Biomarkers in oncology research and treatment: early detection research network: a collaborative approach

The role of alpha-fetoprotein in the tumor microenvironment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a prevalent malignant cancer worldwide, characterized by high morbidity and mortality rates. Alpha-fetoprotein (AFP) is a glycoprotein synthesized by the liver and yolk sac during fetal development. However, the serum levels of AFP exhibit a significant correlation with the onset and progression of HCC in adults. Extensive research has demonstrated that the tumor microenvironment (TME) plays a crucial role in the malignant transformation of HCC, and AFP is a key factor in the TME, promoting HCC development. The objective of this review was to analyze the existing knowledge regarding the role of AFP in the TME. Specifically, this review focused on the effect of AFP on various cells in the TME, tumor immune evasion, and clinical application of AFP in the diagnosis and treatment of HCC. These findings offer valuable insights into the clinical treatment of HCC.

Electrochemistry-based approaches to low cost, high sensitivity, automated, multiplexed protein immunoassays for cancer diagnostics

Early detection and reliable diagnostics are keys to effectively design cancer therapies with better prognoses. The simultaneous detection of panels of biomarker proteins holds great promise as a general tool for reliable cancer diagnostics. A major challenge in designing such a panel is to decide upon a coherent group of biomarkers which have higher specificity for a given type of cancer. The second big challenge is to develop test devices to measure these biomarkers quantitatively with high sensitivity and specificity, such that there are no interferences from the complex serum or tissue matrices. Lastly, integrating all these tests into a technology that does not require exclusive training to operate, and can be used at point-of-care (POC) is another potential bottleneck in futuristic cancer diagnostics. In this article, we review electrochemistry-based tools and technologies developed and/or used in our laboratories to construct low-cost microfluidic protein arrays for the highly sensitive detection of a panel of cancer-specific biomarkers with high specificity which at the same time has the potential to be translated into POC applications.

A novel knowledge representation framework for the statistical validation of quantitative imaging biomarkers

Quantitative imaging biomarkers are of particular interest in drug development for their potential to accelerate the drug development pipeline. The lack of consensus methods and carefully characterized performance hampers the widespread availability of these quantitative measures. A framework to support collaborative work on quantitative imaging biomarkers would entail advanced statistical techniques, the development of controlled vocabularies, and a service-oriented architecture for processing large image archives. Until now, this framework has not been developed. With the availability of tools for automatic ontology-based annotation of datasets, coupled with image archives, and a means for batch selection and processing of image and clinical data, imaging will go through a similar increase in capability analogous to what advanced genetic profiling techniques have brought to molecular biology. We report on our current progress on developing an informatics infrastructure to store, query, and retrieve imaging biomarker data across a wide range of resources in a semantically meaningful way that facilitates the collaborative development and validation of potential imaging biomarkers by many stakeholders. Specifically, we describe the semantic components of our system, QI-Bench, that are used to specify and support experimental activities for statistical validation in quantitative imaging.

Measurement of biomarker proteins for point-of-care early detection and monitoring of cancer

This critical review evaluates progress toward viable point-of-care protein biomarker measurements for cancer detection and diagnostics. The ability to measure panels of specific, selective cancer biomarker proteins in physicians' surgeries and clinics has the potential to revolutionize cancer detection, monitoring, and therapy. The dream envisions reliable, cheap, automated, technically undemanding devices that can analyze a patient's serum or saliva in a clinical setting, allowing on-the-spot diagnosis. Existing commercial products for protein assays are reliable in laboratory settings, but have limitations for point-of-care applications. A number of ultrasensitive immunosensors and some arrays have been developed, many based on nanotechnology. Multilabel detection coupled with high capture molecule density in immunosensors and arrays seems to be capable of detecting a wide range of protein concentrations with sensitivity ranging into the sub pg mL(-1) level. Multilabel arrays can be designed to detect both high and ultralow abundance proteins in the same sample. However, only a few of the newer ultrasensitive methods have been evaluated with real patient samples, which is key to establishing clinical sensitivity and selectivity.

Proteomic and metabolic prediction of response to therapy in gastrointestinal cancers

Despite substantial improvements in the diagnosis and treatment of many gastrointestinal cancers, particularly colorectal cancer, numerous patients are only diagnosed in advanced stages of disease, which can preclude curative treatment. Screening and early diagnosis of high-risk individuals might be the most promising approach to improve prognosis; however, molecular biomarkers for early diagnosis of most gastrointestinal cancers are not yet available. The prognosis of patients with advanced gastrointestinal cancers has improved through the development of multimodal treatments and the introduction of targeted therapies. Nonetheless, not all patients benefit equally from these treatment approaches, and toxicity can be substantial. The ability to predict whether a patient will respond to therapy early in their treatment for gastrointestinal cancer may be of particular value to stratify and individualize patient treatment strategies. Despite improvement in the understanding of cancer pathogenesis and progression at the molecular level, the molecular changes that underlie treatment response and/or drug resistance are still largely unknown. PET is the first technique to show promise in prediction of response to therapy, and has resulted in promising advancements, particularly in esophageal and gastric cancers. Tissue-based and blood-based molecular biomarkers are still subject to validation. Prediction of response to treatment could ultimately lead to an overall improvement in prognosis.