H. Lee Moffitt Cancer Center & Research Institute

  • BAD Phosphorylation Determines Ovarian Cancer Chemosensitivity and Patient Survival

BAD Phosphorylation Determines Ovarian Cancer Chemosensitivity and Patient Survival
Douglas C. Marchion, Hope M. Cottrill, Yin Xiong, Ning Chen, Elona Bicaku, Nisha Bansal, William J Fulp, Hye Sook Chon, Marcia Humphrey, Siddharth G. Kamath, Ardeshir Hakam, Carolina Moreno, Patricia L. Judson, Andrew Berchuck, Timothy Yeatman, Robert M. Wenham, Sachin M. Apte, Gregory C. Bloom, Steven A. Eschrich, Said Sebti, Dung-Tsa Chen, Johnathan M. Lancaster
 
This page includes supplemental information, methods and data for the paper referenced above. Specific questions can be directed to Johnathan.Lancaster@moffitt.org or otherwise as noted below.
 

Index

Ovarian Cancer Cell Lines

Genes associated with OVCA development of cisplatin resistance (as measured by EC50) were identified using Pearson's correlation. The gene lists were generated by load CellLines.rdata and running the processOVCACellLineGenes() function. Files/support scripts/etc are described below: The resulting probeset lists (with correlation coefficients): A Summary Table with annotations is available.
 
These genes were loaded into GeneGO MetaCore and significantly enriched pathways were identified (see the spreadsheet for the full list).

North American Ovarian Cancer Patients

We looked at a set of 142 ovarian cancer patient samples collected from two different sources, for consideration of the BAD Pathway. The full 142 samples are annotated in this file and consist of the following:
  1. 114 samples arrayed and publicly available from Duke University (link). These sample names were standardized according to this mapping, to be consistent with the Moffitt samples.
  2. 28 Moffitt Cancer samples. The expression data is available as GSE23554 in GEO (Gene Expression Omnibus). They are also available here as a zip file of CEL files.
The patient data was normalized together using rma (see exprs-ovcapatients-rma.txt for expression values). An R object (as an ExpressionSet) was created that included the 142 patients with clinical data. There are 101 Complete Responders (CR) and 41 Incomplete Responders (IR). Performing a t test between expression in these two groups yields 397 probesets, as annotated here.

BAD Pathway Signature

This zip file contains R code and all R Datasets needed to create plots and statistics for Figure 4 of the BAD-apoptosis pathway and cancer clinical outcome paper. For any questions regarding this analysis please contact William Fulp in the Biostatistics Department of Moffitt Cancer Center william.fulp@moffitt.org.
 
Zip File Contents:
  1. posted.code.for.paper.r: R code to create all Figure 4 plots and perform analyses.
  2. Gene.List.RData: List of probe set ids and matching gene names.
  3. Moffitt.Duke.FF.142.RData (Fig4A-Fig4C): Clinical and Gene Dataset (n=142). OVCA samples from MCC and Duke University Medical Center (North American OVCA dataset)
  4. GSE9891.RData4 (Fig 4D): Clinical and Gene Dataset (n=238).
  5. Moffitt.Colon205.RData (Fig 4E): Clinical and Gene Dataset (n=205). Colon cancer samples from MCC.
  6. Nutt.RData3 (Fig 4F): Clinical and Gene Dataset (n=50), as well as a linking file matching the probe sets to the cDNA dataset.
  7. GSE13041.RData2 (Fig 4G): Clinical and Gene Dataset (n=182).
  8. Wang.RData5 (Fig 4H): Clinical and Gene Dataset (n=286).
  9. Chanrion.RData1 (Fig 4I): Clinical and Gene Dataset (n=155), as well as a linking file matching the probe sets to the cDNA dataset.
References:
  1. Chanrion M, Negre V, Fontaine H, et al. A gene expression signature that can predict the recurrence of tamoxifen-treated primary breast cancer. Clin Cancer Res 2008;14(6):1744-52.
  2. Lee Y, Scheck AC, Cloughesy TF, et al. Gene expression analysis of glioblastomas identifies the major molecular basis for the prognostic benefit of younger age. BMC medical genomics 2008;1:52.
  3. Nutt CL, Mani DR, Betensky RA, et al. Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer research 2003;63(7):1602-7.
  4. Tothill RW, Tinker AV, George J, et al. Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome. Clin Cancer Res 2008;14(16):5198-208.
  5. Wang Y, Klijn JG, Zhang Y, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 2005;365(9460):671-9.

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