A personalized approach to cancer treatment: how biomarkers can help

Learning chemical sensitivity reveals mechanisms of cellular response

Chemical probes interrogate disease mechanisms at the molecular level by linking genetic changes to observable traits. However, comprehensive chemical screens in diverse biological models are impractical. To address this challenge, we develop ChemProbe, a model that predicts cellular sensitivity to hundreds of molecular probes and drugs by learning to combine transcriptomes and chemical structures. Using ChemProbe, we infer the chemical sensitivity of cancer cell lines and tumor samples and analyze how the model makes predictions. We retrospectively evaluate drug response predictions for precision breast cancer treatment and prospectively validate chemical sensitivity predictions in new cellular models, including a genetically modified cell line. Our model interpretation analysis identifies transcriptome features reflecting compound targets and protein network modules, identifying genes that drive ferroptosis. ChemProbe is an interpretable in silico screening tool that allows researchers to measure cellular response to diverse compounds, facilitating research into molecular mechanisms of chemical sensitivity.

Soluble platelet selectin and platelets in COVID-19: a multifaceted connection

The COVID-19 pandemic has brought to light the intricate relationship between platelets, soluble platelet selectin (sP-selectin), and disease pathogenesis. Platelets, traditionally recognized for their role in hemostasis, have emerged as key contributors to the immunothrombotic complications observed in COVID-19 patients. Concurrently, elevated levels of sP-selectin, indicative of platelet activation and endothelial injury, have been consistently identified in COVID-19 patients and have shown associations with disease severity and adverse outcomes. This multifaceted connection underscores the pivotal role of platelets and sP-selectin in orchestrating thromboinflammation, vascular dysfunction, and disease progression in COVID-19. Platelet activation triggers the release of inflammatory mediators and promotes platelet-leukocyte interactions, amplifying the systemic inflammatory response and exacerbating endothelial injury. Additionally, platelet-derived factors contribute to microvascular thrombosis, further exacerbating tissue damage and organ dysfunction in severe COVID-19. Elevated sP-selectin levels serve as biomarkers for disease severity and prognostication, aiding in risk stratification and early identification of patients at higher risk of adverse outcomes. Therapeutic strategies targeting platelet dysfunction and sP-selectin-mediated pathways hold promise in mitigating thromboinflammation and improving outcomes in COVID-19 patients. Antiplatelet agents, platelet inhibitors, and anti-inflammatory therapies represent potential interventions to attenuate platelet activation, inhibit platelet-leukocyte interactions, and alleviate endothelial dysfunction. A comprehensive understanding of the multifaceted connection between platelets, sP-selectin, and COVID-19 pathogenesis offers opportunities for tailored therapeutic approaches aimed at mitigating thromboinflammation and improving patient outcomes in this complex and challenging clinical setting.

IGSF3 tissue expression in squamous cell carcinoma of the oropharynx: a novel tool for prognosis assessment in HPV-related and HPV-unrelated disease

Biomarkers are not broadly used in the management of head and neck cancers (HNCs). Biomarkers have been beneficial in the management of other cancers, however, not in HNCs. Therefore, we observed the immunopositivity of a novel biomarker called immunoglobulin superfamily member 3 (IGSF3) in tumor tissues in HPV-related and HPV-unrelated OPSCC. Two patient cohorts (C1 and C2) from separate time periods were available for this study (total N = 282). Both consisted of OPSCC patients treated at the Helsinki University Hospital (HUS, Helsinki, Finland) during 2000-2016. For HPV determination, HPV mRNA in situ hybridization was used. Immunohistochemistry was used to assess IGSF3 immunopositivity in cancer tissues. Overall survival (OS) was used as endpoint in the statistical analysis. In C1, stronger immunopositivity of IGSF3 in tumor-infiltrating lymphocytes (TILs) correlated with favorable OS (p = 0.005). Stronger IGSF3 immunopositivity in tumor cells (TCs) was associated with HPV negativity (p = 0.017). Stronger IGSF3 immunopositivity in TILs correlated with HPV positivity (p < 0.001). Elevated IGSF3 immunopositivity in TILs associates with HPV-related tumors and may signify favorable prognosis. The immunopositivity of IGSF3 differs between HPV-related and HPV-unrelated OPSCC.

Quantum dots in noninvasive imaging of oral squamous cell carcinomas: A scoping literature review

ABSTRACT

The current scoping review's objective was to outline existing applications, recent breakthroughs, and quantum dots' applicability in imaging of oral squamous cell cancer. Quantum dots are nanometric semiconductor crystals with customizable optical characteristics and intense, stable fluorescence suited for bioimaging and labeling. We used the Preferred reporting items for systematic reviews and meta-analyses (PRISMA) recommendations for conducting our systematic search. An analysis of the properties and applications of quantum dots in noninvasive detection of oral squamous cell cancer is presented in this study, which comprehensively explores the available evidence. Following searches in the databases PubMed, Ovid SP, and Cochrane using the search terms quantum dots AND oral squamous cell cancer, 55 published publications were chosen for this review. The review identified a total of eight papers that met the criteria. In noninvasive detection of oral squamous cell carcinoma, quantum dots have the potential to offer an array of therapeutic and diagnostic applications. Furthermore, quantum dots emit near-infrared and visible light, which is advantageous in biological imaging since it reduces light dispersion and absorption of tissue. The future may see quantum dots become a popular noninvasive imaging technique for oral squamous cell cancer. The number of studies accessible is quite limited, and further research is required.

Effectiveness of Non-pharmacologic Interventions on Device-measured Physical Activity in Adults With Cancer, and Methodology Used for Assessment: A Systematic Review and Meta-analysis

OBJECTIVE

To investigate the effectiveness of different types of interventions aimed at enhancing device-measured physical activity (PA) and summarize the devices and methodologies used to measure PA in adults with cancer.

DATA SOURCES

A systematic review was prospectively registered on PROSPERO (CRD42020199466). The search was conducted in PubMed, The Cochrane Library, EMBASE (via Ovid), and PEDro from 2005 onward.

STUDY SELECTION

Prospective interventional studies (randomized controlled trials [RCTs], non-randomized controlled trials, and single-group trials), that included adults within 12 months from cancer diagnosis, and device-measured PA before and after commencement of an intervention were included. Studies were excluded if PA was measured at a single time point. Two independent reviewers screened 3033 records and 30 articles met the inclusion criteria.

DATA EXTRACTION

Two reviewers independently extracted the data. PEDro scale and GRADE approach were used to assess methodological quality of RCTs and overall quality of evidence, respectively. A meta-analysis of relevant RCTs was conducted.

DATA SYNTHESIS

Thirty studies were identified, mainly including adults with multiple cancer types. Interventions were behavior change interventions (n=15), exercise training (n=13), neuromuscular electrostimulation (n=1), or a nutritional program (n=1). The meta-analysis showed improvements on moderate-to-vigorous intensity PA (MVPA) in the experimental group (8 studies; standardized mean difference (SMD)=0.23; 95% CI 0.06-0.39); with subgroup analysis showing that findings were mainly driven by behavior change interventions (5 studies; SMD=0.23, 95% CI 0.05-0.41). An uncertain effect on sedentary behavior, daily steps, and light intensity PA was found. PA was measured with medical devices and commercial wearables, quality of the methodology was variable.

CONCLUSIONS

Behavior change interventions increased device-derived MVPA in adult cancer patients who underwent the intervention within 12 months of the cancer diagnosis. Various devices and methodologies were used to assess PA, which limits comparisons across the studies.

Learning chemical sensitivity reveals mechanisms of cellular response

Chemical probes interrogate disease mechanisms at the molecular level by linking genetic changes to observable traits. However, comprehensive chemical screens in diverse biological models are impractical. To address this challenge, we developed ChemProbe, a model that predicts cellular sensitivity to hundreds of molecular probes and drugs by learning to combine transcriptomes and chemical structures. Using ChemProbe, we inferred the chemical sensitivity of cancer cell lines and tumor samples and analyzed how the model makes predictions. We retrospectively evaluated drug response predictions for precision breast cancer treatment and prospectively validated chemical sensitivity predictions in new cellular models, including a genetically modified cell line. Our model interpretation analysis identified transcriptome features reflecting compound targets and protein network modules, identifying genes that drive ferroptosis. ChemProbe is an interpretable in silico screening tool that allows researchers to measure cellular response to diverse compounds, facilitating research into molecular mechanisms of chemical sensitivity.

Effects of Semisupervised Exercise Training on Health Outcomes in People With Lung or Head and Neck Cancer: Protocol for a Randomized Controlled Trial

BACKGROUND

Lung or head and neck cancers are known for their high prevalence and mortality rates. Chemotherapy and radiotherapy are usually recommended as cancer treatment for these malignancies; however, they can negatively impact both the physical and mental status of patients. Hence, it is reasonable to consider resistance and aerobic exercise training to prevent these negative health outcomes. Further, several factors prevent patients from attending outpatient exercise training programs, and, therefore, a semisupervised home-based exercise training program may be seen as a well-accepted alternative.

OBJECTIVE

The aim of this study will be to investigate the effects of a semisupervised home-based exercise training program on physical performance, body composition, and self-reported outcomes; changes in the initial cancer treatment dose prescribed; number of hospitalizations at 3, 6, and 9 months; and 12-month survival in people with primary lung or head and neck cancer.

METHODS

Participants will be randomly allocated to the training group (TG) or control group (CG). The TG will undergo semisupervised home-based resistance and aerobic exercise training throughout their cancer treatment. The resistance training will be performed using elastic bands (TheraBand) twice a week. The aerobic training (ie, brisk walk) will be performed for at least 20 minutes per day outdoors. The equipment and tools used during the training sessions will be provided. This intervention will start the week before treatment commencement, will be performed throughout the duration of the treatment, and will continue for 2 weeks after treatment completion. The CG will undergo usual care (ie, cancer treatment with no formal exercise prescription). Assessments will take place 2 weeks before the beginning of the usual cancer treatment and 2 weeks after treatment completion. The measures of physical function (peripheral muscle strength, functional exercise capacity, and physical activity), body composition, and self-reported outcomes (symptoms of anxiety and depression, health-related quality of life, and symptoms related to the disease and treatment) will be collected. We will report on any change in the initial cancer treatment dose prescribed; number of hospitalizations at 3, 6, and 9 months; and 12-month survival.

RESULTS

In February 2021, the clinical trial registration was approved. Recruitment and data collection for the trial are ongoing (as of April 2023, 20 participants had already been randomized), and findings of this study are likely to be published late in 2024.

CONCLUSIONS

This exercise training as a complementary treatment for patients with cancer is likely to promote positive effects on the health outcomes assessed, over and above any change in the CG, and prevent the reduction of initial cancer treatment dose prescribed. If these positive effects are shown, they will likely impact long-term outcomes such as hospitalizations and 12-month survival.

TRIAL REGISTRATION

Brazilian Clinical Trials Registry (ReBEC) RBR-5cyvzh9; https://ensaiosclinicos.gov.br/rg/RBR-5cyvzh9.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/43547.

Clinical significance of circulating tumor cell (CTC)-specific microRNA (miRNA) in breast cancer

As a noninvasive method, circulating tumor cell (CTC) provides ideal liquid biopsy specimens for early cancer screening and diagnosis. CTCs detection in breast cancer is correlated with patient prognosis such as disease-free survival (DFS) and overall survival (OS). Besides, accumulating evidence supported that CTCs count may be indicator for chemotherapy response as well. The functional roles of microRNA (miRNA) in breast cancer have been well-recognized for the last few years. Due to its stability in circulation, numerous studies have proven that circulating miRNA may serve as promising diagnostic and prognostic biomarkers in breast cancer. The potential ability of miRNAs in disease screening, staging or even molecular subtype classification makes them valuable tools for early breast cancer patients. It would be of great significance to characterize the miRNA expression profile in CTCs, which could provide reliable biological information originated from tumor. However, some issues need to be addressed before the utility of CTC-specific miRNAs in clinical setting. Taken together, we believe that CTC-specific miRNA detection will be trend for early breast cancer screening, diagnosis and treatment monitor in near future.

Unconventional protein post-translational modifications: the helmsmen in breast cancer

Breast cancer is the most prevalent malignant tumor and a leading cause of mortality among females worldwide. The tumorigenesis and progression of breast cancer involve complex pathophysiological processes, which may be mediated by post-translational modifications (PTMs) of proteins, stimulated by various genes and signaling pathways. Studies into PTMs have long been dominated by the investigation of protein phosphorylation and histone epigenetic modifications. However, with great advances in proteomic techniques, several other PTMs, such as acetylation, glycosylation, sumoylation, methylation, ubiquitination, citrullination, and palmitoylation have been confirmed in breast cancer. Nevertheless, the mechanisms, effects, and inhibitors of these unconventional PTMs (particularly, the non-histone modifications other than phosphorylation) received comparatively little attention. Therefore, in this review, we illustrate the functions of these PTMs and highlight their impact on the oncogenesis and progression of breast cancer. Identification of novel potential therapeutic drugs targeting PTMs and development of biological markers for the detection of breast cancer would be significantly valuable for the efficient selection of therapeutic regimens and prediction of disease prognosis in patients with breast cancer.

Predictive and Prognostic Significance of mRNA Expression and DNA Copies Aberrations of ERCC1, RRM1, TOP1, TOP2A, TUBB3, TYMS, and GSTP1 Genes in Patients with Breast Cancer

Increasingly, many researchers are focusing on the sensitivity in breast tumors (BC) to certain chemotherapy drugs and have personalized their research based on the assessment of this sensitivity. One such personalized approach is to assess the chemotherapy’s gene expression, as well as aberrations in the number of DNA copies—deletions and amplifications with the ability to have a significant effect on the gene’s activity. Thus, the aim of this work was to study the predictive and prognostic significance of the expression and chromosomal aberrations of eight chemosensitivity genes in breast cancer patients. Material and methods. The study involved 97 patients with luminal B breast cancer IIB–IIIB stages. DNA and RNA were isolated from samples of tumor tissue before and after treatment. Microarray analysis was performed for all samples on high-density microarrays (DNA chips) of Affymetrix (USA) CytoScanTM HD Array and Clariom™ S Assay, human. Detection of expression level of seven chemosensitivity genes—RRM1, ERCC1, TOP1, TOP2a, TUBB3, TYMS, and GSTP1—was performed using PCR real-time (RT-qPCR). Results. The expression of the RRM1 (AC scheme), TOP2α, TYMS, and TUBB3 genes in patients with an objective response to treatment (complete and partial regression) is higher than in patients with stabilization and progression (p < 0.05). According to our results, the presence of a high level of GSTP1 in a tumor biopsy is associated with the low efficiency of the NAC CP scheme (p = 0.05). The presence of RRM1 deletion is associated with complete and partial regression, as for the TOP1 and TUBB3 genes (p < 0.05). Higher rates of metastatic survival are associated with a high level of expression and amplification of the GSTP1 gene (log-rank test p = 0.02 and p = 0.05). Conclusion. Thus, a complex assessment of the chemotherapy’s gene expression is important not only for understanding the heterogeneity and molecular biology of breast cancer but also to obtain a more accurate disease prognosis.

A Roadmap towards Breast Cancer Therapies Supported by Explainable Artificial Intelligence

In recent years personalized medicine reached an increasing importance, especially in the design of oncological therapies. In particular, the development of patients’ profiling strategies suggests the possibility of promising rewards. In this work, we present an explainable artificial intelligence (XAI) framework based on an adaptive dimensional reduction which (i) outlines the most important clinical features for oncological patients’ profiling and (ii), based on these features, determines the profile, i.e., the cluster a patient belongs to. For these purposes, we collected a cohort of 267 breast cancer patients. The adopted dimensional reduction method determines the relevant subspace where distances among patients are used by a hierarchical clustering procedure to identify the corresponding optimal categories. Our results demonstrate how the molecular subtype is the most important feature for clustering. Then, we assessed the robustness of current therapies and guidelines; our findings show a striking correspondence between available patients’ profiles determined in an unsupervised way and either molecular subtypes or therapies chosen according to guidelines, which guarantees the interpretability characterizing explainable approaches to machine learning techniques. Accordingly, our work suggests the possibility to design data-driven therapies to emphasize the differences observed among the patients.

Feature-weighted Ordinal Classification for Predicting Drug Response in Multiple Myeloma

MOTIVATION

Ordinal classification problems arise in a variety of real-world applications, in which samples need to be classified into categories with a natural ordering. An example of classifying high-dimensional ordinal data is to use gene expressions to predict the ordinal drug response, which has been increasingly studied in pharmacogenetics. Classical ordinal classification methods are typically not able to tackle high-dimensional data and standard high-dimensional classification methods discard the ordering information among the classes. Existing work of high-dimensional ordinal classification approaches usually assume a linear ordinality among the classes. We argue that manually-labeled ordinal classes may not be linearly arranged in the data space, especially in high-dimensional complex problems.

RESULTS

We propose a new approach that can project high-dimensional data into a lower discriminating subspace, where the innate ordinal structure of the classes is uncovered. The proposed method weights the features based on their rank correlations with the class labels and incorporates the weights into the framework of linear discriminant analysis. We apply the method to predict the response to two types of drugs for patients with Multiple Myeloma, respectively. A comparative analysis with both ordinal and nominal existing methods demonstrates that the proposed method can achieve a competitive predictive performance while honoring the intrinsic ordinal structure of the classes. We provide interpretations on the genes that are selected by the proposed approach to understand their drug-specific response mechanisms.

AVAILABILITY AND IMPLEMENTATION

The data underlying this article are available in the Gene Expression Omnibus Database at https://www.ncbi.nlm.nih.gov/geo/ and can be accessed with accession number GSE9782 and GSE68871. The source code for FWOC can be accessed at https://github.com/pisuduo/Feature-Weighted-Ordinal-Classification-FWOC.

Plasmin and Plasminogen System in the Tumor Microenvironment: Implications for Cancer Diagnosis, Prognosis, and Therapy

Simple Summary

In this review, we present a detailed discussion of how the plasminogen-activation system is utilized by tumor cells in their unrelenting attack on the tissues surrounding them. Plasmin is an enzyme which is responsible for digesting several proteins that hold the tissues surrounding solid tumors together. In this process tumor cells utilize the activity of plasmin to digest tissue barriers in order to leave the tumour site and spread to other parts of the body. We specifically focus on the role of plasminogen receptor—p11 which is an important regulatory protein that facilitates the conversion of plasminogen to plasmin and by this means promotes the attack by the tumour cells on their surrounding tissues.

Abstract

The tumor microenvironment (TME) is now being widely accepted as the key contributor to a range of processes involved in cancer progression from tumor growth to metastasis and chemoresistance. The extracellular matrix (ECM) and the proteases that mediate the remodeling of the ECM form an integral part of the TME. Plasmin is a broad-spectrum, highly potent, serine protease whose activation from its precursor plasminogen is tightly regulated by the activators (uPA, uPAR, and tPA), the inhibitors (PAI-1, PAI-2), and plasminogen receptors. Collectively, this system is called the plasminogen activation system. The expression of the components of the plasminogen activation system by malignant cells and the surrounding stromal cells modulates the TME resulting in sustained cancer progression signals. In this review, we provide a detailed discussion of the roles of plasminogen activation system in tumor growth, invasion, metastasis, and chemoresistance with specific emphasis on their role in the TME. We particularly review the recent highlights of the plasminogen receptor S100A10 (p11), which is a pivotal component of the plasminogen activation system.

Immune and metabolic checkpoints blockade: Dual wielding against tumors

Recent advances in cancer immunotherapy have raised hopes for treating cancers that are resistant to conventional therapies. Among the various immunotherapy methods, the immune checkpoint (IC) blockers were more promising and have paved their way to the clinic. Tumor cells induce the expression of ICs on the immune cells and derive them to a hyporesponsive exhausted phenotype. IC blockers could hinder immune exhaustion in the tumor microenvironment and reinvigorate immune cells for an efficient antitumor response. Despite the primary success of IC blockers in the clinic, the growing numbers of refractory cases require an in-depth study of the cellular and molecular mechanisms underlying IC expression and function. Immunometabolism is recently found to be a key factor in the regulation of immune responses. Activated or exhausted immune cells exploit different metabolic pathways. Tumor cells can suppress antitumor responses via immunometabolism alteration. Therefore, it is expected that concurrent targeting of ICs and immunometabolism pathways can cause immune cells to restore their antitumor activity. In this review, we dissected the reciprocal interactions of immune cell metabolism with expression and signaling of ICs in the tumor microenvironment. Recent findings on dual targeting of ICs and metabolic checkpoints have also been discussed.

Integrin alpha5 in human breast cancer is a mediator of bone metastasis and a therapeutic target for the treatment of osteolytic lesions

Bone metastasis remains a major cause of mortality and morbidity in breast cancer. Therefore, there is an urgent need to better select high-risk patients in order to adapt patient's treatment and prevent bone recurrence. Here, we found that integrin alpha5 (ITGA5) was highly expressed in bone metastases, compared to lung, liver, or brain metastases. High ITGA5 expression in primary tumors correlated with the presence of disseminated tumor cells in bone marrow aspirates from early stage breast cancer patients (n = 268; p = 0.039). ITGA5 was also predictive of poor bone metastasis-free survival in two separate clinical data sets (n = 855, HR = 1.36, p = 0.018 and n = 427, HR = 1.62, p = 0.024). This prognostic value remained significant in multivariate analysis (p = 0.028). Experimentally, ITGA5 silencing impaired tumor cell adhesion to fibronectin, migration, and survival. ITGA5 silencing also reduced tumor cell colonization of the bone marrow and formation of osteolytic lesions in vivo. Conversely, ITGA5 overexpression promoted bone metastasis. Pharmacological inhibition of ITGA5 with humanized monoclonal antibody M200 (volociximab) recapitulated inhibitory effects of ITGA5 silencing on tumor cell functions in vitro and tumor cell colonization of the bone marrow in vivo. M200 also markedly reduced tumor outgrowth in experimental models of bone metastasis or tumorigenesis, and blunted cancer-associated bone destruction. ITGA5 was not only expressed by tumor cells but also osteoclasts. In this respect, M200 decreased human osteoclast-mediated bone resorption in vitro. Overall, this study identifies ITGA5 as a mediator of breast-to-bone metastasis and raises the possibility that volociximab/M200 could be repurposed for the treatment of ITGA5-positive breast cancer patients with bone metastases.

Breath markers for therapeutic radiation

Radiation dose is important in radiotherapy. Too little, and the treatment is not effective, too much causes radiation toxicity. A biochemical measurement of the effect of radiotherapy would be useful in personalisation of this treatment. This study evaluated changes in exhaled breath volatile organic compounds (VOC) associated with radiotherapy with thermal desorption gas chromatography mass-spectrometry followed by data processing and multivariate statistical analysis. Further the feasibility of adopting gas chromatography ion mobility spectrometry for radiotherapy point-of-care breath was assessed. A total of 62 participants provided 240 end-tidal 1 dm³ breath samples before radiotherapy and at 1, 3, and 6 h post-exposure, that were analysed by thermal-desorption/gas-chromatography/quadrupole mass-spectrometry. Data were registered by retention-index and mass-spectra before multivariate statistical analyses identified candidate markers. A panel of sulfur containing compounds (thio-VOC) were observed to increase in concentration over the 6 h following irradiation. 3-methylthiophene (80 ng.m^-3 to 790 ng.m^-3) had the lowest abundance while 2-thiophenecarbaldehyde(380 ng.m^-3 to 3.85 μg.m^-3) the highest; note, exhaled 2-thiophenecarbaldehyde has not been observed previously. The putative tumour metabolite 2,4-dimethyl-1-heptene concentration reduced by an average of 73% over the same time. Statistical scoring based on the signal intensities thio-VOC and 3-methylthiophene appears to reflect individuals' responses to radiation exposure from radiotherapy. The thio-VOC are hypothesised to derive from glutathione and Maillard-based reactions and these are of interest as they are associated with radio-sensitivity. Further studies with continuous monitoring are needed to define the development of the breath biochemistry response to irradiation and to determine the optimum time to monitor breath for radiotherapy markers. Consequently, a single 0.5 cm³ breath-sample gas chromatography-ion mobility approach was evaluated. The calibrated limit of detection for 3-methylthiophene was 10 μg.m^-3 with a lower limit of the detector's response estimated to be 210 fg.s^-1; the potential for a point-of-care radiation exposure study exists.

Epidermal growth factor receptor ligands: targets for optimizing treatment of metastatic colorectal cancer

The discovery of epidermal growth factor (EGF) and its receptor (EGFR) revealed the connection between EGF-like ligands, signaling from the EGFR family members and cancer. Over the next fifty years, analysis of EGFR expression and mutation led to the use of monoclonal antibodies to target EGFR in the treatment of metastatic colorectal cancer (mCRC) and this treatment has improved outcomes for patients. The use of the RAS oncogene mutational status has helped to refine patient selection for EGFR antibody therapy, but an effective molecular predictor of likely responders is lacking. This review analyzes the potential utility of measuring the expression, levels and activation of EGF-like ligands and associated processes as prognostic or predictive markers for the identification of patient risk and more effective mCRC therapies.

Prognostic Biomarkers for Gastric Cancer: An Umbrella Review of the Evidence

Introduction: Biomarkers are biological molecules entirely or partially participating in cancerous processes that function as measurable indicators of abnormal changes in the human body microenvironment. Aiming to provide an overview of associations between prognostic biomarkers and gastric cancer (GC), we performed this umbrella review analyzing currently available meta-analyses and grading the evidence depending on the credibility of their associations. Methods: A systematic literature search was conducted by two independent investigators of the PubMed, Embase, Web of Science, and Cochrane Databases to identify meta-analyses investigating associations between prognostic biomarkers and GC. The strength of evidence for prognostic biomarkers for GC were categorized into four grades: strong, highly suggestive, suggestive, and weak. Results: Among 120 associations between prognostic biomarkers and GC survival outcomes, only one association, namely the association between platelet count and GC OS, was supported by strong evidence. Associations between FITC, CEA, NLR, foxp3+ Treg lymphocytes (both 1- and 3-year OS), CA 19-9, or VEGF and GC OS were supported by highly suggestive evidence. Four associations were considered suggestive and the remaining 108 associations were supported by weak or not suggestive evidence. Discussion: The association between platelet count and GC OS was supported by strong evidence. Associations between FITC, CEA, NLR, foxp3+ Treg lymphocytes (both 1- and 3-year OS), CA 19-9, or VEGF and GC OS were supported by highly suggestive evidence, however, the results should be interpreted cautiously due to inadequate methodological quality as deemed by AMSTAR 2.0.

Technologies for circulating tumor cell separation from whole blood

The importance of early cancer diagnosis and improved cancer therapy has been clear for years and has initiated worldwide research towards new possibilities in the care strategy of patients with cancer using technological innovations. One of the key research fields involves the separation and detection of circulating tumor cells (CTC) because of their suggested important role in early cancer diagnosis and prognosis, namely, providing easy access by a liquid biopsy from blood to identify metastatic cells before clinically detectable metastasis occurs and to study the molecular and genetic profile of these metastatic cells. Provided the opportunity to further progress the development of technology for treating cancer, several CTC technologies have been proposed in recent years by various research groups and companies. Despite their potential role in cancer healthcare, CTC methods are currently mainly used for research purposes, and only a few methods have been accepted for clinical application because of the difficulties caused by CTC heterogeneity, CTC separation from the blood, and a lack of thorough clinical validation. Therefore, the standardization and clinical application of various developed CTC technologies remain important subsequent necessary steps. Because of their suggested future clinical benefits, we focus on describing technologies using whole blood samples without any pretreatment and discuss their advantages, use, and significance. Technologies using whole blood samples utilize size-based, immunoaffinity-based, and density-based methods or combinations of these methods as well as positive and negative enrichment during separation. Although current CTC technologies have not been truly implemented yet, they possess high potential as future clinical diagnostic techniques for the individualized therapy of patients with cancer. Thus, a detailed discussion of the clinical suitability of these new advanced technologies could help prepare clinicians for the future and can be a foundation for technologies that would be used to eliminate CTCs in vivo.

Polymorphisms in the promoter region of the CRBN gene as a predictive factor for the first-line CTD therapy in multiple myeloma patients

Cereblon is a primary molecular target for immunomodulatory drugs. The aim of this study was to evaluate the influence of selected clinical and molecular factors including single nucleotide polymorphisms (SNPs) in CRBN gene on the efficacy of first line CTD (cyclophosphamide, thalidomide, dexamethasone) chemotherapy in patients with multiple myeloma. Study group consisted of 68 patients. Analysis of CRBN gene SNPs (rs6768972, rs1672753) was performed using Real-Time PCR genotyping technique. Median progression free survival (PFS) was 15 months and overall survival (OS) 79 months. Factors associated with significantly shorter OS included ISS 3, kidney disease, weight loss, anemia, thrombocytopenia, hypoalbuminemia, elevated β2-microglobuline and CRP. The presence of t(4;14) was associated with significantly shorter PFS and OS. Both examined SNPs proved to be statistically significant, independent predictive factors of efficacy of the CTD chemotherapy. The presence of AA genotype (rs6768972) correlated with longer median PFS (18 vs 9 months; HR=0.49,95% CI: 0.26-0.91, p=0.0062). Conversely, in the carriers of CC genotype (rs1672753) significantly shorter median PFS was observed (4 vs 16 months; HR=3.93, 95% CI: 0.26-59.64, p=0.0321). In conclusion, SNPs of the CRBN gene may be useful in qualifying patients for treatment with regimens containing thalidomide.

The impact of tumor cell metabolism on T cell-mediated immune responses and immuno-metabolic biomarkers in cancer

The role of adaptive immunity is increasingly recognized as an important element both in the process of tumorigenesis and in the patient's response to treatment. While this understanding has led to new therapeutic strategies that potentiate the activities of tumor infiltrating lymphocytes, only a minority of patients attain durable responses. Metabolic activities in the tumor microenvironment, including hypoxia and acidity, can adversely affect immune responses, making the identification of metabolic biomarkers critically important for understanding and employing immunotherapies.

New Developments in Breast Cancer Prognosis: Molecular Predictors of Treatment Response and Survival

Aim

This study aimed to assess the molecular subtypes of breast cancer for patients attending a dedicated breast care center and examine the association with clinicopathological features, treatment and survival outcomes.

Methods

Demographic, clinicopathological and treatment details were collected from women with primary breast cancer. Immunohistochemical subtypes were also collected. The association between breast cancer subtypes and clinicopathological features was assessed using the chi-square or Fisher's exact test. Survival outcomes were compared among subtypes with the log-rank test.

Results

Immunohistochemical subtypes were not associated with tumor size, lymphovascular invasion or lymph node involvement but differed by histological grade (p=0.014) and nuclear grade of tumors (p=0.001). The 5-year overall survival estimates for luminal A, luminal B, HER-2-positive and triple-negative tumors were 100%, 96.2%, 71.4% and 92.3% respectively. Compared to luminal A tumors (93.4%), luminal B (80.8%), HER-2-positive (71.4%) and triple-negative (76.9%) tumors exhibited a reduced disease-free survival (DFS). Patients with ER-positive tumors had a higher DFS than their ER-negative counterparts (p=0.036). Patients with tumors expressing a low Ki-67 level had a more favorable prognosis (p=0.02).

Conclusions

The most prevalent luminal A subtype is associated with relatively better prognosis, whereas HER-2-positive and triple-negative tumors are prone to early relapse with diminished survival.

Predictors of adherence to exercise interventions during and after cancer treatment: A systematic review

Objective

Exercise interventions benefit cancer patients. However, only low numbers of patients adhere to these interventions. This review aimed to identify predictors of exercise intervention adherence in patients with cancer, during and after multimodality cancer treatment.

Methods

A literature search was performed using electronic databases (PubMed, Embase, and Cochrane) to identify relevant papers published before February 1, 2017. Papers reporting randomized controlled trials, conducted in adult cancer patients who participated in an exercise intervention during and/or after multimodality cancer treatment, and providing outcome of factors predicting exercise adherence were included. Papers were assessed for methodological quality by using the Physiotherapy Evidence Database scale.

Results

The search identified 720 potentially relevant papers, of which 15 fulfilled the eligibility criteria. In these 15 studies, 2279 patients were included and 1383 of these patients were randomized to an exercise intervention. During cancer treatment, the factors predicting exercise adherence were as follows: location of the rehabilitation center, extensive exercise history, high motivation for exercise, and fewer exercise limitations. After cancer treatment, factors that predicted adherence were as follows: less extensive surgery, low alcohol consumption, high previous exercise adherence, family support, feedback by trainers, and knowledge and skills of exercise. Methodological quality of the included papers was rated “high”.

Conclusions

The most prominent predictors of adherence to exercise interventions were location of the rehabilitation center, extensive exercise history, high motivation for exercise, and fewer exercise limitations. To increase the number of cancer patients who will benefit, these results should be considered into the development and implementation of future exercise interventions.

CONCORD biomarker prediction for novel drug introduction to different cancer types

Many cancer therapeutic agents have shown to be effective for treating multiple cancer types. Yet major challenges exist toward introducing a novel drug used in one cancer type to different cancer types, especially when a relatively small number of patients with the other cancer type often benefit from anti-cancer therapy with the drug. Recently, many novel agents were introduced to different cancer types together with companion biomarkers which were obtained or biologically assumed from the original cancer type. However, there is no guarantee that biomarkers from one cancer can directly predict a therapeutic response in another. To tackle this challenging question, we have developed a concordant expression biomarker-based technique ("CONCORD") that overcomes these limitations. CONCORD predicts drug responses from one cancer type to another by identifying concordantly co-expressed biomarkers across different cancer systems. Application of CONCORD to three standard chemotherapeutic agents and two targeted agents demonstrated its ability to accurately predict the effectiveness of a drug against new cancer types and predict therapeutic response in patients.

Oxamate potentiates taxol chemotherapeutic efficacy in experimentally-induced solid ehrlich carcinoma (SEC) in mice

Several human cancers including the breast display elevated expression of Lactate dehydrogenase-A (LDH-A), the enzyme that converts pyruvate to lactate and oxidizes NADH to NAD⁺. Indeed, tumor lactate levels correlate with increased metastasis, tumor recurrence, and poor outcome. Lactate also plays roles in promoting tumor inflammation and as a signaling molecule that stimulates tumor angiogenesis. Because of its essential role in cancer metabolism, LDH-A has been considered as a potential target for combination cancer therapy. Therefore, the current study investigated the possible anti-tumor effect of LDH inhibitor (oxamate) in a murine model of breast cancer [Solid Ehrlich Carcinoma (SEC)], alone and in combination with Taxol chemotherapy. The potential underlying mechanisms were also investigated. The results indicated that oxamate induced significant anti-tumor activity against the SEC. Mechanistically, the combination treatment was more efficient than paclitaxel monotherapy in reducing ATP, MDA, TNF-α and Il-17 contents in SEC. Moreover, the apoptotic and anti-angiogenic effects of the combination treatment were triggered more efficiently as compared to paclitaxel monotherapy, Therefore, oxamate may represent a promising agent that enhance the antitumor activity of paclitaxel.

Inter-alpha Inhibitor H4 as a Potential Biomarker Predicting the Treatment Outcomes in Patients with Hepatocellular Carcinoma

Purpose

Early prediction of treatment outcomes represents an essential step towards increased treatment efficacy and survival in patients with hepatocellular carcinoma (HCC). In this study, we performed two-dimensional electrophoresis (2-DE) followed by protein profiling to identify biomarkers predictive of therapeutic outcomes in patients with HCC who received liver-directed therapy (LDTx) involving local radiotherapy (RT), and studied the underlying mechanisms of the identified proteins.

Materials and Methods

2-DE analysis was conducted by pooling sera from patients with a good or poor prognosis; serum proteomic profiles of the two groups were compared and analyzed using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Identified proteins were confirmed via enzyme-linked immunosorbent assay. An invasion assay was performed after overexpression and knockdown of target protein in Huh7 cells.

Results

Levels of inter-alpha inhibitor H4 (ITIH4), fibrinogen gamma chain, keratin 9/1 complex, carbonic anhydrase I, and carbonmonoxyhemoglobin S were changed by more than 4-fold in response to LDTx. In particular, pre-LDTx ITIH4 expression was more than 5-fold higher in patients with a good prognosis, compared to patients with a poor prognosis. The migration ability of Huh7 cells was significantly suppressed and enhanced by ITIH4 overexpression and knockdown, respectively. The tumors of patients with HCC and a good prognosis expressed high levels of ITIH4, compared to those of patients with a poor prognosis.

Conclusion

Taken together, ITIH4 may be a potential therapeutic target that could inhibit cancer metastasis, as well as a prognostic marker for patients with HCC who are receiving LDTx.

Association of SMAD4 mutation with patient demographics, tumor characteristics, and clinical outcomes in colorectal cancer

SMAD4 is an essential mediator in the transforming growth factor-β pathway. Sporadic mutations of SMAD4 are present in 2.1-20.0% of colorectal cancers (CRCs) but data are limited. In this study, we aimed to evaluate clinicopathologic characteristics, prognosis, and clinical outcome associated with this mutation in CRC cases. Data for patients with metastatic or unresectable CRC who underwent genotyping for SMAD4 mutation and received treatment at The University of Texas MD Anderson Cancer Center from 2000 to 2014 were reviewed. Their tumors were sequenced using a hotspot panel predicted to cover 80% of the reported SMAD4 mutations, and further targeted resequencing that included full-length SMAD4 was performed on mutated tumors using a HiSeq sequencing system. Using The Cancer Genome Atlas data on CRC, the characteristics of SMAD4 and transforming growth factor-β pathway mutations were evaluated according to different consensus molecular subtypes of CRC. Among 734 patients with CRC, 90 (12%) had SMAD4 mutations according to hotspot testing. SMAD4 mutation was associated with colon cancer more so than with rectal cancer (odds ratio 2.85; p<0.001), female sex (odds ratio 1.71; p = 0.02), and shorter overall survival than in wild-type SMAD4 cases (median, 29 months versus 56 months; hazard ratio 2.08; p<0.001 [log-rank test]). SMAD4 mutation was not associated with age, stage at presentation, colonic location, distant metastasis, or tumor grade. A subset of patients with metastatic CRC (n = 44) wild-type for KRAS, NRAS, and BRAF who received anti-epidermal growth factor receptor therapy with mutated SMAD4 (n = 13) had shorter progression-free survival duration than did patients wild-type for SMAD4 (n = 31) (median, 111 days versus 180 days; p = 0.003 [log-rank test]). Full-length sequencing confirmed that missense mutations at R361 and P356 in the MH2 domain were the most common SMAD4 alterations. In The Cancer Genome Atlas data, SMAD4 mutation frequently occurred with KRAS, NRAS, and BRAF mutations and was more common in patients with the consensus molecular subtype 3 of CRC than in those with the other 3 subtypes. This is one of the largest retrospective studies to date characterizing SMAD4 mutations in CRC patients and demonstrates the prognostic role and lack of response of CRC to anti-epidermal growth factor receptor therapy. Further studies are required to validate these findings and the role of SMAD4 mutation in CRC.

The Routine Clinical use of Pharmacogenetic Tests: What it Will Require?

Pharmacogenetic testing aims to personalize drug therapy with a view to optimising drug efficacy and minimise toxicity. However, despite the potential benefits, pharmacogenetic testing is mostly confined to specialised medical areas, laboratories and centres. Widespread integration into routine clinical practice has been limited by a complex set of issues including regulatory and reimbursement frameworks, evidence of clinical utility and clinician perspectives, practices and education. Here we assess the current barriers to widespread clinical uptake and identify the key issue necessary to address to accelerate routine testing.

Analytical pitfalls and challenges in clinical metabolomics

Metabolomics-based strategies have become an integral part of modern clinical research, allowing for a better understanding of pathophysiological conditions and disease mechanisms, as well as providing innovative tools for more adequate diagnostic and prognosis approaches. Metabolomics is considered an essential tool in precision medicine, which aims for personalized prevention and tailor-made treatments. Nevertheless, multiple pitfalls may be encountered in clinical metabolomics during the entire workflow, hampering the quality of the data and, thus, the biological interpretation. This review describes the challenges underlying metabolomics-based experiments, discussing step by step the potential pitfalls of the analytical process, including study design, sample collection, storage, as well as preparation, chromatographic and electrophoretic separation, detection and data analysis. Moreover, it offers practical solutions and strategies to tackle these challenges, ensuring the generation of high-quality data.

Mapping ethical and social aspects of cancer biomarkers

Cancer biomarkers represent a revolutionary advance toward personalised cancer treatment, promising therapies that are tailored to subgroups of patients sharing similar generic traits. Notwithstanding the optimism driving this development, biomarkers also present an array of social and ethical questions, as witnessed in sporadic debates across different literatures. This review article seeks to consolidate these debates in a mapping of the complex terrain of ethical and social aspects of cancer biomarker research. This mapping was undertaken from the vantage point offered by a working cancer biomarker research centre called the Centre for Cancer Biomarkers (CCBIO) in Norway, according to a dialectic move between the literature and discussions with researchers and practitioners in the laboratory. Starting in the lab, we found that, with the exception of some classical bioethical dilemmas, researchers regarded many issues relative to the ethos of the biomarker community; how the complexity and uncertainty characterising biomarker research influence their scientific norms of quality. Such challenges to the ethos of cancer research remain largely implicit, outside the scope of formal bioethical enquiry, yet form the basis for other social and ethical issues. Indeed, looking out from the lab we see how questions of complexity, uncertainty and quality contribute to debates around social and global justice; undermining policies for the prioritisation of care, framing the stratification of those patients worthy of treatment, and limiting global access to this highly sophisticated research. We go on to discuss biomarker research within the culturally-constructed 'war on cancer' and highlight an important tension between the expectations of 'magic bullets' and the complexity and uncertainty faced in the lab. We conclude by arguing, with researchers in the CCBIO, for greater reflexivity and humility in cancer biomarker research and policy.

Identifying predictive markers for personalized treatment selection

It is now well recognized that the effectiveness and potential risk of a treatment often vary by patient subgroups. Although trial-and-error and one-size-fits-all approaches to treatment selection remain a common practice, much recent focus has been placed on individualized treatment selection based on patient information (La Thangue and Kerr, 2011; Ong et al., 2012). Genetic and molecular markers are becoming increasingly available to guide treatment selection for various diseases including HIV and breast cancer (Mallal et al., 2008; Zujewski and Kamin, 2008). In recent years, many statistical procedures for developing individualized treatment rules (ITRs) have been proposed. However, less focus has been given to efficient selection of predictive biomarkers for treatment selection. The standard Wald test for interactions between treatment and the set of markers of interest may not work well when the marker effects are nonlinear. Furthermore, interaction-based test is scale dependent and may fail to capture markers useful for predicting individualized treatment differences. In this article, we propose to overcome these difficulties by developing a kernel machine (KM) score test that can efficiently identify markers predictive of treatment difference. Simulation studies show that our proposed KM-based score test is more powerful than the Wald test when there is nonlinear effect among the predictors and when the outcome is binary with nonlinear link functions. Furthermore, when there is high-correlation among predictors and when the number of predictors is not small, our method also over-performs Wald test. The proposed method is illustrated with two randomized clinical trials.

Preliminary Study on Serum Lactate Dehydrogenase (LDH)-Prognostic Biomarker in Carcinoma Breast

INTRODUCTION

Serum Lactate Dehydrogenase (LDH) is one of the biochemical markers for breast cancer. Serum LDH is enzyme required for anaerobic glycolysis. One of its isoenzyme is increased in breast cancer due to up-regulation in its gene. It leads to increase in serum LDH level in breast cancer patients. Serum LDH is economical, easily available and easy to estimate.

AIM

In the present study, we evaluated the LDH levels in circulation of newly diagnosed patients of breast cancer and tried to correlate it with different TNM staging of carcinoma breast before interventions and after adjuvant therapy of these patients.

MATERIALS AND METHODS

This prospective study was done on 83 diagnosed patients of breast cancer was conducted among poor patients in rural area. This study was conducted in the Department of Surgery between October 2008 to October 2010, at MGIMS, Sevagram, Maharashtra, a rural medical college located in Central India. Out of total 83 participants, 10 participants were having adverse events following surgery and remaining 73 participants were without adverse events following surgery. The significant difference in serum LDH levels between two groups, with and without adverse surgical outcome was calculated by Mann-Whitney U test.

RESULTS

Patients with higher clinical TNM staging were having higher serum LDH levels. The serum LDH levels at sixth months following surgery showed a trend of statistically significant difference between patients with and without adverse events. As increased serum LDH levels in breast cancer patients shows poor prognosis, surgical outcome or advanced metastases.

CONCLUSION

Serum LDH monitoring can be used as a prognostic biomarker in patients of breast cancer. For confirmation of this finding, we require further more studies on larger sample size and long-term follow-up in patients specifically with higher serum LDH levels.

BreCAN-DB: a repository cum browser of personalized DNA breakpoint profiles of cancer genomes

BreCAN-DB (http://brecandb.igib.res.in) is a repository cum browser of whole genome somatic DNA breakpoint profiles of cancer genomes, mapped at single nucleotide resolution using deep sequencing data. These breakpoints are associated with deletions, insertions, inversions, tandem duplications, translocations and a combination of these structural genomic alterations. The current release of BreCAN-DB features breakpoint profiles from 99 cancer-normal pairs, comprising five cancer types. We identified DNA breakpoints across genomes using high-coverage next-generation sequencing data obtained from TCGA and dbGaP. Further, in these cancer genomes, we methodically identified breakpoint hotspots which were significantly enriched with somatic structural alterations. To visualize the breakpoint profiles, a next-generation genome browser was integrated with BreCAN-DB. Moreover, we also included previously reported breakpoint profiles from 138 cancer-normal pairs, spanning 10 cancer types into the browser. Additionally, BreCAN-DB allows one to identify breakpoint hotspots in user uploaded data set. We have also included a functionality to query overlap of any breakpoint profile with regions of user's interest. Users can download breakpoint profiles from the database or may submit their data to be integrated in BreCAN-DB. We believe that BreCAN-DB will be useful resource for genomics scientific community and is a step towards personalized cancer genomics.

Validation of the IHC4 Breast Cancer Prognostic Algorithm Using Multiple Approaches on the Multinational TEAM Clinical Trial

CONTEXT

Hormone receptors HER2/neu and Ki-67 are markers of residual risk in early breast cancer. An algorithm (IHC4) combining these markers may provide additional information on residual risk of recurrence in patients treated with hormone therapy.

OBJECTIVE

To independently validate the IHC4 algorithm in the multinational Tamoxifen Versus Exemestane Adjuvant Multicenter Trial (TEAM) cohort, originally developed on the trans-ATAC (Arimidex, Tamoxifen, Alone or in Combination Trial) cohort, by comparing 2 methodologies.

DESIGN

The IHC4 biomarker expression was quantified on TEAM cohort samples (n = 2919) by using 2 independent methodologies (conventional 3,3'-diaminobezidine [DAB] immunohistochemistry with image analysis and standardized quantitative immunofluorescence [QIF] by AQUA technology). The IHC4 scores were calculated by using the same previously established coefficients and then compared with recurrence-free and distant recurrence-free survival, using multivariate Cox proportional hazards modeling.

RESULTS

The QIF model was highly significant for prediction of residual risk (P < .001), with continuous model scores showing a hazard ratio (HR) of 1.012 (95% confidence interval [95% CI]: 1.010-1.014), which was significantly higher than that for the DAB model (HR: 1.008, 95% CI: 1.006-1.009); P < .001). Each model added significant prognostic value in addition to recognized clinical prognostic factors, including nodal status, in multivariate analyses. Quantitative immunofluorescence, however, showed more accuracy with respect to overall residual risk assessment than the DAB model.

CONCLUSIONS

The use of the IHC4 algorithm was validated on the TEAM trial for predicting residual risk in patients with breast cancer. These data support the use of the IHC4 algorithm clinically, but quantitative and standardized approaches need to be used.

HDAC2 deregulation in tumorigenesis is causally connected to repression of immune modulation and defense escape

Histone deacetylase 2 (HDAC2) is overexpressed or mutated in several disorders such as hematological cancers, and plays a critical role in transcriptional regulation, cell cycle progression and developmental processes. Here, we performed comparative transcriptome analyses in acute myeloid leukemia to investigate the biological implications of HDAC2 silencing versus its enzymatic inhibition using epigenetic-based drug(s). By gene expression analysis of HDAC2-silenced vs wild-type cells, we found that HDAC2 has a specific role in leukemogenesis. Gene expression profiling of U937 cell line with or without treatment of the well-known HDAC inhibitor vorinostat (SAHA) identifies and characterizes several gene clusters where inhibition of HDAC2 ‘mimics’ its silencing, as well as those where HDAC2 is selectively and exclusively regulated by HDAC2 protein expression levels. These findings may represent an important tool for better understanding the mechanisms underpinning immune regulation, particularly in the study of major histocompatibility complex class II genes.

The oncomiR miR-197 is a novel prognostic indicator for non-small cell lung cancer patients

Background:

MicroRNA expression signatures can promote personalised care for non-small cell lung cancer (NSCLC) patients. Our aim was to evaluate the previously unexplored prognostic potential of miR-197, a key oncogenic molecule for NSCLC.

Methods:

Total RNA isolation (n=124 NSCLC and n=21 tumour-adjacent normal tissues), was performed using the QIAsymphony SP workstation. The quantity and quality of RNA were assessed by spectrophotometric analysis and an Agilent 2100 bioanalyzer. Polyadenylation and reverse transcription were subsequently carried out. MiR-197 expression levels were measured by qPCR, after quality control (inter-assay CV=7.8%). Internal validation procedures were followed by assigning training and test sets and robust biostatistical analyses were performed, including bootstrap resampling.

Results:

MiR-197 is associated with larger tumours (P=0.042) and the squamous cell carcinoma histotype (P=0.032). Interestingly, after adjusting for important prognostic indicators, miR-197 expression was identified as a novel independent predictor of unfavourable prognosis for NSCLC patients (HR=1.97, 95% CI=1.10–3.38, P=0.013). We also demonstrate that miR-197 retains its prognostic performance in both early-stage I (P=0.045) and more advanced-stage individuals (P=0.036).

Conclusions:

The cost-effective expression analysis of miR-197 could constitute a novel molecular tool for NSCLC management.

Targeted therapy in older patients with solid tumors

The introduction of targeted therapy has ushered in the era of personalized medicine in cancer therapy. The increased understanding of tumor heterogeneity has led to the determination of specific targets that can be exploited in treatment. This review highlights approved drugs in different therapeutic classes, including tyrosine kinase inhibitors, mammalian target of rapamycin inhibitors, drugs targeted to the human epidermal growth factor receptor 2, BRAF-mutation targeted drugs, anti-epidermal growth factor receptor inhibitors, and anti-vascular endothelial growth factor therapy. There have not been elderly patient-specific trials of these therapies. Most of the data are extrapolated from larger trials in which older patients generally were a fraction of the participants. Therapeutic recommendations are made on the basis of this analysis with the recognition that the older clinical trial participants may not be representative of patients seen in daily practice. Patient selection and geriatric evaluation are critical for appropriate drug selection, dosing, and monitoring. With care, these therapies are a major step forward in the safe and effective treatment of older patients with cancer.

Potential of dihydropyrimidine dehydrogenase genotypes in personalizing 5-fluorouracil therapy among colorectal cancer patients

BACKGROUND

Dihydropyrimidine dehydrogenase (DPD) is a pyrimidine catabolic enzyme involved in the initial and rate-limiting step of the catabolic pathway of toxic metabolites of 5-fluorouracil (5-FU). Several studies have reported that deficiency of DPD and polymorphisms of its gene are related to 5-FU toxicities and death. Association between serum concentration of 5-FU and its related toxicity has also been previously demonstrated. Hence, this study aims to understand the role of DPYD variants in serum level of 5-FU and the risk of developing toxicity to prevent adverse reactions and maximize therapy outcome for personalized medicine.

METHODS

A total of 26 patients comprising 3 different ethnic groups (Malay, Chinese, and Indian) diagnosed with colorectal cancer and treated with 5-FU chemotherapy regimen from local hospital were recruited. Polymerase chain reaction and denaturing high-performance liquid chromatography methods were developed to screen polymorphisms of DPYD gene. High-performance liquid chromatography-based quantification assay was developed to measure the serum concentration of 5-FU among these patients.

RESULTS

Patients with DPYD genotypes of deficient enzyme activity had higher median serum levels of 5-FU compared with normal DPD group (median, 11.51 mcg/mL; 95% confidence interval, 10.18-16.11 versus median, 0.83 mcg/mL; 95% confidence interval, 0.55-5.90, Mann-Whitney U test; P = 0.010). Patients with neutropenia (n = 11) had significantly higher serum concentrations of 5-FU as compared with those with normal white blood cell count (n = 15) (Mann-Whitney U test, P = 0.031). Combined regression analysis showed that the predictive power of DPYD*5 (rs1801159) and 1896 T>C (rs17376848) for serum concentrations of 5-FU in the studied group was 36.6% (P = 0.04). Similarly, DPYD*5 and 1896 T>C accounted for 29.9% of the occurrences of neutropenia (analysis of variance, P = 0.017).

CONCLUSIONS

This study revealed that DPYD*5 (rs1801159) and 1896 T>C (rs17376848) are potentially useful predictive markers of patients' responses to 5-FU chemotherapy. Pharmacogenotyping is therefore recommended to guide dosing of 5-FU and prevent neutropenia.

Significance of CASP8AP2 and H2AFZ expression in survival and risk of relapse in children with acute lymphoblastic leukemia

Novel biomarkers for risk refinement and stratification in childhood acute lymphoblastic leukemia (ALL) are needed to optimize treatment results. We studied the expression of CASP8AP2 and H2AFZ associated with relapse and survival in bone marrow samples from newly diagnosed children with ALL. We found: (a) an increased risk for early relapse in those patients with low expression of CASP8AP2 (odds ratio [OR] 3.93, 95% confidence interval [CI] 1.40-11.02, p < 0.05) confirming its usefulness as a predictive risk marker, although H2AFZ did not present the same effect; (b) patients with low expressions of CASP8AP2 and H2AFZ had inferior survival rates (p < 0.001); (c) the predictive values regarding low expressions of H2AFZ and CASP8AP2 and high white blood cell count suggest that these features could help to identify more accurately patients at greater risk of relapse.

A pilot pharmacologic biomarker study in HLA-haploidentical hematopoietic cell transplant recipients

PURPOSE

Eleven patients diagnosed with various hematologic malignancies receiving an HLA-haploidentical hematopoietic cell transplant (HCT) participated in an ancillary biomarker trial. The goal of the trial was to evaluate potential pharmacologic biomarkers pertinent to the conditioning regimen [fludarabine monophosphate (fludarabine) and cyclophosphamide (CY)] or postgrafting immunosuppression [CY and mycophenolate mofetil (MMF)] in these patients.

METHODS

We characterized the interpatient variability of nine pharmacologic biomarkers. The biomarkers evaluated were relevant to fludarabine (i.e., area under the curve (AUC) of 2-fluoro-ara-A or F-ara-A), CY (i.e., AUCs of CY and four of its metabolites), and MMF (i.e., total mycophenolic acid (MPA) AUC, unbound MPA AUC, and inosine monophosphate dehydrogenase (IMPDH) activity).

RESULTS

Interpatient variability in the pharmacologic biomarkers was high. Among those related to HCT conditioning, the interpatient variability ranged from 1.5-fold (CY AUC) to 4.0-fold (AUC of carboxyethylphosphoramide mustard, a metabolite of CY). Among biomarkers evaluated as part of postgrafting immunosuppression, the interpatient variability ranged from 1.7-fold (CY AUC) to 4.9-fold (IMPDH area under the effect curve). There was a moderate correlation (R (2) = 0.441) of within-patient 4-hydroxycyclophosphamide formation clearance.

CONCLUSIONS

Considerable interpatient variability exists in the pharmacokinetic and drug-specific biomarkers potentially relevant to clinical outcomes in HLA-haploidentical HCT recipients. Pharmacodynamic studies are warranted to optimize the conditioning regimen and postgrafting immunosuppression administered to HLA-haploidentical HCT recipients.

Early diagnosis of complex diseases by molecular biomarkers, network biomarkers, and dynamical network biomarkers

Many studies have been carried out for early diagnosis of complex diseases by finding accurate and robust biomarkers specific to respective diseases. In particular, recent rapid advance of high-throughput technologies provides unprecedented rich information to characterize various disease genotypes and phenotypes in a global and also dynamical manner, which significantly accelerates the study of biomarkers from both theoretical and clinical perspectives. Traditionally, molecular biomarkers that distinguish disease samples from normal samples are widely adopted in clinical practices due to their ease of data measurement. However, many of them suffer from low coverage and high false-positive rates or high false-negative rates, which seriously limit their further clinical applications. To overcome those difficulties, network biomarkers (or module biomarkers) attract much attention and also achieve better performance because a network (or subnetwork) is considered to be a more robust form to characterize diseases than individual molecules. But, both molecular biomarkers and network biomarkers mainly distinguish disease samples from normal samples, and they generally cannot ensure to identify predisease samples due to their static nature, thereby lacking ability to early diagnosis. Based on nonlinear dynamical theory and complex network theory, a new concept of dynamical network biomarkers (DNBs, or a dynamical network of biomarkers) has been developed, which is different from traditional static approaches, and the DNB is able to distinguish a predisease state from normal and disease states by even a small number of samples, and therefore has great potential to achieve "real" early diagnosis of complex diseases. In this paper, we comprehensively review the recent advances and developments on molecular biomarkers, network biomarkers, and DNBs in particular, focusing on the biomarkers for early diagnosis of complex diseases considering a small number of samples and high-throughput data (or big data). Detailed comparisons of various types of biomarkers as well as their applications are also discussed.