Well-known Driver Genes Research Articles

Empowering precision medicine in rural India by establishment of a molecular genetics laboratory in Bihar: A prospective observational study

ABSTRACT Background: In the era of precision oncology, molecular profiling and targeted therapies are integral to cancer management. However, accessibility remains a challenge in rural India. Objectives: The primary objective was to showcase the lab’s milestones using in-house next-generation sequencing (NGS)/qualitative real-time polymerase chain reaction (qRT-PCR) diagnostic data from patients with cancer. The secondary objective was to highlight the benefits of integrating NGS with precision medicine in rural India despite resource challenges. Materials and Methods: This prospective observational study was conducted at Homi Bhabha Cancer Hospital and Research Center, Muzaffarpur, Bihar, between November 2022 and December 2023. A 72-gene panel was validated as per National Accreditation Board for Testing and Calibration Laboratories (ISO15189/CAP/AMP/NABL) recommendations and implemented in routine clinical testing across multiple cancers. qRT-PCR was also established for detecting actionable mutations in epidermal growth factor receptor (EGFR exons 18-21) for lung cancer, NRAS/KRAS/HRAS/BRAF genes, and dihydropyrimidine dehydrogenase (DPYD) polymorphisms (DPYD*2A/*13/D949V/IVS10) for recommended cases. Results: In one year (November 2022 to December 2023), we performed 146 tests on Indian patients with cancer: 71% (n = 103) with NGS and the remainder with qRT-PCR. Among NGS cases, 71% (n = 73) involved lung cancer, while the rest covered various cancer types. Alterations in well-known driver genes were identified across the cancer types, and their frequencies were matching with the incidence reported in Indian literature. Conclusion: Operating high-end technology in a peripheral center grapple with unique challenges, often demanding resourcefulness and innovation. Nevertheless, our laboratory has made significant strides with every test performed in implementing precision medicine in a seemingly remote setting in rural India.

A novel computational pathology approach for identifying gene signatures prognostic of disease-free survival for papillary thyroid carcinomas

IntroductionPapillary thyroid carcinoma (PTC) is the most prevalent form of thyroid cancer, with the classical and follicular variants representing most cases. Despite generally favorable prognoses, approximately 10% of patients experience recurrence post-surgery and radioactive iodine therapy. Attempts to stratify risk of recurrence have relied on gene expression-based prognostic and predictive signatures with a focus on mutations of well-known driver genes, while hallmarks of tumor morphology have been ignored. ObjectivesWe introduce a new computational pathology approach to develop prognostic gene signatures for PTC that is informed by quantitative features of tumor and immune cell morphology. MethodsWe quantified nuclear and immune-related features of tumor morphology to develop a pathomic signature, which was then used to inform an RNA-expression signature model provides a notable advancement in risk stratification compared to both standalone and pathology-informed gene-expression signatures. ResultsThere was a 17.8% improvement in the C-index (from 0.605 to 0.783) for 123 cPTCs and 15% (from 0.576 to 0.726) for 38 fvPTCs compared to the standalone gene-expression signature. Hazard ratios also improved for cPTCs from 0.89 (0.67,0.99) to 4.43 (3.65,6.68) and fvPTC from 0.98 (0.76,1.32) to 2.28 (1.87,3.64). We validated the image-based risk model on an independent cohort of 32 cPTCs with hazard ratio 1.8 (1.534,2.167).

A novel heterophilic graph diffusion convolutional network for identifying cancer driver genes.

Identifying cancer driver genes plays a curial role in the development of precision oncology and cancer therapeutics. Although a plethora of methods have been developed to tackle this problem, the complex cancer mechanisms and intricate interactions between genes still make the identification of cancer driver genes challenging. In this work, we propose a novel machine learning method of heterophilic graph diffusion convolutional networks (called HGDCs) to boost cancer-driver gene identification. Specifically, HGDC first introduces graph diffusion to generate an auxiliary network for capturing the structurally similar nodes in a biomolecular network. Then, HGDC designs an improved message aggregation and propagation scheme to adapt to the heterophilic setting of biomolecular networks, alleviating the problem of driver gene features being smoothed by its neighboring dissimilar genes. Finally, HGDC uses a layer-wise attention classifier to predict the probability of one gene being a cancer driver gene. In the comparison experiments with other existing state-of-the-art methods, our HGDC achieves outstanding performance in identifying cancer driver genes. The experimental results demonstrate that HGDC not only effectively identifies well-known driver genes on different networks but also novel candidate cancer genes. Moreover, HGDC can effectively prioritize cancer driver genes for individual patients. Particularly, HGDC can identify patient-specific additional driver genes, which work together with the well-known driver genes to cooperatively promote tumorigenesis.

Novel Molecular Determinants of Response or Resistance to Immune Checkpoint Inhibitor Therapies in Melanoma.

BackgroundImmune checkpoint inhibitor (ICI) therapy dramatically prolongs melanoma survival. Currently, the identified ICI markers are sometimes ineffective. The objective of this study was to identify novel determinants of ICI efficacy.MethodsWe comprehensively curated pretreatment somatic mutational profiles and clinical information from 631 melanoma patients who received blockade therapy of immune checkpoints (i.e., CTLA-4, PD-1/PD-L1, or a combination). Significantly mutated genes (SMGs), mutational signatures, and potential molecular subtypes were determined. Their association with ICI responses was assessed simultaneously.ResultsWe identified 27 SMGs, including four novel SMGs (COL3A1, NRAS, NARS2, and DCC) that are associated with ICI efficacy and well-known driver genes. COL3A1 mutations were associated with improved ICI overall survival (hazard ratio (HR): 0.64, 95% CI: 0.45–0.91, p = 0.012), whereas immune resistance was observed in patients with NRAS mutations (HR: 1.42, 95% CI: 1.10–1.82, p = 0.006). The presence of the tobacco smoking-related signature was significantly correlated with inferior prognoses (HR: 1.42, 95% CI: 1.11–1.82, p = 0.005). In addition, the signature resembling that of alkylating agents and a newly discovered signature both exhibited extended prognoses (both HR < 1, p < 0.05). Based on the activities of the extracted 6 mutational signatures, we identified one immune subtype that was significantly associated with better ICI outcomes (HR: 0.44, 95% CI: 0.23–0.87, p = 0.017).ConclusionWe uncovered several novel SMGs and re-annotated mutational signatures that are linked to immunotherapy response or resistance. In addition, an immune subtype was found to exhibit favorable prognoses. Further studies are required to validate these findings.

Predicting pathogenic non-coding SVs disrupting the 3D genome in 1646 whole cancer genomes using multiple instance learning

Over the past years, large consortia have been established to fuel the sequencing of whole genomes of many cancer patients. Despite the increased abundance in tools to study the impact of SNVs, non-coding SVs have been largely ignored in these data. Here, we introduce svMIL2, an improved version of our Multiple Instance Learning-based method to study the effect of somatic non-coding SVs disrupting boundaries of TADs and CTCF loops in 1646 cancer genomes. We demonstrate that svMIL2 predicts pathogenic non-coding SVs with an average AUC of 0.86 across 12 cancer types, and identifies non-coding SVs affecting well-known driver genes. The disruption of active (super) enhancers in open chromatin regions appears to be a common mechanism by which non-coding SVs exert their pathogenicity. Finally, our results reveal that the contribution of pathogenic non-coding SVs as opposed to driver SNVs may highly vary between cancers, with notably high numbers of genes being disrupted by pathogenic non-coding SVs in ovarian and pancreatic cancer. Taken together, our machine learning method offers a potent way to prioritize putatively pathogenic non-coding SVs and leverage non-coding SVs to identify driver genes. Moreover, our analysis of 1646 cancer genomes demonstrates the importance of including non-coding SVs in cancer diagnostics.

Parent-child signals identify candidate cancer driver genes

Background: The DREAM Challenge evaluated methods to identify molecular pathways facilitating the detection of multiple genes affecting critical interactions and processes. Dysregulation of pathways by well-known driver genes is often found in the development and progression of cancer. We used the gene interaction networks provided and the scoring rounds to test disease module identification methods to nominate candidate driver genes in these modules. Method: Our algorithm calculated the proportion of the whole network accessible in two steps from each node in a combined network, which was defined as a 2-reach gene value. Genes with high 2-reach values were used to form the center of star cover clusters. These clusters were assessed for significant modules. Within these modules we identified novel candidate driver genes, by considering the parent-child relationship of well-known driver genes. Disturbance to such driver genes or their upstream parents, can lead to disruption of highly regulated signals affecting the normal functions of cells. We explored these parents as a potential source for candidate driver genes. Results: An initial list of 57 candidate driver genes was identified from 13 significant modules. Analysis of the parent-child relationships of well-known driver genes in these modules prioritized PRKDC, YWHAB, GSK3B, and PPP1CB. Conclusion: Our method incorporated the simple m-reach topology metric in disease module identification and its relationship with known driver genes to identify candidate genes. The four genes shortlisted have been highlighted in recent publications in the literature, which supports the need for further wet lab experimental investigation.

TOPDRIVER: the novel identifier of cancer driver genes in Gastric cancer and Melanoma

Nowadays, research has found a strong relationship between genomic status and occurrence of disease. Cancer is one of the most common diseases that leads to a high annual mortality rate worldwide, and the disease’s genetic content remains challenging. Detecting driver genes of different cancers could help in early diagnosis and treatment. In this paper, we proposed TOPDRIVER, a network-based algorithm, to detect cancer driver genes in cancers. An initial network was constructed by integrating four different omic datasets: HPRD, NCBI, KEGG, and GTEx. This integration created a gene similarity profile that provided a comprehensive perspective of gene interaction in each subtype of cancer and allocated weights to the edges of the network. The vertex scores were calculated using a gene-disease association dataset (DisGeNet) and a molecular functional disease similarity. In this step, the genes network was jagged and faced with a zero-one gap problem. A diffusion kernel was implemented to smooth the vertex scores to overcome this problem. Finally, potential driver genes were extracted according to the topology of the network, genes overall biological functions, and their involvement in cancer pathways. TOPDRIVER has been applied to two subtypes of gastric cancer and one subtype of melanoma. The method could nominate a considerable number of well-known driver genes of these cancers and also introduce novel driver genes. NKX3-1, KIDINS220, and RIPK4 have introduced for gastrointestinal cancer, UBA3, UBE2M, and RRAGA for hereditary gastric cancer and CIT for invasive melanoma. Biological evidences represents TOPDRIVER’s efficiency in a subtype-specific manner.

Genomic Mutational Signatures in Tumors Induced By High and Low Energy Radiation in Trp53-deficient Mouse Models

Ionizing radiation (IR) is both a potent mutagen and an important forms of cancer therapy. While its mechanism of action is not clearly elucidated, IR is thought to act by inducing DNA damage through a combination of direct (double-strand breaks) and indirect mechanisms (oxidative stress) and its relative potency is related to the function of tumor suppressors, particularly Trp53. To explorethe impact of IR on the evolution and landscape of the cancer genome and the impact of Trp53 deficiency, we subjected two mouse models of Trp53 dysfunction (n=214): heterozygous loss (het) and homozygous delta proline region deletion (delP) to either a single 50cGy dose of gamma or high-LET (Fe-ion) radiation. We employed whole genome (WGS) and/or exome sequencing (WES) to examine over 75 IR-related tumors and identified characteristics specific to IR quality, Trp53 genotype, and tumor histology/site. Transcriptome sequencing was carried to validate functional gene upregulation. Fe-ion IR was a potent inducer of mammary tumors than gamma IR and resulted in earlier tumor-specific death (multivariate HR=2.36, P<1.9e-06). Fe-ion IR is strongly associated with large focal deletions and structural rearrangements, supporting a mechanism of direct DNA damage. Overall, there is a preponderance of mammary tumors (51/160 animals). There was a strong deletion bias in mammary tumors (52%) as compared to lymphoma (21%) or skin tumors (15%) (p<2.9e0-3).Trp53 delP animals were much more likely to develop carcinomas in the skin, mammary gland, and other tissues, versus other histologic subtypes, (P<3.6e-05). In contrast, animals with Trp53 het genotype were enriched for non-focal rearrangements and aneuploidy compared to mice with the delP allele (P-adjusted<0.01 for deletions and inversions). Examining global SNV and INDEL variants identified both known and novel mutation signatures, including SNV signature 18, which has been associated with inflammation and implicated in oxidative stress. ROS signature 18 was enriched in tumors induced by gamma vs Fe-ion IR (p<0.032). In addition, in a subset of samples, we identified a de novo SNV and INDEL signature strongly attributed to focal regions of high mutation rate, resembling a phenomenon of “focal” chromothripsis. Finally, we identified somatic mutations in well-known driver genes including Kras, Apc, and Trp53. A preponderance of focal Met amplifications were observed in mammary tumors, particularly in animals exposed to Fe-ion vs gamma IR (19 versus 4 sequenced tumors). The functional impact of MET amplification were confirmed by RNA-seq analysis showing over 10-100+ fold expression increase. Our study provides compelling evidence in support of both a role for direct and indirect DNA damage, identifies focal and whole chromosomal chromothripsis, and illustrates complex interaction of Trp53 deficiency and radiation quality in the landscape of IR-mediated tumors.

Genomic alterations accompanying tumour evolution in colorectal cancer: tracking the differences between primary tumours and synchronous liver metastases by whole-exome sequencing

BackgroundColorectal cancer (CRC) patients with metastatic disease can become cured if neoadjuvant treatment can enable a resection. The search for predictive biomarkers is often performed on primary tumours tissue. In order to assess the effectiveness of tailored treatment in regard to the primary tumour the differences in the genomic profile needs to be clarified.MethodsFresh-frozen tissue from primary tumours, synchronous liver metastases and adjacent normal liver was collected from 21 patients and analysed by whole-exome sequencing on the Illumina HiSeq 2500 platform. Gene variants designated as ‘damaging’ or ‘potentially damaging’ by Ingenuity software were used for the subsequent comparative analysis. BAM files were used as the input for the analysis of CNAs using NEXUS software.ResultsShared mutations between the primary tumours and the synchronous liver metastases varied from 50 to 96%. Mutations in APC, KRAS, NRAS, TP53 or BRAF were concordant between the primary tumours and the metastases. Among the private mutations were well-known driver genes such as PIK3CA and SMAD4. The number of mutations was significantly higher in patients with right- compared to left-sided tumours (102 vs. 66, p = 0.004). Furthermore, right- compared to left-sided tumours had a significantly higher frequency of private mutations (p = 0.023). Similarly, CNAs differed between the primary tumours and the metastases. The difference was mostly comprised of numerical and segmental aberrations. However, novel CNAs were rarely observed in specific CRC-relevant genes.ConclusionThe examined primary colorectal tumours and synchronous liver metastases had multiple private mutations, indicating a high degree of inter-tumour heterogeneity in the individual patient. Moreover, the acquirement of novel CNAs from primary tumours to metastases substantiates the need for genomic profiling of metastases in order to tailor metastatic CRC therapies. As for the mutational status of the KRAS, NRAS and BRAF genes, no discordance was observed between the primary tumours and the metastases.

Abstract 149: Integrated analysis of intratumor heterogeneity with genetic and epigenetic aberrations during colorectal cancer evolution

Abstract [background] Intratumor heterogeneity (ITH) is presumably generated by branching clonal evolution of cancer cells. Recently, a multiregional sequencing approach, which sequences DNA sampled from geographically separated regions of a single tumor, has revealed branched evolution and ITH. In this study, we present genetic and epigenetic analysis of ITH in a series of nine colorectal cancers [material and method] We conducted analysis of samples from geographically separated regions from nine colorectal tumors. From each of the nine tumor, we obtained 5-21 multiregional samples, which were 75 samples in total, together with 9 paired normal mucosa samples. For two cases, samples from liver metastases were obtained. We performed exome sequencing and copy number (CN), methylation, and mRNA expression array profiling. [Result] Each of the multiregional mutation profiles harbored founder and progressor mutations; founder mutations are shared by all regions while progressor mutations are not. We found that mutations in well-known driver genes such as APC, KRAS, and FBWX7 were acquired as founder mutations, while mutations in PIK3CA recurrently occurred as progressor mutations, suggesting that PIK3CA mutations are a late event in the evolution of colorectal cancer. We counted each category of mutation and then identified a correlation between the number of founder mutations and the age of the patients. Our analysis showed that C &amp;gt; T transitions at CpG sites are more prominent in founder mutations than in progressor mutations. Then, the multiregional CN profiles showed that amplifications of 7p, 13q, 10q, 20p, and 20q frequently occurred across all samples in multiple tumors, namely as founder CN alterations. Finally, our data showed that hypermethylations in CpG islands were more prominent in founder methylations than in progressor methylations, suggesting that CpG island hypermethylations mainly occur in the early phase of colorectal cancer evolution. [Conclusion] In this study, our integrated analysis revealed not only extensive ITH, but also the evolutionary histories of the nine tumors. In particular, by focusing on founder and progressor mutations, we identified clues for decoding the life history of the tumors. Citation Format: Shinya Kidogami, Atsushi Niida, Ryutaro Uchi, Yusuke Takahashi, Masaki Mori, Satoru Miyano, Koshi Mimori. Integrated analysis of intratumor heterogeneity with genetic and epigenetic aberrations during colorectal cancer evolution. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 149.

Correlation of clinicopathologic features and driver gene mutation in non-small cell lung cancer

To study the relationship between mutations of well-known driver genes and clinicopathologic characteristics of non-small cell lung cancers (NSCLC). Scorpions amplification refractory mutation system (scorpions ARMS) fluorescence quantitative PCR was performed to investigate 205 driver gene mutation status in NSCLC in correlation with clinicopathological characteristics of the patients. Driver gene mutations were detected in 146 of 205 (71.2%) patients with NSCLC, including 81.7%(138/169) adenocarcinomas, in which mutations of nine genes were found: EGFR (63.3%, 107/169), KRAS (5.9%, 10/169), PIK3CA (4.1%, 7/169), ALK (4.1%, 7/169), ROS1 (3.0%, 5/169), RET (3.6%, 6/169), HER2 (1.8%, 3/169), NRAS (0.6%, 1/169) and BRAF (0.6%, 1/169). The frequencies of driver gene mutations were higher in adenocarcinomas, female patients and non-smokers (P<0.01, P=0.003, P<0.01, respectively). Driver gene mutation status showed no correlation with either the age or the clinical stage (P=0.281, P=0.490, respectively). However, EGFR mutations tended to occur in adenocarcinoma, female, non-smokers, and patients of ≥62 years of age (P<0.01, P<0.01, P=0.002, P=0.012, respectively). The frequency of EGFR mutation was positively correlated with the tumor histology of lepidic, acinar, papillary and micropapillary predominant growth patterns. There was no relationship between EGFR mutation and the clinical stage (P=0.237). The frequency of KRAS mutation was higher in solid predominant and invasive mucinous adenocarcinomas (P=0.015); that of PIK3CA mutation was higher in patients of ≥62 years of age, invasive mucinous adenocarcinoma and fetal adenocarcinoma (P=0.015, P=0.006, respectively). ALK, ROS1 or RET mutation positive NSCLC tended to occur in nonsmokers and have solid predominant tumors and invasive mucinous adenocarcinoma (P=0.012, P=0.017 respectively). The frequency of EML4-ALK mutation was higher in the early stage patients with solid predominant tumors and invasive mucinous adenocarcinomas (P=0.025, P=0.014, respectively); that of ROS1 rearrangement was higher in invasive mucinous adenocarcinomas (P=0.049). NRAS, BRAF and HER2 gene mutations were infrequent and their clinical significance remained to be elucidated. The relationship between mutations of well-known driver genes and clinicopathological characteristics in patients with NSCLC has diversity, the rate of mutations is higher in non-smoking female patients with adenocarcinoma.

Noninvasive diagnosis and monitoring of mutations by deep sequencing of circulating tumor DNA in esophageal squamous cell carcinoma

Circulating tumor DNA (ctDNA) is becoming an important biomarker in noninvasive diagnosis and monitoring of tumor dynamics. This study tested the feasibility of plasma ctDNA for the non-invasive analysis of tumor mutations in esophageal squamous cell carcinoma (ESCC) by sequencing of tumor, tumor-adjacent, and normal tissue, as well as pre-surgery and post-surgery plasma. Exome sequencing of eight patients identified between 29 and 134 somatic mutations in ESCCs, many of which were also determined in ctDNA. Comparison of pre-surgery and post-surgery plasma has shown that mutations had reduced frequency or disappeared after surgery treatment. We further evaluated the TruSight Cancer sequencing panel by using it to detect mutations in the plasma of three patients. Tumor mutations were only found in one of them. To design a sequencing panel with improved targeting, we identified significantly mutated genes by meta-analysis of 532 ESCC genomes. Our results confirmed the well-known driver genes and found several uncharacterized genes. The new panel consisted of 90 recurrent genes, which theoretically achieved 94% and 75% of sensitivity when detecting at least 1 and 2 mutant genes in ESCC patients, respectively. Our results demonstrate the feasibility of using ctDNA to detect ESCCs and monitor treatment effect. The low-cost and sensitive target panel could facilitate clinical usage of ctDNA as a noninvasive biomarker.

Whole Exome Sequencing Reveals That DNMT3A and ASXL1 Mutation Are Involved in the Development of Tyrosine Kinase Inhibitor Resistance in Chronic Myeloid Leukemia Patients without ABL1 Tyrosine Kinase Domain Mutation

BACKGROUND:Tyrosine kinase inhibitor (TKI) resistance is the most relevant event during the treatment of chronic myeloid leukemia (CML), which correlates with high risk of treatment failure, disease progression and death, explaining half of treatment failed CML patients. However, the remaining half with TKI resistance does not show any ABL1 tyrosine kinase domain (TKD) mutation indicating the presence of alternative pathogenic pathways behind TKI resistance. Thus we hypothesized that the novel mutation besides ABL1-TKD mutation occurs during the development of TKI resistance. Using whole exome sequencing, we screened 13 pairs of CML cases with TKI resistance, but without ABL1-TKD mutation. The present study attempts to: 1) explore novel mutation(s) developing TKI resistance to CML treatment and 2) validate the somatic variants in an independent cohort of CML patients (n=100).METHODS:Thirteen CML cases with TKI resistance but not having ABL1 TKD mutation were included prospectively. Reason for TKI resistance includes Loss of MCyR (n=7) with (n=2) or without additional cytogenetic abnormality (ACA; n=5), progression to blastic crisis (n=3), development of ACA (n=1), development of clonal evolution in Ph neg clone (n=1), primary cytogenetic resistance (n=1). TKI resistance were demonstrated to imatinib (n=12), dasatinib (n=5), nilotinib (n=4) or ponatinib (n=2). The latest treatment includes ponatinib (n=3), dasatinib (n=8) alone (n=4), with smoothen inhibitor (n=2), or with after systemic chemotherapy (n=2), omacetaxine (n=1), and nilotinib (n=1). Disease stage at the time of exome sequencing was chronic phase (n=10) or blastic crisis (n=3). Germline and tumor samples at the time of TKI resistance were compared using whole exome sequencing (Illumina TruSeq kit, HiSeq 2000). Targeted sequencing for selected variants was performed to validate the result. All patients were confirmed the absence of ABL1-TKD mutations using Sanger sequencing.RESULTS:1) Exome sequencing (Illumina Truseq kit) was performed as per the manufacturer's protocol using an Illumina HiSeq 2000 sequencer. DNA from buccal mucosa was used as a control for variant calling. Exome sequencing reads processing includes mapping to human genome hg19, marking PCR duplicates, realignment of indels, fixing mate information, and discard the reads with more than 2 mismatches to increase the true positive rate. In the end, we have on-target-coverage of 57x. Lastly, 72% of target positions are mapped more than 30x.2) One hundred nineteen somatic variants were identified in 13 patients in 108 genes. Among them 5 genes have variants in multiple patients including DNMT3A (n=3), ASXL1 (n=2), NPIPB5 (n=2), ATXN3 (n=2) and EFEMP1 (n=2) . We also found at least 1 mutation in well-known driver genes in 6 patients (6/13 = 46%).3) Three out of 4 patients with ACA carry variants at least one of DNMT3A (n=2), ASXL1 (n=2), and SETBP1 (n=1). Also, 2 out of 3 cases progressed to blastic crisis demonstrate variants in DNMT3A (n=1) and IDH1 (n=1).4) Interestingly, in one patient, exome sequencing reveals ABL1-TKD mutation (T315I), which was not detected at the initial screening by Sanger sequencing.5) The result of targeted sequencing in an independent cohort of CML patients (n=100) will be presented in the annual meeting of American Society of Hematology in Dec 2015.CONCLUSION:Our study suggest that DNMT3A and ASXL1 mutations seem to be the driver mutations involved in the development of TKI resistance/progression, independent of ABL1-TKD mutation. Also, exome sequencing can detect ABL1-TKD mutations including T315I prior to be detected by initial Sanger sequencing. DisclosuresLipton:Ariad: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; Teva: Consultancy, Research Funding; Novartis Pharmaceuticals: Consultancy, Research Funding. Kim:Bristol-Myers Squibb: Consultancy, Research Funding; Novartis Pharmaceuticals: Consultancy, Research Funding.

Gene mutations in gastric cancer: a review of recent next-generation sequencing studies.

Gastric cancer (GC) is one of the most common malignancies worldwide. Although some driver genes have been identified in GC, the molecular compositions of GC have not been fully understood. The development of next-generation sequencing (NGS) provides a high-throughput and systematic method to identify all genetic alterations in the cancer genome, especially in the field of mutation detection. NGS studies in GC have discovered some novel driver mutations. In this review, we focused on novel gene mutations discovered by NGS studies, along with some well-known driver genes in GC. We organized mutated genes from the perspective of related biological pathways. Mutations in genes relating to genome integrity (TP53, BRCA2), chromatin remodeling (ARID1A), cell adhesion (CDH1, FAT4, CTNNA1), cytoskeleton and cell motility (RHOA), Wnt pathway (CTNNB1, APC, RNF43), and RTK pathway (RTKs, RAS family, MAPK pathway, PIK pathway) are discussed. Efforts to establish a molecular classification based on NGS data which is valuable for future targeted therapy for GC are introduced. Comprehensive dissection of the molecular profile of GC cannot only unveil the molecular basis for GC but also identify genes of clinical utility, especially potential and specific therapeutic targets for GC.