An Inflection Point in Cancer Protein Biomarkers: What was and What's Next.

Biomarkers remain the highest value proposition in cancer medicine today-especially protein biomarkers. Despite decades of evolving regulatory frameworks to facilitate the review of emerging technologies, biomarkers have been mostly about promise with very little to show for improvements in human health. Cancer is an emergent property of a complex system, and deconvoluting the integrative and dynamic nature of the overall system through biomarkers is a daunting proposition. The last 2 decades have seen an explosion of multiomics profiling and a range of advanced technologies for precision medicine, including the emergence of liquid biopsy, exciting advances in single-cell analysis, artificial intelligence (machine and deep learning) for data analysis, and many other advanced technologies that promise to transform biomarker discovery. Combining multiple omics modalities to acquire a more comprehensive landscape of the disease state, we are increasingly developing biomarkers to support therapy selection and patient monitoring. Furthering precision medicine, especially in oncology, necessitates moving away from the lens of reductionist thinking toward viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to redefine biomarkers as representations of biological system states at different hierarchical levels of biological order. This definition could include traditional molecular, histologic, radiographic, or physiological characteristics, as well as emerging classes of digital markers and complex algorithms. To succeed in the future, we must move past purely observational individual studies and instead start building a mechanistic framework to enable integrative analysis of new studies within the context of prior studies. Identifying information in complex systems and applying theoretical constructs, such as information theory, to study cancer as a disease of dysregulated communication could prove to be "game changing" for the clinical outcome of cancer patients.

Noninvasive detection of any-stage cancer using free glycosaminoglycans.

Cancer mortality is exacerbated by late-stage diagnosis. Liquid biopsies based on genomic biomarkers can noninvasively diagnose cancers. However, validation studies have reported ~10% sensitivity to detect stage I cancer in a screening population and specific types, such as brain or genitourinary tumors, remain undetectable. We investigated urine and plasma free glycosaminoglycan profiles (GAGomes) as tumor metabolism biomarkers for multi-cancer early detection (MCED) of 14 cancer types using 2,064 samples from 1,260 cancer or healthy subjects. We observed widespread cancer-specific changes in biofluidic GAGomes recapitulated in an in vivo cancer progression model. We developed three machine learning models based on urine (Nurine = 220 cancer vs. 360 healthy) and plasma (Nplasma = 517 vs. 425) GAGomes that can detect any cancer with an area under the receiver operating characteristic curve of 0.83-0.93 with up to 62% sensitivity to stage I disease at 95% specificity. Undetected patients had a 39 to 50% lower risk of death. GAGomes predicted the putative cancer location with 89% accuracy. In a validation study on a screening-like population requiring ≥ 99% specificity, combined GAGomes predicted any cancer type with poor prognosis within 18 months with 43% sensitivity (21% in stage I; N = 121 and 49 cases). Overall, GAGomes appeared to be powerful MCED metabolic biomarkers, potentially doubling the number of stage I cancers detectable using genomic biomarkers.

Prognostic value of CDKN2A in head and neck squamous cell carcinoma via pathomics and machine learning.

This study aims to enhance the prognosis prediction of Head and Neck Squamous Cell Carcinoma (HNSCC) by employing artificial intelligence (AI) to analyse CDKN2A gene expression from pathology images, directly correlating with patient outcomes. Our approach introduces a novel AI-driven pathomics framework, delineating a more precise relationship between CDKN2A expression and survival rates compared to previous studies. Utilizing 475 HNSCC cases from the TCGA database, we stratified patients into high-risk and low-risk groups based on CDKN2A expression thresholds. Through pathomics analysis of 271 cases with available slides, we extracted 465 distinctive features to construct a Gradient Boosting Machine (GBM) model. This model was then employed to compute Pathomics scores (PS), predicting CDKN2A expression levels with validation for accuracy and pathway association analysis. Our study demonstrates a significant correlation between higher CDKN2A expression and improved median overall survival (66.73 months for high expression vs. 42.97 months for low expression, p = 0.013), establishing CDKN2A's prognostic value. The pathomic model exhibited exceptional predictive accuracy (training AUC: 0.806; validation AUC: 0.710) and identified a strong link between higher Pathomics scores and cell cycle activation pathways. Validation through tissue microarray corroborated the predictive capacity of our model. Confirming CDKN2A as a crucial prognostic marker in HNSCC, this study advances the existing literature by implementing an AI-driven pathomics analysis for gene expression evaluation. This innovative methodology offers a cost-efficient and non-invasive alternative to traditional diagnostic procedures, potentially revolutionizing personalized medicine in oncology.

Deciphering the tumour microenvironment of clear cell renal cell carcinoma: Prognostic insights from programmed death genes using machine learning.

Clear cell renal cell carcinoma (ccRCC), a prevalent kidney cancer form characterised by its invasiveness and heterogeneity, presents challenges in late-stage prognosis and treatment outcomes. Programmed cell death mechanisms, crucial in eliminating cancer cells, offer substantial insights into malignant tumour diagnosis, treatment and prognosis. This study aims to provide a model based on 15 types of Programmed Cell Death-Related Genes (PCDRGs) for evaluating immune microenvironment and prognosis in ccRCC patients. ccRCC patients from the TCGA and arrayexpress cohorts were grouped based on PCDRGs. A combination model using Lasso and SuperPC was constructed to identify prognostic gene features. The arrayexpress cohort validated the model, confirming its robustness. Immune microenvironment analysis, facilitated by PCDRGs, employed various methods, including CIBERSORT. Drug sensitivity analysis guided clinical treatment decisions. Single-cell data enabled Programmed Cell Death-Related scoring, subsequent pseudo-temporal and cell-cell communication analyses. A PCDRGs signature was established using TCGA-KIRC data. External validation in the arrayexpress cohort underscored the model's superiority over traditional clinical features. Furthermore, our single-cell analysis unveiled the roles of PCDRG-based single-cell subgroups in ccRCC, both in pseudo-temporal progression and intercellular communication. Finally, we performed CCK-8 assay and other experiments to investigate csf2. In conclusion, these findings reveal that csf2 inhibit the growth, infiltration and movement of cells associated with renal clear cell carcinoma. This study introduces a PCDRGs prognostic model benefiting ccRCC patients while shedding light on the pivotal role of programmed cell death genes in shaping the immune microenvironment of ccRCC patients.

Clinical effectiveness of a multitarget urine DNA test for urothelial carcinoma detection: a double-blinded, multicenter, prospective trial.

Urine-based testing is promising for noninvasive diagnosis of urothelial carcinoma (UC) but has suboptimal sensitivity for early-stage tumors. Herein, we developed a multitarget urine tumor DNA test, UI-Seek, for UC detection and evaluated its clinical feasibility. The prediction model was developed in a retrospective cohort (n = 382), integrating assays for FGFR3 and TERT mutations and aberrant ONECUT2 and VIM methylation to generate a UC-score. The test performance was validated in a double-blinded, multicenter, prospective trial (n = 947; ChiCTR2300076543) and demonstrated a sensitivity of 91.37% and a specificity of 95.09%. The sensitivity reached 75.81% for low-grade Ta tumors and exceeded 93% in high-grade Ta and higher stages (T1 to T4). Simultaneous identification of both bladder and upper urinary tract tumors was enabled with sensitivities exceeding 90%. No significant confounding effects were observed regarding benign urological diseases or non-UC malignancies. The test showed improved sensitivities over urine cytology, the NMP22 test, and UroVysion FISH alongside comparable specificities. The single-target accuracy was greater than 98% as confirmed by Sanger sequencing. Post-surgery UC-score decreased in 97.7% of subjects. Overall, UI-Seek demonstrated robust performance and considerable potential for the early detection of UC.

Perturbations in the blood metabolome up to a decade before prostate cancer diagnosis in 4387 matched case-control sets from the European Prospective Investigation into Cancer and Nutrition.

Measuring pre-diagnostic blood metabolites may help identify novel risk factors for prostate cancer. Using data from 4387 matched case-control pairs from the European Prospective Investigation into Cancer and Nutrition (EPIC) study, we investigated the associations of 148 individual metabolites and three previously defined metabolite patterns with prostate cancer risk. Metabolites were measured by liquid chromatography-mass spectrometry. Multivariable-adjusted conditional logistic regression was used to estimate the odds ratio per standard deviation increase in log metabolite concentration and metabolite patterns (OR1SD) for prostate cancer overall, and for advanced, high-grade, aggressive. We corrected for multiple testing using the Benjamini-Hochberg method. Overall, there were no associations between specific metabolites or metabolite patterns and overall, aggressive, or high-grade prostate cancer that passed the multiple testing threshold (padj <0.05). Six phosphatidylcholines (PCs) were inversely associated with advanced prostate cancer diagnosed at or within 10 years of blood collection. metabolite patterns 1 (64 PCs and three hydroxysphingomyelins) and 2 (two acylcarnitines, glutamate, ornithine, and taurine) were also inversely associated with advanced prostate cancer; when stratified by follow-up time, these associations were observed for diagnoses at or within 10 years of recruitment (OR1SD 0.80, 95% CI 0.66-0.96 and 0.76, 0.59-0.97, respectively) but were weaker after longer follow-up (0.95, 0.82-1.10 and 0.85, 0.67-1.06). Pattern 3 (8 lyso PCs) was associated with prostate cancer death (0.82, 0.68-0.98). Our results suggest that the plasma metabolite profile changes in response to the presence of prostate cancer up to a decade before detection of advanced-stage disease.

Comparison of Primary and Metastatic Fumarate Hydratase-Deficient Renal Cell Carcinomas Documents Morphologic Divergence and Potential Diagnostic Pitfall With Peritoneal Mesothelioma.

Fumarate hydratase (FH)-deficient renal cell carcinomas are rare neoplasms characterized by wide morphologic heterogeneity and pathogenetic mutations in the FH gene. They often show aggressive behavior with rapid diffusion to distant organs, so novel therapeutic scenarios have been explored, including EGFR inhibitors and PD-L1 expression for targeted immunotherapy. Herein, we investigated a series of 11 primary FH-deficient renal cell carcinomas and 7 distant metastases to evaluate tumor heterogeneity even in metastatic sites and estimate the specific spread rates to various organs. Furthermore, the tumors were tested for immunohistochemical PD-L1 expression and EGFR mutations. Most metastatic cases involved the abdominal lymph nodes (4/7; 57%), followed by the peritoneum (3/7; 42%), the liver (2/7; 29%), and the lungs (1/7; 14%). Six metastatic localizations were histologically documented, revealing a morphologic heterogeneous architecture often differing from that of the corresponding primary renal tumor. Peritoneal involvement morphologically resembled a benign reactive mesothelial process or primary peritoneal mesothelioma, thus advocating to perform an accurate immunohistochemical panel, including PAX8 and FH, to reach a proper diagnosis. A pure low-grade succinate dehydrogenase-looking primary FH-deficient renal cell carcinoma was also recorded. As for therapy, significant PD-L1 labeling was found in 60% of primary renal tumors, whereas none of them carried pathogenetic EGFR mutations. Our data show that FH-deficient renal cell carcinoma may be morphologically heterogeneous in metastases as well, which involve the lymph nodes, the liver, and the peritoneum more frequently than other renal tumors. Due to the high frequency of this latter (42%), pathologists should always be concerned about ruling out mesothelial-derived mimickers, and the occurrence of rarer, primary, low-grade-looking types. Finally, contrary to EGFR mutations, PD-L1 expression could be a possible predictive biomarker for the therapy of these tumors.

Identification of epigenetic silencing of the SFRP2 gene in colorectal cancer as a clinical biomarker and molecular significance.

BACKGROUND: Several studies have suggested secreted frizzled-related protein 2 (SFRP2) gene as a potential clinical biomarker in colorectal cancer (CRC). However, its diagnostic role remains unclear. In this study, we aimed to investigate the significance of SFRP2 methylation levels in a large cohort of biological specimens (including blood, adipose and colonic tissues) from patients with CRC, thereby potentially identifying new biomarker utility. METHODS: We examined the expression (by qPCR) and methylation status (by 450 K DNA array and DNA pyrosequencing) of the SFRP2 gene in healthy participants (N = 110, aged as 53.7 (14.2), 48/62 males/females) and patients with CRC (N = 85, aged 67.7 (10.5), 61/24 males/females), across different biological tissues, and assessing its potential as a biomarker for CRC. Additionally, we investigated the effect of recombinant human SFRP2 (rhSFRP2) as a therapeutic target, on cell proliferation, migration, and the expression of key genes related to carcinogenesis and the Wnt pathway. RESULTS: Our findings revealed that SFRP2 promoter methylation in whole blood could predict cancer stage (I + II vs. III + IV) (AUC = 0.653), lymph node invasion (AUC = 0.692), and CRC recurrence (AUC = 0.699) in patients with CRC (all with p < 0.05). Furthermore, we observed a global hypomethylation of SFRP2 in tumors compared to the adjacent area (p < 0.001). This observation was validated in the TCGA-COAD and TCGA-READ cohorts, demonstrating overall hypermethylation (both with p < 0.001) and low expression (p < 0.001), as shown in publicly available scRNA-Seq data. Notably, neoadjuvant-treated CRC patients exhibited lower SFRP2 methylation levels compared to untreated patients (p < 0.05) and low promoter SFRP2 methylation in untreated patients was associated with poor overall survival (p < 0.05), when compared to high methylation. Finally, treatment with 5 µg of rhSFRP2 treatment in CRC cells (HCT116 cells) inhibited cell proliferation (p < 0.001) and migration (p < 0.05), and downregulated the expression of AXIN2 (p < 0.01), a gene involved in Wnt signaling pathway. CONCLUSIONS: These findings establish promoter methylation of the SFRP2 gene as a prognostic candidate in CRC when assessed in blood, and as a therapeutic prognostic candidate in tumors, potentially valuable in clinical practice. SFRP2 also emerges as a therapeutic option, providing new clinical and therapeutical avenues.

Identification of SLC22A17 DNA methylation hotspot as a potential biomarker in cutaneous melanoma.

BACKGROUND: Cancer onset and progression are driven by genetic and epigenetic alterations leading to oncogene activation and the silencing of tumor suppressor genes. Among epigenetic mechanisms, DNA methylation (methDNA) is gaining growing interest in cancer. Promoter hypomethylation is associated with oncogene activation while intragenic methDNA can be involved in transcriptional elongation, alternative spicing, and the activation of cryptic start sites. Several genes involved in the modulation of the tumor microenvironment are regulated by methDNA, including the Solute Carrier Family 22 Member 17 (SLC22A17), which is involved in iron trafficking and extracellular matrix remodeling cooperating with the Gelatinase-Associated Lipocalin (NGAL) ligand. However, the exact role of intragenic methDNA in cancer has not been fully investigated. Therefore, the aim of the present study is to explore the role of methDNA in the regulation of SLC22A17 in cutaneous melanoma (CM), used as a tumor model. METHODS: Correlation and differential analyses between SLC22A17 expression and methDNA were performed using the data contained in The Cancer Genome Atlas and Gene Expression Omnibus databases. Functional studies on melanoma cell lines treated with 5-Azacytidine (5-Aza) were conducted to assess the correlation between methDNA and SLC22A17 expression. A validation study on the diagnostic potential of the in silico-identified SLC22A17 methDNA hotspot was finally performed by analyzing tissue samples obtained from CM patients and healthy controls. RESULTS: The computational analyses revealed that SLC22A17 was significantly downregulated in CM, and its expression was related to promoter hypomethylation and intragenic hypermethylation. Moreover, SLC22A17 overexpression and hypermethylation of two intragenic methDNA hotspots were associated with a better clinical outcome in CM patients. The correlation between SLC22A17 methDNA and expression was confirmed in 5-Aza-treated cells. In agreement with in silico analyses, the SLC22A17 promoter methylation hotspot showed higher methDNA levels in CM samples compared to nevi. In addition, the methDNA levels of this hotspot were positively correlated with advanced CM. CONCLUSIONS: The SLC22A17 methDNA hotspot could represent a promising biomarker for CM, highlighting the regulatory role of methDNA on SLC22A17 expression. These results pave the way for the identification of novel epigenetic biomarkers and therapeutic targets for the management of CM patients.

Harnessing exosomes as cancer biomarkers in clinical oncology.

Exosomes are extracellular vesicles well known for facilitating cell-to-cell communication by distributing essential macromolecules like proteins, DNA, mRNA, lipids, and miRNA. These vesicles are abundant in fluids distributed throughout the body, including urine, blood, saliva, and even bile. They are important diagnostic tools for breast, lung, gastrointestinal cancers, etc. However, their application as cancer biomarkers has not yet been implemented in most parts of the world. In this review, we discuss how OMICs profiling of exosomes can be practiced by substituting traditional imaging or biopsy methods for cancer detection. Previous methods like extensive imaging and biopsy used for screening were expensive, mostly invasive, and could not easily provide early detection for various types of cancer. Exosomal biomarkers can be utilized for routine screening by simply collecting body fluids from the individual. We anticipate that the use of exosomes will be brought to light by the success of clinical trials investigating their potential to enhance cancer detection and treatment in the upcoming years.

The role of zinc finger proteins in malignant tumors.

Zinc finger proteins (ZNFs) are the largest family of transcriptional factors in mammalian cells. Recently, their role in the development, progression, and metastasis of malignant tumors via regulating gene transcription and translation processes has become evident. Besides, their possible involvement in drug resistance has also been found, indicating that ZNFs have the potential to become new biological markers and therapeutic targets. In this review, we summarize the oncogenic and suppressive roles of various ZNFs in malignant tumors, including lung, breast, liver, gastric, colorectal, pancreatic, and other cancers, highlighting their role as prognostic markers, and hopefully provide new ideas for the treatment of malignant tumors in the future.

The prowess of metabolomics in cancer research: current trends, challenges and future perspectives.

Cancer due to its heterogeneous nature and large prevalence has tremendous socioeconomic impacts on populations across the world. Therefore, it is crucial to discover effective panels of biomarkers for diagnosing cancer at an early stage. Cancer leads to alterations in cell growth and differentiation at the molecular level, some of which are very unique. Therefore, comprehending these alterations can aid in a better understanding of the disease pathology and identification of the biomolecules that can serve as effective biomarkers for cancer diagnosis. Metabolites, among other biomolecules of interest, play a key role in the pathophysiology of cancer whose levels are significantly altered while 'reprogramming the energy metabolism', a cellular condition favored in cancer cells which is one of the hallmarks of cancer. Metabolomics, an emerging omics technology has tremendous potential to contribute towards the goal of investigating cancer metabolites or the metabolic alterations during the development of cancer. Diverse metabolites can be screened in a variety of biofluids, and tumor tissues sampled from cancer patients against healthy controls to capture the altered metabolism. In this review, we provide an overview of different metabolomics approaches employed in cancer research and the potential of metabolites as biomarkers for cancer diagnosis. In addition, we discuss the challenges associated with metabolomics-driven cancer research and gaze upon the prospects of this emerging field.

Establishment of a multi-line immunochromatography based on magnetic nanoparticles for simultaneous screening of multiple biomarkers.

Biomarkers screening is a benefit approach for early diagnosis of major diseases. In this study, magnetic nanoparticles (MNPs) have been utilized as labels to establish a multi-line immunochromatography (MNP-MLIC) for simultaneous detection of carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA 19-9), and alpha-fetoprotein (AFP) in a single serum sample. Under the optimal parameters, the three biomarkers can be rapidly and simultaneously qualitative screening within 15 min by naked eye. As for quantitative detection, the MNP-MLIC test strips were precisely positioned and captured by a smartphone, and signals on the test and control lines were extracted by ImageJ software. The signal ratio of test and control lines has been calculated and used to plot quantitative standard curves with the logarithmic concentration, of which the correlation coefficients are more than 0.99, and the limit of detection for CEA, CA 19-9, and AFP were 0.60 ng/mL, 1.21 U/mL, and 0.93 ng/mL, respectively. The recoveries of blank serum were 75.0 ~ 112.5% with the relative standard deviation ranging from 2.5 to 15.3%, and the specificity investigation demonstrated that the MNP-MLIC is highly specific to the three biomarkers. In conclusion, the developed MNP-MLIC offers a rapid, simple, accurate, and highly specific method for simultaneously detecting multiple biomarkers in serum samples, which provides an efficient and accurate approach for the early diagnosis of diseases.

SERS biosensor with plastic antibodies for detection of a cancer biomarker protein.

Surface-enhanced Raman scattering (SERS) is a powerful method for detecting breast cancer-specific biomarkers due to its extraordinary enhancement effects obtained by localized surface plasmon resonance (LSPR) in metallic nanostructures at hotspots. In this research, gold nanostars (AuNSs) were used as SERS probes to detect a cancer biomarker at very low concentrations. To this end, we combined molecularly imprinted polymers (MIPs) as a detection layer with SERS for the detection of the biomarker CA 15-3 in point-of-care (PoC) analysis. This required two main steps: (i) the deposition of MIPs on a gold electrode, followed by a second step (ii) antibody binding with AuNSs containing a suitable Raman reporter to enhance Raman signaling (SERS). The MPan sensor was prepared by electropolymerization of the monomer aniline in the presence of CA 15-3. The template molecule was then extracted from the polymer using sodium dodecyl sulfate (SDS). In parallel, a control material was prepared in the absence of the protein (NPan). Surface modification for the control was performed using electrochemical techniques such as cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The performance of the sensor was evaluated using the SERS technique, in which the MPan sensor is first incubated with the protein and then exposed to the SERS probe. Under optimized conditions, the device showed a linear response to CA 15-3 concentrations from 0.016 to 248.51 U mL-1 in a PBS buffer at pH 7.4 in 1000-fold diluted serum. Overall, this approach demonstrates the potential of SERS as an optical reader and opens a new avenue for biosensing applications.

Raman Imaging-A Valuable Tool for Tracking Fatty Acid Metabolism-Normal and Cancer Human Colon Single-Cell Study.

Altered metabolism of lipids is a key factor in many diseases including cancer. Therefore, investigations into the impact of unsaturated and saturated fatty acids (FAs) on human body homeostasis are crucial for understanding the development of lifestyle diseases. In this paper, we focus on the impact of palmitic (PA), linoleic (LA), and eicosapentaenoic (EPA) acids on human colon normal (CCD-18 Co) and cancer (Caco-2) single cells using Raman imaging and spectroscopy. The label-free nature of Raman imaging allowed us to evaluate FAs dynamics without modifying endogenous cellular metabolism. Thanks to the ability of Raman imaging to visualize single-cell substructures, we have analyzed the changes in chemical composition of endoplasmic reticulum (ER), mitochondria, lipid droplets (LDs), and nucleus upon FA supplementation. Analysis of Raman band intensity ratios typical for lipids, proteins, and nucleic acids (I1656/I1444, I1444/I1256, I1444/I750, I1304/I1256) proved that, using Raman mapping, we can observe the metabolic pathways of FAs in ER, which is responsible for the uptake of exogenous FAs, de novo synthesis, elongation, and desaturation of FAs, in mitochondria responsible for energy production via FA oxidation, in LDs specialized in cellular fat storage, and in the nucleus, where FAs are transported via fatty-acid-binding proteins, biomarkers of human colon cancerogenesis. Analysis for membranes showed that the uptake of FAs effectively changed the chemical composition of this organelle, and the strongest effect was noticed for LA. The spectroscopy studies have been completed using XTT tests, which showed that the addition of LA or EPA for Caco-2 cells decreases their viability with a stronger effect observed for LA and the opposite effect observed for PA. For normal cells, CCD-18 Co supplementation using LA or EPA stimulated cells for growing, while PA had the opposite impact.

Recent Advances in Fluorescent Probes for Cancer Biomarker Detection.

Many important biological species have been identified as cancer biomarkers and are gradually becoming reliable targets for early diagnosis and late therapeutic evaluation of cancer. However, accurate quantitative detection of cancer biomarkers remains challenging due to the complexity of biological systems and the diversity of cancer development. Fluorescent probes have been extensively utilized for identifying biological substances due to their notable benefits of being non-invasive, quickly responsive, highly sensitive and selective, allowing real-time visualization, and easily modifiable. This review critiques fluorescent probes used for detecting and imaging cancer biomarkers over the last five years. Focuses are made on the design strategies of small-molecule and nano-sized fluorescent probes, the construction methods of fluorescence sensing and imaging platforms, and their further applications in detection of multiple biomarkers, including enzymes, reactive oxygen species, reactive sulfur species, and microenvironments. This review aims to guide the design and development of excellent cancer diagnostic fluorescent probes, and promote the broad application of fluorescence analysis in early cancer diagnosis.

Cancer Biomarkers in the era of precision oncology: Addressing the needs of patients and health systems.

Cancer Biomarkers are the key to unlocking the promise of precision oncology, selecting which patients will respond to a more personalised treatment while sparing non-responders the therapy-related toxicity. In this paper, we highlight the primacy of cancer biomarkers, but focus on their importance to patients and to health systems. We also highlight how cancer biomarkers represent value for money. We emphasise the need for cancer biomarkers infrastructure to be embedded into European health systems. We also highlight the need to deploy multiple biomarker testing to deliver the optimal benefit for patients and health systems and consider cancer biomarkers from the perspective of cost, value and regulation. Cancer biomarkers must also be situated in the context of the upcoming In Vitro Diagnostics Regulation, which may pose certain challenges (e.g. non-compliance of laboratory developed tests, leading to cancer biomarker shortages and increased costs) that need to be overcome. Cancer biomarkers must be embedded in the real world of oncology delivery and testing must be implemented across Europe, with the intended aim of narrowing, not widening the inequity gap for patients. Cancer patients must be placed firmly at the centre of a cancer biomarker informed precision oncology care agenda.

Circular RNAs as biomarkers and therapeutic targets in cancer.

Circular RNAs (circRNAs) are a class of single-stranded closed non-coding RNA molecules (ncRNAs), which are formed as a result of reverse splicing of mRNAs. Despite their relative abundance, an interest in understanding their regulatory importance is rather recent. High stability, abundance and evolutionary conservation among species underline some of their important traits. CircRNAs perform a variety of cellular functions ranging from miRNA and proteins sponges to transcriptional modulation and splicing. Additionally, most circRNAs are expressed aberrantly in pathological conditions suggesting their possible exploitation as diagnostic biomarkers. Their covalent closed cyclic structure resulting in resistance to RNases further makes them suitable as cancer biomarkers. Studies involving human tumors have verified differences in the expression profiles of circRNAs, indicating a regulatory role in cancer pathogenesis and metastasis. As endogenous competitive RNA, circRNAs can regulate tumor proliferation and invasion. Further, some circRNAs located in the nucleus can regulate transcription of genes by binding to RNA polymerase II. In this review, we elaborate the characteristics, functions and mechanisms of action of circRNAs in cancer. We also discuss the possibility of using circRNAs as potential therapeutic targets and biomarkers for cancer.

Immune capture and protein profiling of small extracellular vesicles from human plasma.

Extracellular vesicles (EVs), the key players in inter-cellular communication, are produced by all cell types and are present in all body fluids. Analysis of the proteome content is an important approach in structural and functional studies of these vesicles. EVs circulating in human plasma are heterogeneous in size, cellular origin, and functions. This heterogeneity and the potential presence of contamination with plasma components such as lipoprotein particles and soluble plasma proteins represent a challenge in profiling the proteome of EV subsets by mass spectrometry. An immunocapture strategy prior to mass spectrometry may be used to isolate a homogeneous subpopulation of small EVs (sEV) with a specific endocytic origin from plasma or other biofluids. Immunocapture selectively separates EV subpopulations in biofluids based on the presence of a unique protein carried on the vesicle surface. The advantages and disadvantages of EV immune capture as a preparative step for mass spectrometry are discussed.

Selection of optimal quantile protein biomarkers based on cell-level immunohistochemistry data.

BACKGROUND: Protein biomarkers of cancer progression and response to therapy are increasingly important for improving personalized medicine. Advanced quantitative pathology platforms enable measurement of protein expression in tissues at the single-cell level. However, this rich quantitative cell-by-cell biomarker information is most often not exploited. Instead, it is reduced to a single mean across the cells of interest or converted into a simple proportion of binary biomarker-positive or -negative cells. RESULTS: We investigated the utility of retaining all quantitative information at the single-cell level by considering the values of the quantile function (inverse of the cumulative distribution function) estimated from a sample of cell signal intensity levels in a tumor tissue. An algorithm was developed for selecting optimal cutoffs for dichotomizing cell signal intensity distribution quantiles as predictors of continuous, categorical or survival outcomes. The proposed algorithm was used to select optimal quantile biomarkers of breast cancer progression based on cancer cells' cell signal intensity levels of nuclear protein Ki-67, Proliferating cell nuclear antigen, Programmed cell death 1 ligand 2, and Progesterone receptor. The performance of the resulting optimal quantile biomarkers was validated and compared to the standard cancer compartment mean signal intensity markers using an independent external validation cohort. For Ki-67, the optimal quantile biomarker was also compared to established biomarkers based on percentages of Ki67-positive cells. For proteins significantly associated with PFS in the external validation cohort, the optimal quantile biomarkers yielded either larger or similar effect size (hazard ratio for progression-free survival) as compared to cancer compartment mean signal intensity biomarkers. CONCLUSION: The optimal quantile protein biomarkers yield generally improved prognostic value as compared to the standard protein expression markers. The proposed methodology has a broad application to single-cell data from genomics, transcriptomics, proteomics, or metabolomics studies at the single cell level.