Stromal-Based Signatures for the Classification of Gastric Cancer

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.

Xerna™ TME Panel is a machine learning-based transcriptomic biomarker designed to predict therapeutic response in multiple cancers

Introduction

Most predictive biomarkers approved for clinical use measure single analytes such as genetic alteration or protein overexpression. We developed and validated a novel biomarker with the aim of achieving broad clinical utility. The Xerna™ TME Panel is a pan-tumor, RNA expression-based classifier, designed to predict response to multiple tumor microenvironment (TME)-targeted therapies, including immunotherapies and anti-angiogenic agents.

Methods

The Panel algorithm is an artificial neural network (ANN) trained with an input signature of 124 genes that was optimized across various solid tumors. From the 298-patient training data, the model learned to discriminate four TME subtypes: Angiogenic (A), Immune Active (IA), Immune Desert (ID), and Immune Suppressed (IS). The final classifier was evaluated in four independent clinical cohorts to test whether TME subtype could predict response to anti-angiogenic agents and immunotherapies across gastric, ovarian, and melanoma datasets.

Results

The TME subtypes represent stromal phenotypes defined by angiogenesis and immune biological axes. The model yields clear boundaries between biomarker-positive and -negative and showed 1.6-to-7-fold enrichment of clinical benefit for multiple therapeutic hypotheses. The Panel performed better across all criteria compared to a null model for gastric and ovarian anti-angiogenic datasets. It also outperformed PD-L1 combined positive score (>1) in accuracy, specificity, and positive predictive value (PPV), and microsatellite-instability high (MSI-H) in sensitivity and negative predictive value (NPV) for the gastric immunotherapy cohort.

Discussion

The TME Panel's strong performance on diverse datasets suggests it may be amenable for use as a clinical diagnostic for varied cancer types and therapeutic modalities.

Clinical molecular subtyping reveals intrinsic mesenchymal reprogramming in gastric cancer cells

The mesenchymal cancer phenotype is known to be clinically related to treatment resistance and a poor prognosis. We identified gene signature-based molecular subtypes of gastric cancer (GC, n = 547) based on transcriptome data and validated their prognostic and predictive utility in multiple external cohorts. We subsequently examined their associations with tumor microenvironment (TME) features by employing cellular deconvolution methods and sequencing isolated GC populations. We further performed spatial transcriptomics analysis and immunohistochemistry, demonstrating the presence of GC cells in a partial epithelial-mesenchymal transition state. We performed network and pharmacogenomic database analyses to identify TGF-β signaling as a driver pathway and, thus, a therapeutic target. We further validated its expression in tumor cells in preclinical models and a single-cell dataset. Finally, we demonstrated that inhibition of TGF-β signaling negated mesenchymal/stem-like behavior and therapy resistance in GC cell lines and mouse xenograft models. In summary, we show that the mesenchymal GC phenotype could be driven by epithelial cancer cell-intrinsic TGF-β signaling and propose therapeutic strategies based on targeting the tumor-intrinsic mesenchymal reprogramming of medically intractable GC.

Phase Ib Study of Navicixizumab Plus Paclitaxel in Patients With Platinum-Resistant Ovarian, Primary Peritoneal, or Fallopian Tube Cancer

This phase Ib study evaluated the safety and efficacy of paclitaxel plus navicixizumab, a bispecific antiangiogenic antibody to vascular endothelial growth factor and delta-like ligand 4, against platinum-resistant ovarian cancer.

Predictive biomarkers for survival benefit with ramucirumab in urothelial cancer in the RANGE trial

The RANGE study (NCT02426125) evaluated ramucirumab (an anti-VEGFR2 monoclonal antibody) in patients with platinum-refractory advanced urothelial carcinoma (UC). Here, we use programmed cell death-ligand 1 (PD-L1) immunohistochemistry (IHC) and transcriptome analysis to evaluate the association of immune and angiogenesis pathways, and molecular subtypes, with overall survival (OS) in UC. Higher PD-L1 IHC and immune pathway scores, but not angiogenesis scores, are associated with greater ramucirumab OS benefit. Additionally, Basal subtypes, which have higher PD-L1 IHC and immune/angiogenesis pathway scores, show greater ramucirumab OS benefit compared to Luminal subtypes, which have relatively lower scores. Multivariable analysis suggests patients from East Asia as having lower immune/angiogenesis signature scores, which correlates with decreased ramucirumab OS benefit. Our data highlight the utility of multiple biomarkers including PD-L1, molecular subtype, and immune phenotype in identifying patients with UC who might derive the greatest benefit from treatment with ramucirumab.

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.

Spatial transcriptomics and proteomics technologies for deconvoluting the tumor microenvironment

The tumor microenvironment (TME) harbors heterogeneous contents and plays critical roles in tumorigenesis, metastasis, and drug resistance. Therefore, the deconvolution of the TME becomes increasingly essential to every aspect of cancer research and treatment. Novel spatially-resolved high-plex molecular profiling technologies have been emerging rapidly as powerful tools to obtain in-depth understanding from TME perspectives due to their capacity to allow high-plex protein and RNA profiling while keeping valuable spatial information. Based on our practical experience, we review a variety of available spatial proteogenomic technologies, including 10X Visium, GeoMx Digital Spatial Profiler (DSP), cyclic immunofluorescence-based CODEX and Multi-Omyx, mass spectrometry (MS)-based imaging mass spectrometry (IMS) and multiplex ion-beam imaging (MIBI). We also discuss FISSEQ, MERFISH, Slide-seq, and HDST, some of which may become commercially available in the near future. In particular, with our experience, we elaborate on DSP for spatial proteogenomic profiling and discuss its unique features designed for immuno-oncology and propose anticipation towards its future direction. The emerging spatially technologies are rapidly reshaping the magnitude of our understanding of the TME.

Prognostic value of noggin protein expression in patients with resected gastric cancer

Background

Noggin and RNA-binding protein for multiple splicing 2 (RBPMS2) are known to regulate the expression of smooth muscle cells, endothelial cells, and osteoblasts. However, the prognostic role of combined Noggin and RBPMS2 expression in resected gastric cancer (GC) is unclear.

Methods

A total of 163 patients with GC who underwent gastrectomy were included in this study. The expression of Noggin and RBPMS2 proteins in tumor cells at the tumor center and invasive front of resected GC was evaluated by immunohistochemistry, and in conjunction with clinicopathological parameters the patient survival was analyzed.

Results

RBPMS2 protein expression was high at the tumor center (n = 86, 52.8%) and low at the invasive front (n = 69, 42.3%), while Noggin protein expression was high in both tumor center (n = 91, 55.8%) and the invasive front (n = 90, 55.2%). Noggin expression at the invasive front and tumor center was significantly decreased in advanced T stage, non-intestinal-type (invasive front, P = 0.008 and P <  0.001; tumor center lesion, P = 0.013 and P = 0.001). RBPMS2 expression at the invasive front was significantly decreased in non-intestinal-type and positive lymphatic invasion (P <  0.001 and P = 0.013). Multivariate analysis revealed that high Noggin protein expression of the invasive front was an independent prognostic factor for overall survival (hazard ratio [HR], 0.58; 95% confidence interval [CI]; 0.35–0.97, P <  0.036), but not at the tumor center (HR, 1.35; 95% CI; 0.81–2.26, P = 0.251).

Conclusions

Our study indicates that high Noggin expression is a crucial prognostic factor for favorable outcomes in patients with resected GC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08273-x.

The impact of recent next generation sequencing and the need for a new classification in gastric cancer

The phenotypical and molecular heterogeneity of gastric cancer has hampered the introduction in clinical practice of a unifying classification of the disease. However, as next generation sequencing (NGS) technologies enhanced the comprehension of the molecular landscape of gastric cancer, novel molecular classification systems have been proposed, allowing the dissection of molecular tumor heterogeneity and paving the way for the development of new targeted therapies. Moreover, the use of NGS analyses in the molecular profiling of formalin-fixed paraffin-embedded (FFPE) specimens will improve patient selection for the enrolment in novel clinical trials. In conclusion, the application of NGS in precision oncology will revolutionize the diagnosis and clinical management in gastric cancer patients.

Comprehensive molecular characterization of gastric cancer patients from phase II second-line ramucirumab plus paclitaxel therapy trial

BACKGROUND

Gastric cancer (GC) is a heterogenous disease consisted of several subtypes with distinct molecular traits. The clinical implication of molecular classification has been limited especially in association with treatment efficacy of ramucirumab or various targeted agents.

METHODS

We conducted a prospective non-randomized phase II single-arm trial of ramucirumab plus paclitaxel as second-line chemotherapy in 62 patients with metastatic GC who failed to respond to first-line fluoropyrimidine plus platinum treatment. For integrative molecular characterization, all patients underwent pre-ramucirumab treatment tissue biopsy for whole-exome/whole-transcriptome sequencing to categorize patients based on molecular subtypes. We also systematically performed integrative analysis, combining genomic, transcriptomic, and clinical features, to identify potential molecular predictors of sensitivity and resistance to ramucirumab treatment.

RESULTS

Sixty-two patients were enrolled in this study between May 2016 and October 2017. Survival follow-up in all patients was completed as of the date of cut-off on January 2, 2019. No patient attained complete response (CR), while 22 patients achieved confirmed partial response (PR), resulting in a response rate (RR) of 35.5% (95% CI, 23.6-47.4). According to TCGA molecular classification, there were 30 GS, 18 CIN, 3 EBV, and 0 MSI tumors. The RR was 33% in GS (10/30), 33% in CIN (6/18), and 100% in EBV-positive GC patients with significant statistical difference for EBV(+) against EBV(-) tumors (P = 0.016; chi-squared test). Moreover, responsive patients were marked by activation of angiogenesis, VEGF, and TCR-associated pathways, while non-responder patients demonstrated enrichments of sonic hedgehog signaling pathway and metabolism activity. Integrative multi-layer data analysis further identified molecular determinants, including EBV status, and somatic mutation in GNAQ to ramucirumab activity.

CONCLUSIONS

Prospective molecular characterization identified a subset of GC patients with distinct clinical response to ramucirumab therapy, and our results demonstrate the feasibility of personalized therapeutic opportunities in gastric cancer.

TRIAL REGISTRATION

The study was registered on ClinicalTrial.gov ( NCT02628951 ) on June 12, 2015.

Gastric cancer: a comprehensive review of current and future treatment strategies

Gastric cancer remains a major unmet clinical problem with over 1 million new cases worldwide. It is the fourth most commonly occurring cancer in men and the seventh most commonly occurring cancer in women. A major fraction of gastric cancer has been linked to variety of pathogenic infections including but not limited to Helicobacter pylori (H. pylori) or Epstein Barr virus (EBV). Strategies are being pursued to prevent gastric cancer development such as H. pylori eradication, which has helped to prevent significant proportion of gastric cancer. Today, treatments have helped to manage this disease and the 5-year survival for stage IA and IB tumors treated with surgery are between 60 and 80%. However, patients with stage III tumors undergoing surgery have a dismal 5-year survival rate between 18 and 50% depending on the dataset. These figures indicate the need for more effective molecularly driven treatment strategies. This review discusses the molecular profile of gastric tumors, the success, and challenges with available therapeutic targets along with newer biomarkers and emerging targets.

Systematic Elucidation of the Mechanism of Quercetin against Gastric Cancer via Network Pharmacology Approach

This study was aimed at elucidating the potential mechanisms of quercetin in the treatment of gastric cancer (GC). A network pharmacology approach was used to analyze the targets and pathways of quercetin in treating GC. The predicted targets of quercetin against GC were obtained through database mining, and the correlation of these targets with GC was analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Next, the protein-protein interaction (PPI) network was constructed, and overall survival (OS) analysis of hub targets was performed using the Kaplan–Meier Plotter online tool. Finally, the mechanism was further analyzed via molecular docking of quercetin with the hub targets. Thirty-six quercetin-related genes were identified, 15 of which overlapped with GC-related targets. These targets were further mapped to 319 GO biological process terms and 10 remarkable pathways. In the PPI network analysis, six hub targets were identified, including AKT1, EGFR, SRC, IGF1R, PTK2, and KDR. The high expression of these targets was related to poor OS in GC patients. Molecular docking analysis confirmed that quercetin can bind to these hub targets. In conclusion, this study provided a novel approach to reveal the therapeutic mechanisms of quercetin on GC, which will ease the future clinical application of quercetin in the treatment of GC.

Predicting Peritoneal Dissemination of Gastric Cancer in the Era of Precision Medicine: Molecular Characterization and Biomarkers

Gastric cancer (GC) is a leading cause of worldwide cancer-related death. Being a highly heterogeneous disease, the current treatment of GC has been suboptimal due to the lack of subtype-dependent therapies. Peritoneal dissemination (PD) is a common pattern of GC metastasis associated with poor prognosis. Therefore, it is urgently necessary to identify patients at high risk of PD. PD is found to be associated with Lauren diffuse type GC. Molecular profiling of GC, especially diffuse type GC, has been utilized to identify molecular alterations and has given rise to various molecular classifications, shedding light on the underlying mechanism of PD and enabling identification of patients at higher PD risk. In addition, a series of diagnositc and prognostic biomarkers of PD from serum, peritoneal lavages and primary GCs have been reported. This comprehensive review summarizes findings on the multi-omic characteristics of diffuse type GC, the clinical significance of updating molecular classifications of GC in association with PD risk and research advances in PD-associated biomarkers.

Molecular dissection of CRC primary tumors and their matched liver metastases reveals critical role of immune microenvironment, EMT and angiogenesis in cancer metastasis

Metastasis is the primary cause of cancer mortality. The primary tumors of colorectal cancer (CRC) often metastasize to the liver. In this study, we have collected 122 samples from 45 CRC patients. Among them, 32 patients have primary tumors, adjacent normal tissues, and matched liver metastases. Thirteen patients have primary tumors without distant metastasis and matched normal tissues. Characterization of these samples was conducted by whole-exome and RNA sequencing and SNP6.0 analysis. Our results revealed no significant difference in genetic alterations including common oncogenic mutations, whole genome mutations and copy number variations between primary and metastatic tumors. We then assembled gene co-expression networks and identified metastasis-correlated gene networks of immune-suppression, epithelial–mesenchymal transition (EMT) and angiogenesis as the key events and potentially synergistic drivers associated with CRC metastasis. Further independent cohort validation using published datasets has verified that these specific gene networks are up regulated throughout the tumor progression. The gene networks of EMT, angiogenesis, immune-suppression and T cell exhaustion are closely correlated with the poor patient outcome and intrinsic anti-PD-1 resistance. These results offer insights of combinational strategy for the treatment of metastatic CRC.

Natural killer cell and stroma abundance are independently prognostic and predict gastric cancer chemotherapy benefit

BACKGROUNDSpecific features of the tumor microenvironment (TME) may provide useful prognostic information. We conducted a systematic investigation of the cellular composition and prognostic landscape of the TME in gastric cancer.METHODSWe evaluated the prognostic significance of major stromal and immune cells within the TME. We proposed a composite TME-based risk score and tested it in 6 independent cohorts of 1678 patients with gene expression or IHC measurements. Further, we devised a patient classification system based on TME characteristics.RESULTSWe identified NK cells, fibroblasts, and endothelial cells as the most robust prognostic markers. The TME risk score combining these cell types was an independent prognostic factor when adjusted for clinicopathologic variables (gene expression, HR [95% CI], 1.42 [1.22-1.66]; IHC, 1.34 [1.24-1.45], P < 0.0001). Higher TME risk scores consistently associated with worse survival within every pathologic stage (HR range, 2.18-3.11, P < 0.02) and among patients who received surgery only. The TME risk score provided additional prognostic value beyond stage, and combination of the two improved prognostication accuracy (likelihood-ratio test χ2 = 235.4 vs. 187.6, P < 0.0001; net reclassification index, 23%). The TME risk score can predict the survival benefit of adjuvant chemotherapy in nonmetastatic patients (stage I-III) (interaction test, P < 0.02). Patients were divided into 4 TME subtypes that demonstrated distinct genetic and molecular patterns and complemented established genomic and molecular subtypes.CONCLUSIONWe developed and validated a TME-based risk score as an independent prognostic and predictive factor, which has the potential to guide personalized management of gastric cancer.FUNDINGThis project is partially supported by NIH grant 1R01 CA222512.

Genomic characterization of intrinsic and acquired resistance to cetuximab in colorectal cancer patients

Anti-EGFR antibodies are effective in therapies for late-stage colorectal cancer (CRC); however, many tumours are unresponsive or develop resistance. We performed genomic analysis of intrinsic and acquired resistance to anti-EGFR therapy in prospectively collected tumour samples from 25 CRC patients receiving cetuximab (an EGFR inhibitor). Of 25 CRC patients, 13 displayed intrinsic resistance to cetuximab; 12 were intrinsically sensitive. We obtained six re-biopsy samples at acquired resistance from the intrinsically sensitive patients. NCOA4–RET and LMNA–NTRK1 fusions and NRG1 and GNAS amplifications were found in intrinsic-resistant patients. In cetuximab-sensitive patients, we found KRAS K117N and A146T mutations in addition to BRAF V600E, AKT1 E17K, PIK3CA E542K, and FGFR1 or ERBB2 amplifications. The comparison between baseline and acquired-resistant tumours revealed an extreme shift in variant allele frequency of somatic variants, suggesting that cetuximab exposure dramatically selected for rare resistant subclones that were initially undetectable. There was also an increase in epithelial-to-mesenchymal transition at acquired resistance, with a reduction in the immune infiltrate. Furthermore, characterization of an acquired-resistant, patient-derived cell line showed that PI3K/mTOR inhibition could rescue cetuximab resistance. Thus, we uncovered novel genomic alterations that elucidate the mechanisms of sensitivity and resistance to anti-EGFR therapy in metastatic CRC patients.

Targeted therapies in metastatic gastric cancer: Current knowledge and future perspectives

Gastric cancer (GC) represents a leading cause of cancer related morbidity and mortality worldwide accounting for more than 1 million of newly diagnosed cases and thousands of deaths every year. In the last decade, the development of targeted therapies and the optimization of already available chemotherapeutic drugs has expanded the available treatment options for advanced GC and granted better survival expectations to the patients. At the same time, global efforts have been undertaken to investigate in detail the genomic and epigenomic heterogeneity of this disease, resulting in the identification of new specific and sensitive predictive and prognostic biomarkers and in innovative molecular classifications based on gene expression profiling. Nonetheless, several randomized studies aimed at exploring new innovative agents, such as immune checkpoint inhibitors, failed to demonstrate clinically meaningful survival advantages. Therefore, it is essential to further improve the molecular characterization of GC subgroups in order to provide researchers and medical oncologists with new tools for patients’ selection and stratification in future clinical development programs and subsequent trials. The aim of the present manuscript is to provide a global overview of the recent molecular classifications from The Cancer Genome Atlas and the Asian Cancer Research Group and to present key promising developments in the field of immunotherapy and targeted therapies in metastatic GC.

Dissection of gastric cancer heterogeneity for precision oncology

Gastric cancer (GC) remains the fifth most prevalent cancer worldwide and the third leading cause of global cancer mortality. Comprehensive ‐omic studies have unveiled a heterogeneous GC landscape, with considerable molecular diversity both between and within tumors. Given the complex nature of GC, a long‐sought goal includes effective identification of distinct patient subsets with prognostic and/or predictive outcomes to enable tailoring of specific treatments (“precision oncology”). In this review, we highlight various approaches to molecular classification in GC, covering recent genomic, transcriptomic, proteomic and epigenomic features. We pay special attention to the translational significance of classifier systems and examine potential confounding factors which deserve further investigation. In particular, we discuss recent advancements in our knowledge of intra‐subtype, intra‐patient and intra‐tumor heterogeneity, and the pivotal role of the tumor stromal microenvironment.

Risk factors predisposing to cardia gastric adenocarcinoma: Insights and new perspectives

Recent decades have seen an alarming increase in the incidence of cardia gastric adenocarcinoma (CGA) while noncardia gastric adenocarcinoma (NCGA) has decreased. In 2012, 260 000 CGA cases (age‐standardised rate (ASR); 3.3/100 000) and 691 000 NCGA cases (ASR; 8.8/100 000) were reported worldwide. Compared with women, men had greater rates for both the subsites, especially for CGA. Recently, four molecular subtypes of GC have been proposed by the Cancer Genome Atlas (TCGA) and the Asian Cancer Research Group (ACRG); however, these classifications do not take into account predisposing germline variants and their possible interaction with somatic alterations in carcinogenesis. The etiology of adenocarcinoma of the cardia and the gastroesophageal junction (GEJ) is not known. It is thought that CGA is distinct from adenocarcinomas located in the esophagus or distal stomach, both epidemiologically and biologically. Moreover, CGA is often identified in the advanced stage having a poor prognosis. Therefore, understanding the risk and the role of predisposing factors in etiology of CGA can inform clinical practice and counseling for risk reduction. In this paper, we showed that GC family history, lifestyle, demographics, gastroesophageal reflux disease, Helicobacter pylori infection, and multiple genetic and epigenetic risk factors as well as several predisposing conditions may underlie susceptibility to CGA. However, several genome‐wide association studies (GWASs) should be conducted to identify novel high‐penetrance genes and pathways as well as causal germline variants predisposing to CGA. They must include different ethnic groups, especially from high‐incidence countries for CGA, because some risk loci are ancestry‐specific. In parallel, statistical methods can be developed to identify cancer predisposition genes (CPGs) from tumor sequencing data. It is also necessary to find novel long noncoding RNAs related to the risk of CGA. Taken altogether, new cancer risk prediction models, including all genetic and nongenetic factors influencing risk, should be developed to facilitate risk assessment, disease prevention, and early diagnosis and intervention of CGA in the future.

Adenosine signaling: Next checkpoint for gastric cancer immunotherapy?

Adenosine (ADO), generated by the ectonucleotidase CD39 and CD73 from ATP, interacts with its specific G protein-coupled receptors, which can impair anti-tumor immune responses inhibiting the infiltration and function of CD8⁺ T cell and natural killer cell. Recent studies have also identified that ADO pathway plays a critical role in tumor immune surveillance, especially for some non-solid cancers. In addition, although immune checkpoint therapy targeting ADO pathway in gastric cancer is still in an early phase, encouraging results have come out from some drugs targeting ADO pathway. Therefore, target ADO signaling may be a new promising strategy to treat gastric cancer. In this review, we summarized recent works on the role of ADO in cancer immunotherapy and also discussed relative mechanisms underlying the function of ADO signaling in cancer immune responses.

Biomarkers in Tumor Microenvironment? Upregulation of Fibroblast Activation Protein-α Correlates with Gastric Cancer Progression and Poor Prognosis

Gastric cancer is the third leading cause of cancer-related mortality worldwide. Recent evidence points to importance of cross talk between cancer cells and the surrounding stroma on gastric cancer progression. Tumor microenvironment biomarkers thus represent a new opportunity for diagnostics innovation. Reactive stromal fibroblasts selectively express the fibroblast activation protein alpha (FAP-α), a homodimeric integral membrane gelatinase that belongs to the serine protease family. We report here that FAP-α expression is significantly elevated in gastric cancer samples by more than fivefold (p < 0.05), using transcriptome data from The Cancer Genome Atlas. Notably, the greatest FAP-α upregulation was observed in the poorly differentiated group (p < 0.001). Moreover, elevated FAP-α expression levels correlated with adverse clinical-pathological characteristics, such as diffuse histological subtype (p < 0.001), advanced pathological stage (p < 0.01) and poor survival. Functional annotation analysis demonstrated that FAP-α upregulation was associated with activation of biological processes implicated in tumor progression, including cell migration and angiogenesis pathways. These observations underscore the possible prognostic significance of FAP-α in gastric cancer and its potential as a novel biomarker for personalized medicine. We caution, however, that further multiomics, biochemical, and animal studies are necessary to ascertain the role of FAP-α as a causative and mechanistic biomarker. Based on pathway analyses, we hypothesize that gastric cancer patients exhibiting FAP-α upregulation might presumably benefit from antiangiogenic drugs in addition to standard therapeutic regimens. We call for future research focusing on the tumor microenvironment biomarkers in clinical oncology.

Molecular classifications of gastric cancers: Novel insights and possible future applications

Despite some notable advances in the systemic management of gastric cancer (GC), the prognosis of patients with advanced disease remains overall poor and their chance of cure is anecdotic. In a molecularly selected population, a median overall survival of 13.8 mo has been reached with the use of human epidermal growth factor 2 (HER2) inhibitors in combination with chemotherapy, which has soon after become the standard of care for patients with HER2-overexpressing GC. Moreover, oncologists have recognized the clinical utility of conceiving cancers as a collection of different molecularly-driven entities rather than a single disease. Several molecular drivers have been identified as having crucial roles in other tumors and new molecular classifications have been recently proposed for gastric cancer as well. Not only these classifications allow the identification of different tumor subtypes with unique features, but also they serve as springboard for the development of different therapeutic strategies. Hopefully, the application of standard systemic chemotherapy, specific targeted agents, immunotherapy or even surgery in specific cancer subgroups will help maximizing treatment outcomes and will avoid treating patients with minimal chance to respond, therefore diluting the average benefit. In this review, we aim at elucidating the aspects of GC molecular subtypes, and the possible future applications of such molecular analyses.

Phenotyping Multiple Subsets of Immune Cells In Situ in FFPE Tissue Sections: An Overview of Methodologies

The recent clinical success of new cancer immunotherapy agents and methods is driving the need to understand the role of immune cells in solid tissues, especially tumors. Immune cell phenotyping via flow cytometry, while a cornerstone of immunology, is not spatially resolved and cannot analyze immune cell subsets in situ in clinical biopsy sections or to determine their interrelationships. To address this problem, a number of methodologies have been developed in attempts to phenotype immune and other cells in images acquired from tissue sections and to assess their organization in the tumor and its microenvironment. This chapter review the staining and multiplex image analysis methods that have been developed for phenotyping immune and other cells in formalin-fixed, paraffin-embedded (FFPE) tissue sections.

Exploring the role of molecular biomarkers as a potential weapon against gastric cancer: A review of the literature

Gastric cancer (GC) is a global health problem and a major cause of cancer-related death with high recurrence rates ranging from 25% to 40% for GC patients staging II-IV. Unfortunately, while the majority of GC patients usually present with advanced tumor stage; there is still limited evidence-based therapeutic options. Current approach to GC management consists mainly of; endoscopy followed by, gastrectomy and chemotherapy or chemo-radiotherapy. Recent studies in GC have confirmed that it is a heterogeneous disease. Many molecular characterization studies have been performed in GC. Recent discoveries of the molecular pathways underlying the disease have opened the door to more personalized treatment and better predictable outcome. The identification of molecular markers is a useful tool for clinical managementin GC patients, assisting in diagnosis, evaluation of response to treatment and development of novel therapeutic modalities. While chemotherapeutic agents have certain physiological effects on the tumor cells, the prediction of the response is different from one type of tumor to the other. The specificity of molecular biomarkers is a principal feature driving their application in anticancer therapies. Here we are trying to focus on the role of molecular pathways of GC and well-established molecular markers that can guide the therapeutic management.