Effect of anti-COVID-19 drugs on patients with cancer.

The clinical treatment of patients with cancer who are also diagnosed with coronavirus disease (COVID-19) has been a challenging issue since the outbreak of COVID-19. Therefore, it is crucial to understand the effects of commonly used drugs for treating COVID-19 in patients with cancer. Hence, this review aims to provide a reference for the clinical treatment of patients with cancer to minimize the losses caused by the COVID-19 pandemic. In this study, we also focused on the relationship between COVID-19, commonly used drugs for treating COVID-19, and cancer. We specifically investigated the effect of these drugs on tumor cell proliferation, migration, invasion, and apoptosis. The potential mechanisms of action of these drugs were discussed and evaluated. We found that most of these drugs showed inhibitory effects on tumors, and only in a few cases had cancer-promoting effects. Furthermore, inappropriate usage of these drugs may lead to irreversible kidney and heart damage. Finally, we have clarified the use of different drugs, which can provide useful guidance for the clinical treatment of cancer patients diagnosed with COVID-19.

Inhibition of FNDC1 suppresses gastric cancer progression by interfering with Gßγ-VEGFR2 complex formation.

Gastric cancer (GC) is a prevalent digestive tract malignant tumor characterized by an insidious onset, ease of metastasis, rapid growth, and poor prognosis. Here, we report that fibronectin type III domain containing 1 (FNDC1) has high expression in GC and indicates poor outcomes in patients with GC. FNDC1 over-expression or knockdown promotes or inhibits tumorigenesis and metastasis, respectively. The expression of FNDC1 is upregulated by TWIST1, strengthening its interaction with Gßγ and VEGFR2. The formation of the trimers, TWIST1 plus Gßγ and VEGFR2, increases VEGFR2 phosphorylation and Gßγ trafficking, which activates RAS-MAPK and PI3K-AKT signaling, benefiting GC progression. In this study, we demonstrated that arsenite can efficiently suppress FNDC1 expression, attenuating the formation of the trimers and downstream pathways. Altogether, our results indicate that FNDC1 might be a promising target for clinical treatment and prognostic judgment, while FNDC1 inhibition by arsenite provides a new opportunity for overcoming this fatal disease.

Biology-guided deep learning predicts prognosis and cancer immunotherapy response.

Substantial progress has been made in using deep learning for cancer detection and diagnosis in medical images. Yet, there is limited success on prediction of treatment response and outcomes, which has important implications for personalized treatment strategies. A significant hurdle for clinical translation of current data-driven deep learning models is lack of interpretability, often attributable to a disconnect from the underlying pathobiology. Here, we present a biology-guided deep learning approach that enables simultaneous prediction of the tumor immune and stromal microenvironment status as well as treatment outcomes from medical images. We validate the model for predicting prognosis of gastric cancer and the benefit from adjuvant chemotherapy in a multi-center international study. Further, the model predicts response to immune checkpoint inhibitors and complements clinically approved biomarkers. Importantly, our model identifies a subset of mismatch repair-deficient tumors that are non-responsive to immunotherapy and may inform the selection of patients for combination treatments.

Noninvasive imaging evaluation of peritoneal recurrence and chemotherapy benefit in gastric cancer after gastrectomy: a multicenter study.

BACKGROUND: Peritoneal recurrence (PR) is the predominant pattern of relapse after curative-intent surgery in gastric cancer (GC) and indicates a dismal prognosis. Accurate prediction of PR is crucial for patient management and treatment. The authors aimed to develop a noninvasive imaging biomarker from computed tomography (CT) for PR evaluation, and investigate its associations with prognosis and chemotherapy benefit. METHODS: In this multicenter study including five independent cohorts of 2005 GC patients, the authors extracted 584 quantitative features from the intratumoral and peritumoral regions on contrast-enhanced CT images. The artificial intelligence algorithms were used to select significant PR-related features, and then integrated into a radiomic imaging signature. And improvements of diagnostic accuracy for PR by clinicians with the signature assistance were quantified. Using Shapley values, the authors determined the most relevant features and provided explanations to prediction. The authors further evaluated its predictive performance in prognosis and chemotherapy response. RESULTS: The developed radiomics signature had a consistently high accuracy in predicting PR in the training cohort (area under the curve: 0.732) and internal and Sun Yat-sen University Cancer Center validation cohorts (0.721 and 0.728). The radiomics signature was the most important feature in Shapley interpretation. The diagnostic accuracy of PR with the radiomics signature assistance was improved by 10.13-18.86% for clinicians ( P <0.001). Furthermore, it was also applicable in the survival prediction. In multivariable analysis, the radiomics signature remained an independent predictor for PR and prognosis ( P <0.001 for all). Importantly, patients with predicting high risk of PR from radiomics signature could gain survival benefit from adjuvant chemotherapy. By contrast, chemotherapy had no impact on survival for patients with a predicted low risk of PR. CONCLUSION: The noninvasive and explainable model developed from preoperative CT images could accurately predict PR and chemotherapy benefit in patients with GC, which will allow the optimization of individual decision-making.

Non-invasive CT imaging biomarker to predict immunotherapy response in gastric cancer: a multicenter study.

BACKGROUND: Despite remarkable benefits have been provided by immune checkpoint inhibitors in gastric cancer (GC), predictions of treatment response and prognosis remain unsatisfactory, making identifying biomarkers desirable. The aim of this study was to develop and validate a CT imaging biomarker to predict the immunotherapy response in patients with GC and investigate the associated immune infiltration patterns. METHODS: This retrospective study included 294 GC patients who received anti-PD-1/PD-L1 immunotherapy from three independent medical centers between January 2017 and April 2022. A radiomics score (RS) was developed from the intratumoral and peritumoral features on pretreatment CT images to predict immunotherapy-related progression-free survival (irPFS). The performance of the RS was evaluated by the area under the time-dependent receiver operating characteristic curve (AUC). Multivariable Cox regression analysis was performed to construct predictive nomogram of irPFS. The C-index was used to determine the performance of the nomogram. Bulk RNA sequencing of tumors from 42 patients in The Cancer Genome Atlas was used to investigate the RS-associated immune infiltration patterns. RESULTS: Overall, 89 of 294 patients (median age, 57 years (IQR 48-66 years); 171 males) had an objective response to immunotherapy. The RS included 13 CT features that yielded AUCs of 12-month irPFS of 0.787, 0.810 and 0.785 in the training, internal validation, and external validation 1 cohorts, respectively, and an AUC of 24-month irPFS of 0.805 in the external validation 2 cohort. Patients with low RS had longer irPFS in each cohort (p<0.05). Multivariable Cox regression analyses showed RS is an independent prognostic factor of irPFS. The nomogram that integrated the RS and clinical characteristics showed improved performance in predicting irPFS, with C-index of 0.687-0.778 in the training and validation cohorts. The CT imaging biomarker was associated with M1 macrophage infiltration. CONCLUSION: The findings of this prognostic study suggest that the non-invasive CT imaging biomarker can effectively predict immunotherapy outcomes in patients with GC and is associated with innate immune signaling, which can serve as a potential tool for individual treatment decisions.

Noninvasive imaging of the tumor immune microenvironment correlates with response to immunotherapy in gastric cancer.

The tumor immune microenvironment (TIME) is associated with tumor prognosis and immunotherapy response. Here we develop and validate a CT-based radiomics score (RS) using 2272 gastric cancer (GC) patients to investigate the relationship between the radiomics imaging biomarker and the neutrophil-to-lymphocyte ratio (NLR) in the TIME, including its correlation with prognosis and immunotherapy response in advanced GC. The RS achieves an AUC of 0.795-0.861 in predicting the NLR in the TIME. Notably, the radiomics imaging biomarker is indistinguishable from the IHC-derived NLR status in predicting DFS and OS in each cohort (HR range: 1.694-3.394, P < 0.001). We find the objective responses of a cohort of anti-PD-1 immunotherapy patients is significantly higher in the low-RS group (60.9% and 42.9%) than in the high-RS group (8.1% and 14.3%). The radiomics imaging biomarker is a noninvasive method to evaluate TIME, and may correlate with prognosis and anti PD-1 immunotherapy response in GC patients.

PAK6 promotes homologous-recombination to enhance chemoresistance to oxaliplatin through ATR/CHK1 signaling in gastric cancer.

Chemoresistance remains the primary challenge of clinical treatment of gastric cancer (GC), making the biomarkers of chemoresistance crucial for treatment decision. Our previous study has reported that p21-actived kinase 6 (PAK6) is a prognostic factor for selecting which patients with GC are resistant to 5-fluorouracil/oxaliplatin chemotherapy. However, the mechanistic role of PAK6 in chemosensitivity remains unknown. The present study identified PAK6 as an important modulator of the DNA damage response (DDR) and chemosensitivity in GC. Analysis of specimens from patients revealed significant associations between the expression of PAK6 and poorer stages, deeper invasion, more lymph node metastases, higher recurrence rates, and resistance to oxaliplatin. Cells exhibited chemosensitivity to oxaliplatin after knockdown of PAK6, but showed more resistant to oxaliplatin when overexpressing PAK6. Functionally, PAK6 mediates cancer chemoresistance by enhancing homologous recombination (HR) to facilitate the DNA double-strand break repair. Mechanistically, PAK6 moves into nucleus to promote the activation of ATR, thereby further activating downstream repair protein CHK1 and recruiting RAD51 from cytoplasm to the DNA damaged site to repair the broken DNA in GC. Activation of ATR is the necessary step for PAK6 mediated HR repair to protect GC cells from oxaliplatin-induced apoptosis, and ATR inhibitor (AZD6738) could block the PAK6-mediated HR repair, thereby reversing the resistance to oxaliplatin and even promoting the sensitivity to oxaliplatin regardless of high expression of PAK6. In conclusion, these findings indicate a novel regulatory mechanism of PAK6 in modulating the DDR and chemoresistance in GC and provide a reversal suggestion in clinical decision.

Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study.

BACKGROUND: Peritoneal recurrence is the predominant pattern of relapse after curative-intent surgery for gastric cancer and portends a dismal prognosis. Accurate individualised prediction of peritoneal recurrence is crucial to identify patients who might benefit from intensive treatment. We aimed to develop predictive models for peritoneal recurrence and prognosis in gastric cancer. METHODS: In this retrospective multi-institution study of 2320 patients, we developed a multitask deep learning model for the simultaneous prediction of peritoneal recurrence and disease-free survival using preoperative CT images. Patients in the training cohort (n=510) and the internal validation cohort (n=767) were recruited from Southern Medical University, Guangzhou, China. Patients in the external validation cohort (n=1043) were recruited from Sun Yat-sen University Cancer Center, Guangzhou, China. We evaluated the prognostic accuracy of the model as well as its association with chemotherapy response. Furthermore, we assessed whether the model could improve the ability of clinicians to predict peritoneal recurrence. FINDINGS: The deep learning model had a consistently high accuracy in predicting peritoneal recurrence in the training cohort (area under the receiver operating characteristic curve [AUC] 0·857; 95% CI 0·826-0·889), internal validation cohort (0·856; 0·829-0·882), and external validation cohort (0·843; 0·819-0·866). When informed by the artificial intelligence (AI) model, the sensitivity and inter-rater agreement of oncologists for predicting peritoneal recurrence was improved. The model was able to predict disease-free survival in the training cohort (C-index 0·654; 95% CI 0·616-0·691), internal validation cohort (0·668; 0·643-0·693), and external validation cohort (0·610; 0·583-0·636). In multivariable analysis, the model predicted peritoneal recurrence and disease-free survival independently of clinicopathological variables (p<0·0001 for all). For patients with a predicted high risk of peritoneal recurrence and low survival, adjuvant chemotherapy was associated with improved disease-free survival in both stage II disease (hazard ratio [HR] 0·543 [95% CI 0·362-0·815]; p=0·003) and stage III disease (0·531 [0·432-0·652]; p<0·0001). By contrast, chemotherapy had no impact on disease-free survival for patients with a predicted low risk of peritoneal recurrence and high survival. For the remaining patients, the benefit of chemotherapy depended on stage: only those with stage III disease derived benefit from chemotherapy (HR 0·637 [95% CI 0·484-0·838]; p=0·001). INTERPRETATION: The deep learning model could allow accurate prediction of peritoneal recurrence and survival in patients with gastric cancer. Prospective studies are required to test the clinical utility of this model in guiding personalised treatment in combination with clinicopathological criteria. FUNDING: None.

c-Myc-activated intronic miR-210 and lncRNA MIR210HG synergistically promote the metastasis of gastric cancer.

The relationship between microRNA (miRNA) and hosting long non-coding RNA (lncRNA) remains unclear. Here, the expression levels of microRNA-210 (miR-210) and hosting lncRNA MIR210HG are significantly increased and positively correlated in gastric cancer (GC). Gain- and loss-of-function studies demonstrate that miR-210 and MIR210HG synergistically promote the migration and invasion of GC cells in vitro. Furthermore, GC sublines simultaneously expressing miR-210 and MIR210HG display synergistic promotion of lung metastasis in vivo. Mechanistically, MIR210HG interacts with DExH-box helicase 9 (DHX9) to increase DHX9/c-Jun complex's occupancy on the promoter of matrix metallopeptidases (MMPs), and thus promotes migration and invasion of GC cells. Additionally, miR-210 directly suppresses the expression of dopamine receptor D5 (DRD5), serine/threonine kinase 24 (STK24) and MAX network transcriptional repressor (MNT), resulting in enhanced migration and invasion. Finally, MYC proto-oncogene (c-Myc) transactivates miR-210 and MIR210HG. Overexpression of miR-210 or/and MIR210HG can rescue the inhibitory effect on the migration and invasion by silencing c-Myc. Moreover, c-Myc inhibitor significantly decreases lung metastasis of GC in vivo. Collectively, our findings identify a novel mechanism, by which c-Myc-activated miR-210 and MIR210HG synergistically promote the metastasis of GC.

Radiomics signature based on computed tomography images for the preoperative prediction of lymph node metastasis at individual stations in gastric cancer: A multicenter study.

BACKGROUND: Specific diagnosis and treatment of gastric cancer (GC) require accurate preoperative predictions of lymph node metastasis (LNM) at individual stations, such as estimating the extent of lymph node dissection. This study aimed to develop a radiomics signature based on preoperative computed tomography (CT) images, for predicting the LNM status at each individual station. METHODS: We enrolled 1506 GC patients retrospectively from two centers as training (531) and external (975) validation cohorts, and recruited 112 patients prospectively from a single center as prospective validation cohort. Radiomics features were extracted from preoperative CT images and integrated with clinical characteristics to construct nomograms for LNM prediction at individual lymph node stations. Performance of the nomograms was assessed through calibration, discrimination and clinical usefulness. RESULTS: In training, external and prospective validation cohorts, radiomics signature was significantly associated with LNM status. Moreover, radiomics signature was an independent predictor of LNM status in the multivariable logistic regression analysis. The radiomics nomograms revealed good prediction performances, with AUCs of 0.716-0.871 in the training cohort, 0.678-0.768 in the external validation cohort and 0.700-0.841 in the prospective validation cohort for 12 nodal stations. The nomograms demonstrated a significant agreement between the actual probability and predictive probability in calibration curves. Decision curve analysis showed that nomograms had better net benefit than clinicopathologic characteristics. CONCLUSION: Radiomics nomograms for individual lymph node stations presented good prediction accuracy, which could provide important information for individual diagnosis and treatment of gastric cancer.

The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT.

ABSTRACT: In 2020, the new type of coronal pneumonitis became a pandemic in the world, and has firstly been reported in Wuhan, China. Chest CT is a vital component in the diagnostic algorithm for patients with suspected or confirmed COVID-19 infection. Therefore, it is necessary to conduct automatic and accurate detection of COVID-19 by chest CT.The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT.From the COVID-19 cases in our institution, 136 moderate patients and 83 severe patients were screened, and their clinical and laboratory data on admission were collected for statistical analysis. Initial CT Radiomics were modeled by automatic machine learning, and diagnostic performance was evaluated according to AUC, TPR, TNR, PPV and NPV of the subjects. At the same time, the initial CT main features of the two groups were analyzed semi-quantitatively, and the results were statistically analyzed.There was a statistical difference in age between the moderate group and the severe group. The model cohort showed TPR 96.9%, TNR 99.1%, PPV98.4%, NPV98.2%, and AUC 0.98. The test cohort showed TPR 94.4%, TNR100%, PPV100%, NPV96.2%, and AUC 0.97. There was statistical difference between the two groups with grade 1 score (P = .001), the AUC of grade 1 score, grade 2 score, grade 3 score and CT score were 0.619, 0.519, 0.478 and 0.548, respectively.Radiomics' Auto ML model was built by CT image of initial COVID -19 pneumonia, and it proved to be effectively used to predict the clinical classification of COVID-19 pneumonia. CT features have limited ability to predict the clinical typing of Covid-19 pneumonia.

Development and Validation of a Deep Learning CT Signature to Predict Survival and Chemotherapy Benefit in Gastric Cancer: A Multicenter, Retrospective Study.

OBJECTIVE: We aimed to develop a deep learning-based signature to predict prognosis and benefit from adjuvant chemotherapy using preoperative computed tomography (CT) images. BACKGROUND: Current staging methods do not accurately predict the risk of disease relapse for patients with gastric cancer. METHODS: We proposed a novel deep neural network (S-net) to construct a CT signature for predicting disease-free survival (DFS) and overall survival in a training cohort of 457 patients, and independently tested it in an external validation cohort of 1158 patients. An integrated nomogram was constructed to demonstrate the added value of the imaging signature to established clinicopathologic factors for individualized survival prediction. Prediction performance was assessed with respect to discrimination, calibration, and clinical usefulness. RESULTS: The DeLIS was associated with DFS and overall survival in the overall validation cohort and among subgroups defined by clinicopathologic variables, and remained an independent prognostic factor in multivariable analysis (P< 0.001). Integrating the imaging signature and clinicopathologic factors improved prediction performance, with C-indices: 0.792-0.802 versus 0.719-0.724, and net reclassification improvement 10.1%-28.3%. Adjuvant chemotherapy was associated with improved DFS in stage II patients with high-DeLIS [hazard ratio = 0.362 (95% confidence interval 0.149-0.882)] and stage III patients with high- and intermediate-DeLIS [hazard ratio = 0.611 (0.442-0.843); 0.633 (0.433-0.925)]. On the other hand, adjuvant chemotherapy did not affect survival for patients with low-DeLIS, suggesting a predictive effect (Pinteraction = 0.048, 0.016 for DFS in stage II and III disease). CONCLUSIONS: The proposed imaging signature improved prognostic prediction and could help identify patients most likely to benefit from adjuvant chemotherapy in gastric cancer.

Chemotherapy Use and Survival Among Young and Middle-Aged Patients With Gastric Cancer.

INTRODUCTION: Treatments for young patients with gastric cancer (GC) remain poorly defined, and their effects on survival are uncertain. We aimed to investigate the receipt of chemotherapy by age category (18-49, 50-64, and 65-85 years) and explore whether age differences in chemotherapy matched survival gains in patients with GC. METHODS: Patients who were histologically diagnosed with GC were included from a Chinese multi-institutional database and the Surveillance, Epidemiology, and End Results database. There were 5,122 and 31,363 patients aged 18-85 years treated between 2000 and 2014, respectively. Overall survival and stage-specific likelihood of receiving chemotherapy were evaluated. RESULTS: Of the 5,122 and 31,363 patients in China and Surveillance, Epidemiology, and End Result data sets, 3,489 (68.1%) and 18,115 (57.8%) were men, respectively. Younger (18-49 years) and middle-aged (50-64 years) patients were more likely to receive chemotherapy compared with older patients (65-85 years) (64.9%, 56.7%, and 45.4% in the 3 groups from the China data set). Among patients treated with surgery alone, a significantly better prognosis was found in younger and middle-aged patients than their older counterparts; however, no significant differences were found in overall survival among age subgroups in patients who received both surgery and chemotherapy, especially in the China data set. The survival benefit from chemotherapy was superior among older patients (all P < 0.0001) compared with that among younger and middle-aged patients in stage II and III disease. DISCUSSION: Potential overuse of chemotherapy was found in younger and middle-aged patients with GC, but the addition of chemotherapy did not bring about matched survival improvement, especially in the China data set.

Radiomics Nomogram for Prediction of Peritoneal Metastasis in Patients With Gastric Cancer.

Objective: The aim of this study is to evaluate whether radiomics imaging signatures based on computed tomography (CT) could predict peritoneal metastasis (PM) in gastric cancer (GC) and to develop a nomogram for preoperative prediction of PM status. Methods: We collected CT images of pathological T4 gastric cancer in 955 consecutive patients of two cancer centers to analyze the radiomics features retrospectively and then developed and validated the prediction model built from 292 quantitative image features in the training cohort and two validation cohorts. Lasso regression model was applied for selecting feature and constructing radiomics signature. Predicting model was developed by multivariable logistic regression analysis. Radiomics nomogram was developed by the incorporation of radiomics signature and clinical T and N stage. Calibration, discrimination, and clinical usefulness were used to evaluate the performance of the nomogram. Results: In training and validation cohorts, PM status was associated with the radiomics signature significantly. It was found that the radiomics signature was an independent predictor for peritoneal metastasis in multivariable logistic analysis. For training and internal and external validation cohorts, the area under the receiver operating characteristic curves (AUCs) of radiomics signature for predicting PM were 0.751 (95%CI, 0.703-0.799), 0.802 (95%CI, 0.691-0.912), and 0.745 (95%CI, 0.683-0.806), respectively. Furthermore, for training and internal and external validation cohorts, the AUCs of radiomics nomogram for predicting PM were 0.792 (95%CI, 0.748-0.836), 0.870 (95%CI, 0.795-0.946), and 0.815 (95%CI, 0.763-0.867), respectively. Conclusions: CT-based radiomics signature could predict peritoneal metastasis, and the radiomics nomogram can make a meaningful contribution for predicting PM status in GC patient preoperatively.

Genomics Score Based on Genome-Wide Network Analysis for Prediction of Survival in Gastric Cancer: A Novel Prognostic Signature.

PURPOSE: Gastric cancer (GC) is a product of multiple genetic abnormalities, including genetic and epigenetic modifications. This study aimed to integrate various biomolecules, such as miRNAs, mRNA, and DNA methylation, into a genome-wide network and develop a nomogram for predicting the overall survival (OS) of GC. MATERIALS AND METHODS: A total of 329 GC cases, as a training cohort with a random of 150 examples included as a validation cohort, were screened from The Cancer Genome Atlas database. A genome-wide network was constructed based on a combination of univariate Cox regression and least absolute shrinkage and selection operator analyses, and a nomogram was established to predict 1-, 3-, and 5-year OS in the training cohort. The nomogram was then assessed in terms of calibration, discrimination, and clinical usefulness in the validation cohort. Afterward, in order to confirm the superiority of the whole gene network model and further reduce the biomarkers for the improvement of clinical usefulness, we also constructed eight other models according to the different combinations of miRNAs, mRNA, and DNA methylation sites and made corresponding comparisons. Finally, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were also performed to describe the function of this genome-wide network. RESULTS: A multivariate analysis revealed a novel prognostic factor, a genomics score (GS) comprising seven miRNAs, eight mRNA, and 19 DNA methylation sites. In the validation cohort, comparing to patients with low GS, high-GS patients (HR, 12.886; P < 0.001) were significantly associated with increased all-cause mortality. Furthermore, after stratification of the TNM stage (I, II, III, and IV), there were significant differences revealed in the survival rates between the high-GS and low-GS groups as well (P < 0.001). The 1-, 3-, and 5-year C-index of whole genomics-based nomogram were 0.868, 0.895, and 0.928, respectively. The other models have comparable or relatively poor comprehensive performance, while they had fewer biomarkers. Besides that, DAVID 6.8 further revealed multiple molecules and pathways related to the genome-wide network, such as cytomembranes, cell cycle, and adipocytokine signaling. CONCLUSION: We successfully developed a GS based on genome-wide network, which may represent a novel prognostic factor for GC. A combination of GS and TNM staging provides additional precision in stratifying patients with different OS prognoses, constituting a more comprehensive sub-typing system. This could potentially play an important role in future clinical practice.

Young age increases risk for lymph node positivity in gastric cancer: A Chinese multi-institutional database and US SEER database study.

Object: The risk of lymph node positivity (LN+) in gastric cancer (GC) impacts therapeutic recommendations. The aim of this study was to quantify the effect of younger age on LN+. Methods: Data from a Chinese multi-institutional database and the US SEER database on stage I to III resected GC were analyzed for the relationship between age and LN+ status. The association of age and LN+ status was examined with logistic regression separately for each T stage, adjusting for multiple covariates. Poisson regression was used to evaluate age and number of LN+. Results: 4,905 and 14,877 patients were identified in the China and SEER datasets respectively. 479 (9.8%) patients were under age 40 years, with 768 (15.7%) between age 40 and 49 years in China dataset, and 416 (2.8%) patients were under age 40 years, with 1176 (7.9%) between age 40 and 49 years in SEER dataset. Both datasets exhibited significantly proportional decreases of N3a and N3b LN+ with age increasing. Patients younger than age 40 years were more likely to show LN+ compared with the reference age 60 to 69 years. The youngest patients had the highest ORs of N1, N2, N3a, and N3b vs N0 LN+ within T4 stage of China dataset and T3 stage of SEER dataset, the values of ORs decreased with increasing age. Young age was a predictor of an increased number of LNs positive for each T stage. Conclusion: In the two large datasets, young age at diagnosis is associated with an increased risk of LN+.

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.

Frequency and Prognosis of Pulmonary Metastases in Newly Diagnosed Gastric Cancer.

Purpose: The purpose of this study was to analyze the frequency and prognosis of pulmonary metastases in newly diagnosed gastric cancer using population-based data from SEER. Methods: Patients with gastric cancer and pulmonary metastases (GCPM) at the time of diagnosis in advanced gastric cancer were identified using the Surveillance, Epidemiology and End Result (SEER) database of the National Cancer Institute from 2010 to 2014. Multivariable logistic regression was performed to identify predictors of the presence of GCPM at diagnosis. Receiver operator characteristics analysis was performed to significant predictors on multivariable logistic regression and was then assessed with Delong's test. Multivariable Cox regression was developed to identify factors associated with all-cause mortality and gastric cancer-specific mortality. Survival curves were obtained according to the Kaplan-Meier method and compared using the log-rank test. Results: We identified 1,104 patients with gastric cancer and pulmonary metastases at the time of diagnosis, representing 6.02% of the entire cohort and 15.19% of the subset with metastatic disease to any distant site. Among the entire cohort, multivariable logistic regression identified six factors (younger, upper 1/3 of stomach, intestinal-type, T4 staging, N1 staging, and presence of more extrapulmonary metastases to liver, bone, and brain) as positive predictors of the presence of pulmonary metastases at diagnosis. The value of AUC for the multivariable logistic regression model was 0.775. Median survival among the entire cohort with GCPM was 3.0 months (interquartile range: 1.0-9.0 mo). Multivariable Cox model in SEER cohort confirmed five factors (diagnosis at previous period, black race, adverse pathology grade, absence of chemotherapy, and presence of more extrapulmonary metastases to liver, bone, and brain) as negative predictors for overall survival. Conclusions: The findings of this study provided population-based estimates of the frequency and prognosis for GCPM at time of diagnosis. The multivariable logistic regression model had an acceptable performance to predict the presence of PM. These findings may provide preventive guidelines for the screening and treatment of PM in GC patients. Patients with high risk factors should be paid more attention before and after diagnosis.

Liver Metastases in Newly Diagnosed Gastric Cancer: A Population-Based Study from SEER.

Purpose: Population-based data on the proportion and prognosis of liver metastases at diagnosis of gastric cancer are currently lacking. Besides, the treatment of gastric cancer with liver metastases is still controversial now. Methods: Patients with gastric cancer and liver metastases (GCLM) at the time of diagnosis in advanced gastric cancer were identified using the Surveillance, Epidemiology, and End Result (SEER) database of the National Cancer Institute. Multivariable logistic and Cox regression were performed to identify predictors of the presence of GCLM at diagnosis and factors associated with all-cause mortality. Results: We identified 3507 patients with gastric cancer and liver metastases at the time of diagnosis, representing 16.89% of the entire cohort and 44.12% of the subset with metastatic disease to any distant site. Among entire cohort, multivariable logistic regression identified thirteen factors (age, race, sex, original, tumor location, pathology grade, Lauren classification, T staging, N staging, tumor size, number of extrahepatic metastatic sites to bone, lung, and brain, insurance situation and smoking) as predictors of the presence of liver metastases at diagnosis. Median survival among the entire cohort with GCLM was 4.0 months (interquartile range: 1.0-10.0 mo). Patients receiving comprehensive therapy had longer median overall survival, of which the median survival was 12.0 months (interquartile range: 6.0-31.0 mo). Multivariable Cox model in SEER cohort confirmed nine factors (age, tumor location, Lauren classification, T staging, number of extrahepatic metastatic sites to bone, lung, and brain, surgery, chemotherapy, RSC and marital status) as independent predictors for overall survival. Conclusions: The findings of this study provided population-based estimates of the proportion and prognosis for LM at time of GC diagnosis. These findings provide preventive guidelines for screening and treatment of LM in GC patients.

Radiomics Signature on Computed Tomography Imaging: Association With Lymph Node Metastasis in Patients With Gastric Cancer.

Background: To evaluate whether radiomic feature-based computed tomography (CT) imaging signatures allow prediction of lymph node (LN) metastasis in gastric cancer (GC) and to develop a preoperative nomogram for predicting LN status. Methods: We retrospectively analyzed radiomics features of CT images in 1,689 consecutive patients from three cancer centers. The prediction model was developed in the training cohort and validated in internal and external validation cohorts. Lasso regression model was utilized to select features and build radiomics signature. Multivariable logistic regression analysis was utilized to develop the model. We integrated the radiomics signature, clinical T and N stage, and other independent clinicopathologic variables, and this was presented as a radiomics nomogram. The performance of the nomogram was assessed with calibration, discrimination, and clinical usefulness. Results: The radiomics signature was significantly associated with pathological LN stage in training and validation cohorts. Multivariable logistic analysis found the radiomics signature was an independent predictor of LN metastasis. The nomogram showed good discrimination and calibration. Conclusions: The newly developed radiomic signature was a powerful predictor of LN metastasis and the radiomics nomogram could facilitate the preoperative individualized prediction of LN status.