Journal of the National Cancer Center最新文献

Glioblastoma is one of the most devastating central nervous system disorders. Being a highly vascular brain tumor, it is distinguished by aberrant vessel architecture. This lends credence to the idea that endothelial cells (ECs) linked with glioblastoma vary fundamentally from ECs seen in the healthy human brain. To effectively design an antiangiogenic treatment, it is crucial to identify the functional and phenotypic characteristics of tumor-associated ECs. The ECs associated with glioblastoma are less prone to apoptosis than control cells and are resistant to cytotoxic treatments. Additionally, ECs associated with glioblastoma migrate more quickly than control ECs and naturally produce large amounts of growth factors such as endothelin-1, interleukin-8, and vascular endothelial growth factor (VEGF). For designing innovative antiangiogenic drugs that particularly target tumor-related ECs in gliomas, it is critical to comprehend these distinctive features of ECs associated with gliomas. This review discusses the process of angiogenesis, other factors involved in the genesis of tumors, and the possibility of ECs as a potential target in combating glioblastoma. It also sheds light on the association of tumor microenvironment and ECs with immunotherapy. This review, thus gives us the hope that neuro endothelial targeting with growth factors and angiogenesis regulators combined with gene therapy would open up new doorways and change our traditional perspective of treating cancer.

Background

With the popularization of lung cancer screening, more early-stage lung cancers are being detected. This study aims to compare three types of N classifications, including location-based N classification (pathologic nodal classification [pN]), the number of lymph node stations (nS)-based N classification (nS classification), and the combined approach proposed by the International Association for the Study of Lung Cancer (IASLC) which incorporates both pN and nS classification to determine if the nS classification is more appropriate for early-stage lung cancer.

Methods

We retrospectively reviewed the clinical data of lung cancer patients treated at the Cancer Hospital, Chinese Academy of Medical Sciences between 2005 and 2018. Inclusion criteria was clinical stage IA lung adenocarcinoma patients who underwent resection during this period. Sub-analyses were performed for the three types of N classifications. The optimal cutoff values for nS classification were determined with X-tile software. Kaplan‒Meier and multivariate Cox analyses were performed to assess the prognostic significance of the different N classifications. The prediction performance among the three types of N classifications was compared using the concordance index (C-index) and decision curve analysis (DCA).

Results

Of the 669 patients evaluated, 534 had pathological stage N0 disease (79.8%), 82 had N1 disease (12.3%) and 53 had N2 disease (7.9%). Multivariate Cox analysis indicated that all three types of N classifications were independent prognostic factors for prognosis (all P < 0.001). However, the prognosis overlaps between pN (N1 and N2, P = 0.052) and IASLC-proposed N classification (N1b and N2a1 [P = 0.407], N2a1 and N2a2 [P = 0.364], and N2a2 and N2b [P = 0.779]), except for nS classification subgroups (nS0 and nS1 [P < 0.001] and nS1 and nS >1 [P = 0.006]). There was no significant difference in the C-index values between the three N classifications (P = 0.370). The DCA results demonstrated that the nS classification provided greater clinical utility.

Conclusion

The nS classification might be a better choice for nodal classification in clinical stage IA lung adenocarcinoma.

Objective

Immune checkpoint inhibitors (ICIs) targeting programmed cell death-1/ligand-1 (PD-1/PD-L1), cytotoxic T lymphocyte antigen-4 (CTLA-4), and lymphocyte-activation gene-3 (LAG-3) have been widely studied and applied throughout the course of cancer treatment. This study aimed to provide a comprehensive profile of ICI-associated toxicity and elucidate the toxicity patterns of ICIs across different treatment lines.

Methods

In total, 155 cohorts comprising 24 539 eligible patients were included in the safety analysis. Trial name, registration number, cancer type, trial phase, clinical setting, trial design, regimen, dosing schedule, age, sex and ethnicity distributions, number of patients, number of treatment-related adverse events (trAEs), and number of treatment-related death were extracted. We defined a timeline from the neoadjuvant setting to the third-line setting. We also introduced a synthesizing principle for adverse event rates (SPAER) of immunotherapy to ensure the comparability and reliability across different treatment lines. The study protocol was registered and approved by the PROSPERO protocol review committee (CRD42021242368).

Results

After excluding the neoadjuvant setting group, we observed a distinct reduction in the incidence of treatment-related adverse events (trAEs) with an advancement of the line of ICI treatment. The incidence of trAEs was negatively correlated with the line of treatment, irrespective of whether monotherapy or dual-ICI combination therapy was administered. Sensitivity analyses also confirmed the coincident negative correlations.

Conclusion

In summary, using a timeline-based concept centered around treatment lines, we revealed the dynamic landscape of ICI-associated toxicity and found that patients treated with ICIs during later lines of therapy may have a lower risk of trAEs.

Artificial intelligence (AI) is developing rapidly and has found widespread applications in medicine, especially radiotherapy. This paper provides a brief overview of AI applications in radiotherapy, and highlights the research directions of AI that can potentially make significant impacts and relevant ongoing research works in these directions. Challenging issues related to the clinical applications of AI, such as robustness and interpretability of AI models, are also discussed. The future research directions of AI in the field of medical physics and radiotherapy are highlighted.

Detection of minimal/molecular residual disease (MRD) based on ctDNA assay develops from hematological malignancies to solid tumors. Generally, there are two mainstream assays in MRD testing technology: tumor-informed and tumor-agnostic. For colorectal cancer (CRC), MRD is used not only to monitor recurrence and predict prognosis, but also to help in clinical decision making and assessment of clinical efficacy in the settings of curative surgery, radiotherapy, chemotherapy and surveillance. Accumulated clinical trials are exploring roles of MRD in early or advanced stages of CRC. Here, we give an overview of how MRD is and will be used in CRC.

Background

The target definition of consolidation radiotherapy (RT) for extensive stage small-cell lung cancer (ES-SCLC) has not been standardized. This study aimed to demonstrate the feasibility of post-chemotherapy based consolidation RT in ES-SCLC.

Methods

All ES-SCLC patients without initial brain metastases who completed ≥ 4 cycles of systemic therapy at Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University from 2012 to 2021 were included in this retrospective study. We correlated the site of first recurrence to the post-chemotherapy-based radiation volume (small-field). Relapse pattern, progression-free survival (PFS) and overall survival (OS) were compared between those received and did not receive consolidation RT.

Results

A total of 152 patients were followed up for a median of 31.7 months (interquartile range [IQR], 23.9–39.6 months). The median PFS and OS of the cohort were 8.3 months (IQR, 6.1–11.2 months) and 16.2 months (IQR, 9.9–24.9 months), respectively. Thoracic consolidation RT served not only as an independent prognostic factor for improved PFS in the entire cohort, but also significantly prolonged OS in the subgroup without synchronous liver metastases. Small-field consolidation RT markedly reduced in-field recurrences (hazard ratio [HR], 0.28 [95% CI, 0.12–0.38]; P < 0.001) without increasing out-of-field recurrences (HR, 0.40 [95% CI, 0.13–1.16]; P = 0.080). No relapse was observed at the margin of the targets. Treatment-related toxicities were moderate, with grade 3 acute radiation pneumonia, radiation esophagitis, and bone marrow suppression rates of 8.3%, 3.1%, and 12.5%, respectively. No grade 5 toxicity occurred.

Conclusion

Small-field consolidation RT based on post-chemotherapy volume is safe and can significantly improve local control in ES-SCLC.

Esophageal cancer (EC) is particularly common in China. With the continuing progress of multi-disciplinary therapy including early screening, minimally invasive techniques, radiotherapy and chemotherapy, the 5-year survival of EC has been improved in China. However, there are considerable disparities in the diagnosis and treatment quality among different regions. The Esophageal Cancer Expert Committee of the National Cancer Quality Control Center (NCQCC) considers a set of authoritative quality control standards as an opportunity to eliminate the disparities and improve the overall survival and quality of life of EC. To further promote the quality control for standardized diagnosis and treatment of EC, the National Cancer Center commissioned the Esophageal Cancer Quality Control Expert Committee to draft and formulate the Chinese Quality Control Indices for Standardized Diagnosis and Treatment of Esophageal Cancer (2022 edition). The Indices includes 21 items that cover all key areas in the diagnosis and treatment of esophageal cancer, such as medical oncology, radiation oncology, endoscopy, and pathology.

Background

Human epidermal growth factor receptor 2 (HER2) overexpression is related to anti-HER2 therapy in many tumors. RC48- antibody-drug conjugate (ADC) has shown promising efficacy in patients with HER2-positive locally advanced or metastatic urothelial carcinoma (UC). The characteristic expression and scoring systems of HER2 are nonexistent in UC. We aimed to explore HER2 status and its correlation with the efficacy of HER2-targeting ADC therapy in UC.

Methods

A total of 137 and 43 patients were enrolled in cohort 1 and cohort 2, respectively, from March 2009 to December 2018. The patients in cohort 2 were enrolled in a phase II study of RC48-ADC. UC samples were tested for HER2 status using immunohistochemistry (IHC) and/or fluorescence in situ hybridization (FISH). The 2018 ASCO/CAP HER2 scoring system was adopted and modified to score HER2 expression in UC.

Results

The HER2-positive (IHC 2+ or 3+) rate was 24.1% (33/137). In HER2 IHC 2+ or 3+ patients, the HER2 gene amplification rate was 31% (13/42). The objective response rates (ORRs) in RC48-ADC-treated patients with IHC 3+, IHC 2+ and FISH+, IHC 2+ and FISH- were 58.8%, 66.7% and 40%, respectively. The ORR showed a trend toward a better benefit for RC48-ADC therapy in patients with HER2 amplification than in those without amplification (61.5% vs. 44.8%, P = 0.059). The heterogeneity of HER2 expression in the primary tumor was 55.5% (15/27), and the ORR was not significantly different between patients with tumor heterogeneity and homogeneity.

Conclusions

IHC testing should be performed to assess the HER2 status before the initiation of HER2-ADC therapy. There was a trend toward a better benefit for patients with HER2 amplification, and tumor heterogeneity did not influence the drug efficacy.

For many cancers a primary cause of poor survival is that they are detected at a late stage when therapies are less effective. Although screening methods exist to detect some types of cancer at an early stage, there are currently no effective methods to screen for most types of cancer. Biomarkers have the potential to improve detection of early-stage cancers, risk stratification, and prediction of which pre-cancerous lesions are likely to progress and to make screening tests less invasive. Although thousands of research articles on biomarkers for early detection are published every year, few of these biomarkers have been validated and shown to be clinically useful. This reflects both the inherent difficulty in detecting early-stage cancers and a disconnect between the process of discovering biomarkers and their use in the clinic. To overcome this limitation the US National Cancer Institute created the Early Detection Research Network. It is a highly collaborative program that brings together biomarker discoverers, assay developers, and clinicians. It provides an infrastructure that is essential for developing and validating biomarkers and imaging methods for early cancer detection and has successfully completed several multicenter validation studies.