Eight-Signature Classifier for Prediction of Nasopharyngeal Carcinoma Survival

AI-Based Risk Score from Tumour-Infiltrating Lymphocyte Predicts Locoregional-Free Survival in Nasopharyngeal Carcinoma

BACKGROUND

Locoregional recurrence of nasopharyngeal carcinoma (NPC) occurs in 10% to 50% of cases following primary treatment. However, the current main prognostic markers for NPC, both stage and plasma Epstein-Barr virus DNA, are not sensitive to locoregional recurrence.

METHODS

We gathered 385 whole-slide images (WSIs) from haematoxylin and eosin (H&E)-stained NPC sections (n = 367 cases), which were collected from Sun Yat-sen University Cancer Centre. We developed a deep learning algorithm to detect tumour nuclei and lymphocyte nuclei in WSIs, followed by density-based clustering to quantify the tumour-infiltrating lymphocytes (TILs) into 12 scores. The Random Survival Forest model was then trained on the TILs to generate risk score.

RESULTS

Based on Kaplan-Meier analysis, the proposed methods were able to stratify low- and high-risk NPC cases in a validation set of locoregional recurrence with a statically significant result (p < 0.001). This finding was also found in distant metastasis-free survival (p < 0.001), progression-free survival (p < 0.001), and regional recurrence-free survival (p < 0.05). Furthermore, in both univariate analysis (HR: 1.58, CI: 1.13-2.19, p < 0.05) and multivariate analysis (HR:1.59, CI: 1.11-2.28, p < 0.05), we also found that our methods demonstrated a strong prognostic value for locoregional recurrence.

CONCLUSION

The proposed novel digital markers could potentially be utilised to assist treatment decisions in cases of NPC.

Comprehensive integrated analysis of MR and DCE-MR radiomics models for prognostic prediction in nasopharyngeal carcinoma

Although prognostic prediction of nasopharyngeal carcinoma (NPC) remains a pivotal research area, the role of dynamic contrast-enhanced magnetic resonance (DCE-MR) has been less explored. This study aimed to investigate the role of DCR-MR in predicting progression-free survival (PFS) in patients with NPC using magnetic resonance (MR)- and DCE-MR-based radiomic models. A total of 434 patients with two MR scanning sequences were included. The MR- and DCE-MR-based radiomics models were developed based on 289 patients with only MR scanning sequences and 145 patients with four additional pharmacokinetic parameters (volume fraction of extravascular extracellular space (ve), volume fraction of plasma space (vp), volume transfer constant (Ktrans), and reverse reflux rate constant (kep) of DCE-MR. A combined model integrating MR and DCE-MR was constructed. Utilizing methods such as correlation analysis, least absolute shrinkage and selection operator regression, and multivariate Cox proportional hazards regression, we built the radiomics models. Finally, we calculated the net reclassification index and C-index to evaluate and compare the prognostic performance of the radiomics models. Kaplan-Meier survival curve analysis was performed to investigate the model's ability to stratify risk in patients with NPC. The integration of MR and DCE-MR radiomic features significantly enhanced prognostic prediction performance compared to MR- and DCE-MR-based models, evidenced by a test set C-index of 0.808 vs 0.729 and 0.731, respectively. The combined radiomics model improved net reclassification by 22.9%-52.6% and could significantly stratify the risk levels of patients with NPC (p = 0.036). Furthermore, the MR-based radiomic feature maps achieved similar results to the DCE-MR pharmacokinetic parameters in terms of reflecting the underlying angiogenesis information in NPC. Compared to conventional MR-based radiomics models, the combined radiomics model integrating MR and DCE-MR showed promising results in delivering more accurate prognostic predictions and provided more clinical benefits in quantifying and monitoring phenotypic changes associated with NPC prognosis.

An Individualized Prognostic Model in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma Based on Serum Metabolomic Profiling

PURPOSE

This study aims to evaluate the value of a serum metabolomics-based metabolic signature for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients, thereby assisting clinical decisions.

METHODS

In this retrospective study, a total of 320 LA-NPC patients were randomly divided into a training set (ca. 70%; n = 224) and a validation set (ca. 30%; n = 96). Serum samples were analyzed using widely targeted metabolomics. Univariate and multivariate Cox regression analyses were used to identify candidate metabolites related to progression-free survival (PFS). Patients were categorized into high-risk and low-risk groups based on the median metabolic risk score (Met score), and the PFS difference between the two groups was compared using Kaplan-Meier curves. The predictive performance of the metabolic signature was evaluated using the concordance index (C-index) and the time-dependent receiver operating characteristic (ROC), and a comprehensive nomogram was constructed using the Met score and other clinical factors.

RESULTS

Nine metabolites were screened to build the metabolic signature and generate the Met score, which effectively separated patients into low- and high-risk groups. The C-index in the training and validation sets was 0.71 and 0.73, respectively. The 5-year PFS was 53.7% (95% CI, 45.12-63.86) in the high-risk group and 83.0% (95%CI, 76.31-90.26) in the low-risk group. During the construction of the nomogram, Met score, clinical stage, pre-treatment EBV DNA level, and gender were identified as independent prognostic factors for PFS. The predictive performance of the comprehensive model was better than that of the traditional model.

CONCLUSION

The metabolic signature developed through serum metabolomics is a reliable prognostic indicator of PFS in LA-NPC patients and has important clinical significance.

Gene signature developed for predicting early relapse and survival in early-stage pancreatic cancer

BACKGROUND

The aim of this study was to construct a predictive signature integrating tumour-mutation- and copy-number-variation-associated features using machine learning to precisely predict early relapse and survival in patients with resected stage I-II pancreatic ductal adenocarcinoma.

METHODS

Patients with microscopically confirmed stage I-II pancreatic ductal adenocarcinoma undergoing R0 resection at the Chinese PLA General Hospital between March 2015 and December 2016 were enrolled. Whole exosome sequencing was performed, and genes with different mutation or copy number variation statuses between patients with and without relapse within 1 year were identified using bioinformatics analysis. A support vector machine was used to evaluate the importance of the differential gene features and to develop a signature. Signature validation was performed in an independent cohort. The associations of the support vector machine signature and single gene features with disease-free survival and overall survival were assessed. Biological functions of integrated genes were further analysed.

RESULTS

Overall, 30 and 40 patients were included in the training and validation cohorts, respectively. Some 11 genes with differential patterns were first identified; using a support vector machine, four features (mutations of DNAH9, TP53, and TUBGCP6, and copy number variation of TMEM132E) were further selected and integrated to construct a predictive signature (the support vector machine classifier). In the training cohort, the 1-year disease-free survival rates were 88 per cent (95 per cent c.i. 73 to 100) and 7 per cent (95 per cent c.i. 1 to 47) in the low-support vector machine subgroup and the high-support vector machine subgroup respectively (P < 0.001). Multivariable analyses showed that high support vector machine was significantly and independently associated with both worse overall survival (HR 29.20 (95 per cent c.i. 4.48 to 190.21); P < 0.001) and disease-free survival (HR 72.04 (95 per cent c.i. 6.74 to 769.96); P < 0.001). The area under the curve of the support vector machine signature for 1-year disease-free survival (0.900) was significantly larger than the area under the curve values of the mutations of DNAH9 (0.733; P = 0.039), TP53 (0.767; P = 0.024), and TUBGCP6 (0.733; P = 0.023), the copy number variation of TMEM132E (0.700; P = 0.014), TNM stage (0.567; P = 0.002), and differentiation grade (0.633; P = 0.005), suggesting higher predictive accuracy for prognosis. The value of the signature was further validated in the validation cohort. The four genes included in the support vector machine signature (DNAH9, TUBGCP6, and TMEM132E were novel in pancreatic ductal adenocarcinoma) were significantly associated with the tumour immune microenvironment, G protein-coupled receptor binding and signalling, cell-cell adhesion, etc.

CONCLUSION

The newly constructed support vector machine signature precisely and powerfully predicted relapse and survival in patients with stage I-II pancreatic ductal adenocarcinoma after R0 resection.

The prognostic predictive value of systemic immune index and systemic inflammatory response index in nasopharyngeal carcinoma: A systematic review and meta-analysis

Objective

To study the predictive value of systemic immune index (SII) and systemic inflammatory response index (SIRI) in the prognosis of patients with nasopharyngeal carcinoma.

Methods

Two researchers independently searched PubMed, Cochrane, Embase, and Web of Science databases (until March 18, 2022) for all studies on SII, SIRI, and prognosis in patients with nasopharyngeal carcinoma. Quality assessment of included studies was assessed using the Newcastle-Ottawa Scale (NOS). In addition, a bivariate mixed-effects model was used to explore predictive value.

Results

A total of 9 studies that satisfied the requirements were included, involving, 3187 patients with nasopharyngeal carcinoma. The results of the meta-analysis showed that SII could be an independent predictor of OS (HR=1.78, 95%CI [1.44-2.20], Z=5.28, P<0.05), and SII could also be an independent predictor of PFS (HR=1.66, 95%CI [1.36-2.03], Z=4.94, P<0.05). In addition, SIRI could also serve as an independent predictor of OS (HR=2.88, 95%CI [1.97-4.19], Z=5.51, P<0.05). The ROC area was 0.63, the sensitivity was 0.68 (95%CI [0.55-0.78]), and the specificity was 0.55 (95%CI [0.47-0.62]), all of which indicated that SII had a certain predictive value for OS.

Conclusion

SII and SIRI can be used as independent predictors to predict the prognosis and survival status of patients with nasopharyngeal carcinoma and have certain predictive accuracy. Therefore, SII and SIRI should be considered in studies that update survival risk assessment systems.

Systematic Review Registration

https://www.ytyhdyy.com/, identifier PROSPERO (CRD42022319678).

Post-operative anastomotic leakage and collagen changes in patients with rectal cancer undergoing neoadjuvant chemotherapy vs chemoradiotherapy

Background

A significant difference in the anastomotic leakage (AL) rate has been observed between patients with locally advanced rectal cancer who have undergone preoperative chemotherapy and those undergoing preoperative chemoradiotherapy. This study aimed to quantitatively analyse collagen structural changes caused by preoperative chemoradiotherapy and illuminate the relationship between collagen changes and AL.

Methods

Anastomotic distal and proximal "doughnut" specimens from the Sixth Affiliated Hospital of Sun Yat-sen University (Guangzhou, China) were quantitatively assessed for collagen structural changes between patients with and without preoperative radiotherapy using multiphoton imaging. Then, patients treated with preoperative chemoradiotherapy were used as a training cohort to construct an AL-SVM classifier by the Mann-Whitney U test and support vector machine (SVM). An independent test cohort from the Fujian Province Cancer Hospital (Fuzhou, China) was used to validate the AL-SVM classifier.

Results

A total of 207 patients were included from the Sixth Affiliated Hospital of Sun Yat-sen University. The AL rate in the preoperative chemoradiotherapy group (n = 107) was significantly higher than that in the preoperative chemotherapy group (n = 100) (21.5% vs 7.0%, P = 0.003). A fully quantitative analysis showed notable morphological and spatial distribution feature changes in collagen in the preoperative chemoradiotherapy group. Then, the patients who received preoperative chemoradiotherapy were used as a training cohort to construct the AL-SVM classifier based on five collagen features and the tumor distance from the anus. The AL-SVM classifier showed satisfactory discrimination and calibration with areas under the curve of 0.907 and 0.856 in the training and test cohorts, respectively.

Conclusions

The collagen structure may be notably altered by preoperative radiotherapy. The AL-SVM classifier was useful for the individualized prediction of AL in rectal cancer patients undergoing preoperative chemoradiotherapy.

Phosphoserine phosphatase as an indicator for survival through potentially influencing the infiltration levels of immune cells in neuroblastoma

Introduction: Metabolic deregulation, a hallmark of cancer, fuels cancer cell growth and metastasis. Phosphoserine phosphatase (PSPH), an enzyme of the serine metabolism pathway, has been shown to affect patients’ prognosis in many cancers but its significance in neuroblastoma remains unknown. Here, we show that the functional role and potential mechanism of PSPH and it is correlated with survival of neuroblastoma patients.

Patients and Methods: The TARGET dataset (n = 151) and our hospital-based cases (n = 55) were used for assessing the expression level of PSPH associated with survival in neuroblastoma patients, respectively. Then, in vitro experiments were performed to define the role of PSPH in neuroblastoma. The ESTIMATE and TIMER algorithms were utilized to examine the correlation between PSPH expression level and abundance of immune cells. Further, Kaplan-Meier survival analysis was performed to evaluate the effect of both PSPH and immune cells on patients’ prognosis.

Results: High expression of PSPH was significantly associated with unfavorable overall survival (OS) and event-free survival (EFS) in both the TARGET dataset and our hospital-based cases, and was an independent predictor of OS (hazard ratio, 2.00; 95% confidence intervals, 1.21–3.30, p = 0.0067). In vitro experiments showed that high expression of PSPH significantly promoted cell growth and metastasis. Further, the ESTIMATE result suggested that high expression level of PSPH was negatively associated with low stromal and ESTIMATE score. Specifically, high PSPH expression was found to be negatively associated with CD8⁺ T cell, macrophages and neutrophils, which negatively affected survival of neuroblastoma patients (p < 0.0001, p = 0.0005, and p = 0.0004, respectively).

Conclusion: These findings suggested that PSPH expression could be a promising indicator for prognosis and immunotherapy in neuroblastoma patients by potentially influencing infiltration levels of immune cells.

Neutrophil-to-lymphocyte ratio trend: A novel prognostic predictor in patients with nasopharyngeal carcinoma receiving radiotherapy

BACKGROUND

Peripheral neutrophil-lymphocyte ratio (NLR), reflecting immune-inflammation status, shows great potential for tumor progression and outcome. Pre-treatment NLR does not fully reflect the immune-inflammatory response to treatment. This study aimed to introduce the NLR trend as a new indicator and to investigate its prognostic value in patients with nasopharyngeal carcinoma receiving radiotherapy.

METHODS

This retrospective study evaluated patients with nasopharyngeal carcinoma treated with radiotherapy. The NLR trend value was calculated from the fitted line gradient via the NLRs before, during (at least once), and after each patient's first radiotherapy. The Kaplan-Meier curve and log-rank test were used to calculate and compare survival outcomes of different pretreatment NLRs and NLR trends for progression-free survival, locoregional recurrence-free survival (LRFS), and overall survival at 3 and 5 years. Multivariate Cox regression analyses were performed to assess the association between the NLR trend plus 3- and 5-year overall survival.

RESULTS

The study included 528 patients. A lower NLR trend predicted worse progression-free survival, LRFS, plus 3- and 5-year overall survival. Multivariate Cox regression analysis showed that the NLR trend independently predicted 3- and 5-year overall survival. Sub-group analysis showed that the prognosis of patients with a low pretreatment NLR and a high NLR trend were superior to those of other groups.

CONCLUSION

The NLR trend independently predicted the prognosis of patients with nasopharyngeal carcinoma receiving radiotherapy. The NLR trend and the pretreatment NLR combination is more precise than pretreatment NLR in predicting prognosis. A high NLR trend may be evidence of a positive immune response to radiotherapy in patients with nasopharyngeal carcinoma.

Prognostic significance of MRI-based late-course tumor volume in locoregionally advanced nasopharyngeal carcinoma

Background

To validate tumor volume-based imaging markers for predicting local recurrence-free survival (LRFS) in locoregionally advanced nasopharyngeal carcinoma patients, who underwent induction chemotherapy followed by definitive intensity-modulated radiotherapy.

Methods

We enrolled 145 patients with stage III–IVA nasopharyngeal carcinoma in this retrospective study. Pre-treatment tumor volume (V_pre) and late-course volume (LCV) were measured based on the MRIs scanned before treatment and during the first 3 days in the sixth week of radiotherapy, respectively. The volume regression rate (VRR) was calculated according to V_pre and LCV. Receiver operating characteristic (ROC) curves were used to identify the cut-off best separating patient subgroups in assessing the prognostic value of V_pre, LCV and VRR. The Kaplan–Meier method was used for survival analysis. Prognostic analyses were performed using univariate and multivariate COX proportional hazard models.

Results

The LCV was 5.3 ± 0.5 (range 0–42.1) cm³; The VRR was 60.4 ± 2.2% (range 2.9–100.0). The median follow-up period was 36 months (range 6–98 months). The cut-off value of LCV determined by the ROC was 6.8 cm³ for LRFS prediction (sensitivity 68.8%; specificity 79.8%). The combination of LCV and VRR for LRFS prediction (AUC = 0.79, P < 0.001, 95% CI 0.67–0.90), LCV (AUC = 0.74, P = 0.002, 95% CI 0.60–0.88) and V_pre (AUC = 0.71, P = 0.007, 95% CI 0.56–0.85) are better than T category (AUC = 0.64, P = 0.062, 95% CI 0.50–0.79) alone. Patients with LCV ≤ 6.8 cm³ had significantly longer LRFS (P < 0.001), disease-free survival (DFS, P < 0.001) and overall survival (OS, P = 0.005) than those with LCV > 6.8 cm³. Multivariate Cox regression showed LCV was the only independent prognostic factor for local control (HR = 7.80, 95% CI 2.69–22.6, P < 0.001).

Conclusions

LCV is a promising prognostic factor for local control and chemoradiosensitivity in patients with locoregionally advanced NPC. The LCV, and the combination of LCV with VRR are more robust predictors for patient survival than T category.

Clinical Significance of a CD3/CD8-Based Immunoscore in Neuroblastoma Patients Using Digital Pathology

Background

Infiltrating immune cells have been reported as prognostic markers in many cancer types. We aimed to evaluate the prognostic role of tumor-infiltrating lymphocytes, namely CD3+ T cells, CD8+ cytotoxic T cells and memory T cells (CD45RO+), in neuroblastoma.

Patients and Methods

Immunohistochemistry was used to determine the expression of CD3, CD8 and CD45RO in the tumor samples of 244 neuroblastoma patients. We then used digital pathology to calculate the densities of these markers and derived an immunoscore based on such densities.

Results

Densities of CD3+ and CD8+ T cells in tumor were positively associated with the overall survival (OS) and event-free survival (EFS), whereas density of CD45RO+ T cells in tumor was negatively associated with OS but not EFS. An immunoscore with low density of CD3 and CD8 (CD3-CD8-) was indictive of a greater risk of death (hazard ratio 6.39, 95% confidence interval 3.09-13.20) and any event (i.e., relapse at any site, progressive disease, second malignancy, or death) (hazard ratio 4.65, 95% confidence interval 2.73-7.93). Multivariable analysis revealed that the CD3-CD8- immunoscore was an independent prognostic indicator for OS, even after adjusting for other known prognostic indicators.

Conclusions

The new immunoscore based on digital pathology evaluated densities of tumor-infiltrating CD3+ and CD8+ T cells contributes to the prediction of prognosis in neuroblastoma patients.

A Radiomics Nomogram for Non-Invasive Prediction of Progression-Free Survival in Esophageal Squamous Cell Carcinoma

To construct a prognostic model for preoperative prediction on computed tomography (CT) images of esophageal squamous cell carcinoma (ESCC), we created radiomics signature with high throughput radiomics features extracted from CT images of 272 patients (204 in training and 68 in validation cohort). Multivariable logistic regression was applied to build the radiomics signature and the predictive nomogram model, which was composed of radiomics signature, traditional TNM stage, and clinical features. A total of 21 radiomics features were selected from 954 to build a radiomics signature which was significantly associated with progression-free survival (p &lt; 0.001). The area under the curve of performance was 0.878 (95% CI: 0.831–0.924) for the training cohort and 0.857 (95% CI: 0.767–0.947) for the validation cohort. The radscore of signatures' combination showed significant discrimination for survival status. Radiomics nomogram combined radscore with TNM staging and showed considerable improvement over TNM staging alone in the training cohort (C-index, 0.770 vs. 0.603; p &lt; 0.05), and it is the same with clinical data (C-index, 0.792 vs. 0.680; p &lt; 0.05), which were confirmed in the validation cohort. Decision curve analysis showed that the model would receive a benefit when the threshold probability was between 0 and 0.9. Collectively, multiparametric CT-based radiomics nomograms provided improved prognostic ability in ESCC.

Impact of Platelets to Lymphocytes Ratio and Lymphocytes during Radical Concurrent Radiotherapy and Chemotherapy on Patients with Nonmetastatic Esophageal Squamous Cell Carcinoma

Purpose

This study examined the importance of hematological parameters as prognostic markers for people with esophageal cancer receiving radical concurrent chemoradiation.

Methods

106 patients with esophageal cancer are included in this study. Cox regression analysis, Kaplan-Meier method, and chi-square test were used to analyze our data.

Results

The median follow-up time for patients was 15.5 months (3-55). Univariate and multivariate analyses showed that age, the change of platelet-to-lymphocyte ratio (ΔPLR), and the change rate of circulating lymphocyte count (ΔCLC%) were independent influencing factors of OS and DFS. The patients were grouped according to the median of ΔPLR and ΔCLC%, and analysis showed that a higher ΔPLR and a higher ΔCLC% was related to poor OS and DFS (P < 0.001, P < 0.001 and P < 0.001, P < 0.001). By subgroup analysis, the OS of T1-4N1-2 were better in the low ΔPLR group than the high one (P = 0.03, P < 0.001, P = 0.001, P < 0.001, and P = 0.008). DFS of T3-4N1-2 in the low ΔPLR group were better than the high one (P < 0.001, P = 0.016 and P < 0.001, P = 0.022). For patients with T1-4N0-2, the OS in the low ΔCLC% group were better than in the high ΔCLC% group (P = 0.01, P < 0.001, P < 0.002, P = 0.012, P < 0.001, and P = 0.024). For T1-4N1-2, the DFS were better in the low ΔCLC% group than others (P = 0.042, P < 0.001, P < 0.001, P < 0.001, and P = 0.006).

Conclusion

ΔPLR and ΔCLC% are independent factors of OS and DFS, and a lower ΔPLR and ΔCLC% are associated with a better OS and DFS. And T3-4N1-2 patients in the low ΔPLR group and low ΔCLC% group have greater survival benefit.

Radiomics Nomogram Based on Multiple-Sequence Magnetic Resonance Imaging Predicts Long-Term Survival in Patients Diagnosed With Nasopharyngeal Carcinoma

Purpose

Although the tumor–node–metastasis staging system is widely used for survival analysis of nasopharyngeal carcinoma (NPC), tumor heterogeneity limits its utility. In this study, we aimed to develop and validate a radiomics model, based on multiple-sequence magnetic resonance imaging (MRI), to estimate the probability of overall survival in patients diagnosed with NPC.

Methods

Multiple-sequence MRIs, including T1-weighted, T1 contrast, and T2-weighted imaging, were collected from patients diagnosed with NPC. Radiomics features were extracted from the contoured gross tumor volume of three sequences from each patient using the least absolute shrinkage and selection operator with the Cox regression model. The optimal Rad score was determined using 12 of the 851 radiomics features derived from the multiple-sequence MRI and its discrimination power was compared in the training and validation cohorts. For better prediction performance, an optimal nomogram (radiomics nomogram-MS) that incorporated the optimal Rad score and clinical risk factors was developed, and a calibration curve and a decision curve were used to further evaluate the optimized discrimination power.

Results

A total of 504 patients diagnosed with NPC were included in this study. The optimal Rad score was significantly correlated with overall survival in both the training [C-index: 0.731, 95% confidence interval (CI): 0.709–0.753] and validation cohorts (C-index: 0.807, 95% CI: 0.782–0.832). Compared with the nomogram developed with only single-sequence MRI, the radiomics nomogram-MS had a higher discrimination power in both the training (C-index: 0.827, 95% CI: 0.809–0.845) and validation cohorts (C-index: 0.836, 95% CI: 0.815–0.857). Analysis of the calibration and decision curves confirmed the effectiveness and utility of the optimal radiomics nomogram-MS.

Conclusions

The radiomics nomogram model that incorporates multiple-sequence MRI and clinical factors may be a useful tool for the early assessment of the long-term prognosis of patients diagnosed with NPC.

Prognostic Biomarkers for Survival in Nasopharyngeal Carcinoma: A Systematic Review of the Literature

This systematic review aims to identify prognostic molecular biomarkers which demonstrate strong evidence and a low risk of bias in predicting the survival of nasopharyngeal carcinoma (NPC) patients. The literature was searched for on PubMed to identify original clinical studies and meta-analyses which reported associations between molecular biomarkers and survival, including ≥150 patients with a survival analysis, and the results were validated in at least one independent cohort, while meta-analyses must include ≥1000 patients with a survival analysis. Seventeen studies fulfilled these criteria-two studies on single nucleotide polymorphisms (SNPs), three studies on methylation biomarkers, two studies on microRNA biomarkers, one study on mutational signature, six studies on gene expression panels, and three meta-analyses on gene expressions. The comparison between the hazard ratios of high-risk and low-risk patients along with a multivariate analysis are used to indicate that these biomarkers have significant independent prognostic values for survival. The biomarkers also indicate a response to certain treatments and whether they could be used as therapeutic targets. This review highlights that patients' genetics, epigenetics, and signatures of cancer and immune cells in the tumor microenvironment (TME) play a vital role in determining their survival.

Nogo-B promotes invasion and metastasis of nasopharyngeal carcinoma via RhoA-SRF-MRTFA pathway

Distant metastasis remains the major cause for treatment failure in patients with nasopharyngeal carcinoma (NPC). Thus, it is necessary to investigate the underlying regulation mechanisms and potential biomarkers for NPC metastasis. Nogo-B (neurite outgrowth inhibitor B), encoded by reticulon-4, has been shown to be associated with the progression and advanced stage of several cancer types. However, the relationship between Nogo-B and NPC remains unknown. In this study, we found that higher expression of Nogo-B was detected in NPC cells and tissues. Higher expression of Nogo-B was statistically relevant to N stage, M stage, and poor prognosis in NPC patients. Further functional investigations indicated that Nogo-B overexpression could increase the migration, invasion, and metastasis ability of NPC cells in vitro and in vivo. Mechanistically, Nogo-B promoted epithelial-mesenchymal transition (EMT) and enhanced the invasive potency by interacting directly with its receptor NgR3 in NPC. Additionally, overexpression of Nogo-B could upregulate the protein levels of p-RhoA, SRF, and MRTFA. A positive relationship was found between the expression of Nogo-B and the p-RhoA in NPC patients as well as in mouse lung xenografts. Nogo-B^high p-RhoA^high expression was significantly associated with N stage, M stage, and poor prognosis in NPC patients. Notably, CCG-1423, an inhibitor of the RhoA-SRF-MRTFA pathway, could reverse the invasive potency of Nogo-B and NgR3 in NPC cell lines, and decrease the expression of N-Cadherin, indicating that CCG-1423 may be a potential target drug of NPC. Taken together, our findings reveal that Nogo-B enhances the migration and invasion potency of NPC cells via EMT by binding to its receptor NgR3 to regulate the RhoA-SRF-MRTFA pathway. These findings could provide a novel insight into understanding the metastasis mechanism and targeted therapy of advanced NPC.

Development and validation of a transcriptomics-based gene signature to predict distant metastasis and guide induction chemotherapy in locoregionally advanced nasopharyngeal carcinoma

AIM

Metastasis is the primary cause of treatment failure in nasopharyngeal carcinoma (NPC); however, the current tumour-node-metastasis staging system has limitations in predicting distant metastasis and guiding induction chemotherapy (IC) application. Here, we established a transcriptomics-based gene signature to assess the risk of distant metastasis and guide IC in locoregionally advanced NPC.

METHODS

Transcriptome sequencing was performed on NPC biopsy samples from 12 pairs of patients with different metastasis risks. Bioinformatics and qPCR were used to identify differentially expressed genes (DEGs), while univariate and multivariate analyses were used to select prognostic indicators for the gene signature. A signature-based nomogram was established in a training cohort (n = 191) and validated in an external cohort (n = 263).

RESULTS

Eleven DEGs were identified between metastatic and non-metastatic NPC. Four of these (AK4, CPAMD8, DDAH1 and CRTR1) were used to create a gene signature that effectively categorised patients into low- and high-risk metastasis groups (training: 91.1 versus 70.4%, p < 0.0001, C-index = 0.752; validation: 88.4 versus 73.9%, p = 0.00057, C-index = 0.741). IC with concurrent chemoradiotherapy (CCRT) improved distant metastasis-free survival in low-risk patients (94.4 versus 85.0%, p = 0.043), whereas patients in the high-risk group did not benefit from IC (72.6 versus 74.9%, p = 0.946).

CONCLUSIONS

Our transcriptomics-based gene signature was able to reliably predict metastasis in locoregionally advanced NPC and could be used to identify candidates that could benefit from IC + CCRT.

Usefulness of 18F-fluorodeoxyglucose positron-emission tomography/computed tomography combined with the platelet-lymphocyte ratio in predicting the prognosis of nasopharyngeal carcinoma

OBJECTIVES

To investigate the value of ¹⁸F-fluorodeoxyglucose (FDG) positron-emission tomography (PET)/computed tomography (CT) combined with the platelet-lymphocyte ratio (PLR) in predicting the prognosis of nasopharyngeal carcinoma (NPC).

METHODS

This was a retrospective analysis of the data of 73 patients with NPC who underwent ¹⁸F-FDG PET/CT before treatment from January 2010 to December 2014. The maximum standard uptake value (SUVmax) of NPC and the PLR within 1 week before treatment were both measured. The Mann-Whitney U-test was used to compare the differences between the SUVmax and PLR among the different clinical characteristics of patients with NPC and the 5-year progression-free survival (PFS) rate; according to the receiver operating characteristic (ROC) curve, the best cutoff values of the SUVmax and PLR were obtained and used to group patients. The Kaplan-Meier method and Log-rank test were used to conduct univariate analysis of 5-year PFS in patients with NPC, and Cox regression was used to conduct multivariate analysis; differences in the 5-year PFS of patients with different SUVmax values combined with the PLR were compared.

RESULTS

The SUVmax and PLR of patients with disease progression within 5 years were higher than those of patients without disease progression (p = 0.006 and p = 0.026). SUVmax = 9.7 and PLR = 132.98 had the best prognostic diagnostic efficiency for patients. Cox multivariate analysis showed that the SUVmax and PLR are independent factors affecting the prognosis of NPC. The 5-year PFS of patients with SUVmax <9.7 was significantly higher than that of patients with SUVmax ≥9.7 in the high PLR group (PLR ≥132.98) and in the low PLR group (PLR <132.98) (59.3% vs  29.4%, p = 0.033 and 90.9% vs 42.9%, p = 0.006, respectively). For patients with SUVmax <9.7, the 5-year PFS of the high PLR group was significantly lower than the low PLR group (59.3% vs  90.9%, p = 0.016); for patients with SUVmax ≥9.7, there was no significant difference in 5-year PFS between the high PLR group and the low PLR group (29.4% vs  42.9%, p = 0.406).

CONCLUSIONS

Both the SUVmax of the primary tumor and the PLR before treatment have an important influence on the prognosis of NPC. Combining the SUVmax and the PLR can more accurately predict the prognosis of patients with NPC.

ADVANCES IN KNOWLEDGE

In this study, we evaluated the prognostic value of combining pretreatment tumor ¹⁸F-FDG uptake on PET/CT imaging and PLR in NPC patients. We found that both SUVmax and PLR are independent factors for the PFS of NPC patients, and a low SUVmax (SUVmax <9.7) combined with a low PLR (PLR <132.98) revealed significant PFS benefit.

MRI-Based Back Propagation Neural Network Model as a Powerful Tool for Predicting the Response to Induction Chemotherapy in Locoregionally Advanced Nasopharyngeal Carcinoma

BACKGROUND

Pretreatment individualized assessment of tumor response to induction chemotherapy (ICT) is a need in locoregionally advanced nasopharyngeal carcinoma (LANPC). Imaging method plays vital role in tumor response assessment. However, powerful imaging method for ICT response prediction in LANPC is insufficient.

PURPOSE

To establish a robust model for predicting response to ICT in LANPC by comparing the performance of back propagation neural network (BPNN) model with logistic regression model.

STUDY TYPE

Retrospective.

POPULATION

A total of 286 LANPC patients were assigned to training (N = 200, 43.8 ± 10.9 years, 152 male) and testing (N = 86, 43.5 ± 11.3 years, 57 male) cohorts.

FIELD STRENGTH/SEQUENCE

T₂ -weighted imaging, contrast enhanced-T₁ -weighted imaging using fast spin echo sequences at 1.5 T scanner.

ASSESSMENT

Predictive clinical factors were selected by univariate and multivariate logistic models. Radiomic features were screened by interclass correlation coefficient, single-factor analysis, and the least absolute shrinkage selection operator (LASSO). Four models based on clinical factors (Model^clinic ), radiomics features (Model^radiomics ), and clinical factors + radiomics signatures using logistic (Model^combined ), and BPNN (Model^BPNN ) methods were established, and model performances were compared.

STATISTICAL TESTS

Student's t-test, Mann-Whitney U-test, and Chi-square test or Fisher's exact test were used for comparison analysis. The performance of models was assessed by area under the receiver operating characteristic (ROC) curve (AUC) and Delong test. P < 0.05 was considered statistical significance.

RESULTS

Three significant clinical factors: Epstein-Barr virus-DNA (odds ratio [OR] = 1.748; 95% confidence interval [CI], 0.969-3.171), sex (OR = 2.883; 95% CI, 1.364-6.745), and T stage (OR = 1.853; 95% CI, 1.201-3.052) were identified via univariate and multivariate logistic models. Twenty-four radiomics features were associated with treatment response. Model^BPNN demonstrated the highest performance among Model^combined , Model^radiomics , and Model^clinic (AUC of training cohort: 0.917 vs. 0.808 vs. 0.795 vs. 0.707; testing cohort: 0.897 vs. 0.755 vs. 0.698 vs. 0.695).

CONCLUSION

A machine-learning approach using BPNN showed better ability than logistic regression model to predict tumor response to ICT in LANPC.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Septicemic Melioidosis Detection Using Support Vector Machine with Five Immune Cell Types

Melioidosis, caused by Burkholderia pseudomallei (B. pseudomallei), predominantly occurs in the tropical regions. Of various types of melioidosis, septicemic melioidosis is the most lethal one with a mortality rate of 40%. Early detection of the disease is paramount for the better chances of cure. In this study, we developed a novel approach for septicemic melioidosis detection, using a machine learning technique-support vector machine (SVM). Several SVM models were built, and 19 features characterized by the corresponding immune cell types were generated by Cell type Identification Estimating Relative Subsets Of RNA Transcripts (CIBERSORT). Using these features, we trained a binomial SVM model on the training set and evaluated it on the independent testing set. Our findings indicated that this model performed well with means of sensitivity and specificity up to 0.962 and 0.979, respectively. Meanwhile, the receiver operating characteristic (ROC) curve analysis gave area under curves (AUCs) ranging from 0.952 to 1.000. Furthermore, we found that a concise SVM model, built upon a combination of CD8+ T cells, resting CD4+ memory T cells, monocytes, M2 macrophages, and activated mast cells, worked perfectly on the detection of septicemic melioidosis. Our data showed that its mean of sensitivity was up to 0.976 while that of specificity up to 0.993. In addition, the ROC curve analysis gave AUC close to 1.000. Taken together, this SVM model is a robust classification tool and may serve as a complementary diagnostic technique to septicemic melioidosis.

Prediction of Genetic Alterations in Oncogenic Signaling Pathways in Squamous Cell Carcinoma of the Head and Neck: Radiogenomic Analysis Based on Computed Tomography Images

OBJECTIVE

This study investigated the role of radiomics in evaluating the alterations of oncogenic signaling pathways in head and neck cancer.

METHODS

Radiomics features were extracted from 106 enhanced computed tomography images with head and neck squamous cell carcinoma. Support vector machine-recursive feature elimination was used for feature selection. Support vector machine algorithm was used to develop radiomics scores to predict genetic alterations in oncogenic signaling pathways. The performance was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve.

RESULTS

The alterations of the Cell Cycle, HIPPO, NOTCH, PI3K, RTK RAS, and TP53 signaling pathways were predicted by radiomics scores. The AUC values of the training cohort were 0.94, 0.91, 0.94, 0.93, 0.87, and 0.93, respectively. The AUC values of the validation cohort were all greater than 0.7.

CONCLUSIONS

Radiogenomics is a new method for noninvasive acquisition of tumor molecular information at the genetic level.

Establishment and Validation of Nomogram Based on Combination of Pretreatment C-Reactive Protein/Albumin Ratio-EBV DNA Grade in Nasopharyngeal Carcinoma Patients Who Received Concurrent Chemoradiotherapy

Background

A higher ratio of pretreatment C-reactive protein/albumin ratio (CAR) is associated with poor prognosis in nasopharyngeal carcinoma (NPC), and Epstein–Barr virus (EBV) DNA level is known to not only participate in the occurrence of nasopharyngeal carcinoma but also affect the development and prognosis of the disease. Herein, we proposed that a combination of both these markers could improve the predictive prognostic ability.

Methods

In all, 842 NPC patients who received concurrent chemoradiotherapy (CCRT) were entered in this study. We collected all patients’ blood samples and EBV DNA copy numbers within one week before any treatment. Receiver operating characteristic (ROC) curve was used to determine the optimal cut-off. We employed the Kaplan–Meier method for survival analyses and the univariate and multivariate analyses (Cox proportional hazards regression model) for statistical analysis. A nomogram was constructed based on multivariate analyses results of the validation set. The model was internally validated using 1000 bootstrap samples to avoid overfitting. Another validation of 10-fold cross-validation was also applied. Calibration curves and concordance index (C-index) were calculated to determine predictive and discriminatory capacity.

Results

In the whole cohort, we observed that higher CAR, EBV DNA level, and CAR-EBV DNA (C-E) grade were associated with shorter overall survival (OS) and distant metastasis-free survival (DMFS) (all P<0.05). In univariate and multivariate analyses, C-E grade was an independent prognostic factor (all P<0.05). In the training set, we gained the similar results with the whole set. According to multivariate analyses of the training set, we constructed a nomogram. The results of bootstrap samples and 10-fold cross-validation showed favorable predictive efficacy. And calibration curves of the model provided credibility to its predictive capability.

Conclusion

C-E grade was confirmed as an independent prognostic predictor in patients with NPC who received CCRT. Higher level of pretreatment C-E grade could signify a higher risk of metastasis and shorter OS. The prognostic nomogram based on C-E grade was dependable in nasopharyngeal carcinoma patients.

Early risk-assessment of patients with nasopharyngeal carcinoma: the added prognostic value of MR-based radiomics

•

The current study extracted radiomics—a large quantitative data of imaging features—from magnetic resonance images of patients with nasopharyngeal carcinoma.

•

The survival model fitted with radiomic features showed good prognostic performance in predicting the progression-free survival of patients with nasopharyngeal carcinoma (integrated area under the curve, 0.71; 95% confidence interval, 0.71–0.72).

•

Addition of radiomics to clinical survival model improved the prognostication of progression-free survival in patients diagnosed with nasopharyngeal carcinoma (integrated area under the curve from 0.76 to 0.81, p<0.001).

Objectives

To assess the additive prognostic value of MR-based radiomics in predicting progression-free survival (PFS) in patients with nasopharyngeal carcinoma (NPC)

Methods

Patients newly diagnosed with non-metastatic NPC between June 2006 and October 2019 were retrospectively included and randomly grouped into training and test cohorts (7:3 ratio). Radiomic features (n=213) were extracted from T2-weighted and contrast-enhanced T1-weighted MRI. The patients were staged according to the 8^th edition of American Joint Committee on Cancer Staging Manual. The least absolute shrinkage and selection operator was used to select the relevant radiomic features. Univariate and multivariate Cox proportional hazards analyses were conducted for PFS, yielding three different survival models (clinical, stage, and radiomic). The integrated time-dependent area under the curve (iAUC) for PFS was calculated and compared among different combinations of survival models, and the analysis of variance was used to compare the survival models. The prognostic performance of all models was validated using a test set with integrated Brier scores.

Results

This study included 81 patients (training cohort=57; test cohort=24), and the mean PFS was 57.5 ± 43.6 months. In the training cohort, the prognostic performances of survival models improved significantly with the addition of radiomics to the clinical (iAUC, 0.72–0.80; p=0.04), stage (iAUC, 0.70–0.79; p=0.001), and combined models (iAUC, 0.76–0.81; p<0.001). In the test cohort, the radiomics and combined survival models were robustly validated for their ability to predict PFS.

Conclusion

Integration of MR-based radiomic features with clinical and stage variables improved the prediction PFS in patients diagnosed with NPC.

A Nomogram Based on a Collagen Feature Support Vector Machine for Predicting the Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer Patients

BACKGROUND

The relationship between collagen features (CFs) in the tumor microenvironment and the treatment response to neoadjuvant chemoradiotherapy (nCRT) is still unknown. This study aimed to develop and validate a perdition model based on the CFs and clinicopathological characteristics to predict the treatment response to nCRT among locally advanced rectal cancer (LARC) patients.

METHODS

In this multicenter, retrospective analysis, 428 patients were included and randomly divided into a training cohort (299 patients) and validation cohort (129 patients) [7:3 ratio]. A total of 11 CFs were extracted from a multiphoton image of pretreatment biopsy, and a support vector machine (SVM) was then used to construct a CFs-SVM classifier. A prediction model was developed and presented with a nomogram using multivariable analysis. Further validation of the nomogram was performed in the validation cohort.

RESULTS

The CFs-SVM classifier, which integrated collagen area, straightness, and crosslink density, was significantly associated with treatment response. Predictors contained in the nomogram included the CFs-SVM classifier and clinicopathological characteristics by multivariable analysis. The CFs nomogram demonstrated good discrimination, with area under the receiver operating characteristic curves (AUROCs) of 0.834 in the training cohort and 0.854 in the validation cohort. Decision curve analysis indicated that the CFs nomogram was clinically useful. Moreover, compared with the traditional clinicopathological model, the CFs nomogram showed more powerful discrimination in determining the response to nCRT.

CONCLUSIONS

The CFs-SVM classifier based on CFs in the tumor microenvironment is associated with treatment response, and the CFs nomogram integrating the CFs-SVM classifier and clinicopathological characteristics is useful for individualized prediction of the treatment response to nCRT among LARC patients.

The prognostic significance of survivin expression in patients with HNSCC: a systematic review and meta-analysis

Background

Survivin has been recently identified as a promising novel therapeutic target and prognostic marker in different types of cancer. Here we conducted a comprehensive meta-analysis to better clarify they the precise prognostic and diagnostic value of survivin in head and neck squamous cell carcinoma (HNSCC).

Methods

Database of PubMed (Medline), Embase, and Web of Science were systematically searched for related published literature up to September 2020. Pooled hazards ratios (HR) and related 95% confidence intervals (CI) were used to estimate the association of survivin expression and survival outcomes in HNSCC patients.

Results

Twenty eight studies with 4891 patients were finally included in this meta-analysis, the pooled analysis indicated that the survivin expression was significantly correlated with poorer overall survival (OS) (HR, 2.02; 95% CI, 1.65–2.47, P < 0.001), and poorer disease-free survival (DFS)/ disease-specific survival (DSS) (HR = 2.03, 95%CI: 1.64–2.52, P < 0.001; HR = 1.92, 95%CI: 1.41–2.60, P < 0.001, receptively). Similar results were observed in subgroup analysis stratified by different cancer types, such as laryngeal squamous cell carcinoma (LSCC) (HR = 1.35, 95%CI: 1.05–1.74, P < 0.001), oral squamous cell carcinomas (OSCC) (HR = 2.45, 95%CI: 1.89–3.17, P < 0.001), nasopharyngeal carcinoma (NPC) (HR = 2.53, 95%CI: 1.76–3.62, P < 0.001) and HNSCC (HR = 1.52, 95%CI: 1.25–1.86, P < 0.001). Furthermore, ethnicity-stratified analysis indicated that survivin was significantly associated with poorer OS among both Asian and Non- Asian HNSCC patients (HR = 2.16, 95%CI: 1.76–2.66; HR = 1.56, 95%CI: 1.33–1.82, respectively).

Conclusions

Our results suggested that survivin is predictors of worse prognosis in HNSCC patients. Hence, survivin is a potential therapeutic target for HNSCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08170-3.

Differences in Radiomics Signatures Between Patients with Early and Advanced T-Stage Nasopharyngeal Carcinoma Facilitate Prognostication

BACKGROUND

Accurately predicting the risk of death, recurrence, and metastasis of patients with nasopharyngeal carcinoma (NPC) is potentially important for personalized diagnosis and treatment. Survival outcomes of patients vary greatly in distinct stages of NPC. Prognostic models of stratified patients may aid in prognostication.

PURPOSE

To explore the prognostic performance of MRI-based radiomics signatures in stratified patients with NPC.

STUDY TYPE

Retrospective.

POPULATION

Seven hundred and seventy-eight patients with NPC (T1-2 stage: 298, T3-4 stage: 480; training cohort: 525, validation cohort: 253).

FIELD STRENGTH/SEQUENCE

Fast-spin echo (FSE) axial T1-weighted images, FSE axial T2-weighted images, contrast-enhanced FSE axial T1-weighted images at 1.5 T or 3.0 T.

ASSESSMENT

Radiomics signatures, clinical nomograms, and radiomics nomograms combining the radiomic score (Radscore) and clinical factors for predicting progression-free survival (PFS) were constructed on T1-2 stage patient cohort (A), T3-4 stage patient cohort (B), and the entire dataset (C).

STATISTICAL TESTS

Least absolute shrinkage and selection operator (LASSO) method was applied for radiomics modeling. Harrell's concordance indices (C-index) were employed to evaluate the predictive power of each model.

RESULTS

Among 4,410 MRI-extracted features, we selected 16, 16, and 14 radiomics features most relevant to PFS for Models A, B, and C, respectively. Only 0, 1, and 4 features were found overlapped between models A/B, A/C, and B/C, respectively. Radiomics signatures constructed on T1-2 stage and T3-4 stage patients yielded C-indices of 0.820 (95% confidence interval [CI]: 0.763-0.877) and 0.726 (0.687-0.765), respectively, which were larger than those on the entire validation cohort (0.675 [0.637-0.713]). Radiomics nomograms combining Radscore and clinical factors achieved significantly better performance than clinical nomograms (P < 0.05 for all).

DATA CONCLUSION

The selected radiomics features and prognostic performance of radiomics signatures differed per the type of NPC patients incorporated into the models. Radiomics models based on pre-stratified tumor stages had better prognostic performance than those on unstratified dataset.

LEVEL OF EVIDENCE

4 Technical Efficacy Stage: 5.

Oncolytic Virus Therapy Alters the Secretome of Targeted Glioblastoma Cells

Simple Summary

Proteins secreted by cancer cells in response to oncolytic virus anti-tumor therapy constitute the instructions for the immune cells. Yet as there are hundreds of proteins, including those encapsulated in vesicles, whose message drives the mobilization of immune cells, we aimed to decipher the instruction sent by cancer cells in response to therapy. Searching the cataloged vesicle and vesicle-free secreted proteins, we found that the proteins associated with the favorable survival of brain cancer patients were those that have the power to mobilize the immune cells. Thus, this approach established cancer-secreted contributors to the immune–therapeutic effect of the oncolytic virus.

Abstract

Oncolytic virus (OV) therapy, which is being tested in clinical trials for glioblastoma, targets cancer cells, while triggering immune cells. Yet OV sensitivity varies from patient to patient. As OV therapy is regarded as an anti-tumor vaccine, by making OV-infected cancer cells secrete immunogenic proteins, linking these proteins to transcriptome would provide a measuring tool to predict their sensitivity. A set of six patient-derived glioblastoma cells treated ex-vivo with herpes simplex virus type 1 (HSV1) modeled a clinical setting of OV infection. The cellular transcriptome and secreted proteome (separated into extracellular vesicles (EV) and EV-depleted fractions) were analyzed by gene microarray and mass-spectroscopy, respectively. Data validation and in silico analysis measured and correlated the secretome content with the response to infection and patient survival. Glioblastoma cells reacted to the OV infection in a seemingly dissimilar fashion, but their transcriptomes changed in the same direction. Therefore, the upregulation of transcripts encoding for secreted proteins implies a common thread in the response of cancer cells to infection. Indeed, the OV-driven secretome is linked to the immune response. While these proteins have distinct membership in either EV or EV-depleted fractions, it is their co-secretion that augments the immune response and associates with favorable patient outcomes.

A Machine Learning Approach Yields a Multiparameter Prognostic Marker in Liver Cancer

A number of staging systems have been developed to predict clinical outcomes in hepatocellular carcinoma (HCC). However, no general consensus has been reached regarding the optimal model. New approaches such as machine learning (ML) strategies are powerful tools for incorporating risk factors from multiple platforms. We retrospectively reviewed the baseline information, including clinicopathologic characteristics, laboratory parameters, and peripheral immune features reflecting T-cell function, from three HCC cohorts. A gradient-boosting survival (GBS) classifier was trained with prognosis-related variables in the training dataset and validated in two independent cohorts. We constructed a 20-feature GBS model classifier incorporating one clinical feature, 14 laboratory parameters, and five T-cell function parameters obtained from peripheral blood mononuclear cells. The GBS model-derived risk scores demonstrated high concordance indexes (C-indexes): 0.844, 0.827, and 0.806 in the training set and validation sets 1 and 2, respectively. The GBS classifier could separate patients into high-, medium- and low-risk subgroups with respect to death in all datasets (P < 0.05 for all comparisons). A higher risk score was positively correlated with a higher clinical stage and the presence of portal vein tumor thrombus (PVTT). Subgroup analyses with respect to Child-Pugh class, Barcelona Clinic Liver Cancer stage, and PVTT status supported the prognostic relevance of the GBS-derived risk algorithm independent of the conventional tumor staging system. In summary, a multiparameter ML algorithm incorporating clinical characteristics, laboratory parameters, and peripheral immune signatures offers a different approach to identify patients with the greatest risk of HCC-related death.

A Nomogram Based on Serum Biomarkers and Clinical Characteristics to Predict Survival in Patients With Non-Metastatic Nasopharyngeal Carcinoma

Objective

This study focused on developing an effective nomogram for improving prognostication for patients with primary nasopharyngeal carcinoma (NPC) restaged according to the eighth edition of the AJCC/UICC TNM staging system.

Methods

Based on data of 5,903 patients with non-metastatic NPC (primary cohort), we used Cox regression analysis to identify survival risk factors and created a nomogram. We used the nomogram to predict overall survival (OS), distant metastasis-free survival (DMFS) and disease-free survival (DFS) in the primary and independent validation (3,437 patients) cohorts. Moreover, we compared the prognostic accuracy between the 8th TNM system and the nomogram.

Results

The nomogram included gender, age, T stage, N stage, Epstein–Barr virus DNA, hemoglobin, C-reactive protein, lactate dehydrogenase, and radiotherapy with/without induction or concurrent chemotherapy. In the prediction of OS, DMFS and DFS, the nomogram had significantly higher concordance index (C-index) and area under ROC curve (AUC) than the TNM system alone. Calibration curves demonstrated satisfactory agreements between nomogram-predicted and observed survival. The stratification in different groups permitted remarkable differentiation among Kaplan–Meier curves for OS, DMFS, and DFS.

Conclusion

The nomogram led to a more precise prognostic prediction for NPC patients in comparison with the 8th TNM system. Therefore, it could facilitate individualized and personalized patients’ counseling and care.

Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer

Background

We evaluated the ability of radiomics based on intratumoral and peritumoral regions on preoperative gastric cancer (GC) contrast-enhanced CT imaging to predict disease-free survival (DFS) and chemotherapy response in stage II/III GC.

Methods

This study enrolled of 739 consecutive stage II/III GC patients. Within the intratumoral and peritumoral regions of CT images, 584 total radiomic features were computed at the portal venous-phase. A radiomics signature (RS) was generated by using support vector machine (SVM) based methods. Univariate and multivariate Cox proportional hazards models and Kaplan-Meier analysis were used to determine the association of the RS and clinicopathological variables with DFS. A radiomics nomogram combining the radiomics signature and clinicopathological findings was constructed for individualized DFS estimation.

Results

The radiomics signature consisted of 26 features and was significantly associated with DFS in both the training and validation sets (both P<0.0001). Multivariate analysis showed that the RS was an independent predictor of DFS. The signature had a higher predictive accuracy than TNM stage and single radiomics features and clinicopathological factors. Further analysis showed that stage II/III patients with high scores were more likely to benefit from adjuvant chemotherapy.

Conclusion

The newly developed radiomics signature was a powerful predictor of DFS in GC, and it may predict which patients with stage II and III GC benefit from chemotherapy.

A Nomogram for Prediction of Survival in Patients After Gastrectomy Within Enhanced Recovery After Surgery (ERAS): A Single-Center Retrospective Study

BACKGROUND Enhanced Recovery After Surgery (ERAS) programs can optimize clinical outcomes and have been widely used across multiple specialties, but a personalized prediction model involving ERAS for the prognosis of gastric cancer is lacking. MATERIAL AND METHODS We retrospectively collected clinical data on 725 gastric cancer patients within ERAS who underwent curative gastric resection in the Affiliated Hospital of Qingdao University from 2007 to 2014. Kaplan-Meier method, log-rank test, and Cox proportional risk model were used to determine the independent prognostic factors of patients. The accuracy of model was evaluated by C-index, calibration curve, and Decision Curve Analysis (DCA), and the receiver operator characteristic (ROC) curve was used to compare the nomogram model with the predictive value of TNM staging system. RESULTS The 5-year overall survival (OS) of 725 patients within ERAS was 72.5%. Age at diagnosis, T stage, N stage, and postoperative complications were determined to be independent factors affecting the prognosis of patients within ERAS, and nomogram model was constructed. The C-index of the training group was 0.809 and that of the verification group was 0.804; the calibration curves and DCA of the 2 groups showed good accuracy. Through verification, we found that, compared with the TNM staging assessment method, the nomogram model was more accurate in predicting the prognosis of gastric cancer. CONCLUSIONS This study identified factors affecting the prognosis of patients with gastric cancer, and we constructed the first prognostic nomogram model in ERAS mode to facilitate postoperative personalized prognostic evaluation.

Development of a Prognostic Model to Identify the Suitable Definitive Radiation Therapy Candidates in de Novo Metastatic Nasopharyngeal Carcinoma: A Real-World Study

PURPOSE

We aimed to develop an accurate prognostic model to identify suitable candidates for definitive radiation therapy (DRT) in addition to palliative chemotherapy (PCT) among patients with de novo metastatic nasopharyngeal carcinoma (mNPC).

METHODS AND MATERIALS

Patients with de novo mNPC who received first-line PCT with or without DRT were included. Overall survival for patients who received PCT alone versus PCT plus DRT was estimated using inverse probability of treatment weighting-adjusted survival analyses. We developed and validated a prognostic model to predict survival and stratify risks in de novo mNPC. A model-based trees approach was applied to estimate stratified treatment effects using prognostic scores obtained from the prognostic model and to identify suitable DRT candidates. Dominance analysis was used to determine the relative importance of each predictor of receiving DRT.

RESULTS

A total of 460 patients were enrolled; 244 received PCT plus DRT and 216 received PCT alone. The 6-month conditional landmark, inverse probability of treatment weighting-adjusted Cox regression analysis showed that PCT plus DRT was associated with a significant survival benefit (hazard ratio: 0.516; 95% confidence interval, 0.403-0.660; P < .001). A prognostic model based on 5 independent prognostic factors, including serum lactate dehydrogenase, number of metastatic sites, presence of liver metastasis, posttreatment Epstein-Barr virus DNA level, and response of metastases to chemotherapy was developed and subsequently validated. Prognostic scores obtained from the prognostic model were used for risk stratification and efficacy estimation. High-risk patients identified using the proposed model would not benefit from additional DRT, whereas low-risk patients experienced significant survival benefits. Socioeconomic factors, including insurance status and education level, played an important role in receipt of DRT.

CONCLUSIONS

Additional DRT after PCT was associated with increased overall survival in patients with de novo mNPC, especially low-risk patients identified with a newly developed prognostic model.

Identifying ultrasound features of positive expression of Ki67 and P53 in breast cancer using radiomics

PURPOSE

To examine the relationship between ultrasonic findings and positive expression of Ki67 and P53 in breast cancer.

MATERIAL AND METHODS

Surgical resection specimens of 263 breast cancer lesions were examined. Ultrasound examination and pathological examination were performed on each lesion for retrospective analysis. We applied regression analysis to the ultrasonic features related to the positive expression of Ki67 and P53 and obtained the corresponding models. To analyze diagnostic efficiency, we calculated the area under the curve (AUC). Additionally, we created a heat map to show the results of the cluster analysis.

RESULTS

Lesions with higher Ki67 expression were associated with posterior acoustic enhancement, absence of an echo halo and a higher Breast Imaging Reporting and Data System (BI-RADS) category. P53-positive cancer were associated with an absence of an echo halo and a higher BI-RADS category. The AUC of the regression models of Ki67 and P53 was 0.78 and 0.71, respectively.

CONCLUSIONS

Our study revealed that breast cancer ultrasonic findings were closely related to expression of molecular indicators, suggesting that ultrasound can be used to provide useful information to clinicians.

Hypoxic Roadmap of Glioblastoma-Learning about Directions and Distances in the Brain Tumor Environment

Malignant brain tumor-glioblastoma is not only difficult to treat but also hard to study and model. One of the reasons for these is their heterogeneity, i.e., individual tumors consisting of cancer cells that are unlike each other. Such diverse cells can thrive due to the simultaneous co-evolution of anatomic niches and adaption into zones with distorted homeostasis of oxygen. It dampens cytotoxic and immune therapies as the response depends on the cellular composition and its adaptation to hypoxia. We explored what transcriptome reposition strategies are used by cells in the different areas of the tumor. We created the hypoxic map by differential expression analysis between hypoxic and cellular features using RNA sequencing data cross-referenced with the tumor's anatomic features (Ivy Glioblastoma Atlas Project). The molecular functions of genes differentially expressed in the hypoxic regions were analyzed by a systematic review of the gene ontology analysis. To put a hypoxic niche signature into a clinical context, we associated the model with patients' survival datasets (The Cancer Genome Atlas). The most unique class of genes in the hypoxic area of the tumor was associated with the process of autophagy. Both hypoxic and cellular anatomic features were enriched in immune response genes whose, along with autophagy cluster genes, had the power to predict glioblastoma patient survival. Our analysis revealed that transcriptome responsive to hypoxia predicted worse patients' outcomes by driving tumor cell adaptation to metabolic stress and immune escape.

Germline Polymorphisms and Length of Survival of Nasopharyngeal Carcinoma: An Exome-Wide Association Study in Multiple Cohorts

Germline polymorphisms are linked with differential survival outcomes in cancers but are not well studied in nasopharyngeal carcinoma (NPC). Here, a two-phase association study is conducted to discover germline polymorphisms that are associated with the prognosis of NPC. The discovery phase includes two consecutive hospital cohorts of patients with NPC from Southern China. Exome-wide genotypes at 246 173 single nucleotide polymorphisms (SNPs) are determined, followed by survival analysis for each SNP under Cox proportional hazard regression model. Candidate SNP is replicated in another two independent cohorts from Southern China and Singapore. Meta-analysis of all samples (n = 5553) confirms that the presence of rs1131636-T, located in the 3'-UTR of RPA1, confers an inferior overall survival (HR = 1.33, 95% CI = 1.20-1.47, P = 6.31 × 10-8). Bioinformatics and biological assays show that rs1131636 has regulatory effects on upstream RPA1. Functional studies further demonstrate that RPA1 promotes the growth, invasion, migration, and radioresistance of NPC cells. Additionally, miR-1253 is identified as a suppressor for RPA1 expression, likely through regulation of its binding affinity to rs1131636 locus. Collectively, these findings provide a promising biomarker aiding in stratifying patients with poor survival, as well as a potential drug target for NPC.

Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning

PURPOSE

Pancreatic cancer remains a disease of high mortality despite advanced diagnostic techniques. Mucins (MUC) play crucial roles in carcinogenesis and tumor invasion in pancreatic cancers. MUC1 and MUC4 expression are related to the aggressive behavior of human neoplasms and a poor patient outcome. In contrast, MUC2 is a tumor suppressor, and we have previously reported that MUC2 is a favorable prognostic factor in pancreatic neoplasia. This study investigates whether the methylation status of three mucin genes from postoperative tissue specimens from patients with pancreatic neoplasms could serve as a predictive biomarker for outcome after surgery.

EXPERIMENTAL DESIGN

We evaluated the methylation status of MUC1, MUC2, and MUC4 promoter regions in pancreatic tissue samples from 191 patients with various pancreatic lesions using methylation-specific electrophoresis. Then, integrating these results and clinicopathologic features, we used support vector machine-, neural network-, and multinomial-based methods to develop a prognostic classifier.

RESULTS

Significant differences were identified between the positive- and negative-prediction classifiers of patients in 5-year overall survival (OS) in the cross-validation test. Multivariate analysis revealed that these prognostic classifiers were independent prognostic factors analyzed by not only neoplastic tissues but also nonneoplastic tissues. These classifiers had higher predictive accuracy for OS than tumor size, lymph node metastasis, distant metastasis, and age and can complement the prognostic value of the TNM staging system.

CONCLUSIONS

Analysis of epigenetic changes in mucin genes may be of diagnostic utility and one of the prognostic predictors for patients with pancreatic ductal adenocarcinoma.

The prognostic value of integration of pretreatment serum amyloid A (SAA)-EBV DNA (S-D) grade in patients with nasopharyngeal carcinoma

Background

Serum amyloid A (SAA) has been associated with the development and prognosis of cancer. The purpose of this study was to evaluate the predictive value of integration of pretreatment SAA–EBV DNA (S-D) grade and comparison with the TNM staging system in patients with nasopharyngeal carcinoma (NPC). The S-D grade was calculated based on the cut-off values of serum SAA and EBV DNA copy numbers which were determined by receiver operating characteristic (ROC) curves. We evaluated the prognostic value of pretreatment SAA, EBV DNA and S-D grade on overall survival (OS) of NPC patients. We also evaluated the predictive power of S-D grade with TNM staging system using 4 indices: concordance statistics (C-index), time-dependent ROC (ROCt) curve, net reclassification index (NRI) and integrated discrimination improvement (IDI).

Results

A total of 304 NPC patients were enrolled in this study. Multivariate analysis showed that TNM stage (P = 0.007), SAA (P = 0.013), and EBV DNA (P = 0.033) were independent prognostic factors in NPC. The S-D grade was divided into S-D grade 1, S-D grade 2, and S-D grade 3, which had more predictive accuracy for OS than TNM staging according to all 4 indices.

Conclusions

We found that the S-D grade could be used as a new tool to predict the OS in NPC patients.

Integrated analysis of genomic and transcriptomic profiles identified a prognostic immunohistochemistry panel for esophageal squamous cell cancer

Background

The poor outcome of patients with esophageal squamous cell carcinoma (ESCC) highlights the importance of the identification of novel effective prognostic biomarkers. We aimed to identify a clinically applicable prognostic immunohistochemistry (IHC) panel for ESCC.

Methods

An integrated analysis was performed to screen and establish a prognostic panel using exome sequencing profile from 81 pairs of ESCC samples and RNA expression microarray data from 119 ESCC subjects. Two independent ESCC cohorts were recruited as training and validation groups to test the prognostic value.

Results

Three genes were selected, namely, ANO1, GAL, and MMP3, which were aberrantly expressed in ESCC tumor tissues (P < .001). Among them, ANO1 and MMP3 were reserved for the construction of the prognostic panel due to their significant association with the prognosis of ESCC patients (P = .015 and P < .001). Patients with both ANO1+ and MMP3+ had a poorer prognosis than that with ANO1−/MMP3+, ANO1+/MMP3−, or ANO1−/MMP3 − in both the training set and validation set (P < .001). Receiver operating characteristic analysis showed that the combination of IHC panel and eighth American Joint Commission on Cancer staging yielded a better prognostic predictive efficacy compared with the two indexes alone (P < .001, area under curve: 0.752). Finally, a nomogram was created by integrating the IHC markers and clinicopathological risk factors to predict prognosis with a C‐index of 0.695 (95% confidence interval: 0.657‐0.734).

Conclusion

Using an integrated multistage screening strategy, we identified and validated a valuable prognostic IHC panel for ESCC.

Tumor Immune Microenvironment and Chemosensitivity Signature for Predicting Response to Chemotherapy in Gastric Cancer

Current gastric cancer staging alone cannot predict prognosis and adjuvant chemotherapy benefits in stage II and III gastric cancer. Tumor immune microenvironment biomarkers and tumor-cell chemosensitivity might add predictive value to staging. This study aimed to construct a predictive signature integrating tumor immune microenvironment and chemosensitivity-related features to improve the prediction of survival and adjuvant chemotherapy benefits in patients with stage II to III gastric cancer. We used IHC to assess 26 features related to tumor, stroma, and chemosensitivity in tumors from 223 patients and evaluated the association of the features with disease-free survival (DFS) and overall survival (OS). Support vector machine (SVM)-based methods were used to develop the predictive signature, which we call the SVM signature. Validation of the signature was performed in two independent cohorts of 445 patients. The diagnostic signature integrated seven features: CD3⁺ cells at the invasive margin (CD3 IM), CD8⁺ cells at the IM (CD8 IM), CD45RO⁺ cells in the center of tumors (CD45RO CT), CD66b⁺ cells at the IM (CD66b IM), CD34⁺ cells, periostin, and cyclooxygenase-2. Patients fell into low- and high-SVM groups with significant differences in 5-year DFS and OS in the training and validation cohorts (all P < 0.001). The signature was an independent prognosis indicator in multivariate analysis in each cohort. The signature had better prognostic value than various clinicopathologic risk factors and single features. High-SVM patients exhibited a favorable response to adjuvant chemotherapy. Thus, this SVM signature predicted survival and has the potential for identifying patients with stage II and III gastric cancer who could benefit from adjuvant chemotherapy.

Prevalence of NRAS Mutation, PD-L1 Expression and Amplification, and Overall Survival Analysis in 36 Primary Vaginal Melanomas

Background

Primary vaginal melanomas are uncommon and aggressive tumors with poor prognosis, and the development of new targeted therapies is essential. This study aimed to identify the molecular markers occurring in these patients and potentially improve treatment strategies.

Materials and Methods

The clinicopathological characteristics of 36 patients with primary vaginal melanomas were reviewed. Oncogenic mutations in BRAF, KIT, NRAS, GNAQ and GNA11 and the promoter region of telomerase reverse transcriptase (TERT) were investigated using the Sanger sequencing. The expression and copy number of programmed death‐ligand 1 (PD‐L1) were also assessed.

Results

Mutations in NRAS, KIT, and TERT promoter were identified in 13.9% (5/36), 2.9% (1/34), and 5.6% (2/36) of the primary vaginal melanomas, respectively. PD‐L1 expression and amplification were observed in 27.8% (10/36) and 5.6% (2/36) of cases, respectively. PD‐L1 positive expression and/or amplification was associated with older patients (p = .008). Patients who had NRAS mutations had a poorer overall survival compared with those with a wild‐type NRAS (33.5 vs. 14.0 months; hazard ratio [HR], 3.09; 95% CI, 1.08–8.83). Strikingly, two patients with/without PD‐L1 expression receiving immune checkpoint inhibitors had a satisfying outcome. Multivariate analysis demonstrated that >10 mitoses per mm² (HR, 2.96; 95% CI, 1.03–8.51) was an independent prognostic factor.

Conclusions

NRAS mutations and PD‐L1 expression were most prevalent in our cohort of primary vaginal melanomas and can be potentially considered as therapeutic targets.

Implications for Practice

This study used the Sanger sequencing, immunohistochemistry, and fluorescence in situ hybridization methods to detect common genetic mutations and PD‐L1 expression and copy number in 36 primary vaginal melanomas. NRAS mutations and PD‐L1 expression were the most prevalent, but KIT and TERT mutations occurred at a lower occurrence in this rare malignancy. Two patients receiving immune checkpoint inhibitors had a satisfying outcome, signifying that the PD‐L1 expression and amplification can be a possible predictive marker of clinical response. This study highlights the possible prospects of biomarkers that can be used for patient selection in clinical trials involving treatments with novel targeted therapies based on these molecular aberrations.

Little is known about the molecular characteristics of primary vaginal melanoma. This article reports on the molecular markers of this rare and aggressive disease, focusing on improvements in treatment strategies.

Translational genomics of nasopharyngeal cancer

Nasopharyngeal carcinoma (NPC), also named the Cantonese cancer, is a unique cancer with strong etiological association with infection of the Epstein-Barr virus (EBV). With particularly high prevalence in Southeast Asia, the involvement of EBV and genetic aberrations contributive to NPC tumorigenesis have remained unclear for decades. Recently, genomic analysis of NPC has defined it as a genetically homogeneous cancer, driven largely by NF-κB signaling caused by either somatic aberrations of NF-κB negative regulators or by overexpression of the latent membrane protein 1 (LMP1), an EBV viral oncoprotein. This represents a landmark finding of the NPC genome. Exome and RNA sequencing data from new EBV-positive NPC models also highlight the importance of PI3K pathway aberrations in NPC. We also realize for the first time that NPC mutational burden, mutational signatures, MAPK/PI3K aberrations, and MHC Class I gene aberrations, are prognostic for patient outcome. Together, these multiple genomic discoveries begin to shape the focus of NPC therapy development. Given the challenge of NF-κB targeting in human cancers, more innovative drug discovery approaches should be explored to target the unique atypical NF-κB activation feature of NPC. Our next decade of NPC research should focus on further identification of the -omic landscapes of recurrent and metastatic NPC, development of gene-based precision medicines, as well as large-scale drug screening with the newly developed and well-characterized EBV-positive NPC models. Focused preclinical and clinical investigations on these major directions may identify new and effective targeting strategies to further improve survival of NPC patients.

Pre-treatment Serum Lactate Dehydrogenase Predicts Distant Metastasis and Poor Survival in Nasopharyngeal Carcinoma

Background: Pre-treatment serum lactate dehydrogenase (LDH) has emerged as prognostic factor for many cancers. In this study, we evaluated the value of LDH in predicting distant metastasis and poor survival for patients with nasopharyngeal carcinoma (NPC).

Methods: Clinical data from 172 non-metastatic NPC patients were retrospectively collected and serum LDH levels were routinely measured before treatment. The independent-samples t test was used to calculate differences between serum LDH levels from the various patient groups. Receiver-operating characteristic (ROC) curve analysis was performed to select the optimal cutoff points. The Kaplan-Meier method and log-rank test were adopted to calculate and compare the distant metastasis free survival (DMFS) and overall survival (OS) rates. The Cox proportional hazards model was used to carry out univariate and multivariate analyses.

Results: NPC patients progressed with distant metastasis often have higher pre-treatment serum LDH levels than those did not develop distant metastasis (mean LDH level was 237.1U/L and 108.8U/L, respectively, p=0.001). Elevated LDH level was identified as an independent prognostic factor for poor DMFS (hazard ratio (HR), 8.31; 95% confidence interval (CI), 2.44-28.32; p=0.001) and OS (HR, 4.45; 95% CI, 1.77-11.21; p=0.002). Moreover, subgroup analyses revealed significant associations between serum LDH level and worse survival in advanced stage patients.

Conclusions: Pre-treatment serum LDH level can predict distant metastasis and associate with the poor survival in patients with NPC.

LSCDFS-MKL: A multiple kernel based method for lung squamous cell carcinomas disease-free survival prediction with pathological and genomic data

Lung squamous cell carcinoma (SCC) is a fatal disease in both male and female, for which current treatments are inadequate. Surgical resection is regarded as the cornerstone of treatment for patients with lung SCC, but even for the same stage patients, the wide spectrum of disease-free survival (DFS) times exits. Therefore, how to improve the DFS prediction performance of lung SCC becomes one major research area. In this study, we proposed a novel method called LSCDFS-MKL, which was on the basis of multiple kernel learning to predict DFS of lung SCC. In LSCDFS-MKL, we first efficiently integrated pathological images and genomic data (copy number aberration, gene expression, protein expression) from lung SCC. The results of LSCDFS-MKL between different types of data show that the features extracted from pathological images play an important role in DFS prediction of lung SCC. Then we compared our method LSCDFS-MKL with other existing methods and performance analysis indicates that LSCDFS-MKL has a significantly better performance than other prediction methods. After that, we applied the proposed method on different stage stratums and the performance demonstrates that LSCDFS-MKL remains efficient in DFS prediction of lung SCC patients. Finally, we performed LSCDFS-MKL on an independent validation dataset and the accuracy of DFS prediction achieves 100%, which is promising.

Post-radiation sinusitis is associated with recurrence in nasopharyngeal carcinoma patients treated with intensity-modulated radiation therapy

BACKGROUND

This study investigated the impact of post-radiation sinusitis on the prognosis of nasopharyngeal carcinoma (NPC) patients treated with intensity-modulated radiation therapy (IMRT).

METHODS

Two hundred and thirty patients with non-metastatic NPC were analyzed in terms of freedom from local failure (FFLF), freedom from distant failure (FFDF), overall survival (OS), and disease-free survival (DFS). For each patient, the status of the sinus mucosa was flexibly assessed by documenting mucosal changes as indicated by differences between images obtained before radiotherapy and more than 6 months post-radiation.

RESULTS

With a median follow-up of 39.7 months (8 to 81 months), 19 (8.26%) patients relapsed locally, 13 (5.65%) patients failed in the neck, and 26 (11.3%) patients developed distant metastases. The presence of sinusitis noted in images post-radiation was a significant predictor for DFS (p = 0.001), FFLF (p = 0.004), and FFDF (p = 0.015), in addition to having high negative predictive value for local relapse (97.5%).

CONCLUSIONS

This is the first study to investigate the prognostic value of post-radiation sinusitis in NPC patients treated with IMRT. Post-radiation sinusitis was found to be a significant predictor for DFS, FFLF, and FFDF, and was also found to have high negative predictive value for local recurrence (97.5%). It may thus be used as an additional tool for clinicians to determine the possibility of recurrence.

Interfering Expression of Chimeric Transcript SEPT7P2-PSPH Promotes Cell Proliferation in Patients with Nasopharyngeal Carcinoma

Introduction

Nasopharyngeal carcinoma (NPC) is a distinct type of head and neck cancer which is mostly prevalent in southern China. The development of NPC involves accumulation of multiple genetic changes. Chromosomal translocation is always thought to be accompanied with the fusion chimeric products. To data, the role of the fusion chimeric transcript remains obscure.

Materials and Methods

We performed RNA sequencing to detect the fusion genes in ten NPC tissues. Sanger sequencing and quantitative RT-PCR were used to measure the level of the fusion chimeric transcript in NPC tissues and cell lines. The functional experiments such as CCK8 assay, colony formation, and migration/invasion were conducted to analyze the role of this transcript in NPC in vitro.

Results

We demonstrated that the chimeric transcript SEPT7P2-PSPH was formed by trans-splicing of adjacent genes in the absence of chromosomal rearrangement and observed in both NPC patients and cell lines in parallel. Low-expression of the SEPT7P2-PSPH chimeric transcript induced the protein expression of PSPH and promoted cell proliferation, metastasis/invasion, and transforming ability in vitro.

Conclusions

Our findings indicate that the chimeric transcript SEPT7P2-PSPH is a product of trans-splicing of two adjacent genes and might be a tumor suppressor gene, potentially having the role of anticancer activity.

Baseline hemoglobin <11.0 g/dL has stronger prognostic value than anemia status in nasopharynx cancers treated with chemoradiotherapy

BACKGROUND

To retrospectively investigate the influence of pretreatment anemia and hemoglobin levels on the survival of nasopharyngeal carcinoma patients treated with concurrent chemoradiotherapy (C-CRT).

METHODS

A total of 149 nasopharyngeal carcinoma patients who received C-CRT were included. All patients had received 70 Gy to the primary tumor plus the involved lymph nodes, and 59.4 Gy and 54 Gy to the intermediate- and low-risk neck regions concurrent with 1-3 cycles of cisplatin. Patients were dichotomized into non-anemic and anemic (hemoglobin <12 g/dL (women) or <13 g/dL (men)) groups according to their pre-treatment hemoglobin measures. Receiver operating characteristic (ROC) curve analysis was utilized for accessibility of a pre-treatment hemoglobin cut-off that impacts outcomes. Potential interactions between baseline anemia status and hemoglobin measures and overall survival, locoregional progression-free survival (LRPFS), and progression-free survival were assessed.

RESULTS

Anemia was evident in 36 patients (24.1%), which was related to significantly shorter overall survival (P=0.007), LRPFS (P<0.021), and progression-free survival (P=0.003) times; all three endpoints retained significance in multivariate analyses (P<0.05, for each). A baseline hemoglobin value of 11.0 g/dL exhibited significant association with outcomes in ROC curve analysis: hemoglobin <11.0 g/dL (N=26) was linked with shorter median overall survival (P<0.001), LRPFS (P=0.004), and progression-free survival (P<0.001) times, which also retained significance for all three endpoints in multivariate analyses and suggested a stronger prognostic worth for the hemoglobin <11.0 g/dL cut-off value than the anemia status.

CONCLUSION

Pre-C-CRT hemoglobin <11.0 g/dL has a stronger prognostic worth than the anemia status with regard to LRPFS, progression-free survival, and overall survival for nasopharyngeal carcinoma patients.

Development and validation of a magnetic resonance imaging-based model for the prediction of distant metastasis before initial treatment of nasopharyngeal carcinoma: A retrospective cohort study

Background

We aimed to identify a magnetic resonance imaging (MRI)-based model for assessment of the risk of individual distant metastasis (DM) before initial treatment of nasopharyngeal carcinoma (NPC).

Methods

This retrospective cohort analysis included 176 patients with NPC. Using the PyRadiomics platform, we extracted the imaging features of primary tumors in all patients who did not exhibit DM before treatment. Subsequently, we used minimum redundancy-maximum relevance and least absolute shrinkage and selection operator algorithms to select the strongest features and build a logistic model for DM prediction. The independent statistical significance of multiple clinical variables was tested using multivariate logistic regression analysis.

Findings

In total, 2780 radiomic features were extracted. A DM MRI-based model (DMMM) comprising seven features was constructed for the classification of patients into high- and low-risk groups in a training cohort and validated in an independent cohort. Overall survival was significantly shorter in the high-risk group than in the low-risk group (P < 0·001). A radiomics nomogram based on radiomic features and clinical variables was developed for DM risk assessment in each patient, and it showed a significant predictive ability in the training [area under the curve (AUC), 0·827; 95% confidence interval (CI), 0.754–0.900] and validation (AUC, 0.792; 95% CI, 0.633–0.952) cohorts.

Interpretation

DMMM can serve as a visual prognostic tool for DM prediction in NPC, and it can improve treatment decisions by aiding in the differentiation of patients with high and low risks of DM.

Fund

This research received financial support from the National Natural Science Foundation of China (81571664, 81871323, 81801665, 81771924, 81501616, 81671851, and 81527805); the National Natural Science Foundation of Guangdong Province (2018B030311024); the Science and Technology Planning Project of Guangdong Province (2016A020216020); the Scientific Research General Project of Guangzhou Science Technology and Innovation Commission (201707010328); the China Postdoctoral Science Foundation (2016M600145); and the National Key R&D Program of China (2017YFA0205200, 2017YFC1308700, and 2017YFC1309100).

Immunomarker Support Vector Machine Classifier for Prediction of Gastric Cancer Survival and Adjuvant Chemotherapeutic Benefit

Purpose: Current tumor-node-metastasis (TNM) staging system cannot provide adequate information for prediction of prognosis and chemotherapeutic benefits. We constructed a classifier to predict prognosis and identify a subset of patients who can benefit from adjuvant chemotherapy.Experimental Design: We detected expression of 15 immunohistochemistry (IHC) features in tumors from 251 gastric cancer (GC) patients and evaluated the association of their expression level with overall survival (OS) and disease-free survival (DFS). Then, integrating multiple clinicopathologic features and IHC features, we used support vector machine (SVM)-based methods to develop a prognostic classifier (GC-SVM classifier) with features. Further validation of the GC-SVM classifier was performed in two validation cohorts of 535 patients.Results: The GC-SVM classifier integrated patient sex, carcinoembryonic antigen, lymph node metastasis, and the protein expression level of eight features, including CD3_{invasive margin (IM)}, CD3_{center of tumor (CT)}, CD8_IM, CD45RO_CT, CD57_IM, CD66b_IM, CD68_CT, and CD34. Significant differences were found between the high- and low-GC-SVM patients in 5-year OS and DFS in training and validation cohorts. Multivariate analysis revealed that the GC-SVM classifier was an independent prognostic factor. The classifier had higher predictive accuracy for OS and DFS than TNM stage and can complement the prognostic value of the TNM staging system. Further analysis revealed that stage II and III GC patients with high-GC-SVM were likely to benefit from adjuvant chemotherapy.Conclusions: The newly developed GC-SVM classifier was a powerful predictor of OS and DFS. Moreover, the GC-SVM classifier could predict which patients with stage II and III GC benefit from adjuvant chemotherapy. Clin Cancer Res; 24(22); 5574-84. ©2018 AACR.