METHODS FOR ASSEMBLING PANELS OF CANCER CELL LINES FOR USE IN TESTING THE EFFICACY OF ONE OR MORE PHARMACEUTICAL COMPOSITIONS

• US 20100144554A1
• Filed: 10/28/2009
• Published: 06/10/2010
• Est. Priority Date: 10/31/2008
• Status: Active Grant

• Current Assignee: Abbvie Incorporated
• Original Assignee: Abbott Laboratories Incorporated
• Inventors: Zhang, Ke, Lu, Xin, Semizarov, Dimitri, Lesniewski, Rick R.
• Granted Patent: US 9,002,653 B2
• Time in Patent Office: Days
• US Class Current: 506/24
• CPC Class Codes: G06F 17/10 Complex mathematical operations function generation by table look-up G06F1/03; evaluation of elementary functions by calculation G06F7/544
• CPC Class Codes: G06F 17/11 for solving equations , e.g. nonlinear equations, general mathematical optimization problems optimization specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: G06F 17/15 Correlation function computation including computation of convolution operations arithmetic circuits for sum of products per se, e.g. multiply-accumulators G06F7/5443; digital filters, e.g. FIR, IIR, adaptive filters H03H17/00
• CPC Class Codes: G06F 17/16 Matrix or vector computation , e.g. matrix-matrix or matrix-vector multiplication, matrix factorization matrix transposition G06F7/78
• CPC Class Codes: G06F 17/17 Function evaluation by approximation methods, e.g. inter- or extrapolation, smoothing, least mean square method G06F17/18 takes precedence ; interpolation for numerical control G05B19/18
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/10 Ploidy or copy number detection
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/30 Unsupervised data analysis
• CPC Class Codes: G16B 5/00 ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• CPC Class Codes: G16B 5/20 Probabilistic models
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

The present invention relates to algorithms for use in defining genomic subgroups of tumors and cancer cell lines. The present invention also relates to methods for assembling panels of tumors and cancer cell lines according to genomic subgroups for use in testing the efficacy of one or more pharmaceutical compounds in the treatment of subjects suffering from at least one cancer.

Claims

1. An algorithm for use in clustering tumors and cell lines to define genomic subgroups, the method comprising the steps of:

(a) obtaining a plurality of m samples comprising at least one tumor or cancer cell line;

(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, copy number alteration information obtained from 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 (1);

2. The algorithm of claim 1, wherein the unsupervised clustering algorithm is a hierarchical clustering.

3. The algorithm of claim 1, wherein Cophenetic correlation is used to provide a final number of clusters from the data set.

4. The algorithm of claim 1, wherein Bayesian Information Criterion is used to provide a final number of clusters from the data set.

5. The algorithm of claim 1, wherein Cophenetic correlation and Bayesian Information Criterion are used to provide a final number of clusters from the data set.

6. A method for assembling panels of tumor and cancer cell lines according to genomic subgroups, the method comprising the steps of:

(a) obtaining a plurality of m samples comprising at least one tumor or cancer cell line;

(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, copy number alteration information obtained from 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 unsupervised clustering 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 (1);

7. The method of claim 6, wherein the cancer is selected from the group consisting of:

small cell lung carcinoma, non-small cell lung carcinoma, colorectal cancer, and melanoma.

8. The method of claim 6, wherein the copy number alteration is a gain or loss or copy number.