Cancer patient selection for administration of therapeutic agents using mass spectral analysis of blood-based samples
Methods using mass spectral data analysis and a classification algorithm provide an ability to determine whether a solid epithelial tumor cancer patient is likely to benefit from a therapeutic agent or a combination of therapeutic agents targeting agonists of the receptors, receptors or proteins involved in MAPK (mitogen-activated protein kinase) pathways or the PKC (protein kinase C) pathway upstream from or at Akt or ERK/JNK/p38 or PKC, such as therapeutic agents targeting EGFR and/or HER2. The methods also provide the ability to determine whether the cancer patient is likely to benefit from the combination of a therapeutic agent targeting EFGR and a therapeutic agent targeting COX2; or whether the cancer patient is likely to benefit from the treatment with an NF-κB inhibitor.
1 . A method of identifying a solid epithelial tumor cancer patient as being likely to benefit from treatment with a therapeutic agent or a combination of therapeutic agents targeting agonists of the receptors, receptors or proteins involved in MAPK (mitogen-activated protein kinase) pathways or the PKC (protein kinase C) pathway upstream from or at Akt or ERK/JNK/p38 or PKC or not likely to benefit from treatment with the therapeutic agent or the combination of therapeutic agents, comprising the steps of:
a) obtaining a mass spectrum from a blood-based sample from the solid epithelial tumor cancer patient;
b) performing one or more predefined pre-processing steps on the mass spectrum obtained in step a);
c) obtaining integrated intensity values of selected features in said spectrum at one or more predefined m/z ranges after the pre-processing steps on the mass spectrum in step b) have been performed;
d) using the values obtained in step c) in classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from other solid tumor patients to identify the patient as being either likely or not likely to benefit from treatment with the therapeutic agent or a combination of therapeutic agents.
2 . The method of claim 1 , wherein the therapeutic agent or combination of therapeutic agents targets EGFR and VEGF.
3 . The method of claim 1 , wherein the therapeutic agent or combination of therapeutic agents targets HER2.
4 . The method of claim 1 , wherein the therapeutic agent or combination of therapeutic agents comprises Trastuzumab.
5 . The method of claim 1 , wherein the therapeutic agent or combination of therapeutic agents targets EGFR, and wherein the classification algorithm produces a class label that identifies the patient as likely to benefit from the treatment with a COX2 inhibitor in combination with the therapeutic agent targeting EGFR and as not likely to benefit from the treatment with only a therapeutic agent targeting EGFR.
6 . The method of claim 5 , wherein the COX2 inhibitor comprises colecoxib.
7 . The method of claim 5 , wherein the COX2 inhibitor comprises rofecoxib.
8 . The method of claim 1 , wherein the patient is further identified by the class label as likely to benefit from the treatment with a NF-κB inhibitor.
9 . The method of claim 1 , wherein the one or more predefined m/z ranges are selected from the group of m/z ranges consisting of:
5732 to 5795
5811 to 5875
6398 to 6469
11376 to 11515
11459 to 11599
11614 to 11756
11687 to 11831
11830 to 11976
12375 to 12529
23183 to 23525
23279 to 23622 and
65902 to 67502.
10 . Apparatus configured to identify a solid epithelial tumor cancer patient as being likely to benefit from treatment with a therapeutic agent or a combination of therapeutic agents targeting agonists of the receptors, receptors or proteins involved in MAPK (mitogen-activated protein kinase) pathways or the PKC (protein kinase C) pathway upstream from or at Akt or ERK/JNK/p38 or PKC or not likely to benefit from treatment with the therapeutic agent or combination of therapeutic agents:
a storage device storing a mass spectrum of a blood-based sample from the solid epithelial tumor cancer patient, and
a processor executing software instructions configured to a) perform one or more predefined pre-processing steps on the mass spectrum, b) obtain integrated intensity values of features in said mass spectrum at one or more predefined m/z ranges; and c) use the values obtained in step b) in a classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from other solid epithelial tumor cancer patients to identify the patient as being either likely or not likely to benefit from the therapeutic agent or a combination of therapeutic agents.
11 . The apparatus of claim 10 , wherein the therapeutic agent or combination of therapeutic agents targets HER2.
12 . The apparatus of claim 10 , wherein the therapeutic agent or combination of therapeutic agents targets EGFR and VEGF.
13 . The apparatus of claim 10 , wherein the therapeutic agent or combination of therapeutic agents comprises Tastuzumab.
14 . The apparatus of claim 10 , wherein the therapeutic agent targets EGFR, and wherein the classification algorithm produces a class label that identifies the patient as likely to benefit from the treatment with a COX2 inhibitor in combination with the therapeutic agent targeting EGFR and as not likely to benefit from the treatment with only a therapeutic agent targeting EGFR.
15 . The apparatus of claim 14 , wherein the COX2 inhibitor comprises colecoxib.
16 . The apparatus of claim 14 , wherein the COX2 inhibitor comprises rofecoxib.
17 . The apparatus of claim 10 , wherein the patient is further identified as likely to benefit from the treatment with a NF-κB inhibitor.
18 . The apparatus of claim 10 , wherein the predefined m/z ranges are selected from the group of m/z ranges consisting of:
5732 to 5795
5811 to 5875
6398 to 6469
11376 to 11515
11459 to 11599
11614 to 11756
11687 to 11831
11830 to 11976
12375 to 12529
23183 to 23525
23279 to 23622 and
65902 to 67502.
19 . A method for predicting whether a cancer patient is likely to benefit from administration of the combination of a COX2 inhibitor and an EGFR inhibitor, comprising the steps of:
a) obtaining a mass spectrum from a blood-based sample from the cancer patient;
b) performing one or more predefined pre-processing steps on the mass spectrum obtained in step a);
c) obtaining integrated intensity values of selected features in said spectrum at one or more predefined m/z ranges after the pre-processing steps on the mass spectrum in step b) have been performed; and
d) using the values obtained in step c) in classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from other solid epithelial tumor patients to identify the patient as being either likely or not likely to benefit from treatment by administration of a combination of a COX2 inhibitor and an EGFR inhibitor.
20 . The method of claim 19 , wherein the predefined m/z ranges are selected from the group of m/z ranges consisting of:
5732 to 5795
5811 to 5875
6398 to 6469
11376 to 11515
11459 to 11599
11614 to 11756
11687 to 11831
11830 to 11976
12375 to 12529
23183 to 23525
23279 to 23622 and
65902 to 67502.
21 . The method of claim 1 , wherein the method is implemented in a laboratory test center.
22 . The method of claim 19 , wherein the method is implemented in a laboratory test center.
23 . Apparatus configured to identify a predicting whether a cancer patient is likely to benefit from administration of the combination of a COX2 inhibitor and an EGFR inhibitor, comprising:
a storage device storing a mass spectrum of a blood-based sample from the cancer patient, and
a processor executing software instructions configured to a) perform one or more predefined pre-processing steps on the mass spectrum, b) obtain integrated intensity values of selected features in said spectrum at one or more predefined m/z ranges after the pre-processing steps on the mass spectrum in step a) have been performed; and c) use the values obtained in step b) in classification algorithm using a training set comprising class-labeled spectra produced from blood-based samples from other cancer patients to identify the patient as being either likely or not likely to benefit from treatment by administration of a combination of a COX2 inhibitor and an EGFR inhibitor.