Serum Patterns Predictive of Breast Cancer
Models for classifying a biological sample are developed from samples taken from a mammalian subject into one of at least two possible biological states related to breast cancer. Samples may be processed by mass spectral and other high-throughput analytical techniques.
1 . A model for classifying a biological sample taken from a mammalian subject into one of at least two possible biological states related to breast cancer using a data stream that is obtained by performing a mass spectral analysis of the biological sample, the data stream including magnitude values for a range of mass-to-charge values, comprising:
at least one classifying hypervolume associated with one of the at least two biological states related to breast cancer and disposed within a vector space having n dimensions, each dimension corresponding to a different mass-to-charge value;
wherein n is at least three and at least a first of the dimensions corresponds to a mass-to-charge value in a range of m/z values selected from the m/z ranges consisting of between 200 to 300, 300 to 400, 400 to 500, 500 to 600, 600 to 700, and 700 to 900.
2 . The model of claim 1 , wherein n is at least 5.
3 . The model of claim 1 , wherein n is between 5 and 25.
4 . The model of claim 1 , wherein at least a second of the dimensions corresponds to a mass-to-charge value of between 500 and 1100.
5 . The model of claim 1 , wherein at least a second of the dimensions corresponds to a mass-to-charge value of between 500 and 900.
6 . The model of claim 1 , wherein at least a second of the dimensions corresponds to a mass-to-charge value of between 700 and 900.
7 . The model of claim 1 , the at least one classifying hypervolume being a first classifying hypervolume, further comprising:
a second classifying hypervolume disposed within the vector space;
the first classifying hypervolume being associated with a presence of breast cancer, the second classifying hypervolume being associated with an absence of breast cancer.
8 . The model of claim 1 , the at least one classifying hypervolume being a first classifying hypervolume, further comprising:
a second classifying hypervolume disposed within the vector space;
the first classifying hypervolume and the second classifying hypervolume being associated with a presence of breast cancer.
9 . The model of claim 1 , the at least one classifying hypervolume being a first classifying hypervolume, further comprising:
a second classifying hypervolume disposed within the vector space;
the first classifying hypervolume and the second classifying hypervolume being associated with an absence of breast cancer.
10 . The model of claim 1 , wherein the classifying hypervolume is associated with a presence of breast cancer.
11 . The model of claim 10 , wherein the classifying hypervolume is associated with a presence of in situ breast cancer.
12 . The model of claim 10 , wherein the classifying hypervolume is associated with a presence of invasive breast cancer.
13 . The model of claim 10 , wherein the classifying hypervolume is associated with a likelihood of metastasis of the invasive breast cancer.
14 . The model of claim 1 , wherein the classifying hypervolume is associated with an absence of breast cancer.
15 . The model of claim 14 , wherein the classifying hypervolume is associated with a benign breast condition.
16 . The model of claim 14 , wherein the benign breast condition is selected from the group consisting of hyperplasia, radial scar, calcification, and fibroadenoma.
17 . The model of claim 14 , wherein the classifying hypervolume is associated with a likelihood of a future occurrence of breast cancer
18 . The model of claim 1 , wherein the model has at least a 65% accuracy.
19 . The model of claim 1 , wherein the model has at least a 70% accuracy.
20 . The model of claim 1 , wherein the model has at least a 80% sensitivity.
21 . The model of claim 1 , wherein the model has at least a 80% specificity.
22 . The model of claim 1 , where in the hypervolume is a hypersphere.
23 . A method of classifying a biological sample taken from a subject into one of at least two possible biological states related to breast cancer by analyzing a data stream that is obtained by performing a mass spectral analysis of the biological sample, the data stream including magnitude values for a range of mass-to-charge values, comprising:
abstracting the data stream to produce a sample vector that characterizes the data stream in a vector space having n dimensions and containing a diagnostic hypervolume, the vector space having at least a first dimension, a second dimension, and a third dimension, the first dimension corresponding to a mass-to-charge value of between 500 and 600, the second dimension corresponding to a mass-to-charge value of between 700 and 900, the diagnostic hypervolume corresponding to one of the presence or absence of breast cancer; and
determining whether the sample vector rests within the diagnostic hypervolume.
24 . The method of claim 23 , wherein the hypervolume corresponds to the presence of breast cancer and further comprising:
if the sample vector rests within the diagnostic hypervolume, identifying the biological sample as indicating that the subject has breast cancer.
25 . The method of claim 23 , wherein the third dimension corresponds to a mass-to-charge value of between 500 and 1100.
26 . The method of claim 23 , wherein the third dimension corresponds to a mass-to-charge value of between 500 and 900.
27 . The method of claim 23 , the diagnostic hypervolume is a first diagnostic hypervolume, wherein the vector space contains a second diagnostic hypervolume, the first diagnostic hypervolume and the second diagnostic hypervolume corresponding to the presence of breast cancer.
28 . The model of claim 23 , the diagnostic hypervolume is a first diagnostic hypervolume corresponding to the presence of breast cancer, wherein the vector space contains a second diagnostic hypervolume, the second diagnostic hypervolume corresponding to an absence of breast cancer.
29 . The method of claim 23 , wherein the hypervolume is a hypersphere.
30 . The method of claim 23 , wherein the hypervolume corresponds to the presence of in situ breast cancer.
31 . The method of claim 23 , wherein the hypervolume corresponds to the presence of invasive breast cancer.
32 . The method of claim 24 , wherein the hypervolume corresponds to the absence of breast cancer and to the presence of a benign breast condition.
33 . The model of claim 32 , wherein the benign breast condition is selected from the group consisting of hyperplasia, radial scar, calcification, and fibroadenoma.
34 . A model for classifying a biological sample taken from a mammalian subject into one of at least two possible biological states related to breast cancer using a data stream that is obtained by performing an mass spectral analysis of the biological sample, the data stream including magnitude values for a range of mass-to-charge values, comprising:
at least one classifying hypervolume disposed within an vector space having n-dimensions, each dimension corresponding to a different mass-to-charge value,
wherein n is greater than three, at least two of the dimensions correspond to mass-to-charge values in table A.
35 . The model of claim 34 , wherein at least three of the dimensions correspond to mass-to-charge values in table A.
36 . The model of claim 34 , wherein n is between 5 and 25.
37 . The model of claim 34 , the at least one classifying hypervolume being a first classifying hypervolume, further comprising:
a second classifying hypervolume disposed within the vector space,
the first classifying hypervolume being associated with a presence of breast cancer, the second classifying hypervolume being associated with an absence of breast cancer.
38 . The model of claim 34 , the at least one classifying hypervolume being a first classifying hypervolume, further comprising:
a second classifying hypervolume disposed within the vector space,
the first classifying hypervolume and the second classifying hypervolume being associated with a presence of breast cancer.
39 . The model of claim 34 , the at least one classifying hypervolume being a first classifying hypervolume, further comprising:
a second classifying hypervolume disposed within the vector space,
the first classifying hypervolume and the second classifying hypervolume being associated with an absence of breast cancer.
40 . The model of claim 34 , wherein the classifying hypervolume is associated with a presence of breast cancer.
41 . The model of claim 40 , wherein the classifying hypervolume is associated with a presence of in situ breast cancer.
42 . The model of claim 40 , wherein the classifying hypervolume is associated with a presence of invasive breast cancer.
43 . The model of claim 42 , wherein the classifying hypervolume is associated with a likelihood of metastasis of the invasive breast cancer.
44 . The model of claim 34 , wherein the classifying hypervolume is associated with an absence of breast cancer.
45 . The model of claim 44 , wherein the classifying hypervolume is associated with a benign breast condition.
46 . The model of claim 45 , wherein the benign breast condition is selected from the group consisting of hyperplasia, radial scar, calcification, and fibroadenoma.
47 . The model of claim 44 , wherein the classifying hypervolume is associated with a likelihood of a future occurrence of breast cancer.
48 . The model of claim 34 , wherein the model has at least a 65% accuracy.
49 . The model of claim 34 , wherein the model has at least a 70% accuracy.
50 . The model of claim 34 , wherein the model has at least a 80% sensitivity.
51 . The model of claim 34 , wherein the model has at least a 80% specificity.
52 . A model for classifying a biological sample taken from a mammalian subject using a data stream that is obtained by performing a mass spectral analysis of the biological sample, the data stream including magnitude values for a range of mass-to-charge values, comprising:
at least one classifying hypervolume disposed within a vector space having n dimensions, each dimension corresponding to a different mass-to-charge value,
wherein n is at least three, at least a first of the dimensions corresponds to a mass-to-charge value of between 500 and 600, at least a second of the dimensions corresponds to a mass-to-charge value of between 600 and 700.
53 . The model of claim 52 , wherein n is at least 5.
54 . The model of claim 52 , wherein n is between 5 and 25.
55 . The model of claim 52 , wherein the model has at least a 65% accuracy.
56 . The model of claim 52 , wherein the model has at least a 70% accuracy.
57 . A model for classifying a biological sample taken from a mammalian subject using a data stream that is obtained by performing a mass spectral analysis of the biological sample, comprising:
at least two classifying hypervolumes disposed within a vector space having at least three dimensions, one of the at least two classifying hypervolumes being associated with a presence of a disease, another of the at least two classifying hypervolumes being associated with an absence of the disease,
the model having at least a 65% accuracy.
58 . The model of claim 57 , wherein the vector space has at least 5 dimensions.
59 . The model of claim 57 , wherein the disease is breast cancer.
60 . The model of claim 57 , wherein the data stream includes magnitude values for a range of mass-to-charge values, a first of the at least three dimensions corresponds to a mass-to-charge value of between 500 and 600, and a second of the at least three dimensions corresponds to a mass-to-charge value of between 600 and 700.
61 . The model of claim 57 , wherein the data stream includes magnitude values for a range of mass-to-charge values, at least two of the at least three dimensions correspond to mass-to-charge values in table 1.
62 . The model of claim 1 , wherein the first of the dimensions corresponds to a mass-to-charge value of between 520 and 590.
63 . The model of claim 1 , wherein the first of the dimensions corresponds to a mass-to-charge value of about 537.
64 . The model of claim 1 , wherein the first of the dimensions corresponds to a mass-to-charge value of about 579.
65 . The model of claim 1 , wherein the first of the dimensions corresponds to a mass-to-charge value of between 535 and 540.
66 . The model of claim 1 , wherein the first of the dimensions corresponds to a mass-to-charge value of between 575 and 580.
67 . The model of claim 1 , wherein the second of the dimensions corresponds to a mass-to-charge value of about 827.
68 . The model of claim 1 , wherein the second of the dimensions corresponds to a mass-to-charge value of between 820 and 830.
69 . A model for classifying a biological sample taken from a mammalian subject into one of at least two possible biological states using a data stream that is obtained by performing a mass spectral analysis of the biological sample, the data stream including magnitude values for a range of mass-to-charge values, comprising:
at least one classifying hypervolume associated with the presence of ductal carcinoma in situ and disposed within a vector space having n dimensions, each dimension corresponding to a different mass-to-charge value.
70 . The model of claim 69 , wherein n is at least three, at least a first of the dimensions corresponds to a mass-to-charge value of between 900 and 905, and at least a second of the dimensions corresponds to a mass-to-charge value of between 610 and 620.
71 . The model of claim 69 , the at least one classifying hypervolume being a first classifying hypervolume, further comprising:
a second classifying hypervolume associated with the presence of lobular carcinoma in situ and disposed within the vector space.
72 . A model for classifying a biological sample taken from a mammalian subject into one of at least two possible biological states using a data stream that is obtained by performing a mass spectral analysis of the biological sample, the data stream including magnitude values for a range of mass-to-charge values, comprising:
at least one classifying hypervolume associated with the presence of lobular carcinoma in situ and disposed within a vector space having n dimensions, each dimension corresponding to a different mass-to-charge value.
73 . The model of claim 72 , wherein n is at least three, at least a first of the dimensions corresponds to a mass-to-charge value of between 1050 and 1060, and at least a second of the dimensions corresponds to a mass-to-charge value of between 610 and 620.
74 . A model for classifying a biological sample taken from a mammalian subject into one of at least two possible biological states associated with breast pathology using a data stream that is obtained by performing a mass spectral analysis of the biological sample, the data stream including magnitude values for a range of mass-to-charge values, comprising:
at least one classifying hypervolume disposed within a vector space having n dimensions, each dimension corresponding to a different mass-to-charge value.