Discrimination of Head and Neck Squamous Cell Carcinoma Patients and Healthy Adults by 10-Color Flow Cytometry: Development of a Score Based on Leukocyte Subsets

EphA4 signaling is involved in the phenotype of well-differentiated oral squamous cell arcinoma with decreased tumor immunity

Metronomic chemotherapy is defined as a high-frequency low-dose schedule of chemotherapy drug administration. Although metronomic chemotherapy is widely used, the mechanisms underlying resistance to metronomic chemotherapy remain unclear. Therefore, we herein conducted a single institutional phase I/II trial to assess the efficacy and safety of metronomic chemotherapy with bleomycin plus S-1, an oral 5-FU prodrug, in the neoadjuvant setting for patients with oral squamous cell carcinoma (OSCC). The response rate of well-differentiated OSCC to metronomic chemotherapy was significantly lower. We investigated differences in molecular profiles between poorly or moderately differentiated head and neck squamous cell carcinoma (HNSCC) and well-differentiated HNSCC from patients with HNSCC TCGA data. EphA4 expression positively correlated with histological differentiation. An upstream regulator analysis correlated with EphA4 expression identified pathways associated with decreased mTORC1 signaling and T cell activation, including TCR, CD3, CD28, and CD40LG. An EphA4 blocking peptide (KYL) induced mTOR activation in well-differentiated OSCC cell lines. Plasmacytoid dendritic cell and CD8⁺ T cell numbers were higher in the microenvironment of poorly or moderately differentiated HNSCC than in that of well-differentiated HNSCC. Well-differentiated HNSCC had the characteristics of "cold tumors" (immune-excluded tumors). Moreover, KYL used with chemotherapeutic drugs synergistically increased cancer cell death. Well-differentiated OSCC is depleted of immune cells, which may be partly explained by the receptor tyrosine kinase EphA4.

Pre-Therapeutic VEGF Level in Plasma Is a Prognostic Bio-Marker in Head and Neck Squamous Cell Carcinoma (HNSCC)

Simple Summary

In the context of a growing variety in treatment strategies for patients with cancer, especially approaches based on antiangiogenetic pathways, we aimed to identify a useful biomarker for patients with head and neck squamous cell carcinoma (HNSCC). Our experimental results detected vascular endothelial growth factor (VEGF) in patients’ pre-therapeutic plasma, and not serum, which serves as a suitable biomarker for outcome prognostication. Results were validated in an independent cohort, confirming VEGF as an independent predictor (Pi) of outcomes in HNSCC patients. Therefore, pre-therapeutic VEGF in plasma may be an attractive biomarker in future HNSCC studies.

Abstract

Vascular endothelial growth factor (VEGF) is centrally involved in cancer angiogenesis. We hypothesized that pre-therapeutic VEGF levels in serum and plasma differ in their potential as biomarkers for outcomes in head and neck squamous cell carcinoma (HNSCC) patients. As prospectively defined in the study protocols of TRANSCAN-DietINT and NICEI-CIH, we measured VEGF in pretreatment serum and plasma of 75 HNSCC test cohort (TC) patients. We analyzed the prognostic value of VEGF concentrations in serum (VEGF_Serum) and plasma (VEGF_Plasma) for event-free survival (EFS) utilizing receiver-operating characteristics (ROC). Mean VEGF concentrations in plasma (34.6, 95% CI 26.0–43.3 ng/L) were significantly lower (p = 3.35 × 10⁻¹⁸) than in serum (214.8, 95% CI 179.6–250.0 ng/L) but, based on ROC (area under the curve, AUC_Plasma = 0.707, 95% CI 0.573–0.840; p = 0.006 versus AUC_Serum = 0.665, 95% CI 0.528–0.801; p = 0.030), superiorly correlated with event-free survival (EFS) of TC patients. Youden indices revealed optimum binary classification with VEGF_Plasma 26 ng/L and VEGF_Serum 264 ng/L. Kaplan–Meier plots demonstrated superiority of VEGF_Plasma in discriminating patients regarding outcome. Patients with VEGF_Plasma < 26 ng/L had superior nodal (NC), local (LC) and loco-regional control (LRC) leading to significant prolonged progression-free survival (PFS) and EFS. We successfully validated VEGF_Plasma according the cut-off <26 ng/L as predictive for superior outcome in an independent validation cohort (iVC) of 104 HNSCC patients from the studies DeLOS-II and LIFE and found better outcomes including prolonged tumor-specific (TSS) and overall survival (OS). Outcomes in TC and iVC combined again was related to VEGF_Plasma, and multivariate Cox regression revealed that VEGF_Plasma was an independent outcome predictor. In HNSCC, pre-therapeutic VEGF_Plasma is prognostic for outcomes.

New Biomarkers in Cancers

In this Special Issue of Cancers, the latest insights on biomarkers in cancers are presented in 33 up-to-the-minute research papers and reviews summing up the tremendous progress in this interesting and important field of research. The recent development of new therapeutic approaches has provided clinicians with more efficient tools than ever before for the treatment of cancerous diseases. However, choosing the right option requires to [...].

Machine Learning-Assisted Evaluation of Circulating DNA Quantitative Analysis for Cancer Screening

While the utility of circulating cell-free DNA (cfDNA) in cancer screening and early detection have recently been investigated by testing genetic and epigenetic alterations, here, an original approach by examining cfDNA quantitative and structural features is developed. First, the potential of cfDNA quantitative and structural parameters is independently demonstrated in cell culture, murine, and human plasma models. Subsequently, these variables are evaluated in a large retrospective cohort of 289 healthy individuals and 983 patients with various cancer types; after age resampling, this evaluation is done independently and the variables are combined using a machine learning approach. Implementation of a decision tree prediction model for the detection and classification of healthy and cancer patients shows unprecedented performance for 0, I, and II colorectal cancer stages (specificity, 0.89 and sensitivity, 0.72). Consequently, the methodological proof of concept of using both quantitative and structural biomarkers, and classification with a machine learning method are highlighted, as an efficient strategy for cancer screening. It is foreseen that the classification rate may even be improved by the addition of such biomarkers to fragmentomics, methylation, or the detection of genetic alterations. The optimization of such a multianalyte strategy with this machine learning method is therefore warranted.