You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: Oncogenic Pathway Combinations Predict Clinical Prognosis in Gastric Cancer}
Article:
Abstract:
Many solid cancers are known to exhibit a high degree of heterogeneity in their deregulation of different oncogenic pathways. We sought to identify major oncogenic pathways in gastric cancer( GC) with significant relationships to patient survival. Using gene expression signatures, we devised an in silico strategy to map patterns of oncogenic pathway activation in 301 primary gastric cancers, the second highest cause of global cancer mortality. We identified three oncogenic pathways( proliferation/stem cell, NF-κB, and Wnt/β-catenin) deregulated in the majority( >70%) of gastric cancers. We functionally validated these pathway predictions in a panel of gastric cancer cell lines. Patient stratification by oncogenic pathway combinations showed reproducible and significant survival differences in multiple cohorts, suggesting that pathway interactions may play an important role in influencing disease behavior. Individual GCs can be successfully taxonomized by oncogenic pathway activity into biologically and clinically relevant subgroups. Predicting pathway activity by expression signatures thus permits the study of multiple cancer-related pathways interacting simultaneously in primary cancers, at a scale not currently achievable by other platforms.
Introduction:
Gastric cancer( GC) is the second leading cause of global cancer mortality[1]. Particularly prevalent in Asia, most GC patients are diagnosed with advanced stage disease[2]. Deregulation of canonical oncogenic pathways such as E2F, K-RAS, p53, and Wnt/β-catenin signaling are known to occur with varying frequencies in GC[3]–[6], indicating that GC is a molecularly heterogeneous disease. Previous studies describing GC diversity in primary tumors have typically focused on single pathways, measuring only one or a few biomarkers per experiment[4],[6],[7]. In contrast, experimental evidence indicates that most cancer phenotypes( uncontrolled growth, resistance to apoptosis, etc) are largely governed not just by single pathways, but complex interactions between multiple pro- and anti-oncogenic signaling circuits[8]. Narrowing this gap between the clinical and experimental arenas will require strategies capable of measuring and relating activity patterns of multiple oncogenic pathways simultaneously in primary tumors. Previous studies have proposed using gene expression signatures to predict the activity of oncogenic pathways in cancers[9] – here, we hypothesized that patterns of oncogenic pathway activation could be used to develop a genomic taxonomy of GC. Importantly, this pathway-centric strategy differs substantially from previous microarray studies describing expression changes associated with morphological and tissue type differences in GC[10],[11], as pathway signatures( rather than individual genes) are used as the basis for cancer classification. We developed an in silico method to map activation levels of different pathways in cohorts of complex primary tumor profiles and validated this pathway-directed classification approach using proof-of-concept examples from breast cancer. We then applied this method to GC to evaluate eleven oncogenic pathways previously implicated in gastric carcinogenesis[3]–[7],[12]–[17]. In total, we analyzed over 300 primary GCs derived from three independent patient cohorts, performing to the best of our knowledge the largest genomic analysis of GC to date. We identified three oncogenic pathways( nuclear factor-κB( NF-κB), Wnt/β-catenin, and proliferation/stem cell) that were deregulated in the vast majority( >70%) of GCs, and functionally validated the pathway predictions in vitro using a panel of GC cell lines. Although patient stratification at the level of individual pathways failed to consistently demonstrate significant differences in clinical outcome, patient stratification by oncogenic pathway combinations( e. g. high proliferation/high NF-κB vs. low proliferation/low NF-κB) showed reproducible and significant survival differences in multiple independent patient cohorts, suggesting a critical role for pathway combinations in influencing GC clinical behavior. Our results thus demonstrate that GCs can be successfully taxonomized using oncogenic pathway activity into biologically, functionally, and clinically relevant subtypes.
Discussion:
In this study, we sought to subdivide GCs into molecularly homogenous subgroups as a first step to individualizing patient treatments and improving outcomes. Importantly, unlike previous GC microarray studies relating gene expression patterns to histology or anatomical type[10],[11], we chose to base our GC subdivisions on patterns of oncogenic pathway activity. After developing and validating this novel classification approach, we were able to describe, for the first time, a genomic taxonomy of GC based on patterns of oncogenic pathway activity. Our approach is particularly suited for gene expression microarrays, since these platforms interrogate thousands of mRNA transcripts in each sample, thereby permitting the assessment of multiple pathways simultaneously in a single experiment. In contrast, such an approach is not currently possible at the protein level due to lack of appropriate platforms. Using this strategy, we identified three dominant pathways showing activation in the majority( >70%) of GCs: proliferation/stem cell, Wnt/β-catenin, and NF-κB signaling. The ability to perform such “high-throughput pathway profiling” opens many interesting avenues. For example, several studies have previously reported inconsistent results regarding the prognostic impact of different oncogenic pathways in GC- the prognostic implications of proliferation-related antigens such as Ki-67 in GC are not firmly established[29], and high NF-κB activation in GC has been associated with both good and bad GC patient outcome in different studies[7],[30]. It is quite possible that some of this inconsistency may have been due to a historical focus on using conventional methods and analyzing either single pathways or individual pathway components( genes/proteins). Our observation that pathway combinations are predictive of patient outcome suggests that pathway combinations, rather than single pathways alone, may play a critical role in influencing tumor behavior. Another benefit of high-throughput pathway profiling is the ability to define higher order relationships between distinct oncogenic pathways. In the current study, we consistently observed concomitant activation of E2F, MYC, p21(-repression), and stem cell pathways in tumors( the ‘proliferation/stem cell’ pathway cluster). This is most likely due to increased cellular proliferation in tumor cells, as E2F is important in cell proliferation control and MYC is both a p21-repressor and inducer of cyclin D2 and cyclin-dependent kinase binding protein CksHs2[31]. Furthermore, stem cells, particularly embryonic stem cells( ESCs), are also known to exhibit high cell proliferation rates[32]. More intriguingly, we also observed close associations between apparently functionally different pathways, such as β-catenin and SRC, as well as HDAC inhibition and BRCA1. Such pathway co-activation patterns may suggest functional interactions between these pathways, which deserve to be studied further. For example, it is possible that activated c-SRC may enhance the expression of the Wnt signaling pathway[33]. Exploring the relationships between pathways showing co-activation may thus provide valuable information regarding the ability of the cancer cell to coordinate the activity of multiple pathways. A third benefit of the pathway profiling approach is that it facilitates identification of major disease-related pathways. Of the pathways analyzed in this study, the finding that NF-κB signaling may be elevated in a significant proportion of GCs deserves some attention as this pathway has been relatively less explored in GC. Interestingly, while we observed a significant difference in both p50 and p65( the NF-κB subunits) gene expression between NF-κB/on and NF-κB/off GCCLs, we did not observe overt differential p50 protein expression in these lines, in contrast to p65( Figure 4C). This may be due to a combination of three reasons. First, the absolute range of p65 gene expression across the cell lines is markedly greater than the absolute range of p50 gene expression( >3×, Figure S4). Second, the Western blotting assay used to perform these protein measurements is known to be highly non-quantitative, which may mask subtle differences in expression. Third, beyond gene expression, p50 expression is also subject to a variety of post-transcriptional regulatory mechanisms such as precursor cleavage that might affect the final level of p50 protein, while p65 is not generated from a precursor protein[34]. NF-κB has been shown to be activated by H. pylori[35], a known GC carcinogen, and aberrant NF-κB signaling has also been implicated in multiple inflammation-linked cancers such as GC[36]. NF-κB has been suggested to be constitutively activated in primary gastric cancers in a few studies[7]. Targeted NF-κB-inhibitors are currently being actively developed in many anticancer drug development programs and a subset of GC patients( i. e. those with elevated NF-κB activity) may represent a suitable subclass for evaluating the efficacy of these compounds. The in silico method used in our study is conceptually similar to the work of Bild et al, which used a binary regression model to classify tumors based on the predicted activity of five oncogenic pathways[9]. Unlike binary regression, our approach, which makes use of a rank-based connectivity metric[37], requires no elaborate training process on each pathway signature and also does not require the availability of raw expression data, facilitating the use of the many publicly available pathway signatures in the literature[27]. However, the gene expression-based approach does have limitations. First, because our pathway predictions are based on gene expression rather than proteins, such predictions are admittedly molecular surrogates of true pathway signaling activity. Second, we are currently limited to analyzing known oncogenic pathways previously identified in the literature. Third, although we were able to use pathway signatures from very different tissue contexts to predict pathway activation status, an examination of the initial proof-of-principle breast cancer examples revealed that the association of ER status to estrogen responsiveness as predicted using the osteosarcoma signature, although significant, was markedly weaker compared to the association of ER status to tamoxifen sensitivity predicted using a signature derived from the same tissue type( i. e. breast). This result implies that there may also exist tissue-specific differences in pathway signatures that may affect prediction accuracy. Fourth, compared to our study which focused on pathways of known biological relevance in GC, it is unclear if this method can be applied to diseases where prior knowledge of involved pathways may not be available. However, it should be noted that a wealth of pathway signatures( >1000) associated with diverse biochemical and signaling pathways already exists in the literature, which can be accessed from public databases such as MSigDB( http://www. broad. mit. edu/gsea/msigdb/genesets. jsp? collection=CGP). Since our approach can be applied to virtually any disease dataset for which gene expression information is available, testing every signature in a high-throughput manner for evidence of pathway deregulation is both conceivable and feasible. In such cases, pathway exhibiting high frequencies of deregulation would then represent candidate pathways involved in the disease in question, which can then be targeted for focused investigation and experimentation. Addressing these issues will form the ground for much future research. In conclusion, we have shown in this work that pathways signatures can be successfully used to predict the activation status of cellular signaling pathways, even in biological entities as complex as a human GC. One obvious immediate application of such pathway-based taxonomies may relate to the use of targeted therapies. Initial trials assessing the role of targeted therapies in GC have demonstrated only modest results[38]; however, most of these studies have been performed without pre-stratifying patients using molecular or histopathologic criteria. Pathway-based taxonomies may prove very useful in developing personalized treatment regimens for different subgroups of GC, since such oncogenic pathway activation patterns can be readily linked to potential pathway inhibitors and targeted therapies.
Gastric cancer is the second leading cause of global cancer mortality . With current treatments , less than a quarter of patients survive longer than five years after surgery . Individual gastric cancers are highly disparate in their cellular characteristics and responses to standard chemotherapeutic drugs , making gastric cancer a complex disease . Pathway based approaches , rather than single gene studies , may help to unravel this complexity . Here , we make use of a computational approach to identify connections between molecular pathways and cancer profiles . In a large scale study of more than 300 patients , we identified subgroups of gastric cancers distinguishable by their patterns of driving molecular pathways . We show that these identified subgroups are clinically relevant in predicting survival duration and may prove useful in guiding the choice of targeted therapies designed to interfere with these molecular pathways . We also identified specific gastric cancer cell lines mirroring these pathway subgroups , which should facilitate the pre-clinical assessment of responses to targeted therapies in each subgroup .