Prognostic and predictive biomarkers: tools in personalized oncology

Advance computational tools for multiomics data learning

The burgeoning field of bioinformatics has seen a surge in computational tools tailored for omics data analysis driven by the heterogeneous and high-dimensional nature of omics data. In biomedical and plant science research multi-omics data has become pivotal for predictive analytics in the era of big data necessitating sophisticated computational methodologies. This review explores a diverse array of computational approaches which play crucial roles in processing, normalizing, integrating, and analyzing omics data. Notable methods such similarity-based methods, network-based approaches, correlation-based methods, Bayesian methods, fusion-based methods and multivariate techniques among others are discussed in detail, each offering unique functionalities to address the complexities of multi-omics data. Furthermore, this review underscores the significance of computational tools in advancing our understanding of data and their transformative impact on research.

NK/DC crosstalk-modulating antitumor activity via Sema3E/PlexinD1 axis for enhanced cancer immunotherapy

The complex relationship between natural killer (NK) cells and dendritic cells (DCs) within the tumor microenvironment significantly impacts the success of cancer immunotherapy. Recent advancements in cancer treatment have sought to bolster innate and adaptive immune responses through diverse modalities, aiming to tilt the immune equilibrium toward tumor elimination. Optimal antitumor immunity entails a multifaceted interplay involving NK cells, T cells and DCs, orchestrating immune effector functions. Although DC-based vaccines and NK cells' cytotoxic capabilities hold substantial therapeutic potential, their interaction is frequently hindered by immunosuppressive elements such as myeloid-derived suppressor cells (MDSCs) and regulatory T cells. Chemokines and cytokines, such as CXCL12, CCL2, interferons, and interleukins, play crucial roles in modulating NK/DC interactions and enhancing immune responses. This review elucidates the mechanisms underlying NK/DC interaction, emphasizing their pivotal roles in augmenting antitumor immune responses and the impediments posed by tumor-induced immunosuppression. Furthermore, it explores the therapeutic prospects of restoring NK/DC crosstalk, highlighting the significance of molecules like Sema3E/PlexinD1 in this context, offering potential avenues for enhancing the effectiveness of current immunotherapeutic strategies and advancing cancer treatment paradigms. Harnessing the dynamic interplay between NK and DC cells, including the modulation of Sema3E/PlexinD1 signaling, holds promise for developing more potent therapies that harness the immune system's full potential in combating cancer.

Prognostic and Clinical Significance of the Proliferation Marker MCM7 in Breast Cancer

INTRODUCTION

Minichromosome maintenance complex component 7 (MCM7) plays an essential role in proliferation and DNA replication of cancer cells. However, the expression and prognostic significance of MCM7 in breast cancer (BC) remain to be defined. In this study, we aimed to evaluate the role of MCM7 in BC.

METHODS

We conducted immunohistochemistry staining of MCM7 in 1,156 operable early-stage BC samples and assessed MCM7 at the transcriptomic levels using publicly available cohorts (n = 13,430). MCM7 expression was evaluated and correlated with clinicopathological parameters including Ki67 labelling index and patient outcome.

RESULTS

At the transcriptomic level, there was a significant association between high MCM7 mRNA levels and shorter patient survival in the whole cohort and in luminal BC class but not in the basal-like molecular subtype. High MCM7 protein expression was detected in 43% of patients and was significantly associated with parameters characteristic of aggressive tumour behaviour. MCM7 was independently associated with shorter survival, particularly in oestrogen receptor-positive (luminal) BC. MCM7 stratified luminal tumours with aggressive clinicopathological features into distinct prognostic groups. In endocrine therapy-treated BC patients, high MCM7 was associated with poor outcome, but such association disappeared with administration of adjuvant chemotherapy. Patients with high expression of Ki67 and MCM7 showed worst survival, while patients with double low expression BC showed the best outcome compared with single expression groups.

CONCLUSION

The current findings indicate that MCM7 expression has a prognostic value in BC and can be used to identify luminal BC patients who can benefit from adjuvant chemotherapy.

Personalized treatment using predictive biomarkers in solid organ malignancies: A review

In recent years, the influence of specific biomarkers in the diagnosis and prognosis of solid organ malignancies has been increasingly prominent. The relevance of the use of predictive biomarkers, which predict cancer response to specific forms of treatment provided, is playing a more significant role than ever before, as it affects diagnosis and initiation of treatment, monitoring for efficacy and side effects of treatment, and adjustment in treatment regimen in the long term. In the current review, we explored the use of predictive biomarkers in the treatment of solid organ malignancies, including common cancers such as colorectal cancer, breast cancer, lung cancer, prostate cancer, and cancers associated with high mortalities, such as pancreatic cancer, liver cancer, kidney cancer and cancers of the central nervous system. We additionally analyzed the goals and types of personalized treatment using predictive biomarkers, and the management of various types of solid organ malignancies using predictive biomarkers and their relative efficacies so far in the clinical settings.

Combining Molecular and Traditional Prognostic Factors: A Holistic Approach to Breast Cancer Prognostication

Breast cancer is a heterogeneous disease with various morphologies and molecular features, and it is the second leading cause of cancer death in women in developed countries. According to the literature, we currently lack both prognostic biomarkers and therapeutic targets. The most important prognostic factors are disease stage and Nottingham grade. We conducted a retrospective analysis involving 273 patients with BC who underwent neoadjuvant therapy before proceeding to curative surgical treatment between 1 January 2014 and 31 December 2023. Pathological procedures were conducted at the Department of Pathology, Emergency County Hospital of Targu Mureș, Romania. A statistical analysis was performed. Regarding the relationship between Nottingham grade and Ki67, grade I was associated with a Ki67 of less than 14. The relationship between tumor grade and luminal was similar (p = 0.0001): Grade I was associated with luminal A. Regarding TNM stage, it was statistically significantly correlated with TILs (p = 0.01) and RCB (p = 0.0001). Stages III and IV were associated with a high RCB and poor prognosis. Regarding the prognostic value, Nottingham grade 3 and TNM stages III and IV were correlated with low overall survival and disease-free survival, with poor prognosis, and, among the molecular variables, RCB played the most important prognostic role.

An update on the molecular mechanisms of ZFAS1 as a prognostic, diagnostic, or therapeutic biomarker in cancers

Zinc finger antisense 1 (ZFAS1), a newly discovered long noncoding RNA, is expressed in various tissues and organs and has been introduced an oncogenic gene in human malignancies. In various cancers, ZFAS1 regulates apoptosis, cell proliferation, the cell cycle, migration, translation, rRNA processing, and spliceosomal snRNP assembly; targets signaling cascades; and interacts with transcription factors via binding to key proteins and miRNAs, with conflicting findings on its effect on these processes. ZFAS1 is elevated in different types of cancer, like colorectal, colon, osteosarcoma, and gastric cancer. Considering the ZFAS1 expression pattern, it also has the potential to be a diagnostic or prognostic marker in various cancers. The current review discusses the mode of action of ZFAS1 in various human cancers and its regulation function related to chemoresistance comprehensively, as well as the potential role of ZFAS1 as an effective and noninvasive cancer-specific biomarker in tumor diagnosis, prognosis, and treatment. We expected that the current review could fill the current scientific gaps in the ZFAS1-related cancer causative mechanisms and improve available biomarkers.

Multiomics approach towards characterization of tumor cell plasticity and its significance in precision and personalized medicine

Cellular plasticity refers to the ability of cells to change their identity or behavior, which can be advantageous in some cases (e.g., tissue regeneration) but detrimental in others (e.g., cancer metastasis). With a better understanding of cellular plasticity, the complexity of cancer cells, their heterogeneity, and their role in metastasis is being unraveled. The plasticity of the cells could also prove as a nemesis to their characterization. In this review, we have attempted to highlight the possibilities and benefits of using multiomics approach in characterizing the plastic nature of cancer cells. There is a need to integrate fragmented evidence at different levels of cellular organization (DNA, RNA, protein, metabolite, epigenetics, etc.) to facilitate the characterization of different forms of plasticity and cell types. We have discussed the role of cellular plasticity in generating intra-tumor heterogeneity. Different omics level evidence is being provided to highlight the variety of molecular determinants discovered using different techniques. Attempts have been made to integrate some of this information to provide a quantitative assessment and scoring of the plastic nature of the cells. However, there is a huge gap in our understanding of mechanisms that lead to the observed heterogeneity. Understanding of these mechanism(s) is necessary for finding targets for early detection and effective therapeutic interventions in metastasis. Targeting cellular plasticity is akin to neutralizing a moving target. Along with the advancements in precision and personalized medicine, these efforts may translate into better clinical outcomes for cancer patients, especially in metastatic stages.

Breast cancer stem cells as novel biomarkers

Breast cancer is the most common cancer and the leading cause of mortality worldwide. Despite advancements in detection and treatment, it remains a major cause of cancer-related deaths in women. Breast cancer stem cells (BCSCs) are a crucial group of cells responsible for carcinogenesis, metastasis, medication resistance, and tumor recurrence. Identifying and understanding their molecular pathways is essential for developing effective breast cancer therapy. BCSCs are responsible for tumor genesis, development, metastasis, treatment resistance, and recurrence. Biomarkers are essential tools for identifying high-risk patients, improving diagnostic accuracy, developing follow-up programs, assessing treatment susceptibility, and predicting prognostic outcomes. Stem cell intervention therapy can provide specialized tools for precision therapy. Biomarker analysis in cancer patients is crucial to identify cells associated with disease progression and post-therapeutic relapse. However, negative post-therapeutic impacts can enhance cancer stemness by boosting BCSCs plasticity phenotypes, activating stemness pathways in non-BCSCs, and promoting senescence escape, leading to tumor relapse and metastasis. Despite the advancements in precision medicine, challenges persist in identifying stem cell markers, limiting the number of eligible patients for treatment. The diversity of biomedical research hinders the development of individualization-based preventative, monitoring, and treatment strategies, especially in oncology. Integrating and interpreting clinical and scientific data remains challenging. The development of stem cell-related indicators could significantly improve disease precision, enabling stem cell-targeted therapy and personalized treatment plans, although BCSCs are promising for breast cancer treatment optimization, serving as biomarkers for current therapy modalities. This summary discusses recent advancements in breast cancer stem cell research, including biomarkers, identification methods, molecular mechanisms, and tools for studying their biological origin and lineage development for precision medicine.

Salivary and serum levels of lactate dehydrogenase in oral submucous fibrosis: A meta-analysis

BACKGROUND

The occurrence of oral submucous fibrosis (OSF) is often accompanied by an increase in lactate dehydrogenase (LDH) levels. In this meta-analysis, we compared the salivary and serum levels of LDH levels between OSF patients and controls.

MATERIAL AND METHODS

A comprehensive search was conducted in PubMed, Embase, Web of Science, and Cochrane Library from the establishment of the database to June 2023, and the quality of the studies was checked by the Newcastle-Ottawa Quality Assessment scale. The mean difference (MD) and 95% confidence interval (CI) were calculated using RevMan 5.4 software.

RESULTS

A total of 28 studies were retrieved from the database, and we included 5 studies in this meta-analysis. The salivary LDH level of OSF patients was higher than healthy controls (MD: 423.10 pg/L 95%CI: 276.42-569.77 pg/mL, P < .00001), the serum LDH level of OSF patients was also higher than that of healthy controls (MD: 226.20 pg/mL, 95%CI: 147.71-304.69 pg/mL, P < .00001).

CONCLUSIONS

This meta-analysis showed that salivary and serum LDH levels were higher in OSF patients than in healthy controls, suggesting that LDH may be a potential biomarker for OSF.

A multiparameter liquid biopsy approach allows to track melanoma dynamics and identify early treatment resistance

Melanoma heterogeneity is a hurdle in metastatic disease management. Although the advent of targeted therapy has significantly improved patient outcomes, the occurrence of resistance makes monitoring of the tumor genetic landscape mandatory. Liquid biopsy could represent an important biomarker for the real-time tracing of disease evolution. Thus, we aimed to correlate liquid biopsy dynamics with treatment response and progression by devising a multiplatform approach applied to longitudinal melanoma patient monitoring. We conceived an approach that exploits Next Generation Sequencing (NGS) and droplet digital PCR, as well as the FDA-cleared platform CellSearch, to analyze circulating tumor DNA (ctDNA) trend and circulating melanoma cell (CMC) count, together with their customized genetic and copy number variation analysis. The approach was applied to 17 stage IV melanoma patients treated with BRAF/MEK inhibitors, followed for up to 28 months. BRAF mutations were detected in the plasma of 82% of patients. Single nucleotide variants known or suspected to confer resistance were identified in 70% of patients. Moreover, the amount of ctDNA, both at baseline and during response, correlated with the type and duration of the response itself, and the CMC count was confirmed to be a prognostic biomarker. This work provides proof of principle of the power of this approach and paves the way for a validation study aimed at evaluating early ctDNA-guided treatment decisions in stage IV melanoma. The NGS-based molecular profile complemented the analysis of ctDNA trend and, together with CMC analysis, revealed to be useful in capturing tumor evolution.

Identification of biomarkers for the early detection of non-small cell lung cancer: a systematic review and meta-analysis

Lung cancer (LC) causes few symptoms in the earliest stages, leading to one of the highest mortality rates among cancers. Low-dose computerised tomography (LDCT) is used to screen high-risk individuals, reducing the mortality rate by 20%. However, LDCT results in a high number of false positives and is associated with unnecessary follow-up and cost. Biomarkers with high sensitivities and specificities could assist in the early detection of LC, especially in patients with high-risk features. Carcinoembryonic antigen (CEA), cytokeratin 19 fragments and cancer antigen 125 have been found to be highly expressed during the later stages of LC but have low sensitivity in the earliest stages. We determined the best biomarkers for the early diagnosis of LC, using a systematic review of eight databases. We identified 98 articles that focussed on the identification and assessment of diagnostic biomarkers and achieved a pooled area under curve of 0.85 (95% CI 0.82-0.088), indicating that the diagnostic performance of these biomarkers when combined was excellent. Of the studies, 30 focussed on single/antigen panels, 22 on autoantibodies, 31 on miRNA and RNA panels, and 15 suggested the use of circulating DNA combined with CEA or neuron-specific enolase (NSE) for early LC detection. Verification of blood biomarkers with high sensitivities (Ciz1, exoGCC2, ITGA2B), high specificities (CYFR21-1, antiHE4, OPNV) or both (HSP90α, CEA) along with miR-15b and miR-27b/miR-21 from sputum may improve early LC detection. Further assessment is needed using appropriate sample sizes, control groups that include patients with non-malignant conditions, and standardised cut-off levels for each biomarker.

A Bayesian survival treed hazards model using latent Gaussian processes

Survival models are used to analyze time-to-event data in a variety of disciplines. Proportional hazard models provide interpretable parameter estimates, but proportional hazard assumptions are not always appropriate. Non-parametric models are more flexible but often lack a clear inferential framework. We propose a Bayesian treed hazards partition model that is both flexible and inferential. Inference is obtained through the posterior tree structure and flexibility is preserved by modeling the log-hazard function in each partition using a latent Gaussian process. An efficient reversible jump Markov chain Monte Carlo algorithm is accomplished by marginalizing the parameters in each partition element via a Laplace approximation. Consistency properties for the estimator are established. The method can be used to help determine subgroups as well as prognostic and/or predictive biomarkers in time-to-event data. The method is compared with some existing methods on simulated data and a liver cirrhosis dataset.

An update on methods for detection of prognostic and predictive biomarkers in melanoma

The approval of immunotherapy for stage II-IV melanoma has underscored the need for improved immune-based predictive and prognostic biomarkers. For resectable stage II-III patients, adjuvant immunotherapy has proven clinical benefit, yet many patients experience significant adverse events and may not require therapy. In the metastatic setting, single agent immunotherapy cures many patients but, in some cases, more intensive combination therapies against specific molecular targets are required. Therefore, the establishment of additional biomarkers to determine a patient's disease outcome (i.e., prognostic) or response to treatment (i.e., predictive) is of utmost importance. Multiple methods ranging from gene expression profiling of bulk tissue, to spatial transcriptomics of single cells and artificial intelligence-based image analysis have been utilized to better characterize the immune microenvironment in melanoma to provide novel predictive and prognostic biomarkers. In this review, we will highlight the different techniques currently under investigation for the detection of prognostic and predictive immune biomarkers in melanoma.

Building a large gene expression-cancer knowledge base with limited human annotations

Cancer prevention is one of the most pressing challenges that public health needs to face. In this regard, data-driven research is central to assist medical solutions targeting cancer. To fully harness the power of data-driven research, it is imperative to have well-organized machine-readable facts into a knowledge base (KB). Motivated by this urgent need, we introduce the Collaborative Oriented Relation Extraction (CORE) system for building KBs with limited manual annotations. CORE is based on the combination of distant supervision and active learning paradigms and offers a seamless, transparent, modular architecture equipped for large-scale processing. We focus on precision medicine and build the largest KB on 'fine-grained' gene expression-cancer associations-a key to complement and validate experimental data for cancer research. We show the robustness of CORE and discuss the usefulness of the provided KB. Database URL https://zenodo.org/record/7577127.

A novel adenosine signalling-based prognostic signature in gastric cancer and its association with cancer immune features and immunotherapy response

Reliable prognostic signatures that can reflect the intrinsic characteristics of gastric cancer (GC) are still rare. Here, we developed an adenosine-based prognostic signature and explored its association with the tumour immune in GC patients, aiming at confirming the prognostic value of adenosine-related genes and guiding the GC risk stratification and immunotherapeutic response prediction. We collected adenosine pathway-related genes from STRING websites and manual searching. We enrolled the The Cancer Genome Atlas cohort and four gene expression omnibus cohorts of GC for generating and validating the adenosine pathway-based signature using the Cox regression method. Gene expression in the signature was verified using polymerase chain reaction. We also performed gene set enrichment analysis, immune infiltration assessment and immunotherapy response prediction based on this signature. Our study resulted in a six-gene adenosine signature (GNAS, CXCR4, PPP1R1B, ADCY6, NT5E and NOS3) for risk stratification of GC prognosis, with the highest area under the receiver operating characteristic curve up to 0.767 for predicting 10-year overall survival (OS). In the training cohort, patients with signature-defined high risk had significantly poorer OS than those with low risk (p < .001). Multivariate analysis identified the signature as an independent prognostic factor (hazard ratio 2.863, 95% confidence interval [1.871-4.381], p < .001). These findings were confirmed in four independent cohorts. Expression detection showed that all signature genes were upregulated in both GC tissues and cell lines. Further analysis revealed that the signature-defined high-risk patients were characterised by immunosuppressive states and associated with a poor immunotherapy response. In conclusion, the adenosine pathway-based signature represents a promising risk stratification tool for GC in guiding individualised prognostication and immunotherapy.

Genetic contribution of caspase-8 variants and haplotypes to breast cancer risk and prognosis: a case-control study in Iran

PURPOSE

Multiple genome-wide and candidate-gene association studies have been conducted to search for common risk variants of breast cancer. Recent large meta-analyses and consolidating evidence have highlighted the role of the caspase-8 gene in breast cancer pathogenesis. Therefore, this study aimed to identify common variations and haplotypes associated with risk and overall survival of breast cancer with respect to underlying susceptibility variants in the CASP8 gene region in a group of the Iranian population.

METHODS

In a case-control study with a total of 1008 samples (455 cases and 553 controls), genotyping of 12 candidate polymorphisms, consisting of rs3834129, rs2037815, rs7608692, rs12990906, rs3769821, rs6435074, rs3754934, rs3817578, rs10931936, rs1045485, rs1045487, and rs13113, were performed using PCR-based methods, including ARMS-PCR, AS-PCR, RFLP-PCR, HRM-PCR, and TaqMan-PCR.

RESULTS

rs3834129, rs3754934, rs12990906, and rs10931936 were associated with the risk and overall survival of breast cancer. Several haplotypes were also identified an associated with a higher risk of breast cancer, including a three-SNP haplotype rs3817578-rs10931936-rs1045485 [p < 0.001, OR = 1.78(1.32-2.41)]. rs3754934-C allele showed an association with a lower risk of death in all patients [p = 0.022; HR = 0.46(0.23-0.89)] and in the hormone-receptor-positive group [p = 0.038; HR = 0.37(0.14-0.95)], as well as CC genotype in the hormone-receptor-positive group [p = 0.002; HR = 0.09(0.02-0.43)].

CONCLUSION

The present study suggests a diagnostic and prognostic role of CASP8 gene variations in breast cancer. The risky haplotypes are likely to have one or more underlying breast cancer susceptibility alleles. Understanding the mode of action of these alleles will aid individual-level risk prediction. It also may help identify at-risk patients to provide them with better surveillance.

Multimodal data fusion for cancer biomarker discovery with deep learning

Technological advances now make it possible to study a patient from multiple angles with high-dimensional, high-throughput multi-scale biomedical data. In oncology, massive amounts of data are being generated ranging from molecular, histopathology, radiology to clinical records. The introduction of deep learning has significantly advanced the analysis of biomedical data. However, most approaches focus on single data modalities leading to slow progress in methods to integrate complementary data types. Development of effective multimodal fusion approaches is becoming increasingly important as a single modality might not be consistent and sufficient to capture the heterogeneity of complex diseases to tailor medical care and improve personalised medicine. Many initiatives now focus on integrating these disparate modalities to unravel the biological processes involved in multifactorial diseases such as cancer. However, many obstacles remain, including lack of usable data as well as methods for clinical validation and interpretation. Here, we cover these current challenges and reflect on opportunities through deep learning to tackle data sparsity and scarcity, multimodal interpretability, and standardisation of datasets.

A Boolean-based machine learning framework identifies predictive biomarkers of HSP90-targeted therapy response in prostate cancer

Precision medicine has emerged as an important paradigm in oncology, driven by the significant heterogeneity of individual patients' tumour. A key prerequisite for effective implementation of precision oncology is the development of companion biomarkers that can predict response to anti-cancer therapies and guide patient selection for clinical trials and/or treatment. However, reliable predictive biomarkers are currently lacking for many anti-cancer therapies, hampering their clinical application. Here, we developed a novel machine learning-based framework to derive predictive multi-gene biomarker panels and associated expression signatures that accurately predict cancer drug sensitivity. We demonstrated the power of the approach by applying it to identify response biomarker panels for an Hsp90-based therapy in prostate cancer, using proteomic data profiled from prostate cancer patient-derived explants. Our approach employs a rational feature section strategy to maximise model performance, and innovatively utilizes Boolean algebra methods to derive specific expression signatures of the marker proteins. Given suitable data for model training, the approach is also applicable to other cancer drug agents in different tumour settings.

Cancer Immunotherapy: The Checkpoint between Chronic Colitis and Colorectal Cancer

Inflammatory Bowel Disease (IBD) is a group of diseases that cause intestinal inflammation and lesions because of an abnormal immune response to host gut microflora. Corticosteroids, anti-inflammatories, and antibiotics are often used to reduce non-specific inflammation and relapse rates; however, such treatments are ineffective over time. Patients with chronic colitis are more susceptible to developing colorectal cancer, especially those with a longer duration of colitis. There is often a limit in using chemotherapy due to side effects, leading to reduced efficacy, leaving an urgent need to improve treatments and identify new therapeutic targets. Cancer immunotherapy has made significant advances in recent years and is mainly categorized as cancer vaccines, adoptive cellular immunotherapy, or immune checkpoint blockade therapies. Checkpoint markers are expressed on cancer cells to evade the immune system, and as a result checkpoint inhibitors have transformed cancer treatment in the last 5-10 years. Immune checkpoint inhibitors have produced long-lasting clinical responses in both single and combination therapies. Winnie mice are a viable model of spontaneous chronic colitis with immune responses like human IBD. Determining the expression levels of checkpoint markers in tissues from these mice will provide insights into disease initiation, progression, and cancer. Such information will lead to identification of novel checkpoint markers and the development of treatments with or without immune checkpoint inhibitors or vaccines to slow or stop disease progression.

Cohesin RAD21 Gene Promoter Methylation Correlated with Better Prognosis in Breast Cancer Patients

RAD21 plays multiple roles in numerous cancers. In breast cancer (BC), a high level of RAD21 correlates with poor disease outcomes and resistance to chemotherapy. However, data regarding RAD21 promoter methylation in BC tissue and its correlation with clinical outcomes in patients with BC remain limited. Here, we investigated the clinicopathological features associated with the methylation status of RAD21 in BC to figure out its possible role in pathogenesis and the formation of breast carcinogenesis. The methylation status of the RAD21 gene was significantly associated with better clinical outcomes in patients with BC.

The Legend of ATP: From Origin of Life to Precision Medicine

Adenosine triphosphate (ATP) may be the most important biological small molecule. Since it was discovered in 1929, ATP has been regarded as life’s energy reservoir. However, this compound means more to life. Its legend starts at the dawn of life and lasts to this day. ATP must be the basic component of ancient ribozymes and may facilitate the origin of structured proteins. In the existing organisms, ATP continues to construct ribonucleic acid (RNA) and work as a protein cofactor. ATP also functions as a biological hydrotrope, which may keep macromolecules soluble in the primitive environment and can regulate phase separation in modern cells. These functions are involved in the pathogenesis of aging-related diseases and breast cancer, providing clues to discovering anti-aging agents and precision medicine tactics for breast cancer.

Breast Cancer; Discovery of Novel Diagnostic Biomarkers, Drug Resistance, and Therapeutic Implications

Breast cancer is the second most reported cancer in women with high mortality causing millions of cancer-related deaths annually. Early detection of breast cancer intensifies the struggle towards discovering, developing, and optimizing diagnostic biomarkers that can improve its prognosis and therapeutic outcomes. Breast cancer-associated biomarkers comprise macromolecules, such as nucleic acid (DNA/RNA), proteins, and intact cells. Advancements in molecular technologies have identified all types of biomarkers that are exclusively studied for diagnostic, prognostic, drug resistance, and therapeutic implications. Identifying biomarkers may solve the problem of drug resistance which is a challenging obstacle in breast cancer treatment. Dysregulation of non-coding RNAs including circular RNAs (circRNAs) and microRNAs (miRNAs) initiates and progresses breast cancer. The circulating multiple miRNA profiles promise better diagnostic and prognostic performance and sensitivity than individual miRNAs. The high stability and existence of circRNAs in body fluids make them a promising new diagnostic biomarker. Many therapeutic-based novels targeting agents have been identified, including ESR1 mutation (DNA mutations), Oligonucleotide analogs and antagonists (miRNA), poly (ADP-ribose) polymerase (PARP) in BRCA mutations, CDK4/6 (cell cycle regulating factor initiates tumor progression), Androgen receptor (a steroid hormone receptor), that have entered clinical validation procedure. In this review, we summarize the role of novel breast cancer diagnostic biomarkers, drug resistance, and therapeutic implications for breast cancer.

Immunohistochemical Biomarkers in Oral Submucous Fibrosis - A Scoping Review

INTRODUCTION

This scoping review was done to study the immunohistochemical biomarkers involved in pathogenesis and malignant transformation (MT) of oral submucous fibrosis (OSF), in literature published from 2010 to 2021.

METHOD

The protocol was adapted from the Joanna Briggs Institute Reviewer's Manual (2017, and reported according to the PRISMA guidelines for Scoping Reviews (PRISMA-ScR).

RESULTS

Eighty-six studies included in this review reported 84 immunohistochemical (IHC) biomarkers in OSF: 9 epithelial markers, 29 connective tissue markers, 22 proliferative markers, and 24 other biomarkers that are transcription factors, cancer stem cell markers, cell signaling markers, proteins, and enzymes. The commonly reported IHC biomarkers were alpha-smooth muscle actin (α-SMA) and E-cadherin (7 articles each) followed by vascular endothelial growth factor (VEGF) and CD34 (6 articles each), p53, p63 and Ki67 (5 articles each). α-SMA, Ki67, CD105, and hTERT were significantly increased in oral squamous cell carcinoma arising in a background of OSF (OSCC-OSF) compared to OSF and normal subjects.

CONCLUSION

The identified surrogate IHC biomarkers reported in OSF in this scoping review require validation with long-term prospective studies to facilitate early diagnosis, for use in risk assessment, and plan appropriate treatment for OSF in clinical practice.

ProCanBio: A Database of Manually Curated Biomarkers for Prostate Cancer

Prostate cancer (PCa) is the second lethal malignancy in men worldwide. In the past, numerous research groups investigated the omics profiles of patients and scrutinized biomarkers for the diagnosis and prognosis of PCa. However, information related to the biomarkers is widely scattered across numerous resources in complex textual format, which poses hindrance to understand the tumorigenesis of this malignancy and scrutinization of robust signature. To create a comprehensive resource, we collected all the relevant literature on PCa biomarkers from the PubMed. We scrutinize the extensive information about each biomarker from a total of 412 unique research articles. Each entry of the database incorporates PubMed ID, biomarker name, biomarker type, biomolecule, source, subjects, validation status, and performance measures such as sensitivity, specificity, and hazard ratio (HR). In this study, we present ProCanBio, a manually curated database that maintains detailed data on 2053 entries of potential PCa biomarkers obtained from 412 publications in user-friendly tabular format. Among them are 766 protein-based, 507 RNA-based, 157 genomic mutations, 260 miRNA-based, and 122 metabolites-based biomarkers. To explore the information in the resource, a web-based interactive platform was developed with searching and browsing facilities. To the best of the authors' knowledge, there is no resource that can consolidate the information contained in all the published literature. Besides this, ProCanBio is freely available and is compatible with most web browsers and devices. Eventually, we anticipate this resource will be highly useful for the research community involved in the area of prostate malignancy.

Peripherally-driven myeloid NFkB and IFN/ISG responses predict malignancy risk, survival, and immunotherapy regime in ovarian cancer

Background

Tumors can influence peripheral immune macroenvironment, thereby creating opportunities for non-invasive serum/plasma immunobiomarkers for immunostratification and immunotherapy designing. However, current approaches for immunobiomarkers’ detection are largely quantitative, which is unreliable for assessing functional peripheral immunodynamics of patients with cancer. Hence, we aimed to design a functional biomarker modality for capturing peripheral immune signaling in patients with cancer for reliable immunostratification.

Methods

We used a data-driven in silico framework, integrating existing tumor/blood bulk-RNAseq or single-cell (sc)RNAseq datasets of patients with cancer, to inform the design of an innovative serum-screening modality, that is, serum-functional immunodynamic status (sFIS) assay. Next, we pursued proof-of-concept analyses via multiparametric serum profiling of patients with ovarian cancer (OV) with sFIS assay combined with Luminex (cytokines/soluble immune checkpoints), CA125-antigen detection, and whole-blood immune cell counts. Here, sFIS assay’s ability to determine survival benefit or malignancy risk was validated in a discovery (n=32) and/or validation (n=699) patient cohorts. Lastly, we used an orthotopic murine metastatic OV model, with anti-OV therapy selection via in silico drug–target screening and murine serum screening via sFIS assay, to assess suitable in vivo immunotherapy options.

Results

In silico data-driven framework predicted that peripheral immunodynamics of patients with cancer might be best captured via analyzing myeloid nuclear factor kappa-light-chain enhancer of activated B cells (NFκB) signaling and interferon-stimulated genes' (ISG) responses. This helped in conceptualization of an ‘in sitro’ (in vitro+in situ) sFIS assay, where human myeloid cells were exposed to patients’ serum in vitro, to assess serum-induced (si)-NFκB or interferon (IFN)/ISG responses (as active signaling reporter activity) within them, thereby ‘mimicking’ patients’ in situ immunodynamic status. Multiparametric serum profiling of patients with OV established that sFIS assay can: decode peripheral immunology (by indicating higher enrichment of si-NFκB over si-IFN/ISG responses), estimate survival trends (si-NFκB or si-IFN/ISG responses associating with negative or positive prognosis, respectively), and coestimate malignancy risk (relative to benign/borderline ovarian lesions). Biologically, we documented dominance of pro-tumorigenic, myeloid si-NFκB response^HIGHsi-IFN/ISG response^LOW inflammation in periphery of patients with OV. Finally, in an orthotopic murine metastatic OV model, sFIS assay predicted the higher capacity of chemo-immunotherapy (paclitaxel–carboplatin plus anti-TNF antibody combination) in achieving a pro-immunogenic peripheral milieu (si-IFN/ISG response^HIGHsi-NFκB response^LOW), which aligned with high antitumor efficacy.

Conclusions

We established sFIS assay as a novel biomarker resource for serum screening in patients with OV to evaluate peripheral immunodynamics, patient survival trends and malignancy risk, and to design preclinical chemo-immunotherapy strategies.

Integrating Molecular Biomarker Inputs Into Development and Use of Clinical Cancer Therapeutics

Biomarkers can contribute to clinical cancer therapeutics at multiple points along the patient’s diagnostic and treatment course. Diagnostic biomarkers can screen or classify patients, while prognostic biomarkers predict their survival. Biomarkers can also predict treatment efficacy or toxicity and are increasingly important in development of novel cancer therapeutics. Strategies for biomarker identification have involved large-scale genomic and proteomic analyses. Pathway-specific biomarkers are already in use to assess the potential efficacy of immunotherapy and targeted cancer therapies. Judicious application of machine learning techniques can identify disease-relevant features from large data sets and improve predictive models. The future of biomarkers likely involves increasing utilization of liquid biopsy and multiple samplings to better understand tumor heterogeneity and identify drug resistance.

Multiplexed magnetic beads-assisted amperometric bioplatforms for global detection of methylations in nucleic acids

This work reports the first electrochemical bioplatform developed for the multidetection of 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC) in DNA, DNA N6-methyladenine (6mA) and RNA N6-methyladenosine (m6A) methylations at global level. Direct competitive immunoassays were implemented on the surface of magnetic beads (MBs) and optimized for the single amperometric determination of different targets varying in length, sequence and number of methylations on screen-printed carbon electrodes. After evaluating the sensitivity and selectivity of such determinations and the confirmation of no cross-reactivity, a multiplexed disposable platform allowing the simultaneous determination of the mentioned four methylation events in only 45 min has been prepared. The multiplexed bioplatform was successfully applied to the determination of m6A in cellular total RNA and of 5-mC, 5-hmC and 6mA in genomic DNA extracted from tissues. The developed bioplatform showed its usefulness to discriminate the aggressiveness of cancerous cells and between healthy and tumor tissues of colorectal cancer patients.

A call to action: molecular pathology in Brazil

Background

Adoption of molecular pathology in Brazil is currently very limited. Of note, there are no programs for training new molecular pathologists in the country; thus, documents compiling nationally applicable information on molecular pathology are few.

Methods

A selected panel of Brazilian experts in fields related to molecular pathology were provided with a series of relevant questions to address prior to the multi-day conference. Within this conference, each narrative was discussed and edited by the entire group, through numerous drafts and rounds of discussion until a consensus was achieved.

Results

The panel proposes specific and realistic recommendations for implementing molecular pathology in cancer care in Brazil. In creating these recommendations, the authors strived to address all barriers to the widespread use and impediments to access mentioned previously within this manuscript.

Conclusion

This manuscript provides a review of molecular pathology principles as well as the current state of molecular pathology in Brazil. Additionally, the panel proposes practical and actionable recommendations for the implementation of molecular pathology throughout the country in order to increase awareness of the importance molecular pathology in Brazil.

Using machine learning approaches for multi-omics data analysis: A review

With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from various omics sources such as genetics, proteomics, and metabolomics can be integrated to unravel the intricate working of systems biology using machine learning-based predictive algorithms. Machine learning methods offer novel techniques to integrate and analyse the various omics data enabling the discovery of new biomarkers. These biomarkers have the potential to help in accurate disease prediction, patient stratification and delivery of precision medicine. This review paper explores different integrative machine learning methods which have been used to provide an in-depth understanding of biological systems during normal physiological functioning and in the presence of a disease. It provides insight and recommendations for interdisciplinary professionals who envisage employing machine learning skills in multi-omics studies.

Pharmacogenomic-based personalized medicine: Multistakeholder perspectives on implementational drivers and barriers in the Canadian healthcare system

Pharmacogenomics (PGx)-based personalized medicine (PM) is increasingly utilized to guide treatment decisions for many drug-disease combinations. Notably, London Health Sciences Centre (LHSC) has pioneered a PGx program that has become a staple for London-based specialists. Although implementational studies have been conducted in other jurisdictions, the Canadian healthcare system is understudied. Herein, the multistakeholder perspectives on implementational drivers and barriers are elucidated. Using a mixed-method qualitative model, key stakeholders, and patients from LHSC's PGx-based PM clinic were interviewed and surveyed, respectively. Interview transcripts were thematically analyzed in a stepwise process of customer profiling, value mapping, and business model canvasing. Value for LHSC located specialist users of PGx was driven by the quick turnaround time, independence of the PGx clinic, and the quality of information. Engagement of external specialists was only limited by access and awareness, whereas other healthcare nonusers were limited by education and applicability. The major determinant of successful adoption at novel sites were institutional champions. Patients valued and approved of the service, expressed a general willingness to pay, but often traveled far to receive genotyping. This paper discusses the critical pillars of education, awareness, advocacy, and efficiency required to address implementation barriers to healthcare service innovation in Canada. Further adoption of PGx practices into Canadian hospitals is an important factor for advancing system-level changes in care delivery, patient experiences, and outcomes. The findings in this paper can help inform efforts to advance clinical PGx practices, but also the potential adoption and implementation of other innovative healthcare service solutions.

The Role of Epigenetics in the Progression of Clear Cell Renal Cell Carcinoma and the Basis for Future Epigenetic Treatments

Simple Summary

The accumulated evidence on the role of epigenetic markers of prognosis in clear cell renal cell carcinoma (ccRCC) is reviewed, as well as state of the art on epigenetic treatments for this malignancy. Several epigenetic markers are likely candidates for clinical use, but still have not passed the test of prospective validation. Development of epigenetic therapies, either alone or in combination with tyrosine-kinase inhibitors of immune-checkpoint inhibitors, are still in their infancy.

Abstract

Clear cell renal cell carcinoma (ccRCC) is curable when diagnosed at an early stage, but when disease is non-confined it is the urologic cancer with worst prognosis. Antiangiogenic treatment and immune checkpoint inhibition therapy constitute a very promising combined therapy for advanced and metastatic disease. Many exploratory studies have identified epigenetic markers based on DNA methylation, histone modification, and ncRNA expression that epigenetically regulate gene expression in ccRCC. Additionally, epigenetic modifiers genes have been proposed as promising biomarkers for ccRCC. We review and discuss the current understanding of how epigenetic changes determine the main molecular pathways of ccRCC initiation and progression, and also its clinical implications. Despite the extensive research performed, candidate epigenetic biomarkers are not used in clinical practice for several reasons. However, the accumulated body of evidence of developing epigenetically-based biomarkers will likely allow the identification of ccRCC at a higher risk of progression. That will facilitate the establishment of firmer therapeutic decisions in a changing landscape and also monitor active surveillance in the aging population. What is more, a better knowledge of the activities of chromatin modifiers may serve to develop new therapeutic opportunities. Interesting clinical trials on epigenetic treatments for ccRCC associated with well established antiangiogenic treatments and immune checkpoint inhibitors are revisited.

Elevated peripheral absolute monocyte count related to clinicopathological features and poor prognosis in solid tumors: Systematic review, meta-analysis, and meta-regression

BACKGROUND

Absolute monocyte count (AMC) is often used to be assessed in cancer follow-up, which has regained interest as a potential prognostic indicator in many solid tumors, though not consistently or comprehensively. In the present study, we set out to perform a comprehensive meta-analysis of all available data regarding the prognostic significance of AMC in solid tumors. We also evaluated the association between AMC and clinical features in solid tumors.

METHODS

A hazard ratio (HR) and corresponding 95% confidence interval (CI) or a p value (p) from eligible studies were extracted and subsequently pooled analyzed. Subgroup analyses and meta-regression analyses were conducted according to the confounders of included studies. In addition, the relationships between AMC and clinical characteristics were also explored in the meta-analysis.

RESULTS

Overall, ninety-three articles comprising 104 studies with 32229 patients were finally included. The results showed that elevated AMC was associated with worse overall survival (OS) (HR = 1.615; 95% CI: 1.475-1.768; p < 0.001), disease-free survival (DFS) (HR:1.488; 95% CI: 1.357-1.633; p < 0.001), progressive-free survival (PFS) (HR: 1.533; 95% CI: 1.342-1.751; p < 0.001) and cancer-specific survival (CSS) (HR: 1.585; 95% CI: 1.253-2.006; p < 0.001) in non-hematological tumors. Subgroup analyses according to each confounder further proved the consistent prognostic value of AMC in solid tumor outcomes. Moreover, elevated AMC was more likely to be observed in male group and patients with smoking history, and associated with longer tumor length and advanced T stage.

CONCLUSION

In short, the meta-analysis found that elevated AMC might indicate poor long-term outcomes in non-hematologic cancers, thus AMC may be a valuable marker in the prognosis for patients with solid tumors.

A New Horizon of Liquid Biopsy in Thymic Epithelial Tumors: The Potential Utility of Circulating Cell-Free DNA

BACKGROUND

Thymic epithelial tumors (TETs) are rare thoracic malignancies, commonly divided into two different histopathological entities, thymoma (T) and thymic carcinoma (TC). To date, there are no specific biomarkers for monitoring the biological course of these rare tumors. We carried out a single center study aiming at the detection of circulating cell-free DNA (ccfDNA) and the correlation of its levels with metastatic dissemination and histological subtype in patients with TETs.

METHODS

From July 2018 to January 2020, 5-ml blood samples from 26 patients with advanced TET (aTET) (11 patients with TC and 15 patients with T) and from six patients with completely resected TET (cr-TET), were prospectively obtained before the initiation of systemic therapy. Blood samples from 10 healthy donors were used as control. The QIAamp MinElute ccfDNA Kits was used for ccfDNA isolation from plasma; real-time PCR was used for cfDNA quantification.

RESULTS

We found significantly higher ccfDNA amount in patients with T and TC compared to controls, with median ccfDNA level of 3.3 ng/µl, 11.4 ng/µl and 25.6 ng/µl, for healthy donors, T and TC patients, respectively (p<0.001). No significant difference was found between cr-TET and controls (p = 0.175). ccfDNA concentrations were higher in metastatic (M1a and M1b) compared to non-metastatic (M0) TETs (25.6 ng/µl versus 7.2 ng/µl; p= 0.037). No significant correlation was found either between ccfDNA and disease stage, according to both the Masaoka-Koga (p= 0.854) and the TNM 8th edition staging systems (p = 0.66), or between ccfDNA levels and overall tumor burden, estimated according RECIST 1.1 criteria (r = 0.07, p = 0.725).

CONCLUSIONS

To the best of our knowledge, this is the first study that prospectively explores detection and quantification of ccfDNA in TETs. Higher baseline cfDNA levels have been observed in both advanced T and TC comparing to the control group.

Recent Discoveries of Macromolecule- and Cell-Based Biomarkers and Therapeutic Implications in Breast Cancer

Breast cancer is the most commonly diagnosed cancer type and the leading cause of cancer-related mortality in women worldwide. Breast cancer is fairly heterogeneous and reveals six molecular subtypes: luminal A, luminal B, HER2+, basal-like subtype (ER−, PR−, and HER2−), normal breast-like, and claudin-low. Breast cancer screening and early diagnosis play critical roles in improving therapeutic outcomes and prognosis. Mammography is currently the main commercially available detection method for breast cancer; however, it has numerous limitations. Therefore, reliable noninvasive diagnostic and prognostic biomarkers are required. Biomarkers used in cancer range from macromolecules, such as DNA, RNA, and proteins, to whole cells. Biomarkers for cancer risk, diagnosis, proliferation, metastasis, drug resistance, and prognosis have been identified in breast cancer. In addition, there is currently a greater demand for personalized or precise treatments; moreover, the identification of novel biomarkers to further the development of new drugs is urgently needed. In this review, we summarize and focus on the recent discoveries of promising macromolecules and cell-based biomarkers for the diagnosis and prognosis of breast cancer and provide implications for therapeutic strategies.

Variants of Genes Involved in Metabolism of Folate Among Patients with Breast Cancer: Association of TYMS 3R Allele with Susceptibility to Breast Cancer and Metastasis

Background & Objective:

Breast cancer (BC) is known to be the most prevalent cancer among women. One-carbon metabolism disturbance might play an important role in the etiology of BC. The present study aimed to investigate the thymidylate synthase (TYMS), 5-methyltetrahydrofolate-homocysteine methyltransferase (MTR), and methionine synthase reductase (MTRR) variants as good candidates for studying the role of genetic variants of folate metabolizing enzymes in the risk of BC.

Methods:

The present case-control study includes 100 BC patients and 141 healthy females. The TYMS 2R/3R (rs34743033), MTR c.2756A>G (rs1805087), and MTRR c.66A>G (rs1801394) variants were detected by polymerase chain reaction (PCR), PCR-restriction fragment length polymorphism (RFLP), and a designed amplification-refractory mutation system (ARMS) method, respectively.

Results:

The 3R allele of TYMS enhanced the risk of BC by 2.84-fold (P<0.001). In the presence of TYMS 3R/3R, compared to TYMS 2R/3R, there was a trend toward enhancing the risk of metastasis by 4.15-fold (95% CI: 0.96-17.85, P=0.055). The frequencies of MTR c.2756A>G and MTRR c.66A>G variants were not significantly different among patients and controls.

Conclusion:

We observed that the TYMS 3R is a risk allele for susceptibility to BC and this allele may increase the risk of metastasis in BC patients. .

A Meta-Analysis of Proteomic Blood Markers of Colorectal Cancer

BACKGROUND

Early diagnosis will significantly improve the survival rate of colorectal cancer (CRC); however, the existing methods for CRC screening were either invasive or inefficient. There is an emergency need for novel markers in CRC's early diagnosis. Serum proteomics has gained great potential in discovering novel markers, providing markers that reflect the early stage of cancer and prognosis prediction of CRC. In this paper, the results of proteomics of CRC studies were summarized through a meta-analysis in order to obtain the diagnostic efficiency of novel markers.

METHODS

A systematic search on bibliographic databases was performed to collect the studies that explore blood-based markers for CRC applying proteomics. The detection and validation methods, as well as the specificity and sensitivity of the biomarkers in these studies, were evaluated. Newcastle- Ottawa Scale (NOS) case-control studies version was used for quality assessment of included studies.

RESULTS

Thirty-four studies were selected from 751 studies, in which markers detected by proteomics were summarized. In total, fifty-nine proteins were classified according to their biological function. The sensitivity, specificity, or AUC varied among these markers. Among them, Mammalian STE20-like protein kinase 1/ Serine threonine kinase 4 (MST1/STK4), S100 calcium-binding protein A9 (S100A9), and Tissue inhibitor of metalloproteinases 1 (TIMP1) were suitable for effect sizes merging, and their diagnostic efficiencies were recalculated after merging. MST1/STK4 obtained a sensitivity of 68% and a specificity of 78%. S100A9 achieved a sensitivity of 72%, a specificity of 83%, and an AUC of 0.88. TIMP1 obtained a sensitivity of 42%, a specificity of 88%, and an AUC of 0.71.

CONCLUSION

MST1/STK4, S100A9, and TIMP1 showed excellent performance for CRC detection. Several other markers also presented optimized diagnostic efficacy for CRC early detection, but further verification is still needed before they are suitable for clinical use. The discovering of more efficient markers will benefit CRC treatment.

Identification of a Five-Gene Signature for Predicting Survival in Malignant Pleural Mesothelioma Patients

Malignant pleural mesothelioma (MPM), predominantly caused by asbestos exposure, is a highly aggressive cancer with poor prognosis. The staging systems currently used in clinics is inadequate in evaluating the prognosis of MPM. In this study, a five-gene signature was developed and enrolled into a prognostic risk score model by LASSO Cox regression analysis based on two expression profiling datasets (GSE2549 and GSE51024) from Gene Expression Omnibus (GEO). The five-gene signature was further validated using the Cancer Genome Atlas (TCGA) MPM dataset. Univariate and multivariate Cox analyses proved that the five-gene signature was an independent prognostic factor for MPM. The signature remained statistically significant upon stratification by Brigham stage, AJCC stage, gender, tumor size, and lymph node status. Time-dependent receiver operating characteristic (ROC) curve indicated good performance of our model in predicting 1- and 2-years overall survival in MPM patients. The C-index was 0.784 for GSE2549 and 0.753 for the TCGA dataset showing moderate predictive accuracy of our model. Furthermore, Gene Set Enrichment Analysis suggested that the five-gene signature was related to pathways resulting in MPM tumor progression. Together, we have established a five-gene signature significantly associated with prognosis in MPM patients. Hence, the five-genes signature may serve as a potentially useful prognostic tool for MPM patients.

Pretreatment albumin-to-alkaline phosphatase ratio as a prognostic indicator in solid cancers: A meta-analysis with trial sequential analysis

BACKGROUND

Albumin-to-alkaline phosphatase ratio (AAPR), a novel and economic serum biomarker, is associated with survival in patients with cancer. This study aimed to evaluate the potential role of AAPR as a prognostic indicator of solid cancers.

METHODS

This meta-analysis with trial sequential analysis of retrospective studies was designed to investigate the relationship between AAPR and overall survival (OS) in solid cancers. The meta-analysis included 5951 patients from 20 cohorts. The main predictor variable was AAPR, and the main outcome was OS. Statistical tests were performed using Stata 12.0, Revman 5.3, and R 3.6.1.

RESULTS

Compared to patients with a lower AAPR, those with a higher AAPR had a better OS (hazard ratio [HR]: 0.50; 95% confidence interval [CI]: 0.43-0.58; p < 0.001). Subgroup analysis by tumor type indicated that a higher AAPR was associated with a better OS in non-small cell lung cancer (HR: 0.45; 95% CI: 0.26-0.78; p < 0.001), small cell lung cancer (HR: 0.60; 95% CI: 0.44-0.82; p < 0.001), hepatocellular carcinoma (HR: 0.49; 95% CI: 0.34-0.69; p < 0.001), pancreatic ductal adenocarcinoma (HR: 0.47; 95% CI: 0.31-0.71; p < 0.001), and nasopharyngeal carcinoma (HR: 0.42; 95% CI: 0.21-0.85; p = 0.016).

CONCLUSION

Pretreatment AAPR may be a useful prognostic indicator in solid cancers.

Why the hype - What are microRNAs and why do they provide unique investigative, diagnostic, and therapeutic opportunities in veterinary medicine?

MicroRNAs (miRNAs) are small, non-coding RNAs that regulate gene expression by inhibiting translation or inducing transcript degradation. MiRNAs act as fine-tuning factors that affect the expression of up to 60% of all mammalian protein coding genes. In contrast to proteins, there is widespread conservation of miRNA sequences across species. This conservation strongly suggests that miRNAs appeared early in evolution and have retained their functional importance. Cross-species conservation provides advantages when compiling candidate markers for health and disease compared to protein-based discoveries. This broad utility is accompanied by the emergence of inexpensive sequencing protocols for the identification of all RNAs in a sample (including miRNAs). With the use of miRNA mimics and antagonists, unique research questions can be answered in biological systems with 'cause and effect' methodology. MiRNAs are readily detectable in blood making them attractive candidates as biomarkers for disease. Here, we review their utility as biomarkers and their potential as therapeutic agents or targets to combat disease.

Guideline-recommended incorporation of biomarker testing results in metastatic colorectal cancer therapy

Aim: To describe pharmacogenomic tumor testing among patients with metastatic colorectal cancer. Methods: This was a retrospective study of patients with metastatic colorectal cancer diagnosed between 1 January 2014 and 30 June 2018. Patients were assessed for pharmacogenomic testing and appropriateness of chemotherapy use. Results: Overall, 112/167 (67.1%) patients had at least one of the three recommended pharmacogenomic tests and 41/167 (24.6%) had all tests. Twenty-four patients were treated with cetuximab with 8/167 (4.7%) identified as being treated with a RAS variant (n = 3) or incomplete testing (n = 5); thus, not in accordance with guidelines. Conclusion: Uptake of testing was variable but increased over time; however, a small proportion of patients received cetuximab with a variant or not all recommended tests being performed.

Clinical Relevance of Liquid Biopsy in Melanoma and Merkel Cell Carcinoma

Melanoma and Merkel cell carcinoma are two aggressive skin malignancies with high disease-related mortality and increasing incidence rates. Currently, invasive tumor tissue biopsy is the gold standard for their diagnosis, and no reliable easily accessible biomarker is available to monitor patients with melanoma or Merkel cell carcinoma during the disease course. In these last years, liquid biopsy has emerged as a candidate approach to overcome this limit and to identify biomarkers for early cancer diagnosis, prognosis, therapeutic response prediction, and patient follow-up. Liquid biopsy is a blood-based non-invasive procedure that allows the sequential analysis of circulating tumor cells, circulating cell-free and tumor DNA, and extracellular vesicles. These innovative biosources show similar features as the primary tumor from where they originated and represent an alternative to invasive solid tumor biopsy. In this review, the biology and technical challenges linked to the detection and analysis of the different circulating candidate biomarkers for melanoma and Merkel cell carcinoma are discussed as well as their clinical relevance.

Identification of Pan-Cancer Prognostic Biomarkers Through Integration of Multi-Omics Data

Prognostic biomarkers dedicating to treat cancer are very difficult to identify. Although high-throughput sequencing technology allows us to mine prognostic biomarkers much deeper by analyzing omics data, there is lack of effective methods to comprehensively utilize multi-omics data. In this work, we integrated multi-omics data [DNA methylation (DM), gene expression (GE), somatic copy number alternation, and microRNA expression (ME)] and proposed a method to rank genes by desiring a “Score.” Applying the method, cancer-specific prognostic biomarkers for 13 cancers were obtained. The prognostic powers of the biomarkers were further assessed by C-indexes (ranged from 0.76 to 0.96). Moreover, by comparing the 13 survival-related gene lists, seven genes (SLK, API5, BTBD2, PTAR1, VPS37A, EIF2B1, and ZRANB1) were found to be associated with prognosis in a variety of cancers. In particular, SLK was more likely to be cancer-related due to its high missense mutation rate and associated with cell adhesion. Furthermore, after network analysis, EPRS, HNRNPA2B1, BPTF, LRRK1, and PUM1 were demonstrated to have a broad correlation with cancers. In summary, our method has a better integration of multi-omics data that can be extended to the researches of other diseases. And the prognostic biomarkers had a better prognostic power than previous methods. Our results could provide a reference for translational medicine researchers and clinicians.

Applications and strategies in nanodiagnosis and nanotherapy in lung cancer

Lung cancer is the second most common cancer and the leading cause of death in both men and women in the world. Lung cancer is heterogeneous in nature and diagnosis is often at an advanced stage as it develops silently in the lung and is frequently associated with high mortality rates. Despite the advances made in understanding the biology of lung cancer, progress in early diagnosis, cancer therapy modalities and considering the mechanisms of drug resistance, the prognosis and outcome still remains low for many patients. Nanotechnology is one of the fastest growing areas of research that can solve many biological problems such as cancer. A growing number of therapies based on using nanoparticles (NPs) have successfully entered the clinic to treat pain, cancer, and infectious diseases. Recent progress in nanotechnology has been encouraging and directed to developing novel nanoparticles that can be one step ahead of the cancer reducing the possibility of multi-drug resistance. Nanomedicine using NPs is continuingly impacting cancer diagnosis and treatment. Chemotherapy is often associated with limited targeting to the tumor, side effects and low solubility that leads to insufficient drug reaching the tumor. Overcoming these drawbacks of chemotherapy by equipping NPs with theranostic capability which is leading to the development of novel strategies. This review provides a synopsis of current progress in theranostic applications for lung cancer diagnosis and therapy using NPs including liposome, polymeric NPs, quantum dots, gold NPs, dendrimers, carbon nanotubes and magnetic NPs.

Electrochemical biosensing to move forward in cancer epigenetics and metastasis: A review

Early detection and effective treatment are crucial to reduce the physical, emotional, and financial pressure exerted by growing cancer burden on individuals, families, communities, and health systems. Currently, it is clear that the accurate analysis of emerging cancer epigenetic and metastatic-related biomarkers at different molecular levels is envisaged as an exceptional solution for early and reliable diagnosis and the improvement of therapy efficiency through personalized treatments. Within this field, electrochemical biosensing has demonstrated to be competitive over other emerging and currently used methodologies for the determination of these biomarkers accomplishing the premises of user-friendly, multiplexing ability, simplicity, reduced costs and decentralized analysis, demanded by clinical oncology, thus priming electrochemical biosensors to spark a diagnostic revolution for cancer prediction and eradication. This review article critically discusses the main characteristics, opportunities and versatility exhibited by electrochemical biosensing, through highlighting representative examples published during the last two years, for the reliable determination of these emerging biomarkers, with great diagnostic, predictive and prognostic potential. Special attention is paid on electrochemical affinity biosensors developed for the single or multiplexed determination of methylation events, non-coding RNAs, ctDNA features and metastasis-related protein biomarkers both in liquid and solid biopsies of cancer patients. The main challenges to which further work must be addressed and the impact of these advances should have in the clinical acceptance of these emerging biomarkers are also discussed which decisively will contribute to understand the molecular basis involved in the epigenetics and metastasis of cancer and to apply more efficient personalized therapies.

Wearable Lab on Body: Combining Sensing of Biochemical and Digital Markers in a Wearable Device

Wearables are being widely researched for monitoring individual's health and wellbeing. Current generation wearable devices sense an individual's physiological data such as heart rate, respiration, electrodermal activity, and EEG, but lack in sensing their biological counterparts, which drive the majority of individual's physiological signals. On the other hand, biosensors for detecting biochemical markers are currently limited to one-time use, are non-continuous and don't provide flexibility in choosing which biomarker they sense. We present "wearable lab on body", a platform for active continuous monitoring of human biomarkers from the biological fluid. Our platform contains both digital sensors such as IMU for activity recognition, as well as an automated system for continuous sampling of biomarkers from saliva by leveraging already existing paper-based biochemical sensors. The platform could aid with longitudinal studies of biomarkers and early diagnosis of diseases.

Drug Resistance Biomarkers and Their Clinical Applications in Childhood Acute Lymphoblastic Leukemia

Biomarkers are biological molecules found in body fluids or tissues, which can be considered as indications of a normal or abnormal process, or of a condition or disease. There are various types of biomarkers based on their application and molecular alterations. Treatment-sensitivity or drug resistance biomarkers include prognostic and predictive molecules with utmost importance in selecting appropriate treatment protocols and improving survival rates. Acute lymphoblastic leukemia (ALL) is the most prevalent hematological malignancy diagnosed in children with nearly 80% cure rate. Despite the favorable survival rates of childhood ALL (chALL), resistance to chemotherapeutic agents and, as a consequence, a dismal prognosis develops in a significant number of patients. Therefore, there are urgent needs to have robust, sensitive, and disease-specific molecular prognostic and predictive biomarkers, which could allow better risk classification and then better clinical results. In this article, we review the currently known drug resistance biomarkers, including somatic or germ line nucleic acids, epigenetic alterations, protein expressions and metabolic variations. Moreover, biomarkers with potential clinical applications are discussed.

Prognostic role of microRNAs in breast cancer: A systematic review

MicroRNAs (miRNAs) have been found to play an important role in breast cancer, functioning either as potential oncogenes or tumor suppressor genes, but their role in the prognosis of patients remains unclear. The aim of the present review study is to highlight recent preclinical and clinical studies performed on both circulating and tissue-specific miRNAs and their potential role as prognostic markers in breast cancer. We systematically searched the PubMed database to explore the prognostic value of miRNAs in breast cancer. After performing the literature search and review, 117 eligible studies were identified. We found that 110 aberrantly expressed miRNAs have been associated with prognosis in breast cancer. In conclusion, the collective data presented in this review indicate that miRNAs could serve as novel prognostic tools in breast cancer, while the clinical application of these findings has yet to be verified.