Multiplexed Cancer Pathway Analysis
All cancers must evolve a means of sustaining self-sufficient growth and evading apoptosis. This process typically occurs via the accumulation of mutational events that confer a growth advantage through deregulation of the molecular pathways controlling cell growth and cell fate. Mutations in over 100 genes are known to drive tumorgenesis and within any given tumor there are between 2-8 mutated "driver genes" modulating the activity of critical molecular pathways. Studying the deregulation of molecular pathways impacted by mutational events as well as monitoring expression of these driver genes is critical to gaining a complete understanding of the biology underlying cancer.
Browse samples of the white paper or download the complete version.
Multiplexed Cancer Pathway Analysis: Complete Download
- Introduction cont.
- Panel Design
- Pathway Deregulation
- Pathway Analysis
- Download White Paper
Molecular pathways are an attractive organizing principle for analysis of gene expression data as they provide a means to combine the noisy information in individual genes into stable and meaningful representations of fundamental biological processes. Gene expression profiling has long been used within the cancer field to stratify cell populations and classify tumors. This powerful ability is largely due to the fact that the gene expression state of a cell or tissue contains information about the biological processes occurring within a sample. Pathway-based analyses provide a holistic view of the changes to fundamental biological processes allowing for deregulation of regulatory pathways to be linked back to "driver gene" status.
Pathways of the PanCancer Pathways Panel
In their seminal paper, Vogelstein et al., argue that understanding the deregulation of pathways is integral to understanding the biology of any cancer. A growing number of studies have demonstrated that pathway based analysis of gene expression information provides a framework for understanding the discrete changes between the biology of different cancers and cancer subtypes. To better understand the intricate network of pathways and interactions, NanoString has taken a biologyguided, data-driven approach to identify over 700 essential genes that capture the activity of 13 canonical cancer pathways* and associated driver genes. Each of the pathways was mapped to publicly available data-sources (KEGG http:// www.genome.jp/kegg/, Reactome http://www.reactome.org/, GO http://www.geneontology.org/) as described below (see Panel Design) in order to create a tool designed to enable a pathway-based approach to exploring the molecular mechanisms of cancer and cancer subtyping.
Register to view the entire white paper.
Proportion of Pathway Genes Included in the Panel
Click on the thumbnail to see the full-size image.
Identification of Cancer Pathway Genes
Proportion of total variance in pathways captured by the selected pathway genes. Individual black lines denote the proportion of pathway gene variance captured by the selected gene set as gene number increases. Each line represents a pathway, and each line's upper-right terminus corresponds to the number of genes ultimately selected for inclusion in the gene list for a given pathway. The thick red line denotes the proportion of pathway gene variance captured on average across all pathways and highlight that 60% of genes in a pathway are sufficient to capture 90% of the gene expression variance within a pathway.
Register to view the entire section on Panel Design.
Pathway Deregulation Analysis
Pathway-based Analyses of Gene Expression Data
Pathway deregulation scores in breast cancer samples. Pathifier was used to calculate deregulation scores for each pathway (x-axis) in each sample (y-axis). Samples are colored according to intrinsic subtype, with Basal-like (red), HER2-enriched (pink), Luminal A (dark blue), Luminal B (light blue), and normal samples (green). Deregulation scores were generated relative to expression in normal breast tissue and are shown on a continuum from no deregulation (red) to highly deregulated (yellow).
Boxplots of pathway deregulation scores by intrinsic subtype. The distribution of Pathifier deregulation scores of each pathway is plotted for each intrinsic subtype. Samples are colored according to intrinsic subtype, with Basal-like (red), HER2-enriched (pink), Luminal A (dark blue), Luminal B (light blue), and normal samples (green). The top and bottom of the box delineate the upper and lower quartiles, with the thick line within each box representative of the median. Whiskers extend to capture all data within two standard deviations of the mean.
Deregulation scores of selected cell fate pathways in TCGA breast cancer data. Pathifier-derived deregulation scores from five pathways related to cell fate are plotted against each other in order to highlight patterns of coexpression within instrinsic subtypes. Samples are colored according to intrinsic subtype, with Basal-like (red), HER2-enriched (pink), Luminal A (dark blue), Luminal B (light blue), and normal samples (green). (A) Hedgehog and Wnt pathway regulation is consistent for all breast cancer subtypes. (B) Chromatin Modification and Wnt pathway deregulation and discordant in the majority of samples from each intrinsic subtype. (C) Notch and Wnt pathway regulation is consistent except for a subgroup of Luminal tumors. (D) Transcriptional Regulation and Wnt pathway regulation is consistent in Basal and HER2-enriched and discordant in Luminal A and B tumors.
Register to view the entire section on Pathway Deregulation.
Click on the thumbnail to see the full-size image.
Differential expression in Basal-like tumors relative to normal tissue within the cell cycle pathway. Differential expression results comparing expression of individual cell cycle genes between normal and Basal-like samples are mapped to a KEGG representation of the pathway using Pathview. Proteins whose corresponding genes are up-regulated in Basal-like samples are colored red; proteins with down-regulated genes are colored green.
Differential expression in Basal-like tumors relative to normal tissue within the apoptosis pathway. Differential expression results comparing expression of individual apoptosis genes between normal and Basal-like samples are mapped to a KEGG representation of the pathway using Pathview. Proteins whose corresponding genes are up-regulated in Basal-like samples are colored red; proteins with down-regulated genes are colored green.
Register to view the entire section on Pathway Analysis.
Download the Complete White Paper