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US 10,095,829 B2Granted

Computer implemented methods of treating lung cancer

AssigneeWorldwide Innovative Network

InventorsVladimir Lazar, Jean-Charles Soria, Michel Ducreux, Thomas Tursz

Life of this patent

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Abstract Drawings Claims Description

Abstract

The present invention concerns a method for predicting the relative efficacy of a plurality of drugs for treating a tumor in an individual comprising the molecular characterization of the tumor, and the calculation of a score for the plurality of drugs essentially based on the percentage of deregulated target genes.

Claims32

Dependent claims

Claim 1A method for treating a patient having lung cancer · 402 words6 dependent Claim 8A method for treating a patient having lung cancer the method · 563 words5 dependent Claim 14A method for treating a patient having lung cancer the method · 582 words5 dependent Claim 20A method for treating a patient having lung cancer the method · 699 words5 dependent Claim 26A method for treating a patient having lung cancer the method · 706 words5 dependent Claim 32A method for treating a patient having lung cancer the method · 397 wordsNo dependents

A method for treating a patient having lung cancer, the method comprising:

a) characterizing molecular anomalies of a lung cancer sample from the patient in comparison to a normal sample from the same patient which is a normal histological counterpart of the lung cancer sample, said molecular anomalies characterization comprising determining genes differentially expressed in the lung cancer sample in comparison to the normal sample by oligonucleotide array and optionally determining the gain or loss of gene copy number in the lung cancer sample in comparison to the normal sample by Comparative Genomic Hybridization and determining deregulated genes in the lung cancer sample based on the genes differentially expressed and optionally the gain or loss of gene copy number;
b) providing a drug database comprising target genes associated with a plurality of drugs disclosed in Table 1;
c) determining a score for each drug of said plurality of drugs comprising calculating, for each drug of the plurality of drugs, a percentage of deregulated genes in the lung cancer sample from the patient as characterized in step (a) that are target genes for each drug of the plurality of drugs as provided in step (b) and determining the score for each drug of the plurality of drugs based on the percentage of deregulated genes among the target genes in the lung cancer sample from the patient, wherein a higher score is predictive of a higher relative efficacy of the drug for treating the lung cancer in the patient and wherein the step of characterizing molecular anomalies of the lung cancer sample comprises determining a fold change for the differentially expressed genes and optionally for the gain or loss of gene copy number;
and
d) selecting a drug with a high score for treating the patient, wherein the score (W) for a given drug is determined by the following algorithm:
W
=
Pz
⁢
(
Σ
C
⁢
F
c
>
2
)
n
C
⁢
F
c
>
2
wherein:
W is the score for the given drug;
P is the percentage of target genes for the given drug which are deregulated in the lung cancer of the patient;
Σ is sum;
Fc>2 is the Fold Change of each deregulated target gene for the given drug with a Fold Change higher than 2;
and
nCFc>2 refers to the number of target genes for the given drug with a Fold Change higher than 2.

29 more elements

A method for treating a patient having lung cancer the method comprising:

a) characterizing molecular anomalies of a lung cancer sample from the patient in comparison to a normal sample from the same patient which is a normal histological counterpart of the lung cancer sample, said molecular anomalies characterization comprising determining genes differentially expressed in the lung cancer sample in comparison to the normal sample by oligonucleotide array and optionally determining the gain or loss of gene copy number in the lung cancer sample in comparison to the normal sample by Comparative Genomic Hybridization and determining deregulated genes in the lung cancer sample based on the genes differentially expressed and optionally the gain or loss of gene copy number;
b) providing a drug database comprising target genes associated with a plurality of drugs disclosed in Table 1;
c) determining a score for each drug of said plurality of drugs comprising calculating, for each drug of the plurality of drugs, a percentage of deregulated genes in the lung cancer sample from the patient as characterized in step (a) that are target genes for each drug of the plurality of drugs as provided in step (b) and determining the score for each drug of the plurality of drugs based on the percentage of deregulated genes among the target genes in the lung cancer sample from the patient, wherein a higher score is predictive of a higher relative efficacy of the drug for treating the lung cancer in the patient and wherein the step of characterizing molecular anomalies of the lung cancer sample comprises determining a fold change for the differentially expressed genes and optionally for the gain or loss of gene copy number;
and
d) selecting a drug with a high score for treating the patient, and
wherein the method further comprises in step a) the detection of the presence of a mutation in a gene in the lung cancer sample in comparison to the normal sample by sequencing and wherein the score (W) for a given drug is determined by the following algorithm:
W
=
P
⁡
(
(
Σ
CM
⁢
F
CM
)
n
1
⁢
CM
⁢
q
1
⁢
z
1
+
(
Σ
Cm
⁢
F
Cm
)
n
2
⁢
Cm
⁢
q
2
⁢
z
2
-
(
Σ
CR
⁢
F
CR
)
n
3
⁢
CR
⁢
q
3
⁢
z
3
)
wherein:
W is the score for the given drug;
P is the percentage of target genes for the given drug which are deregulated in the lung cancer of the patient;
Σ is sum;
CM refers to major target genes for the given drug;
Cm refers to minor target genes for the given drug;
CR refers to resistance genes for the given drug;
n1CM, n2Cm and n3CR are respectively the number of deregulated target genes with a defined threshold for major target genes, minor target genes and resistance genes;
FCM, FCm and FCR are the Fold Change of each gene higher than the defined threshold for major target genes, minor target genes and resistance genes, respectively;
q1, q2 and q3 are multiplication coefficients for major target genes, minor target genes and resistance genes, respectively;
and
z1, z2 and z3 are multiplication coefficients associated with the presence of a mutation in a major target gene, a minor target gene and a resistance gene, respectively.

74 more elements

A method for treating a patient having lung cancer the method comprising:

a) characterizing molecular anomalies of a lung cancer sample from the patient in comparison to a normal sample from the same patient which is a normal histological counterpart of the lung cancer sample, said molecular anomalies characterization comprising determining genes differentially expressed in the lung cancer sample in comparison to the normal sample by oligonucleotide array and optionally determining the gain or loss of gene copy number in the lung cancer sample in comparison to the normal sample by Comparative Genomic Hybridization and determining deregulated genes in the lung cancer sample based on the genes differentially expressed and optionally the gain or loss of gene copy number;
b) providing a drug database comprising target genes associated with a plurality of drugs disclosed in Table 1;
c) determining a score for each drug of said plurality of drugs comprising calculating, for each drug of the plurality of drugs, a percentage of deregulated genes in the lung cancer sample from the patient as characterized in step (a) that are target genes for each drug of the plurality of drugs as provided in step (b) and determining the score for each drug of the plurality of drugs based on the percentage of deregulated genes among the target genes in the lung cancer sample from the patient, wherein a higher score is predictive of a higher relative efficacy of the drug for treating the lung cancer in the patient and wherein the step of characterizing molecular anomalies of the lung cancer sample comprises determining a fold change for the differentially expressed genes and optionally for the gain or loss of gene copy number;
and
d) selecting a drug with a high score for treating the patient, and
wherein the method further comprises in step a) the detection of the presence of a mutation in a gene in the lung cancer sample in comparison to the normal sample by sequencing and wherein the score (W) for a given drug is determined by the following algorithm:
W
=
P
CM
⁡
(
Σ
CM
⁢
F
CM
)
n
1
⁢
CM
⁢
q
1
⁢
z
1
+
P
Cm
⁡
(
Σ
Cm
⁢
F
Cm
)
n
2
⁢
Cm
⁢
q
2
⁢
z
2
-
P
CR
⁡
(
Σ
CR
⁢
F
CR
)
n
3
⁢
CR
⁢
q
3
⁢
z
3
wherein W is the score for the given drug;
Σ is sum;
CM refers to major target genes for the given drug;
Cm refers to minor target genes for the given drug;
CR refers to resistance genes for the given drug;
n1CM, n2Cm and n3CR are respectively the number of deregulated target genes with a defined threshold for major target genes, minor target genes and resistance genes;
FCM, FCm and FCR are the Fold Change of each gene higher than the defined threshold for major target genes, minor target genes and resistance genes, respectively;
q1, q2 and q3 are multiplication coefficients for major target genes, minor target genes and resistance genes, respectively;
z1, z2 and z3 are multiplication coefficients associated with the presence of a mutation in a major target gene, a minor target gene and a resistance gene, respectively;
and PCM, PCm and PCR are the percentage of target genes for the given drug which are deregulated in the lung cancer of the patient for major target genes, minor target genes and resistance genes, respectively.

77 more elements

A method for treating a patient having lung cancer the method comprising:

159 more elements

A method for treating a patient having lung cancer the method comprising:

199 more elements

A method for treating a patient having lung cancer the method comprising:

a) characterizing molecular anomalies of a lung cancer sample from the patient in comparison to a normal sample from the same patient which is a normal histological counterpart of the lung cancer sample, said molecular anomalies characterization comprising determining genes differentially expressed in the lung cancer sample in comparison to the normal sample by oligonucleotide array, and optionally determining the gain or loss of gene copy number in the lung cancer sample in comparison to the normal sample by Comparative Genomic Hybridization in the lung cancer sample in comparison to the normal sample by sequencing and determining deregulated genes in the lung cancer sample based on the genes differentially expressed and optionally the gain or loss of gene copy number;
b) providing a drug database comprising target genes associated with a plurality of drugs disclosed in Table 1;
c) determining a score for each drug of said plurality of drugs comprising calculating, for each drug of the plurality of drugs, a percentage of deregulated genes in the lung cancer sample from the patient as characterized in step (a) that are target genes for each drug of the plurality of drugs as provided in step (b) and determining the score for each drug of the plurality of drugs based on the percentage of deregulated genes among the target genes in the lung cancer sample from the patient, wherein a higher score is predictive of a higher relative efficacy of the drug for treating lung cancer in the patient;
d) selecting a drug with a high score for treating the patient;
and
e) administering the drug with the high score to the patient and treating the patient's lung cancer, wherein the score (W) for a given drug is determined by the following algorithm:
W
=
Pz
⁢
(
Σ
C
⁢
F
c
>
2
)
n
C
⁢
F
c
>
2
wherein:
W is the score for the given drug;
P is the percentage of target genes for the given drug which are deregulated in the lung cancer of the patient;
Σ is sum;
Fc>2 is the Fold Change of each deregulated target gene for the given drug with a Fold Change higher than 2;
and
nCFc>2 refers to the number of target genes for the given drug with a Fold Change higher than 2.

30 more elements

Dependencies are inferred from the claim text.

Facts

Application: 13/382,585Filed 6 Jul 2010 · us provisional application
Priority: 8 Jul 2009
Published: US 2012/0136583 A131 May 2012
Granted: 9 Oct 2018
Term ends: Not estimatedThe source records don’t support an estimate.

Classification11 codes

G16H 20/10
ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient…:…
G16B 20/00
Ict specially adapted for functional genomics or proteomics
G16B 25/10
ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression: gene or protein expression profiling;…
G16H 50/20
ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or…: for…

Family

Application61/223,798Us provisional application · filed 8 Jul 2009
US 2012/0136583 A1Pre-grant publication · published 31 May 2012
US 10,095,829 B2This patent

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

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