of serum RNAs for the early detection of LC in smokers at different prediagnostic time intervals and histological subtypes. In total, 1061 samples from 925 individuals were analyzed. RNA sequencing with an average of 18 million reads per sample was performed. We generated machine learning models using normalized serum RNA levels and found that smokers later diagnosed with LC in 10 years can be robustly separated from healthy controls regardless of histology with an average area under the ROC curve (AUC) of 0.76 (95% CI, 0.68-0.83). Furthermore, the strongest models that took both time to diagnosis and histology into account successfully predicted non-small cell LC (NSCLC) between 6 and 8 years, with an AUC of 0.82 (95% CI, 0.76-0.88), and SCLC between 2 and 5 years, with an AUC of 0.89 (95% CI, 0.77-1.0), before diagnosis. The most important separators were microRNAs, miscellaneous RNAs, isomiRs, and tRNA-derived fragments. We have shown that LC can be detected years before diagnosis and manifestation of disease symptoms independently of histological subtype. However, the highest AUCs were achieved for specific subtypes and time intervals before diagnosis. The collection of models may therefore also predict the severity of cancer development and its histology. Our study demonstrates that serum RNAs can be promising prediagnostic biomarkers in an LC screening setting, from early detection to risk assessment.