MATTE: a pipeline of transcriptome module alignment for anti-noise phenotype-gene-related analysis.

A phenotype may be associated with multiple genes that interact with each other in the form of a gene module or network. How to identify these relationships is one important aspect of comparative transcriptomics. However, it is still a challenge to align gene modules associated with different phenotypes. Although several studies attempted to address this issue in different aspects, a general framework is still needed. In this study, we introduce Module Alignment of TranscripTomE (MATTE), a novel approach to analyze transcriptomics data and identify differences in a modular manner. MATTE assumes that gene interactions modulate a phenotype and models phenotype differences as gene location changes. Specifically, we first represented genes by a relative differential expression to reduce the influence of noise in omics data. Meanwhile, clustering and aligning are combined to depict gene differences in a modular way robustly. The results show that MATTE outperformed state-of-the-art methods in identifying differentially expressed genes under noise in gene expression. In particular, MATTE could also deal with single-cell ribonucleic acid-seq data to extract the best cell-type marker genes compared to other methods. Additionally, we demonstrate how MATTE supports the discovery of biologically significant genes and modules, and facilitates downstream analyses to gain insight into breast cancer. The source code of MATTE and case analysis are available at https://github.com/zjupgx/MATTE.

Integrated analysis reveals a novel 5-fluorouracil resistance-based prognostic signature with promising implications for predicting the efficacy of chemotherapy and immunotherapy in patients with colorectal cancer.

BACKGROUND: 5-Fluorouracil (5-FU) has been used as a standard first-line treatment for colorectal cancer (CRC) patients. Although 5-FU-based chemotherapy and immune checkpoint blockade (ICB) have achieved success in treating CRC, drug resistance and low response rates remain substantial limitations. Thus, it is necessary to construct a 5-FU resistance-related signature (5-FRSig) to predict patient prognosis and identify ideal patients for chemotherapy and immunotherapy. METHODS: Using bulk and single-cell RNA sequencing data, we established and validated a novel 5-FRSig model using stepwise regression and multiple CRC cohorts and evaluated its associations with the prognosis, clinical features, immune status, immunotherapy, neoadjuvant therapy, and drug sensitivity of CRC patients through various bioinformatics algorithms. Unsupervised consensus clustering was performed to categorize the 5-FU resistance-related molecular subtypes of CRC. The expression levels of 5-FRSig, immune checkpoints, and immunoregulators were determined using quantitative real-time polymerase chain reaction (RT‒qPCR). Potential small-molecule agents were identified via Connectivity Map (CMap) and molecular docking. RESULTS: The 5-FRSig and cluster were confirmed as independent prognostic factors in CRC, as patients in the low-risk group and Cluster 1 had a better prognosis. Notably, 5-FRSig was significantly associated with 5-FU sensitivity, chemotherapy response, immune cell infiltration, immunoreactivity phenotype, immunotherapy efficiency, and drug selection. We predicted 10 potential compounds that bind to the core targets of 5-FRSig with the highest affinity. CONCLUSION: We developed a valid 5-FRSig to predict the prognosis, chemotherapeutic response, and immune status of CRC patients, thus optimizing the therapeutic benefits of chemotherapy combined with immunotherapy, which can facilitate the development of personalized treatments and novel molecular targeted therapies for patients with CRC.

Unraveling the Drivers of Tumorigenesis in the Context of Evolution: Theoretical Models and Bioinformatics Tools.

Cancer originates from somatic cells that have accumulated mutations. These mutations alter the phenotype of the cells, allowing them to escape homeostatic regulation that maintains normal cell numbers. The emergence of malignancies is an evolutionary process in which the random accumulation of somatic mutations and sequential selection of dominant clones cause cancer cells to proliferate. The development of technologies such as high-throughput sequencing has provided a powerful means to measure subclonal evolutionary dynamics across space and time. Here, we review the patterns that may be observed in cancer evolution and the methods available for quantifying the evolutionary dynamics of cancer. An improved understanding of the evolutionary trajectories of cancer will enable us to explore the molecular mechanism of tumorigenesis and to design tailored treatment strategies.

SIGLEC10+ macrophages drive gastric cancer progression by suppressing CD8+ T cell function.

Existing immune checkpoint inhibitors focus on activating T cells and show limited effectiveness in gastric cancer (GC). SIGLEC10 is identified as a novel tumor-associated macrophage-related immune checkpoint in other cancer types. However, its immunosuppressive role and clinical significance in GC remain unclear. In this study, we find a dominant expression of SIGLEC10 on CD68+ macrophages in GC. SIGLEC10 can suppress the proliferation and function of tumor-infiltrating CD8+ T cells in vitro via the Akt/P38/Erk signaling pathway. Furthermore, in ex vivo and in vivo models, SIGLEC10 blockade promotes CD8+ T cell effector function. Finally, SIGLEC10+ macrophages are positively correlated with the adverse prognosis of GC. Our study highlights that SIGLEC10 directly suppresses T cell function and serves as a promising target for immunotherapy and suggests SIGLEC10+ macrophages as a novel potential predictor of the clinical prognosis of GC.

CanDriS: posterior profiling of cancer-driving sites based on two-component evolutionary model.

Current cancer genomics databases have accumulated millions of somatic mutations that remain to be further explored. Due to the over-excess mutations unrelated to cancer, the great challenge is to identify somatic mutations that are cancer-driven. Under the notion that carcinogenesis is a form of somatic-cell evolution, we developed a two-component mixture model: while the ground component corresponds to passenger mutations, the rapidly evolving component corresponds to driver mutations. Then, we implemented an empirical Bayesian procedure to calculate the posterior probability of a site being cancer-driven. Based on these, we developed a software CanDriS (Cancer Driver Sites) to profile the potential cancer-driving sites for thousands of tumor samples from the Cancer Genome Atlas and International Cancer Genome Consortium across tumor types and pan-cancer level. As a result, we identified that approximately 1% of the sites have posterior probabilities larger than 0.90 and listed potential cancer-wide and cancer-specific driver mutations. By comprehensively profiling all potential cancer-driving sites, CanDriS greatly enhances our ability to refine our knowledge of the genetic basis of cancer and might guide clinical medication in the upcoming era of precision medicine. The results were displayed in a database CandrisDB (http://biopharm.zju.edu.cn/candrisdb/).

MODIG: integrating multi-omics and multi-dimensional gene network for cancer driver gene identification based on graph attention network model.

MOTIVATION: Identifying genes that play a causal role in cancer evolution remains one of the biggest challenges in cancer biology. With the accumulation of high-throughput multi-omics data over decades, it becomes a great challenge to effectively integrate these data into the identification of cancer driver genes. RESULTS: Here, we propose MODIG, a graph attention network (GAT)-based framework to identify cancer driver genes by combining multi-omics pan-cancer data (mutations, copy number variants, gene expression and methylation levels) with multi-dimensional gene networks. First, we established diverse types of gene relationship maps based on protein-protein interactions, gene sequence similarity, KEGG pathway co-occurrence, gene co-expression patterns and gene ontology. Then, we constructed a multi-dimensional gene network consisting of approximately 20 000 genes as nodes and five types of gene associations as multiplex edges. We applied a GAT to model within-dimension interactions to generate a gene representation for each dimension based on this graph. Moreover, we introduced a joint learning module to fuse multiple dimension-specific representations to generate general gene representations. Finally, we used the obtained gene representation to perform a semi-supervised driver gene identification task. The experiment results show that MODIG outperforms the baseline models in terms of area under precision-recall curves and area under the receiver operating characteristic curves. AVAILABILITY AND IMPLEMENTATION: The MODIG program is available at https://github.com/zjupgx/modig. The code and data underlying this article are also available on Zenodo, at https://doi.org/10.5281/zenodo.7057241. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Improving the tumor selectivity of T cell engagers by logic-gated dual tumor-targeting.

Targeting single tumor antigens makes it difficult to provide sufficient tumor selectivity for T cell engagers (TCEs), leading to undesirable toxicity and even treatment failure, which is particularly serious in solid tumors. Here, we designed novel trispecific TCEs (TriTCEs) to improve the tumor selectivity of TCEs by logic-gated dual tumor-targeting. TriTCE can effectively redirect and activate T cells to kill tumor cells (∼18 pM EC50) by inducing the aggregation of dual tumor antigens, which was ∼70- or 750- fold more effective than the single tumor-targeted isotype controls, respectively. Further in vivo experiments indicated that TriTCE has the ability to accumulate in tumor tissue and can induce circulating T cells to infiltrate into tumor sites. Hence, TriTCE showed a stronger tumor growth inhibition ability and significantly prolonged the survival time of the mice. Finally, we revealed that this concept of logic-gated dual tumor-targeted TriTCE can be applied to target different tumor antigens. Cumulatively, we reported novel dual tumor-targeted TriTCEs that can mediate a robust T cell response by simultaneous recognition of dual tumor antigens at the same cell surface. TriTCEs allow better selective T cell activity on tumor cells, resulting in safer TCE treatment.

Proteomic profiling of gastric cancer with peritoneal metastasis identifies a protein signature associated with immune microenvironment and patient outcome.

BACKGROUND: Peritoneal metastasis (PM) frequently occurs in patients with gastric cancer (GC) and is a major cause of mortality. Risk stratification for PM can optimize decision making in GC treatment. METHODS: A total of 25 GC patients (13 with synchronous, 6 with metachronous PM and 6 PM-free) were included in this study. Quantitative proteomics by high-depth tandem mass tags labeling and whole-exome sequencing were conducted in primary GC and PM samples. Proteomic signature and prognostic model were established by machine learning algorithms in PM and PM-free GC, then validated in two external cohorts. Tumor-infiltrating immune cells in GC were analyzed by CIBERSORT. RESULTS: Heterogeneity between paired primary and PM samples was observed at both genomic and proteomic levels. Compared to primary GC, proteome of PM samples was enriched in RNA binding and extracellular exosomes. 641 differently expressed proteins (DEPs) between primary GC of PM group and PM-free group were screened, which were enriched in extracellular exosome and cell adhesion pathways. Subsequently, a ten-protein signature was derived based on DEPs by machine learning. This signature was significantly associated with patient prognosis in internal cohort and two external proteomic datasets of diffuse and mixed type GC. Tumor-infiltrating immune cell analysis showed that the signature was associated with immune microenvironment of GC. CONCLUSIONS: We characterized proteomic features that were informative for PM progression of GC. A protein signature associated with immune microenvironment and patient outcome was derived, and it could guide risk stratification and individualized treatment.

GSDMD, an executor of pyroptosis, is involved in IL-1ß secretion in Aspergillus fumigatus keratitis.

The protein GSDMD is an important performer of pyroptosis and a universal substrate for the inflammatory caspase. However, the role and regulatory mechanism of GSDMD in Aspergillus fumigatus keratitis is remains unknown. Here we detected GSDMD protein in the cornea of normal and fungal-infected C57BL/6 mice. Human corneal epithelial cell (HCECs) were preincubated with a hydrochloride solution (IFNR inhibitor), ruxolitinib (JAK/STAT inhibitor), belnacasan (caspase-1 inhibitor) before infection with A. fumigatus conidia. Mice corneas were infected with Aspergillus fumigatus after pretreatment of GSDMD siRNA via subconjunctival injection. After, samples were harvested at specific time points and the expression of GSDMD and IL-1ß was assessed by PCR, Western blot and immunofluorescence staining. Compared with the control group, we observed that the expression of GSDMD in fungal-infected mice cornea was significantly increased. After pretreatment with IFNR, JAK/STAT and caspase-1 inhibitors before fungal infection, the expression of GSDMD was significantly inhibited compared to the DMSO control in HCECs. Moreover, the GSDMD siRNA treatment have significantly weaken corneal inflammatory response, decreasing the proinflammatory factor IL-1ß secretion and reducing neutrophils and macrophages recruitment in mice infected corneas. In summary, the data here provided evidences that GSDMD, an executor of pyroptosis, is involved in the early immune response of A. fumigatus keratitis. Additionally, the inhibition of GSDMD expression can affect the secretion of IL-1ß and the recruitment of neutrophil and macrophages by blocking IFNR, JAK/STAT and caspase-1 signaling pathway. The protein GSDMD may emerge as a potential therapeutic target for A. fumigatus keratitis.

A novel genomic classification system of gastric cancer via integrating multidimensional genomic characteristics.

BACKGROUND: Gastric cancer (GC) is one of the leading causes of cancer deaths with high heterogeneity. There is currently a paucity of clinically applicable molecular classification system to guide precise medicine. METHODS: A total of 70 Chinese patients with GC were included in this study and whole-exome sequencing was performed. Unsupervised clustering was undertaken to identify genomic subgroups, based on mutational signature, copy number variation, neoantigen, clonality, and essential genomic alterations. Subgroups were characterized by clinicopathological factors, molecular features, and prognosis. RESULTS: We identified 32 significantly mutated genes (SMGs), including TP53, ARID1A, PIK3CA, CDH1, and RHOA. Of these, PREX2, PIEZO1, and FSIP2 have not been previously reported in GC. Using a novel genome-based classification method that integrated multidimensional genomic features, we categorized GC into four subtypes with distinct clinical phenotypes and prognosis. Subtype 1, which was predominantly Lauren intestinal type, harbored recurrent TP53 mutation and ERBB2 amplification, high tumor mutation burden (TMB)/tumor neoantigen burden (TNB), and intratumoral heterogeneity, with a liver metastasis tendency. Subtype 2 tended to occur at an elder age, accompanying with frequent TP53 and SYNE1 mutations, high TMB/TNB, and was associated with poor prognosis. Subtype 3 and subtype 4 included patients with mainly diffuse/mixed type tumors, high frequency of peritoneal metastasis, and genomical stability, whereas subtype 4 was associated with a favorable prognosis. CONCLUSIONS: By integrating multidimensional genomic characteristics, we proposed a novel genomic classification system of GC associated with clinical phenotypes and provided a new insight to facilitate genome-guided risk stratification and disease management.

Ring finger protein 31-mediated atypical ubiquitination stabilizes forkhead box P3 and thereby stimulates regulatory T-cell function.

The CD4+CD25+FOXP3+ regulatory T (Treg) cells are critical for maintaining immune tolerance in healthy individuals and are reported to restrict anti-inflammatory responses and thereby promote tumor progression, suggesting them as a target in the development of antitumor immunotherapy. Forkhead box P3 (FOXP3) is a key transcription factor governing Treg lineage differentiation and their immune-suppressive function. Here, using Treg cells, as well as HEK-293T and Jurkat T cells, we report that the stability of FOXP3 is directly and positively regulated by the E3 ubiquitin ligase ring finger protein 31 (RNF31), which catalyzes the conjugation of atypical ubiquitin chains to the FOXP3 protein. We observed that shRNA-mediated RNF31 knockdown in human Treg cells decreases FOXP3 protein levels and increases levels of interferon-γ, resulting in a Th1 helper cell-like phenotype. Human Treg cells that ectopically expressed RNF31 displayed stronger immune-suppressive capacity, suggesting that RNF31 positively regulates both FOXP3 stability and Treg cell function. Moreover, we found that RNF31 is up-regulated in Treg cells that infiltrate human gastric tumor tissues compared with their counterparts residing in peripheral and normal tissue. We also found that elevated RNF31 expression in intratumoral Treg cells is associated with poor survival of gastric cancer patients, suggesting that RNF31 supports the immune-suppressive functions of Treg cells. Our results suggest that RNF31 could be a potential therapeutic target in immunity-based interventions against human gastric cancer.

Correction to: The lncRNA UCA1 promotes proliferation, migration, immune escape and inhibits apoptosis in gastric cancer by sponging anti-tumor miRNAs.

Following publication of the original article [1], authors reported Pro. Gang Zhao has to be considered as another corresponding author, according to his important contribution.

The lncRNA UCA1 promotes proliferation, migration, immune escape and inhibits apoptosis in gastric cancer by sponging anti-tumor miRNAs.

BACKGROUND: UCA1 is a long non-coding RNA which was found overexpressed in various human cancers including gastric cancer (GC). It is identified that UCA1 promotes GC cells proliferation, migration and invasion, however, the role of UCA1 during the processes of immune escape is still not unclear. METHODS: We collected 40 paired GC and non-tumor tissue samples. The level of UCA1 in GC and control tissue samples were determined by in situ hybridization and qRT-PCR. Cell viability was determined by MTT assay. GC cells' migration capacities were examined by transwell assay. To understand the roles of UCA1 during immune escape, wildtype or UCA1 KO GC cells co-cultured with peripheral blood mononuclear cells or cytokine-induced killer cells in vitro. Mouse model was used to examine the function of UCA1 in vivo. RESULTS: UCA1 promoted GC cells proliferation and migration, and inhibit apoptosis. UCA1 repressed miR-26a/b, miR-193a and miR-214 expression through direct interaction and then up-regulated the expression of PDL1. UCA1-KO GC cells could induce a higher IFNγ expression when co-cultured with peripheral blood mononuclear cells (PBMCs), and have a lower survival rate when co-cultured with cytokine-induced killer (CIK) cells in vitro. UCA1-KO GC cells formed smaller tumors, had higher miR-26a, -26b, -193a and - 214 level, reduced cell proliferation and increased apoptosis in xenograft mouse model. CONCLUSIONS: UCA1 overexpression protected PDL1 expression from the repression of miRNAs and contributed to the GC cells immune escape. UCA1 could serve as a potential novel therapeutic target for GC treatment.

Somatic mutation of DNAH genes implicated higher chemotherapy response rate in gastric adenocarcinoma patients.

BACKGROUND: The dynein axonemal heavy chain (DNAH) family of genes encode the dynein axonemal heavy chain, which is involved in cell motility. Genomic variations of DNAH family members have been frequently reported in diverse kinds of malignant tumors. In this study, we analyzed the genomic database to evaluate the mutation status of DNAH genes in gastric adenocarcinoma and further identified the significance of mutant DNAH genes as effective molecular biomarkers for predicting chemotherapy response in gastric cancer patients. METHODS: We analyzed the clinical and genomic data of gastric cancer patients published in The Cancer Genome Atlas (TCGA) project. Data on chemotherapy response, overall survival (OS) and chemotherapy-free survival were retrieved. Then, we verified the results via targeted sequencing of gastric cancer patients with similar clinical characteristics but different chemotherapeutic outcomes. RESULTS: In total, 132 gastric adenocarcinoma patients undergoing chemotherapy treatment from TCGA were included in our study. Somatic mutations in all 13 members of the DNAH family of genes were associated with different chemotherapy responses. Compared with patients with wild-type DNAH genes (n = 59), a significantly higher proportion of those with mutations in DNAH genes (n = 73) (55.9% vs 80.8%) responded to chemotherapy (P = 0.002). Moreover, DNAH mutations were correlated with significantly better OS (P = 0.027), chemotherapy-free survival (P = 0.027), fluoropyrimidine-free survival (P = 0.048) and platinum-free survival (P = 0.014). DNAH mutation status was an independent risk factor for OS (P = 0.015), chemotherapy-free survival (P = 0.015) and platinum-free survival (P = 0.011). We identified somatic mutations in 27 (42.2%) of the 64 stage III gastric adenocarcinoma patients receiving fluoropyrimidine-based chemotherapy by targeted exon sequencing with strict screening conditions. In our own cohort, a significantly higher proportion of patients (n = 32) with DNAH mutations than patients with wild-type DNAH genes (n = 32) had a good prognosis (OS > 48 months) (70.4% vs 35.1%) (P = 0.005). CONCLUSIONS: Dynein axonemal heavy chain gene mutations contribute positively to chemotherapy sensitivity in gastric cancer patients.

Regulatory T cells and M2 macrophages present diverse prognostic value in gastric cancer patients with different clinicopathologic characteristics and chemotherapy strategies.

BACKGROUND: Gastric cancer (GC) remains a refractory cancer worldwide. Currently, exploring the differences of the immune status in GC patients with different subgroups might provide promising immunotherapeutic approaches for the treatment of GC. METHODS: In this study, a total of 598 surgically resected FFPE primary gastric cancer samples were assessed for FOXP3, CD163, CD3, CD8, and PD-L1 markers. The correlations between the immune markers expression and clinicopathological features and prognosis were investigated retrospectively. RESULTS: In general, PD-L1, CD3, and CD8 could be regarded as favorable prognostic factors. Our data demonstrated that high infiltration of FOXP3+ Treg indicates better prognosis in stage I-II patients, while the converse outcome was noted in stage III-IV patients. Our data also confirmed different prognostic value in different pathological classifications, chemotherapy strategies, and locations, with or without lymph node metastasis. Also, M2 macrophages indicated poor prognosis in general. However, high M2 macrophage infiltration suggests a favorable prognosis in signet ring cell carcinoma and mucinous adenocarcinoma. Moreover, the prognostic value of the two indices when they are combined is reported. CONCLUSIONS: These results suggested that different immune statuses are exhibited in different subgroups of GC, which may direct further understanding of the immune status of GC as well as provide a further theoretical basis and potential targets for GC immunotherapy.

Correlations of fascin-1 and cadherin-17 protein expression with clinicopathologic features and prognosis of patients with gastric cancer.

We aim to explore the associations of fascin-1 and cadherin-17 in gastric cancer (GC) to the clinicopathologic features and prognosis of GC. Case group included 204 GC tissues while control group comprised 204 paired adjacent cancer tissues. Expressions of fascin-1 and cadherin-17 were measured with immunohistochemistry and western blot and then analyzed statistically in relation to clinicopathologic features and survival time. Survival curve was drawn by Kaplan-Meier method, and independent prognostic factors were identified with Cox proportional hazards regression model. Fascin-1 was positively expressed in 45.1 % of GC tissues and in 27.5 % of adjacent cancer tissues, respectively (P < 0.05); cadherin-17 was positively expressed in 51.5 % of GC tissues and in 33.8 % of adjacent cancer tissues (P < 0.05). Fascin-1 expression in GC tissues was related to tumor size (P = 0.001) and Lauren classification (P = 0.001). Cadherin-17 expression in GC tissues was related to tumor size (P < 0.001), Lauren classification (P = 0.009), clinical staging (P = 0.001), and distant metastasis (P = 0.002). Fascin-1 expression was positively correlated with cadherin-17 expression in GC tissues (r = 0.828, P < 0.01). Patients with positive expression of both fascin-1 and cadherin-17 had lower survival rates than those with negative expression (all P < 0.01). Cox regression analysis showed that fascin-1 expression, cadherin-17 expression, tumor size, and differentiation were independent risk factors for GC (all P < 0.05). Fascin-1 and cadherin-17 are related to clinicopathologic features of GC and are independent adverse prognostic factors for GC.

JPC-2997, a new aminomethylphenol with high in vitro and in vivo antimalarial activities against blood stages of Plasmodium.

4-(tert-Butyl)-2-((tert-butylamino)methyl)-6-(6-(trifluoromethyl)pyridin-3-yl)-phenol (JPC-2997) is a new aminomethylphenol compound that is highly active in vitro against the chloroquine-sensitive D6, the chloroquine-resistant W2, and the multidrug-resistant TM90-C2B Plasmodium falciparum lines, with 50% inhibitory concentrations (IC50s) ranging from 7 nM to 34 nM. JPC-2997 is >2,500 times less cytotoxic (IC50s > 35 µM) to human (HepG2 and HEK293) and rodent (BHK) cell lines than the D6 parasite line. In comparison to the chemically related WR-194,965, a drug that had advanced to clinical studies, JPC-2997 was 2-fold more active in vitro against P. falciparum lines and 3-fold less cytotoxic. The compound possesses potent in vivo suppression activity against Plasmodium berghei, with a 50% effective dose (ED50) of 0.5 mg/kg of body weight/day following oral dosing in the Peters 4-day test. The radical curative dose of JPC-2997 was remarkably low, at a total dose of 24 mg/kg, using the modified Thompson test. JPC-2997 was effective in curing three Aotus monkeys infected with a chloroquine- and pyrimethamine-resistant strain of Plasmodium vivax at a dose of 20 mg/kg daily for 3 days. At the doses administered, JPC-2997 appeared to be well tolerated in mice and monkeys. Preliminary studies of JPC-2997 in mice show linear pharmacokinetics over the range 2.5 to 40 mg/kg, a low clearance of 0.22 liters/h/kg, a volume of distribution of 15.6 liters/kg, and an elimination half-life of 49.8 h. The high in vivo potency data and lengthy elimination half-life of JPC-2997 suggest that it is worthy of further preclinical assessment as a partner drug.

Elevated Rictor expression is associated with tumor progression and poor prognosis in patients with gastric cancer.

The rapamycin insensitive companion of mTOR (Rictor) is an essential subunit of mTOR complex 2 (mTORC2), maintains the integrity of the complex and functions as regulator of Akt full activation. Rictor has been implicated to be involved in growth and progression of malignancies, however, little is known about its expression and prognostic role in gastric cancer in particular. Therefore, we investigated the relationship of Rictor expression with clinical outcomes, together with pAktSer473 and pS6, two downstream substrates of mTORC2 and mTORC1, in 396 gastric cancer tissue samples via immunohistochemistry. The results showed that 74.0% and 55.8% of tumors were Rictor and pAktSer473 positive staining, respectively, which correlated well with each other. Patients with positive expressions had poorer overall survival and relapse-free survival compared with those negative staining. Both Rictor and pAktSer473 expression were associated with lymph node metastasis, TNM stage, and WHO grading. Rictor was also correlated with tumor size, depth of invasion, and tumor thrombus, while pAktSer473 was also correlated with distant metastasis. In spite of 67.4% expression rate was presented in gastric cancer tissues, no significant association was observed between pS6Ser235/236, representing mTORC1 activity, and clinicopathological features or prognosis. These results suggest that mTORC2/Rictor/pAkt may play a more important role than mTORC1/pS6 in tumor progression, which could act as a prognostic biomarker or potential therapeutic target in gastric cancer.

Evaluation of high-risk clinicopathological indicators in gastrointestinal stromal tumors for prognosis and imatinib treatment outcome.

BACKGROUND: Although the clinical benefit of imatinib adjuvant therapy for high-risk patients with gastrointestinal stromal tumor (GIST) has been proven, the recurrence rate still remains high. This study aimed to sub-divide high-risk GIST patients with some "very high-risk" factors for more precise prognostic indicator, and possible association with efficiency of imatinib adjuvant therapy. METHODS: Clinicopathological data were confirmed by pathological diagnosis and clinical records. Recurrence-free survivals (RFS) were evaluated in 370 GIST patients (212 cases as test cohort and 158 cases as validation cohort) and 48 high-risk GISTs with imatinib adjuvant therapy after R0 resection. RESULTS: Mitosis count > 10/50 high-power fields (HPF) and serosal invasion are independent prognostic factors for RFS of GIST patients. Mitosis count > 10/50HPF and serosal invasion can sub-divide high-risk GIST patients effectively and significantly improve the area under the curve (AUC) of receiver operating characteristics (ROC) curve for prognostic indicator both in test and validation cohort. Patients with serosal invasion after R0 resection showed a poorer prognosis with imatinib adjuvant therapy. CONCLUSIONS: Sub-division of high-risk GIST patients helps to more precisely predicting the prognosis. Serosal invasion may be an adverse predictive factor in high-risk patients and imatinib treatment outcome.