US2019005190A1PendingUtilityA1
Method for predicting efficacy of drugs in a patient
Assignee: WORLDWIDE INNOVATIVE NETWORKPriority: Jul 8, 2009Filed: Sep 10, 2018Published: Jan 3, 2019
Est. expiryJul 8, 2029(~2.9 yrs left)· nominal 20-yr term from priority
G16H 70/40G16B 25/00G16H 50/20G16H 70/00G16B 20/00G06F 19/20G06F 19/3456G06F 19/18Y02A90/26G16H 20/10G16B 25/10Y02A90/10
45
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Claims
Abstract
The present invention concerns a method for predicting the relative efficacy of a plurality of drugs for treating a tumour in an individual comprising the molecular characterization of the tumour, and the calculation of a score for the plurality of drugs essentially based on the percentage of deregulated target genes.
Claims
exact text as granted — not AI-modifiedWhat is claimed:
1 . A method for treating a patient having cancer, the method comprising:
a) characterizing molecular anomalies of a cancer sample from the patient in comparison to a normal sample from the same patient which is a normal histological counterpart of the cancer sample, said molecular anomalies characterization comprising determining genes differentially expressed in the cancer sample in comparison to the normal sample by oligonucleotide array and optionally determining the gain or loss of gene copy number in the cancer sample in comparison to the normal sample by Comparative Genomic Hybridization and determining deregulated genes in the 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 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 cancer sample from the patient, wherein a higher score is predictive of a higher relative efficacy of the drug for treating the cancer in the patient and wherein the step of characterizing molecular anomalies of the 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 one of the following algorithms:
W
=
P
(
∑
C
F
c
>
2
)
n
C
F
c
>
2
;
(
i
)
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 cancer of the patient;
Σ is sum;
F c>2 is the Fold Change of each deregulated target gene for the given drug with a Fold Change higher than 2; and
n C F c>2 refers to the number of target genes for the given drug with a Fold Change higher than 2;
W
=
P
(
(
∑
CM
F
CM
)
n
1
CM
q
1
z
1
+
(
∑
Cm
F
Cm
)
n
2
Cm
q
2
z
2
-
(
∑
CR
F
CR
)
n
3
CR
q
3
z
3
)
;
(
ii
)
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 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;
n 1 CM, n 2 Cm and n 3 CR are respectively the number of deregulated target genes with a defined threshold for major target genes, minor target genes and resistance genes;
F CM , F Cm and F CR are the Fold Change of each gene higher than the defined threshold for major target genes, minor target genes and resistance genes, respectively;
q 1 , q 2 and q 3 are multiplication coefficients for major target genes, minor target genes and resistance genes, respectively; and
z 1 , z 2 and z 3 are multiplication coefficients associated with the presence of a mutation in a major target gene, a minor target gene and a resistance gene, respectively;
W
=
P
CM
(
∑
CM
F
CM
)
n
1
CM
q
1
z
1
+
P
Cm
(
∑
Cm
F
Cm
)
n
2
Cm
q
2
z
2
-
P
CR
(
∑
CR
F
CR
)
n
3
CR
q
3
z
3
;
(
iii
)
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;
n 1 CM, n 2 Cm and n 3 CR are respectively the number of deregulated target genes with a defined threshold for major target genes, minor target genes and resistance genes;
F CM , F Cm and F CR are the Fold Change of each gene higher than the defined threshold for major target genes, minor target genes and resistance genes, respectively;
q 1 , q 2 and q 3 are multiplication coefficients for major target genes, minor target genes and resistance genes, respectively;
z 1 , z 2 and z 3 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 P CM , P Cm and P CR are the percentage of target genes for the given drug which are deregulated in the cancer of the patient for major target genes, minor target genes and resistance genes, respectively;
W
=
P
(
(
∑
CM
F
CM
×
Int
CM
)
n
1
CM
q
1
z
1
+
(
∑
Cm
F
Cm
×
Int
Cm
)
n
2
Cm
q
2
z
2
-
(
∑
CR
F
CR
×
Int
CR
)
n
3
CR
q
3
z
3
)
or
W
=
P
CM
(
∑
CM
F
CM
×
Int
CM
)
n
1
CM
q
1
z
1
+
P
Cm
(
∑
Cm
F
Cm
×
Int
Cm
)
n
2
Cm
q
2
z
2
-
P
CR
(
∑
CR
F
CR
×
Int
CR
)
n
3
CR
q
3
z
3
;
(
iv
)
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 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;
n 1 CM, n 2 Cm and n 3 CR are respectively the number of deregulated target genes with a defined threshold for major target genes, minor target genes and resistance genes;
F CM , F Cm and F CR are the Fold Change of each gene higher than the defined threshold for major target genes, minor target genes and resistance genes, respectively;
q 1 , q 2 and q 3 are multiplication coefficients for major target genes, minor target genes and resistance genes, respectively;
z 1 , z 2 and z 3 are multiplication coefficients associated with the presence of a mutation in a major target gene, a minor target gene and a resistance gene, respectively;
P CM , P Cm and P CR are the percentage of target genes for the given drug which are deregulated in the cancer of the patient for major target genes, minor target genes and resistance genes, respectively; and
Int CM , Int Cm and Int CR are the intensity for major target genes, minor target genes and resistance genes, respectively; or
W
=
P
(
(
∑
CM
F
CM
)
n
1
CM
q
1
z
1
-
(
∑
CR
F
CR
)
n
3
CR
q
3
z
3
)
or
W
=
P
CM
(
∑
CM
F
CM
)
n
1
CM
q
1
z
1
-
P
CR
(
∑
CR
F
CR
)
n
3
CR
q
3
z
3
or
W
=
P
(
(
∑
CM
F
CM
×
Int
CM
)
n
1
CM
q
1
z
1
-
(
∑
CR
F
CR
×
Int
CR
)
n
3
CR
q
3
z
3
)
or
W
=
P
CM
(
∑
CM
F
CM
×
Int
CM
)
n
1
CM
q
1
z
1
-
P
CR
(
∑
CR
F
CR
×
Int
CR
)
n
3
CR
q
3
z
3
;
(
v
)
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 cancer of the patient;
Σ is sum;
CM refers to major target genes for the given drug;
CR refers to resistance genes for the given drug;
n 1 CM and n 3 CR are respectively the number of deregulated target genes with a defined threshold for major target genes and resistance genes;
F CM and F CR are the Fold Change of each gene higher than the defined threshold for major target genes and resistance genes, respectively;
q 1 and q 3 are multiplication coefficients for major target genes and resistance genes, respectively;
z 1 and z 3 are multiplication coefficients associated with the presence of a mutation in a major target gene and a resistance gene, respectively;
P CM , and P CR are the percentage of genes for the given drug which are deregulated in the cancer of the patient for major target genes and resistance genes, respectively; and
Int CM and Int CR are the intensity for major target genes and resistance genes, respectively.
2 . The method according to claim 1 , wherein the target genes for each drug are classified in the database into:
major target genes (CM) which have been demonstrated to have a clear cause and effect link with the drug's mechanism of action; minor target genes (Cm), the level of regulation of which is modified in the presence of the drug, without a direct link with the drug's mechanism of action; and resistance genes (CR) which induce a direct resistance to the drug or are associated with a major toxicity.
3 . The method according to claim 1 , wherein F c>2 is the Fold Change of each over-expressed target gene for the given drug with a Fold Change higher than 2 and n C F c>2 is either the number of target genes for the given drug with a Fold Change higher than 2, or the number of over-expressed target genes for the given drug with a Fold Change higher than 2.
4 . The method according to claim 1 , wherein for algorithm (ii) F CM , F Cm and F CR are the Fold Change of each over-expressed target gene for the given drug with the defined threshold and wherein n 1 CM, n 2 Cm and n 3 CR are either the number of target genes for the given drug with the defined threshold or the number of over-expressed target genes for the given drug with the defined threshold.
5 . The method according to claim 1 , wherein for algorithm (iii) F CM , F Cm and F CR are the Fold Change of each over-expressed target gene for the given drug with the defined threshold and wherein n 1 CM, n 2 Cm and n 3 CR are either the number of target genes for the given drug with the defined threshold or the number of over-expressed target genes for the given drug with the defined threshold.
6 . The method according to claim 1 , wherein for algorithm (iv) F CM , F Cm and F CR are the Fold Change of each over-expressed target gene for the given drug with the defined threshold and wherein n 1 CM, n 2 Cm and n 3 CR are either the number of target genes for the given drug with the defined threshold or the number of over-expressed target genes for the given drug with the defined threshold.
7 . The method according to claim 1 , wherein for algorithm (v) F CM and F CR are the Fold Change of each over-expressed target gene for the given drug with the defined threshold and wherein n 1 CM and n 3 CR are either the number of target genes for the given drug with the defined threshold or the number of over-expressed target genes for the given drug with the defined threshold.
8 . The method according to claim 4 , wherein the defined threshold is a Fold Change of at least 2.
9 . The method according to claim 5 , wherein the defined threshold is a Fold Change of at least 2.
10 . The method according to claim 6 , wherein the defined threshold is a Fold Change of at least 2.
11 . The method according to claim 7 , wherein the defined threshold is a Fold Change of at least 2.
12 . The method according to claim 1 , wherein for algorithm (ii) the multiplication coefficients for the target genes are between 10 and 1,000 for major target genes (q 1 ), between 0.1 and 10 for minor target genes (q 2 ) and between 10 to 1,000 for resistance genes (q 3 ).
13 . The method according to claim 1 , wherein for algorithm (iii) the multiplication coefficients associated with a mutation z 1 , z 2 and z 3 are 1 when no mutation exists and, depending on the functional impact of the mutation, are between 10 and 1,000.
14 . The method according to claim 1 , wherein said plurality of drugs are Axitinib, Capecitabine, Trastuzumab, Erlotinib, Gefitinib, Lapatinib, Bevacizumab, Imatinib, Temsirolimus, Dasatinib, Sorafenib, Nilotinib, Bosutinib, Sunitinib, Cetuximab, Vinorelbine, Dacarbazine, Docetaxel, Paclitaxel, Pemetrexed, Gemcitabine, Irinotecan and Topotecan.
15 . The method according to claim 1 , wherein the molecular anomalies of said cancer sample are determined by northern analysis, mRNA microarrays, cDNA microarrays or RT-PCR.
16 . The method according to claim 1 , wherein said cancer is leukemia, lymphoma, bladder cancer, breast cancer, stomach cancer, thyroid cancer, prostate cancer, testis cancer, liver cancer, pancreatic cancer, bone cancer, kidney cancer, endometrial cancer, melanoma, lung cancer, gastric cancer, colorectal cancer, head or neck cancer, brain cancer, neuroblastoma, or ovarian cancer.
17 . The method according to claim 1 , said method further comprising treating said patient with the drug selected by one of said algorithms.Join the waitlist — get patent alerts
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