Cancer Biomarkers in Liquid Biopsy for Early Detection of Breast Cancer: A Systematic Review

Random forest algorithm identifies miRNA signatures for breast cancer detection and classification from patient urine samples

Background and objectives

Breast cancer is the most common cancer in women, with one in eight women suffering from this disease in her lifetime. The implementation of centrally organized mammography screening for women between 50 and 69 years of age was a major step in the direction of early detection. However, the participation rate reaches approximately 50% of the eligible women, one reason being the painful compression of the breast, cited as a major issue for not participating in this very important program. Therefore, focusing current research on less painful and less invasive techniques for the detection of breast cancer is highly clinically relevant. Liquid biopsies offer this option by detection of distinct molecules such as microRNAs (miRNAs) or circulating tumor DNA (ctDNA) or disseminated tumor cells.

Design and methods

Here, we present the first proof-of-concept approach for sequencing miRNAs in female urine to detect breast cancer and, subsequently, intrinsic subtype-specific miRNA patterns and implement in this regard a novel random forest algorithm. To this end, we performed miRNA sequencing on 82 urine samples, 32 samples from breast cancer patients (9× luminal A, 8× luminal B, 9× triple-negative, and 6× HER2) and 50 healthy control samples.

Results and conclusion

Using a random forest algorithm, we identified a signature of 275 miRNAs that allows the detection of invasive breast cancer in urine. Furthermore, we identified distinct miRNA expression patterns for the major intrinsic subtypes of breast cancer, specifically luminal A, luminal B, HER2-enriched, and triple-negative breast cancer. This experimental approach specifically validates miRNA sequencing as a technique for breast cancer detection in urine samples and opens the door to a new, easy, and painless procedure for different breast cancer-related medical procedures such as screening but also treatment monitoring.

The VISTA/VSIG3/PSGL-1 axis: crosstalk between immune effector cells and cancer cells in invasive ductal breast carcinoma

A checkpoint protein called the V-domain Ig suppressor of T cell activation (VISTA) is important for controlling immune responses. Immune cells that interact with VISTA have molecules, or receptors, known as VISTA receptors. Immune system activity can be modified by the interaction between VISTA and its receptors. Since targeting VISTA or its receptors may be beneficial in certain conditions, VISTA has been studied in relation to immunotherapy for cancer and autoimmune illnesses. The purpose of this study was to examine the expression levels and interactions between VISTA and its receptors, VSIG3 and PSGL-1, in breast cancer tissues. IHC analysis revealed higher levels of proteins within the VISTA/VSIG3/PSGL-1 axis in cancer tissues than in the reference samples (mastopathies). VISTA was found in breast cancer cells and intratumoral immune cells, with membranous and cytoplasmic staining patterns. VISTA was also linked with pathological grade and VSIG3 and PSGL-1 levels. Furthermore, we discovered that the knockdown of one axis member boosted the expression of the other partners. This highlights the significance of VISTA/VSIG3/PSGL-1 in tumor stroma and microenvironment remodeling. Our findings indicate the importance of the VISTA/VSIG3/PSGL-1 axis in the molecular biology of cancer cells and the immune microenvironment.

Optimization of Surface Functionalizations for Ring Resonator-Based Biosensors

Liquid biopsy is expected to become widespread in the coming years thanks to point of care devices, which can include label-free biosensors. The surface functionalization of biosensors is a crucial aspect that influences their overall performance, resulting in the accurate, sensitive, and specific detection of target molecules. Here, the surface of a microring resonator (MRR)-based biosensor was functionalized for the detection of protein biomarkers. Among the several existing functionalization methods, a strategy based on aptamers and mercaptosilanes was selected as the most highly performing approach. All steps of the functionalization protocol were carefully characterized and optimized to obtain a suitable protocol to be transferred to the final biosensor. The functionalization protocol comprised a preliminary plasma treatment aimed at cleaning and activating the surface for the subsequent silanization step. Different plasma treatments as well as different silanes were tested in order to covalently bind aptamers specific to different biomarker targets, i.e., C-reactive protein, SARS-CoV-2 spike protein, and thrombin. Argon plasma and 1% v/v mercaptosilane were found as the most suitable for obtaining a homogeneous layer apt to aptamer conjugation. The aptamer concentration and time for immobilization were optimized, resulting in 1 µM and 3 h, respectively. A final passivation step based on mercaptohexanol was also implemented. The functionalization protocol was then evaluated for the detection of thrombin with a photonic biosensor based on microring resonators. The preliminary results identified the successful recognition of the correct target as well as some limitations of the developed protocol in real measurement conditions.

Identification of hub genes to determine drug-disease correlation in breast carcinomas

The exact molecular mechanism underlying the heterogeneous drug response against breast carcinoma remains to be fully understood. It is urgently required to identify key genes that are intricately associated with varied clinical response of standard anti-cancer drugs, clinically used to treat breast cancer patients. In the present study, the utility of transcriptomic data of breast cancer patients in discerning the clinical drug response using machine learning-based approaches were evaluated. Here, a computational framework has been developed which can be used to identify key genes that can be linked with clinical drug response and progression of cancer, offering an immense opportunity to predict potential prognostic biomarkers and therapeutic targets. The framework concerned utilizes DeSeq2, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Cytoscape, and machine learning techniques to find these crucial genes. Total RNA extraction and qRT-PCR were performed to quantify relative expression of few hub genes selected from the networks. In our study, we have experimentally checked the expression of few key hub genes like APOA2, DLX5, APOC3, CAMK2B, and PAK6 that were predicted to play an immense role in breast cancer tumorigenesis and progression in response to anti-cancer drug Paclitaxel. However, further experimental validations will be required to get mechanistic insights of these genes in regulating the drug response and cancer progression which will likely to play pivotal role in cancer treatment and precision oncology.

Identification of Endometrial Cancer-Specific microRNA Biomarkers in Endometrial Fluid

Abnormal uterine bleeding is a common benign gynecological complaint and is also the most common symptom of endometrial cancer (EC). Although many microRNAs have been reported in endometrial carcinoma, most of them were identified from tumor tissues obtained at surgery or from cell lines cultured in laboratories. The objective of this study was to develop a method to detect EC-specific microRNA biomarkers from liquid biopsy samples to improve the early diagnosis of EC in women. Endometrial fluid samples were collected during patient-scheduled in-office visits or in the operating room prior to surgery using the same technique performed for saline infusion sonohysterography (SIS). The total RNA was extracted from the endometrial fluid specimens, followed by quantification, reverse transcription, and real-time PCR arrays. The study was conducted in two phases: exploratory phase I and validation phase II. In total, endometrial fluid samples from 82 patients were collected and processed, with 60 matched non-cancer versus endometrial carcinoma patients used in phase I and 22 in phase II. The 14 microRNA biomarkers, out of 84 miRNA candidates, with the greatest variation in expression from phase I, were selected to enter phase II validation and statistical analysis. Among them, three microRNAs had a consistent and substantial fold-change in upregulation (miR-429, miR-183-5p, and miR-146a-5p). Furthermore, four miRNAs (miR-378c, miR-4705, miR-1321, and miR-362-3p) were uniquely detected. This research elucidated the feasibility of the collection, quantification, and detection of miRNA from endometrial fluid with a minimally invasive procedure performed during a patient in-office visit. The screening of a larger set of clinical samples was necessary to validate these early detection biomarkers for endometrial cancer.