Analysis of chemotherapy response programs in ovarian cancers by the next-generation sequencing technologies

Lihua Cheng, Wei Lu, Bhushan Kulkarni, Tanja Pejovic, Xiaowei Yan, Jung Hsien Chiang, Leroy Hood, Kunle Odunsi, Biaoyang Lin

Research output: Contribution to journalArticle

39 Citations (Scopus)

Abstract

Objective: To understand the chemotherapy response program in ovarian cancer cells at deep transcript sequencing levels. Methods: Two next-generation sequencing technologies - MPSS (massively parallel signature sequencing) and SBS (sequencing by synthesis) - were used to sequence the transcripts of IGROV1 and IGROV1-CP cells, and to sequence the transcripts of a highly chemotherapy responsive and a highly chemotherapy resistant ovarian cancer tissue. Results: We identified 3422 signatures (2957 genes) that are significantly different between IGROV1 and IGROV1-CP cells (P <0.001). Gene Ontology (GO) term GO:0001837 (epithelial-to-mesenchymal transition) and GO:0034330 (cell junction assembly and maintenance) are enriched in genes that are over expressed in IGROV1-CP cells while apoptosis-related GO terms are enriched in genes over expressed in IGROV1 cells. We identified 1187 tags (corresponding to 1040 genes) that are differentially expressed between the chemotherapy responsive and the persistently chemotherapy resistant ovarian cancer tissues. GO term GO:0050673 (epithelial cell proliferation) and GO:0050678 (regulation of epithelial cell proliferation) are enriched in the genes over expressed in the chemotherapy resistant tissue while the GO:0007229 (integrin-mediated signaling pathway) is enriched in the genes over expressed in the chemotherapy sensitive tissue. An integrative analysis identified 111 common differentially expressed genes including two bone morphogenetic proteins (BMP4 and BMP7), six solute carrier proteins (SLC10A3, SLC16A3, SLC25A1, SLC35B3, SLC7A5 and SLC7A7), transcription factor POU5F1 (POU class 5 homeobox 1), and KLK10 (kallikrein-related peptidase 10). A network analysis revealed a subnetwork with three genes BMP7, NR2F2 and AP2B1 that were consistently over expressed in the chemoresistant tissue or cells compared to the chemosensitive tissue or cells. Conclusion: Our database offers the first comprehensive view of the digital transcriptomes of ovarian cancer cell lines and tissues with different chemotherapy response phenotypes.

Original languageEnglish (US)
Pages (from-to)159-169
Number of pages11
JournalGynecologic Oncology
Volume117
Issue number2
DOIs
StatePublished - May 2010

Fingerprint

Gene Ontology
Ovarian Neoplasms
Technology
Drug Therapy
Genes
High-Throughput Nucleotide Sequencing
Large Neutral Amino Acid-Transporter 1
POU Domain Factors
Epithelial Cells
Cell Proliferation
Tissue Kallikreins
Bone Morphogenetic Proteins
Epithelial-Mesenchymal Transition
Intercellular Junctions
Homeobox Genes
Transcriptome
Integrins
Peptide Hydrolases
Maintenance
Databases

Keywords

  • Chemotherapy
  • EMT
  • MPSS
  • Next-generation sequencing
  • Ovarian cancer
  • SBS

ASJC Scopus subject areas

  • Obstetrics and Gynecology
  • Oncology

Cite this

Analysis of chemotherapy response programs in ovarian cancers by the next-generation sequencing technologies. / Cheng, Lihua; Lu, Wei; Kulkarni, Bhushan; Pejovic, Tanja; Yan, Xiaowei; Chiang, Jung Hsien; Hood, Leroy; Odunsi, Kunle; Lin, Biaoyang.

In: Gynecologic Oncology, Vol. 117, No. 2, 05.2010, p. 159-169.

Research output: Contribution to journalArticle

Cheng, Lihua ; Lu, Wei ; Kulkarni, Bhushan ; Pejovic, Tanja ; Yan, Xiaowei ; Chiang, Jung Hsien ; Hood, Leroy ; Odunsi, Kunle ; Lin, Biaoyang. / Analysis of chemotherapy response programs in ovarian cancers by the next-generation sequencing technologies. In: Gynecologic Oncology. 2010 ; Vol. 117, No. 2. pp. 159-169.
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abstract = "Objective: To understand the chemotherapy response program in ovarian cancer cells at deep transcript sequencing levels. Methods: Two next-generation sequencing technologies - MPSS (massively parallel signature sequencing) and SBS (sequencing by synthesis) - were used to sequence the transcripts of IGROV1 and IGROV1-CP cells, and to sequence the transcripts of a highly chemotherapy responsive and a highly chemotherapy resistant ovarian cancer tissue. Results: We identified 3422 signatures (2957 genes) that are significantly different between IGROV1 and IGROV1-CP cells (P <0.001). Gene Ontology (GO) term GO:0001837 (epithelial-to-mesenchymal transition) and GO:0034330 (cell junction assembly and maintenance) are enriched in genes that are over expressed in IGROV1-CP cells while apoptosis-related GO terms are enriched in genes over expressed in IGROV1 cells. We identified 1187 tags (corresponding to 1040 genes) that are differentially expressed between the chemotherapy responsive and the persistently chemotherapy resistant ovarian cancer tissues. GO term GO:0050673 (epithelial cell proliferation) and GO:0050678 (regulation of epithelial cell proliferation) are enriched in the genes over expressed in the chemotherapy resistant tissue while the GO:0007229 (integrin-mediated signaling pathway) is enriched in the genes over expressed in the chemotherapy sensitive tissue. An integrative analysis identified 111 common differentially expressed genes including two bone morphogenetic proteins (BMP4 and BMP7), six solute carrier proteins (SLC10A3, SLC16A3, SLC25A1, SLC35B3, SLC7A5 and SLC7A7), transcription factor POU5F1 (POU class 5 homeobox 1), and KLK10 (kallikrein-related peptidase 10). A network analysis revealed a subnetwork with three genes BMP7, NR2F2 and AP2B1 that were consistently over expressed in the chemoresistant tissue or cells compared to the chemosensitive tissue or cells. Conclusion: Our database offers the first comprehensive view of the digital transcriptomes of ovarian cancer cell lines and tissues with different chemotherapy response phenotypes.",
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AU - Yan, Xiaowei

AU - Chiang, Jung Hsien

AU - Hood, Leroy

AU - Odunsi, Kunle

AU - Lin, Biaoyang

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N2 - Objective: To understand the chemotherapy response program in ovarian cancer cells at deep transcript sequencing levels. Methods: Two next-generation sequencing technologies - MPSS (massively parallel signature sequencing) and SBS (sequencing by synthesis) - were used to sequence the transcripts of IGROV1 and IGROV1-CP cells, and to sequence the transcripts of a highly chemotherapy responsive and a highly chemotherapy resistant ovarian cancer tissue. Results: We identified 3422 signatures (2957 genes) that are significantly different between IGROV1 and IGROV1-CP cells (P <0.001). Gene Ontology (GO) term GO:0001837 (epithelial-to-mesenchymal transition) and GO:0034330 (cell junction assembly and maintenance) are enriched in genes that are over expressed in IGROV1-CP cells while apoptosis-related GO terms are enriched in genes over expressed in IGROV1 cells. We identified 1187 tags (corresponding to 1040 genes) that are differentially expressed between the chemotherapy responsive and the persistently chemotherapy resistant ovarian cancer tissues. GO term GO:0050673 (epithelial cell proliferation) and GO:0050678 (regulation of epithelial cell proliferation) are enriched in the genes over expressed in the chemotherapy resistant tissue while the GO:0007229 (integrin-mediated signaling pathway) is enriched in the genes over expressed in the chemotherapy sensitive tissue. An integrative analysis identified 111 common differentially expressed genes including two bone morphogenetic proteins (BMP4 and BMP7), six solute carrier proteins (SLC10A3, SLC16A3, SLC25A1, SLC35B3, SLC7A5 and SLC7A7), transcription factor POU5F1 (POU class 5 homeobox 1), and KLK10 (kallikrein-related peptidase 10). A network analysis revealed a subnetwork with three genes BMP7, NR2F2 and AP2B1 that were consistently over expressed in the chemoresistant tissue or cells compared to the chemosensitive tissue or cells. Conclusion: Our database offers the first comprehensive view of the digital transcriptomes of ovarian cancer cell lines and tissues with different chemotherapy response phenotypes.

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