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The variability in the prognosis of individuals with hepatocellular carcinoma (HCC) suggests that HCC may comprise several distinct biological phenotypes. These phenotypes may result from activation of different oncogenic pathways during tumorigenesis and/or from a different cell of origin. Here we address whether the transcriptional characteristics of HCC can provide insight into the cellular origin of the tumor. We integrated gene expression data from rat fetal hepatoblasts and adult hepatocytes with HCC from human and mouse models. Individuals with HCC who shared a gene expression pattern with fetal hepatoblasts had a poor prognosis. The gene expression program that distinguished this subtype from other types of HCC included markers of hepatic oval cells, suggesting that HCC of this subtype may arise from hepatic progenitor cells. Analyses of gene networks showed that activation of AP-1 transcription factors in this newly identified HCC subtype might have key roles in tumor development.

Typically observed at 2 y after surgical resection, late recurrence is a major challenge in the management of hepatocellular carcinoma (HCC). We aimed to develop a genomic predictor that can identify patients at high risk for late recurrence and assess its clinical implications. Systematic analysis of gene expression data from human liver undergoing hepatic injury and regeneration revealed a 233-gene signature that was significantly associated with late recurrence of HCC. Using this signature, we developed a prognostic predictor that can identify patients at high risk of late recurrence, and tested and validated the robustness of the predictor in patients (n = 396) who underwent surgery between 1990 and 2011 at four centers (210 recurrences during a median of 3.7 y of follow-up). In multivariate analysis, this signature was the strongest risk factor for late recurrence (hazard ratio, 2.2; 95% confidence interval, 1.3-3.7; p = 0.002). In contrast, our previously developed tumor-derived 65-gene risk score was significantly associated with early recurrence (p = 0.005) but not with late recurrence (p = 0.7). In multivariate analysis, the 65-gene risk score was the strongest risk factor for very early recurrence (<1 y after surgical resection) (hazard ratio, 1.7; 95% confidence interval, 1.1-2.6; p = 0.01). The potential significance of STAT3 activation in late recurrence was predicted by gene network analysis and validated later. We also developed and validated 4- and 20-gene predictors from the full 233-gene predictor. The main limitation of the study is that most of the patients in our study were hepatitis B virus-positive. Further investigations are needed to test our prediction models in patients with different etiologies of HCC, such as hepatitis C virus. Two independently developed predictors reflected well the differences between early and late recurrence of HCC at the molecular level and provided new biomarkers for risk stratification. Please see later in the article for the Editors' Summary.

Genetically modified mice have been extensively used for analyzing the molecular events that occur during tumor development. In many, if not all, cases, however, it is uncertain to what extent the mouse models reproduce features observed in the corresponding human conditions 1 , 2 , 3 . This is due largely to lack of precise methods for direct and comprehensive comparison at the molecular level of the mouse and human tumors. Here we use global gene expression patterns of 68 hepatocellular carcinomas (HCCs) from seven different mouse models and 91 human HCCs from predefined subclasses 4 to obtain direct comparison of the molecular features of mouse and human HCCs. Gene expression patterns in HCCs from Myc , E2f1 and Myc E2f1 transgenic mice were most similar to those of the better survival group of human HCCs, whereas the expression patterns in HCCs from Myc Tgfa transgenic mice and in diethylnitrosamine-induced mouse HCCs were most similar to those of the poorer survival group of human HCCs. Gene expression patterns in HCCs from Acox1 −/− mice and in ciprofibrate-induced HCCs were least similar to those observed in human HCCs. We conclude that our approach can effectively identify appropriate mouse models to study human cancers.

Recent investigations reported that some subtypes from the Lund or The Cancer Genome Atlas (TCGA) classifications were most responsive to PD-L1 inhibitor treatment. However, the association between previously reported subtypes and immune checkpoint inhibitor (ICI) therapy responsiveness has been insufficiently explored. Despite these contributions, the ability to predict the clinical applicability of immune checkpoint inhibitor therapy in patients remains a major challenge. Here, we aimed to re-classify distinct subtypes focusing on ICI responsiveness using gene expression profiling in the IMvigor 210 cohort (n = 298). Based on the hierarchical clustering analysis, we divided advanced urothelial cancer patients into three subgroups. To confirm a prognostic impact, we performed survival analysis and estimated the prognostic value in the IMvigor 210 and TCGA cohort. The activation of CD8+ T effector cells was common for patients of classes 2 and 3 in the TCGA and IMvigor 210 cohort. Survival analysis showed that patients of class 3 in the TCGA cohort had a poor prognosis, while patients of class 3 showed considerably prolonged survival in the IMvigor 210 cohort. One of the distinct characteristics of patients in class 3 is the inactivation of the TGFβ and YAP/TAZ pathways and activation of the cell cycle and DNA replication and DNA damage (DDR). Based on our identified transcriptional patterns and the clinical outcomes of advanced urothelial cancer patients, we constructed a schematic summary. When comparing clinical and transcriptome data, patients with downregulation of the TGFβ and YAP/TAZ pathways and upregulation of the cell cycle and DDR may be more responsive to ICI therapy.

Although the 5-year survival rate of patients diagnosed with nonmuscle invasive bladder cancer (NMIBC) has reached 85%, more than 50% of patients suffer from frequent recurrences. To identify molecular targets associated with recurrence of NMIBC, we analyzed gene expression data and found that FOXM1 and FANCD2 were involved in recurrence. Therefore, we investigated how these genes were involved in the mechanism of recurrence and confirmed their usefulness as biomarkers. Investigation have shown that FOXM1 directly regulated the transcription of FANCD2, which is the key gene of the Fanconi anemia (FA) pathway. Depletion of FOXM1 resulted in DNA repair defects in the FA pathway and in decreased resistance to chemotherapy. Thus, the FANCD2-associated FA pathway activated by FOXM1 is an important mechanism involved in chemotherapy-related recurrence. In conclusion, FOXM1 and FANCD2 can be used as prognostic factors that are associated with high risk of recurrence and with anticancer drug resistance properties in NMIBC patients.

Ex-vivo expanded mesenchymal stromal cells (MSCs) are increasingly used for paracrine support of hematopoietic stem cell (HSC) regeneration, but inconsistent outcomes have hindered ongoing clinical trials. Here, we show that significant heterogeneity in the niche activity of MSCs is created during their culture in various serum-supplemented media. The MSCs cultured under stimulatory or non-stimulatory culture conditions exhibited differences in colony forming unit-fibroblast contents, expression levels of cross-talk molecules (Jagged-1 and CXCL-12) and their support for HSC self-renewal. Accordingly, the enhancing effects of MSCs on hematopoietic engraftment were only visible when HSCs were co-transplanted with MSCs under stimulatory conditions. Of note, these differences in MSCs and their effects on HSCs were readily reversed by switching the cultures, indicating that the difference in niche activity can be caused by distinct functional state, rather than by clonal heterogeneity. Supporting the findings, transcriptomic analysis showed distinct upstream signaling pathways such as inhibition of P53 and activation of ER-stress response gene ATF4 for MSCs under stimulatory conditions. Taken together, our study shows that the niche activity of MSCs can vary rapidly by the extrinsic cues during culture causing variable outcomes in hematopoietic recoveries, and point to the possibility that MSCs can be pre-screened for more predictable efficacy in various cell therapy trials.

Chemotherapy resistance is an obstacle to cancer therapy and is considered a major cause of recurrence. Thus, understanding the mechanisms of chemoresistance is critical to improving the prognosis of patients. Here, we have established a stepwise gemcitabine-resistant T24 bladder cancer cell line to understand the molecular mechanisms of chemoresistance within cancer cells. The characteristics of the stepwise chemoresistance cell line were divided into 4 phases (parental, early, intermediate, and late phases). These four phase cells showed increasingly aggressive phenotypes in vitro and in vivo experiments with increasing phases and revealed the molecular properties of the biological process from parent cells to phased gemcitabine-resistant cell line (GRC). Taken together, through the analysis of gene expression profile data, we have characterized gene set of each phase indicating the response to anticancer drug treatment. Specifically, we identified a multigene signature (23 genes including GATA3 , APOBEC3G , NT5E , MYC , STC1 , FOXD1 , SMAD9 ) and developed a chemoresistance score consisting of that could predict eventual responsiveness to gemcitabine treatment. Our data will contribute to predicting chemoresistance and improving the prognosis of bladder cancer patients.

Although several prognostic signatures have been developed in lung cancer, their application in clinical practice has been limited because they have not been validated in multiple independent data sets. Moreover, the lack of common genes between the signatures makes it difficult to know what biological process may be reflected or measured by the signature. By using classical data exploration approach with gene expression data from patients with lung adenocarcinoma (n = 186), we uncovered two distinct subgroups of lung adenocarcinoma and identified prognostic 193-gene gene expression signature associated with two subgroups. The signature was validated in 4 independent lung adenocarcinoma cohorts, including 556 patients. In multivariate analysis, the signature was an independent predictor of overall survival (hazard ratio, 2.4; 95% confidence interval, 1.2 to 4.8; p = 0.01). An integrated analysis of the signature revealed that E2F1 plays key roles in regulating genes in the signature. Subset analysis demonstrated that the gene signature could identify high-risk patients in early stage (stage I disease), and patients who would have benefit of adjuvant chemotherapy. Thus, our study provided evidence for molecular basis of clinically relevant two distinct two subtypes of lung adenocarcinoma.

Gastric cancer is a heterogeneous cancer, making treatment responses difficult to predict. Here we show that we identify two distinct molecular subtypes, mesenchymal phenotype (MP) and epithelial phenotype (EP), by analyzing genomic and proteomic data. Molecularly, MP subtype tumors show high genomic integrity characterized by low mutation rates and microsatellite stability, whereas EP subtype tumors show low genomic integrity. Clinically, the MP subtype is associated with markedly poor survival and resistance to standard chemotherapy, whereas the EP subtype is associated with better survival rates and sensitivity to chemotherapy. Integrative analysis shows that signaling pathways driving epithelial-to-mesenchymal transition and insulin-like growth factor 1 (IGF1)/IGF1 receptor (IGF1R) pathway are highly activated in MP subtype tumors. Importantly, MP subtype cancer cells are more sensitive to inhibition of IGF1/IGF1R pathway than EP subtype. Detailed characterization of these two subtypes could identify novel therapeutic targets and useful biomarkers for prognosis and therapy response. The prognosis and treatment of gastric cancer is complicated by heterogeneity. Here, the authors reveal two molecular subtypes, the mesenchymal subtype associated with poor survival and chemoresistance, and the epithelial phenotype associated with better survival and sensitivity to chemotherapy.

FOXM1 (Forkhead box M1) is a key regulator of tumorigenesis. Previous studies demonstrated that FOXM1 overexpression was strongly correlated with poor prognosis in various cancers, including hepatocellular carcinoma (HCC). In this study, we examined an association between the gene expression signature of FOXM1 and HCC patient outcome. The co-expressed gene set of FOXM1 , which is significantly associated with the prognosis of HCC patients, was identified by analyzing the gene expression profiles of 100 patients with HCC, and this gene set was validated in two independent HCC patient cohorts ( n =573). In multivariate analysis, the co-expressed gene set of FOXM1 was the most significant prognostic factor for overall survival in patients with HCC (hazard ratio=1.706, 95% confidence intervals=1.176–2.475, P =0.005). We also analyzed different types of cancer, including pancreatic adenocarcinoma, lung adenocarcinoma, breast carcinoma and bladder urothelial carcinoma, to verify the association between the co-expressed gene set of FOXM1 and patient prognosis, and we found a consistent prognostic significance, regardless of tumor type. Finally, we identified a putative signaling pathway in which miR-34a acts as an upstream regulator of the FOXM1-MYC signaling network; this pathway may be ultimately responsible for the poor prognosis of HCC patients. The prognostic subgroups defined by the gene expression signature of FOXM1 could help predict high-risk patients and may guide selection of the best treatment strategy. Liver cancer: FOXM1 gene expression predictive of patient outcome The expression of the FOXM1 gene could help predict high-risk patients and guide treatment strategies in hepatocellular carcinoma (HCC). Overexpression of FOXM1 , a key regulator of tumor development and progression, has been linked to poor prognosis in various forms of cancer. In-Sun Chu and Bic-Na Song at the Korea Research Institute of Bioscience and Biotechnology in Daejeon investigated the association between FOXM1 expression and prognosis in patients with HCC, the most common form of liver cancer. They found the survival rates of patients with high levels of FOXM1 expression were significantly worse than those with low levels. Similar results were found in several other types of cancer. The authors concluded that the expression of FOXM1 and its associated genes has potential as a prognostic and predictive indicator in various cancers.