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.

CD177 drives the transendothelial migration of Treg cells enriched in human colorectal cancer.

Objectives: Regulatory T (Treg) cells regulate immunity in autoimmune diseases and cancers. However, immunotherapies that target tumor-infiltrating Treg cells often induce unwanted immune responses and tissue inflammation. Our research focussed on exploring the expression pattern of CD177 in tumor-infiltrating Treg cells with the aim of identifying a potential target that can enhance immunotherapy effectiveness. Methods: Single-cell RNA sequencing (scRNA-seq) data and survival data were obtained from public databases. Twenty-one colorectal cancer patient samples, including fresh tumor tissues, peritumoral tissues and peripheral blood mononuclear cells (PBMCs), were analysed using flow cytometry. The transendothelial activity of CD177+ Treg cells was substantiated using in vitro experiments. Results: ScRNA-seq and flow cytometry results indicated that CD177 was exclusively expressed in intratumoral Treg cells. CD177+ Treg cells exhibited greater activation status and expressed elevated Treg cell canonical markers and immune checkpoint molecules than CD177- Treg cells. We further discovered that both intratumoral CD177+ Treg cells and CD177-overexpressing induced Treg (iTreg) cells had lower levels of PD-1 than their CD177- counterparts. Moreover, CD177 overexpression significantly enhanced the transendothelial migration of Treg cells in vitro. Conclusions: These results demonstrated that Treg cells with higher CD177 levels exhibited an enhanced activation status and transendothelial migration capacity. Our findings suggest that CD177 may serve as an immunotherapeutic target and that overexpression of CD177 may improve the efficacy of chimeric antigen receptor T (CAR-T) cell therapy.

FOXP3+ regulatory T cell perturbation mediated by the IFNγ-STAT1-IFITM3 feedback loop is essential for anti-tumor immunity.

Targeting tumor-infiltrating regulatory T cells (Tregs) is an efficient way to evoke an anti-tumor immune response. However, how Tregs maintain their fragility and stability remains largely unknown. IFITM3 and STAT1 are interferon-induced genes that play a positive role in the progression of tumors. Here, we showed that IFITM3-deficient Tregs blunted tumor growth by strengthening the tumor-killing response and displayed the Th1-like Treg phenotype with higher secretion of IFNγ. Mechanistically, depletion of IFITM3 enhances the translation and phosphorylation of STAT1. On the contrary, the decreased IFITM3 expression in STAT1-deficient Tregs indicates that STAT1 conversely regulates the expression of IFITM3 to form a feedback loop. Blocking the inflammatory cytokine IFNγ or directly depleting STAT1-IFITM3 axis phenocopies the restored suppressive function of tumor-infiltrating Tregs in the tumor model. Overall, our study demonstrates that the perturbation of tumor-infiltrating Tregs through the IFNγ-IFITM3-STAT1 feedback loop is essential for anti-tumor immunity and constitutes a targetable vulnerability of cancer immunotherapy.

Computational identification and clinical validation of a novel risk signature based on coagulation-related lncRNAs for predicting prognosis, immunotherapy response, and chemosensitivity in colorectal cancer patients.

Background: Coagulation is critically involved in the tumor microenvironment, cancer progression, and prognosis assessment. Nevertheless, the roles of coagulation-related long noncoding RNAs (CRLs) in colorectal cancer (CRC) remain unclear. In this study, an integrated computational framework was constructed to develop a novel coagulation-related lncRNA signature (CRLncSig) to stratify the prognosis of CRC patients, predict response to immunotherapy and chemotherapy in CRC, and explore the potential molecular mechanism. Methods: CRC samples from The Cancer Genome Atlas (TCGA) were used as the training set, while the substantial bulk or single-cell RNA transcriptomics from Gene Expression Omnibus (GEO) datasets and real-time quantitative PCR (RT-qPCR) data from CRC cell lines and paired frozen tissues were used for validation. We performed unsupervised consensus clustering of CRLs to classify patients into distinct molecular subtypes. We then used stepwise regression to establish the CRLncSig risk model, which stratified patients into high- and low-risk groups. Subsequently, diversified bioinformatics algorithms were used to explore prognosis, biological pathway alteration, immune microenvironment, immunotherapy response, and drug sensitivity across patient subgroups. In addition, weighted gene coexpression network analysis was used to construct an lncRNA-miRNA-mRNA competitive endogenous network. Expression levels of CRLncSig, immune checkpoints, and immunosuppressors were determined using RT-qPCR. Results: We identified two coagulation subclusters and constructed a risk score model using CRLncSig in CRC, where the patients in cluster 2 and the low-risk group had a better prognosis. The cluster and CRLncSig were confirmed as the independent risk factors, and a CRLncSig-based nomogram exhibited a robust prognostic performance. Notably, the cluster and CRLncSig were identified as the indicators of immune cell infiltration, immunoreactivity phenotype, and immunotherapy efficiency. In addition, we identified a new endogenous network of competing CRLs with microRNA/mRNA, which will provide a foundation for future mechanistic studies of CRLs in the malignant progression of CRC. Moreover, CRLncSig strongly correlated with drug susceptibility. Conclusion: We developed a reliable CRLncSig to predict the prognosis, immune landscape, immunotherapy response, and drug sensitivity in patients with CRC, which might facilitate optimizing risk stratification, guiding the applications of immunotherapy, and individualized treatments for CRC.

Blockade of TNF-α/TNFR2 signalling suppresses colorectal cancer and enhances the efficacy of anti-PD1 immunotherapy by decreasing CCR8+ T regulatory cells.

Enrichment of regulatory T cells (Tregs) in the tumour microenvironment (TME) has been recognized as one of the major factors in the initiation and development of resistance to immune checkpoint inhibitors. C-C motif chemokine receptor 8 (CCR8), a marker of activated suppressive Tregs, has a significant impact on the functions of Tregs in the TME. However, the regulatory mechanism of CCR8 in Tregs remains unclear. Here, we reveal that a high level of TNF-α in the colorectal cancer (CRC) microenvironment upregulates CCR8 expression in Tregs via the TNFR2/NF-κB signalling pathway and the FOXP3 transcription factor. Furthermore, in both anti-PD1-responsive and anti-PD1-unresponsive tumour models, PD1 blockade induced CCR8+ Treg infiltration. In both models, Tnfr2 depletion or TNFR2 blockade suppressed tumour progression by reducing CCR8+ Treg infiltration and thus augmented the efficacy of anti-PD1 therapy. Finally, we identified that TNFR2+CCR8+ Tregs but not total Tregs are positively correlated with adverse prognosis in CRC and gastric cancer. Our work reveals the regulatory mechanisms of CCR8 in Tregs and identifies TNFR2 as a promising target for immunotherapy.

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.

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.

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.

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.

Long noncoding RNA SPRY4-IT1 acts as a miR-101-5p sponge to promote gastrointestinal stromal tumor progression by inhibiting ZEB1.

OBJECTIVES: Research on long noncoding RNAs (lncRNAs) has been conducted in different areas of oncology. Currently, the biological significance of lncRNAs and their regulatory features in gastrointestinal stromal tumors (GIST) remain largely unknown. We have previously identified SPRY4-IT1 overexpression in GIST through lncRNA sequencing of GIST tissues. Coincidentally, SPRY4-IT1 is an intron of the SPRY4 gene, and SPRY4 is specifically highly expressed in GIST. Thus the aim of the present study was to investigate the role of lncRNA SPRY4-IT1 in GIST pathogenesis. METHODS: Herein, we screened for SPRY4-IT1 and analyzed its possible phenotypes using Gene set enrichment analysis (GSEA). The phenotypes of GIST were verified using CCK-8, colony formation, and wound-healing assays. The ceRNA mechanism was determined by the location of lncRNA SPRY4-IT1, and its relationship to the Ago2 protein. The SPRY4-IT1/miR-101-5p/ZEB1 axis was predicted using online software and sequencing. Luciferase and pull-down assays were performed for verification. Pathway-associated and phenotype-associated proteins were detected by western blotting. RESULTS: Sequencing analysis revealed 117 differentially expressed lncRNAs in GIST and normal gastric tissue samples. Accordingly, SPRY4-IT1 was screened out and its phenotype was predicted by GSEA. Mechanistically, SPRY4-IT1 was identified as a competing endogenous RNA (ceRNA) that downregulated miR-101-5p and upregulated ZEB1, which activated extracellular signal-regulated kinase (ERK) signaling to stimulate GIST proliferation, invasion, and epithelial-mesenchymal transition. Although this effect was regulated by a negative feedback loop through SPRY4, it was still controlled by SPRY4-IT1. CONCLUSIONS: In GIST, we revealed a ceRNA mechanism by which SPRY4-IT1 modulates ZEB1 by sponging miR-101-5p, eventually driving tumor cell proliferation, migration, and epithelial-mesenchymal transition (EMT).

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.

High-level of intratumoral GITR+ CD4 T cells associate with poor prognosis in gastric cancer.

Immunotherapy targeting glucocorticoid-induced TNFR-related protein (GITR) exhibited strong anti-tumor capacity in mouse model but poor efficacy in clinical trials. This may be attributed to the different GITR expression mode between human and mouse. In this study, we analyzed single-cell RNA sequencing (scRNA-seq) data of human gastric cancer (GC) and used flow to explore the GITR expression across T cell subsets and tissue types in GC patients. We revealed that GITR+ CD4 T cells, including regulatory CD4 T (Treg) cells and conventional CD4 T (Tconv) cells, might contribute to the immunosuppressive microenvironment in GC. The enrichment of these cells was associated with a worse prognosis. Moreover, we found the cellular distribution of GITR protein in Treg cells was microenvironment dependent. In conclusion, GITR is still an important immune checkpoint need to be studied.

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.

Integrative analysis reveals a clinicogenomic landscape associated with liver metastasis and poor prognosis in hepatoid adenocarcinoma of the stomach.

Hepatoid adenocarcinoma of the stomach (HAS) is a rare subtype of gastric cancer (GC) that histologically resembles hepatocellular carcinoma (HCC). Despite its low incidence, HAS had a poor 5-year survival rate. Currently, the linkages between clinicopathological and genomic features of HAS and its therapeutic targets remain largely unknown. Herein, we enrolled 90 HAS patients and 270 stage-matched non-HAS patients from our institution for comparing clinicopathological features. We found that HAS had worse overall survival and were more prone to develop liver metastasis than non-HAS in our cohort, which was validated via meta-analysis. By comparing whole-exome sequencing data of HAS (n=30), non-HAS (n=63), and HCC (n=355, The Cancer Genome Atlas), we identified a genomic landscape associated with unfavorable clinical features in HAS, which contained frequent somatic mutations and widespread copy number variations. Notably, signaling pathways regulating pluripotency of stem cells affected by frequent genomic alterations might contribute to liver metastasis and poor prognosis in HAS patients. Furthermore, HAS developed abundant multiclonal architecture associated with liver metastasis. Encouragingly, target analysis suggested that HAS patients might potentially benefit from anti-ERBB2 or anti-PD-1 therapy. Taken together, this study systematically demonstrated a high risk of liver metastasis and poor prognosis in HAS, provided a clinicogenomic landscape underlying these unfavorable clinical features, and identified potential therapeutic targets, laying the foundations for developing precise diagnosis and therapy in this rare but lethal disease.

The nerve-tumour regulatory axis GDNF-GFRA1 promotes tumour dormancy, imatinib resistance and local recurrence of gastrointestinal stromal tumours by achieving autophagic flux.

Complete surgical resection, accessible therapeutic targets and effective tyrosine kinase inhibitors (TKIs) have not completely cured gastrointestinal stromal tumours (GISTs), with most patients suffering from residual tumours and recurrence. The existence of nerve infiltration in GIST provides a way for tumour cells to escape local resection and systemic targeted therapy, which may challenge the previous understanding of its behaviour patterns and inspire the development of more radical excision and more precise targeted therapy. Moreover, tumour dormancy has emerged as a major cause of drug resistance and tumour relapse. Among these pathways, the nerve-tumour regulatory axis GDNF-GFRA1 is activated in GISTs, assists tumour cells in achieving dormancy and protects them from apoptosis under environmental stress by enhancing autophagic flux. The concrete mechanism is that the GDNF-regulating interaction between GFRA1 and the lysosomal calcium channel MCOLN1 activates Ca2+-dependent TFEB signalling. Activated TFEB transcriptionally regulates intracellular lysosome levels, which could achieve feedback upregulation of cellular autophagy flux during TKI treatment. This dormancy-transition axis fills parts of the mechanistic vacancy before the onset of secondary mutations, and strategies for TKIs combined with targeting GFRA1-dependent autophagy have distinct promise as prospective clinical therapies.

TSNAD v2.0: A one-stop software solution for tumor-specific neoantigen detection.

TSNAD is a one-stop software solution for predicting neoantigens from the whole genome/exome sequencing data of tumor-normal pairs. Here we present TSNAD v2.0 which provides several new features such as the function of RNA-Seq analysis including gene expression and gene fusion analysis, the support of different versions of the reference genome. Most importantly, we replace the NetMHCpan with DeepHLApan we developed previously, which considers both the binding between peptide and major histocompatibility complex (MHC) and the immunogenicity of the presented peptide-MHC complex (pMHC). TSNAD v2.0 achieves good performamce on a standard dataset. For better usage, we provide the Docker version and the web service of TSNAD v2.0. The source code of TSNAD v2.0 is freely available at https://github.com/jiujiezz/tsnad. And the web service of TSNAD v2.0 is available at http://biopharm.zju.edu.cn/tsnad/.

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.

Whole-exome sequencing of alpha-fetoprotein producing gastric carcinoma reveals genomic profile and therapeutic targets.

Alpha-fetoprotein producing gastric carcinoma (AFPGC) is a rare and aggressive subtype of gastric cancer. However, little is known about the genomic features of this disease. We perform whole-exome sequencing analysis of AFPGC, and identify 34 significantly mutated genes. Somatic copy number alterations analysis reveals several significant focal amplifications (e.g. 19q12, 17q12) and focal deletions (e.g. 1p36.11, 9p21.3), and some of these negatively affect the patient prognosis. Comparative analyses reveal that AFPGC has distinct genomic features from gastric cancer of The Cancer Genome Atlas as well as four molecular subtypes. Several frequently altered genes with potential as therapeutic targets are identified in AFPGC. Further analysis reveals that AFPGC with amplification of CCNE1 at 19q12 and/or ERBB2 at 17q12 show poorer survival and more aggressive. Subsequently, based on our established patient-derived xenograft models for AFPGC, translational research is performed and the therapeutic value of targeting CCNE1 and ERBB2 is validated. In this work, we provide an understanding of genomic characteristics of AFPGC and propose a platform to explore and validate the genome-guided personalized treatment for this disease.

Evolution and functional divergence of the ERBB receptor family.

Aim: The ERBB gene family plays an important role in cell proliferation and differentiation, and aberrant activations could result in tumorigenesis, which makes this gene family an attractive drug target in the area of precision oncology. Materials & methods: Functional divergence analysis and conservation analysis were performed using ClustalW, MEGA7 and DIVERGE3 software. Results: One hundred and forty five functional divergence residues sites, 94 totally conserved sites and averagely 345 conserved sites of individual gene member were obtained. Some have been reported to play role in drug binding, tumorigenesis and drug resistance. Conclusion: Functional divergence residues with high posterior probabilities and conserved residues may possess certain functions, and aberrant alterations may confer drug resistance or contribute to tumorigenesis.