The combination of platelet count and lymphocyte to monocyte ratio is a prognostic factor in patients with resected breast cancer

The use of platelets as a clinical tool in oncology: opportunities and challenges

Platelets are small circulating anucleated cells mainly involved in thrombosis and hemostasis processes. Moreover, platelets play an active role in tumorigenesis and cancer progression, stimulating angiogenesis and vascular remodelling, and protecting circulating cancer cells from shear forces and immune surveillance. Several reports indicate that platelet number in the blood circulation of cancer patients is associated with prognosis and response to treatment. However, the mechanisms of platelets "education" by cancer cells and the crosstalk between platelets and tumor are still unclear, and the role of "tumor educated platelets" (TEPs) is achieving growing interest in cancer research. TEPs are a biological source of cancer-derived biomarkers, especially RNAs that are protected by platelets membrane from circulating RNases, and could serve as a non-invasive tool for tumor detection, molecular profiling and evolution during therapy in clinical practice. Moreover, short platelet lifespan offers the possibility to get a snapshot assessment of cancer molecular profile, providing a real-time tool. We review and discuss the potential and the clinical utility, in terms of cancer diagnosis and monitoring, of platelet count together with other morphological parameters and of the more recent and innovative TEP profiling.

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer

BACKGROUND

Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis.

METHODS

Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve.

RESULTS

From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients.

CONCLUSIONS

This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

Prognostic role of preoperative circulating systemic inflammatory response markers in primary breast cancer: meta-analysis

BACKGROUND

Circulating markers of the systemic inflammatory response are prognostic in several cancers, but their role in operable breast cancer is unclear. A systematic review and meta-analysis of the literature was carried out.

METHODS

A search of electronic databases up to August 2020 identified studies that examined the prognostic value of preoperative circulating markers of the systemic inflammatory response in primary operable breast cancer. A meta-analysis was carried out for each marker with more than three studies, reporting a HR and 95 per cent confidence interval for disease-free survival (DFS), breast cancer-specific survival (BCSS) or overall survival (OS).

RESULTS

In total, 57 studies were reviewed and 42 were suitable for meta-analysis. Higher neutrophil-to-lymphocyte ratio (NLR) was associated with worse overall survival (OS) (pooled HR 1.75, 95 per cent c.i. 1.52 to 2.00; P < 0.001), disease-free survival (DFS) (HR 1.67, 1.50 to 1.87; P < 0.001), and breast cancer-specific survival (BCSS) (HR 1.89, 1.35 to 2.63; P < 0.001). This effect was also seen with an arithmetically-derived NLR (dNLR). Higher platelet-to-lymphocyte ratio (PLR) was associated with worse OS (HR 1.29, 1.10 to 1.50; P = 0.001) and DFS (HR 1.58, 1.33 to 1.88; P < 0.001). Higher lymphocyte-to-monocyte ratio (LMR) was associated with improved DFS (HR 0.65, 0.51 to 0.82; P < 0.001), and higher C-reactive protein (CRP) level was associated with worse BCSS (HR 1.22, 1.07 to 1.39; P = 0.002) and OS (HR 1.24, 1.14 to 1.35; P = 0.002).

CONCLUSION

Current evidence suggests a role for preoperative NLR, dNLR, LMR, PLR, and CRP as prognostic markers in primary operable breast cancer. Further work should define their role in clinical practice, particularly reproducible thresholds and molecular subtypes for which these may be of most value.

Diagnostic Value of Neutrophil-to-Lymphocyte, Platelet-to-Lymphocyte, and Monocyte-to-Lymphocyte Ratios for the Assessment of Rheumatoid Arthritis in Patients with Undifferentiated Inflammatory Arthritis

Background: To investigate the diagnostic performance of the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR) in the diagnosis of rheumatoid arthritis (RA) in subjects with undifferentiated inflammatory arthritis (UIA). Methods: This retrospective cohort study investigated 201 female patients with UIA (≥1 swollen joint) and 280 age-matched, healthy female controls. “Clinical RA” was defined based on the clinical judgment of a rheumatologist and “disease-modifying anti-rheumatic drugs (DMARDs) RA” was defined as a case of initiating DMARDs treatment within 6 months after the first visit. “Classified RA” was defined as fulfilling the 2010 classification criteria for RA. Receiver operating characteristics were used to determine the optimal cut-off value. Results: UIA patients had a significantly higher NLR, PLR, and MLR than the controls. Among the 201 UIA patients, 65 (32.3%), 63 (31.3%), and 61 (30.3%) subjects were classified as clinical RA, DMARDs RA, and classified RA, respectively. At a cut-off of 0.24, MLR showed moderate accuracy for the diagnosis of DMARDs RA (sensitivity, 65.1%; specificity, 62.3%; area under the curve [AUC], 0.701; p < 0.001). However, the diagnostic accuracies of NLR and PLR were low. Conclusions: MLR may be used as a complementary diagnostic indicator for RA diagnosis in patients with UIA.