US 2010/0145893 A1Published application

Genomic classification of non-small cell lung carcinoma based on patterns of gene copy number alterations

AssigneeAbbott Laboratories

InventorsDimitri Semizarov, Xin Lu, Ke Zhang, Rick R. Lesniewski

Life of this patent

Estimating the term…

Abstract

The invention is directed to methods and kits that allow for classification of non-small cell lung carcinoma tumors and cell lines according to genomic profiles, and methods of diagnosing, predicting clinical outcomes, and stratifying patient populations for clinical testing and treatment using the same.

Claims23

Dependent claims

A method for obtaining a database of non-small cell lung carcinoma genomic subgroups, the method comprising the steps of:

(a) obtaining a plurality of m samples comprising at least one NSCLC 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 Vij is the ith row and jth column of matrix V, (WH)ij is the ith row and jth 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, Ci,j is the ith row and jth column in the matrix C, H,i and H,j are the ith and jth column vector in matrix H, ρ(H,i, H,j) is the Pearson correlation coefficient between H,i 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.

120 more elements

A method of classifying a NSCLC tumor or cell line, comprising:

(a) providing a database, developed through a method comprising:
(i) obtaining a plurality of m samples comprising at least one NSCLC tumor or cell line;
(ii) acquiring a first data set comprising copy number alteration information from at least one locus from each chromosome from each sample obtained in step (i);
(iii) identifying in the first data set samples contaminated by normal cells and eliminating the contaminated samples from the first 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 first data set for each sample scoring 50% or greater probability of containing normal cells;
(iv) 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;
(v) 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 Vij is the ith row and jth column of matrix V, (WH)ij is the ith row and jth 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 (v)(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 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, Ci,j is the ith row and jth column in the matrix C, H,i and H,j are the ith and jth column vector in matrix H, ρ(H,i, H,j) is the Pearson correlation coefficient between H,i 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 (iv);
(4) averaging the Pearson correlation coefficient matrices for each run of the algorithm obtained from step (v)(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 (v)(4) and cutting the dendrogram into r clusters;
(vi) 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 sample;
and
(vii) optionally evaluating the stability of the final number of clusters selected in step (vi) using a ten-fold stability test;
(b) providing a sample suspected of containing NSCLC cells,
(c) acquiring a second data set, Vsample, comprising copy number alteration information from the same at least one locus from step (ii);
and
(d) classifying the sample from Vsample, by comparing Vsample to the clusters determined in steps (i)-(vii).

125 more elements

A method of classifying a therapeutic intervention for arresting or killing non-small cell lung carcinoma (NSCLC) cells, comprising:

(a) from a panel of NSCLC cells classified according to genomic subgroups, selecting at least one NSCLS cell line from each subgroup, wherein the panel is assembled from a method comprising:
(i) obtaining a plurality of m samples comprising at least one NSCLC tumor or cell line;
(ii) acquiring a first data set comprising copy number alteration information from at least one locus from each chromosome from each sample obtained in step (i);
(iii) identifying in the first data set samples contaminated by normal cells and eliminating the contaminated samples from the first 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 first data set for each sample scoring 50% or greater probability of containing normal cells;
(iv) 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;
(v) 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 Vij is the ith row and jth column of matrix V, (WH)ij is the ith row and jth 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 (v)(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 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, Ci,j is the ith row and jth column in the matrix C, H,i and H,j are the ith and jth column vector in matrix H, ρ(H,i, H,j) is the Pearson correlation coefficient between H,i 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 (iv);
(4) averaging the Pearson correlation coefficient matrices for each run of the algorithm obtained from step (v)(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 (v)(4) and cutting the dendrogram into r clusters;
(vi) 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 sample;
and
(vii) optionally evaluating the stability of the final number of clusters selected in step (vi) using a ten-fold stability test
(viii) selecting at least one NSCLC cell from each cluster selected in step (vi) and assembling into panels defined according to genomic subgroups.
(b) contacting the at least one NSCLC cell from each subgroup with the therapeutic intervention;
(c) assaying the effectiveness of the therapeutic intervention to arrest or kill the at least one NSCLC cell from each subgroup;
(d) classifying the therapeutic intervention according to the determined effectiveness of the therapeutic intervention to arrest or kill the at least one NSCLC cell from each subgroup,
wherein arresting or killing the at least one NSCLC cell from one subgroup, but not another indicates specificity of the therapeutic intervention to arrest or kill NSCLC cells of that subgroup.

126 more elements

A method of assembling a probe panel for classifying a NSCLC cell from a sample, comprising:

(a) assembling a database, comprising:
(i) obtaining a plurality of m samples comprising at least one NSCLC tumor or cell line;
(ii) acquiring a first data set comprising copy number alteration information from at least one locus from each chromosome from each sample obtained in step (i);
(iii) identifying in the first data set samples contaminated by normal cells and eliminating the contaminated samples from the first 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 first data set for each sample scoring 50% or greater probability of containing normal cells;
(iv) 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;
(v) 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 Vij is the ith row and jth column of matrix V, (WH)ij is the ith row and jth 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 (v)(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 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, Ci,j is the ith row and jth column in the matrix C, H,i and H,j are the ith and jth column vector in matrix H, ρ(H,i, H,j) is the Pearson correlation coefficient between H,i 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 (iv);
(4) averaging the Pearson correlation coefficient matrices for each run of the algorithm obtained from step (v)(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 (v)(4) and cutting the dendrogram into r clusters;
(vi) 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 sample;
and
(vii) optionally evaluating the stability of the final number of clusters selected in step (vi) using a ten-fold stability test
(viii) selecting at least one sample from each cluster selected in step (vi) and assembling into panels defined according to genomic subgroups;
(b) analyzing the database of step (a) to determine characteristic copy number abnormalities for each subgroup;
(c) designing a plurality of probes based on the determined characteristic copy number abnormalities for each subgroup and assigning each probe to a genomic subgroup.

124 more elements

A kit for classifying a NSCLC tumor sample or a cell line, comprising:

(a) instructions to assemble a database, comprising instructions for:
(i) obtaining a plurality of m samples comprising at least one NSCLC tumor or cell line;
(ii) acquiring a first data set comprising copy number alteration information from at least one locus from each chromosome from each sample obtained in step (i);
(iii) identifying in the first data set samples contaminated by normal cells and eliminating the contaminated samples from the first 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 first data set for each sample scoring 50% or greater probability of containing normal cells;
(iv) 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;
(v) 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 Vij is the ith row and jth column of matrix V, (WH)ij is the ith row and jth 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 (v)(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 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, Ci,j is the ith row and jth column in the matrix C, Hi, and H,j are the ith and jth column vector in matrix H, ρ(H,i, H,j) is the Pearson correlation coefficient between H,i 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 (iv);
(4) averaging the Pearson correlation coefficient matrices for each run of the algorithm obtained from step (v)(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 (v)(4) and cutting the dendrogram into r clusters;
(vi) 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 sample;
and
(vii) optionally evaluating the stability of the final number of clusters selected in step (vi) using a ten-fold stability test;
and
(b) optionally, a first, second and third cell line, or isolated genomic DNA thereof, wherein the first cell line is selected from the group consisting of HCC827, NCI-H1437, NCI-H1563, NCI-H1568, NCI-H1623, NCI-H1651, NCI-H1693, NCI-H1755, NCI-H1793, NCI-H1838, NCI-H1944, NCI-H1975, NCI-H1993, NCI-H2023, NCI-H2073, NCI-H2085, NCI-H2087, NCI-H2122, NCI-H2126, NCI-H2228, NCI-H2291, NCI-H23, NCI-H2342, NCI-H2347, NCI-H647, NCI-H920, NCI-H969, CLS-54, LX-289, SK-LU-1, H2882, Calu-6, H358, and H460;
the second cell line is selected from the group consisting of NCI-H2405, NCI-H522, SK-MES-1, H157, H1819, H2009, H2887, HCC1171, HCC1359, HCC15, HCC193, HCC366, HCC461, HCC515, HCC78, HOP-62, HOP-92, and NCI-H266;
and
the third cell line is selected from the group consisting of A549, Calu-3, NCI-H1734, NCI-H838, and HCC95.

126 more elements

Dependencies are inferred from the claim text.

Facts

Application: 12/607,079Filed 28 Oct 2009
Priority: 31 Oct 2008
Published: 10 Jun 2010
Term ends: Not estimatedThe source records don’t support an estimate.

Classification13 codes

G16H 70/60
ICT specially adapted for the handling or processing of medical references: relating to pathologies
C12Q 1/6886
For diseases caused by alterations of genetic material: for cancer
G16B 20/20
ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations: allele or variant detection, e.g. single…
G16B 40/00
Ict specially adapted for biostatistics

Family

Application61/110,317Us provisional application · filed 31 Oct 2008
Application61/110,281Us provisional application · filed 31 Oct 2008
US 8,498,820 B2Related patent
US 2010/0145893 A1This patent

From the US filing history. Foreign family members aren’t listed.

Timeline

Citations

Open elsewhere

Building an agent or pulling many patents? Use the API or MCP server instead of scraping pages. Get an API key

patentgrep

Loading patent…

Abstract

A method for obtaining a database of non-small cell lung carcinoma genomic subgroups, the method comprising the steps of:

(a) obtaining a plurality of m samples comprising at least one NSCLC 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 Vij is the ith row and jth column of matrix V, (WH)ij is the ith row and jth 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, Ci,j is the ith row and jth column in the matrix C, H,i and H,j are the ith and jth column vector in matrix H, ρ(H,i, H,j) is the Pearson correlation coefficient between H,i 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.

120 more elements

Facts

Application

12/607,079Filed 28 Oct 2009

Priority

31 Oct 2008

Published

10 Jun 2010

Term ends

Not estimatedThe source records don’t support an estimate.

Classification13 codes

G16H 70/60
ICT specially adapted for the handling or processing of medical references: relating to pathologies
C12Q 1/6886
For diseases caused by alterations of genetic material: for cancer
G16B 20/20
ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations: allele or variant detection, e.g. single…
G16B 40/00
Ict specially adapted for biostatistics