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Abstract Background Accurate and up-to-date estimates of the global cancer burden in adolescents and young adults (AYA) are scarce. This study aims to assess the global burden and trends of AYA cancer, with a focus on socioeconomic disparities, to inform global cancer control strategies. Methods AYA cancer, defined as cancer occurring in individuals aged 15–39, was analyzed using data from the Global Burden of Disease (GBD) 2021 study and the Global Cancer Observatory (GLOBOCAN) 2022 project. We examined the global burden by age, sex, geographic location, and Human Development Index (HDI), as well as its temporal trends. Primary outcomes included age-standardized incidence and mortality rates (ASIR, ASMR) and the average annual percent change (AAPC). Results In 2022, an estimated 1,300,196 incidental cases and 377,621 cancer-related deaths occurred among AYAs worldwide, with an ASIR of 40.3 per 100,000 and an ASMR of 11.8 per 100,000. The most common cancers were breast, thyroid, and cervical, while the leading causes of death were breast, cervical, and leukemia. The incidence and mortality were disproportionately higher among females (ASIR: 52.9 for females vs. 28.3 for males; ASMR: 13.1 for females vs. 10.6 for males). Countries with higher HDI experienced a higher incidence of AYA cancers (ASIR: 32.0 [low HDI] vs. 54.8 [very high HDI]), while countries with lower HDI faced a disproportionately higher mortality burden (ASMR: 17.2 [low HDI] vs. 8.4 [very high HDI]) despite their relatively low incidence. Disproportionality and regression measures highlighted significant HDI-related inequalities. AYA cancer incidence was stable from 2000 to 2011 (AAPC: − 0.04) but increased from 2012 to 2021 (AAPC: 0.53), driven by growing gonadal and colorectal cancers. Mortality decreased substantially from 2000 to 2011 (AAPC: − 1.64), but the decline slowed from 2012 (AAPC: − 0.32) probably due to increased deaths from gonadal cancers. These trends varied by sex, cancer type, geography, and HDI. Conclusion AYA cancers present a significant and growing global burden, with marked disparities across sex, geographic locations, and HDI levels. Policymakers should prioritize equitable resource allocation and implement targeted interventions to reduce these inequalities, particularly in low-HDI regions and with regard to gonadal cancers.

Abstract Background Combinatory therapeutic strategy containing immunochemotherapy as part of induction therapy components is one of the current trends in the treatment of high‐risk metastatic locally advanced nasopharyngeal carcinoma (NPC). However, the mechanism underlying the heterogeneity of response at the single‐cell level has not been underexplored. Methods 18 bulks and 11 single‐cell RNA sequencing from paired before‐treatment and on‐treatment samples in patients with treatment‐naive high‐risk metastatic locally advanced NPCs were obtained. Following quality control, a total of 87 191 cells were included in the subsequence bioinformatics analysis. Results Immunochemotherapy was associated with on‐treatment tumour microenvironment (TME) remodelling, including upregulation of anti‐TMEs signatures, downregulation of pro‐TMEs signatures, reversing CD8+ T exhaustion, and repolarizing proinflammatory TAMs. For the patients achieving a complete response, the cytotoxic activity of CD8+ T cells was stimulated and more interferon‐gamma was provided, which would be the key for TAMs proinflammatory repolarization and eventually promote the CD8+ T cells maturation in turn. Among patients who did not reach complete response, differentiation and hypoxia signatures for endothelial cells were elevated after therapy. These patients exhibited higher levels of immune checkpoint genes in malignant cells at the baseline (before treatment), and decreased tumour antigen presentation activity, which may underlie the resistance mechanism to therapy. Conclusions This study pictures a map of TME modulation following immunochemotherapy‐based combination induction therapy and provides potential future approaches. Highlights Immunochemotherapy remodeled T cell phenotypes. For the patients achieving complete response, more interferon gamma was provided by CD8+ T cells after therapy, which would be the key for TAMs pro‐inflammatory repolarization and eventually promote the CD8+ T cells maturation in turns. Among patients who did not reach complete response, malignant cells exhibited higher level of immune checkpoint genes before therapy, and decreased tumor antigen presentation activity, which may underlie the resistance mechanism to therapy.

Abstract To assess the efficacy and safety of the combination of immune checkpoint inhibitors (ICIs) and target therapy (anti-angiogenesis or EGFR inhibitors) as a second-line or subsequent treatment for recurrent or metastatic nasopharyngeal carcinoma (R/M NPC), we conducted a retrospective study. In this study, previously treated R/M NPC patients were administered one of the following treatment: ICIs combined with target therapy and chemotherapy (ITC), ICIs combined with target therapy alone (IT), ICIs combined with chemotherapy (IC), or chemotherapy alone (C). The primary endpoint under consideration was progression-free survival (PFS), while secondary endpoints included overall survival (OS), objective response rate (ORR), disease control rate (DCR), and safety measures. A total of 226 patients participated in this study, with 70 receiving the ITC regimen, 48 receiving IT, 48 treated with IC, and 60 undergoing C alone. The median PFS for the four cohorts was 20.67, 13.63, 12.47, and 7.93 months respectively. Notably, ITC regimen yielded the most favorable PFS among these cohorts. The ITC cohort exhibited a comparable tumor response and safety profile to the IT and IC cohorts (p > 0.05), but superior tumor response compared to the C cohort (p

Objectives Risk prediction for patients with polymyositis/dermatomyositis-associated interstitial lung disease (PM/DM-ILD) is challenging due to heterogeneity in the disease course. We aimed to develop a mortality risk prediction model for PM/DM-ILD.Methods This prognostic study analysed patients with PM/DM-ILD admitted to Nanjing Drum Hospital from 2016 to 2021. The primary outcome was mortality within 1 year. We used a least absolute shrinkage and selection operator (LASSO) logistic regression model to identify predictive laboratory indicators. These indicators were used to create a laboratory risk score, and we developed a mortality risk prediction model by incorporating clinical factors. The evaluation of model performance encompassed discrimination, calibration, clinical utility and practical application for risk prediction and prognosis.Results Overall, 418 patients with PM/DM-ILD were enrolled and randomly divided into development (n=282) and validation (n=136) cohorts. LASSO logistic regression identified four optimal features in the development cohort, forming a laboratory risk score: C reactive protein, lactate dehydrogenase, CD3+CD4+ T cell counts and PO2/FiO2. The final prediction model integrated age, arthralgia, anti-melanoma differentiation-associated gene 5 antibody status, high-resolution CT pattern and the laboratory risk score. The prediction model exhibited robust discrimination (area under the receiver operating characteristic: 0.869, 95% CI 0.811 to 0.910), excellent calibration and valuable clinical utility. Patients were categorised into three risk groups with distinct mortality rates. The internal validation, sensitivity analyses and comparative assessments against previous models further confirmed the robustness of the prediction model.Conclusions We developed and validated an evidence-based mortality risk prediction model with simple, readily accessible clinical variables in patients with PM/DM-ILD, which may inform clinical decision-making.

BackgroundEpstein-Barr virus (EBV) DNA seronegative (Sero-) and seropositive (Sero+) nasopharyngeal carcinoma (NPC) are distinctly different disease subtypes. Patients with higher baseline EBV DNA titers seem to benefit less from anti-PD1 immunotherapy, but underlying mechanisms remain unclear. Tumor microenvironment (TME) characteristics could be the important factor affecting the efficacy of immunotherapy. Here, we illuminated the distinct multicellular ecosystems of EBV DNA Sero- and Sero+ NPCs from cellular compositional and functional perspectives at single-cell resolution.MethodWe performed single-cell RNA sequencing analyses of 28,423 cells from ten NPC samples and one non-tumor nasopharyngeal tissue. The markers, function, and dynamics of related cells were analyzed.ResultsWe found that tumor cells from EBV DNA Sero+ samples exhibit low-differentiation potential, stronger stemness signature, and upregulated signaling pathways associated with cancer hallmarks than that of EBV DNA Sero- samples. Transcriptional heterogeneity and dynamics in T cells were associated with EBV DNA seropositivity status, indicating different immunoinhibitory mechanisms employed by malignant cells depending on EBV DNA seropositivity status. The low expression of classical immune checkpoints, early-triggered cytotoxic T-lymphocyte response, global activation of IFN-mediated signatures, and enhanced cell-cell interplays cooperatively tend to form a specific immune context in EBV DNA Sero+ NPC.ConclusionsCollectively, we illuminated the distinct multicellular ecosystems of EBV DNA Sero- and Sero+ NPCs from single-cell perspective. Our study provides insights into the altered tumor microenvironment of NPC associated with EBV DNA seropositivity, which will help direct the development of rational immunotherapy strategies.

BackgroundAnti-melanoma differentiation-associated gene 5 antibody-positive dermatomyositis with interstitial lung disease (anti-MDA5 DM-ILD) is a disease with high mortality. We sought to develop an effective and convenient prediction tool to estimate mortality risk in patients with anti-MDA5 DM-ILD and inform clinical decision-making early.MethodsThis prognostic study included Asian patients with anti-MDA5 DM-ILD hospitalized at the Nanjing Drum Hospital from December 2016 to December 2020. Candidate laboratory indicators were retrospectively collected. Patients hospitalized from 2016 to 2018 were used as the discovery cohort and applied to identify the optimal predictive features using a least absolute shrinkage and selection operator (LASSO) logistic regression model. A risk score was determined based on these features and used to construct the mortality risk prediction model in combination with clinical characteristics. Results were verified in a temporal validation comprising patients treated between 2019 and 2020. The primary outcome was mortality risk within one year. The secondary outcome was overall survival. The prediction model’s performance was assessed in terms of discrimination, calibration, and clinical usefulness.ResultsThis study included 127 patients, (72 men [56.7%]; median age, 54 years [interquartile range, 48-63 years], split into discovery (n = 87, 70%) and temporal validation (n=37, 30%) cohorts. Five optimal features were selected by LASSO logistic regression in the discovery cohort (n = 87) and used to construct a risk score, including lymphocyte counts, CD3+CD4+ T-cell counts, cytokeratin 19 fragment (CYFRA21-1), oxygenation index, and anti-Ro52 antibody. The retained predictive variables in the final prediction model were age, Heliotrope, fever, and risk score, and the most predictive factor was the risk score. The prediction model showed good discrimination (AUC: 0.915, 95% CI: 0.846–0.957), good calibration (Hosmer–Lemeshow test, P = 0.506; Brier score, 0.12), and fair clinical usefulness in the discovery cohort. The results were verified among patients in the temporal validation cohort (n = 38). We successfully divided patients into three risk groups with very different mortality rates according to the predictive score in both the discovery and validation cohorts (Cochran-Armitage test for trend, P < 0.001).ConclusionsWe developed and validated a mortality risk prediction tool with good discrimination and calibration for Asian patients with anti-MDA5 DM-ILD. This tool can offer individualized mortality risk estimation and inform clinical decision-making.

We aimed to develop a nomogram integrating MRI-based tumor burden features (MTBF), nodal necrosis, and some clinical factors to forecast the distant metastasis-free survival (DMFS) of patients suffering from non-metastatic nasopharyngeal carcinoma (NPC). A total of 1640 patients treated at Sun Yat-sen University Cancer Center (Guangzhou, China) from 2011 to 2016 were enrolled, among which 1148 and 492 patients were randomized to a training cohort and an internal validation cohort, respectively. Additionally, 200 and 257 patients were enrolled in the Foshan and Dongguan validation cohorts, respectively, which served as independent external validation cohorts. The MTBF were developed from the stepwise regression of six multidimensional tumor burden variables, based on which we developed a nomogram also integrating nodal necrosis and clinical features. This model divided the patients into high- and low-risk groups by an optimal cutoff. Compared with those of patients in the low-risk group, the DMFS [hazard ratio (HR): 4.76, 95% confidence interval (CI): 3.39–6.69; p < 0.0001], and progression-free survival (PFS; HR: 4.11, 95% CI: 3.13–5.39; p < 0.0001) of patients in the high-risk group were relatively poor. Furthermore, in the training cohort, the 3-year DMFS of high-risk patients who received induction chemotherapy (ICT) combined with concurrent chemoradiotherapy (CCRT) was better than that of those who were treated with CCRT alone (p = 0.0340), whereas low-risk patients who received ICT + CCRT had a similar DMFS to those who only received CCRT. The outcomes we obtained were all verified in the three validation cohorts. The survival model can be used as a reliable prognostic tool for NPC patients and is helpful to determine patients who will benefit from ICT.

Abstract Background Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperplasia, the positive rate for malignancy identification during biopsy is low, thus leading to delayed or missed diagnosis for nasopharyngeal malignancies upon initial attempt. Here, we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies under endoscopic examination based on deep learning. Methods An endoscopic images-based nasopharyngeal malignancy detection model (eNPM-DM) consisting of a fully convolutional network based on the inception architecture was developed and fine-tuned using separate training and validation sets for both classification and segmentation. Briefly, a total of 28,966 qualified images were collected. Among these images, 27,536 biopsy-proven images from 7951 individuals obtained from January 1st, 2008, to December 31st, 2016, were split into the training, validation and test sets at a ratio of 7:1:2 using simple randomization. Additionally, 1430 images obtained from January 1st, 2017, to March 31st, 2017, were used as a prospective test set to compare the performance of the established model against oncologist evaluation. The dice similarity coefficient (DSC) was used to evaluate the efficiency of eNPM-DM in automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images, by comparing automatic segmentation with manual segmentation performed by the experts. Results All images were histopathologically confirmed, and included 5713 (19.7%) normal control, 19,107 (66.0%) nasopharyngeal carcinoma (NPC), 335 (1.2%) NPC and 3811 (13.2%) benign diseases. The eNPM-DM attained an overall accuracy of 88.7% (95% confidence interval (CI) 87.8%–89.5%) in detecting malignancies in the test set. In the prospective comparison phase, eNPM-DM outperformed the experts: the overall accuracy was 88.0% (95% CI 86.1%–89.6%) vs. 80.5% (95% CI 77.0%–84.0%). The eNPM-DM required less time (40 s vs. 110.0 ± 5.8 min) and exhibited encouraging performance in automatic segmentation of nasopharyngeal malignant area from the background, with an average DSC of 0.78 ± 0.24 and 0.75 ± 0.26 in the test and prospective test sets, respectively. Conclusions The eNPM-DM outperformed oncologist evaluation in diagnostic classification of nasopharyngeal mass into benign versus malignant, and realized automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images.