Biomarker-based selection of therapy for colorectal cancer

Biomarkers for major depressive and bipolar disorders using metabolomics: A systematic review

Major depressive disorder (MDD) and bipolar disorder (BD) lack robust biomarkers useful for screening purposes in a clinical setting. A systematic review of the literature was conducted on metabolomic studies of patients with MDD or BD through the use of analytical platforms such as in vivo brain imaging, mass spectrometry, and nuclear magnetic resonance. Our search identified a total of 7,590 articles, of which 266 articles remained for full-text revision. Overall, 249 metabolites were found to be dysregulated with 122 of these metabolites being reported in two or more of the studies included. A list of biomarkers for MDD and BD established from metabolites found to be abnormal, along with the number of studies supporting each metabolite and a comparison of which biological fluids they were reported in, is provided. Metabolic pathways that may be important in the pathophysiology of MDD and BD were identified and predominantly center on glutamatergic metabolism, energy metabolism, and neurotransmission. Using online drug registries, we also illustrate how metabolomics can facilitate the discovery of novel candidate drug targets.

Broad Detection of Alterations Predicted to Confer Lack of Benefit From EGFR Antibodies or Sensitivity to Targeted Therapy in Advanced Colorectal Cancer

INTRODUCTION

A KRAS mutation represented the first genomic biomarker to predict lack of benefit from anti-epidermal growth factor receptor (EGFR) antibody therapy in advanced colorectal cancer (CRC). Expanded RAS testing has further refined the treatment approach, but understanding of genomic alterations underlying primary and acquired resistance is limited and further study is needed.

MATERIALS AND METHODS

We prospectively analyzed 4,422 clinical samples from patients with advanced CRC, using hybrid-capture based comprehensive genomic profiling (CGP) at the request of the individual treating physicians. Comparison with prior molecular testing results, when available, was performed to assess concordance.

RESULTS

We identified a RAS/RAF pathway mutation or amplification in 62% of cases, including samples harboring KRAS mutations outside of the codon 12/13 hotspot region in 6.4% of cases. Among cases with KRAS non-codon 12/13 alterations for which prior test results were available, 79 of 90 (88%) were not identified by focused testing. Of 1,644 RAS/RAF wild-type cases analyzed by CGP, 31% harbored a genomic alteration (GA) associated with resistance to anti-EGFR therapy in advanced CRC including mutations in PIK3CA, PTEN, EGFR, and ERBB2. We also identified other targetable GA, including novel kinase fusions, receptor tyrosine kinase amplification, activating point mutations, as well as microsatellite instability.

CONCLUSION

Extended genomic profiling reliably detects alterations associated with lack of benefit to anti-EGFR therapy in advanced CRC, while simultaneously identifying alterations potentially important in guiding treatment. The use of CGP during the course of clinical care allows for the refined selection of appropriate targeted therapies and clinical trials, increasing the chance of clinical benefit and avoiding therapeutic futility.

IMPLICATIONS FOR PRACTICE

Comprehensive genomic profiling (CGP) detects diverse genomic alterations associated with lack of benefit to anti-epidermal growth factor receptor therapy in advanced colorectal cancer (CRC), as well as targetable alterations in many other genes. This includes detection of a broad spectrum of activating KRAS alterations frequently missed by focused molecular hotspot testing, as well as other RAS/RAF pathway alterations, mutations shown to disrupt antibody binding, RTK activating point mutations, amplifications, and rearrangements, and activating alterations in downstream effectors including PI3K and MEK1. The use of CGP in clinical practice is critical to guide appropriate selection of targeted therapies for patients with advanced CRC.

High expression levels of unc-51-like kinase 1 as a predictor of poor prognosis in colorectal cancer

Colorectal cancer (CRC) is one of the most common and lethal cancers worldwide. Unc-51-like kinase 1 (ULK1) plays an important role in autophagy, which is widely involved in human CRC. The aim of the present study was to investigate the clinical and prognostic significance of the expression of ULK1 in human CRC. Expression of ULK1 in 339 CRC specimens (tumor-node-metastasis stages I-IV) was assessed by immunohistochemistry. The optimal cutpoint of the expression of ULK1 was assessed by the X-tile program, and the patients were divided into 2 groups of high or low expression levels of ULK1, accordingly. Correlation analysis between the expression of ULK1 and the clinicopathological variables in CRC demonstrated that the expression of ULK1 was significantly associated with gender and tumor differentiation. Univariate Cox regression analysis indicated that high expression levels of ULK1 were a risk factor for overall and disease-free survival. Therefore, the high expression levels of ULK1 may be a useful independent biomarker for predicting a poor prognosis in patients with CRC.

Biomarkers in major depressive disorder: the role of mass spectrometry

Major depressive disorder (MDD) is common. Despite numerous available treatments, many individuals fail to improve clinically. MDD continues to be diagnosed exclusively via behavioral rather than biological methods. Biomarkers-which include measurements of genes, proteins, and patterns of brain activity-may provide an important objective tool for the diagnosis of MDD or in the rational selection of treatments. Proteomic analysis and validation of its results as biomarkers is less explored than other areas of biomarker research in MDD. Mass spectrometry (MS) is a comprehensive, unbiased means of proteomic analysis, which can be complemented by directed protein measurements, such as Western Blotting. Prior studies have focused on MS analysis of several human biomaterials in MDD, including human post-mortem brain, cerebrospinal fluid (CSF), blood components, and urine. Further studies utilizing MS and proteomic analysis in MDD may help solidify and establish biomarkers for use in diagnosis, identification of new treatment targets, and understanding of the disorder. The ultimate goal is the validation of a biomarker or a biomarker signature that facilitates a convenient and inexpensive predictive test for depression treatment response and helps clinicians in the rational selection of next-step treatments.

Potential biomarkers in psychiatry: focus on the cholesterol system

Measuring biomarkers to identify and assess illness is a strategy growing in popularity and relevance. Although already in clinical use for treating and predicting cancer, no biological measurement is used clinically for any psychiatric disorder. Biomarkers could predict the course of a medical problem, and aid in determining how and when to treat. Several studies have indicated that of candidate psychiatric biomarkers detected using proteomic techniques, cholesterol and associated proteins, specifically apolipoproteins (Apos), may be of interest. Cholesterol is necessary for brain development and its synthesis continues at a lower rate in the adult brain. Apos are the protein component of lipoproteins responsible for lipid transport. There is extensive evidence that the levels of cholesterol and Apos may be disturbed in psychiatric disorders, including autistic spectrum disorders (ASD). Here, we describe putative serum biomarkers for psychiatric disorders, and the role of cholesterol and Apos in central nervous system (CNS) disorders.

High accordance in prognosis prediction of colorectal cancer across independent datasets by multi-gene module expression profiles

A considerable portion of patients with colorectal cancer have a high risk of disease recurrence after surgery. These patients can be identified by analyzing the expression profiles of signature genes in tumors. But there is no consensus on which genes should be used and the performance of specific set of signature genes varies greatly with different datasets, impeding their implementation in the routine clinical application. Instead of using individual genes, here we identified functional multi-gene modules with significant expression changes between recurrent and recurrence-free tumors, used them as the signatures for predicting colorectal cancer recurrence in multiple datasets that were collected independently and profiled on different microarray platforms. The multi-gene modules we identified have a significant enrichment of known genes and biological processes relevant to cancer development, including genes from the chemokine pathway. Most strikingly, they recruited a significant enrichment of somatic mutations found in colorectal cancer. These results confirmed the functional relevance of these modules for colorectal cancer development. Further, these functional modules from different datasets overlapped significantly. Finally, we demonstrated that, leveraging above information of these modules, our module based classifier avoided arbitrary fitting the classifier function and screening the signatures using the training data, and achieved more consistency in prognosis prediction across three independent datasets, which holds even using very small training sets of tumors.