Journal of the National Cancer Center最新文献

Pancreatic ductal adenocarcinoma (PDAC) stands as a profoundly heterogeneous and aggressive malignancy, manifesting a discouragingly limited response to conventional therapeutic interventions. Within the intricate tapestry of the tumor microenvironment (TME), cancer-associated fibroblasts (CAFs) emerge as pivotal constituents, wielding the capacity to propel the malignant attributes of neoplastic cells while bolstering their deftness in thwarting treatments. The rapid evolution of nanomedicinal technologies ushers in fresh avenues for therapeutic paradigms meticulously honed to target CAFs. Notably, a recent proposition by Yuan et al. introduces a PDAC treatment strategy metaphorically akin to “shooting fish in a barrel.” By adeptly capitalizing on the spatial distribution of the CAF barricade encircling the tumor, this innovative approach orchestrates a metamorphosis of CAFs, transitioning them from impediments to drug delivery into reservoirs of therapeutic agents. The resultant outcome, an augmentation of chemotherapy and immunotherapy efficacy, attests to the transformative potential of this concept. The study not only bequeaths novel insights and methodologies to surmount barriers in drug delivery for tumor treatment but also holds promise in elevating the precision, efficacy, and safety of tailored therapeutic regimens. Within this discourse, we meticulously evaluate Yuan et al.'s research, scrutinizing its merits and limitations, and cast a forward-looking gaze upon the formulation, validation of efficacy, and clinical translation of nanomedicines targeting CAFs.

Treatment of breast cancer with low expression of human epidermal growth factor receptor 2 (HER2; HER2-low) has drawn much attention in recent years. With the proven therapeutic effect of trastuzumab deruxtecan (T-DXd) in patients with HER2-low (immunohistochemistry [IHC] 1+, or IHC2+/in situ hybridization [ISH]-) breast cancer, HER2-low may become a new subtype of targeted therapy for breast cancer. The expert committee formulated this consensus based on the current clinical studies and clinical medication experience. The current consensus is the collaborative work of an interdisciplinary working group, including experts in the fields of pathology and oncology. The purpose of this consensus was to guide the clinical diagnosis and treatment of HER2-low breast cancer, thereby prolonging the overall survival of patients.

Cellular senescence is characterized by a generally irreversible cell cycle arrest and the secretion of bioactive factors known as the senescence-associated secretory phenotype (SASP). In an oncogenic context, senescence is considered a tumor suppressive mechanism as it prevents cell proliferation and inhibits the progression from pre-malignant to malignant disease. However, recent studies have demonstrated that senescent tumor cells, which could spontaneously exist within cancer tissues or arise in response to various cancer interventions (the so-called therapy-induced senescence, TIS), can acquire pro-tumorigenic properties and are capable of driving local and metastatic relapse. This highlights the complex and multifaceted nature of cellular senescence in cancer biology. Here, we summarize the current knowledge of the pathological function of therapy-induced senescent tumor cells and discuss possible mechanisms by which tumor cell senescence contributes to cancer relapse. We also discuss implications for future studies toward targeting these less appreciated cells.

Background

Although poor oral health and several lifestyle factors have been found to be associated with cancer risk, their joint relationship has rarely been studied.

Methods

We prospectively examined the associations of oral health and healthy lifestyle factors with cancer risk among 0.5 million rural and urban residents from the China Kadoorie Biobank (2004–2015). Oral health status was assessed from self-reported baseline questionnaires. A healthy lifestyle index comprising non-smoking, non-drinking, ideal body shape, physical activity and healthy diet was calculated for each participant, and categorized into favorable, intermediate and unfavorable lifestyle behavior. We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) relating oral health and healthy lifestyle index to cancer risk using Cox proportional hazards models. We estimated the population attributable risk percent (PAR%) and 95% CIs using multivariate models.

Results

During a median follow-up of 9 years, 23,805 new cancer cases were documented, with 52% from rural areas and 48% from urban areas. Compared with those with good oral health and favorable lifestyle, participants with poor oral health and unfavorable lifestyle had a higher risk of developing cancer in both rural (adjusted HR, 1.55 [95% CI, 1.39–1.74]; P for trend < 0.001) and urban areas (adjusted HR, 1.44 [95% CI, 1.24–1.67]; P for trend < 0.001). A significant multiplicative interaction between oral health and healthy lifestyle index on cancer risk was found in rural residents (P for interaction = 0.004) rather than in urban residents (P for interaction = 0.973). Assuming poor oral health as an additional risk factor, the PAR% of total cancer increased by 3.0% and 1.1% for participants with intermediate lifestyle and unfavorable lifestyle, respectively.

Conclusions

These findings suggest a joint effect of oral health and common lifestyle factors on cancer risk. Promotion of healthy lifestyle by integration of good oral health would be beneficial to consider in cancer prevention strategies.

Objective

Accurate prognostic predictions and personalized decision-making on induction chemotherapy (IC) for individuals with locally advanced nasopharyngeal carcinoma (LA-NPC) remain challenging. This research examined the predictive function of tumor burden-incorporated machine-learning algorithms for overall survival (OS) and their value in guiding treatment in patients with LA-NPC.

Methods

Individuals with LA-NPC were reviewed retrospectively. Tumor burden signature-based OS prediction models were established using a nomogram and two machine-learning methods, the interpretable eXtreme Gradient Boosting (XGBoost) risk prediction model, and DeepHit time-to-event neural network. The models' prediction performances were compared using the concordance index (C-index) and the area under the curve (AUC). The patients were divided into two cohorts based on the risk predictions of the most successful model. The efficacy of IC combined with concurrent chemoradiotherapy was compared to that of chemoradiotherapy alone.

Results

The 1 221 eligible individuals, assigned to the training (n = 813) or validation (n = 408) set, showed significant respective differences in the C-indices of the XGBoost, DeepHit, and nomogram models (0.849 and 0.768, 0.811 and 0.767, 0.730 and 0.705). The training and validation sets had larger AUCs in the XGBoost and DeepHit models than the nomogram model in predicting OS (0.881 and 0.760, 0.845 and 0.776, and 0.764 and 0.729, P < 0.001). IC presented survival benefits in the XGBoost-derived high-risk but not low-risk group.

Conclusion

This research used machine-learning algorithms to create and verify a comprehensive model integrating tumor burden with clinical variables to predict OS and determine which patients will most likely gain from IC. This model could be valuable for delivering patient counseling and conducting clinical evaluations.

Objective

Considering that there are no effective biomarkers for the screening of cardia gastric cancer (CGC), we developed a noninvasive diagnostic approach, employing data-independent acquisition (DIA) proteomics to identify candidate protein markers.

Methods

Plasma samples were obtained from 40 subjects, 10 each for CGC, cardia high-grade dysplasia (CHGD), cardia low-grade dysplasia (CLGD), and healthy controls. Proteomic profiles were obtained through liquid chromatography-mass spectrometry (LC-MS/MS-based DIA proteomics. Candidate plasma proteins were identified by weighted gene co-expression network analysis (WGCNA) combined with machine learning and further validated by the Human Protein Atlas (HPA) database. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the biomarker panel.

Results

There was a clear distinction in proteomic features among CGC, CHGD, CLGD, and the healthy controls. According to the WGCNA, we found 42 positively associated and 164 inversely associated proteins related to CGC progression and demonstrated several canonical cancer-associated pathways. Combined with the results from random forests, LASSO regression, and immunohistochemical results from the HPA database, we identified three candidate proteins (GSTP1, CSRP1, and LY6G6F) that could together distinguish CLGD (AUC = 0.91), CHGD (AUC = 0.99) and CGC (AUC = 0.98) from healthy controls with excellent accuracy.

Conclusions

The panel of protein biomarkers showed promising diagnostic potential for CGC and precancerous lesions. Further validation and a larger-scale study are warranted to assess its potential clinical applications, suggesting a potential avenue for CGC prevention in the future.

Background

The tumor microenvironment (TME) performs a crucial function in the tumorigenesis and response to immunotherapies of clear cell renal cell carcinoma (ccRCC). However, a lack of recognized pre-clinical TME-based risk models poses a great challenge to investigating the risk factors correlated with prognosis and treatment responses for patients with ccRCC.

Methods

Stromal and immune contexture were assessed to calculate the TMErisk score of a large sample of patients with ccRCC from public and real-world cohorts using machine-learning algorithms. Next, analyses for prognostic efficacy, correlations with clinicopathological features, functional enrichment, immune cell distributions, DNA variations, immune response, and heterogeneity were performed and validated.

Results

Clinical hub genes, including INAFM2, SRPX, DPYSL3, VSIG4, APLNR, FHL5, A2M, SLFN11, ADAMTS4, IFITM1, NOD2, CCR4, HLA-DQB2, and PLAUR, were identified and incorporated to develop the TMErisk signature. Patients in the TME^high risk group (category) exhibited a considerably grim prognosis, and the TMErisk model was shown to independently function as a risk indicator for the overall survival (OS) of ccRCC patients. Expression levels of immune checkpoint genes were substantially increased in TME^high risk group, while those of the human leukocyte antigen (HLA) family genes were prominently decreased. In addition, tumors in the TME^high group showed significantly high infiltration levels of tumor-infiltrated lymphocytes, including M2 macrophages, CD8⁺ T cells, B cells, and CD4⁺ T cells. In heterogeneity analysis, more frequent somatic mutations, including pro-tumorigenic BAP1 and PBRM1, were observed in the TME^high group. Importantly, 19.3% of patients receiving immunotherapies in the TME^high group achieved complete or partial response compared with those with immune tolerance in the TME^low group, suggesting that TMErisk prominently differentiates prognosis and responses to immunotherapy for patients with ccRCC.

Conclusions

We first established the TMErisk score of ccRCC using machine-learning algorithms based on a large-scale population. The TMErisk score can be utilized as an innovative independent prognosis predictive marker with high sensitivity and accuracy. Our discovery also predicted the efficacy of immunotherapy in ccRCC patients, indicating the intimate link between tumor immune microenvironment and intratumoral heterogeneity.

Objective

The incidence and mortality of hepatocellular carcinoma (HCC) have been increasing around the world. Current guidelines recommend HCC screening in high-risk population. However, the strength of evidence of benefits and harms of HCC screening to support the recommendation was unclear. The objective is to systematically synthesize current evidence on the benefits and harms of HCC screening.

Methods

We searched PubMed and nine other databases until August 20, 2021. We included cohort studies and RCTs that compared the benefits and harms of screening and non-screening in high-risk population of HCC. Case series studies that reported harms of HCC screening were also included. Pooled risk ratio (RR), according to HCC screening status, was calculated for each benefit outcome (e.g., HCC mortality, survival rate, proportion of early HCC), using head-to-head meta-analysis. The harmful outcomes (e.g., proportion of physiological harms provided by non-comparative studies were pooled by prevalence of meta-analysis. Analysis on publication bias and quality of life, subgroup analysis, and sensitivity analysis were also conducted.

Results

We included 70 studies, including four random clinical trials (RCTs), 63 cohort studies,three case series studies. The meta-analysis of RCTs showed HCC screening was significantly associated with reduced HCC mortality (RR [risk ratio], 0.73 [95% CI, 0.56–0.96]; I² = 75.1%), prolonged overall survival rates (1-year, RR, 1.72 [95% CI, 1.13–2.61]; I² = 72.5%; 3-year, RR, 2.86 [95% CI, 1.78–4.58]; I² = 10.1%; and 5-year, RR, 2.76 [95% CI, 1.37–5.54]; I² = 28.3%), increased proportion of early HCC detection (RR, 2.68 [95% CI, 1.77–4.06]; I² = 50.4%). Similarly, meta-analysis of cohort studies indicated HCC screening was more effective than non-screening. However, pooled proportion of physiological harms was 16.30% (95% CI: 8.92%–23.67%) and most harms were of a mild to moderate severity.

Conclusion

The existing evidence suggests HCC screening is more effective than non-screening in high-risk population. However, harms of screening should not be ignored.