Patent Application
20160222458
The foregoing applications, and each document cited or referenced in each of the present and foregoing applications, including during the prosecution of each of the foregoing applications (“application and article cited documents”), and any manufacturer's instructions or catalogues for any products cited or mentioned in each of the foregoing applications and articles and in any of the application and article cited documents, are hereby incorporated herein by reference. Furthermore, all documents cited in this text, and all documents cited or reference in documents cited in this text, and any manufacturer's instructions or catalogues for any products cited or mentioned in this text or in any document hereby incorporated into this text, are hereby incorporated herein by reference. Documents incorporated by reference into this text or any teachings therein may be used in the practice of this invention. Documents incorporated by reference into this text are not admitted to be prior art.
The present invention relates to the fields of medicine, cell biology, molecular biology and genetics. More particularly, the invention relates to a method of assigning a grade to a breast tumour which reflects its aggressiveness.
The effective treatment of cancer depends, to a large extent, on the accuracy with which malignant tissue can be subtyped according to clinicopathological features that reflect disease aggressiveness.
Some clinical subtypes, despite phenotypic homogeneity, are associated with substantial clinical heterogeneity (e.g., refractory response to treatment) confounding their clinical meaning. Recent studies using DNA microarray technology suggest that such clinical heterogeneity may be resolvable at the molecular level (1-4). Indeed, some have demonstrated that gene expression signatures underlying specific biological properties of cancer cells may be superior indicators of clinical subtypes with robust prognostic value (1, 2). Thus, global analysis of gene expression has the potential to uncover molecular determinants of clinical heterogeneity providing a more objective and biologically-rational approach to cancer subtyping.
Accordingly, there is a need in the art for gene markers which are diagnostic or reflective of tumourigenicity.
In breast cancer, histologic grade is an important parameter for classifying tumours into morphological subtypes informative of patient risk. Grading seeks to integrate measurements of cellular differentiation and replicative potential into a composite score that quantifies the aggressive behaviour of the tumour.
The most studied and widely used method of breast tumour grading is the Elston-Ellis modified Scarff, Bloom, Richardson grading system, also known as the Nottingham grading system (NGS) (5, 6, Haybittle et al, 1982). The NGS is based on a phenotypic scoring procedure that involves the microscopic evaluation of morphologic and cytologic features of tumour cells including degree of tubule formation, nuclear pleomorphism and mitotic count (6). The sum of these scores stratifies breast tumours into Grade I (G1) (well-differentiated, slow-growing), Grade II (G2) (moderately differentiated), and Grade III (G3) (poorly-differentiated, highly-proliferative) malignancies.
Multivariate analyses in large patient cohorts have consistently demonstrated that the histologic grade of invasive breast cancer is a powerful prognostic indicator of disease recurrence and patient death independent of lymph node status and tumour size (6-9). Untreated patients with G1 disease have a ˜95% five-year survival rate, whereas those with G2 and G3 malignancies have survival rates at 5 years of ˜75% and ˜50%, respectively.
However, the value of histologic grade in patient prognosis has been questioned by reports of substantial inter-observer variability among pathologists (10-13) leading to debate over the role that grade should play in therapeutic planning (14, 15). Furthermore, where the prognostic significance of G1 and G3 disease is of more obvious clinical relevance, it is less clear what the prognostic value is of the more heterogeneous, moderately differentiated Grade II tumours, which comprise approximately 50% of all breast cancer cases (9, 15, 16).
There is therefore a need for methods which are capable of discriminating between heterogeneous tumour grades, particularly Grade II breast tumours.
We have now demonstrated that a gene expression signature comprising one or more of a set of 232 genes, represented by 264 probesets (e.g., Affymetrix probesets), is capable of discriminating between high and low grade tumours. Such a gene expression signature may be used to provide an objective and clinically valuable measure of tumour grade.
We further describe a novel strategy of clinical class discovery that combines gene discovery and class prediction algorithms with patient survival analysis, and between-group statistical analyses of conventional clinical markers and gene ontologies represented by differentially expressed genes.
Our findings show that the genetic reclassification of histologic grade reveals new clinical subtypes of invasive breast cancer and can improve therapeutic planning for patients with moderately differentiated tumours.
Furthermore, our results support the view that tumours of low and high grade, as defined genetically, may reflect independent pathobiological entities rather than a continuum of cancer progression.
According to a 1st aspect of the present invention, we provide a method of assigning a grade to a breast tumour, which grade is indicative of the aggressiveness of the tumour, the method comprising detecting the expression of a gene selected from the genes set out in Table D0 (6g-TAG) or Table D1 (SWS Classifier 0).
The method may comprise detecting the expression of level of 5 or more genes. The 5 or more genes may comprise the genes set out in Table D0 (6g-TAGs).
The method may comprise detecting the expression of BRRN1 (GenBank Accession No. NM_015341), AURKA (GenBank Accession No. NM_003600), MELK (GenBank Accession No. NM_014791), PRR11 (GenBank Accession No. NM_018304), CENPW (GenBank Accession No. NM_001012507) and E2F1 (GenBank Accession No. NM_005225).
There is provided, according to a 2nd aspect of the present invention, a method of classifying a histological Grade 2 tumour into a low aggressiveness tumour or a high aggressiveness tumour, the method comprising assigning a grade to the histological Grade 2 tumour according to the 1st aspect of the invention.
We provide, according to a 3rd aspect of the present invention, a method of predicting a survival rate for an individual with a histological Grade 2 breast tumour, the method comprising assigning a grade to the breast tumour by a method according to any preceding aspect of the invention.
As a 4th aspect of the present invention, there is provided a method of prognosis of an individual with a breast tumour, the method comprising assigning a grade to the breast tumour by a method as described,
We provide, according to a 5th aspect of the present invention, a method of diagnosis of aggressive breast cancer in an individual, the method comprising assigning a grade indicative of high aggressiveness to a breast tumour of the individual by a method as described.
The present invention, in a 6th aspect, provides a method of choosing a therapy for an individual with breast cancer, the method comprising assigning a grade to the breast tumour by a method as described, and choosing an appropriate therapy based on the aggressiveness of the breast tumour.
In a 7th aspect of the present invention, there is provided a method of treatment of an individual with breast cancer, the method comprising assigning a grade to the breast tumour by a method as described, and administering an appropriate therapy to the individual based on the aggressiveness of the breast tumour.
According to an 8th aspect of the present invention, we provide a method of determining the likelihood of success of a particular therapy on an individual with a breast tumour, the method comprising comparing the therapy with the therapy determined by a such a method.
We provide, according to a 9th aspect of the invention, a method of assigning a breast tumour patient into a prognostic group, the method comprising applying the Nottingham Prognostic Index to a breast tumour, in which the histologic grade score of the breast tumour is replaced by a grade obtained by a method as described.
There is provided, in accordance with a 10th aspect of the present invention, a method of assigning a breast tumour patient into a prognostic group, the method comprising deriving a score which is the sum of the following: (a) (0.2× tumour size in cm); (b) tumour grade in which the tumour grade is assigned by a method as described; and (c) lymph node stage; in which the tumour size and the lymph node stage are determined according to the Nottingham Prognostic Index, in which a patient with a score of 2.4 or less is categorised to a EPG (excellent prognostic group), a patient with a score of less than 3.4 is categorised to a GPG (good prognostic group), a patient with a score of between 3.4 and 5.4 is categorised to a MPG (moderate prognostic group), a patient with a score of greater than 5.4 is categorised to a PPG (poor prognostic group).
As an 11th aspect of the invention, we provide a method of determining whether a breast tumour is a metastatic breast tumour, the method comprising assigning a grade to the breast tumour by a method as described.
We provide, according to a 12th aspect of the invention, a method of identifying a molecule capable of treating or preventing breast cancer, the method comprising: (a) grading a breast tumour; (b) exposing the breast tumour to a candidate molecule; and (c) detecting a change in tumour grade; in which the grade or change thereof, or both, is assigned by a method as described.
According to a 13th aspect of the present invention, we provide a molecule identified by such a method.
There is provided, according to a 14th aspect of the present invention, use of such a molecule in a method of treatment or prevention of cancer in an individual.
We provide, according to a 15th aspect of the present invention, a method of treatment of an individual suffering from breast cancer, the method comprising modulating the expression of a gene set out in Table D0 (6g-TAG) or Table D1 (SWS Classifier 0).
According to a 16th aspect of the present invention, we provide a method of determining the proliferative state of a cell, the method comprising detecting the expression of a gene selected from the genes set out in Table D1 (SWS Classifier 0), in which: (a) a high level of expression of a gene which is annotated “3” in Column 7 (“Grade with Higher Expression”) indicates a highly proliferative cell; (b) a high level of expression of a gene which is annotated “1” in Column 7 (“Grade with Higher Expression”) indicates a non-proliferating cell or a slow-growing cell; (c) a low level of expression of a gene which is annotated “3” in Column 8 (“Grade with Lower Expression”) indicates a highly proliferative cell; and (d) a low level of expression of a gene which is annotated “1” in Column 8 (“Grade with Lower Expression”) indicates a non-proliferating cell or a slow-growing cell.
According to a 17th aspect of the present invention, we provide a combination comprising the genes set out in Table D1 (SWS Classifier 0).
We provide, according to an 18th aspect of the present invention, a combination comprising the probesets set out in Table D1 (SWS Classifier 0). According to a 19th aspect of the present invention, we provide a combination comprising the genes set out in the above aspects of the invention. As an 20th aspect of the invention, we provide a combination comprising the probesets set out in the above aspects of the invention. According to a 21st aspect of the present invention, we provide a combination according to any of the above aspects of the invention in the form of an array. According to a 21st aspect of the present invention, we provide a combination according to the above aspects of the invention in the form of a microarray.
There is provided, according to a 22nd aspect of the present invention, a kit comprising such a combination, array or microarray, together with instructions for use in a method as described. We provide, according to a 23rd aspect of the present invention, use of such a combination, array or a microarray or kit in a method as described.
The method may comprise a method of assigning a grade to a breast tumour as described.
As a 24th aspect of the present invention, there is provided a computer implemented method of assigning a grade to a breast tumour, the method comprising processing expression data for one or more genes set out in Table D1 (SWS Classifier 0) and obtaining a grade indicative of aggressiveness of the breast tumour.
We provide, according to a 25th aspect of the present invention, a program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform method of assigning a grade to a breast tumour, the method comprising: processing expression data for one or more genes set out in Table D1 (SWS Classifier 0); and obtaining a grade indicative of aggressiveness of the breast tumour.
According to a 1st aspect of the present invention, we provide a method of assigning a grade to a breast tumour, which grade is indicative of the aggressiveness of the tumour, the method comprising detecting the expression of a gene selected from the genes set out in Table D1 (SWS 0 Classifier).
There is provided, according to a 2nd aspect of the present invention, a method of classifying a histological Grade 2 tumour into a low aggressiveness tumour or a high aggressiveness tumour, the method comprising assigning a grade to the histological Grade 2 tumour according to the 1st aspect of the invention.
We provide, according to a 3rd aspect of the present invention, a method of predicting a survival rate for an individual with a histological Grade 2 breast tumour, the method comprising assigning a grade to the breast tumour by a method according to the 1st or 2nd aspect of the invention.
As a 4th aspect of the present invention, there is provided a method of prognosis of an individual with a breast tumour, the method comprising assigning a grade to the breast tumour by a method according to the 1st aspect of the invention.
We provide, according to a 5th aspect of the present invention, a method of diagnosis of aggressive breast cancer in an individual, the method comprising assigning a grade indicative of high aggressiveness to a breast tumour of the individual by a method according to the 1st aspect of the invention.
The present invention, in a 6th aspect, provides a method of choosing a therapy for an individual with breast cancer, the method comprising assigning a grade to the breast tumour by a method according the 1st aspect of the invention, and choosing an appropriate therapy based on the aggressiveness of the breast tumour.
In a 7th aspect of the present invention, there is provided a method of treatment of an individual with breast cancer, the method comprising assigning a grade to the breast tumour by a method according to the 1st aspect of the invention, and administering an appropriate therapy to the individual based on the aggressiveness of the breast tumour.
According to an 8th aspect of the present invention, we provide a method of determining the likelihood of success of a particular therapy on an individual with a breast tumour, the method comprising comparing the therapy with the therapy determined.
We provide, according to a 9th aspect of the invention, a method of assigning a breast tumour patient into a prognostic group, the method comprising applying the Nottingham Prognostic Index to a breast tumour, in which the histologic grade score of the breast tumour is replaced by a grade obtained by a method according to the 1st aspect of the invention.
There is provided, in accordance with a 10th aspect of the present invention, a method of assigning a breast tumour patient into a prognostic group, the method comprising deriving a score which is the sum of the following: (a) (0.2× tumour size in cm); (b) tumour grade in which the tumour grade is assigned by a method according to the 1st aspect of the invention, and (c) lymph node stage; in which the tumour size and the lymph node stage are determined according to the Nottingham Prognostic Index, in which a patient with a score of 2.4 or less is categorised to a EPG (excellent prognostic group), a patient with a score of less than 3.4 is categorised to a GPG (good prognostic group), a patient with a score of between 3.4 and 5.4 is categorised to a MPG (moderate prognostic group), and a patient with a score of greater than 5.4 is categorised to a PPG (poor prognostic group).
As an 11th aspect of the invention, we provide a method of determining whether a breast tumour is a metastatic breast tumour, the method comprising assigning a grade to the breast tumour by a method according to the 1st aspect of the invention.
According to a 12th aspect of the present invention, we provide a method of identifying a molecule capable of treating or preventing breast cancer, the method comprising (a) grading a breast tumour; (b) exposing the breast tumour to a candidate molecule; and (c) detecting a change in tumour grade, in which the grade is assigned by a method according to the 1st aspect of the invention.
There is provided, according to a 13th aspect of the present invention, a molecule identified by such a method.
We provide, according to a 14th aspect of the present invention, a method of treatment of an individual suffering from breast cancer, the method comprising modulating the expression of a gene set out in Table D1 (SWS 0 Classifier).
According to a 15th aspect of the present invention, we provide a method of determining the proliferative state of a cell, the method comprising detecting the expression of a gene selected from the genes set out in Table D1 (SWS 0 Classifier), in which: (a) a high level of expression of a gene which is annotated “3” in Column 7 indicates a highly proliferative cell; (b) a high level of expression of a gene which is annotated “1” in Column 7 indicates a non-proliferating cell or a slow-growing cell; (c) a low level of expression of a gene which is annotated “3” in Column 8 indicates a highly proliferative cell; and (d) a low level of expression of a gene which is annotated “1” in Column 8 indicates a non-proliferating cell or a slow-growing cell.
According to a 16th aspect of the present invention, we provide an array, preferably a microarray, comprising the genes set out in Table D1 (SWS 0 Classifier).
We provide, according to a 17th aspect of the present invention, an array, preferably a microarray, comprising the probesets set out in Table D1 (SWS 0 Classifier).
According to an 18th aspect of the present invention, we provide use of an array or microarray according to the 16th or 17th aspect of the invention in a method of assigning a grade to a breast tumour.
As a 19th aspect of the invention, we provide such a use, in which the method comprises the 1st aspect of the invention.
According to a 20th aspect of the present invention, we provide a computer implemented method of assigning a grade to a breast tumour, the method comprising processing expression data for one or more genes set out in Table D1 (SWS 0 Classifier) and obtaining a grade indicative of aggressiveness of the breast tumour.
There is provided, according to a 21st aspect of the present invention, use of Statistically Weighted Syndromes (SWS) on gene expression data, preferably microarray gene expression data.
We provide, according to a 22nd aspect of the present invention, use of Statistically Weighted Syndromes (SWS) for gene discovery.
As a 23rd aspect of the present invention, there is provided such use in combination with Prediction Analysis of Microarrays (PAM).
We provide, according to a 24th aspect of the present invention, use of Statistically Weighted Syndromes (SWS) in combination with Prediction Analysis of Microarrays (PAM) to identify gene sets diagnostic of cancer status, preferably breast cancer status, or proliferative status.
The practice of the present invention will employ, unless otherwise indicated, conventional techniques of chemistry, molecular biology, microbiology, recombinant DNA and immunology, which are within the capabilities of a person of ordinary skill in the art. Such techniques are explained in the literature. See, for example, J. Sambrook, E. F. Fritsch, and T. Maniatis, 1989, Molecular Cloning: A Laboratory Manual, Second Edition, Books 1-3, Cold Spring Harbor Laboratory Press; Ausubel, F. M. et al. (1995 and periodic supplements; Current Protocols in Molecular Biology, ch. 9, 13, and 16, John Wiley & Sons, New York, N.Y.); B. Roe, J. Crabtree, and A. Kahn, 1996, DNA Isolation and Sequencing: Essential Techniques, John Wiley & Sons; J. M. Polak and James O'D. McGee, 1990, In Situ Hybridization: Principles and Practice; Oxford University Press; M. J. Gait (Editor), 1984, Oligonucleotide Synthesis: A Practical Approach, Irl Press; D. M. J. Lilley and J. E. Dahlberg, 1992, Methods of Enzymology: DNA Structure Part A: Synthesis and Physical Analysis of DNA Methods in Enzymology, Academic Press; Using Antibodies: A Laboratory Manual: Portable Protocol NO. I by Edward Harlow, David Lane, Ed Harlow (1999, Cold Spring Harbor Laboratory Press, ISBN 0-87969-544-7); Antibodies: A Laboratory Manual by Ed Harlow (Editor), David Lane (Editor) (1988, Cold Spring Harbor Laboratory Press, ISBN 0-87969-314-2), 1855, Lars-Inge Larsson “Immunocytochemistry: Theory and Practice”, CRC Press Inc., Baca Raton, Fla., 1988, ISBN 0-8493-6078-1, John D. Pound (ed.); “Immunochemical Protocols, vol. 80”, in the series: “Methods in Molecular Biology”, Humana Press, Totowa, N.J., 1998, ISBN 0-89603-493-3, Handbook of Drug Screening, edited by Ramakrishna Seethala, Prabhavathi B. Fernandes (2001, New York, N.Y., Marcel Dekker, ISBN 0-8247-0562-9); Lab Ref: A Handbook of Recipes, Reagents, and Other Reference Tools for Use at the Bench, Edited Jane Roskams and Linda Rodgers, 2002, Cold Spring Harbor Laboratory, ISBN 0-87969-630-3; and The Merck Manual of Diagnosis and Therapy (17th Edition, Beers, M. H., and Berkow, R, Eds, ISBN: 0911910107, John Wiley & Sons). Each of these general texts is herein incorporated by reference.
As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” include plural referents, unless the context clearly dictates otherwise. The terms “a” (or “an”), as well as the terms “one or more,” and “at least one” can be used interchangeably.
Furthermore, “and/or” is to be taken as specific disclosure of each of the two specified features or components with or without the other. Thus, the term “and/or” as used in a phrase such as “A and/or B” is intended to include A and B, A or B, A (alone), and B (alone). Likewise, the term “and/or” as used in a phrase such as “A, B, and/or C” is intended to include A, B, and C; A, B, or C; A or B; A or C; B or C; A and B; A and C; B and C; A (alone); B (alone); and C (alone).
Units, prefixes, and symbols are denoted in their Système International de Unites (SI) accepted form. Numeric ranges are inclusive of the numbers defining the range. The headings provided herein are not limitations of the various aspects or embodiments of the invention, which can be had by reference to the specification as a whole. Accordingly, the terms defined immediately below are more fully defined by reference to the specification in its entirety.
Wherever embodiments are described with the language “comprising,” otherwise analogous embodiments described in terms of “consisting of” and/or “consisting essentially of” are included.
Unless the context indicates otherwise, the following acronyms as used in this document have the indicated meanings: “BC”: Breast cancer; “TAG”: Tumour Aggressive Grading; “6g-TAGs”: 6 gene—Tumour Aggressive Grading signature; “G1”: histologic grade 1; “G2”: histologic grade 2; “G1-like”: histologic grade 1-like; “G3-like”: histologic grade 3-like; G3 histologic grade 3; “GLG”: genetic low grade; “GHG”: genetic high grade; “GG1”: Genetic grade 1; “GG3”: Genetic grade 3; “qRT-PCR”: quantitative reverse transcriptase-polymerase chain reaction; “BII-US”: microarray data generated in Bioinformatics Institute of Singapore.
We have identified a number of genes whose expression is indicative of breast tumour aggressiveness. Accordingly, we provide for methods of grading breast tumours, and therefore assigning a measure of their aggressiveness, by detecting the level of expression of one or more of these genes. The genes are provided in a number of gene sets, or classifiers.
We provide for the detection of and/or the determination of the expression level of at least one, a plurality, or all of the genes of a 6 gene set which we term “6g-TAGs”. The 6 genes of the 6g-TAGs gene set comprise BRRN1, AURKA, MELK, PRR11, CENPW and E2F1 and are set out in Table D0 below.
The GenBank Accession Numbers of each of the genes are as follow: BRRN1 (NM_015341), AURKA (NM_003600), MELK (NM_014791), PRR11 (NM_018304), CENPW (NM_001012507) and E2F1 (NM_005225).
In a general aspect, we provide for the detection of any one or more of a small set of 264 gene probesets, which we term the “SWS Classifier 0”. This classifier represents 232 genes. In some embodiments, the expression of all of the 264 gene probesets are detected. For example, the expression of all the 232 genes represented by such probesets may be detected.
The genes comprised in this classifier are set out in Table D1 in the section “SWS Classifier 0” below, in Table S1 in Example 20, as well as in Appendix A1. This and the other tables D2, D3, D4 and D5 (see below) contain the GenBank ID and the Gene Symbol of the gene, as well as the “Affi ID”, or the “Affymetrix ID” number of a probe. Affymetrix probe set IDs and their corresponding oligonucleotide sequences, as well as the GenBank mRNA sequences they are designed from, can be accessed on the world wide web at the ADAPT website, hosted by the Paterson Institute for Cancer Research. Table D0 also contains this information.
In such an embodiment, therefore, our method comprises determining the expression level of at least one of the genes of the 264 gene probesets (for example, at least one of the 232 genes) in the classifier which we term the “SWS Classifier 0”. More than one, for example, a plurality of the genes of such a set may also be detected. The 264 gene probesets of the SWS Classifier 0 gene set are set out in Table D1 below.
In some embodiments, the expression level of more than one gene is detected. For example, the expression level of 5 or more genes may be detected. The expression level of a plurality of genes may therefore be determined. In some embodiments, the expression level of all 264 gene probesets (for example, the expression level of all 232 genes) may be detected, though it will be clear that this does not need to be so, and a smaller subset may be detected.
We therefore provide for the detection of one or more, a plurality or all, of subsets comprising 17 genes and several subsets of 5-17 genes from the 264 gene probesets.
Alternatively, or in addition, our method may comprise determining the expression level of at least one, a plurality, or all of the genes of a 5 gene set which we term the “SWS Classifier 1”. The 5 genes of the SWS Classifier 1 gene set are set out in Table D2 below.
In other embodiments, our method may comprise determining the expression level of at least one, a plurality, or all of the genes of an 17 gene set which we term the “SWS Classifier 2”. The 17 genes of the SWS Classifier 2 gene set are set out in Table D3 below.
In other embodiments, our method may comprise determining the expression level of at least one, a plurality, or all of the genes of a 7 gene set which we term the “SWS Classifier 3”. The 7 genes of the SWS Classifier 3 gene set are set out in Table D4 below.
In other embodiments, our method may comprise determining the expression level of at least one, a plurality, or all of the genes of a 7 gene set which we term the “SWS Classifier 4”. The 7 genes of the SWS Classifier 4 gene set are set out in Table D5 below.
In specific embodiments, the methods comprise detection of the expression level of all of the genes in the gene set of interest. For example, all 6 genes in the “6g-TAGs” are detected, all 5 genes in the “SWS Classifier 1” are detected, all 17 genes in the “SWS Classifier 2” are detected, all 7 genes in the “SWS Classifier 3” are detected and all 7 genes of the SWS Classifier 4 gene set are detected in these embodiments.
Where the 6g-TAGs, SWS Classifier 1, the SWS Classifier 2, the SWS Classifier 3 or the SWS Classifier 4 are used, each of Tables D0, D2, D3, D4 and D5 provide indications of the grades to be assigned to the tumour depending on the level of expression of the relevant gene which is detected (in Columns 7 and 8 respectively).
Thus, the tables also contain columns showing the grades associated with high and low levels of expression of a particular gene, in Columns 7 and 8 of Table D1 for example. Thus, for example, the gene Barren homolog (Drosophila) is annotated to the effect that the “Grade with Higher Expression” is 3, while the “Grade with Lower Expression” is 1. Accordingly, our method provides that the tumour has a grade of 3 if a high level of expression of Barren homolog (Drosophila) is detected in or from the tumour. If a low level of this gene is detected in or from the tumour, then a grade of 1 may be assigned to that tumour.
Detection of gene expression, for example for tumour grading, may suitably be done by any means as known in the art, and as described in further detail below.
The methods described here for gene expression analysis and tumour grading may be automated, or partially or completely controlled by a controller such as a microcomputer. Thus, any of the methods described here may comprise computer implemented methods of assigning a grade to a breast tumour. For example, such a method may comprise processing expression data for one or more genes set out in Table D1 (SWS 0 Classifier) and obtaining a grade indicative of aggressiveness of the breast tumour.
The methods described here are suitably capable of classifying a breast tumour to an accuracy of at least 85%, at least 90% accuracy, or at least 95% accuracy, with reference to the grade obtained by conventional means, such as for example grading of the breast tumour by histological grading. For example, the methods may be capable of classifying tumours with grades corresponding to histological Grade 1 and histological Grade 3 tumours with an accuracy of 70% or above, 80% or above, or 90% or above.
In a refinement of our methods, we provide for a “cut-off” level of expression, by which the expression of a gene in or from a tumour may be judged in order to establish whether the expression is at a “high” level, or at a “low” level. The cut-off level is set out in Column 9 of Tables D0, D1, D2, D3, D4 and D5.
Accordingly, in some embodiments, our methods include assigning a grade based on whether the level of expression falls below or exceeds the cut-off. In some embodiments, the cut-off values are determined as the natural log transform normalised signal intensity measurement for Affymetrix arrays. In such embodiments, the cut-off values may be determined as a global mean normalisation with a scaling factor of 500.
For example, referring back to Table 1, the cut-off level of expression for the gene Barren homolog (Drosophila) is 5.9167 units (see above and formula (1), Microarray Method). Where a given tumour contains a level of expression of this gene that exceeds this level, then it is determined to be a “high” level of expression. A grade of 3 may then be assigned to that tumour. On the other hand, if the expression of the Barren homologue falls below this cut-off level, then the expression is judged to be a “low” level of expression. A grade of 1 may be assigned to the tumour in this event.
Thus, we provide for a method which comprises detecting a high level of expression of a gene in SWS Classifier 0 and assigning the grade set out in Column 7 of Table D1 to the breast tumour. The method may comprise, or optionally further comprise detecting a low level of expression of the gene and assigning the grade set out in Column 8 of Table D1 to the breast tumour. A high level of expression may be detected if the expression level of the gene is above the expression level set out in Column 9 of Table D1, and a low level of expression is detected if the expression level of the gene is below that level.
We further provide for a method which comprises detecting a high level of expression of a gene in 6g-TAGs and assigning the grade set out in Column 7 of Table D0 to the breast tumour. The method may comprise, or optionally further comprise detecting a low level of expression of the gene and assigning the grade set out in Column 8 of Table D0 to the breast tumour. A high level of expression may be detected if the expression level of the gene is above the expression level set out in Column 9 of Table D0, and a low level of expression is detected if the expression level of the gene is below that level.
Our methods may comprise detecting a high expression level of any one or more of the 6g-TAGs genes.
Our methods may comprise detecting a high level of expression of BRRN1 (GenBank Accession No. NM_015341), a high level of expression of AURKA (GenBank Accession No. NM_003600), a high level of expression of MELK (GenBank Accession No. NM_014791), a high level of expression of PRR11 (GenBank Accession No. NM_018304), a high level of expression of CENPW (GenBank Accession No. NM_001012507) and/or a high level of expression of E2F1 (GenBank Accession No. NM_005225).
Where a high level of expression of a particular gene or genes is detected, this may be used to establish that a tumour is a high-aggressiveness tumour, e.g., a Grade 3 tumour, or to establish that a tumour is a metastatic tumour, or that cell is a highly proliferative cell, etc, as described in detail in this document.
A high level of expression of any single gene, a pair of the above genes, or a set of three, a set of four, a set of five, or all six of the 6g-TAGs genes may be detected for the purposes of this document.
Our methods may comprise detection of a high level of expression of aurora kinase A. Aurora kinase A (AURKA) has an Entrez_ID of 6790 and a Refseq ID of NM_003600. As the term is used in this document, a “high level of expression” of AURKA is an expression level that is above 6.65262, above 6.30082, above 6.77578. In certain embodiments, a “high level of expression” is an expression level of AURKA that is above 6.576406667. Conversely, a “low level of expression” is an expression level of this gene that is below that level.
The expression of AURKA may be detected for example by use of Affymetrix probe set id 208079_s_at.
Our methods may comprise detection of a high level of expression of centromere protein W. Centromere protein W (CENPW) has an Entrez_ID of 387103 and a Refseq ID of NM_001286524. As the term is used in this document, a “high level of expression” of CENPW is an expression level that is above 7.56154, above 7.40448, above 7.46601. In certain embodiments, a “high level of expression” is an expression level of CENPW that is above 7.477343333. Conversely, a “low level of expression” is an expression level of this gene that is below that level.
The expression of CENPW may be detected for example by use of Affymetrix probe set id 226936_at.
Our methods may comprise detection of a high level of expression of maternal embryonic leucine zipper kinase. Maternal embryonic leucine zipper kinase (MELK) has an Entrez_ID of 9833 and a Refseq ID of NM_014791. As the term is used in this document, a “high level of expression” of MELK is an expression level that is above 7.1069, above 6.63834, above 6.9252. In certain embodiments, a “high level of expression” is an expression level of MELK that is above 6.890146667. Conversely, a “low level of expression” is an expression level of this gene that is below that level.
The expression of MELK may be detected for example by use of Affymetrix probe set id 204825_at.
Our methods may comprise detection of a high level of expression of non-SMC condensin I complex, subunit H. non-SMC condensin I complex, subunit H (NCAPH) has an Entrez_ID of 23397 and a Refseq ID of NM_015341. As the term is used in this document, a “high level of expression” of NCAPH is an expression level that is above 5.91723, above 5.33539, above 5.65104. In certain embodiments, a “high level of expression” is an expression level of NCAPH that is above 5.634553333. Conversely, a “low level of expression” is an expression level of this gene that is below that level.
The expression of NCAPH may be detected for example by use of Affymetrix probe set id 12949_at.
Our methods may comprise detection of a high level of expression of proline rich 11. Proline rich 11 (PRR11/FLJ11029) has an Entrez_ID of 55771 and a Refseq ID of NM_018304. As the term is used in this document, a “high level of expression” of PRR11/FLJ11029 is an expression level that is above 7.70616, above 7.16871, above 7.12064. In certain embodiments, a “high level of expression” is an expression level of PRR11/FLJ11029 that is above 7.331836667. Conversely, a “low level of expression” is an expression level of this gene that is below that level.
The expression of PRR11/FLJ11029 may be detected for example by use of Affymetrix probe set id 228273_at.
Our methods may comprise detection of a high level of expression of E2F transcription factor 1. E2F transcription factor 1 (E2F1) has an Entrez_ID of 1869 and a Refseq ID of NM_005225. As the term is used in this document, a “high level of expression” of E2F1 is an expression level that is above 6.47071, above 5.9933, above 6.48464. In certain embodiments, a “high level of expression” is an expression level of E2F1 that is above 6.316216667. Conversely, a “low level of expression” is an expression level of this gene that is below that level.
The expression of E2F1 may be detected for example by use of Affymetrix probe set id 2028 s_at.
There are various methods by which expression levels of a gene may be detected, and these are known in the art. Examples include RT-PCR, RNAse protection, Northern blotting, Western blotting etc. The gene expression level may be determined at the transcript level, or at the protein level, or both. The detection may be manual, or it may be automated. It is envisaged that any one or a combination of these methods may be employed in the methods and compositions described here.
The detection of expression of a plurality of genes is suitably detected in the form of an expression profile of the plurality of genes, by conventional means known in the art. In some embodiments, the detection is by means of microarray hybridisation.
For example, a sample of a tumour may be taken from a patient and processed for detection of gene expression levels. Gene expression levels may be detected in the form of nucleic acid or protein levels or both, for example. Analysis of nucleic acid expression levels may be suitably performed by amplification techniques, such as polymerase chain reaction (PCR), rolling circle amplification, etc. Detection of expression levels is suitably performed by detecting RNA levels. This can be performed by means known in the art, for example, real time polymerase chain reaction (RT-PCR) or RNAse protection, etc. For this purpose, we provide for sets of one or more primers or primer pairs which are capable of amplifying any one or more of the genes in the classifiers disclosed herein. Specifically, we provide for a set of primer pairs capable of amplifying all of the genes in the SWS Classifier 0, 6g-TAGs, SWS Classifier 1, SWS Classifier 2, SWS Classifier 3 or SWS Classifier 4 sets.
Suitably, RNA expression levels may be detected by hybridisation to a microchip or array, for example, a microchip or array comprising the genes or probesets corresponding to the specific classifier of interest, as described in the Examples. In some embodiments, the gene expression data or profile is derived from microarray hybridisation to for example an Affymetrix microarray.
Detection of protein levels may be performed by for example, immunoassays including ELISA or sandwich immunoassays using antibodies against the protein or proteins of interest (for example as described in U.S. Pat. No. 6,664,114. The detection may be performed by use of a “dip stick” which comprises impregnated antibodies against polypeptides of interest, such as described in US2004014094.
We provide therefore for sets of one or more antibodies which are capable of binding specifically to any one or more of the proteins encoded by the genes in the classifiers disclosed herein. Specifically, we provide for a set of antibodies capable of amplifying all of the genes in the SWS Classifier 0, 6g-TAGs, SWS Classifier 1, SWS Classifier 2, SWS Classifier 3 or SWS Classifier 4 sets.
The grade may be assigned by any suitable method. For example, it may be assigned applying a class prediction algorithm comprising a nearest shrunken centroid method (Tibshirani, et al., 2002, Proc Natl Acad Sci USA. 99(10): 6567-6572) to the expression data of the plurality of genes. The class prediction algorithm may suitably comprise Statistically Weighted Syndromes (SWS) or Prediction Analysis of Microarrays (PAM).
In some embodiments, the grade of the tumour may be assigned by applying a class prediction algorithm comprising one or more of the steps set out here. First, a set of predictor parameters (i.e., probesets) may be obtained based on predictors which discriminate the histologic tumours G1 and G3. Next, the potentially predictive parameters (i.e. signal intensity values of micro-array) may be recoded to obtain cut-off values for robust discrete-valued variables. The recoding may be done in such a way as to maximize an informativity measure of discrimination ability of the parameter and minimize its instability to the discrimination object (i.e. patients) belonging to distinct classes (i.e. G1 and G3). Then, statistically robust discrete-valued variables and combinations thereof may be selected for further construction of class prediction algorithm. A sum of the statistically weighted discrete-valued variables and combinations thereof may be obtained based on the Weighted Voting Procedure procedure described in SWS method section. Finally, a predictive outcome (classification) score of breast cancer subtypes based on the sum for sub-typing (re-classification) histologic G2 tumours may be obtained.
In suitable embodiments, the method is applied to grade breast tumours which are traditionally graded as Grade 2 by conventional means, such as by histological grading as known in the art. Our method is capable of distinguishing the aggressiveness of tumours within the group of tumours in Grade 2 (which were hitherto thought to be homogenous) into Grade 1 like tumours (i.e., more aggressive) and Grade 3 like tumours (i.e., less aggressive). This is described in detail in the Examples.
Accordingly, we provide for a method of classifying a histological Grade 2 tumour into a low aggressiveness tumour or a high aggressiveness tumour. In other words, we provide a method for reassigning a more precise grading to a tumour which has been graded histologically as a Grade 2 tumour.
Such a method comprises assigning a grade to the histological Grade 2 tumour according to any of the methods described above. For example, the expression of any one or more genes, for example, all the genes, in any of the SWS Classifiers described here may be detected and a grade of 1 or 3 assigned using Columns 7, 8 or 9 individually or in combination, as described above.
Such a tumour which has been reassigned will suitably have one or more characteristics or features of the reassigned grade. The characteristics or features may include one or more histological or morphological features, susceptibility to treatment, rate of growth or proliferation, degree of differentiation, aggressiveness, etc. As an example, the characteristic or feature may comprise aggressiveness.
For example, a histological Grade 2 breast tumour which has been assigned a low aggressiveness grade by the gene expression detection methods described here may suitably have at least one feature of a histological Grade 1 breast tumour. Similarly, a breast tumour assigned a high aggressiveness grade may have at least one feature of a histological Grade 3 breast tumour.
Such a feature may comprise degree of differentiation (e.g., well-differentiated, moderately differentiated or poorly-differentiated). The feature may comprise rate of growth (e.g., slow-growing, fast-growing). The feature may comprise rate of proliferation (e.g., slow-proliferation, highly-proliferative). The feature may comprise likelihood of tumour recurrence post-surgery. The feature may comprise survival rate. The feature may comprise likelihood of tumour recurrence post-surgery and survival rate. The feature may comprise a disease free survival rate. The feature may comprise susceptibility to treatment.
Accordingly, application of the grading methods described here enables the classification of the histological Grade 2 tumour into a Grade 1 tumour or a Grade 3 tumour, so as to allow the clinician to treat the tumour accordingly in view of its aggressiveness, prognosis, etc.
Such regrading using our methods is suitably capable of classifying histological Grade 2 tumours into Grade 1 like and/or Grade 3 like tumours with an accuracy of 70% or above, 80% or above, or 90% or above.
The histological grading may be performed by any means known in the art. For example, the breast tissue or tumour may be graded by the Nottingham Grading System (NGS) or the Elston-Ellis Modified Scarf, Bloom, Richardson Grading System, both methods being well known in the art.
The information obtained from the regrading may be used to predict any of the parameters which may be useful to the clinician. The parameter may include, for example, likelihood of tumour metastasis, prognosis of the patient, survival rate, possibility of recovery and recurrence, etc, depending on the grade of the tumour which has been reassigned to the histological Grade 2 tumour. We therefore describe a method of determining whether a breast tumour is a metastatic breast tumour, the method comprising assigning a grade to the breast tumour as described using gene expression data.
We describe a method of predicting a survival rate for an individual with a histological Grade 2 breast tumour, the method comprising assigning a grade to the breast tumour using gene expression data as described. A low aggressiveness grade may suitably indicate a high probability of survival and a high aggressiveness grade may suitably indicate a low probability of survival. We also provide for a method of prognosis of an individual with a breast tumour, the method comprising assigning a grade to the breast tumour by a method as described, and a method of diagnosis of aggressive breast cancer in an individual, the method comprising assigning a grade indicative of high aggressiveness to a breast tumour of the individual by a method as described.
The methods of gene expression analysis may be employed for determining the proliferative state of a cell. For example, such a method may comprise detecting the expression of a gene selected from the genes set out in Table D1 (SWS Classifier 0) and/or a gene selected from the genes set out in Table D0 (6g-TAGs). Where a high level of expression of a gene which is annotated “3” in Column 7 is detected, this may indicate a highly proliferative cell. Similarly, where a high level of expression of a gene which is annotated “1” in Column 7 is detected, a non-proliferating cell or a slow-growing cell may be indicated. If a low level of expression of a gene which is annotated “3” in Column 8 is detected, this may indicate a highly proliferative cell and where a low level of expression of a gene which is annotated “1” in Column 8 is detected, this indicates a non-proliferating cell or a slow-growing cell.
The classifiers are described herein as combinations of probesets, and the skilled person will be aware that more than one probeset can correspond to one gene. Accordingly, the SWS Classifier 0 contains 264 probesets which represent 232 genes. It will be clear therefore that the invention encompasses detection of expression level of one or more genes, and/or one or more probesets within the relevant classifiers, or any combination of this.
Furthermore, it will also be clear that the detection of expression level of one or more genes, and/or one or more probesets within for example a 6g-TAGs geneset is also encompassed.
Suitably, the information obtained by the regarding may also be used by the clinician to recommend a suitable treatment, in line with the grade of the tumour which has been reassigned.
Thus, a tumour which has been reassigned to Grade 1 may require less aggressive treatment than a tumour which has been reassigned to Grade 3, for example. We therefore describe a method of choosing a therapy for an individual with breast cancer, the method comprising assigning a grade to the breast tumour by a method as described herein, and choosing an appropriate therapy based on the aggressiveness of the breast tumour. In general, the method may be employed for the treatment of an individual with breast cancer, by assigning a grade to the breast tumour and administering an appropriate therapy to the individual based on the aggressiveness of the breast tumour.
In general, we disclose a method of treatment of an individual suffering from breast cancer, the method comprising modulating the expression of a gene set out in Table D0 (6g-TAGs), Table D1 (SWS Classifier 0), Table D2 (SWS 1 Classifier), Table D3 (SWS Classifier 2), Table D4 (SWS Classifier 3) and/or Table D5 (SWS Classifier 4).
Treatment of High-Aggressiveness Tumours
Tumours classified as high-aggressive, such as Grade 3 tumours, may be treated by therapeutic agents that work directly by inhibiting dividing (proliferating) cells.
Such therapeutic agents include chemotherapeutic agents. The chemotherapeutic agent may comprise an antiproliferative chemotherapeutic agent. Examples of chemotherapeutic agents include taxanes such as docetaxel and paclitaxel.
The chemotherapeutic agent may comprise a vinca alkaloid or a condensin inhibitors. The chemotherapeutic agent may comprise vinblastine, vincristine, vindesine, vinorelbine, desoxyvincaminol, vincaminol, vinburnine, vincamajine, vineridine, vinburnine or vinpocetine.
A further example of a chemotherapeutic agent suitable for treating high-aggressive cells is a taxane. Taxanes include paclitaxel (taxol), docetaxel (taxotere) and cabazitaxel.
Inhibitors of AURKA or MELK may also be used as agents for treating high-aggressive cells. An example of an AURKA inhibitor is alisertib. An example of a MELK inhibitor is OTS167.
Further examples of chemotherapeutic agents suitable for treating high-aggressive cells include anthracyclines such as doxorubicin, idarubicin and epirubicin.
These are described in further detail in Joerger M, Thürlimann B. Chemotherapy regimens in early breast cancer: major controversies and future outlook. Expert Rev Anticancer Ther. 2013 February; 13(2):165-78. doi: 10.1586/era.12.172.
Other suitable chemotherapeutic agents may include agents that specifically target cell cycle machinery such as a CDK 4/6 inhibitor. A suitable agent may comprise palbociclib.
Agents suitable for targeting cell cycle machinery are described in detail in Mayer EL. Targeting breast cancer with CDK inhibitors. Curr Oncol Rep. 2015 May; 17(5):443. doi: 10.1007/s11912-015-0443-3.
Treatment of Low-Aggressiveness Tumours
Tumours classified as low-aggressive, such as Grade 3 tumours, are expected to be largely resistant to therapies suitable for treating high-aggressiveness tumours.
Such low-aggressiveness tumours are more suitably treated with agents that do not directly target cell division. Such agents may instead target other growth-related requirements of tumours, such as the mTOR pathway that mediates mRNA translation.
Examples of such therapies suitable for treating low-aggressiveness tumours include everolimus and temsirolimus, described in detail in Vicier C, Dieci M V, Arnedos M, Delaloge S, Viens P, Andre F. Clinical development of mTOR inhibitors in breast cancer. Breast Cancer Res. 2014 Feb. 17; 16(1):203. doi: 10.1186/bcr3618.
Further examples of therapies suitable for treating low-aggressiveness tumours include agents which mediate the growth of blood vessels that provide blood supply to tumours.
An example of such an agent is bevacizumab, described in Keating G M. Bevacizumab: a review of its use in advanced cancer. Drugs. 2014 October; 74(16):1891-925. doi: 10.1007/s40265-014-0302-9.
Other examples of therapeutics suitable for treatment of low-aggressive tumours include agents capable of mediating hormone-related growth signaling pathways such as the estrogen signaling pathways in estrogen receptor-positive breast cancers. Such drugs may comprise tamoxifen, anastrozole, letrozole, exemestane and goserelin. These are described in detail in Schiavon G, Smith I E. Status of adjuvant endocrine therapy for breast cancer. Breast Cancer Res. 2014; 16(2):206.
It will be evident that any of the diagnosis and treatment methods may suitably be combined with other methods of assessing the aggressiveness of the tumour, the patient's health and susceptibility to treatment, etc. For example, the diagnosis or choice of therapy may be determined by further assessing the size of the tumour, or the lymph node stage or both, optionally together or in combination with other risk factors
Specifically, the choice of therapy may be determined by assessing the Nottingham Prognostic Index (NPI). The NPI is described in detail in Haybittle, et al., 1982. In combination with the grading methods described here, the method is suitable for assigning a breast tumour patient into a prognostic group. Such a combined method comprises deriving a score which is the sum of the following: (a) (0.2× tumour size in cm); (b) tumour grade in which the tumour grade is assigned by a method according to any of the gene expression detection methods described herein; and (c) lymph node stage; in which the tumour size and the lymph node stage are determined according to the Nottingham Prognostic Index, in which a patient with a score of 2.4 or less is categorised to a EPG (excellent prognostic group), a patient with a score of less than 3.4 is categorised to a GPG (good prognostic group), a patient with a score of between 3.4 and 5.4 is categorised to a MPG (moderate prognostic group), a patient with a score of greater than 5.4 is categorised to a PPG (poor prognostic group).
Alternatively, or in addition, a method of assigning a breast tumour patient into a prognostic group may comprise applying the Nottingham Prognostic Index to a breast tumour, but modified such that the histologic grade score of the breast tumour is replaced by a grade obtained by a gene expression detection method as described in this document.
Other factors which may of course be assessed for determining the choice of therapy may include receptor status, such as oestrogen receptor (ER) or progesterone receptor (PR) status, as known in the art. For example, the choice of therapy may be determined by further assessing the oestrogen receptor (ER) status of the breast tumour.
We further provide for combinations of genes according to the various classifiers disclosed in this document. Such combinations may comprise mixtures of genes or corresponding probes, such as in a form which is suitable for detection of expression. For example, the combination may be provided in the form of DNA in solution.
In other embodiments, a microarray or chip is provided which comprises any combination of genes or probes, in the form of cDNA, genomic DNA, or RNA, within the classifiers. In some embodiments, the microarray or chip comprises all the genes or probes in 6g-TAGs, SWS Classifier 0, SWS Classifier 1, SWS Classifier 2, SWS Classifier 3 or SWS Classifier 4. The genes may be synthesised or obtained by means known in the art, and attached on the microarray or chip by conventional means, as known in the art. Such microarrays or chips are useful in monitoring gene expression of any one or more of the genes comprised therein, and may be used for tumour grading or detection as described here.
We further describe a probe set consisting of a probe or probes having Affymetrix ID numbers as set out in Column 6 of Table D0, Table D1, Table D2, Table D3, Table D4 or Table D5. Specifically, we describe an array, such as a microarray, comprising the probesets set out in Table D0 (6g-TAGs). We also describe an array such as a microarray comprising the probesets set out in Table D1 (SWS Classifier 0). We also describe an array such as a microarray comprising the genes or probesets set out in Table D2 (SWS 1 Classifier), an array such as a microarray comprising the genes or probesets set out in Table D3 (SWS Classifier 2), an array such as a microarray comprising the genes or probesets set out in Table D4 (SWS3 Classifier), and an array such as a microarray comprising the genes or probesets set out in Table D5 (SWS Classifier 4).
The probes or probe sets are suitably synthesised or made by means known in the art, for example by oligonucleotide synthesis, and may be attached to a microarray for easier carriage and storage. They may be used in a method of assigning a grade to a breast tumour as described herein.
We describe the use of Statistically Weighted Syndromes (SWS) on gene expression data which may comprise microarray gene expression data. We describe the use of SWS for gene discovery. We further describe such use in combination with Prediction Analysis of Microarrays (PAM). We describe the use of SWS to identify gene sets diagnostic of cancer status, such as breast cancer status or proliferative status.
The methods and compositions described here may be used for identifying molecules capable of treating or preventing breast cancer, which may be used as drugs for cancer treatment. Such a method comprises: (a) grading a breast tumour as described using gene expression data; (b) exposing the breast tumour to a candidate molecule; and (c) detecting a change in tumour grade. The change in tumour grade is suitably determined by grading a breast tumour as described using gene expression data before and after exposure of the breast tumour to a candidate molecule. We provide molecule identified by such a method, for example for use in breast cancer treatment.
Particular screening applications relate to the testing of pharmaceutical compounds in drug research. The reader is referred generally to the standard textbook “In vitro Methods in Pharmaceutical Research”, Academic Press, 1997, and U.S. Pat. No. 5,030,015). Assessment of the activity of candidate pharmaceutical compounds generally involves combining the breast cancer cells with the candidate compound, determining any change in the tumour grade, as determined by the gene expression detection methods described herein of the cells that is attributable to the compound (compared with untreated cells or cells treated with an inert compound), and then correlating the effect of the compound with the observed change.
The screening may be done, for example, either because the compound is designed to have a pharmacological effect on certain cell types such as tumour cells, or because a compound designed to have effects elsewhere may have unintended side effects. Two or more drugs can be tested in combination (by combining with the cells either simultaneously or sequentially), to detect possible drug-drug interaction effects. In some applications, compounds are screened initially for potential toxicity (Castell et al., pp. 375-410 in “In vitro Methods in Pharmaceutical Research,” Academic Press, 1997). Cytotoxicity can be determined in the first instance by the effect on cell viability, survival, morphology, and expression or release of certain markers, receptors or enzymes. Effects of a drug on chromosomal DNA can be determined by measuring DNA synthesis or repair. [3H]thymidine or BrdU incorporation, especially at unscheduled times in the cell cycle, or above the level required for cell replication, is consistent with a drug effect. The reader is referred to A. Vickers (PP 375-410 in “In vitro Methods in Pharmaceutical Research,” Academic Press, 1997) for further elaboration.
Candidate molecules subjected to the assay and which are found to be of interest may be isolated and further studied. Methods of isolation of molecules of interest will depend on the type of molecule employed, whether it is in the form of a library, how many candidate molecules are being tested at any one time, whether a batch procedure is being followed, etc.
The candidate molecules may be provided in the form of a library. In an embodiment, more than one candidate molecule is screened simultaneously. A library of candidate molecules may be generated, for example, a small molecule library, a polypeptide library, a nucleic acid library, a library of compounds (such as a combinatorial library), a library of antisense molecules such as antisense DNA or antisense RNA, an antibody library etc, by means known in the art. Such libraries are suitable for high-throughput screening. Tumour cells may be exposed to individual members of the library, and the effect on tumour grade, if any, cell determined. Array technology may be employed for this purpose. The cells may be spatially separated, for example, in wells of a microtitre plate.
In an embodiment, a small molecule library is employed. By a “small molecule”, we refer to a molecule whose molecular weight may be less than about 50 kDa. In particular embodiments, a small molecule has a molecular weight may be less than about 30 kDa, such as less than about 15 kDa, or less than 10 kDa or so. Libraries of such small molecules, here referred to as “small molecule libraries” may contain polypeptides, small peptides, for example, peptides of 20 amino acids or fewer, for example, 15, 10 or 5 amino acids, simple compounds, etc.
Alternatively or in addition, a combinatorial library, as described in further detail below, may be screened for candidate modulators of tumour function.
Libraries, in particular, libraries of candidate molecules, may suitably be in the form of combinatorial libraries (also known as combinatorial chemical libraries).
A “combinatorial library”, as the term is used in this document, is a collection of multiple species of chemical compounds that consist of randomly selected subunits. Combinatorial libraries may be screened for molecules which are capable of changing the choice by a stem cell between the pathways of self-renewal and differentiation.
Various combinatorial libraries of chemical compounds are currently available, including libraries active against proteolytic and non-proteolytic enzymes, libraries of agonists and antagonists of G-protein coupled receptors (GPCRs), libraries active against non-GPCR targets (e.g., integrins, ion channels, domain interactions, nuclear receptors, and transcription factors) and libraries of whole-cell oncology and anti-infective targets, among others. A comprehensive review of combinatorial libraries, in particular their construction and uses is provided in Dolle and Nelson (1999), Journal of Combinatorial Chemistry, Vol 1 No 4, 235-282. Reference is also made to Combinatorial peptide library protocols (edited by Shmuel Cabilly, Totowa, N.J.: Humana Press, c1998. Methods in Molecular Biology; v. 87). Specific combinatorial libraries and methods for their construction are disclosed in U.S. Pat. No. 6,168,914 (Campbell, et al), as well as in Baldwin et al. (1995), “Synthesis of a Small Molecule Library Encoded with Molecular Tags,” J. Am. Chem. Soc. 117:5588-5589, and in the references mentioned in those documents.
Further references describing chemical combinatorial libraries, their production and use include The Chemical Generation of Molecular Diversity. Michael R. Pavia, Sphinx Pharmaceuticals, A Division of Eli Lilly (Published July, 1995); Combinatorial Chemistry: A Strategy for the Future—MDL Information Systems discusses the role its Project Library plays in managing diversity libraries (Published July, 1995); Solid Support Combinatorial Chemistry in Lead Discovery and SAR Optimization, Adnan M. M. Mjalli and Barry E. Toyonaga, Ontogen Corporation (Published July, 1995); Non-Peptidic Bradykinin Receptor Antagonists From a Structurally Directed Non-Peptide Library. Sarvajit Chakravarty, Babu J. Mavunkel, Robin Andy, Donald J. Kyle*, Scios Nova Inc. (Published July, 1995); Combinatorial Chemistry Library Design using Pharmacophore Diversity Keith Davies and Clive Briant, Chemical Design Ltd. (Published July, 1995); A Database System for Combinatorial Synthesis Experiments—Craig James and David Weininger, Daylight Chemical Information Systems, Inc. (Published July, 1995); An Information Management Architecture for Combinatorial Chemistry, Keith Davies and Catherine White, Chemical Design Ltd. (Published July, 1995); Novel Software Tools for Addressing Chemical Diversity, R. S. Pearlman, Laboratory for Molecular Graphics and Theoretical Modeling, College of Pharmacy, University of Texas (Published June/July, 1996); Opportunities for Computational Chemists Afforded by the New Strategies in Drug Discovery: An Opinion, Yvonne Connolly Martin, Computer Assisted Molecular Design Project, Abbott Laboratories (Published June/July, 1996); Combinatorial Chemistry and Molecular Diversity Course at the University of Louisville: A Description, Arno F. Spatola, Department of Chemistry, University of Louisville (Published June/July, 1996); Chemically Generated Screening Libraries: Present and Future. Michael R. Pavia, Sphinx Pharmaceuticals, A Division of Eli Lilly (Published June/July, 1996); Chemical Strategies For Introducing Carbohydrate Molecular Diversity Into The Drug Discovery Process. Michael J. Sofia, Transcell Technologies Inc. (Published June/July, 1996); Data Management for Combinatorial Chemistry. Maryjo Zaborowski, Chiron Corporation and Sheila H. DeWitt, Parke-Davis Pharmaceutical Research, Division of Warner-Lambert Company (Published November, 1995); and The Impact of High Throughput Organic Synthesis on R&D in Bio-Based Industries, John P. Devlin (Published March, 1996).
Techniques in combinatorial chemistry are gaining wide acceptance among modern methods for the generation of new pharmaceutical leads (Gallop, M. A. et al., 1994, J. Med. Chem. 37:1233-1251; Gordon, E. M. et al., 1994, J. Med. Chem. 37:1385-1401). One combinatorial approach in use is based on a strategy involving the synthesis of libraries containing a different structure on each particle of the solid phase support, interaction of the library with a soluble receptor, identification of the ‘bead’ which interacts with the macromolecular target, and determination of the structure carried by the identified ‘bead’ (Lam, K. S. et al., 1991, Nature 354:82-84). An alternative to this approach is the sequential release of defined aliquots of the compounds from the solid support, with subsequent determination of activity in solution, identification of the particle from which the active compound was released, and elucidation of its structure by direct sequencing (Salmon, S. E. et al., 1993, Proc. Natl. Acad. Sci. USA 90:11708-11712), or by reading its code (Kerr, J. M. et al., 1993, J. Am. Chem. Soc. 115:2529-2531; Nikolaiev, V. et al., 1993, Pept. Res. 6:161-170; Ohlmeyer, M. H. J. et al., 1993, Proc. Natl. Acad. Sci. USA 90:10922-10926).
Soluble random combinatorial libraries may be synthesized using a simple principle for the generation of equimolar mixtures of peptides which was first described by Furka (Furka, A. et al., 1988, Xth International Symposium on Medicinal Chemistry, Budapest 1988; Furka, A. et al., 1988, 14th International Congress of Biochemistry, Prague 1988; Furka, A. et al., 1991, Int. J. Peptide Protein Res. 37:487-493). The construction of soluble libraries for iterative screening has also been described (Houghten, R. A. et al. 1991, Nature 354:84-86). K. S. Lam disclosed the novel and unexpectedly powerful technique of using insoluble random combinatorial libraries. Lam synthesized random combinatorial libraries on solid phase supports, so that each support had a test compound of uniform molecular structure, and screened the libraries without prior removal of the test compounds from the support by solid phase binding protocols (Lam, K. S. et al., 1991, Nature 354:82-84).
Thus, a library of candidate molecules may be a synthetic combinatorial library (e.g., a combinatorial chemical library), a cellular extract, a bodily fluid (e.g., urine, blood, tears, sweat, or saliva), or other mixture of synthetic or natural products (e.g., a library of small molecules or a fermentation mixture).
A library of molecules may include, for example, amino acids, oligopeptides, polypeptides, proteins, or fragments of peptides or proteins; nucleic acids (e.g., antisense; DNA; RNA; or peptide nucleic acids, PNA); aptamers; or carbohydrates or polysaccharides. Each member of the library can be singular or can be a part of a mixture (e.g., a compressed library). The library may contain purified compounds or can be “dirty” (i.e., containing a significant quantity of impurities).
Commercially available libraries (e.g., from Affymetrix, ArQule, Neose Technologies, Sarco, Ciddco, Oxford Asymmetry, Maybridge, Aldrich, Panlabs, Pharmacopoeia, Sigma, or Tripose) may also be used with the methods described here.
In addition to libraries as described above, special libraries called diversity files can be used to assess the specificity, reliability, or reproducibility of the new methods. Diversity files contain a large number of compounds (e.g., 1000 or more small molecules) representative of many classes of compounds that could potentially result in nonspecific detection in an assay. Diversity files are commercially available or can also be assembled from individual compounds commercially available from the vendors listed above.
The breast tumour is surgically resected, processed, and snap frozen. A frozen portion of the tumour is processed for total RNA extraction using the Qiagen RNeasy kit (Qiagen, Valencia, Calif.). Briefly, frozen tumours are cut into minute pieces, and pieces totalling ˜50-100 milligrams (mg) are homogenized for 40 seconds in RNeasy Lysis Buffer (RLT). Proteinase K is added, and the samples are incubated for 10 minutes at 55 degrees C., followed by centrifugation and the addition of ethanol. After transferring the supernatant into RNeasy columns, DNase is added. Collected RNA is then assessed for quality using an Agilent 2100 bioanalyzer (Agilent Technologies, Rockville, Md.) or by agarose gel. The RNA is stored at minus −70 degrees C.
Labeled cRNA target is generated for microarray hybridization essentially according to the Affymetrix protocol (Affymetrix, Santa Clara, Calif.). Briefly, approximately 5 micrograms (μg) of total RNA are reversed transcribed into first-strand cDNA using a T7-linked oligo-dT primer, followed by second strand synthesis. A T7 RNA polymerase is then used to linearly amplify antisense RNA. This “cRNA” is biotinylated and chemically fragmented at 95° C. Ten μg of the fragmented, biotinylated cRNA is hybridized at 45° C. for 16 hours to an Affymetrix high-density oligonucleotide GenChip array. The array is then washed and stained with streptavidin-phycoerythrin (10 μg/ml). Signal amplification is achieved using a biotinylated anti-streptavidin antibody. The scanned images are inspected for the presence of artifacts. In case of defects, the hybridization procedure is repeated. Expression values and detection calls are computed from raw data following the procedures outlined for the Affymetrix MAS 5.0 analysis software. Global mean normalization of the gene expression by hybridization signals across all arrays is used to control for differences in chip hybridization signal intensity values. To do that for a given array j (j=1, 2, . . . M), we calculated normalization coefficients kj (j=1, 2, . . . , n), by the following formula:
where n is the number of observed probe sets, aij is the signal intensity value of the i-th Affymetrix probesets representing a gene expression. Then the natural logarithm of the signal intensity value of the given array j was multiplied by this normalization coefficient. A normalisation coefficient of 500 is used in determining the cut-offs shown in the Tables in this document.
The microarray-derived normalized numerical expression values corresponding to the genetic grade signature genes are used as input for the SWS algorithm.
The RNA purification and microarray analysis methodologies above reflect only our “preferred methods”, and that other variants exist that could be used in conjunction with our Process for Predicting Patient Outcome. . . . For example, the starting material could be formalin-fixed paraffin-embedded tumour material instead of fresh frozen material, or the RNA might be extracted using a Cesium Chloride Gradient method, or the RNA could be analyzed by NimbleGen Microarrays that include DNA probes corresponding to our genes of interest. And it should also be noted that a microarray may not be necessary at all to determine the expression levels of our signature genes, but rather their expression could be quantitatively measured by PCR-based techniques such as real time-PCR.
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Clinical characteristics of patient and tumour samples of the Uppsala, Stockholm and Singapore cohorts are summarized in Table E1.
The Uppsala cohort originally comprised of 315 women representing 65% of all breast cancers resected in Uppsala County, Sweden from Jan. 1, 1987 to Dec. 31, 1989. Information pertaining to patient therapies, clinical follow up, and sample processing are described elsewhere (41).
Histological Grading
For histological grading, new tumour sections are prepared from the original paraffin blocks, stained with eosin, and graded in a blinded fashion by H. N. according to the Nottingham grading system (6, Haybittle et al., 1982) as follows:
Tubule Formation: 3=poor, if <10% of the tumour showed definite tubule formation, 2=moderate, if ≧10% but ≦75%, and 1=well, if >75%.
Mitotic Index: 1=low, if <10 mitoses, 2=medium, if 10-18 mitoses, and 3=high, if >18 mitoses (per 10 high-power fields). The field diameter was 0.57 mm.
Nuclear Grade: 1=low, if there was little variation in the size and shape of the nuclei, 2=medium for moderate variation, and 3=high for marked variation and large size.
Scores are then summed, and tumour samples with scores ranging from 3-5 are classified as Grade I; 6-7 as Grade II; and 8-9 as Grade III.
Protein Assays
Protein levels of Estrogen Receptor (ER) and Progesterone Receptor (PgR) are assessed by immunoassay (monoclonal 6F11 anti-ER and monoclonal NCL-PGR, respectively, Novocastra Laboratories Ltd, Newcastle upon Tyne, UK) and deemed positive if >0.1 fmol/ug DNA. VEGF was measured in tumour cytosol by a quantitative immunoassay kit (Quantikine-human VEGF; R&D Systems, Minneapolis, Minn., USA) as described (42). Protein levels of Ki-67 are analyzed using anti-Ki67 antibody (MIB-1) by the grid-graticula method with cut-offs: low=2, medium>2 and <6, high=6. Cyclin E was measured using the antibody HE12 (Santa Cruz Inc., USA) with cutoffs: low=0-4%, medium=5-49%, and high=50-100% stained tumour cells (43).
S-phase fraction was determined by flow cytometry and defined as high if >7% in diploid tumours, or >12% in aneuploid tumours. TP53 mutational status was determined by cDNA sequencing as previously described (41). The Uppsala tumour samples are approved for microarray profiling by the ethical committee at the Karolinska Institute, Stockholm, Sweden.
Stockholm Cohort
The Stockholm samples are derived from breast cancer patients that were operated on at the Karolinska Hospital from Jan. 1, 1994 through Dec. 31, 1996 and identified in the Stockholm-Gotland breast cancer registry.
Information on patient age, tumour size, number of metastatic axillary lymph nodes, hormonal receptor status, distant metastases, site and date of relapse, initial therapy, and date and cause of death are obtained from patient records filed with the Stockholm-Gotland registry.
Tumour sections are classified using the Nottingham grading system (Haybittle et al., 1982). The Stockholm tumour samples are approved for microarray profiling by the ethical committee at the Karolinska Hospital, Stockholm, Sweden.
Singapore Cohort
The Singapore samples are derived from patients that were operated on at the National University Hospital (Singapore) from Feb. 1, 2000 through Jan. 31, 2002.
Information on patient age, tumour size, number of metastatic lymph nodes and hormonal receptor status are obtained from hospital records.
Tumour sections are graded in a blinded fashion according to the Nottingham grading system (Haybittle et al., 1982) as applied to the Uppsala and Stockholm cohorts, with the following exception: Mitotic Index: 1=low, if <8 mitoses, 2=medium, if 9-16 mitoses, and 3=high, if >16 mitoses (per 10 high-power fields). The field diameter is 0.55 mm. The Singapore tumour samples are approved for microarray profiling by the Singapore National University Hospital ethics board.
After exclusions based on tissue availability, RNA integrity, clinical annotation and microarray quality control, expression profiles of 249, 147, and 98 tumours from the Uppsala, Stockholm and Singapore cohorts, respectively, were deemed suitable for further analysis.
All tumour samples are profiled on the Affymetrix U133A and B genechips. Microarray analysis of the Uppsala and Singapore samples was carried out at the Genome Institute of Singapore (44). The Stockholm samples are analyzed by microarray at Bristol-Myers Squibb, Princeton, N.J., USA. RNA processing and microarray hybridizations are carried out essentially as described (44).
Microarray data processing: all microarray data are processed as previously described (44).
GO analysis is facilitated by PANTHER software, available on the Applied Biosystems' website (46). Selected gene lists are statistically compared (Mann-Whitney) with a reference list (ie, NCBI Build 35) comprised of all genes represented on the microarray to identify significantly over- and under-represented GO terms.
The Kaplan Meier estimate is used to compute survival curves, and the p-value of the likelihood-ratio test is used to assess the statistical significance of the resultant hazard ratios. For standardization, events occurring beyond 10 years are censored. All cases of contralateral disease are censored. Disease-free survival (DFS) is defined as the time interval from surgery until the first recurrence or last follow-up.
Multivariate analysis by Cox proportional hazard regression, including a stepwise model selection procedure based on the Akaike information criterion, and all survival statistics are performed in the R survival package. Remaining predictors in the Cox models are assessed by Likelihood-ratio test p-values.
NPI scores (Haybittle et al., 1982) are calculated according to the following formula: NPI score=(0.2× tumour size (cm))+grade (1, 2 or 3)+LN stage (1, 2 or 3)
Tumour size is defined as the longest diameter of the resected tumour. LN stage is 1, if lymph node negative, 2, if 3 or fewer nodes involved, and 3, if >3 nodes involved (47). As the number of cancerous lymph nodes are not available for the Uppsala cohort, a stage score of 2 is assigned if 1 or more nodes are involved, and a score of 3 is assigned if nodal involvement showed evidence of periglandular growth. For ggNPI calculations, grade scores (1, 2 or 3) are replaced by genetic grade predictions (1 or 3).
NPI scores <3.4=GPG (good prognostic group); scores of 3.4 to 5.4=MPG (moderate prognostic group); scores >5.4 PPG (poor prognostic group). Scores of 2.4 or less=EPG (excellent prognostic group).
For inter-group comparisons using the clinicopathological measurements, non-parametric Mann-Whitney U-test statistics are used for continuous variables and one-sided Fisher's exact test used for categorical variables. This work is facilitated by the Statistica-6 and StatXact-6 software packages.
In simplified terms, the algorithm of genetic re-classification of Grade 2 tumour, based on SWS approach can be described as follows.
A training set consisting of samples of known classes (eg, histologic Grade I (G1) and histologic Grade III (G3) tumours is used to select the variables e, gene expression measurements; probesets or predictors), that allow the most accurate discrimination (or prediction) of the samples in the training set. Once the SWS algorithm is trained on the optimal set of variables, it is then applied to an independent exam set (ie, a new set of samples not used in training) to validate it's prediction accuracy. More details are given below.
Briefly, for constructing the class prediction function, the SWS method uses the training set {tilde over (S)}0 (comprised of G1 and G3 tumour samples) to evaluate statistically the weight of the graduated “informative” variables (predictors), and all possible pairs of these predictors. The predictors are automatically selected by SWS from n (n=44,500) probe sets (which represents the gene expression measurements) on U133A and U1133B Affymetrix Genechips.
The description of each patient includes n (potential) prognostic variables X1, . . . , Xn (signals from probe sets of the U133A and U1133B chips) and information about class to which a patient belongs. In particular, the predictors might be able to discriminate G1 and G3 tumours with minimum “a posteriori probability”. Reliability of the SWS class prediction function is based on the standard “leave-one-out procedure” and on an additional exam of the class prediction ability on one or more independent sample populations (ie, patient cohorts). In this application of SWS, the G2 tumour samples of the Uppsala cohort and two other cohorts (NUH and Stockholm cohorts; see Methods) have been used as exam datasets to test the SWS class prediction function.
Let us consider the available n-dimension domain of the variables (the probesets) X1, . . . , Xn as prognostic variable space. The SWS algorithm is based on calculating the a posteriori probabilities of the tumours belonging to one of two classes using a weighted voting scheme involving the sets of so called “syndromes”. A syndrome is the sub-region of prognostic variable space. For a syndrome to be useful in the algorithm, within the syndrome, one class of samples (for instance, G3 tumours) must be significantly highly represented than another class (for instance, G1s), and in other sub-region(s) the inverse relationship should be observed. In the present version of the SWS method, one-dimensional and two-dimensional sub-regions (syndromes) are used.
Let b′i and b″i, denote the boundaries of the sub-region for the variable Xi (the i-th probe set); bi′≧Xi>bi″. One-dimensional syndrome for the variable Xi is defined as the set of points in variable space for which inequalities bi′≧Xi>bi″ are satisfied. Two-dimensional syndrome for variables Xi′ and Xi″ is defined as a set of points in variable space for which inequalities bi′′≧Xi′>bi′″, and bi″′≧Xi″>bi″″ are satisfied. The syndromes are constructed at the initial stage of training using the optimal partitioning (OP) algorithm described below.
SWS Training Algorithm
SWS training algorithm is based on three major steps:
1) optimal recoding (partitioning) of the given covariates (signal intensity values) to obtain discrete-valued variables with low and high gradation;
2) selection of the most informative and robust of these discrete-valued variables and their paired combinations (termed syndromes) that together best characterize the classes of interest;
3) tallying the statistically weighted votes of these syndromes to allow us to compute the value of the outcome prediction function.
Optimal Partitioning (OP)
The OP method is used for constructing the optimal syndromes for each class (G1 and (13) using the training set {tilde over (S)}0. The OP is based on the optimal partitioning of some potential prognostic variable Xi range that allows the best separation of the samples belonging to different classes. To evaluate the separating ability of partition R (see below) in the training set {tilde over (S)}0 the chi-2 functional is used (Kuznetsov et al, 1998). The optimal partitions are searched inside observed variable domain that contain partitions with cut-off values not greater than a fixed threshold (defined below). The partition with the maximal value of the chi-2 functional is considered optimal for the given variable.
Stability of Partitioning
Another important characteristic that allows evaluation the prognostic ability of partitioning model for specific variables is the index of boundary instability. Let Ro, Rl, . . . , Rm be optimal partitions of variable Xi ranges that is calculated by training set {tilde over (S)}0, {tilde over (S)}1, . . . , {tilde over (S)}m, where {tilde over (S)}k is the training set without description of the kth sample. Let Kj denote the different classes (j=1, 2). Let b1k, . . . , br−1k be boundary points of optimal partition Rk found by training set {tilde over (S)}k; Di is the variance of variable Xi. The boundary instability index κ({tilde over (S)}0, Kj, r) for partitioning with r elements is calculated as the ratio (Kuznetsov et al, 1996):
Selecting of Optimal Variables Set
The OP can be used at the initial stage of training for reducing the dimension of the prognostic variables set. Selection of the optimal set of prognostic variables depends on a sufficiently high partition value determined by the Chi-2 function. The additional criterion of selection of prognostic variables is the instability index κ({tilde over (S)}0, Kj, r). The variable is used if value κ({tilde over (S)}0, Kj, r) is less than threshold κ0, defined a priori by the user. When the partition of the given variable is instable (κ({tilde over (S)}0, Kj, r)<κ0), the variable is removed from the final optimal set of prognostic variables. Finally, the optimal set of prognostic variables is defined if both selection criteria are fulfilled.
The Weighted Voting Procedure
Let {tilde over (Q)}j0 denote the set of constructed syndromes for class Kj. Let x* denote the point of parametric space. The SWS estimates a posteriori probability Pjsv(x*) of the class Kj at the point x* that belongs to the intersection of syndromes q1, . . . , qr from {tilde over (Q)}j0 as follows:
where vij is the fraction of class Kj among objects with prognostic variables vectors belonging to syndrome qi, wi is the so-called “weight” of syndrome qi. The weight wi is calculated by the formula.
where
(Kuznetsov, 1996.) The estimate of fraction vij variance has the second term
which is used to avoid a value {circumflex over (d)}i equal to zero in cases when the given syndrome is associated only with objects of one class from the training set.
The results of testing applied and simulated tasks have demonstrated that formula (1) gives too low of estimates of conditional probabilities for classes that are of smaller fraction in the training set. So the additional correction of estimates in (1) has been implemented. The final estimates of conditional probability at point x* are calculated as Pjsws(x*)=Pjsv(x*)χ({tilde over (S)}0,Kj),
where
is the vector of prognostic variables for the k-th samples from the training set.
Our methodology is based on the schema presented in
Beginning with the Uppsala dataset comprised of 68 G1 and 55 G3 tumours, we used SWS optimal partitioning (OP) at the initial stage of training to reduce the dimension of the prognostic variables set. SWS rank orders the set of probes according to specific algorithmic criteria for assessing differential expression between classes.
Based on this two-criteria (chi-2 and instability index) selection algorithm, we used SWS chi-2 values bigger than 24.38 (at p-value less then 0.00001); in combination with low boundary instability index criteria (κ0<0.1 for 90% of the selected informative variables and κ0<0.4 for 10% of the other informative variables). Visual presentation on scatchard plot (log κ0, chi-2) distribution of probesets, these two cut-off values discriminated the relatively small and compact group of probesets. We observed that this group of probesets provide a local minima on the Class Error Rate (CER) function and provide an optimal selection of 264 probesets classifier of G1 and G3. Using these 264 probe sets, the both SWS and PAM methods provide a small misclassification error (4.5% for G1, and 5.5% for G3, respectively) when the leave-one-out cross-validation procedure is used. We also used the U-test with critical value p=0.05 (with Bonferroni correction) and all 264 probesets follow this cut-off value.
Based on our selection criteria, we selected a classifier comprising 264 probe sets, which we term the “SWS Classifier 0”. See Table D1 in section “SWS Classifier 0” of the Description as well as Appendix 1.
Details are shown in Appendix 1A, Appendix 2, Appendix 3 and Appendix 4.
A posterior probability for G1 and G3 was also estimated by SWS Classifier 2 for each tumour sample by the classical leave-one-out cross-validation procedure.
We estimated the class error rate based on the misclassification error rate plot (Tibshirani et al, 2002) and found that for the 264 selected probe sets, CER consists of 5% for G1, and 6% for G3, respectively. Similar discrimination was obtained with SWS methods (see above).
Based on consistency between SWS and U-tests and PAM CER validation of the selection procedure, we further considered the classification results using the 264 variables. In two-group comparisons, high CER were observed in the G1-G2 and G2-G3 predictions (data not shown), while G1-G3 classification accuracy was high (<5% errors). Complementary to SWS classification method, the PAM method confirms that G2 tumours are not molecularly distinct from either low or high grade tumours, possibly owing to substantial molecular heterogeneity within the G2 class.
To extract the smallest possible classifier from the 264 variables, we varied the initial parameters of the SWS algorithm to minimize the number of predictors in training set providing the maximum correlation coefficient between posteriori probabilities and true class indicators (specifically, 1 was the indicator of G1 tumours, and 3 was the indicator of G3 tumours in the G1-G3 comparison). The predictive power of the predictor set was estimated using standard leave-one-out procedure and counting the numbers of errors of class predictions.
We derived a classifier comprising 6 gene probe sets (5 genes) which we term the “SWS Classifier 1”. 4.4% for class G1; and 5.5% for class G3 CERs were obtained with the SWS Classifier 1. See
Appendix 5A, Appendix 5B and Appendix 5C show detailed information about selected gene probe sets, optimal partition boundaries, true classes, posterior probabilities and clinical significance of the SWS Classifier 1 predictor (estimated by patient survival analysis).
By SWS, for the G1-G3 comparisons, maximal prediction accuracies are obtained with 18 probe sets (17 genes). We refer to this 18 probe set as the “SWS Classifier 2”. See Table D3 in section “SWS Classifier 2” of the Description. This classifier includes all five genes represented by SWS Classifier 1.
Appendix 6A, Appendix 6B and Appendix 6C show detailed information about selected gene probe sets, optimal partition boundaries, true classes, posterior probabilities and clinical significance of the SWS Classifier 2 (estimated by patient survival analysis).
With the 18 probe sets, both the SWS Classifier 2 and PAM correctly classify ˜96% (65/68) of the G1s and ˜95% (52/55) of the G3s (by leave one-out method).
The smaller number of probes sets required by SWS Classifier 1 (6 probe sets) compared to PAM (18 probe sets, data not presented) may reflect the ability of SWS to use more diverse interaction and/or co-expression patterns during variable selection.
The posterior probability (Pr) is an estimate of the likelihood that a sample from the exam group of tumours belongs to one class (termed “G1-like”) or the other (ie, “G3-like”). Both 18 probesets SWS and PAM classifiers scored the vast majority of G1 and G3 tumours with high probabilities of class membership.
Due to the highly informative and stable nature of each gene (represented by Affymetrix probe-sets) of the 264 predictor set we hypothesized that there are many small alternative gene sub-sets that could be used to classify tumours with high accuracy (and therefore classify patients according to outcome with high prognostic significance). For example, high Pr scores for the class assignments of G1 and G3 by SWS classifier 1 (6 probesets, as shown in Table D2 in section “SWS classifier 1” of the Description and Appendix 5A) and SWS class assignments of G1-like and G3-like classes within G2 class were observed.
Notably, 95% of the tumours of the Uppsala cohort showed >75% probability of belonging to either the G1-like or G3-like class, indicating a highly discriminant statistical basis for the class prediction function of the SWS classifier 1 for the G2 class.
To find other classifiers, we excluded the best 6 probe sets (SWS classifier 1) from the 264 probe sets, and randomly selected two non-overlapping subsets (each of 40 probe sets) from the remaining 258 probe sets and applied the SWS algorithm to each subset.
In this way, we selected two additional classifiers: SWS classifier 3 (6-probe sets; Table D4 in section “SWS Classifier 3” of the Description and Appendix 7A) and SWS classifier 4 (7-probe sets; Table D5 in section “SWS Classifier 4” of the Description and Appendix 8A).
Tables D4 and D5 are organized as Table D3. For Uppsala, Stockholm and Singapore cohorts, each of three SWS classifiers provide similar high accuracy of classification in G1-G3 comparisons (Tables D3-D5). SWS also provided high and reproducible levels of separation of G2a and G2b sub-groups for different cohorts and highly significant differences in G2a-G2b comparison based on survival analysis (Tables D3-D5).
These tables show the values of parameters of SWS algorithm for selected classifies, predicted individual probabilities of belonging to the given class, and gene annotation, clinical significance etc.
Thus, we could consider the 264 probe sets as a general genetic classifier of the G2a (G1-like) and G2b (G3-like) tumour types.
We next applied our grade classifiers directly to the 126 G2 tumours of the Uppsala cohort to ask if these genetic determinants of low and high grade might resolve moderately differentiated G2 tumours into separable classes. Using SWS for the 264 predictor set, we observed that the G2 tumours could be separated into G1-like (n=83) and G3-like (n=43) classes with few tumours exhibiting intermediate Pr scores (Appendix 2).
The probabilities of the SWS class assignments are shown in
We found 96% of the G2 tumours were assigned by the SWS classifier (and 94% by the PAM classifier, data not shown) to either the G1-like or G3-like classes with >75% probability, indicating that almost all G2 tumours can be molecularly well separated into distinct low- and high-grade-like classes (henceforth referred to as “G2a” and “G2b” genetic grades) (Appendix 2).
We validated the separation ability of G1a and G2b based on individual predictors and showed that all of them are statistically significant by U-test and t-test (Appendix 3).
Clinical validation (survival analysis) of G2a and G2b tumour subtypes based on the predictor set (or genetic classifier), showed a highly significant difference between survival curves of the G1a and G2b patients (Appendix 4).
To determine if the genetic grade classification correlates with patient outcome, we compared the disease-free survival (DFS) of patients with histologic G2 tumours classified as G2a or G2b by the SWS algorithm. (Due to space limitations and high concordance between the SWS and PAM classifiers, only data for the SWS classifier are presented hereafter.)
The Kaplan-Meier survival curves for these patients are shown in
This finding is consistent in specific therapeutic contexts including untreated patients (
For external validation, we directly applied the SWS classifier to two large independent cohorts of primary breast cancer cases that are also graded according to the NGS guidelines and profiled on the Affymetrix platform (albeit at different times and in different laboratories). The results of the grade classifications are shown in
In both the Stockholm and Singapore cohorts, the G1 tumours are correctly classified with high accuracies similar to that observed in the training set: 96% (27/28) for Stockholm and 91% (10/11) for Singapore (
As clinical histories are available on the Stockholm patients, we tested the prognostic performance of the classifier on this new G2 population of which 79% (46/58) of tumours are classified as G2a and 21% (12/58) are classified as G2b. Though this set is considerably smaller than the Uppsala G2 set, similar survival associations are observed.
As
To assess the prognostic novelty of the classifier, we used multivariate Cox regression models to compare its performance to that of other conventional prognostic indicators assessed in the Uppsala cohort including lymph node status, tumour size, patient age, and estrogen (ER) and progesterone (PgR) receptor status. See Table E3 below.
As Table E3 shows, the genetic grade signature remained significantly associated with outcome in the different therapeutic contexts independent of the classical predictors, and is superior to both LN status and tumour size in all four treatment subgroups with the exception of systemic therapy where only tumour size is more significant.
This finding is further substantiated by a robust model selection approach (the Akaike Information Criterion) whereby the genetic grade classifier remained more significant than LN status and tumour size in all therapeutic subgroups (data not shown). These results demonstrate a powerful and additive contribution of the genetic grade classifier to patient prognosis.
The prognostic performance of the classifier suggests that G2a and G2b genetic grades may in fact represent distinct pathological entities previously unrecognized. We investigated this possibility by several approaches.
First we examined the histopathological composition of the G2a and G2b tumours and found that the predominant histologic subtypes—ductal, lobular and tubular—are equally distributed within the two classes and therefore not correlated with genetic grade (data not shown). Next, we analyzed the expression levels of the selected 264 probesets (i.e., representing ˜232 genes) as the maximum number of probesets capable of recapitulating a high G1/G3 classification accuracy (see Methods). These genes represent the top most significantly differentially expressed genes between G1 and G3 tumours after correcting for false discovery (see Table D1 above).
As shown in
This finding shows that extensive molecular heterogeneity exists within the G2 tumour population, and this heterogeneity is robustly defined by the major determinants of G1 and G3 cancer. It also demonstrates that a much larger and pervasive transcriptional program underlies the genetic grade predictions of the SWS signature—despite its composition of a mere 5 genes. Furthermore, statistical analysis of the gene ontology (GO) terms associated with the G2a-G2b differentially expressed genes revealed the significant enrichment of numerous biological processes and molecular functions.
Table E4 displays a selected set of significantly enriched GO categories which includes cell cycle, inhibition of apoptosis, cell motility and stress response, suggesting an imbalance of these cellular processes between the G2a- and G2b-type tumour cells.
Table S2 below shows the complete list of GO categories and their p values.
To extend our analysis beyond the transcript level, we investigated the differences between G2a and G2b tumours using conventional clinicopathological markers.
Of the three histologic grading criteria, both mitotic count and nuclear pleomorphism are found to significantly vary between the G2a and G2b tumours (p=0.007 and p=0.05;
These findings, together with those of the gene ontology analysis, suggest that the genetic grade classifier may largely mirror cell proliferation and thus reflect the replicative potential of the breast tumour cells. However, proliferation is not the only oncogenic factor found to be associated with genetic grade. In the G2b tumours, protein levels of VEGF (
Further analysis of bio-markers revealed yet more oncogenic differences. P53 mutations are found in only 6% of the G2a tumours, whereas 44% of the G2b tumours are p53 mutants (p<0.0001;
Finally, we observed a significant difference in hormonal status between the G2a and G2b tumours, with an increasing fraction of ER negative (7% versus 19%; p=0.06) and PgR negative (8.5% versus 23%; p=0.02) tumours in the G2b class, indicating differences in hormone sensitivity and dependence.
Taken together, these results show that multiple tumourigenic properties measured at the RNA, DNA, protein, and cellular levels can subdivide the G2a and G2b tumour subtypes—a finding that may explain, in part, the different patient survival outcomes observed between these two genetic classes.
The genetic and clinicopathological evidence suggests that the genetic grade signature reflects, among other properties, the proliferative capacity of tumour cells. That proliferation rate is positively correlated with poor outcome in breast cancer (23) could explain the prognostic capacity of the genetic grade signature.
To further investigate this possibility, we analyzed the major proliferation markers, Ki67, S-phase fraction and mitotic index, together with the genetic grade signature, for survival correlations in Cox regression models (Table S3).
Multivariate analysis showed that the genetic grade signature remained a significant independent predictor of recurrence (p=0.0075) in the presence of these proliferation markers, suggesting that the prognostic power of the grade signature derives from more than just and association with cell proliferation.
In the survival analysis (
To address this, we further analyzed the expression patterns of the 264 grade-associated probesets described in
Notably, FOS and FOSB, central components of the AP-1 transcription factor complex, are expressed at higher levels in the G1 tumours, while genes involved in cell cycle progression such as CCNE2, MAD2L1, ASK and ECT2 are expressed at higher levels in the G2a tumours. In a similar fashion, the G3 tumours showed higher expression of cell cycle genes such as CDC20, BRRN1 and TTK as well as proliferative genes with oncogenic potential including MYBL2, ECT2 and CCNE1 when compared to the G2b tumours, while the anti-apoptotic gene, BCL2, is expressed at higher levels in the G2b tumours.
GO analysis of these differentially expressed genes indicated larger biological differences. In the G1-G2a comparison, the differentially expressed genes pointed to differences primarily in cell cycle-related processes and oncogenesis, while differences between the G2a and G3 tumours included cell cycle-related processes, inhibition of apoptosis, oncogenesis and cell motility (Table E4, Table S2).
Statistical analysis of conventional clinicopathological markers revealed further distinctions in the G1-G2a and the G2b-G3 tumour comparisons. As shown in
Taken together, these data indicate that the G2a and G2b populations, though highly similar to G1 and G3 tumours in terms of survival and transcriptional configuration, remain separable at multiple molecular and clinicopathological levels.
The prognostic performance of the genetic grade signature in the G2 population suggests that the molecular “misclassifications” in the G1-G3 comparisons might correlate with survival differences. Of the 68 Uppsala and 28 Stockholm G1 tumours, too few are classified as G3-like (ie, 4 in total) for a reliable Kaplan-Meier estimate.
However, among the 55 Uppsala and 61 Stockholm G3 tumours, a total of 18 are classified as G1-like. Kaplan-Meier analysis could not confirm a significant disease-free-survival advantage for these patients, though a trend is observed (
This finding suggests that the prognostic significance of the classifier may extend also to the poorly differentiated G3 tumours, and that scaling based on the classifier Pr score may allow the fine tuning of prognostic sensitivity and/or specificity, depending on the clinical application.
The Nottingham Prognostic Index (NPI) is a widely accepted method of stratifying patients into prognostic groups (good (GPG), moderate (MPG) and poor (PPG)) based on lymph node stage, tumour size, and histologic grade (24). It is described in detail in Haybittle et al., 1982. We investigated whether incorporating genetic grade into the NPI could improve patient stratification. A simplified substitution method was explored.
For all tumours of the Uppsala and Stockholm cohorts for which NPI scores and survival information could be obtained (n=382), histologic grade (1, 2 or 3) is replaced by the genetic grade prediction (1 or 3) and new NPI (ie, ggNPI) scores are computed (see Methods). The survival of patients stratified into risk groups is then compared between classic NPI and ggNPI.
Though the survival curves of the NPI and ggNPI prognostic groups are comparable (
Practical guidelines that use the NPI in therapeutic decision making often recognize an excellent prognostic group (EPG) comprised of patients with NPI scores </=2.4 (25, 26). Untreated patients in this group with lymph node negative disease have a 95% 10-year survival probability—equivalent to that of an age-matched female population without breast cancer (26). Thus, patients in this group are routinely not recommended for post-operative adjuvant therapy (25-27).
We compared the NPI and ggNPI stratifications on a subset of 161 lymph-node-negative patients who received no adjuvant systemic therapy. Forty-three and 87 patients are classified into the EPG by the classic NPI and ggNPI, respectively. Of the 43 patients classified into the EPG by the classic NPI, only one was considered different by the ggNPI; whereas, of those classified as needing adjuvant therapy by the classic NPI (ie, scores >2.4), 45 are reclassified by the ggNPI into the EPG.
When examined for outcome, the survival curves of the 43 and 87 EPG patients by NPI and ggNPI, respectively, are statistically indistinguishable, both showing ˜94% survival at 10 years (
Thus, twice as many patients could be accurately classified into the EPG by the ggNPI, suggesting that the use of genetic grade can improve prediction of which patients should be spared systemic adjuvant therapy.
The clinical subtyping of cancer directly impacts disease management. Subtypes indicative of tumour recurrence or drug resistance indicate the need for more aggressive or specific therapeutic strategies, while those that suggest less aggressive disease may specify milder therapeutic options. While clinical subtyping has historically been based primarily on the phenotypic properties of cancer, comprehensive genomic and transcriptomic analyses are beginning to reveal robust genotypic determinants of tumour subtype. In this context, we have studied the transcriptomes of primary invasive breast cancers using expression microarray technology to elucidate the genetic underpinnings of histologic grade, and to use this information to resolve the clinical heterogeneity associated with histologic grade.
Using two different supervised learning algorithms, SWS and PAM, we identified small gene subsets capable of classifying histologic Grade I and Grade III tumours with high accuracy. The smallest gene signature (SWS), comprised of a mere 5 genes (6 probesets), partitioned the large majority of G2 tumours into two highly distinguishable subclasses with G1-like and G3-like properties (G2a and G2b, respectively). Not only are the G2a and G2b tumours molecularly similar to those of histologic G1 and G3, respectively, but the disease-free survival curves of G2a and G2b patients are also highly resemblant of those of G1 and G3 patients. Moreover, these observations are confirmed in a large independent breast cancer cohort. Further analysis revealed that extensive genetic differences between the G2a and G2b classes are accompanied by a host of biological and tumourigenic differences know to separate low and high grade cancer (28) including proliferation rate (mitotic index, Ki67), angiogenic potential (VEGF, vascular growth), p53 mutational status, and estrogen and progesterone dependence, to name a few. Together, these findings demonstrate that the genetic grade signature recognizes and delineates two novel grade-related clinical subtypes among moderately differentiated G2 tumours.
Ma et. al. (2003) were the first to report a histologic grade signature capable of distinguishing low and high grade breast tumours. Using 12K cDNA microarrays to analyse material from 10 G1, 11G2 and 10 G3 micro-dissected tumours, they identified from a list of 1,940 variably expressed, well-measured genes (the top 200 differentially expressed between G1 and G3 tumours (p<0.01 after false discovery correction) (29). Using these genes to cluster their graded tumours, they observed that the majority of G2 tumours possessed a hybrid signature intermediate to that of G1 and G3 with few exceptions (see
To address this discrepancy, we cross-compared their list of 200 grade-associated genes to our list of 232 and observed a significant overlap of 35 genes (p<1.0×10-7; Monte Carlo simulation) including 2 of our 5 SWS signature genes, MELK and STK6. However, this overlap, despite its significance, represents only a small percentage of either gene list. That the two lists are mostly dissimilar in composition, and that the Ma et. al. study included both invasive (IDC) and noninvasive (DCIS) tumours could explain, to some degree, the variable results observed. Nevertheless, our finding that G2 tumours are predominantly G1-like or G3-like is clinically substantiated by the significant and reproducible survival differences observed between the G2a and G2b classes. It is also possible that differences in sample size (we have much larger number of patients than in Ma etc work), sample preparation, sample size, RNA purification, data normalization could have contributed to the variable results.
To better understand the prognostic value of the genetic grade signature, we compared its performance to other major indicators of outcome in multivariate Cox regression models. In G2 tumours, not only did the classifier remain an independent predictor of disease recurrence, but it is consistently a more powerful predictor than lymph node status and tumour size, underscoring its value as a new prognostic indicator. When incorporated into the Nottingham Prognostic Index (Haybittle et al., 1982), the genetic grade signature improved risk stratification for 25% of patients (compared to the classic NPI) and more than doubled the fraction of lymph node negative patients that should be classified into the excellent prognostic group and thus spared adjuvant treatment.
Breast cancer is thought to progress from a hyperplastic state, to a noninvasive malignant form (carcinoma in situ), to invasive carcinoma and, ultimately, to metastatic disease (30-32). Both the noninvasive and invasive forms can be stratified according to histologic grade. Whether grade is a continuum through which breast cancer progresses, or whether it is merely the endpoint of distinct genetic pathways has been debated (33-38). Studies comparing primary tumours to their subsequent metastases have supported the grade progression model, particularly when multiple metachronous recurrences are analyzed (38, 39). However, comparative genomic studies have identified reproducible chromosomal alterations that distinguish low and high grade disease including a 16q deletion unique to G1 carcinomas (36, 37, 40). These studies argue against the progression model and point to genetic origins of histologic grade. In our study of 494 invasive primary tumours, 94% could be molecularly classified with high probability of being G1-like or G3-like, while only 6% showed intermediate Pr scores (ie, <0.75 for G1-like or G3-like). Notably, we observed these same percentages in the G2 population of 224 tumours. These findings support the genetic pathways model of grade origin, as they suggest that the large majority of breast cancers fundamentally exist in one of two predominant forms marked by the molecular and clinical essence of low or high grade. Whether these forms correlate with the grade-specific genomic alterations previously reported (36, 37, 40) remains to be elucidated.
It should also be noted that although a small percentage (˜6%) of the tumours in our study had intermediate genetic grade measurements (ie, analogous to the hybrid signature observed in Ma et. al. (2003)), too few were discovered to determine the clinical relevance of this intermediate genotype. Furthermore, it is unclear whether these intermediates arise as homogeneous cells that truly borderline low and high grade, or rather represent heterogeneous tumours comprised of distinct low and high grade cell types, such as that observed in tubular mixed carcinoma (38). Alternatively, that we observed the same percentage of intermediacy in tumour classification of all grades and across cohorts, suggests that this class represent a baseline level of uncertainty owing the technical noise.
In conclusion, our results show that the genetic essence of histologic grade can be distilled down to the expression patterns of a mere 5 genes with powerful prognostic implications, particularly in the Grade II setting and in the context of the NPI. The results indicate that G2 invasive breast cancer, at least in genetic terms, does not exist as a significant clinical entity. Indeed, our genetic grade signature dichotomized G2 tumours into two biologically and clinically distinct subtypes that could further be distinguished from G1 and G3 populations. Thus histologic grading, together with measurements of genetic grade, provide a rational basis for the refinement of the G2 subtype into subgrades “2a” and “2b” with immediate clinical ramifications.
Furthermore, our finding that the genetic grade signature could further resolve outcome prediction in G3 tumours, and in a manner dependent on Pr score thresholding, suggests that the genetic grade classifier, viewed as a scalable continuous variable, may have robust prognostic benefit in the diagnosis of all breast tumours. How to optimally weight the genetic grade measurement in combination with other risk factors for greatest prognostic return is a clinical challenge that must next be addressed.
Breast cancer (BC) is one of common malignant disease in women [1-4]. BC comprises heterogeneous tumours with different clinical characteristics, distinct molecular subtypes, and responses to specific treatments.
One of the major challenges of breast cancer therapy is lack of uniform, accurate and reproducible molecular signatures/classifiers that can assist clinicians for treatment decisions across different clinical factors, including histologic grades, clinical stage, tumour mass, ER (+/−)-status or LN(+/−)-status etc. The current existing microarray gene expression or qRT-PCR prognostic/predictive assays in the market [5-8] still have their own limitations in the assisting only specific patient subgroups for treatment recommendation [9-11]. Significant discordance remains between clinical assay-defined subsets and intrinsic subtype. Such situation is occurred for tumours with borderline hormone receptor (HR; ER, PG, HER2) expression are highly biologically heterogeneous, which raises the question of whether these tumours should be considered indeterminate. A significant proportion of clinically defined HER2-negative tumours were defined as molecular HER2-positive subtype; however, whether they are suitable for anti-HER2 therapy needs to be determined [85].
Clinical influence of the most popular in USA the RT-PCR-based 21-gene recurrence score assay (Oncotype DX) in woman with early-stage, estrogen receptor-positive, lymph node-negative breast cancers was recently evaluated in 70,802 Medicare recipients diagnosed with breast cancer between 2005 and 2009 [12]. In 2005-th assay was used for just 1.1% of woman compared to 10.1% in 2009. The test was assumed to be informative regarding the potential benefits of adjuvant chemotherapy. Nevertheless, the authors noted that chemotherapy rates in this sub-set intermediate-risk BC patients, was not significantly changed from 2005 to 2009 year and concluded that factor influencing adoption of the assay and its impact on adjuvant chemotherapy use in clinical practice remain important area of study.
An extensive search is still on-going to assess the patient's treatment modalities. Majority of gene signature-based assay panels are problematic due to lack of robust performance (reproducibility) at the level of multi-cohort datasets and their inability to stratify effectively distinct patient groups and intra-tumour heterogeneity. Further, the computational predicted post-surgery treatment breast cancer risk recurrence lack extensive experimental screening methods [13-17] leading to poor prediction and suboptimal therapeutic capabilities. Moreover, majority of micro-array or qRT-PCR-based prognostic/prediction assays are inconsistent in digging underlying regulatory mechanisms of the genes included and/or associated with the signatures, leading to a scepticism of the oncologists and poorly prognostic performance.
It is known that overexpression of cell cycle/mitotic genes play a major role in BC stem cell initiation, clonal expansion, tumour progression and they determined outcome of the disease and therapeutic intervention. Expression of the proliferative genes correlate with BC histologic grading system(s), scoring tumour aggressiveness based on proliferative rate and a level of dedifferentiation of breast epithelial cells, accompanied with morphological disorder in transformed mammary tissue. In general, tumours are graded as 1, 2, 3, or 4, depending on the amount of abnormality. In histologic grade 1 (G1) tumours, the tumour cells and the organization of the tumour tissue appear close to normal. These tumours tend to grow and spread slowly. In contrast, the cells and tissue of histologic grades 3 and 4 (HG3, HG4) tumours do not look like normal cells and tissue. G3 and G4 tumours tend to grow rapidly and often spread faster than tumours with a lower grade (G1). Histologic grade 2 (G2), consist of about 50% of breast cancer patients and is classified as moderately differentiated (intermediate grade). However, G2 is not homogeneous; for instance, it includes ER-positive and ER-negative BC tumours. Farther more HG2 ER-positive tumours consist of two clinically distinct intrinsic subtypes classified molecularly as Luminal A and Luminal B [84].
The genetic tumour aggressiveness grading signature (TAGs), included 232 genes [18] is a computationally-derived microarray-based molecular analogue of the histologic grading system of BC, consisting of mostly the transcribed genes related to mitosis, chromosome condensation, chromosome segregation, mitosis, and kinetochore machineries [18] which are the cell cycle/proliferation genes,—key hallmark of cancers [19, 20]. Moreover, 232g-TAGs reclassifies the histologic grade II (G2) breast tumours in histologic grade I-like (G1-like) and in histologic grade 3-like (G3-like) molecular sub-classes, stratifying G2 tumours of BC patients onto low- and high-aggressive types with significantly distinct clinical outcomes. Several small representative signatures have been also derived, which independently from ER, PR, tumour size, lymph node status of the patients provided a very similar and robust genetic and clinical features, as the 232g-TAGs [18].
There exist various prognostic gene signature panels in market such as Mammaprint, Theraprint, Targetprint, OncotypeDx, and PAM50 that could assess the risk of disease development of breast cancer patients [21-24].
In contrast to the conventional prognostic signatures (e.g. MammaPrint, Oncotype DX, or MiK67 test), TAGs quantitatively stratifies BC patients with respect to clinical outcome equally well, without pre-selection of the patient based on ER, PR and LN status, tumour size and also assists in re-classifying the histologic grade II BC patients onto low- and high-risk subgroups, which are similar to the histologic grade I and grade III, respectively [18], which are well-known are strongly correlated with p53 status and chromosome alteration pattern in low and high-aggressive breast cancers.
Herein, we study patho-biological and clinical values of six cell-cycle genes (BRRN1 (NM_015341), AURKA (NM_003600), MELK (NM_014791), PRR11 (NM_018304), CENPW (NM_001012507) and E2F1 (NM_005225)), called hear 6g-TAG), representing the 232g-TAGgenes, reported previously (Ivshina et al, 2006)). We test the hypotheses that these 6 genes and their products could be coincident in cancer cell functions and potentially utilized in clinical practice as (i) the early diagnostic multi-gene biomarker having the recurrence free survival and treatment outcome significances; (ii) the accurate and reproducible cell cycle-based clinical classifier of the low- and high-grade aggressive tumours (including primary tumours, local and distant metastases).
We proposed a method of quantification of pathobiological and clinical significance of the six cell-cycle genes. We demonstrated that these six genes and their products (RNA, proteins) could be transcriptionally co-regulated by E2F1 transcription factor in cell cycle, over-performed in the comparison with commercial BC prognostic assays and potentially can be utilized in clinical practice as (i) a reproducible cell cycle-based clinical classifier of the low- and high-molecular grade aggressive tumours and (ii) the early diagnostic multi-gene biomarker and (iii) the predicting function of the recurrence within the patient cohorts of the given histological grade, ER-status, LN-status, molecular tumour subtype and metastatic states of breast cancers.
A prototype of qPCR-based assay is developed and validated. We characterized the functions of the genes and validated the 6g-TAGs in several BC microarray data and over our sets tumour samples via qRT-PCR analysis. We showed that these 6 genes and their products are co-expressed in G1/S, G2/M transition of cell cycle, and form in BR CA cells the interactive network hubs transcriptionally controlled by E2F1. At protein-protein interaction level, we demonstrated that PRR11, BRRN1 and MELK can be co-localized and realize their functions within breast individual cancer cells. Our bioinformatics and statistical analyses suggested that the 6g-TAGs genes act collectively as inter-connecting network hubs, with critical regulatory role in G1/S, G2/M transition. The 6g-TAGs dichotomized of the histologic grade-2 (G2) tumours onto histologic grade 1-like (G1-like) and histologic grade 3-like (G3-like) sub-classes and robustly stratified BC patient' survival pattern according the recurrence risks onto genetically low-grade (GLG=G1+G1-like) and genetically high-grade (GHG=G3-like+G3) tumour classes. In summary, our integrative microarray and qRT-PCR analysis in combination with experimental and clinical data suggests that 6g-TAGs assay is a perspective clinical biomarker with strong early cancer diagnostic, classification, prognostic and therapeutic value.
Commercial total RNA samples of 58 breast adenocarcinoma patients and 4 normal breast tissue samples were obtained from OriGene. BC patients were classified based on comprehensive clinical information including TNM, stage, histological grade (grade 1 (G1): 5 samples; grade 2 (G2): 16 samples; grade 3 (G3): 37 samples) and survival information.) Microarray gene expression studies were carried out using U133 Plus 2.0 Affymetrix. The microarray dataset was normalized using RMA (Robust Multichip Average) method. The dataset was uploaded recently to NCBI Gene Expression Omnibus (GSE61304).
The quality of total RNA of each patient samples obtained was analysed using Agilent 2100 Bio Analyzer (all samples have RIN value of above 8). The GeneChip 3′ in vitro transcription (IVT) protocol that includes reverse transcription to synthesize first strand cDNA, second-strand cDNA, biotin-modified RNA labelling, RNA purification and fragmentation have been carried out using Affymterix manufacturer's protocol. A total of 500 ng of RNA were used from each RNA sample for the above procedure. Positive control RNA provided by manufacturer's were used for quality control checking. Hybridization, subsequent washing, and staining of the arrays were carried out as outlined in the GeneChip® Expression Technical Manual. All the hybridization and scanning procedures were done at Biopolis Shared Facility (BSF), A-STAR.
Additionally, three microarray gene expression datasets based on Affimetrix U133 A&B platform, called Stockholm, Uppsala and Singapore cohorts were used along with in-house microarray dataset (GSE61304)
To assess diagnostic significance of TAGs genes, GSE10780 data set was downloaded from GEO NCBI. These samples are categorized into three different histological types Normal, IDC-normal like and IDC [24].
Two breast cancer cell lines were selected, MCF10A (normal like, non-tumourigenic, low grade, and MDA-MB-436 (invasive tumourigenic high grade) to quantify the protein expression levels of 6 TAGs genes. MCF-10A and MDA-MB-436 cells were obtained from the ATCC. MCF-10A cells were cultured in supplements of Insulin, Cholera toxin and epidermal growth factors along with 10% fetal bovine serum (FBS). MCF-10A cells were dissociated using trypsin 5% for 15 minutes at 37° C. and then cells were then spun down at 1000 rpm for 5 minutes. The supernatant was subsequently aspirated and the pellet of cells was supplanted with based media for further downstream processes. For MBA-MB-436 DMEM F-12 medium with essential amino acids along with 10% fetal bovine serum were used.
cDNA was synthesized from 62 total RNA samples using Qiagen cDNA synthesis kit. These cDNA were tested initially with endogenous control b-actin (primers provided by OriGene), to ensure equal amount of cDNA loaded in each plate well. The 58 tumour cDNA samples were used for further downstream qPCR analysis. Primers were designed for CENPW (Forward—CGTCATACGGACCGGATTGT (SEQ ID NO: 1), Reverse—GGAGACTATGGTCGACAGCG (SEQ ID NO: 2)), PRR11 (Forward—CAAAGCTGCTACTGCCATTG (SEQ ID NO: 3), Reverse—CTGGTTGCCATTCAGTCTCA(SEQ ID NO: 4)), MELK (Forward—CAAACTTGCCTGCCATATCCT (SEQ ID NO: 5), Reverse—GGCTGTCTCTAGCACATGGTA (SEQ ID NO: 6)), AURKA(Forward—AGCTAGAGGCATCATGGACCG (SEQ ID NO: 7), Reverse—GCTCAGCTGGAGAAAGCCGGA (SEQ ID NO: 8)), and BRRN1 (Forward—TGCCAAAAAGATGGACATGA (SEQ ID NO: 9), Reverse—CCGCTAAGCATCTTCTCGTC(SEQ ID NO: 10)), E2F1 (forward—GCTGTTCTTCTGCCCCATAC (SEQ ID NO: 11), Reverse—GAAGGCCCATCTCATATCCA(SEQ ID NO: 12)) and run q-PCR experiment and further extracted CT values using ABI 7300. Relative quantification was estimated using ddCT method [25-27] for each gene and further estimated mean average mRNA levels of G1 and G3 patients for the genes. Applied Biosystems 7300 Real Time PCR machine was used with compatible SYBR green master mix.
Breast cancer MDA-MB-436 cells were isolated at G1, S and G2/M cell cycle phases using propidium iodide (PI) dye by FACS analysis (detailed in Flow cytometer method) and further extracted total RNA from each sub-population of cells and carried out cDNA synthesis followed by PCR amplification using above specified primers of TAGs genes and run DNA agarose gel for further RT-PCR analysis.
Pelleted cells were lysed using lysis buffer (commercial Bio-Rad) and estimated proteins (Bio-Rad protein assay) and loaded equal amount of protein and separated by SDS-PAGE [28-30]. After transfer, the membranes were probed with commercial rabbit polyclonal antibodies of Actin, C6orf173, AURKA, MELK and PRR11 (Cell sciences, Sigma Aldrich). Commercial mouse monoclonal antibody available for BRRN1 was obtained from Cell Signalling. Commercial rabbit polyclonal antibody of E2F1 obtained from Thermo Scientific. B-Actin (cell signalling) was used as internal control to relatively compare the expression levels of 6-TAGs genes. Secondary antibodies (anti-rabbit and anti-mouse IgG horseradish peroxidase-conjugated) were purchased from GE Healthcare Bio-Sciences AB. Proteins were visualized using an enhanced chemiluminescence (ECL) reagent kit (GE Healthcare Bio-Sciences AB). Densitometry analysis of Western Blot images was done using ImageJ open source software.
MDA-MB-436 cells (primary and transfected (GFP-PRR11) were cultured at 370 C, described above with appropriate antibiotics. Prior to immunostaining experiments, the cells were grown on coverslips. Immunostaining and digital image capturing was performed as described earlier [31]. Briefly, cells on coverslips were fixed in a 1:1 mixture of cold methanol and acetone (−20° C.). After re-hydration in phosphate buffer saline, cells were stained with antibodies. Hoechst 33258 (Sigma-Aldrich) was added at a concentration of 0.4 μg/ml to the secondary antibody for DNA staining when necessary. 510 laser scanning confocal microscope with ORCA-ER CCD camera (Hamamatsu). Confocal microscopy images of MDA-MB-436 cells were acquired in a point scanning confocal microscope Zeiss LSM 510 Meta (Zeiss, Germany), with a 40×EC Plan-Neofluar oil immersion objective, and diode (405 nm), argon (488 nm), DPSS (561 nm) and helium-neon (633 nm) lasers; cells were excited at 405 nm (Hoechst 33342), 488 nm (FAM) and 561 nm (rhodamine). Differential interference contrast (DIC) images were obtained using the helium-neon laser (633 nm). Digital images were acquired using the LSM 510 Meta software. All instrumental parameters pertaining to fluorescence detection and image analyses were held constant to allow sample comparison.
For the immunoprecipiation 5 ug of the antibodies (rabbit anti-PRR11 and mouse anti-BRRN1) were coupled to the CN-Br sepharose 4 Fast Flow according manufacturer protocol (GE Healthcare Bio-Sciences AB) and such supports were used to capture the corresponding proteins from the NP40 cell lysates (usually 1×107 MDA-MB-436 cells were used for one probe). After extensive washing with NP40 lysis buffer once and PBS (at least 20 volumes) the protein complexes were eluted by a heat (940 C) and separated on the SDS-PAGE.
MDA-MB-436 breast cancer cells were harvested and spun down and remove supernatant and resuspend pelleted cells and add 1 ml of fresh medium (described above) and filter cells trough cup with cell stainer filter (BD commercial) to avoid clumps add working solution of Hoechst 15 ul (stock: 1 mg/ml in DMSO) and foil (Aluminum) the tube to avoid light incubate @37 C for 15 min prepare one more tube with 1 ml of fresh medium for cells to be collected for cell cycle analysis (re-suspend cells) using BD FACs Ariallu SORT available at our Bioshared Facility services. The collected cells at various cell cycle phases were subjected for RNA isolation followed by cDNA synthesis and RT-PCR experiments. Verity Software (Modfit LT3.3) was used to assess percentage of cells at various cell cycle phases after siRNA silencing of the 6g-TAGs genes. To measure the proliferation rate of MDA-MB-436 in siRNA treated 6g-TAGs genes, we seeded 5,000 cells in 12-well plates and counted cells at various time points indicated and compared relatively with control siRNA treatment as represented in
Data driven grouping (DDg) is a computational method for the genome wide identification/selection of the survival significant genes and patient grouping/stratification in to disease development risk groups, reflecting training patient set groping according the disease survival events and last follow up of the patients. This method, based on fitting a semi-parametric Cox proportional hazard regression model, is used to fit patients' survival times/last follow-up and events to gene expression value data. In this study, disease free survival (DFS) data were used. One dimensional data driven grouping (1D DDg) method [32] was used for fast and efficient screening of massive gene expression datasets to identify/select potential individual genes-candidates (predictors) and these gene expression discriminative cut-off values for construction rule of the prognostic/predictive patient stratification [33]. The model estimates the optimal partition (cut-off) of expression level values of a gene by maximizing the separation of the survival (Kaplan-Meier) curves related to the different (high- and low-) risks of the disease behaviour [32]. We also used SurvExpress web resource and the online Kaplan-Meier Plotter for selection of multi-gene classifiers, stratification of the patients into significant survival subgroups, comparison of these groups These two programs were used on validation stage of our prognostic classifiers.
Statistically weighted Syndrome grouping (SWVg) grouping method is based on a dichotomization of survival data and selection of optimal (best) prognostic features and weighted used to obtain consensus grouping decisions from the patient survival grouping information generated by multiple prognostic covariates (e.g., expression values of genes) [32, 34]. SWVg is a multivariate voting classification and feature selection algorithm deriving the prognostic covariate (e.g. expressed gene subset) composed of a prognostic signature that is able to robustly separate the patients of two (or more) groups. It has taken all the grouping information across the list of SWVg-selected the selected prognostic covariate (selected genes). Each survival significant covariate after applying DDg provides patients' grouping and SWVg further synergizes survival information of all such prognostic covariate and separates the patients into robust (overall) survival groups discriminated by SWVg with log-rank statistics p-value smaller then each of the selected prognostic covariate along.
The sub-classification of G2 was performed using Statistically Weighted Syndrome (SWS) algorithm based on G1 and G3 tumours [Kuznetsov et al, 1996; Kuznetsov 2006]. G1 and G3 tumours were used as training subsets and the G2 tumours were used as class discovery set. The classifier assigned each tumour of G2 as either G1-like or G3-like tumours with the estimated probability. We applied this procedure for classification of testing group consists of 62 tumours. These tumour samples include 4 normal, 5 G1, 16 G2 and 37 G3 tumours. Due to the limited number of G1 tumours we combined the 4 normal tumours with HG1 tumours to obtain 9 tumours as low grades during the training of the classifier. Also there is an imbalance between low grade (LG) and G3 tumours, therefore we split G3 tumours randomly into two non-overlapping subgroups and performed two training-prediction iterations. The obtained training accuracies for both balanced iterations were accuracy was 96.4% and 92.6%, respectively. (Table EE6).
G2 tumour samples were used as class prediction set and sub-classified into HG1-like and G3-like tumours based on the assigning probability of both training-prediction iterations. According to this procedure, six tumours were assigned to G1-like and 10 tumours were assigned to G3-like subclasses. The expression levels of the 6g -TAG genes in G1, G1-like, G3-like and G3 for Uppsala, Stockholm and Illumina data sets is depicted in
For analysis of the gene co-expression patterns and for selection of potential gene network interactors, microarray expression probes with significant Kendall correlation coefficients (|τ|≧0.2 and P(τ, FDR)≦0.01) correlated with a given target gene, were selected. Next, strongly correlating probes were separately analyzed using the “1-D DDg algorithm” [19]. The probes with significant impact on the survival of the patients were selected according to the criterion FDR≦0.05.
Network analysis of the 6-TAGs genes was carried out using MetaCore™ software. The genes PRR11, MELK, BRRN1, AURKA, and MELK were used as seed nodes to extent the network using MetaCore, automatic expand to 50 nodes network building option had been used to build the TAGs network. Result in the network consists of nodes (protein or protein complex) among them AURKA, MELK, and E2F1 forms a network hub. The network nodes were extracted for further gene co-expression analysis. David gene ontology studies were conducted in parallel comparison to metacore for better statistical reliability [35, 36].
Cyclebase 3.0 is a web tool with a overview of cell-cycle regulation and phenotypes for a given gene of interest. Its main features include (a) aiming to provide a concise overview of cell-cycle regulation and phenotypes for a gene. (b) For a more detailed view of the transcriptome data, the tool normalizes and aligns the individual time course studies, to allow all expression data for a gene to be plotted on a common time scale (percentage of cell cycle). (c) Further detail on PTMs, degradation signals and organism-specific phenotypes is provided in the form of tables with linkouts to the original sources whenever possible. [37-39].
Proliferative or cell cycle/mitotic genes, transcription factors, oncogenes and tumour suppressors are highly-enriched and consist of a major fraction of the 232g-TAGs (represented by 264 U133A&B probsets). This genetic tumour grading classifier provides a classification of the breast cancers of two major tumour classes (G1+G1-like and G3-like+G3) [5], [21] strongly associated with low- and high-risk of BC recurrence, p53 wide-type and p53-mutation status, low- and high-aggressive tumour and patient survival outcomes across many conventional clinical factors including ER-status, LN-status and tumour size. To better understand the regulatory mechanism of the TAGs genes in breast cancers and its ability to use some of these genes as breast cancer clinical biomarkers, we first provided a meta-analysis of various transcription factors that are positively correlated with TAGs genes in various breast cancer datasets (Uppsala, Stockholm and Singapore and GSE61304 dataset (in-house)). Further we found that the representative genes of the 232g-TAGs (BRRN1 (NM_015341), AURKA (NM_003600), MELK (NM_014791), PRR11 (NM_018304), CENPW (NM_001012507) and E2F1 (NM_005225)) have higher expression levels in various stages of breast cancer relative to normal breast tissue (
In this study, we used the extreme discriminative analysis using Modified Wilcoxon Test (MWT) and binomial tests [41]. The method used a cross normalization for matched pair samples. Each gene of 6g-TAG demonstrates a strong discrimination between the tumour and adjacent breast tissue samples (Table EE4).
We further investigated the regulatory role of various transcription factors (TF) on TAG genes in breast cancer. E2F1 is a key regulator of transcription activity in breast and many other cancers. We found that E2F1 (which gene is belonging to 232g-TAGs) correlates positively with many other TAGs genes (
We suggested that E2F1 could play regulatory role as a transcription factor (TF) controlling the proliferation, cell cycle/mitosis genes included in our TAG signature. We screened ChIP-seq (Chromatin immunoprecipitation sequencing) tracks in UCSC genome browser and investigated MCF-7 breast cancer cell line dataset (Chromatin Immunoprecipitation using HA tagged E2F1 antibody) and found that all the TAGs genes showed significant ChIP-seq E2F1 binding peaks in their upstream promoter regions. We observed significant E2F1 promoter binding ChIP-seq peaks at upstream promoter regions of the 6g-TAGs genes.
Based on co-expression analysis and promoter binding site studies, we suggest that E2F1 could regulate our o TAGs genes. To check if TAGs genes act as targets of E2F1 transcription factor, we conducted siRNA silencing experiments by knocking down E2F1 transcript in breast cancer cell line (MDA-MB-436) and estimated the mRNA levels of TAGs genes using qPCR studies.
Based on co-expression studies on various breast cancer datasets and E2F1 promoter binding analysis of the TAGs genes, along with siRNA-E2F1 validation experiments, we strongly suggest that E2F1 transcription factor could regulate the TAGs genes in breast cancer. This led to further extend gene panel by including E2F1 transcription factor and investigate further by experiments the proliferative potential and prognostic significance of TAGs genes. In all our future sections, we included E2F1 (NM_005225) along with the origin 5 TAGs genes (BRRN1 (NM_015341), AURKA (NM_003600), MELK (NM_014791), PRR11 (NM_018304), CENPW (NM_001012507), Table EE1) as the 6g-TAGs.
To understand the grade signature potential of 6 TAGs genes, we extracted Affymetrix probsets intensity values in various Uppsala, Stockholm and Singapore cohort public microarray datasets.
To reconfirm this phenomenon, Affymetrix microarray probe intensity values of the 6g-TAGs genes were extracted from in-house cohort microarray dataset (GSE61304) and estimated mean values for G1 and G3 patient samples respectively.
To validate further the observations based on microarray experiments, we conducted real time quantitative PCR (qRT-PCR) using commercial tissue array experiments.
Then we further checked if these 6g-TAGs genes also show similar expression pattern and discriminate grades at protein level. To test this phenomenon, we selected two well established breast cancer cell lines, MCF10A (immortal, non-tumourigenic, low grade), and MDA-MB-436 (invasive tumourigenic high grade) to quantify the protein expression levels of 6g-TAGs genes.
Patients with histological G2 have ‘moderate’ risk BC development on average. A better treatment options can be provided, if underlying heterogeneity of G2 tumours be delineated further into G1 like and G3 like categories [42]. We analysed 4 different breast cancer datasets to test if 6g-TAGs can delineate G2 patients into either HG1 like and/or HG3 like groups.
To understand the underlying mechanisms of breast cancer with respect to 6g-TAGs genes, we conducted co-expression studies on various breast cancer microarray datasets (Uppsala, Stockholm, Singapore, US). Based on 6g-TAGs genes, we extended the interacting gene network components using Metacore (GeneGo) software with an arbitrary cut-off of 50 nodes (genes).
To understand the biological and functional significance of these co-expressed network components of 6g-TAGs genes in BC cells, we conducted gene ontology functional studies using David, GeneGo software's. Table EE2 enlists various gene ontology (GO) functions of the gene network components obtained based on Metacore software (Methods). These genes and it network components have a strong functional role in cell cycle (p=7.19 E-26), chromosome condensation (p=7.19E-26), regulation at G1/S (p=1.56 E-13), G2/M transition (p=4.43 E-35), regulation at kinetochore complex and chromosome segregation (1.26 E-12). Further represents list of various other gene ontology functions obtained using 6g-TAGs-related genes and its gene interaction network components.
To reconfirm the above observation we submitted the TAGs gene network components in DAVID Bioinformatics GO software, representing various biological functions attributing to 6g-TAGs genes and its gene interaction network components having strong statistical significance at FDR. Interestingly, both the software showed similar biological functions, re-affirming that TAGs network components have strong functional role in breast cancer via cell cycle and other downstream biological processes.
To validate the above co-expression phenomenon observed in breast cancer cohort datasets, we conducted qRT-PCR experiments using tissue array qPCR experiments. cDNA was synthesized from 58 breast tumour samples of RNA's from GSE61304 dataset and conducted qRT-PCR studies and estimated relative fold change values with respect to normal samples. Co-efficient of correlation was estimated for 6g-TAGs genes using 58 breast cancer patient samples.
Based on previous publications [43-45] it was shown that co-expressed genes may be co-regulated and might have a possibility to interact with each other and attributing to critical biological functions. To assess further, if the positively correlated 6g-TAGs genes co-occurrence in BC, we conducted co-localization studies on PRR11, BRRN1, MELK and CENPW (part of TAGs genes) using immuno-fluorescent experiments (confocal microscopy).
To support above observation, we tested if the above mentioned proteins (PRR11, BRNN1, MELK, and AURKA) form any complexes with each other by performing immunoprecipitations of MDA-MB-436 cell lysates, using anti-PRR11 and anti-BRRN1 antibodies coupled to the surface of CNBr sepharose beads.
Gene ontology functions of TAGs genes and its interacting gene network components showed that these genes have a significant role at various check points of cell cycle (G1/S, G2/M). To understand the functional role of TAGs genes at various cell cycle phases, MDA-MB 436 cells were synchronized and cells were further sorted at G1, S and G2/M phases. RT-PCR gene expression studies were carried out on 6g-TAGs genes at various synchronized cell cycle phases.
Further siRNA silencing studies were conducted on all TAGs genes to check at which phase of cell cycle these siRNA treated cells were arrested.
One of the key questions to be addressed is to check if 6g-TAGs genes can show prognostic potential indiscriminate low risk and high risk patients with respect to recurrence free survival. We investigated this phenomenon in various breast cancer microarray cohort datasets (Uppsala, Stockholm, Singapore and BII-US). All the breast cancer microarray datasets have been analysed using disease free survival information defined as, the time interval from surgery until the first recurrence (local, regional, or distant) or last date of follow-up.
To validate prognostic ability of the 6g-TAGs genes observed in various microarray breast cancer cohort datasets, we conducted qPCR experiments of all 6g-TAGs genes using cDNA of 62 breast cancer patient samples (with DFS clinical information). qPCR assay delta Ct-values were extracted as explained in Methods section and used in our 1D DDg analysis.
Further experiments were carried out to check the synergistic prognostic potential of the 6g-TAGs genes indiscriminating low and high risk breast cancer patients. This was tested using our Statistical Weighted Voting classification method (see Methods). We used 6g-TAGs genes of Uppsala cohort microarray data.
We compared the prognostic performance of the 6-g TAGs classification with several other known clinical risk factors in various breast cancer cohorts using univariate and multivariate Cox regression analyses (Table EE3).
In the Uppsala cohort, LN (4.73E-04), Size (2.08E-03) and 6g-TAGs genes (1.13E-06) have statistically significant Hazard ratio (>1). In Stockholm, Singapore and BII-US cohort the univariate hazard ratio for 6g-TAGs genes was relatively higher than other clinical risk factors with p value of 7.57E-04, 6.89e-03, and 9.5E-03 respectively. We then included all significant clinical variables in a multivariate Cox regression analysis; the 6g-TAGs classification retained its independent prognostic value with p values of 2.2E-05, 1.32E-02, 6.48E-02 and 4.0E-02 for Uppsala, Stockholm, Singapore and BII-US cohorts respectively. Table EE3 clearly represented details risk hazard ratios of various clinical risk factors selected from various datasets.
To test the robustness of the 6g-TAG genes prognostic ability, we explored Express Survival Web application containing multiple datasets within breast cancer. We selected various breast cancer datasets (
Importantly, the 6g-TAG signature able to stratify the patients within very specific clinical and molecular BC sub-classes (
Our method and 6g-TAG assay could be used for classification and prognosis other (non-breast) cancers including (
Herein, we present the 6g-TAGs gene subset (module) as (i) the proliferative multi-gene low- and high-grades tumour classifier, (ii) early detection genetic signature of breast cancers and (iii) disease outcome predictor. This signature includes transcription factor E2F1 regulating other 5 periodic cell cycle genes of this structural and functional genetic module of the breast cancers and perhaps many other cancers.
Many gene signatures studied previously lack underlying functional mechanism attributing to breast cancer [46, 47]. In this current study, we represented 6g-TAGs genes as potential interacting network hubs with various components co-expressing in breast cancer datasets (
The predicted cell cycle regulatory role of 6g-TAGs genes was experimentally validated using RT-PCR studies on MDA-MB 436 cells sorted at various cell cycle (G1, S and G2/M) phases. AURKA-A, E2F1 showed high expression at G2/M check point as evident from its key role during mitotic chromosomal segregation [48]. E2F1 also showed high expression in G1/S [49-51]. BRRN1, CENPW are relatively higher in G2/M compared to other cell cycle phases (
The 6g-TAGs genes functional role at cell cycle check points was further corroborated by CycleBase 3.0 web tool studied on Hela cancer cells.
We further assessed the proliferation potential of 6g-TAGs genes by independently silencing 6g-TAGs genes in MDA-MB-436 cells at various time points (12, 24, 36, 48, 60 and 72 hrs).
The results consisted of our previous finding (Ivshina et al, 2006; Kuznetsov et al, 2006) that the TAGs genes can be prognostic markers of breast cancer.
PRR11
Our previous microarray studies strongly suggested that the products of poorly-annotated gene, PRR11 can be strictly associated cell cycle, breast cancer aggressiveness and patient' treatment outcome. Specifically FLJ11029 (detected by Affymetrix probsets 228273_at), RNA transcript of PRR11 gene could play important pro-oncogenic and prognostic role in BC (Ivshina et al, 2006; Kuznetsov et al, 2006). We have observed that the transcribed locus FLJ11029 was strongly expressed in BC and positively correlated with expression of other genes 232g-TAGs. These findings suggest that transcriptional regulation of FLJ11029 could be related to cells cycle/mitosis. Additionally,
BRRN1/NCAPH/Condensin I
This gene encodes a member of the barr gene family and a regulatory subunit of the condensin complex. This complex is required for the conversion of interphase chromatin into condensed chromosomes. [55-58] BRRN1/NCAPH Condensin I defects could be associated with genome instability—the inherent feature of the most cancers and is the basis for selective killing of cancer cells by genotoxic therapeutics (Taxol, Vinblastine). Our current studies indicated that NCAPH interacts with PRR11 and further based on RT-PCR and siRNA silencing experiments it was shown that NCAPH could play critical regulatory role in cell cycle (G1/S phase) in breast cancer cells (
AURKA
This gene is one of the relatively well characterised members of our 6g-TAGs. AURKA protein is well known for its role in spindle assembly [59] and deregulation of this gene is known to have profound affect in chromosomal abnormalities in colorectal carcinoma progression [60]. In our current study it is shown to have critical role in breast cancer progression by regulating G2/M check point and further silencing of AURKA in breast cancer cell lines proved to be detrimental to cancer cells, indicating potential target for cancer therapy (
Further it was reported that genetic polymorphisms in AURKA and BRACA1 are associated with breast cancer susceptibility in Chinese Han population. [61]. It is a key regulator of chromosome segregation and cytokinesis and is currently undergoing clinical trials. Alisertib is an investigational, oral, selective inhibitor of AURKA used with several others specific Aurora A kinase inhibitors (e.g. MLN8237) and studied in clinical trials [62, 63]. These inhibitors could stop the growth of tumour cells by blocking some of the specific enzymes needed for cell proliferation and could be used starting from phase I and II of clinical trials as the common proliferative and tumour aggressiveness markers. Aurora A kinase inhibitors work in treating patients with high aggressive (triple-negative) tumours and/or at late stages/high-grade of BC and other cancers. Moreover, down regulation of AURKA can also reverse estrogen-mediated growth in breast cancer cells [84]. These findings also suggest that AURKA and their products could be used efficient therapeutic targets for different subtypes BCs (see above). Our 6-gene TAGs qPCR assay (including AURKA) should be useful in estimating the degree of clinical benefit based on objective clinical responses with AURKA inhibitor in breast and other cancer patients.
MELK
The maternal embryonic leucine zipper kinase (MELK) is the upregulated gene in high-grade prostate cancer [64], brain tumours [65], colorectal cancer [66], and also in breast cancer. [67, 68] MELK is part of our 6g-TAGs gene signature, which together or separately with its products could be used as early diagnostic, prognostic and periodic cell cycle marker, playing critical role in quantification of cell proliferation and tumour aggressiveness (
CENPW
CENPW is a centromere protein coding gene [70, 71]. It has been initially called C6orf173 orCUG2, cancer upregulated gene 2 [72-77] and was computationally selected as a part of our 6g-TAGs. In this work we showed its early diagnostic capacity and also proliferative capability and survival prognostic potential in breast cancer patients (
It is well documented that Retinoblastoma protein (Rb, tumour suppressor gene) regulates cell cycle by forming protein complex with E2F1 [78, 79]. Based on previous studies, it was shown that loss of Rb leads to genomic instability and disruption of kinetochore complex with underlying mechanism unclear [49, 80]. In our current study, we showed that 6-g TAGs genes act as targets of E2F1 (
One of the major draw backs of various previously predicted biomarkers of breast cancer is lack of analysis at multi-cohort microarray datasets and the biomarkers predicted were not supported by experimental data. [81, 82]. To investigate 6g-TAGs genes grade signature potential, we analysed multi-cohort datasets (Singapore, Uppsala and Stockholm cohorts) and also in-house dataset (BII-US cohort) and further validated by breast cancer cell lines and by qPCR experiments (
Further, we could show 6 TAGs genes as potential early diagnostic markers of cancer.
This study provides a quantification of patho-biological and clinical significance of the six cell-cycle genes (BRRN1 (NM_015341), AURKA (NM_003600), MELK (NM_014791), PRR11 (NM_018304), CENPW (NM_001012507) and E2F1 (NM_005225)), representing the tumour aggressiveness grading (TAGs) signature (232 genes reported previously). We demonstrate that all of our TAGs genes are under regulation of E2F1 TF, these genes act as an inter-connecting gene network hubs, with regulatory role in G1/S, G2/M transition in BC. 6g-TAGs provides a dichotomization of proliferative capacity of the tumour cells in the low- and high-aggressive grades of BC with strong early cancer diagnostic, tumours classification, prognostic and therapeutic value. Each of these six genes can act as (i) a reproducible cell cycle-based clinical classifier of the low- and high-grade aggressive tumours and (ii) the early diagnostic multi-gene biomarker (iii) having the disease free survival and treatment outcome significances.
Based on finding we developed and validated a prototype of a qPCR-based method for early diagnostics, low- and high-aggressiveness grading classification and risk of recurrence prediction of BC. The method well reflects quantitatively the cancer cell cycle/mitosis rate, transcriptome over-expression and tumour aggressiveness of the different tumour types, subtypes and subclasses. Our TAG signature detection method could be implemented as uniform and objective prognostic factor, because it i) reflects and improves a measure of tumour aggressiveness previously based on clinical classification of tumours on low- and high-grade tumour classes and ii) it predicts outcome of BC patients without patient's preselection for assay conduction; our method could be apply for any cohorts regardless nuclear receptor status; tumour mass, tumour stages and subtypes. Therefore, we assume that our method could be useful on any phase of clinical trials and regular clinical practice for personalization of diagnosis and clinical outcome of many tumours, tumour' classes and subtypes.
Overall, our results could improve current clinical breast cancer classification (e.g. histologic grade, cancer recurrence risk assessment, management and counseling), and further provide a solution for the easily detection, outcome prognosis, and optimization of personalized medicine strategy of treating breast cancers in a clinical setting.
Further Aspects
Further aspects and embodiments of the invention are now set out in the following numbered Paragraphs; it is to be understood that the invention encompasses these aspects:
Paragraph 1. A method of assigning a grade to a breast tumour, which grade is indicative of the aggressiveness of the tumour, the method comprising detecting the expression of a gene selected from the genes set out in Table D1 (SWS Classifier 0).
Paragraph 2. A method according to Paragraph 1, in which the method comprises detecting a high level of expression of the gene and assigning the grade set out in Column 7 (“Grade with Higher Expression”) of Table D1 to the breast tumour or detecting a low level of expression of the gene and assigning the grade set out in Column 8 (“Grade with Lower Expression”) of Table D1 to the breast tumour.
Paragraph 3. A method according to Paragraph 1 or 2, in which a high level of expression is detected if the expression level of the gene is above the expression level set out in Column 9 (“Cut-Off”) of Table D1, and a low level of expression is detected if the expression level of the gene is below that level.
Paragraph 4. A method according to Paragraph 1, 2 or 3, in which the expression of a plurality of genes is detected, for example in the form of an expression profile of the plurality of genes.
Paragraph 5. A method according to any preceding Paragraph, in which the gene expression data or profile is derived from microarray hybridisation such as hybridisation to an Affymetrix microarray, or by real time polymerase chain reaction (RT-PCR).
Paragraph 6. A method according to any preceding Paragraph, in which the expression level of the gene or genes is detected using microarray analysis with a probe set consisting of a probe or probes having Affymetrix ID numbers as set out in Column 6 (“Affi ID”) of Table D1.
Paragraph 7. A method according to any preceding Paragraph, in which the method is capable of classifying a breast tumour to an accuracy of at least 85%, at least 90% accuracy, or at least 95% accuracy, with reference to the grade obtained of the breast tumour by histological grading.
Paragraph 8. A method according to any preceding Paragraph, in which the expression level of 5 or more genes is detected.
Paragraph 9. A method according to Paragraph 8, in which the 5 or more genes comprises the genes set out in Table D2 (SWS Classifier 1), viz: Barren homolog (Drosophila) (BRRN1, GenBank Accession No. D38553); Hypothetical protein F1111029 (F1111029, GenBank Accession No. BG165011); cDNA clone IMAGE:4452583, partial cds (GenBank Accession No. BG492359); Serine/threonine-protein kinase 6 (STK6); and Maternal embryonic leucine zipper kinase (MELK, GenBank Accession No. NM_014791).
Paragraph 10. A method according Paragraph 8 or 9, in which the expression level of the genes is detected using microarray analysis with a probe set consisting of probes having Affymetrix ID numbers as set out in Column 6 (“Affi ID”) of Table D2 (SWS Classifier 1), viz: B.228273_at, A.208079_s_at, B.226936_at, A.212949_at, A.204825_at, A.204092_s_at.
Paragraph 11. A method according to Paragraph 8, in which the 5 or more genes comprises the genes set out in Table D4 (SWS Classifier 3), viz: TPX2, microtubule-associated protein homolog (Xenopus laevis) (TPX2, GenBank Accession No. AF098158), Protein regulator of cytokinesis 1 (PRC1, GenBank Accession No. NM_003981), Neuro-oncological ventral antigen 1 (NOVA1, GenBank Accession No. NM_002515), Stanniocalcin 2 (STC2, GenBank Accession No. AI435828), Cold inducible RNA binding protein (CIRBP, GenBank Accession No. AL565767), Chemokine (C-X-C motif) ligand 14 (CXCL14, GenBank Accession No. NM_004887), Signal peptide, CUB domain, EGF-like 2 (SCUBE2, GenBank Accession No. AI424243).
Paragraph 12. A method according Paragraph 8 or 11, in which the expression level of the genes is detected using microarray analysis with a probe set consisting of probes having Affymetrix ID numbers as set out in Column 6 (“Affi ID”) of Table D4 (SWS Classifier 3), viz: A.210052_s_at, A.218009_s_at, A.205794_s_at, A.203438_at, B.225191_at, A.218002_s_at, A.219197_s_at.
Paragraph 13. A method according to Paragraph 8, in which the 5 or more genes comprises the genes set out in Table D5 (SWS Classifier 4), viz: cell division cycle associated 8 (CDCA8, GenBank Accession No. BC001651), centromere protein E, 312 kDa (CENPE, GenBank Accession No. NM_001813), steroid-5-alpha-reductase, alpha polypeptide 1 (3-oxo-5 alpha-steroid delta 4-dehydrogenase alpha 1) (SRD5A1, GenBank Accession No. BC006373), microtubule-associated protein tau (MAPT, GenBank Accession No. NM_016835), leucine zipper protein (FKSG14, GenBank Accession No. FKSG14), BC005400 (GenBank Accession No. R38110), EH-domain containing 2 (EHD2, GenBank Accession No. AI417917).
Paragraph 14. A method according Paragraph 8 or 13, in which the expression level of the genes is detected using microarray analysis with a probe set consisting of probes having Affymetrix ID numbers as set out in Column 6 (“Affi ID”) of Table D5 (SWS Classifier 4), viz: A.221520_s_at, A.205046_at, A.211056_s_at, A.203929_s_at, B.222848_at, B.240112_at, A.221870_at.
Paragraph 15. A method according to any of Paragraphs 1 to 7, in which the expression level of 17 or more genes in Table D1 is detected.
Paragraph 16. A method according to Paragraph 15, in which the 17 or more genes comprises the genes set out in Table D3 (SWS Classifier 2), viz: Barren homolog (Drosophila) (BRRN1, GenBank Accession No. D38553); Cell division cycle associated 8 (CDCA8, GenBank Accession No. BC001651); V-myb myeloblastosis viral oncogene homolog (avian)-like 2 (MYBL2, GenBank Accession No. NM_002466); Hypothetical protein F1111029 (F1111029, GenBank Accession No. BG165011); FBJ murine osteosarcoma viral oncogene homolog B (FOSB, GenBank Accession No. NM_006732); CDNA clone IMAGE:4452583, partial cds (GenBank Accession No. BG492359); Serine/threonine-protein kinase 6 (STK6, GenBank Accession No. BC027464); Anillin, actin binding protein (scraps homolog, Drosophila) (ANLN, GenBank Accession No. AK023208); Centromere protein E, 312 kDa (CENPE, GenBank Accession No. NM_001813); TTK protein kinase (TTK, GenBank Accession No. NM_003318); Signal peptide, CUB domain, EGF-like 2 (SCUBE2, GenBank Accession No. AI424243); V-fos FBJ murine osteosarcoma viral oncogene homolog (FOS, GenBank Accession No. BC004490); TPX2, microtubule-associated protein homolog (Xenopus laevis) (TPX2, GenBank Accession No. AF098158); Kinetochore protein Spc24 (Spc24, GenBank Accession No. AI469788); Forkhead box M1 (FOXM1, GenBank Accession No. NM_021953); Maternal embryonic leucine zipper kinase (MELK, GenBank Accession No. NM_014791); Cell division cycle associated 5 (CDCA5, GenBank Accession No. BE614410); and Cell division cycle associated 3 (CDCA3, GenBank Accession No. NM_031299).
Paragraph 17. A method according Paragraph 15 or 16, in which the expression level of the genes is detected using microarray analysis with a probe set consisting of probes having Affymetrix ID numbers as set out in Column 6 (“Affi ID”) of Table D3, viz: A.212949_at; A.221520_s_at; A.201710_at; B.228273_at; A.202768_at; B.226936_at; A.208079_s_at; B.222608_s_at; A.205046_at; A.204822_at; A.219197_s_at; A.209189_at; A.210052_s_at; B.235572_at; A.202580_x_at; A.204825_at; B.224753_at; and A.221436 s_at.
Paragraph 18. A method according to any of Paragraphs 8 to 17, in which the method comprises detecting a high level of expression of the gene, and assigning the grade set out in Column 7 (“Grade with Higher Expression”) of Table D2 (SWS Classifier 1), Table D3 (SWS Classifier 2), Table D4 (SWS Classifier 3) or Table D5 (SWS Classifier 4) to the breast tumour.
Paragraph 19. A method according to any of Paragraphs 8 to 17, in which the method comprises detecting a low level of expression of the gene, and assigning the grade set out in Column 8 (“Grade with Lower Expression”) of Table D2 (SWS Classifier 1), Table D3 (SWS Classifier 2), Table D4 (SWS Classifier 3) or Table D5 (SWS Classifier 4) to the breast tumour.
Paragraph 20. A method according to any of Paragraphs 8 to 18, in which a high level of expression is detected if the expression level of the gene is above the expression level set out in Column 9 (“Cut-Off”) of Table D2 (SWS Classifier 1), Table D3 (SWS Classifier 2), Table D4 (SWS Classifier 3) or Table D5 (SWS Classifier 4), and a low level of expression is detected if the expression level of the gene is below that level.
Paragraph 21. A method according to any preceding Paragraph, in which the expression level of all of the genes in Table D1 is detected.
Paragraph 22. A method according to any preceding Paragraph, in which the grade is assigned by applying a class prediction algorithm comprising a nearest shrunken centroid method (Tibshirani, et al., 2002, Proc Natl Acad Sci USA. 99(10): 6567-6572) to the expression data of the plurality of genes.
Paragraph 23. A method according to Paragraph 22, in which the class prediction algorithm comprises Prediction Analysis of Microarrays (PAM).
Paragraph 24. A method according to any preceding Paragraph, in which the grade is assigned by applying a class prediction algorithm comprising the steps of: (a) obtaining a set of predictor parameters; (b) re-coding the parameters to obtain discrete-valued variables; (c) selecting statistically robust discrete-valued variables and combinations thereof; (d) obtaining a sum of the statistically weighted discrete-valued variables and combinations thereof; and (e) obtaining a predictive outcome of breast cancer subtype based on the sum.
Paragraph 25. A method according to any preceding Paragraph, in which the grade is assigned by applying a class prediction algorithm comprising Statistically Weighted Syndromes (SWS) to the gene expression data.
Paragraph 26. A method according to any preceding Paragraph, in which the breast tumour comprises a histological Grade 2 breast tumour.
Paragraph 27. A method of classifying a histological Grade 2 tumour into a low aggressiveness tumour or a high aggressiveness tumour, the method comprising assigning a grade to the histological Grade 2 tumour according to any preceding Paragraph.
Paragraph 28. A method according to Paragraph 27, in which a histological Grade 2 breast tumour assigned a low aggressiveness grade has at least one feature of a histological Grade 1 breast tumour.
Paragraph 29. A method according to Paragraph 27, in which a breast tumour assigned a high aggressiveness grade has at least one feature of a histological Grade 3 breast tumour.
Paragraph 30. A method according to Paragraph 28 or 29, in which the feature comprises likelihood of tumour recurrence post-surgery or survival rate, such as disease free survival rate.
Paragraph 31. A method according to Paragraph 28 or 29, in which the feature comprises susceptibility to treatment.
Paragraph 32. A method according to any preceding Paragraph, in which the method is capable of classifying histological Grade 1 and histological Grade 3 tumours with an accuracy of 70% or above, 80% or above, or 90% or above.
Paragraph 33. A method of predicting a survival rate for an individual with a histological Grade 2 breast tumour, the method comprising assigning a grade to the breast tumour by a method according to any preceding Paragraph.
Paragraph 34. A method according to Paragraph 33, in which a low aggressiveness grade indicates a high probability of survival and a high aggressiveness grade indicates a low probability of survival.
Paragraph 35. A method of prognosis of an individual with a breast tumour, the method comprising assigning a grade to the breast tumour by a method according to any of Paragraphs 1 to 32.
Paragraph 36. A method of diagnosis of aggressive breast cancer in an individual, the method comprising assigning a grade indicative of high aggressiveness to a breast tumour of the individual by a method according to Paragraphs 1 to 32.
Paragraph 37. A method of choosing a therapy for an individual with breast cancer, the method comprising assigning a grade to the breast tumour by a method according to any of Paragraphs 1 to 32, and choosing an appropriate therapy based on the aggressiveness of the breast tumour.
Paragraph 38. A method of treatment of an individual with breast cancer, the method comprising assigning a grade to the breast tumour by a method according to any of Paragraphs 1 to 32, and administering an appropriate therapy to the individual based on the aggressiveness of the breast tumour.
Paragraph 39. A method according to Paragraph 36, 37 or 38, in which the diagnosis or choice of therapy is determined by further assessing the size of the tumour, or the lymph node stage or both, optionally together or in combination with other risk factors.
Paragraph 40. A method according to any of Paragraphs 36 to 39, in which the choice of therapy is determined by assessing the Nottingham Prognostic Index (Haybittle, et al., 1982).
Paragraph 41. A method according to any of Paragraphs 36 to 40, in which the choice of therapy is determined by further assessing the oestrogen receptor (ER) status of the breast tumour.
Paragraph 42. A method according to any preceding Paragraph, in which the histological grading comprises the Nottingham Grading System (NGS) or the Elston-Ellis Modified Scarff, Bloom, Richardson Grading System.
Paragraph 43. A method of determining the likelihood of success of a particular therapy on an individual with a breast tumour, the method comprising comparing the therapy with the therapy determined by a method according to Paragraph 37.
Paragraph 44. A method of assigning a breast tumour patient into a prognostic group, the method comprising applying the Nottingham Prognostic Index to a breast tumour, in which the histologic grade score of the breast tumour is replaced by a grade obtained by a method according to any of Paragraphs 2 to 32.
Paragraph 45. A method of assigning a breast tumour patient into a prognostic group, the method comprising deriving a score which is the sum of the following: (a) (0.2× tumour size in cm); (b) tumour grade in which the tumour grade is assigned by a method according to any of Paragraphs 2 to 32; and (c) lymph node stage; in which the tumour size and the lymph node stage are determined according to the Nottingham Prognostic Index, in which a patient with a score of 2.4 or less is categorised to a EPG (excellent prognostic group), a patient with a score of less than 3.4 is categorised to a GPG (good prognostic group), a patient with a score of between 3.4 and 5.4 is categorised to a MPG (moderate prognostic group), a patient with a score of greater than 5.4 is categorised to a PPG (poor prognostic group).
Paragraph 46. A method of determining whether a breast tumour is a metastatic breast tumour, the method comprising assigning a grade to the breast tumour by a method according to any of Paragraphs 1 to 32.
Paragraph 47. A method of identifying a molecule capable of treating or preventing breast cancer, the method comprising: (a) grading a breast tumour; (b) exposing the breast tumour to a candidate molecule; and (c) detecting a change in tumour grade; in which the grade or change thereof, or both, is assigned by a method according to any of Paragraphs 1 to 32.
Paragraph 48. A molecule identified by a method according to Paragraph 47.
Paragraph 49. Use of a molecule according to Paragraph 48 in a method of treatment or prevention of cancer in an individual.
Paragraph 50. A method of treatment or prevention of breast cancer in an individual, the method comprising modulating the expression of a gene set out in Table D1 (SWS Classifier 0).
Paragraph 51. A method of determining the proliferative state of a cell, the method comprising detecting the expression of a gene selected from the genes set out in Table D1 (SWS Classifier 0), in which: (a) a high level of expression of a gene which is annotated “3” in Column 7 (“Grade with Higher Expression”) indicates a highly proliferative cell; (b) a high level of expression of a gene which is annotated “1” in Column 7 (“Grade with Higher Expression”) indicates a non-proliferating cell or a slow-growing cell; (c) a low level of expression of a gene which is annotated “3” in Column 8 (“Grade with Lower Expression”) indicates a highly proliferative cell; and (d) a low level of expression of a gene which is annotated “1” in Column 8 (“Grade with Lower Expression”) indicates a non-proliferating cell or a slow-growing cell.
Paragraph 52. A method according to Paragraph 51, which comprises the features of any of Paragraphs 5 to 32.
Paragraph 53. A combination comprising the genes set out in Table D1 (SWS Classifier 0).
Paragraph 54. A combination comprising the probesets set out in Table D1 (SWS Classifier 0).
Paragraph 55. A combination comprising the genes set out in Paragraph 9, 11, 13 or 16.
Paragraph 56. A combination comprising the probesets set out in Paragraph 10, 12, 14 or 17.
Paragraph 57. A combination according to any of Paragraphs 53, 54, 55 or 56 in the form of an array.
Paragraph 58. A combination according to any of Paragraphs 53, 54, 55 or 56 in the form of a microarray.
Paragraph 59. A kit comprising a combination, array or microarray according to any of Paragraphs 53 to 58, together with instructions for use in a method according to any of Paragraphs 1 to 47 and 50 to 52.
Paragraph 60. Use of a combination, array or a microarray according to any of Paragraphs 53 to 58 or a kit according to Paragraph 59 in a method according to any of Paragraphs 1 to 47 and 50 to 52.
Paragraph 61. Use according to Paragraph 60, in which the method comprises a method of assigning a grade to a breast tumour according to any of Paragraphs 1 to 32.
Paragraph 62. A computer implemented method of assigning a grade to a breast tumour, the method comprising processing expression data for one or more genes set out in Table D1 (SWS Classifier 0) and obtaining a grade indicative of aggressiveness of the breast tumour.
Paragraph 63. A program storage device readable by a machine, tangibly embodying a program of instructions executable by the machine to perform method of assigning a grade to a breast tumour, the method comprising: processing expression data for one or more genes set out in Table D1 (SWS Classifier 0); and obtaining a grade indicative of aggressiveness of the breast tumour.
Each of the applications and patents mentioned in this document, and each document cited or referenced in each of the above applications and patents, including during the prosecution of each of the applications and patents (“application cited documents”) and any manufacturer's instructions or catalogues for any products cited or mentioned in each of the applications and patents and in any of the application cited documents, are hereby incorporated herein by reference. Furthermore, all documents cited in this text, and all documents cited or referenced in documents cited in this text, and any manufacturer's instructions or catalogues for any products cited or mentioned in this text, are hereby incorporated herein by reference.
Various modifications and variations of the described methods and system of the invention will be apparent to those skilled in the art without departing from the scope and spirit of the invention. Although the invention has been described in connection with specific preferred embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments and that many modifications and additions thereto may be made within the scope of the invention. Indeed, various modifications of the described modes for carrying out the invention which are obvious to those skilled in molecular biology or related fields are intended to be within the scope of the claims. Furthermore, various combinations of the features of the following dependent claims can be made with the features of the independent claims without departing from the scope of the present invention.
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| Number | Date | Country | Kind |
|---|---|---|---|
| 200607354-8 | Oct 2006 | SG | national |
This application is a continuation-in-part of U.S. patent application Ser. No. 13/954,050 filed Jul. 30, 2013, which is a continuation of U.S. patent application Ser. No. 12/446,195 filed Apr. 17, 2009 (with a 371(c) date of Oct. 12, 2010), which is a 371 of PCT/SG2007/000357 filed Oct. 19, 2007, which claims the benefit of U.S. Patent Application No. 60/862,519 filed Oct. 23, 2007. This application claims priority from Singapore Patent Application No 200607354-8, filed Oct. 20, 2006.
| Number | Date | Country | |
|---|---|---|---|
| 60862519 | Oct 2006 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 12446195 | Oct 2010 | US |
| Child | 13954050 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 13954050 | Jul 2013 | US |
| Child | 14737807 | US |
