Sudeep Gupta, R. Chaubal, N. Gardi, Sunil Pachakar, Dimple R. Bhatia, P. Gera, N. Nair, S. Joshi, V. Parmar, P. Thakkar, G. Chitkara, Rasika Kadam, S. Gujarathi, H. Oza, R. Hawaldar, V. Vanmali, K. Buetow, A. Dutt, R. Badwe
{"title":"摘要P3-05-01:手术切除对乳腺原发肿瘤的分子效应","authors":"Sudeep Gupta, R. Chaubal, N. Gardi, Sunil Pachakar, Dimple R. Bhatia, P. Gera, N. Nair, S. Joshi, V. Parmar, P. Thakkar, G. Chitkara, Rasika Kadam, S. Gujarathi, H. Oza, R. Hawaldar, V. Vanmali, K. Buetow, A. Dutt, R. Badwe","doi":"10.1158/1538-7445.SABCS19-P3-05-01","DOIUrl":null,"url":null,"abstract":"Rationale: Surgery results in rapid and progressively severe exposure of tumor tissue to hypoxia, up to the point of complete removal, but its effect on tumor gene expression is not well characterized. We document the molecular effects of surgery on primary breast cancer tumor with a serial tissue sampling strategy, including an intra-operative sample. Methods: We included treatment naive, non-metastatic breast cancer patients and sampled tumor and tumour adjacent normal tissue during surgery at three time points: beginning of surgery (Sample A), after half the tumor circumference had been devascularized (Sample B) and from completely resected tumor (Sample C). Patients were divided into two groups: discovery and validation. Tumor or adjacent normal samples from the discovery group underwent whole transcriptome paired-end sequencing (RNA-Seq) generating at least 50 million reads using next generation sequencing. Findings from discovery group were evaluated in a validation group using qRT-PCR and Nanostring nCounter gene expression profiling. Results: 81 breast cancer patients were eligible for this study of whom 46 with at least 1 quality passed sample at time-point A, B or C comprised the discovery group whose samples underwent RNA-seq. Validation group comprised two subsets: 35 independent patients (8 patients’ samples qRT-PCR, data included here; 27 patients’ samples nCounter gene expression, data will be presented) and 17 patients from discovery group whose samples also underwent nCounter gene expression profiling (data will be presented). Individual patient based analyses for A vs B vs C in discovery group revealed 249 significantly de-regulated genes in at least 20% of patients, in at least one comparison (AvB or BvC or AvC). Genes involved in stress response (FOSB, FOS, JUN, JUNB, DUSP1, CYR61, EGR1-3, ATF3, RGS1, RGS2), inflammation (IL20, IL8, SOCS3, GABRP, PIGR, NR4A1-2, CCL2- 3, CCL21, CCL14, CCL18-19, CXCL2, CXCL9-10, CXCL14), invasion & migration (PTGS2, MMP9-13), lipid metabolism (CD36, LIPE, TAT, FABP4, PLIN1, PLIN4, LPL, LEP), epithelial markers (KRT5, KRT7, KRT14-17, KRT23, KRT6A-6B) and cellular differentiation(SOX10, LRP2, S100A2, S100A7-A9, S100B, S100P) were significantly deregulated at time-point B versus A and many of these genes were also significantly deregulated in C versus A comparison, suggesting sustained deregulation through surgical resection. Replicative analysis involving comparison of tumors grouped by time points (A vs B vs C) identified 192 genes uniquely de-regulated in any one of the 3 comparisons, of which 42 overlapped with patient-wise analysis. These 42 genes included all the AP-1 transcription factor network signaling genes, epithelial markers (KRT14, KRT6A), inflammation related genes (SOCS3, PIGR, GABRP, NR4A1, NR4A2), lipid metabolism related genes (PLIN1) and cellular differentiation & cell fate related genes (SOX10, LRP2). Pathway analyses will be presented. qRT-PCR on paired samples in 8 independent patients validated the differential expression of eight genes in either AvB or AvC or both comparisons: FOS, FOSB, JUNB, DUSP1, RGS1, NR4A2, ZFP36, andMMP13. Comparison of biopsy (corresponding to A) with surgical samples (corresponding to C) in 6 cancer types (breast, cervical, lymphoma, mesothelioma, esophageal and sarcoma) in TCGA studies showed statistically significant de-regulation of 11 AP-1 transcription factor network signaling related genes in at least 4 cancers, further validating our experimental results. Conclusions: Our experiment, uniquely including an intra-operative tumor sample, shows that surgical removal induces a conserved stress response in the primary tumor that is potentially capable of bestowing metastatic capability on the tumor cells. Citation Format: Sudeep Gupta, Rohan Chaubal, Nilesh Gardi, Sunil Pachakar, Dimple Bhatia, Poonam Gera, Nita Nair, Shalaka Joshi, Vani Parmar, Purvi Thakkar, Garvit Chitkara, Rasika Kadam, Sejal Gujarathi, Harsh Oza, Rohini Hawaldar, Vaibhav Vanmali, Kenneth Buetow, Amit Dutt, Rajendra Badwe. Molecular effects of surgical resection on primary breast tumor [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-05-01.","PeriodicalId":20307,"journal":{"name":"Poster Session Abstracts","volume":"11 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Abstract P3-05-01: Molecular effects of surgical resection on primary breast tumor\",\"authors\":\"Sudeep Gupta, R. Chaubal, N. Gardi, Sunil Pachakar, Dimple R. Bhatia, P. Gera, N. Nair, S. Joshi, V. Parmar, P. Thakkar, G. Chitkara, Rasika Kadam, S. Gujarathi, H. Oza, R. Hawaldar, V. Vanmali, K. Buetow, A. Dutt, R. Badwe\",\"doi\":\"10.1158/1538-7445.SABCS19-P3-05-01\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Rationale: Surgery results in rapid and progressively severe exposure of tumor tissue to hypoxia, up to the point of complete removal, but its effect on tumor gene expression is not well characterized. We document the molecular effects of surgery on primary breast cancer tumor with a serial tissue sampling strategy, including an intra-operative sample. Methods: We included treatment naive, non-metastatic breast cancer patients and sampled tumor and tumour adjacent normal tissue during surgery at three time points: beginning of surgery (Sample A), after half the tumor circumference had been devascularized (Sample B) and from completely resected tumor (Sample C). Patients were divided into two groups: discovery and validation. Tumor or adjacent normal samples from the discovery group underwent whole transcriptome paired-end sequencing (RNA-Seq) generating at least 50 million reads using next generation sequencing. Findings from discovery group were evaluated in a validation group using qRT-PCR and Nanostring nCounter gene expression profiling. Results: 81 breast cancer patients were eligible for this study of whom 46 with at least 1 quality passed sample at time-point A, B or C comprised the discovery group whose samples underwent RNA-seq. Validation group comprised two subsets: 35 independent patients (8 patients’ samples qRT-PCR, data included here; 27 patients’ samples nCounter gene expression, data will be presented) and 17 patients from discovery group whose samples also underwent nCounter gene expression profiling (data will be presented). Individual patient based analyses for A vs B vs C in discovery group revealed 249 significantly de-regulated genes in at least 20% of patients, in at least one comparison (AvB or BvC or AvC). Genes involved in stress response (FOSB, FOS, JUN, JUNB, DUSP1, CYR61, EGR1-3, ATF3, RGS1, RGS2), inflammation (IL20, IL8, SOCS3, GABRP, PIGR, NR4A1-2, CCL2- 3, CCL21, CCL14, CCL18-19, CXCL2, CXCL9-10, CXCL14), invasion & migration (PTGS2, MMP9-13), lipid metabolism (CD36, LIPE, TAT, FABP4, PLIN1, PLIN4, LPL, LEP), epithelial markers (KRT5, KRT7, KRT14-17, KRT23, KRT6A-6B) and cellular differentiation(SOX10, LRP2, S100A2, S100A7-A9, S100B, S100P) were significantly deregulated at time-point B versus A and many of these genes were also significantly deregulated in C versus A comparison, suggesting sustained deregulation through surgical resection. Replicative analysis involving comparison of tumors grouped by time points (A vs B vs C) identified 192 genes uniquely de-regulated in any one of the 3 comparisons, of which 42 overlapped with patient-wise analysis. These 42 genes included all the AP-1 transcription factor network signaling genes, epithelial markers (KRT14, KRT6A), inflammation related genes (SOCS3, PIGR, GABRP, NR4A1, NR4A2), lipid metabolism related genes (PLIN1) and cellular differentiation & cell fate related genes (SOX10, LRP2). Pathway analyses will be presented. qRT-PCR on paired samples in 8 independent patients validated the differential expression of eight genes in either AvB or AvC or both comparisons: FOS, FOSB, JUNB, DUSP1, RGS1, NR4A2, ZFP36, andMMP13. Comparison of biopsy (corresponding to A) with surgical samples (corresponding to C) in 6 cancer types (breast, cervical, lymphoma, mesothelioma, esophageal and sarcoma) in TCGA studies showed statistically significant de-regulation of 11 AP-1 transcription factor network signaling related genes in at least 4 cancers, further validating our experimental results. Conclusions: Our experiment, uniquely including an intra-operative tumor sample, shows that surgical removal induces a conserved stress response in the primary tumor that is potentially capable of bestowing metastatic capability on the tumor cells. Citation Format: Sudeep Gupta, Rohan Chaubal, Nilesh Gardi, Sunil Pachakar, Dimple Bhatia, Poonam Gera, Nita Nair, Shalaka Joshi, Vani Parmar, Purvi Thakkar, Garvit Chitkara, Rasika Kadam, Sejal Gujarathi, Harsh Oza, Rohini Hawaldar, Vaibhav Vanmali, Kenneth Buetow, Amit Dutt, Rajendra Badwe. Molecular effects of surgical resection on primary breast tumor [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. 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Abstract P3-05-01: Molecular effects of surgical resection on primary breast tumor
Rationale: Surgery results in rapid and progressively severe exposure of tumor tissue to hypoxia, up to the point of complete removal, but its effect on tumor gene expression is not well characterized. We document the molecular effects of surgery on primary breast cancer tumor with a serial tissue sampling strategy, including an intra-operative sample. Methods: We included treatment naive, non-metastatic breast cancer patients and sampled tumor and tumour adjacent normal tissue during surgery at three time points: beginning of surgery (Sample A), after half the tumor circumference had been devascularized (Sample B) and from completely resected tumor (Sample C). Patients were divided into two groups: discovery and validation. Tumor or adjacent normal samples from the discovery group underwent whole transcriptome paired-end sequencing (RNA-Seq) generating at least 50 million reads using next generation sequencing. Findings from discovery group were evaluated in a validation group using qRT-PCR and Nanostring nCounter gene expression profiling. Results: 81 breast cancer patients were eligible for this study of whom 46 with at least 1 quality passed sample at time-point A, B or C comprised the discovery group whose samples underwent RNA-seq. Validation group comprised two subsets: 35 independent patients (8 patients’ samples qRT-PCR, data included here; 27 patients’ samples nCounter gene expression, data will be presented) and 17 patients from discovery group whose samples also underwent nCounter gene expression profiling (data will be presented). Individual patient based analyses for A vs B vs C in discovery group revealed 249 significantly de-regulated genes in at least 20% of patients, in at least one comparison (AvB or BvC or AvC). Genes involved in stress response (FOSB, FOS, JUN, JUNB, DUSP1, CYR61, EGR1-3, ATF3, RGS1, RGS2), inflammation (IL20, IL8, SOCS3, GABRP, PIGR, NR4A1-2, CCL2- 3, CCL21, CCL14, CCL18-19, CXCL2, CXCL9-10, CXCL14), invasion & migration (PTGS2, MMP9-13), lipid metabolism (CD36, LIPE, TAT, FABP4, PLIN1, PLIN4, LPL, LEP), epithelial markers (KRT5, KRT7, KRT14-17, KRT23, KRT6A-6B) and cellular differentiation(SOX10, LRP2, S100A2, S100A7-A9, S100B, S100P) were significantly deregulated at time-point B versus A and many of these genes were also significantly deregulated in C versus A comparison, suggesting sustained deregulation through surgical resection. Replicative analysis involving comparison of tumors grouped by time points (A vs B vs C) identified 192 genes uniquely de-regulated in any one of the 3 comparisons, of which 42 overlapped with patient-wise analysis. These 42 genes included all the AP-1 transcription factor network signaling genes, epithelial markers (KRT14, KRT6A), inflammation related genes (SOCS3, PIGR, GABRP, NR4A1, NR4A2), lipid metabolism related genes (PLIN1) and cellular differentiation & cell fate related genes (SOX10, LRP2). Pathway analyses will be presented. qRT-PCR on paired samples in 8 independent patients validated the differential expression of eight genes in either AvB or AvC or both comparisons: FOS, FOSB, JUNB, DUSP1, RGS1, NR4A2, ZFP36, andMMP13. Comparison of biopsy (corresponding to A) with surgical samples (corresponding to C) in 6 cancer types (breast, cervical, lymphoma, mesothelioma, esophageal and sarcoma) in TCGA studies showed statistically significant de-regulation of 11 AP-1 transcription factor network signaling related genes in at least 4 cancers, further validating our experimental results. Conclusions: Our experiment, uniquely including an intra-operative tumor sample, shows that surgical removal induces a conserved stress response in the primary tumor that is potentially capable of bestowing metastatic capability on the tumor cells. Citation Format: Sudeep Gupta, Rohan Chaubal, Nilesh Gardi, Sunil Pachakar, Dimple Bhatia, Poonam Gera, Nita Nair, Shalaka Joshi, Vani Parmar, Purvi Thakkar, Garvit Chitkara, Rasika Kadam, Sejal Gujarathi, Harsh Oza, Rohini Hawaldar, Vaibhav Vanmali, Kenneth Buetow, Amit Dutt, Rajendra Badwe. Molecular effects of surgical resection on primary breast tumor [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-05-01.