Reanalysis of Petricoin et al. Ovarian Cancer Data Set 3

Russ Wolfinger and Geoff Mann

SAS Institute Inc.

NISS Proteomics Workshop

March 6, 2003

Ovarian Cancer Mass Spec Data from http://clinicalproteomics.steem.com

91 Normals

162 Cancers

What We’d Love to See

What We Are Seeing

Green: Cancer, Red: Normal

Left: Green in Front, Right: Red in Front

New Paper from MD Anderson

 

 

 

Baggerly, K.A, Morris, J.S., and Coombes, K.R. (2003). Cautions about Reproducibility in Mass Spectrometry Patterns: Joint Analysis of Several Proteomic Data Sets

Email: kabagg@mdanderson.org

• Reanalyses of all three ovarian cancer data sets

• For data set 3, they note that two pairs of m/z values provide perfect discrimination: 435.46 & 465.57, and 2.79 & 245.2. Easy to find with simple t-tests; genetic algorithm unnecessary.

First Pair: 435.46 and 465.57 Da

Green: Cancer, Red: NormalLeft: Green in Front, Right: Red in Front

Second Pair: 2.79 and 245.2 Da

Green: Cancer, Red: NormalLeft: Green in Front, Right: Red in Front

Questions

• What’s going on here?

• Are discriminators <500 Da generalizable?

• How about >500 Da?

Going Small: 435 Da

• At least 100 peptide fragments (including permutations) add up to 435, e.g. AFY, SMY, PPW, KNH, GGGAC, SSGGG

• 30 Hits from ChemFinder.com, including Sphingosyl-phosphocholine, a lipid molecule

• Similar kind of story for 465 Da

Going Large: Cross-Validated Stepwise Discriminant Analysis

1. Subtract baselines and determine 330 most prominent peak areas, all with m/z > 600.

2. Form 500 random partitions of the 253 spectra, with a 33% stratified holdout sample in each.

3. Perform stepwise discriminant analysis on each partition, using entry p = 0.05, exit p = 0.20, and max variables = 5.

4. Compute misclassification rate on each trial.

Results of Cross-Validated Stepwise Discriminant Analysis

1. Always picked 5 variables

2. Misclassification rate = 5%.

3. Most common discriminators:

• 681, appeared in 100% of selected quintuples

• 7379, in 63%

• 869, in 54%

• 4004, in 44%

Partial Least Squares on the Same 330 Peak Areas

Parting Shots

• Statistical discrimination is relatively easy for these data, but what are the real explanations for the clear differences in data set 3?

• Can statisticians overcome their biases and win the day?

• Is this kind of approach a red herring or a red snapper?