Progress of Biomarkers to Predict Response to Immune Checkpoint Inhibitors in Microsatellite Stability Colorectal Cancer / 肿瘤防治研究

The exploration of biomarkers predicting response to immune checkpoint inhibitors in microsatellite stability colorectal cancer can enable more patients to benefit from immunotherapy. Tumor mutational burden (TMB), POLE/POLD1 mutation, CMS classifications, MGMT methylation, and other indicators own the potential and value of predicting response to immune checkpoint inhibitors in microsatellite stability colorectal cancer. In this paper, we reviewed the related research on predictive biomarkers of immune checkpoint inhibitors in microsatellite stability colorectal cancer, provide a reference for the best treatment strategy for microsatellite stability colorectal cancer.

The metabolomics signature associated with responsiveness to steroid therapy in focal segmental glomerulosclerosis: a pilot study

Abstract Background Focal segmental glomerulosclerosis (FSGS) is considered one of the most severe glomerular diseases and around 80% of cases are resistant to steroid treatment. Since a large proportion of steroid-resistant (SR) FSGS patients progress to end-stage renal disease, other therapeutic strategies may benefit this population. However, identification of non-invasive biomarkers to predict this high-risk population is needed. Objective We aimed to identify the biomarker candidates to distinguish SR from steroid-sensitive (SS) patients using metabolomics approach and to identify the possible molecular mechanism of resistance. Methods Urine was collected from biopsy-proven FSGS patients eligible for monotherapy with prednisolone. Patients were followed for 6-8 weeks and categorized as SS or SR. Metabolite profile of urine samples was analyzed by one-dimensional 1H-nuclear magnetic resonance (1H-NMR). Predictive biomarker candidates and their diagnostic importance impaired molecular pathways in SR patients, and the common target molecules between biomarker candidates and drug were predicted. Results Homovanillic acid, 4-methylcatechol, and tyrosine were suggested as the significant predictive biomarker candidates, while L-3,4-dihydroxyphenylalanine, norepinephrine, and gentisic acid had high accuracy as well. Tyrosine metabolism was the most important pathway that is perturbed in SR patients. Common targets of the action of biomarker candidates and prednisolone were molecules that contributed in apoptosis. Conclusion Urine metabolites including homovanillic acid, 4-methylcatechol, and tyrosine may serve as potential non-invasive predictive biomarkers for evaluating the responsiveness of FSGS patients.

PD-L1 Testing in Non-small Cell Lung Cancer: Past, Present, and Future

Blockade of the programmed cell death-1 (PD-1) axis has already been established as an effective treatment of non-small cell lung cancer. Immunohistochemistry (IHC) for programmed death-ligand 1 (PD-L1) protein is the only available biomarker that can guide treatment with immune checkpoint inhibitors in non-small cell lung cancer. Because each PD-1/PD-L1 blockade was approved together with a specific PD-L1 IHC assay used in the clinical trials, pathologists have been challenged with performing various assays with a limited sample. To provide a more unified understanding of this, several cross-validation studies between platforms have been performed and showed consistent results. However, the interchangeability of assays may be limited in practice because of the risk of misclassification of patients for the treatment. Furthermore, several issues, including the temporal and spatial heterogeneity of PD-L1 expression in the tumor, and the potential for cytology specimens to be used as an alternative to tissue samples for PD-L1 testing, have still not been resolved. In the future, one of the main aims of immunotherapy research should be to find a novel predictive biomarker for PD-1 blockade therapy and a way to combine it with PD-L1 IHC and other tests.

Molecular diagnostics of lung cancer and its research progress / 中国实用内科杂志

Molecular markers are research hotspot in the field of malignant tumor research, and have important clinical significance for early diagnosis, prognosis monitoring and treatment of tumors. The precise treatment of individualized patients through molecular diagnosis has gradually become the consensus of clinical treatment of tumors. This article introduces the research progress of molecular diagnostic techniques in lung cancer.

Long Pentraxin 3 as a Predictive Marker of Mortality in Severe Septic Patients Who Received Successful Early Goal-Directed Therapy

PURPOSE: Pentraxin 3 (PTX3) has been suggested to be a prognostic marker of mortality in severe sepsis. Currently, there are limited data on biomarkers including PTX3 that can be used to predict mortality in severe sepsis patients who have undergone successful initial resuscitation through early goal-directed therapy (EGDT). MATERIALS AND METHODS: A prospective cohort study was conducted among 83 severe sepsis patients with fulfillment of all EGDT components and the achievement of final goal. Plasma PTX3 levels were measured by sandwich ELISA on hospital day (HD) 0, 3, and 7. The data for procalcitonin, C-reactive protein and delta neutrophil index were collected by electric medical record. The primary outcome was 28-day all-cause mortality. RESULTS: 28-day all-cause mortality was 19.3% and the median (interquartile range) APHCH II score of total patients was 16 (13–19). The non-survivors (n=16) had significantly higher PTX3 level at HD 0 [201.4 (56.9–268.6) ng/mL vs. 36.5 (13.7–145.3) ng/mL, p=0.008]. PTX3 had largest AUC(ROC) value for the prediction of mortality among PTX3, procalcitonin, delta neutrophil index, CRP and APACHE II/SOFA sore at HD 0 [0.819, 95% confidence interval (CI) 0.677–0.961, p=0.008]. The most valid cut-off level of PTX3 at HD 0 was 140.28 ng/mL (sensitivity 66.7%, specificity 73.8%). The PTX3 and procalcitonin at HD 0 showed strong correlation (r=0.675, p<0.001). However, PTX3 at HD 0 was the only independent predictive marker in Cox's proportional hazards model (≥140 ng/mL; hazard rate 7.16, 95% CI 2.46–15.85, p=0.001). CONCLUSION: PTX3 at HD 0 could be a powerful predictive biomarker of 28-day all-cause mortality in severe septic patients who have undergone successful EGDT.

Predictive Perspectives of Disease-Transformed Protein Biomarkers.

With advancement in instrumentation, computation and understanding of disease etiology, proteomics has been expanded to harness the knowledge of change in protein folding and misfolding, protein-protein interaction, protein modification, etc. during progression of disease which is a source of discovery for various biomarkers including predictive biomarkers. Various methodologies for disease prediction are reported using ‘omics’ technology; however, advancement in proteomics with discovery of protein biomarker allows for the estimation of disease risk from years to decades before any disease even manifests internally. Specific proteins as disease biomarkers that appear in the body fluid/diseased tissues are generally measured. Recently, new proteomics technologies are also being developed in order to facilitate both the highthroughput and high-sensitivity requirements of diseaserelated applications of proteomics and possibly providing the framework for prediction of diseases. Therefore, there is a growing interest in proteomics technologies to discover processes that are involved in various diseases, to discover new biomarkers that correlates with the prediction and early detection of diseases. Now there is change in research thinking where already known biomarkers alone or in combination of others are under investigation for advanced application like in prediction and early detection of chronic diseases. In this review, we have emphasized the prediction perspective of some of the protein biomarkers like CA-125, Lp-PLA2 and tau protein for diseases like cancers, cardiovascular diseases, and Alzheimer’s respectively.

Personalized Medicine in Breast Cancer: A Systematic Review / 한국유방암학회지

The recent advent of "-omics" technologies have heralded a new era of personalized medicine. Personalized medicine is referred to as the ability to segment heterogeneous subsets of patients whose response to a therapeutic intervention within each subset is homogeneous. This new paradigm in healthcare is beginning to affect both research and clinical practice. The key to success in personalized medicine is to uncover molecular biomarkers that drive individual variability in clinical outcomes or drug responses. In this review, we begin with an overview of personalized medicine in breast cancer and illustrate the most encountered statistical approaches in the recent literature tailored for uncovering gene signatures.