Patent Document ID: 20100145894
Application ID: 12607082
Patent Flag: 0

Claim One:
1. A method for obtaining a database of colorectal cancer genomic subgroups, the method comprising the steps of: (a) obtaining a plurality of m samples comprising at least one CRC cell, wherein the samples comprise cell lines or tumors; (b) acquiring a data set comprising copy number alteration information from at least one locus from each chromosome from each sample obtained in step (a); (c) identifying in the data set samples contaminated by normal cells and eliminating the contaminated samples from the data set, wherein the identifying and eliminating comprises: (1) applying a machine learning algorithm tuned to parameters that represent the differences between tumor and normal samples to the data; (2) assigning a probability score for normal cell contamination to each sample as determined by the machine learning algorithm; (3) eliminating data from the data set for each sample scoring 50% or greater probability of containing normal cells; (d) estimating a number of subgroups, r, in the data set by applying an unsupervised clustering algorithm using Pearson linear dissimilarity algorithm to the data set; (e) assigning each sample in the data set to at least one cluster using a modified genomic Non-negative Matrix Factorization (gNMF) algorithm, wherein the modified gNMF algorithm comprises: (1) calculating divergence of the algorithm after every 100 steps of multiplicative updating using formula (11): D ( V   WH ) = ∑ i = 1 n ∑ j = 1 m ( V ij log V ij ( WH ) ij - V ij + ( WH ) ij ) ( 11 ) wherein the V ij is the i th row and j th column of matrix V, (WH) ij is the i th row and j th column of matrix (W*H), i runs from 1 to n and n is the number of segments in the data set, and j runs from 1 to m and m is the number of samples in the data set; (2) stopping the algorithm if the divergence calculated in step (e)(1) does not decrease by more than about 0.001% when compared to the divergence calculated for the previous 100 steps of multiplicative updating of the algorithm; (3) randomly repeating the algorithm for a selected number of runs and calculating a Pearson correlation coefficient matrix of H for the each of run the algorithm using formula (12): C i , j = ρ ( H , i , H , j ) = 1 r - 1 ∑ k ( H k , i - H , i _ ) ( H k , j - H , j _ ) s H , i s H , j ( 12 ) wherein C is the correlation matrix, C i,j is the i th row and j th column in the matrix C, H ,i and H ,j are the i th and j th column vector in matrix H, ρ(H ,i , H ,j ) is the Pearson correlation coefficient between H ,j and H ,j, i and j run from 1 to m and m is the number of samples in the data set, k runs from 1 to r and r is the number of subgroups from step (d); (4) averaging the Pearson correlation coefficient matrices for each run of the algorithm obtained from step (e)(3) to arrive at an average correlation matrix; (5) assigning samples into r subgroups by applying a unsupervised clustering algorithm using 1 minus the average correlation matrix determined in step (e)(4) and cutting the dendrogram into r clusters; (f) applying a Cophenetic correlation, Bayesian information criterion, or a combination thereof to provide a final number of clusters from the data set, wherein each final cluster defines a genomic subgroup for each tumor or cell line sample; and (g) optionally evaluating the stability of the final number of clusters selected in step (f) using a ten-fold stability test.