Molecular pathogenesis and precision medicine in gastric cancer

Chapter 15

Molecular pathogenesis and precision medicine in gastric cancer Renu Verma and Prakash Chand Sharma University School of Biotechnology, Guru Gobind Singh Indraprastha University, Dwarka, New Delhi, India

Introduction According to the World Health Organization (WHO), cancer-related health disorders caused 9.6 million deaths in 2018 and remained the second leading cause of deaths globally. The most common types of cancer in men include lung, prostate, colorectal, stomach and liver, while breast, colorectal, lung, cervix, and thyroid cancers are more common among women. Approximately 30%e50% of cancer-related deaths could be prevented by addressing key risk factors, like consumption of tobacco products and alcohol, minimizing infection-related factors, and by maintaining a healthy lifestyle. Cancer results from a large number of genetic and epigenetic changes in the genome that affect mismatch repair genes, tumor suppressor genes, and oncogenes. These alterations interrupt molecular pathways responsible for proper functioning and regulation of cell growth, apoptosis, and metastasis. Worldwide, GC is the sixth most common cancer (1.03 million cases in 2018), and the third leading cause of cancer-related mortality (783,000 deaths in 2018) [1]. GC is more common in developing countries; however, it is relevant in all continents. The scarcity of biomarkers for early detection, classification, and prognosis, has been a barrier in the management of GC.

Next-generation sequencing (NGS) techniques Illumina sequencing Illumina utilizes the sequencing-by-synthesis approach, with a flow channel (8-channel sealed glass microfabricated device), which allows bridge amplification, namely amplification of fragments over a solid surface. For incorporation

of nucleotides into the cluster fragments, DNA polymerase along with four 30 -OH blocked fluorescently labeled nucleotides, are simultaneously added to the flow channel. These fragments are primed with oligomeric units. After each incorporation event, remaining molecules are washed away. Next, the imaging step conducted on an optic instrument scans each lane of flow cells in 100-tile segments. Once it is done, chemicals which block the 30 -OH blocking groups, are added to flow cell, so that each strand is prepared for another round of incorporation. Poor quality sequences are removed, by a quality checking pipeline, and a base-calling algorithm assigns sequences and quality value to each read.

454 sequencing Roche 454 sequencing can sequence much longer reads simultaneously, for the detection of minor variations. Also known as 454 FLX pyrosequencing, it was the first developed next-generation sequencing technique. The downstream reaction takes place with the release of pyrophosphate, after a DNA polymerase incorporates a nucleotide. It produces light with the help of the luciferase enzyme, which can be registered by a suitable detector. In the Roche approach, agarose beads, which carry oligonucleotides on their surface, are mixed with the fragment library to amplify single-stranded DNA copies. A fragment: bead complex mixture is formed, which is encapsulated into oil-water micelles containing PCR reactants. Clonal amplification takes place in aqueous micro-reactors. Each bead is decorated with one million copies of DNA fragment, which are then sequenced together. Substitution error is common in Roche sequencing because each nucleotide is incorporated specifically.

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Ion Torrent semiconductor sequencing

Microsatellite unstable GC

Ion Torrent uses semiconductor-based technology. About one million DNA molecules are present on the surface of the semiconductor chip micro hole. For sequencing, this chip is passed through the flow of nucleotides, and complementary nucleotides are incorporated in the DNA, followed by the release of hydrogen ion, which is detected by a hypersensitive ion sensor. As it is a direct detection method, no scanning or light is required. The high concentration of Hþ ion causes a change in pH and produces a high electronic signal, which is converted into a digital signal. Although being a simple, less expensive, and reliable technique with a smaller machine set up, the technique may not be suitable for sequencing large genomes.

This subtype has been observed in 22% of GC incidences, being characterized by microsatellite instability (MSI). CpG island methylation phenotype is registered, including hypermethylation of the MLH1 promoter. Mutational analysis of MSI samples has identified 37 significantly mutated genes, including TP53, PIK3A, KRAS, and ARID1A. Unlike colorectal cancer, BRAF and V600E mutations are not associated with microsatellite instable GCs.

SOLiD sequencing Applied Biosystems SOLiD sequencer is based on the principle of two base encoding. It uses a library consisting of adaptor-flanked fragments. Similar to other NGStechniques, emulsion PCR is the approach to amplify DNA fragments on the surfaces of 1-mm magnetic beads, for a signal during a reaction. When these fragments are deposited on flow cell slide, primer is annealed to the adaptor sequences, followed by addition of DNA ligase and fluorescently labeled octamers, whose fourth and fifth bases are encoded by fluorescent labels. After fluorescence detection, labeled bases are removed from the ligated octamer, and then another round of hybridization and ligation takes place. Other NGS platforms include pacific biosciences, sequel, and nanopore alternatives, with less read length and higher error rate.

Classification of gastric cancer A significant advance has been the genomic and molecular classification of GC, provided by The Cancer Genome Atlas (TCGA), based on whole genome sequencing, whole exome sequencing, RNA sequencing, and microRNA sequencing. TCGA system categorizes GC into four subtypes, namely EBV positive, microsatellite unstable, genomically stable, and chromosomal instability [2].

EpsteineBarr virus (EBV) positive GC This category is represented by 9% of gastric cancers and is characterized by CpG island methylation phenotype and high levels of DNA hypermethylation. Overexpression of programmed death ligand 1 and 2 (PD-L1 and PD-L2) has also been associated and could be used for therapeutic purposes. There is a strong affinity of PIK3CA mutations with EBV positive gastric cancers.

Genomically stable GC GS subgroup comprises 20% of cases with gastric cancer, exhibiting diffuse histology with CDH1 mutations. Other features of genomically stable gastric cancers include the presence of mutations in the RHOA gene and overexpression of cell adhesion pathway genes. The fusion of CLDN18-ARHGAP26 has also been observed.

GC with chromosomal instability It is noticed in the remaining 50% of gastric cancers, mainly with intestinal histology. The focus here is aneuploidy and amplifications of receptor tyrosine kinases (RTKs). The group displays a high propensity for TP53 mutations. Based on recurrent amplifications of the VEGFA gene, angiogenesis has been predicted as an important feature of chromosomal unstable gastric cancers. Older classifications of GC based on histology are given by Lauren and WHO. Lauren [3] subdivided GCs into intestinal type (54%), diffuse type (32%), and indeterminate type (15%). The WHO has classified GC into four histological subtypes viz. tubular, papillary, mucinous, and poorly cohesive [4] (Fig. 15.1).

Genomic, transcriptomic, microbiomic, metabolomic and proteomic studies in gastric cancer The alluded to technologies or related ones, able to sequence a vast number of short reads of DNA and RNA, much more quickly and cheaply than the previously used Sanger method [5], have been widely applied for gastric cancer studies, within a number of omics settings (Fig. 15.2).

Genomics and transcriptomics To ascertain the role of guanosine triphosphate-binding protein 4 (GTPBP4) in GC, transcriptome profiling using Illumina platform was performed in a cancer cell line MKN45, with and without GTPBP4 knockdown. The expression of the tumor suppressor gene, p53, was found to

Molecular pathogenesis and precision medicine in gastric cancer Chapter | 15

FIGURE 15.1 Molecular classification of gastric cancer (TCGA), compared to older clinical and histological guidelines (WHO, Lauren).

FIGURE 15.2

Relevant omics techniques for gastric cancer diagnosis, treatment, prognosis, and classification (TCGA).

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be increased in knockdown mutants, while its negative effectors were downregulated [6]. A novel method “nonoverlapping integrated reads (NOIR),” was introduced for detection and quantification of mutations in circulating DNA, using Ion Torrent sequencing. Frequency of mutation in a tumor suppressor gene, TP53, was determined at five different progression stages, showing an increase in mutation level with the progression of gastric cancer [7]. Exploiting a noninvasive approach for ion personal genome machine (PGM) based, targeted sequencing using stool specimens, five hotspots of mutations in APC, CDKN2A, and EGFR genes, and seven novel mutations in APC, CDH1, DDR2, HRAS, NRAS, PTEN, and SMARCB1 were detected [8]. Differentially expressed 74 long noncoding RNAs, and 449 mRNAs, were identified in 3 GC samples through Illumina HiSeq sequencing. Genes FEZF1-AS1, HOTAIR, and LINC01234 were perceived to have potential diagnostic value in gastric cancer [9]. Similarly, transcriptome sequencing revealed differentially expressed 1181 mRNAs and 390 long noncoding RNAs in GC, using Illumina platform. Also, the role of four lncRNAs, including AC016735.2, AP001626.1, RP11400N13.3, and RP11-243M5.2, was recognized, as a source of potential biomarkers in GC [10].

Helicobacter pylori and gastric microbiome Presence of Helicobacter pylori and Epstein Barr virus in the microbiome of endoscopic biopsies has been documented through whole-genome sequencing. The bacterial content of the gastric microbiome in actively infected H. pylori-positive individuals is increased. Whole genome sequencing performed on patients undergoing phase II pazopanib treatment, revealed a mutation in BRAF V600E, causing drug resistance, which could lead to metastasis [11]. A study on integrated transcriptome with exome sequencing analyzed 24 significantly mutated genes in microsatellite stable (MSS) tumors, and 16 in microsatellite unstable (MSI) tumors, along with splice site variants. An isoform of ZAK gene, TV1, was found to be upregulated, inducing robust transcriptional activation of several cancerrelated signaling genes such as AP1 and NFkB, known to be modulated by ZAK activity, while isoform TV2 displayed variable levels in GC [12]. A new approach using a green fluorescent protein (GFP) expressing attenuated adenovirus, wherein telomerase promoter regulates viral replication (TelomeScan, OBP401), has been developed, to identify biologically malignant subpopulations in cytology-positive GC patients, Using a panel of target genes on MiSeq platform, peritoneal washes from positive TelomeScan patients revealed 774 genetic variants, including single-nucleotide polymorphisms (SNP), deletions, insertions, and point mutations [13]. A study on RNA-Seq and microarray data from

TCGA, reported lncRNA as a key regulator of gastric cancer development and progression. Shorter survival and poorer prognosis occurred in patients with high HOXA11AS expression [14]. Liquid biopsy Circulating tumor DNA (ctDNA), one of the modalities of liquid biopsy, refers to DNA released from cancer cells into the bloodstream. Targeted deep sequencing has shown TP53 mutation in primary GC tissues [15]. NGS-based genomic profiling has provided a better picture of amplification followed by base substitutions of activating mutations in ERBB2 in tumor tissues. Patients with these mutations can be benefitted from approved targeted ERBB2 inhibitor therapy [16]. Deep sequencing revealed an amplification of FGFR2, that was found exclusively in the primary lesion, and a deletion in the gene TGFBR2 occurring exclusively during metastasis [17]. Whole exome and genome sequencing employed in autosomal-dominant cancer-predisposition syndrome GAPPS (Gastric adenocarcinoma and proximal polyposis of the stomach), could not detect causal point mutations, which were detected through Sanger sequencing, emphasizing a shortcoming of the NGS technique [18]. Transcription factors, splicing factors, tumor suppressor genes, and many other genes were looked upon, for splice variants in EpsteineBarr virus-associated gastric cancer through RNA sequencing. Various splice variants were found to be linked with EBV positive GC samples acquired from the TCGA database [19]. Targeted sequencing of 46 cancer-related genes, helped in identifying differences in mutation frequency pattern, in gastroesophageal junction and gastric carcinoma. TP53 mutations were the most common in gastroesophageal junction, while mutations in APC and CTNNB1 were prevalent in gastric carcinoma [20]. CTNNB1 mutations were also detected in all the gastrointestinal tumor samples in another study using the same targeted multigene NGS approach [21]. A high proportion of 78% of 116 GC cases, harbored at least one clinically relevant genomic alteration in KRAS, CDKN2A, CCND1, ERBB2, PIK3CA, MLL2, MET, PTEN, ATM, DNMT3A, NF1, NRAS, and MDM2, and 116 cases had alterations in TP53, ARID1A, and CDH1 [22]. A mutation common to cancers that activates the PI3/AKT signaling pathway, PIK3CA gene mutation, was quantified using pyrosequencing in GC patients, suggesting no prognostic relationship of the gene [23].

Transcriptome analysis Transcriptome refers to the complete set of transcripts of a cell or population of cells. RNA-Seq approach has surpassed the well-known microarray technique, for the

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assessment of the level of gene expression. Unlike microarrays, RNA-Seq can be used for the analysis of expression of novel transcripts without using probes. Transcriptome profiling using Illumina platform revealed a high number of expressed genes in tumor (13,228) and normal (13,674) tissues in GC patients. Also, Cadherin-1 gene (CDH1), with 309 fold upregulation (24), was highlighted in GC, while another study reported expression change to be 36 fold [24]. Dermatopontin gene (DPT) plays an important role in cell-matrix interactions and is a key gene in TGF-b signaling. DPT gene has been postulated to modify the behavior of TGFBR2, through interaction with decorin [25]. Low expression (w40 fold) of DPT was detected in a study on Chinese GC patients, along with downregulated TGFBR2. Other reports have recorded downregulation of these genes [24], corroborating the low expression of DPT in oral cancer validated by qRTPCR [26]. TGFBR2 gene has also been linked with the microsatellite instability and is being explored as a potential biomarker in GC. Length polymorphism at microsatellite loci, in coding regions of genes, affects their expression by the premature occurrence of a stop codon. We have also observed microsatellite instability in coding regions of some tumor suppressor and mismatch genes, which have led to the formation of truncated proteins in GC tissues (unpublished work). The findings emphasized the significance of the particular TCGA subgroup (MSI unstable). TGFBR2 showed lack of expression in MSI-H samples. Genes having MSI in their untranslated regions displayed differential expression, as compared to genes without UTR mutations. Upregulated and downregulated genes (137 and 139, respectively), containing mutations at microsatellite loci were observed, and 96% of these mutations were present in the UTR regions. These observations suggest an influence of mutations in UTR on gene expression. Transcriptome results validated by q-PCR revealed significantly downregulated expression of MGLL, SORL1, C20orf194, WWC3, and PXDC1 genes in MSI-H cell lines. Mutations in 30 UTR region of MGLL gene, resulted in 42.6% downregulation of recombinant luciferase, indicating the presence of aberrant gene products as a consequence of MSI [27].

factors. Long recurrence-free survival from mutation or deficiency of protein of ARID1A [28] has been predicted. A tyrosine kinase receptor gene EGFR exhibited amplification and overexpression in GC [29]. Inhibitors of another gene of the RTK family, fibroblast growth factor receptor 2 (FGFR2), have shown some clinical efficacy in GC [30]. Ki23057, one of the FGFR inhibitors, along with 5-fluorouracil, has displayed synergistic antitumor effects for GC treatment [31]. Loss of function of the SMAD4 gene helps in epithelialemesenchymal transition, and its reexpression has been seen in reversing the process [32]. Expression of one of the important genes involved in breast cancer, BRCA1, is correlated with sensitivity to chemotherapeutics in gastric cancer [33].

Receptor tyrosine kinases

Compared to human genome, epigenome, transcriptome, and proteome, the metabolome is not directly involved in the information flow of the central dogma, which encompasses the steps by which DNA instructions are converted in a functional product. However, metabolomics measures both upstream and downstream changes that are close to environmental exposures and phenotypic changes [39]. The two main techniques to explore the metabolomic status of the target tissue are nuclear magnetic resonance (NMR) and mass spectrometry (MS).

Receptor tyrosine kinases (RTKs) play a crucial role in the activation of various intracellular signaling pathways. Role of several RTKs inhibitors, in the antiproliferative activity, has been witnessed in clinical trials in target-specific therapy. Silencing and overexpression of the ARID1A gene led to both increased and decreased proliferation, respectively, in tissue culture. Silencing of the ARID1A gene also increases the level of E2F1 and cyclin E1 transcription

Microbiomics Helicobacter pylori, a gram-negative bacteria, has infected half of the world’s human population, out of which 1% e3% develop GC [34]. Virulence factors affecting gastric cancer risk include cag and VacA pathogenicity. Although H. pylori has been defined as one of the strong risk factors for GC, other gastric microbes could also influence the development of the disease. Pyrosequencing of GC samples showed an abundance of Bacilli and members of the Streptococcaceae family when compared to samples of chronic gastritis and intestinal metaplasia [35]. Decreased acidity of the gastric lumen has been associated with the increased risk of Clostridium difficile infection [36]. Gastric microbiota was found to be abundantly represented by H. pylori, Haemophilus, Serratia, Neisseria and Stenotrophomonas using MiSeq platform, and an increased abundance was observed in the bacterial diversity after eradication of H. pylori [37]. Frequency of H. pylori significantly decreased in a tumoral microenvironment, as compared to normal and peritumoral microhabitats. Prevotella copri, Bacteroides uniformis, and H. pylori count decreased while Prevotella melaninogenica, Streptococcus anginosus, and Propionibacterium acnes increased in tumoral gastric microbiota. Overall, the enrichment of bacterial diversity decreased in tumoral and peritumoral microhabitat [38].

Metabolomics

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TABLE 15.1 Summary of metabolites found in gastric cancer. S. No. 1.

2.

Sample Type Gastric Juice

Serum

Technique SIFT-MS

GC-MS

Metabolites

Expression

References

Acetaldehyde, Acetone, Acetic acid, Hexanoic acid, Hydrogen cyanide, Hydrogen sulfide, Methanol, Methyl phenol

Upregulated

Kumar et al. [43]

Formaldehyde

Downregulated

Hexadecanenitrile, Sarcosine, Valine

Upregulated

Cholesterol,

Downregulated

Song et al. [44]

1,2,4,- Benzenetricarboxylic acid, 2-Amino-4-hydroxypteridinone, 9,12 Octadecadienoic acid, 9-Octadecenoic acid, 9-Octadecenoic acid, Fumaric acid, Glutamine, Hexanedioic acid Mesyl-arabinose, Benzeneacetonitrile, Nonahexacontanoic acid, Trans-13- octadecenoic acid 3.

4.

Serum

Serum

GC-MS

GC-MS

3-Hydroxypropionic acid, 3-Hydroxyisobutyric acid

Upregulated

Octanoic acid, Phosphoric acid, Pyruvic acid

Downregulated

11-Eicosenoic acid, 2-Hydroxybutyrate, Asparagine, Azelaic acid, Glutamic acid, Ornithine, Pyroglutamate, Urate, y-tocopherol

Upregulated

Creatinine, Threonate

Downregulated

Ikeda et al. [45]

Yu et al. [46]

5.

Tissue

HR-MAS-MRS

Alanine, Choline, Glycine, Triacylglycerides

Upregulated

Calabrese et al. [47]

6.

Tissue

GC-MS

1-Phenanthrene, a-Ketoglutaric acid, Benzenepropanoic acid, Carboxylic acid, Fumaric acid, Octadecanoic acid, Squalene, Valeric acid, Xylonic acid

Upregulated

Song et al. [48]

3-Hydroxybutanoic acid, 9-Hexadecanoic acid, 9-Octadecenamide, Arachidonic acid, Cis-vaccenic acid, Hexadecanoic acid

Downregulated

Acetamide, Butanetriol, Butenoic acid, Galactofuranoside, Glutamine, Hypoxanthine, Isoleucine, L-Cysteine, L-Tryosine, Naphtalene, Oxazolethione, Phenanthrenol, Serine, Valine

Upregulated

D-Ribofuranose, L-Altrose, L-Mannofuranose, Phosphoserine

Downregulated

7.

Tissue

GC-MS

Wu et al. [49]

Continued

Molecular pathogenesis and precision medicine in gastric cancer Chapter | 15

159

TABLE 15.1 Summary of metabolites found in gastric cancer.dcont’d S. No. 8.

Sample Type Tissue

Technique

Metabolites

Expression

References

GC-MS

Fructose, Glyceraldehyde, Isocitric acid, Lactic acid, Pyruvic acid

Upregulated

Cai et al. [50]

Fumaric acid

Downregulated

9.

Gastric juice

HPLC

Phenylalanine, Tryptophan, Tyrosine

Upregulated

Deng et al. [51]

10.

Gastric juice

LC-MS

Anthranilic acid, Indole-3-lactic acid, Kynurenic acid, Kynurenine, Nicotinic acid, Tryptophan

Upregulated

Choi et al. [52]

11.

Tissue

HR-MASNMR

Alanine, Glutamate, Isoleucine, Lactate, Leucine, Lysine, Phenylalanine, Taurine, Valine

Upregulated

Jung et al. [41]

12.

Serum

GC-MS

b-Hydroxybutyrate, Citrate, Succinate, Docosahexaenoic acid, Fumurate, Glutamic acid, Hepatanoic acid, Hexadecenoic acid, Succinate

Upregulated

Aa et al. [53]

Glucose

Downregulated

GC-MS, Gas Chromatography Mass Spectrometry; HPLC, High Performance Liquid Chromatography; HR-MAS-MRS, High Resolution Magic Angle Spinning Magnetic Resonance Spectroscopy; HR-MAS-NMR, High Resolution Magic Angle Spinning Nuclear Magnetic Resonance Spectroscopy; LC-MS, Liquid Chromatography- Mass Spectrometry; SIFT-MS, Selected Ion Flow Tube Mass Spectrometry.

Historical vignettes Metabolic reprogramming is a hallmark of cancer, linked to tumorigenesis. Otto Warburg (1883e1970) observed a characteristic metabolic pattern, of large glucose consumption for glycolysis in tumor cells even under conditions of sufficient oxygen (Warburg effect). Lactic acid concentration increases in urine and tissue samples in gastric cancer patients [40,41]. The utility of metabolomics in diagnosis and prognosis has been recognized [42]. A list of different metabolites in gas chromatography (GC) is given in Table 15.1.

Proteomics Proteomics addresses virtually all proteins expressed in a cell, tissue, or organism [54]. Proteomics-related approaches have been used to identify differentially expressed proteins between normal and GC samples (Table 15.2). Enhanced coverage of protein sequences is required to detect low abundance proteins in proteomic studies [64]. The proteomic approaches use electrophoresis, mainly twodimensional electrophoresis, liquid chromatography (LC), and mass spectrometry (MS) analysis for quantification and identification of expressed proteins [65]. MALDI-TOF, widely used in microbiology laboratories, as well as its variation SELDI-TOF, are two techniques of mass spectrometry used to identify proteins associated with gastric cancer. HSP27 has been found upregulated and downregulated, in gastric cancer indicating heterogeneity pattern

[61,66]. Proteins enolase-alpha (ENOA), nicotinamide N-methyltransferase (NNMT), annexin 2 (ANXA2) and transgelin (TGLN), were found to be upregulated in GC samples. Gastrokine-1(GKN1) and carbonic anhydrase 2 (CA2), involved in energy metabolism, exhibit downregulation in GC samples [50,67]. Downregulation of lactate dehydrogenase (LDH) subunit LDHA and upregulation of pyruvate dehydrogenase (PDH) subunit PDHB has been observed to inhibit cell growth and cell migration [50]. Annexins are calciumdependent and membrane-binding intracellular proteins. One such protein, ANXA2, has been reported to have an increased expression in GC [57]. Also, increased ANXA1 expression in a GC cell line with lymph node metastasis, compared with a GC cell line derived from a primary tumor, was observed [71]. Various proteins have been described in the new TCGA classification including caspase 7 (CASP7), proliferating cell nuclear antigen (PCNA), BCL2-associated X protein (BAX), spleen tyrosine kinase (SYK), Src family tyrosine kinase LCK (LCK), to have elevated expression in EBV positive subgroup, whereas upregulated expression of claudin 7 (CLDN7), von Hippel-Lindau tumor suppressor (VHL), and cyclin B1 (CCNB1) was detected in the microsatellite instability subtype. On the other hand, KIT proto-oncogene receptor tyrosine kinase (KIT), v-myc avian myelocytomatosis viral oncogene homolog (MYC), v-akt murine thymoma viral oncogene homolog (AKT), and protein kinase C alpha (PRKCA) expressions, were highly elevated in the genomically stable subtype [2].

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TABLE 15.2 Details of proteomic studies in gastric cancer.

S.No.

Sample Size

No. of Differentially Expressed Proteins

Techniques

Important Protein(s)

References

1.

107

20[

LC-MS/MS

EPHA2

Kikuchi et al. [55]

2.

9

15[, 13Y

2DE, MS

S100A2

Liu et al. [56]

3.

12

15[, 9Y

2DE, MS/MS

GAL4, HADHA, HADHB, HNRNPM

Kocevar et al. [57]

4.

15

42[, 39Y

Nano-RPLC-MS/MS

ANXA1

Zhang et al. [58]

5.

12

19[, 11Y

2DE, MS

SEPT2, UBE2N, TALDO1, GKN1, MRPL12, PACAP, GSTM3, TPT1

Kocevar et al. [59]

6.

8

26[, 6Y

2DE, MALDI-TOF MS

ENOA, GDI2, GRP78, GRP94, PPIA, PRDX1, PTEN,

Bai et al. [60]

7.

3

7[, 16Y

DIGE-MS, MS

HSP60, HSP27, ZNF160, SELENBP1, EEF1A1, mutant desmin, fibrinogen gamma, tubulin alpha 6, prostaglandin F synthase

Wu et al. [61]

8.

6

57[, 50Y

2DE, MS/MS

HYOU1, TTHY, KPYM, GRP78, FUMH, ALDOA, LDHA

Liu et al. [62]

9.

3

12[, 7Y

2DE, MS/MS

ANXA2, ANXA4

Lin et al. [63]

[, denotes upregulation; Y, denotes downregulation; 2DE, Two Dimensional Gel Electrophoresis; DIGE-MS, Two Dimensional-Differential In Gel Electrophoresis Mass Spectrometry; LC-MS/MS, Liquid Chromatography Tandem Mass Spectrometry; MALDI-TOF-MS, Matrix-Assisted Laser Desorption Ionization Time of Flight Mass Spectrometry; MS, Mass Spectrometry; Nano-RPLC-MS/MS, Nanoliter Reverse-Phase Liquid Chromatography Mass/Mass Spectrometry

Epigenomic influences

Tumor suppressor genes

Methylation across the genome is unraveled through whole-genome bisulfite sequencing, as well as targeted sequencing aiming to screen the specific desirable genomic regions of interest. An epigenetic trait has been defined as a “stably heritable phenotype resulting from changes in a chromosome, without alterations in the DNA sequence” [72]. Aberrant DNA methylation profiles and histone modifications are linked to developmental defects, obesity, asthma, and neurodegenerative disorders, besides cancer [73]. However, given the complexity of epigenetic mechanisms, which are influenced by aging, genetic variations such as polymorphisms, and environmental factors, deciphering epigenetic information is a challenge [74]. Epigenetic changes are somewhat similar to genetic mutations, that change the underlying structure of the DNA, contributing toward the initiation and progression of cancer [75]. For normal gene expression, epigenetic machinery responsible for DNA methylation, DNA hydroxymethylation, post-translational modifications (PTMs) of histone proteins, nucleosome remodeling, and regulation by noncoding RNAs, performs in harmony with cis and trans acting elements [76,77].

Aberrant DNA methylation in the promoter region of genes, which leads to inactivation of tumor suppressor and other cancer-related genes, is the most well-defined epigenetic activity during gastric tumorigenesis. In mammalian cells, DNA methylation consists of covalent attachment of a methyl group, to the 50 position of cytosine residues in CG dinucleotides. CG dinucleotides are not randomly distributed throughout the genome but tend to cluster in regions called CpG islands, mainly present in the promoter region of the genes [76,77]. An accepted definition of CpG islands describes them as DNA sequences, more than 200 base pair long, with CG content greater than 50%, and an observed/expected CpG ratio of more than 60% [76]. Methylation can also occur at nonpromoter CpG islands, defined as CpG shores, located in the vicinity of CpG islands up to 2 kb in length [78]. Methylation of CpG islands is typically associated with gene silencing, while demethylation of these sites enables transcription [76]. Various risk factors like age, diet, chronic inflammation, infection with H. pylori, and EBV, are also causative agents of aberrant gene methylation in GC [79].

Molecular pathogenesis and precision medicine in gastric cancer Chapter | 15

The methylation status of LPHN2 has been found to be a potential novel epigenetic biomarker, for cisplatin treatment in GC [80]. Defective DNA methylation in CDH1, CHFR, DAPK, GSTP1, p15, p16, RARb, RASSF1A, RUNX3, and TFPI2, has been considered as a serum biomarker for the detection of GC [79]. A large number of genes have been identified to be methylated in the gastric mucosa of GC patients. Among them, RASGRF1 methylation has been found to be significantly elevated, in mucosa from patients with either intestinal- or diffuse-type GC [81]. Silencing of miRNAs is also associated with hypermethylation of CpG islands. Methylation of miR34-b/c was ubiquitous in GC cell lines, but not in normal gastric mucosa from healthy H. pylori-negative individuals [82]. Aberrant DNA methylation in noncancerous gastric mucosa has been implicated in gastric carcinogenesis. Pyrosequencing has been proved to be a more reliable method, in comparison to both methylation-specific polymerase chain reaction (MSP), and bisulfite sequencing [83]. In a comparative analysis, the frequency of promoter region methylation in the TCF4 gene was reported to be higher, when analyzed by using pyrosequencing than MSP in advanced GC samples [84]. Hypermethylation in GPX3 promoter region with a 10% cut off, was observed using pyrosequencing in 60% of the GC samples, and six out of nine cell lines [85]. Hypermethylation in the EDNRB gene in GC tissues has been observed and correlated with tumor infiltration. Similarly, loss of expression of the FAT4 gene was observed in highly methylated GC cell lines, and removal of methylation by demethylating agent restored its expression. Methylation status of FAT4 has also been associated with H. pylori infection in GC [86]. By analyzing 295 GC samples for CpG methylation level in 86 genes and 14 miRNAs, the Cancer Genome Atlas (TCGA) has grouped the hypermethylated genes into three categories: hypermethylated in EBV-positive subtype, hypermethylated in both EBV-positive and MSI-high subtypes, and other hypermethylated genes. Prominent methylation changes were observed in RUNX1, ARHGDIB, PSME1, GZMB, and RBM5 genes, while VAMP5 and POLG genes showed a marginal methylation difference between normal and GC cells.

Molecular biomarkers in gastric cancer Current markers used for GC diagnosis in clinical use include CA 19-9, CA-50, and CA-72. They lack high sensitivity and specificity, which hampers their large-scale efficient and unambiguous use. Other molecular

161

biomarkers can be classified into genetic, epigenetic, and protein markers.

Genetic markers of chemotherapy response DPD and heparin-binding epidermal growth factor (HBEGF) like genes are considered as related to 5FU resistance. Metallothionein-IG and HB-EGF are also potential molecular marker candidates for cisplatin resistance genes. Paclitaxel and cisplatin treatment have been predicted with TP53 codon 72 SNP.

Epigenetic markers Micro-RNA miR-21 was found linked to trastuzumab resistance in which miR-21 has been shown to have a regulatory effect on the treatment response. In blood and gastric secretions, long noncoding RNAs (lncRNAs) have been found to be potential biomarkers for GC. LncRNAs, such as H19, HOTAIR, and MEG3, have been suggested to have a functional role in tumorigenesis and tumor progression. Decreased methylation leads to increased expression of the secreted protein BMP4. Bcl-2/adenovirus E1B1 19 kDa interacting protein three and DAPK (deathassociated protein kinase) methylation products, lead to lower response to fluoropyrimidine-based chemotherapy.

Protein markers Thymidylate synthetase (TS) and DPD are indicative of 5FU tumor sensitivity. In serum, AMBP protein has been observed to correlate with chemotherapeutic response to paclitaxel and capecitabine. Another protein in serum, TUBB3, has been suggested to be involved in resistance to paclitaxel and capecitabine. FOXM1 protein in tissue predicts resistance to docetaxel. REG4 predicted resistance to docetaxel (Table 15.3).

Conclusions Recent advances in medical science concerning prevention and treatment of GC have recorded significant success, yet the National Cancer Database (NCDB) indicates 5-year survival rate of 31% for GC, which is lower than for many tumors. Role of perioperative chemotherapy and/or radiotherapy in the improvement of overall survival (OS) has been recognized; however, the tendency to metastasis and recurrence still remains an area of concern. Adoption of precision medicine helps clinicians to customize treatment options according to patient needs, using various molecular diagnostic methods to design a better curative regimen.

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TABLE 15.3 Details of drugs used for the treatment of metastatic gastric cancer. Line of Treatment

Target

Drug

Phase

Median overall Survival (months)

References

EGFR

EOX  panitumumab

III

11.3 versus 8.8

Langer et al. [87]

First

TCF  panitumumab

III

11.7 versus 10

Tebbutt et al. [88]

First

CX  cetuximab

III

9.4 versus 10.7

Ott et al. [89]

Second

Gefitinib versus placebo

III

3.73 versus 3.67

Langer et al. [90]

Second

Gefitinib

II

5.4

Janmaat et al. [91]

First

CX/CF  trastuzumab

III

13.8 versus 11.1

Yoshikawa at al. [68]

First

OX  lapatinib

III

12.2 10.5

Metzger et al. [92]

Second

Paclitaxel  lapatinib

III

11.0 versus 8.9

Matsubara et al. [93]

Second

TDM-1

III

7.9 versus 8.6

Igney et al. [94]

Second

Everolimus

II

8.3

Yoon et al. [95]

Everolimus

II

10.1

Doi et al. [96]

First

HER2

mTOR

Second Second

VEGF

CX  bevacizumab

II

12.1 versus 10.1

Zhao et al. [97]

First

VEGF-2

FOLFOX  ramucirumab

II

11.7 versus 11.5

Yoon et al. [98]

Second

Ramucirumab  placebo

III

5.2 versus 3.8

Cunningham et al. [69]

Second

Ramucirumab  paclitaxel

III

9.6 versus 7.4

Ajani et al. [70]

Second

Apatinib  placebo

III

6.5  4.7

Li et al. [99]

CF, Cisplatin 5-Fluorouracil; CX, Cisplatin Capecitabine; EOX, Epirubicin Oxaliplatin Capecitabine; FOLFOX, 5-Fluorouracil Leucovorin Oxaliplatin; OX, Oxaliplatin Capecitabine; TCF, Docetaxel Cisplatin 5-Fluorouracil; TDM-1, ado Trastuzumab Emtansine.

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164 PART | II Precision medicine for practitioners

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