Effects of Hepatitis B Surface Antigen on Virus-Specific and Global T Cells in Patients With Chronic Hepatitis B Virus infection.

BACKGROUND & AIMS: Chronic hepatitis B virus (HBV) infection is characterized by the presence of defective viral envelope proteins (hepatitis B surface antigen [HBsAg]) and the duration of infection-most patients acquire the infection at birth or during the first years of life. We investigated the effects of these factors on patients' lymphocyte and HBV-specific T-cell populations. METHODS: We collected blood samples and clinical data from 243 patients with HBV infection (3-75 years old) in the United Kingdom and China. We measured levels of HBV DNA, HBsAg, hepatitis B e antigen, and alanine aminotransferase; analyzed HBV genotypes; and isolated peripheral blood mononuclear cells (PBMCs). In PBMCs from 48 patients with varying levels of serum HBsAg, we measured 40 markers on nature killer and T cells by mass cytometry. PBMCs from 189 patients with chronic infection and 38 patients with resolved infections were incubated with HBV peptide libraries, and HBV-specific T cells were identified by interferon gamma enzyme-linked immune absorbent spot (ELISpot) assays or flow cytometry. We used multivariate linear regression and performed variable selection using the Akaike information criterion to identify covariates associated with HBV-specific responses of T cells. RESULTS: Although T- and natural killer cell phenotypes and functions did not change with level of serum HBsAg, numbers of HBs-specific T cells correlated with serum levels of HBsAg (r = 0.3367; P < .00001). After we performed the variable selection, the multivariate linear regression model identified patient age as the only factor significantly associated with numbers of HBs-specific T cells (P = .000115). In patients younger than 30 years, HBs-specific T cells constituted 28.26% of the total HBV-specific T cells; this value decreased to 7.14% in patients older than 30 years. CONCLUSIONS: In an analysis of immune cells from patients with chronic HBV infection, we found that the duration of HBsAg exposure, rather than the quantity of HBsAg, was associated with the level of anti-HBV immune response. Although the presence of HBs-specific T cells might not be required for the clearance of HBV infection in all patients, strategies to restore anti-HBV immune responses should be considered in patients younger than 30 years.

Nomogram based on blood lipoprotein for estimation of mortality in patients with hepatitis B virus-related acute-on-chronic liver failure.

BACKGROUND: Acute-on-chronic liver failure (ACLF) is a clinic syndrome with substantial high short-term mortality. It is very important to stratify patients according to prognosis to decide management strategy. This study aimed to formulate and validate a nomogram model based on blood lipoprotein for prediction of 3-month mortality in patients with hepatitis B virus (HBV)-related ACLF. METHODS: Data on 393 consecutive patients who were diagnosed as HBV-related ACLF at the Third Affiliated Hospital of Sun Yat-sen University between June 1, 2013, and February 1, 2015, were prospectively collected. Of these, 260 patients who were collected in an earlier period formed the training cohort for the development of nomogram, while 133 patients who were collected thereafter formed the validation cohort for confirming the performance of nomogram. RESULTS: Multivariate analysis showed that low density lipoprotein cholesterol (LDL-C), age, prothrombin time, and creatinine were independently associated with 3-month mortality of patients with HBV-related ACLF. Kaplan-Meier survival analysis revealed that the high LDL-C (LDL-C ≥ 1.0 mmol/L, cut-off value) was significantly associated with elevated overall survival (P < 0.001). All independent factors for survival were selected into the nomogram. The calibration plot for the probability of survival showed good agreement between prediction by nomogram and actual observation. CONCLUSION: This study highlighted that reduction of serum LDL-C level was an independent risk factor for the survival in patients with HBV-related ACLF, and the nomogram based on serum LDL-C was an accurate and practical model for predicting the 3-month mortality in patients with this disease.

Clinical significance of LINC02532 in hepatitis B virus-associated hepatocellular carcinoma and its regulatory effect on tumor progression.

BACKGROUND AND AIM: Long non-coding RNAs (lncRNAs) play an important role in tumor progression, including in hepatocellular carcinoma (HCC) induced by hepatitis B virus (HBV). Therefore, the aim of this study was to investigate the role of LINC02532 in HCC, mainly for diagnostic prognostic value and cellular function, as well as mechanistic aspects. METHODS: Initially, GEO and VirBase databases were used to screen for aberrant lncRNAs in HBV-HCC.Then, HBV-HCC persons followed up in our center were retrospectively studied to investigate the diagnostic, prognostic value of LINC02532 in HBV-HCC. Subsequently, the role of LINC02532 in HBV-HCC was measured using cellular function assay methods and possible mechanisms were analyzed in conjunction with bioinformatic predictive science. RESULTS: LINC02532 was a lncRNA abnormally expressed in HBV-HCC. LINC02532 was significantly up-regulated in the expression level in HBV-HCC tissues compared with normal tissues from patients. Moreover, LINC02532 could distinguish HBV-HCC and predict the prognosis of HBV-HCC. In vitro experiments showed that LINC02532 could regulate miR-455-3p and promote the malignant characterization of HBV-HCC cells. CHEK2 was a target gene of miR-455-3p. CONCLUSIONS: The prognosis and diagnosis of HBV-HCC can rely on the expression of LINC02532. LINC02532 was important for further progression of HBV-HCC, by moderating miR-455-3p/CHEK2.

Chrysophanol induces apoptosis and ferroptosis of gastric cancer cells by targeted regulation of mTOR.

Programmed cell death (PCD) induction is a promising strategy for killing gastric cancer cells. In this study, we investigated the effects of chrysophanol on apoptosis and ferroptosis in gastric cancer cells. Chrysophanol in concentrations ranging from 0 to 100 µM were used to treat GES-1, HGC-27 and AGS cells. Cell counting kit-8 assay, colony formation assay, 5-ethynyl-2'-deoxyuridine staining, flow cytometry, JC-1 probe insertion, dihydroethidium staining and western blotting were performed. The effects of chrysophanol on gastric cancer cells were evaluated in vivo using a xenograft mouse model. Chrysophanol had no cytotoxic effects on GES-1 cells. Chrysophanol with concentrations higher than 25 µM inhibited gastric cancer cell colony formation and proliferation. Chrysophanol induces gastric cancer cell apoptosis in a dose-dependent manner, accompanied by mitochondrial membrane potential dysfunction and cytochrome c release. Additionally, chrysophanol increased the levels of reactive oxygen species, total iron, and Fe2+ in HGC-27 and AGS cells, in a dose-dependent manner. Treatment of cells with the ferroptosis inhibitor ferrostatin-1 attenuated the effects of chrysophanol on cell survival and the expression of ferroptosis markers SLC7A11 and GPX4. Screening by GEO software indicated that the mTOR signalling pathway is possibly regulated by chrysophanol. Furthermore, mTOR overexpression significantly reversed the inhibitory effects of chrysophanol on gastric cancer cells. In gastric cancer xenograft mouse models, chrysophanol treatment inhibited tumour growth and downregulated SLC7A11 and GPX4. Chrysophanol induces apoptosis and ferroptosis, making it a potential candidate for killing gastric cancer cells. The beneficial effects of chrysophanol may be attribute to the targeted regulation of mTOR.

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.

Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics.

The tumor microenvironment (TME) plays a critical role in disease progression and is a key determinant of therapeutic response in cancer patients. Here, we propose a noninvasive approach to predict the TME status from radiological images by combining radiomics and deep learning analyses. Using multi-institution cohorts of 2,686 patients with gastric cancer, we show that the radiological model accurately predicted the TME status and is an independent prognostic factor beyond clinicopathologic variables. The model further predicts the benefit from adjuvant chemotherapy for patients with localized disease. In patients treated with checkpoint blockade immunotherapy, the model predicts clinical response and further improves predictive accuracy when combined with existing biomarkers. Our approach enables noninvasive assessment of the TME, which opens the door for longitudinal monitoring and tracking response to cancer therapy. Given the routine use of radiologic imaging in oncology, our approach can be extended to many other solid tumor types.

Novel Hub genes co-expression network mediates dysfunction in a model of polycystic ovary syndrome.

BACKGROUND: This study aimed to integrate DNA methylation, miRNA, and mRNA microarray data to construct a gene co-expression network for polycystic ovarian syndrome (PCOS). METHODS: The weighted gene co-expression network analysis (WGCNA) was conducted to construct a PCOS-related co-expression network by using the GEO public datasets. We performed Gene Ontology and KEGG pathway enrichment analyses for a further exploration of gene function in networks. Finally, the dysfunction module consisting of a co-expression network was mapped to the PCOS patients and tried to provide guidance to the PCOS phenotyping. RESULTS: Three modules (Midnightbule, Pink, and Red) were identified to be PCOS-related by WGCNA analysis. These module-related genes were enriched in cell response to stimulus, PI3K-Akt signaling pathway, insulin biological process, signaling pathway, and cytokine-cytokine receptor interaction biological processes. The multiple-factor network, including miRNA-lncRNA and DNA methylation-mRNA interaction, was closely associated with PCOS dysfunction. CONCLUSION: Our study render a novel insight into the mechanisms and might provide candidate biomarkers and therapeutic targets for the classification of PCOS dysfunction.

Activated Hepatic Stellate Cells Induce Infiltration and Formation of CD163+ Macrophages via CCL2/CCR2 Pathway.

Background: Activated hepatic stellate cells (aHSCs) regulate the function of immune cells during liver fibrosis. As major innate cells in the liver, macrophages have inducible plasticity. Nevertheless, the mechanisms through which aHSCs regulate macrophages' phenotype and function during liver fibrosis and cirrhosis remain unclear. In this study, we examined the immunoregulatory function of aHSCs during liver fibrosis and explored their role in regulating macrophage phenotype and function. Methods: A total of 96 patients with different stages of chronic hepatitis B-related liver fibrosis were recruited in the study. Metavir score system was used to evaluate the degree of fibrosis. The expression of hepatic CCL2 and M2 phenotype macrophage marker CD163 were detected by immunohistochemistry, and the relationship among hepatic CD163, CCL2, and fibrosis scores were also explored. In the in vitro model, the aHSCs isolated from human liver tissues and THP-1-derived M0-type macrophages (M0MΦ) were co-cultured to observe whether and how aHSCs regulate the phenotype and function of macrophages. To explore whether CCL2/CCR2 axis has a crucial role in macrophage phenotypic changes during liver fibrosis, we treated the M0MΦ with recombinant human CCL2 or its specific receptor antagonist INCB-3284. Furthermore, we used LX2 and TGF-ß-activated LX2 to mimic the different activation statuses of aHSCs to further confirm our results. Results: In patients, the infiltration of M2 macrophages increased during the progression of liver fibrosis. Intriguingly, as a key molecule for aHSC chemotactic macrophage aggregation, CCL2 markedly up-regulated the expression of CD163 and CD206 on the macrophages, which was further confirmed by adding the CCR2 antagonist (INCB 3284) into the cell culture system. In addition, the TGF-ß stimulated LX2 further confirmed that aHSCs up-regulate the expression of CD163 and CD206 on macrophages. LX2 stimulated with TGF-ß could produce more CCL2 and up-regulate other M2 phenotype macrophage-specific markers, including IL-10, ARG-1, and CCR2 besides CD163 and CD206 at the gene level, indicating that the different activation status of aHSCs might affect the final phenotype and function of macrophages. Conclusions: The expression of the M2 macrophage marker increases during liver fibrosis progression and is associated with fibrosis severity. AHSCs can recruit macrophages through the CCL2/CCR2 pathway and induce M2 phenotypic transformation.

Radiographical assessment of tumour stroma and treatment outcomes using deep learning: a retrospective, multicohort study.

BACKGROUND: The tumour stroma microenvironment plays an important part in disease progression and its composition can influence treatment response and outcomes. Histological evaluation of tumour stroma is limited by access to tissue, spatial heterogeneity, and temporal evolution. We aimed to develop a radiological signature for non-invasive assessment of tumour stroma and treatment outcomes. METHODS: In this multicentre, retrospective study, we analysed CT images and outcome data of 2209 patients with resected gastric cancer from five independent cohorts recruited from two centres (Nanfang Hospital of Southern Medical University [Guangzhou, China] and Sun Yat-sen University Cancer Center [Guangzhou, China]). Patients with histologically confirmed gastric cancer, at least 15 lymph nodes harvested, preoperative abdominal CT available, and complete clinicopathological and follow-up data were eligible for inclusion. Tumour tissue was collected for patients in the training cohort (321 patients), internal validation cohort one (246 patients), and external validation cohort one (128 patients). Four stroma classes were defined according to the protein expression of α-smooth muscle actin and periostin assessed by immunohistochemistry. The primary objective was to predict the histologically based stroma classes by using preoperative CT images. We trained a deep convolutional neural network model using the training cohort and tested the model in the internal and external validation cohort one. We evaluated the model's association with prognosis in the training cohort, two internal, and two external validation cohorts and compared outcomes of patients who received or did not receive adjuvant chemotherapy. FINDINGS: The deep-learning model achieved a high diagnostic accuracy for assessing tumour stroma in both internal validation cohort one (area under the receiver operating characteristic curve [AUC] 0·96-0·98]) and external validation cohort one (AUC 0·89-0·94). The stromal imaging signature was significantly associated with disease-free survival and overall survival in all cohorts (p<0·0001). The predicted stroma classes remained an independent prognostic factor adjusting for clinicopathological variables including tumour size, stage, differentiation, and Lauren histology. In patients with stage II or III disease in predicted stroma classes one and two subgroups, patients who received adjuvant chemotherapy had improved survival compared with those who did not (in those with stage II disease hazard ratio [HR] 0·48 [95% CI 0·29-0·77], p=0·0021; and in those with stage III disease HR 0·70 [0·57-0·85], p=0·00042). However, in the other two subgroups adjuvant chemotherapy was not associated with survival and might even be detrimental in the predicted stroma class 4 subgroup (HR 1·48 [1·08-2·03], p=0·013). INTERPRETATION: The deep-learning model could allow for accurate and non-invasive evaluation of tumour stroma from CT images in gastric cancer. The radiographical model predicted chemotherapy outcomes and could be used in combination with clinicopathological criteria to refine prognosis and inform treatment decisions of patients with gastric cancer. FUNDING: None.

Prognostic Factors among Brain Metastases in Newly Diagnosed Ovary Cancer: A Large Real-world Study.

Background: Population-based data on the prognosis of brain metastases at initial diagnosis of ovary cancer (OCBM) are currently lacking. Besides, the effective treatment for OCBM patients is still controversial now. The study aimed to explore the prognostic factors among OCBM. Methods: We retrospectively reviewed the OCBM patients from the Surveillance, Epidemiology, and End Result (SEER) database of the National Cancer Institute to investigate predictors of the presence of OCBM and its' prognostic factors related to all-cause mortality. We employed multivariable logistic and Cox regression analysis. Furthermore, to minimize the impact of potential confounding factors, we conducted a 1:1 propensity score matching (PSM) analysis. Results: A total of 29,512 cases of OC patients entered into the study, including 89 patients with brain metastases of ovarian cancer, which accounted for 0.30% of the entire cohort and 12.02% of the metastatic disease subset. We identified eight factors, including laterality, histology, surgery, radiotherapy, chemotherapy, and extracranial metastatic sites to bone, liver, and lung, as predictors of OCBM based on multivariable logistic regression among the entire cohort. The median survival time of OCBM was 2.0 months, and the interquartile range was 2.0-10.0 mo. The patients who received comprehensive treatment had better prognosis. Based on the multivariable Cox model, marital status, surgery, chemotherapy, and extensive therapy (including RSC, SC, and RC) were identified as predictors of OS. Besides, a new factor (brain metastasis) was identified by 1:1 PSM -based multiple Cox regression, apart from the above prognostic factors for OS. Conclusions: This study provided a population-based estimate of the proportion and prognosis for newly diagnosed ovary cancer with brain metastases. These findings may add materials to guidelines for preliminary screening and optimal treatment of OCBM patients.

Tubal ectopic pregnancy occurrence is associated with high expressions of prokineticin receptors and aberrant secretion of inflammatory cytokines.

OBJECTIVE: Tubal ectopic pregnancy (TEP) remains the most common cause of maternal morbidity and mortality in the early months of pregnancy. The aim of this study is to perform the correlation between PROKRs and pro-inflammatory genes and explore the role of novel genes in pathogenesis of TEP. METHODS: Here, quantitative real time PCR and immunohistochemistry were used to assess the expression of the novel genes in 120 TEP patients and 30 age-matched non-TEP patients. The correlation between PROKRs and pro-inflammatory genes were analyzed by Pearson correlation coefficient. Univariate and multivariate Cox regression analyses were used to assess the risk prediction rate of novel genes. Receiver operating characteristic was used to assess the performance of our model. RESULTS: PROKRs (PROKR1 and PROKR2) and pro-inflammatory genes (TNF-α, IL-6, and IL-8) expression levels significantly enhanced in TEP patients, and significantly positive correlation with pro-inflammatory genes for PROKRs. A multivariate Cox regression analysis demonstrated that 2 genes (PROKR2 and IL8) had significant diagnostic value, which were associated with the occurrence and development of TEP. CONCLUSION: Our data further denote that dysregulation of PROKR2 and IL-8 were risk factor and played an important role in the pathogenesis of TEP.

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.

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.

Efficacy of long-term treatment with tenofovir in Chinese nucleos(t)ide-naïve chronic hepatitis B patients regardless of baseline viral load.

The aim of the present study was to analyze the efficacy and safety of tenofovir (TDF) treatment for up to 5 years in nucleos(t)ide-naïve chronic hepatitis B (CHB) patients, particularly those with a high viral load, a in real-life scenario. A total of 144 nucleos(t)ide-naïve CHB patients who received TDF monotherapy for at least 3 months were retrospectively analyzed. The primary endpoint measure was the achievement of virological response (VR; undetectable serum HBV DNA, <100 IU/ml). The secondary endpoints were alanine aminotransferase (ALT) normalization (ALT < upper limit of normal), hepatitis B e antigen (HBeAg) seroconversion and safety. The median follow-up period was 120 weeks (range, 12-264 weeks). In total, 144, 130, 114, 78, 67, 40 and 13 patients were followed up for at least 12, 24, 48, 96, 144, 192 and 240 weeks, respectively. An incremental trend was observed in the rate of VR: 73.1, 91.3, 98.1, 100, 100 and 100% of the patients exhibited VR at 24, 48, 96, 144, 192 and 240 weeks, respectively. Furthermore, 29 patients with hepatitis B virus (HBV) DNA ≥8 log10 IU/ml at baseline achieved VR during the follow-up period. The proportions of patients achieving normal ALT levels were 72.1, 78.6, 91.2, 95, 96 and 100%, at 24, 48, 96, 144, 192 and 240 weeks, respectively. The rate of HBeAg loss reached 35.6% at week 240. Among the 130 patients, HBV DNA was detectable [partial VR (PVR)] in 35 patients at 24 weeks of follow-up, and 30 of those 35 patients (85.7%) required >24 weeks of further TDF therapy to achieve VR. No serious adverse events were reported. In conclusion, long-term TDF treatment of nucleos(t)ide-naïve chronic hepatitis B patients, regardless of high viral load at baseline, was effective and safe in a real-life scenario. Adjustment of TDF monotherapy may be unnecessary in nucleos(t)ide-naïve patients with PVR at 24 weeks.

Immunomarker Support Vector Machine Classifier for Prediction of Gastric Cancer Survival and Adjuvant Chemotherapeutic Benefit.

Purpose: Current tumor-node-metastasis (TNM) staging system cannot provide adequate information for prediction of prognosis and chemotherapeutic benefits. We constructed a classifier to predict prognosis and identify a subset of patients who can benefit from adjuvant chemotherapy.Experimental Design: We detected expression of 15 immunohistochemistry (IHC) features in tumors from 251 gastric cancer (GC) patients and evaluated the association of their expression level with overall survival (OS) and disease-free survival (DFS). Then, integrating multiple clinicopathologic features and IHC features, we used support vector machine (SVM)-based methods to develop a prognostic classifier (GC-SVM classifier) with features. Further validation of the GC-SVM classifier was performed in two validation cohorts of 535 patients.Results: The GC-SVM classifier integrated patient sex, carcinoembryonic antigen, lymph node metastasis, and the protein expression level of eight features, including CD3invasive margin (IM), CD3center of tumor (CT), CD8IM, CD45ROCT, CD57IM, CD66bIM, CD68CT, and CD34. Significant differences were found between the high- and low-GC-SVM patients in 5-year OS and DFS in training and validation cohorts. Multivariate analysis revealed that the GC-SVM classifier was an independent prognostic factor. The classifier had higher predictive accuracy for OS and DFS than TNM stage and can complement the prognostic value of the TNM staging system. Further analysis revealed that stage II and III GC patients with high-GC-SVM were likely to benefit from adjuvant chemotherapy.Conclusions: The newly developed GC-SVM classifier was a powerful predictor of OS and DFS. Moreover, the GC-SVM classifier could predict which patients with stage II and III GC benefit from adjuvant chemotherapy. Clin Cancer Res; 24(22); 5574-84. ©2018 AACR.

Radiomic signature of 18F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits.

We aimed to evaluate whether radiomic feature-based fluorine 18 (18F) fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging signatures allow prediction of gastric cancer (GC) survival and chemotherapy benefits. Methods: A total of 214 GC patients (training (n = 132) or validation (n = 82) cohort) were subjected to radiomic feature extraction (80 features). Radiomic features of patients in the training cohort were subjected to a LASSO cox analysis to predict disease-free survival (DFS) and overall survival (OS) and were validated in the validation cohort. A radiomics nomogram with the radiomic signature incorporated was constructed to demonstrate the incremental value of the radiomic signature to the TNM staging system for individualized survival estimation, which was then assessed with respect to calibration, discrimination, and clinical usefulness. The performance was assessed with concordance index (C-index) and integrated Brier scores. Results: Significant differences were found between the high- and low-radiomic score (Rad-score) patients in 5-year DFS and OS in training and validation cohorts. Multivariate analysis revealed that the Rad-score was an independent prognostic factor. Incorporating the Rad-score into the radiomics-based nomogram resulted in better performance (C-index: DFS, 0.800; OS, 0.786; in the training cohort) than TNM staging system and clinicopathologic nomogram. Further analysis revealed that patients with higher Rad-scores were prone to benefit from chemotherapy. Conclusion: The newly developed radiomic signature was a powerful predictor of OS and DFS. Moreover, the radiomic signature could predict which patients could benefit from chemotherapy.

Prognostic and Predictive Value of p21-activated Kinase 6 Associated Support Vector Machine Classifier in Gastric Cancer Treated by 5-fluorouracil/Oxaliplatin Chemotherapy.

To determine whether p21-activated Kinase (PAK) 6 is a prognostic and predictive marker in gastric cancer (GC) and to construct a classifier that can identify a subset of patients who are highly sensitive to 5-fluorouracil/oxaliplatin chemotherapy. We retrospectively analyzed the expression levels of PAK6, cyclooxygenase 2, p21WAF1, Ki-67, excision repair cross-complementing gene 1, and thymidylate synthase in 242 paraffin-embedded GC specimens of the training cohort by immunohistochemistry. Then, we used support vector machine (SVM)-based methods to develop a predictive classifier for chemotherapy (chemotherapy score - CS-SVM classifier). Further validation was performed in an independent cohort of 279 patients. High PAK6 expression was associated with poor prognosis and increased chemoresistance to 5-FU/oxaliplatin chemotherapy. The CS-SVM classifier distinguished patients with stage II and III GC into low- and high-CS-SVM groups, with significant differences in the 5-year disease-free survival (DFS) and overall survival (OS) in chemotherapy patients. Moreover, chemotherapy significantly prolonged the DFS and OS of the high CS-SVM patients in the training and validation cohorts. In conclusion, PAK6 was an independent prognostic factor and increased chemoresistance. The CS-SVM classifier distinguished a subgroup of stage II and III patients who would highly benefit from chemotherapy, thus facilitating patient counseling and individualizing the management.