Association of delayed chemoradiotherapy with elevated Epstein-Barr virus DNA load and adverse clinical outcome in nasopharyngeal carcinoma treatment during the COVID-19 pandemic: a retrospective study.

BACKGROUND: To summarize the impact of radiotherapy (RT) and chemotherapy delays on patients with nasopharyngeal carcinoma (NPC) during the COVID-19 pandemic. METHODS: We retrospectively included 233 patients with stage II-IVa NPC treated with RT and chemotherapy between December 11, 2019 and March 11, 2020. The outcomes were elevation in the EBV DNA load between two adjacent cycles of chemotherapy or during RT, and 1-year disease-free survival (DFS). RESULTS: RT delay occurred in 117 (50%) patients, and chemotherapy delay occurred in 220 (94%) patients. RT delay of ≥ 6 days was associated with a higher EBV DNA elevation rate (20.4% vs. 3.6%, odds ratio [OR] = 6.93 [95% CI = 2.49-19.32], P < 0.001), and worse 1-year DFS (91.2% vs. 97.8%, HR = 3.61 [95% CI = 1.37-9.50], P = 0.006), compared with on-schedule RT or delay of < 6 days. Chemotherapy delay of ≥ 10 days was not associated with a higher EBV DNA elevation rate (12.5% vs. 6.8%, OR = 1.94 [95% CI = 0.70-5.40], P = 0.20), or worse 1-year DFS (93.8% vs. 97.1%, HR = 3.73 [95% CI = 0.86-16.14], P = 0.059), compared with delay of < 10 days. Multivariable analyses showed RT delay of ≥ 6 days remained an independent adverse factor for both EBV DNA elevation and DFS. CONCLUSION: To ensure treatment efficacy for patients with nonmetastatic NPC, initiation of RT should not be delayed by more than 6 days; the effect of chemotherapy delay requires further investigation.

Evolving landscape and academic attitudes toward the controversies of global immuno-oncology trials.

This cross-sectional and longitudinal descriptive analysis aimed to track the evolving landscape of global immuno-oncology (IO) trials and provide insight into the resolution of IO-related controversies. Clinical trials (n = 4510) registered on ClinicalTrials.gov in 2007 to 2019 studying immune checkpoint inhibitors (ICIs), adoptive cell transfer (ACT), cancer vaccines and immune modulators were included. Most of IO trials are Phase 2 and focus on ICIs and multiple IO therapies. The United States leads global IO research, with stable growth and the best methodological quality. Mainland China ranks first in the number of ACT trials but has the lowest article publication rate (6.2%). A multiple-arm comparative design is often adopted in multiple IO therapies trials (44.0%). Trials studying ICIs and multiple IO therapies are likely to use early registration (80.0% and 86.6%) and stringent corticosteroid-/infection-related criteria. Hospitals have provided the most extensive and strongest support for all IO categories. Big pharma prefers to fund Phase 3-4 ICI trials (6.98%), while small pharma has a wider sponsorship favoring Phase 1-2 trials. The "partial-use-of-corticosteroids" strategy is generally well accepted in ICI trials with a definitive trend (32.5%; P < .001) but is associated with the poor dissemination of results (P ≤ .020), while the complete disclosure and standardization of dose/timing limits are still lacking. Disparities in design features and dissemination of results are widespread in IO trials and are modulated by IO category, cancer type and sponsor. We propose policy reforms to redefine the timely publication of IO trials and standardize the resolution of corticosteroid-/infection-related issues.

Transcription factor ATMIN facilitates chemoresistance in nasopharyngeal carcinoma.

Despite that the docectaxel-cisplatin-5-fluorouracil (TPF) induction chemotherapy has greatly improved patients' survival and became the first-line treatment for advanced nasopharyngeal carcinoma (NPC), not all patients could benefit from this therapy. The mechanism underlying the TPF chemoresistance remains unclear. Here, by analyzing gene-expression microarray data and survival of patients who received TPF chemotherapy, we identify transcription factor ATMIN as a chemoresistance gene in response to TPF chemotherapy in NPC. Mass spectrometry and Co-IP assays reveal that USP10 deubiquitinates and stabilizes ATMIN protein, resulting the high-ATMIN expression in NPC. Knockdown of ATMIN suppresses the cell proliferation and facilitates the docetaxel-sensitivity of NPC cells both in vitro and in vivo, while overexpression of ATMIN exerts the opposite effect. Mechanistically, ChIP-seq combined with RNA-seq analysis suggests that ATMIN is associated with the cell death signaling and identifies ten candidate target genes of ATMIN. We further confirm that ATMIN transcriptionally activates the downstream target gene LCK and stabilizes it to facilitate cell proliferation and docetaxel resistance. Taken together, our findings broaden the insight into the molecular mechanism of chemoresistance in NPC, and the USP10-ATMIN-LCK axis provides potential therapeutic targets for the management of NPC.

Development and validation of a joint model for dynamic prediction of overall survival in nasopharyngeal carcinoma based on longitudinal post-treatment plasma cell-free Epstein-Barr virus DNA load.

OBJECTIVES: To develop and validate a joint model for dynamic prediction of overall survival (OS) in nasopharyngeal carcinoma (NPC) based on longitudinal post-treatment plasma cell-free Epstein-Barr virus (cfEBV) DNA load. PATIENTS AND METHODS: We analyzed 695 patients with non-metastatic NPC and detectable post-treatment cfEBV DNA load who did not receive adjuvant therapy. We fitted the trajectories of post-treatment cfEBV DNA load as a function of time into a linear mixed-effect model and fitted a Cox regression model with covariates including age, T and N stages, and lactate dehydrogenase level. Finally, we combined both via joint modeling to develop and validate our dynamic model. RESULTS: A strong positive correlation was found between the individual longitudinal post-treatment cfEBV DNA load and the risk of death from any cause (P < 0.001). We developed a joint model capable of providing subject-specific dynamic prediction of conditional OS based on the evolution of the individual plasma cfEBV DNA load trajectory. The joint model showed reliable performance in both training and validation cohorts, with a large area under the curve (interquartile range [IQR]: training cohort, 0.775-0.850; validation cohort, 0.826-0.900) and low prediction errors (IQR: training cohort, 0.017-0.078; validation cohort, 0.034 -0.071). An increasing amount of data on cfEBV DNA load was associated with better model performance. CONCLUSION: Our model provided reliable subject-specific dynamic prediction of conditional OS, which could help guide individualized post-treatment surveillance, risk stratification, and management of NPC in the future.

Expression Profiles and Prognostic Value of Multiple Inhibitory Checkpoints in Head and Neck Lymphoepithelioma-Like Carcinoma.

Background: Inhibitory checkpoints are promising antitumor targets and predictive biomarkers in a variety of cancers. We aimed to identify the expression levels and prognostic value of multiple inhibitory checkpoints supported by preclinical and clinical evidence in head and neck lymphoepithelioma-like carcinoma (HNLELC). Methods: The expression of seven inhibitory checkpoints were evaluated in the tumor nest (TN) and tumor stroma (TS) of 102 HNLELC specimens using immunohistochemistry and digital pathology, and an inhibitory checkpoint-based signature (ICS) was subsequently constructed using the LASSO Cox regression model. Results: PD-L1, B7H3, and IDO-1 were mostly expressed in the TN, with median H-score of TN vs TS: 63.6 vs 14.6; 8.1 vs 1.0; 61.5 vs 34.7 (all P < 0.001), whereas PD-1, TIM-3, LAG-3, and VISTA were mainly observed in the TS, with median H-score of TN vs TS: 0.2 vs 12.4, 3.4 vs 7.1, 6.2 vs 11.9, 16.4 vs 47.2 (all P < 0.001), respectively. The most common simultaneously expressed combinations consisted of PD-L1 + B7H3 + IDO-1 + TIM-3 + LAG-3 + VISTA and B7H3 + IDO-1 + TIM-3 + LAG-3 in the TN (both occurring in 8.8% of patients) and PD-L1 + B7H3 + IDO-1 in the TS (4.9%). In addition, high-ICS patients had shorter 5-year disease-free (40.6% vs 81.7%; P < 0.001), regional recurrence-free (63.5% vs 88.2%; P = 0.003), and overall survival (73.5% vs 92.9%; P = 0.006) than low-ICS patients. Multivariate analysis revealed that ICS represented an independent predictor, which could significantly complement the predictive performance of TNM stage for 3-year (AUC 0.724 vs 0.619, P = 0.014), 5-year (AUC 0.727 vs 0.640, P = 0.056), and 10-year disease-free survival (AUC 0.815 vs 0.709, P = 0.023). Conclusions: The expression of inhibitory checkpoints and ICS classifier may increase the prognostic value of the TNM staging system and guide the rational design of personalized inhibitory checkpoint blockade therapy in HNLELC.

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.

A deep learning MR-based radiomic nomogram may predict survival for nasopharyngeal carcinoma patients with stage T3N1M0.

PURPOSE: To estimate the prognostic value of deep learning (DL) magnetic resonance (MR)-based radiomics for stage T3N1M0 nasopharyngeal carcinoma (NPC) patients receiving induction chemotherapy (ICT) prior to concurrent chemoradiotherapy (CCRT). METHODS: A total of 638 stage T3N1M0 NPC patients (training cohort: n = 447; test cohort: n = 191) were enrolled and underwent MRI scans before receiving ICT + CCRT. From the pretreatment MR images, DL-based radiomic signatures were developed to predict disease-free survival (DFS) in an end-to-end way. Incorporating independent clinical prognostic parameters and radiomic signatures, a radiomic nomogram was built through multivariable Cox proportional hazards method. The discriminative performance of the radiomic nomogram was assessed using the concordance index (C-index) and the Kaplan-Meier estimator. RESULTS: Three DL-based radiomic signatures were significantly correlated with DFS in the training (C-index: 0.695-0.731, all p < 0.001) and test (C-index: 0.706-0.755, all p < 0.001) cohorts. Integrating radiomic signatures with clinical factors significantly improved the predictive value compared to the clinical model in the training (C-index: 0.771 vs. 0.640, p < 0.001) and test (C-index: 0.788 vs. 0.625, p = 0.001) cohorts. Furthermore, risk stratification using the radiomic nomogram demonstrated that the high-risk group exhibited short-lived DFS compared to the low-risk group in the training cohort (hazard ratio [HR]: 6.12, p < 0.001), which was validated in the test cohort (HR: 6.90, p < 0.001). CONCLUSIONS: Our DL-based radiomic nomogram may serve as a noninvasive and useful tool for pretreatment prognostic prediction and risk stratification in stage T3N1M0 NPC.