Innovative methods for biomarker discovery in the evaluation and development of cancer precision therapies

In situ generated CdTe quantum dot-encapsulated hafnium polymer membrane to boost electrochemiluminescence analysis of tumor biomarkers

Exploring the construction of an interface with bright emission, fabulous stability, and good function to develop high-performance electrochemiluminescence (ECL) biosensors for tumor biomarkers is in high demand but faces a huge challenge. Herein, we report an oriented attachment and in situ self-assembling strategy for one-step fabrication of CdTe QD-encapsulated Hf polymer membrane onto an ITO surface (Hf-CP/CdTe QDs/APS/ITO). Hf-CP/CdTe QDs/APS/ITO is fascinating with excellent stability, high ECL emission, and specific adsorption toward ssDNA against dsDNA and mononucleotides (mNs). These interesting properties make it an ideal interface to rationally develop an immobilization-free ECL biosensor for cancer antigen 125 (CA125), used as a proof-of-concept analyte, based on target-aptamer recognition-promoted exonuclease III (Exo III)-assisted digestion. The recognition of ON by CA125 leads to the formation of CA125@ON, which hybridizes with Fc-ssDNA to switch Exo III-assisted digestion, decreasing the amount of Fc groups anchored onto the electrode's surface and blocking electron transfer. As compared to the case where CA125 was absent, significant ECL emission recovery is determined and relies on CA125 concentration. Thus, highly sensitive analysis of CA125 against other biomarkers was achieved with a limit of detection down to 2.57 pg/mL. We envision this work will provide a new path to develop ECL biosensors with excellent properties, which shows great potential for early and accurate diagnosis of cancer.

A Review on Graphene Analytical Sensors for Biomarker-based Detection of Cancer

The engineering of nanoscale materials has broadened the scope of nanotechnology in a restricted functional system. Today, significant priority is given to immediate health diagnosis and monitoring tools for point-of-care testing and patient care. Graphene, as a one-atom carbon compound, has the potential to detect cancer biomarkers and its derivatives. The atom-wide graphene layer specialises in physicochemical characteristics, such as improved electrical and thermal conductivity, optical transparency, and increased chemical and mechanical strength, thus making it the best material for cancer biomarker detection. The outstanding mechanical, electrical, electrochemical, and optical properties of two-dimensional graphene can fulfil the scientific goal of any biosensor development, which is to develop a more compact and portable point-of-care device for quick and early cancer diagnosis. The bio-functionalisation of recognised biomarkers can be improved by oxygenated graphene layers and their composites. The significance of graphene that gleans its missing data for its high expertise to be evaluated, including the variety in surface modification and analytical reports. This review provides critical insights into graphene to inspire research that would address the current and remaining hurdles in cancer diagnosis.

Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases

The use of biomarkers as early warning systems in the evaluation of disease risk has increased markedly in the last decade. Biomarkers are indicators of typical biological processes, pathogenic processes, or pharmacological reactions to therapy. The application and identification of biomarkers in the medical and clinical fields have an enormous impact on society. In this review, we discuss the history, various definitions, classifications, characteristics, and discovery of biomarkers. Furthermore, the potential application of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the last decade are reviewed. The present review aims to inspire readers to explore new avenues in biomarker research and development.

Apelin, a Circulating Biomarker in Cancer Evaluation: A Systematic Review

Apelin is a promising biomarker for the detection and prognosis of cancer. This review aims to synthesize current knowledge on associations of circulating apelin with cancer, illustrate knowledge gaps, and discuss future research. Following PRISMA guidelines, CINAHL, EMBASE, and PubMed were searched using terms “cancer AND apelin” between 2011 and 2021, full text, and English language. Inclusion criteria: measured circulating apelin in adults 18 years or older with cancer, and observational, cross-sectional, longitudinal, case–control, cohort, quasi-experimental, or randomized control trials. Excluded were studies with animal models, tissue samples only, secondary data analyses, systematic reviews, literature reviews, grey literature, and conference abstracts. 16 articles were included. There were significant variations in measurement methods between studies. Comparison of circulating apelin between cases and controls and associations of circulating apelin with clinicopathological characteristics were inconsistent. Variations in results suggest that the relationship between circulating apelin and cancer differs among cancer types. Differences in measurement methods between studies highlight the need for consistency in future research to draw meaningful conclusions. Future research should seek to standardize methods of detecting circulating apelin and examine its associations with specific cancer types to determine what role that circulating apelin may play in cancer development and progression.

Non-coding RNA in cancer drug resistance: Underlying mechanisms and clinical applications

Cancer is one of the most frequently diagnosed malignant diseases worldwide, posing a serious, long-term threat to patients’ health and life. Systemic chemotherapy remains the first-line therapeutic approach for recurrent or metastatic cancer patients after surgery, with the potential to effectively extend patient survival. However, the development of drug resistance seriously limits the clinical efficiency of chemotherapy and ultimately results in treatment failure and patient death. A large number of studies have shown that non-coding RNAs (ncRNAs), particularly microRNAs, long non-coding RNAs, and circular RNAs, are widely involved in the regulation of cancer drug resistance. Their dysregulation contributes to the development of cancer drug resistance by modulating the expression of specific target genes involved in cellular apoptosis, autophagy, drug efflux, epithelial-to-mesenchymal transition (EMT), and cancer stem cells (CSCs). Moreover, some ncRNAs also possess great potential as efficient, specific biomarkers in diagnosis and prognosis as well as therapeutic targets in cancer patients. In this review, we summarize the recent findings on the emerging role and underlying mechanisms of ncRNAs involved in cancer drug resistance and focus on their clinical applications as biomarkers and therapeutic targets in cancer treatment. This information will be of great benefit to early diagnosis and prognostic assessments of cancer as well as the development of ncRNA-based therapeutic strategies for cancer patients.

Diagnostic and prognostic role of TFF3, Romo-1, NF-кB and SFRP4 as biomarkers for endometrial and ovarian cancers: a prospective observational translational study

Purpose

This study aimed to evaluate trefoil factor 3 (TFF3), secreted frizzled-related protein 4 (sFRP4), reactive oxygen species modulator 1 (Romo1) and nuclear factor kappa B (NF-κB) as diagnostic and prognostic markers of endometrial cancer (EC) and ovarian cancer (OC).

Methods

Thirty-one patients with EC and 30 patients with OC undergone surgical treatment were enrolled together with 30 healthy controls in a prospective study. Commercial ELISA kits determined serum TFF-3, Romo-1, NF-кB and sFRP-4 concentrations.

Results

Serum TFF-3, Romo-1 and NF-кB levels were significantly higher in patients with EC and OC than those without cancer. Regarding EC, none of the serum biomarkers differs significantly between endometrial and non-endometrioid endometrial carcinomas. Mean serum TFF-3 and NF-кB levels were significantly higher in advanced stages. Increased serum levels of TFF-3 and NF-кB were found in those with a higher grade of the disease. Regarding OC, none of the serum biomarkers differed significantly among histological subtypes. Significantly increased serum levels of NF-кB were observed in patients with advanced-stage OC than those with stage I and II diseases. No difference in serum biomarker levels was found between those who had a recurrence and those who had not. The sensibility and specificity of these four biomarkers in discriminating EC and OC from the control group showed encouraging values, although no one reached 70%.

Conclusions

TFF-3, Romo-1, NF-кB and SFRP4 could represent new diagnostic and prognostic markers for OC and EC. Further studies are needed to validate our results.

Recent Progress on the Synergistic Antitumor Effect of a Borneol-Modified Nanocarrier Drug Delivery System

Borneol, a traditional Chinese medicine, can enhance therapeutic efficacy by guiding the active ingredients to the target site. Reportedly, borneol improves the penetration capacity of the nasal, cornea, transdermal, intestinal, and blood-brain barriers. Although nanotechnology dramatically changed the face of oncology by targeting tumor sites, the efficiency of nanoparticles delivered to tumor sites is very low, with only 0.7% of the total particles delivered. Thus, based on the penetration ability and the inhibition drug efflux of borneol, it was expected to increase the targeting and detention efficacy of drugs into tumor sites in nanocarriers with borneol modification. Borneol modified nanocarriers used to improve drug-targeting has become a research focus in recent years, but few studies in this area, especially in the antitumor application. Hence, this review summarizes the recent development of nanocarriers with borneol modification. We focus on the updated works of improving therapeutic efficacy, reducing toxicity, inhibiting tumor metastasis, reversing multidrug resistance, and enhancing brain targeting to expand their application and provide a reference for further exploration of targeting drug delivery systems for solid tumor treatment.

Cystatin-B Negatively Regulates the Malignant Characteristics of Oral Squamous Cell Carcinoma Possibly Via the Epithelium Proliferation/Differentiation Program

Disturbance in the proteolytic process is one of the malignant signs of tumors. Proteolysis is highly orchestrated by cysteine cathepsin and its inhibitors. Cystatin-B (CSTB) is a general cysteine cathepsin inhibitor that prevents cysteine cathepsin from leaking from lysosomes and causing inappropriate proteolysis. Our study found that CSTB was downregulated in both oral squamous cell carcinoma (OSCC) tissues and cells compared with normal controls. Immunohistochemical analysis showed that CSTB was mainly distributed in the epithelial structure of OSCC tissues, and its expression intensity was related to the grade classification. A correlation analysis between CSTB and clinical prognosis was performed using gene expression data and clinical information acquired from The Cancer Genome Atlas (TCGA) database. Patients with lower expression levels of CSTB had shorter disease-free survival times and poorer clinicopathological features (e.g., lymph node metastases, perineural invasion, low degree of differentiation, and advanced tumor stage). OSCC cell models overexpressing CSTB were constructed to assess the effects of CSTB on malignant biological behaviors and upregulation of CSTB inhibited cell proliferation, migration, and invasion in vitro. Weighted gene correlation network analysis (WGCNA) and gene set enrichment analysis (GSEA) were performed based on the TCGA data to explore potential mechanisms, and CSTB appeared to correlate with squamous epithelial proliferation-differentiation processes, such as epidermal cell differentiation and keratinization. Moreover, in WGCNA, the gene module most associated with CSTB expression (i.e., the brown module) was also the one most associated with grade classification. Upregulation of CSTB promoted the expression levels of markers (LOR, IVL, KRT5/14, and KRT1/10), reflecting a tendency for differentiation and keratinization in vitro. Gene expression profile data of the overexpressed CSTB cell line were obtained by RNA sequencing (RNA-seq) technology. By comparing the GSEA enrichment results of RNA-seq data (from the OSCC models overexpressing CSTB) and existing public database data, three gene sets (i.e., apical junction, G2/M checkpoint, etc.) and six pathways (e.g., NOTCH signaling pathway, glycosaminoglycan degradation, mismatch repair, etc.) were enriched in the data from both sources. Overall, our study shows that CSTB is downregulated in OSCC and might regulate the malignant characteristics of OSCC via the epithelial proliferation/differentiation program.

Transcriptomic profiling in canines and humans reveals cancer specific gene modules and biological mechanisms common to both species

Understanding relationships between spontaneous cancer in companion (pet) canines and humans can facilitate biomarker and drug development in both species. Towards this end we developed an experimental-bioinformatic protocol that analyzes canine transcriptomics data in the context of existing human data to evaluate comparative relevance of canine to human cancer. We used this protocol to characterize five canine cancers: melanoma, osteosarcoma, pulmonary carcinoma, B- and T-cell lymphoma, in 60 dogs. We applied an unsupervised, iterative clustering method that yielded five co-expression modules and found that each cancer exhibited a unique module expression profile. We constructed cancer models based on the co-expression modules and used the models to successfully classify the canine data. These canine-derived models also successfully classified human tumors representing the same cancers, indicating shared cancer biology between canines and humans. Annotation of the module genes identified cancer specific pathways relevant to cells-of-origin and tumor biology. For example, annotations associated with melanin production (PMEL, GPNMB, and BACE2), synthesis of bone material (COL5A2, COL6A3, and COL12A1), synthesis of pulmonary surfactant (CTSH, LPCAT1, and NAPSA), ribosomal proteins (RPL8, RPS7, and RPLP0), and epigenetic regulation (EDEM1, PTK2B, and JAK1) were unique to melanoma, osteosarcoma, pulmonary carcinoma, B- and T-cell lymphoma, respectively. In total, 152 biomarker candidates were selected from highly expressing modules for each cancer type. Many of these biomarker candidates are under-explored as drug discovery targets and warrant further study. The demonstrated transferability of classification models from canines to humans enforces the idea that tumor biology, biomarker targets, and associated therapeutics, discovered in canines, may translate to human medicine.

Divining responder populations from survival data

BACKGROUND

Biomarkers that predict treatment response are the foundation of precision medicine in clinical decision-making and have the potential to significantly improve the efficiency of clinical trials. Such biomarkers may be identified before clinical testing but many trials enroll unselected populations. We hypothesized that time-varying treatment effects in unselected trials may result from identifiable responder subpopulations that may have associated biomarkers.

MATERIALS AND METHODS

We first simulated scenarios of clinical trials with biomarker populations of varying prevalence and prognostic and predictive associations to illustrate the impact of subgroup-specific effects on overall population estimates. To show a real-world example of time-dependent treatment effects resulting from a prognostic and predictive biomarker, we re-analyzed data from a published clinical trial (RTOG, Radiation Therapy Oncology Group, 9402). We then demonstrated a quantitative framework to fit survival data from clinical trials using statistical models incorporating known estimates of biomarker prevalence and prognostic value to prioritize predictive biomarker hypotheses.

RESULTS

Our simulation studies demonstrate how biomarker subgroups that are both predictive and prognostic can manifest as time-dependent treatment effects in overall populations. RTOG 9402 provides a representative example where 1p/19q co-deletion and IDH mutation biomarker-specific effects led to time-varying treatment effects and a considerable deviation from proportional hazards in the overall trial population. Finally, using biomarker data from The Cancer Genome Atlas, we were able to generate statistical models that correctly identified and prioritized a commonly used biomarker through retrospective analysis of published clinical trial data.

CONCLUSIONS

Biomarkers that are both predictive and prognostic can result in characteristic changes in survival results. Retrospectively analyzing survival data from clinical trials may highlight potential indications for which an underlying predictive biomarker may be found.

Patient specific circulating tumor DNA fingerprints to monitor treatment response across multiple tumors

BACKGROUND

Circulating tumor DNA (ctDNA) offers a convenient way to monitor tumor progression and treatment response. Because tumor mutational profiles are highly variable from person to person, a fixed content panel may be insufficient to track treatment response in all patients.

METHODS

We design ctDNA fingerprint panels specific to individual patients which are based on whole exome sequencing and target to high frequency clonal population clusters in patients. We test the fingerprint panels in 313 patients who together have eight tumor types (colorectal, hepatocellular, gastric, breast, pancreatic, and esophageal carcinomas and lung cancer and cholangiocarcinoma) and exposed to multiple treatment methods (surgery, chemotherapy, radiotherapy, targeted-drug therapy, immunotherapy, and combinations of them). We also monitor drug-related mutations in the patients using a pre-designed panel with eight hotspot genes.

RESULTS

291 (93.0%) designed fingerprint panels harbor less than ten previously known tumor genes. We detected 7475 ctDNA mutations in 238 (76%) patients and 6196 (96.0%) of the mutations are detected in only one test. Both the level of ctDNA content fraction (CCF) and fold change of CCF (between the definitive and proceeding tests) are highly correlated with clinical outcomes (p-values 1.36e-6 for level and 5.64e-10 for fold change, Kruskal-Wallis test). The CCFs of PD patients are an order of magnitude higher than the CCFs of SD and OR patients (median/mean 2.22%/8.96% for SD, 0.18/0.21% for PD, and 0.31/0.54% for OR; pairwise p-values 7.8e-6 for SD ~ PD, 2.7e-4 for OR ~ PD, and 7.0e-3 for SD ~ OR, Wilcoxon rank sum test). The fold change of CCF distinguishes the patient groups even better, which increases for PD, remains stable for SD, and decreases for OR patients (p-values 0.002, ~ 1, and 0.0001 respectively, Wilcoxon signed-rank test). Eleven drug-related mutations are identified from nine out of the 313 patients.

CONCLUSIONS

The ctDNA fingerprint method improves both specificity and sensitivity of monitoring treatment response across several tumor types. It can identify tumor relapse/recurrence potentially earlier than imaging-based diagnosis. When augmented with tumor hotspot genes, it can track acquired drug-related mutations in patients.

Urinary Volatile Terpenes Analyzed by Gas Chromatography-Mass Spectrometry to Monitor Breast Cancer Treatment Efficacy in Mice

Urinary volatile terpene (VT) levels are significantly altered with induced models of breast cancer in mice. The question arises whether VTs can detect the efficacy of antitumor treatments. BALB/c mice were injected with 4T1.2 murine tumor cells in the mammary pad or iliac artery to model localized breast cancer and induced bone metastasis. The effect of two dopaminergic antitumor agents was tested by conventional histology and altered VT levels. The headspace of urine specimens was analyzed by gas chromatography-mass spectrometry. In the localized model, the statistical significance (p < 0.05) was identified for 26% of VTs, and in the metastasis model, 19% of VTs. The authors discovered separate VT panels classifying localized/control [area under the curve (AUC) = 1.0] and metastasis/control (AUC = 0.98). Treatment samples were tested using these panels, which showed that mice treated with either agent were statistically significantly different from cancer samples, which is consistent with conventional analysis.