Patent Document ID: 20100145897
Application ID: 12607080
Patent Flag: 0

Claim One:
1. A method for obtaining a database of malignant melanoma genomic subgroups, the method comprising the steps of: (a) obtaining a plurality of m samples comprising at least one MM cell; (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 the formula: 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 the formula: 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 a correlation matrix, C i,j is an row and j th column in matrix C, H ,i and H ,j are an i th and j th column vector in matrix H, ρ(H ,i , H ,j ) is a Pearson correlation coefficient between H ,i and H ,j , i and j run from 1 to m, 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 tumors and cell lines in the data set 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 cancer 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.