- Email: [email protected]
Tepotinib inhibits the epithelial-mesenchymal transition and tumour growth of gastric cancers via decreasing MUC5B, MMP7, and COX2
abstracts 30P
Protein interactome network analysis predicts novel breast cancer candidate genes with molecular signatures
M. Peer Mohammed Bioinformatics Infrastructure Facility of DBT, Tamilnadu, India
31P
Tepotinib inhibits the epithelial-mesenchymal transition and tumour growth of gastric cancers via decreasing MUC5B, MMP7, and COX2
D.Y. Zang1, S-H. Sohn2, H.J. Sul2, B. Kim2, B.Y. Choi2, H.S. Kim1 Hallym University Medical Center-Hallym University College of Medicine, Anyang, Republic of Korea, 2Hallym Translational Research Institute, Hallym University Sacred Heart Hospital, Anyang, Republic of Korea
1
Background: Aberrant expression of mucins can promote the epithelial-mesenchymal transition (EMT), which leads to enhanced tumorigenesis. Carcinogenesis-related pathways involving c-MET and b-catenin involve mucins. Among the mucins, MUC5AC and MUC6 are characteristic for stomach mucins. This study characterized expressions of MET, MUC5AC, MUC5B, and MUC6 EMT signaling in human gastric cancer (GC) cell lines, and further characterized the differential susceptibility of these cell lines to tepotinib. Methods: We assessed the antitumor activity of tepotinib in GC cell lines. The effect of tepotinib on cell viability (IC50), apoptotic cell death, the EMT, and c-MET and b-catenin signaling were evaluated by MTS, flow cytometry, western blotting, and qRT-PCR. Antitumor efficacy was assessed in MKN45 xenograft mice. Results: Tepotinib treatment showed dose-dependent growth inhibition of c-METamplified SNU620, MKN45, and KATO III cells with concomitant induction of apoptosis, but tepotinib treatment did not have an effect on c-MET-reduced MKN28 and AGS cells. Tepotinib treatment also significantly reduced expressions of phosphor-cMET, total c-MET, phosphor-ERK, total ERK, beta-catenin, and c-MYC protein in SNU620 and MKN45 cells. In contrast, this drug was only slightly active against KATO III cells. Notably, tepotinib significantly reduced the expressions of EMT promotion genes such as MMP7, COX-2, WNT1, MUC5B, and c-MYC in c-MET-expressed GC cells, and increased expressions of EMT suppression genes such as MUC5AC, MUC6, GSK3b, and ECAD. In a murine xenograft model, tumor volumes were significantly reduced in the tepotinib-treated group, when administered by daily oral gavage at a dose of 10 mg/kg/day. Histologically, tepotinib induced more necrosis than in the control group. Conclusions: These results are consistent with clinical evaluations of tepotinib in cMET and MUC5B-expressed GCs. Editorial acknowledgement: National R&D Program for Cancer Control, Ministry of Health and Welfare (HA17C0054), the National Research Foundation of Korea grant funded by the Korean Ministry of Science and ICT (NRF-2017R1A2B4005055), the Ministry of Food and Drug Safety (awarded in 2018, 18183MFDS491) of Korea, the
vii10 | Molecular Analysis for Personalised Therapy
Hallym University Medical Center Research Fund, and the Hallym University Internal Translational Research Fund (No. HURF-2015-38). Legal entity responsible for the study: The authors. Funding: Has not received any funding. Disclosure: All authors have declared no conflicts of interest.
32P
Application of variant interpretation software to decipher pathogenicity of mutations for a molecular tumour board (MTB)
S. Southam1, M. Ayub1, M. Krebs2, D. Rothwell1, D. Graham2, J. Stevenson1 Clinical and Experimental Pharmacology, Cancer Research UK Manchester Institute, Manchester, UK, 2Experimental Cancer Medicine, The Christie NHS Foundation Trust, Manchester, UK
1
Background: Comprehensive genomic profiling (CGP) of tumours using next generation sequencing (NGS) is increasingly used to guide management. Broad panel sequencing may increase treatment options by identifying more potential targets but generates large quantities of data. Therefore, determining actionable alterations is a challenge. To address this the MTB for the Tumour chARacterisation to Guide Experimental Targeted therapy (TARGET) trial at the Manchester Cancer Research Centre has evaluated a variant interpretation software package. Methods: CGP of circulating tumour DNA (ctDNA) was undertaken using a somatic NGS 641 gene panel that included 24 cancer related genes and further exploratory genes. The variant allele frequency threshold was 2.5 and functional annotation of somatic variants used ANNOVAR. For cases with 2 mutations the vcf file was analysed using the Qiagen Clinical Insight platform (QCI) variant interpretation software. Results: Over 18 months, 122 cases with a total of 1313 mutations were analysed by QCI. The variant interpretation provided information on the pathogenicity of the mutations and actionability, which was based on clinical trials identified by QCI. 11% of the total mutations were from the NGS 24 genes, of which 88% were pathogenic and of these 63% were actionable. The NGS exploratory genes accounted for the majority of mutations (89%). In the NGS exploratory genes a smaller percentage of mutations were pathogenic (17%) and of these 9% were actionable. The benefit for the MTB was the ability to focus on the relevant pathogenic mutations. Results were discussed in MTB meetings and pathogenic mutations captured in a digital tool, eTARGET. Conclusions: Utilising variant interpretation software to identify pathogenic mutations from a broader CGP panel enabled more streamlined discussion and decisionmaking. Whilst a broader panel reveals numerically more mutations and the actionability rates are lower this is important to understand the biological relevance of single mutations and patterns of co-mutations. Applying a pathogenicity filter for ctDNA mutations using variant analysis software has scientific and potential clinical utility. Legal entity responsible for the study: Cancer Research UK Manchester Institute, The University of Manchester. Funding: Partly funded by AstraZeneca iDecide Programme (grant no 119106, C.D.). Disclosure: S. Southam: Shareholder / Stockholder / Stock options: AstraZeneca; Shareholder / Stockholder / Stock options: GSK; Research grant / Funding (institution): AstraZeneca. All other authors have declared no conflicts of interest.
33P
Combination of mTOR inhibition and paclitaxel as a personalised strategy in the context of MYC-amplified high-grade serous ovarian cancer
F.C. Martins1, D-L. Couturier2, I. de Santiago2, M. Vias2, D. Sanders2, A. Piskorz2, J. Hall2, M. Jimenez-Linan3, K. Hosking4, R. Crawford4, J. Brenton2 1 Obstetrics & Gynaecology, Cancer Research UK & University of Cambridge UK, Cambridge, UK, 2Cancer Research UK Cambridge Institute, Cambridge, UK, 3Pathology, Cambridge University Hospitals, Cambridge, UK, 4Cambridge University Hospitals, Cambridge, UK Background: The extreme genomic heterogeneity of high-grade serous ovarian carcinoma (HGSOC) is a major barrier to precision medicine approaches. We have developed mutational signature analysis from shallow whole genome sequencing (sWGS) for molecular stratification and therapeutic prediction. PARP inhibitor therapy has significant survival advantage for women with germline BRCA1/2 mutations, but treatments and predictive biomarkers for women without homologous recombination deficiency have not been developed. MYC amplification is the most common driver in HGSOC, present in 42% of the TCGA cases. The best responder from the phase 1 trial of AZD2014 (dual m-TORC1/2 inhibitor) and paclitaxel in HGSOC had a mutation in MYC and we have hypothesised that biosynthetic effects of MYC are mediated through the mTOR pathway and blocked by AZD2014 perturbation. Methods: Primary ovarian cancer spheroids were purified from 28 human ascites and profiled with sWGS. Ex-vivo therapeutic response was measured to four standard of care chemotherapeutic agents and eight inhibitors targeting nodal points of the PI3K and DNA damage repair pathways. RNA seq data from the TCGA HGSOC cohort was
Volume 30 | Supplement 7 | November 2019
Downloaded from https://academic.oup.com/annonc/article-abstract/30/Supplement_7/mdz413.036/5613334 by guest on 24 December 2019
Background: Advancement in molecular biology research has allowed the measurement of multiomics data points from a single tumor biopsy sample in a reasonable time frame for making significant clinical decisions. There have been tremendous advances from the bioinformatics and systems biology perspective to analyse integrated multiscale analysis of the data will prove invaluable for a combined systems-level understanding of the important biological network processes contributing to the initiation, progression and management of cancer. Cancer networks have greater challenge due to the aberrant functions of hundreds of genes that are translated to altered protein function within each cancer cell, to understand this complexity. Network-based methods have been used to analyse the complexity molecular interactions in the cell and can contribute to prediction of candidate genes responsible for cancer. Methods: HI-II- 14 is the largest experimentally determined binary protein – protein interaction map is used for the study. These interactions were mapped to NCBI Entrez gene ID and excluded self-loop and redundant interactions. The known human breast cancer genes were obtained from the Sanger Cancer Gene Census which is a comprehensive catalogue of genes implicated in breast cancer and from Uniprot database. In total, we obtained 913 genes from both for analyses. The genes degree of connections and the centrality in the protein -protein networks were considered. The genome-wide mutation datasets of breast cancer were downloaded from TCGA as maf files. For each gene, we obtained the mutation frequency for each gene is calculated which is defined as the number of mutated samples divided by the total number of samples. The frequency of the top 100 ranked genes and the bottom 100 ranked genes were compared by Wilcox rank sum test. Results: In this study, HI-II-14 human protein-protein interactome network is applied to prediction of novel breast cancer genes. Conclusions: Our study suggests integrating interactome networks with multiomics datasets could provide novel insights into breast cancer-associated genes and underlying molecular mechanisms. Legal entity responsible for the study: M. Peer Mohammed. Funding: Has not received any funding. Disclosure: The author has declared no conflicts of interest.
Annals of Oncology
Protein interactome network analysis predicts novel breast cancer candidate genes with molecular signatures
M. Peer Mohammed Bioinformatics Infrastructure Facility of DBT, Tamilnadu, India
31P
Tepotinib inhibits the epithelial-mesenchymal transition and tumour growth of gastric cancers via decreasing MUC5B, MMP7, and COX2
D.Y. Zang1, S-H. Sohn2, H.J. Sul2, B. Kim2, B.Y. Choi2, H.S. Kim1 Hallym University Medical Center-Hallym University College of Medicine, Anyang, Republic of Korea, 2Hallym Translational Research Institute, Hallym University Sacred Heart Hospital, Anyang, Republic of Korea
1
Background: Aberrant expression of mucins can promote the epithelial-mesenchymal transition (EMT), which leads to enhanced tumorigenesis. Carcinogenesis-related pathways involving c-MET and b-catenin involve mucins. Among the mucins, MUC5AC and MUC6 are characteristic for stomach mucins. This study characterized expressions of MET, MUC5AC, MUC5B, and MUC6 EMT signaling in human gastric cancer (GC) cell lines, and further characterized the differential susceptibility of these cell lines to tepotinib. Methods: We assessed the antitumor activity of tepotinib in GC cell lines. The effect of tepotinib on cell viability (IC50), apoptotic cell death, the EMT, and c-MET and b-catenin signaling were evaluated by MTS, flow cytometry, western blotting, and qRT-PCR. Antitumor efficacy was assessed in MKN45 xenograft mice. Results: Tepotinib treatment showed dose-dependent growth inhibition of c-METamplified SNU620, MKN45, and KATO III cells with concomitant induction of apoptosis, but tepotinib treatment did not have an effect on c-MET-reduced MKN28 and AGS cells. Tepotinib treatment also significantly reduced expressions of phosphor-cMET, total c-MET, phosphor-ERK, total ERK, beta-catenin, and c-MYC protein in SNU620 and MKN45 cells. In contrast, this drug was only slightly active against KATO III cells. Notably, tepotinib significantly reduced the expressions of EMT promotion genes such as MMP7, COX-2, WNT1, MUC5B, and c-MYC in c-MET-expressed GC cells, and increased expressions of EMT suppression genes such as MUC5AC, MUC6, GSK3b, and ECAD. In a murine xenograft model, tumor volumes were significantly reduced in the tepotinib-treated group, when administered by daily oral gavage at a dose of 10 mg/kg/day. Histologically, tepotinib induced more necrosis than in the control group. Conclusions: These results are consistent with clinical evaluations of tepotinib in cMET and MUC5B-expressed GCs. Editorial acknowledgement: National R&D Program for Cancer Control, Ministry of Health and Welfare (HA17C0054), the National Research Foundation of Korea grant funded by the Korean Ministry of Science and ICT (NRF-2017R1A2B4005055), the Ministry of Food and Drug Safety (awarded in 2018, 18183MFDS491) of Korea, the
vii10 | Molecular Analysis for Personalised Therapy
Hallym University Medical Center Research Fund, and the Hallym University Internal Translational Research Fund (No. HURF-2015-38). Legal entity responsible for the study: The authors. Funding: Has not received any funding. Disclosure: All authors have declared no conflicts of interest.
32P
Application of variant interpretation software to decipher pathogenicity of mutations for a molecular tumour board (MTB)
S. Southam1, M. Ayub1, M. Krebs2, D. Rothwell1, D. Graham2, J. Stevenson1 Clinical and Experimental Pharmacology, Cancer Research UK Manchester Institute, Manchester, UK, 2Experimental Cancer Medicine, The Christie NHS Foundation Trust, Manchester, UK
1
Background: Comprehensive genomic profiling (CGP) of tumours using next generation sequencing (NGS) is increasingly used to guide management. Broad panel sequencing may increase treatment options by identifying more potential targets but generates large quantities of data. Therefore, determining actionable alterations is a challenge. To address this the MTB for the Tumour chARacterisation to Guide Experimental Targeted therapy (TARGET) trial at the Manchester Cancer Research Centre has evaluated a variant interpretation software package. Methods: CGP of circulating tumour DNA (ctDNA) was undertaken using a somatic NGS 641 gene panel that included 24 cancer related genes and further exploratory genes. The variant allele frequency threshold was 2.5 and functional annotation of somatic variants used ANNOVAR. For cases with 2 mutations the vcf file was analysed using the Qiagen Clinical Insight platform (QCI) variant interpretation software. Results: Over 18 months, 122 cases with a total of 1313 mutations were analysed by QCI. The variant interpretation provided information on the pathogenicity of the mutations and actionability, which was based on clinical trials identified by QCI. 11% of the total mutations were from the NGS 24 genes, of which 88% were pathogenic and of these 63% were actionable. The NGS exploratory genes accounted for the majority of mutations (89%). In the NGS exploratory genes a smaller percentage of mutations were pathogenic (17%) and of these 9% were actionable. The benefit for the MTB was the ability to focus on the relevant pathogenic mutations. Results were discussed in MTB meetings and pathogenic mutations captured in a digital tool, eTARGET. Conclusions: Utilising variant interpretation software to identify pathogenic mutations from a broader CGP panel enabled more streamlined discussion and decisionmaking. Whilst a broader panel reveals numerically more mutations and the actionability rates are lower this is important to understand the biological relevance of single mutations and patterns of co-mutations. Applying a pathogenicity filter for ctDNA mutations using variant analysis software has scientific and potential clinical utility. Legal entity responsible for the study: Cancer Research UK Manchester Institute, The University of Manchester. Funding: Partly funded by AstraZeneca iDecide Programme (grant no 119106, C.D.). Disclosure: S. Southam: Shareholder / Stockholder / Stock options: AstraZeneca; Shareholder / Stockholder / Stock options: GSK; Research grant / Funding (institution): AstraZeneca. All other authors have declared no conflicts of interest.
33P
Combination of mTOR inhibition and paclitaxel as a personalised strategy in the context of MYC-amplified high-grade serous ovarian cancer
F.C. Martins1, D-L. Couturier2, I. de Santiago2, M. Vias2, D. Sanders2, A. Piskorz2, J. Hall2, M. Jimenez-Linan3, K. Hosking4, R. Crawford4, J. Brenton2 1 Obstetrics & Gynaecology, Cancer Research UK & University of Cambridge UK, Cambridge, UK, 2Cancer Research UK Cambridge Institute, Cambridge, UK, 3Pathology, Cambridge University Hospitals, Cambridge, UK, 4Cambridge University Hospitals, Cambridge, UK Background: The extreme genomic heterogeneity of high-grade serous ovarian carcinoma (HGSOC) is a major barrier to precision medicine approaches. We have developed mutational signature analysis from shallow whole genome sequencing (sWGS) for molecular stratification and therapeutic prediction. PARP inhibitor therapy has significant survival advantage for women with germline BRCA1/2 mutations, but treatments and predictive biomarkers for women without homologous recombination deficiency have not been developed. MYC amplification is the most common driver in HGSOC, present in 42% of the TCGA cases. The best responder from the phase 1 trial of AZD2014 (dual m-TORC1/2 inhibitor) and paclitaxel in HGSOC had a mutation in MYC and we have hypothesised that biosynthetic effects of MYC are mediated through the mTOR pathway and blocked by AZD2014 perturbation. Methods: Primary ovarian cancer spheroids were purified from 28 human ascites and profiled with sWGS. Ex-vivo therapeutic response was measured to four standard of care chemotherapeutic agents and eight inhibitors targeting nodal points of the PI3K and DNA damage repair pathways. RNA seq data from the TCGA HGSOC cohort was
Volume 30 | Supplement 7 | November 2019
Downloaded from https://academic.oup.com/annonc/article-abstract/30/Supplement_7/mdz413.036/5613334 by guest on 24 December 2019
Background: Advancement in molecular biology research has allowed the measurement of multiomics data points from a single tumor biopsy sample in a reasonable time frame for making significant clinical decisions. There have been tremendous advances from the bioinformatics and systems biology perspective to analyse integrated multiscale analysis of the data will prove invaluable for a combined systems-level understanding of the important biological network processes contributing to the initiation, progression and management of cancer. Cancer networks have greater challenge due to the aberrant functions of hundreds of genes that are translated to altered protein function within each cancer cell, to understand this complexity. Network-based methods have been used to analyse the complexity molecular interactions in the cell and can contribute to prediction of candidate genes responsible for cancer. Methods: HI-II- 14 is the largest experimentally determined binary protein – protein interaction map is used for the study. These interactions were mapped to NCBI Entrez gene ID and excluded self-loop and redundant interactions. The known human breast cancer genes were obtained from the Sanger Cancer Gene Census which is a comprehensive catalogue of genes implicated in breast cancer and from Uniprot database. In total, we obtained 913 genes from both for analyses. The genes degree of connections and the centrality in the protein -protein networks were considered. The genome-wide mutation datasets of breast cancer were downloaded from TCGA as maf files. For each gene, we obtained the mutation frequency for each gene is calculated which is defined as the number of mutated samples divided by the total number of samples. The frequency of the top 100 ranked genes and the bottom 100 ranked genes were compared by Wilcox rank sum test. Results: In this study, HI-II-14 human protein-protein interactome network is applied to prediction of novel breast cancer genes. Conclusions: Our study suggests integrating interactome networks with multiomics datasets could provide novel insights into breast cancer-associated genes and underlying molecular mechanisms. Legal entity responsible for the study: M. Peer Mohammed. Funding: Has not received any funding. Disclosure: The author has declared no conflicts of interest.
Annals of Oncology