Method for prognosis of high-grade serous ovarian cancer

文档序号：384854 发布日期：2021-12-10 浏览：23次中文

阅读说明：本技术 高级别浆液性卵巢癌的预后方法 (Method for prognosis of high-grade serous ovarian cancer ) 是由 A·范德斯托尔佩 W·F·J·费尔哈格于 2020-05-01 设计创作，主要内容包括：本申请主要涉及一种用于确定被诊断为高级别浆液性卵巢癌的对象的预后的方法。所述方法包括基于对象样本中至少两种细胞信号传导路径的活性来确定预后,所述至少两种细胞信号传导路径包括磷脂酰肌醇3-激酶(PI3K)路径和核因子-kappa B(NFkB或NFκB)路径。本申请还涉及一种用于识别将适合PI3K路径靶向治疗或NFkB路径靶向治疗的被诊断患有高级别浆液性卵巢癌的对象的方法。所述方法包括基于对象样本中的包括PI3K路径和NFkB路径的至少两种细胞信号传导路径的活性来识别对象。本申请还涉及相应的装置、非瞬态存储介质、计算机程序和套件。(The present application relates generally to a method for determining the prognosis of a subject diagnosed with high-grade serous ovarian cancer. The method comprises determining a prognosis based on the activity of at least two cellular signaling pathways in a sample of the subject, the at least two cellular signaling pathways comprising a phosphatidylinositol 3-kinase (PI3K) pathway and a nuclear factor-kappa B (NFkB or NFkB) pathway. The present application also relates to a method for identifying a subject diagnosed with high-grade serous ovarian cancer who would be suitable for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy. The method comprises identifying the subject based on activity of at least two cellular signaling pathways in the subject sample, including the PI3K pathway and the NFkB pathway. The application also relates to a corresponding apparatus, non-transitory storage medium, computer program and kit.)

1. A method for determining the prognosis of a subject diagnosed with high-grade serous ovarian cancer, wherein the method comprises:

determining a prognosis based on the activity of at least two cellular signaling pathways in a sample of the subject, the at least two cellular signaling pathways including a phosphatidylinositol 3-kinase (PI3K) pathway and a nuclear factor-kappa B (NFkB or NF κ B) pathway,

wherein the cell signaling pathway activity is based on the expression level of three or more target genes directed to the cell signaling pathway, and wherein,

the three or more PI3K target genes are selected from the group consisting of: AGRP, BCL2L11, BCL6, BNIP3, BTG1, CAT, CAV1, CCND1, CCND2, CCNG2, CDK1A, CDK1B, ESRl, FASLG, FBX032, GADD45A, INSR, MXIl, NOS3, PCKl, POMC, PPARGCIA, PRDX3, RBL2, SOD2, and TNFSF10, or selected from the group comprising: ATP8a1, BCL2L11, BNIP3, BTGl, C10orf10, CAT, CBLB, CCNDl, CCND2, CDKNIB, DDB1, DYRK2, ERBB3, EREG, ESRl, EXT1, FASLG, FGFR2, GADD45A, IGF1R, IGFBP1, IGFBP3, INSR, LGMN, MXI1, PPM1D, SEMA3C, SEPP1, SESN1, SLC5a3, SMAD4, SOD2, TLE4, and TNFSF10, or selected from the group comprising: SOD2, BNIP3, MXI1, PCK1, PPARGC1A and CAT, and

the three or more NFkB target genes are selected from the group comprising:

wherein, when the activity of PI3K pathway is low and the activity of NFkB pathway is high, the prognosis is good, and

wherein when the activity of PI3K pathway is high and the activity of NFkB pathway is low, the prognosis is poor, and

wherein the prognosis is intermediate when the PI3K pathway activity is low and the NFkB pathway activity is low or when the PI3K pathway activity is high and the NFkB pathway activity is high.

2. The method according to claim 1, wherein the activity of the at least two cell signaling pathways in the sample is inferred or inferable by a method comprising:

receiving expression levels of three or more target gene in each of the respective cell signaling pathways measured in the sample,

determining a level of activity of a Transcription Factor (TF) element associated with a cellular signaling pathway that controls transcription of three or more target genes, the determining based on evaluating a calibrated mathematical pathway model that correlates the expression levels of the three or more target genes with the level of activity of the TF element, and,

inferring said activity of said corresponding cellular signaling pathway based on the determined level of activity of a TF element associated with said cellular signaling pathway.

3. Method according to claim 1 or 2, wherein the calibrated mathematical pathway model is a probabilistic model, preferably a bayesian network model, based on conditional probabilities relating activity levels of TF elements to the expression levels of the three or more target genes, or wherein the mathematical pathway model is based on one or more linear combinations of the expression levels of the three or more target genes.

4. A method for identifying a subject diagnosed as having high grade serous ovarian cancer who will be eligible for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy, wherein the method comprises:

identifying a subject based on the activity of at least two cellular signaling pathways comprising a phosphatidylinositol 3-kinase (PI3K) pathway and a nuclear factor-kappa B (NFkB or NF κ B) pathway in a sample of the subject,

wherein the cell signaling pathway activity is based on the expression level of three or more target genes directed to the cell signaling pathway, and wherein,

the three or more NFkB target genes are selected from the group comprising:

5. The method of claim 4, wherein the subject is identified as suitable for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy when the activity of the PI3K pathway is low and the activity of the NFkB pathway is high.

6. The method of claim 4 or 5, wherein the method further comprises:

providing PI3K pathway-targeted therapy or NFkB pathway-targeted therapy to the identified subject.

7. An apparatus for determining the prognosis of a subject diagnosed with high-grade serous ovarian cancer, comprising a digital processor configured to perform the method of any one of claims 1 to 3, or configured to identify the prognosis of a subject diagnosed with high-grade serous ovarian cancer that would be suitable for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy, the digital processor comprising a digital processor configured to perform the method of any one of claims 4 to 6.

8. A non-transitory storage medium storing instructions executable by a digital processing device to perform a method according to any one of claims 1 to 3, or to identify a subject diagnosed with high grade serous ovarian cancer that will be eligible for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy, for determining a prognosis of the subject diagnosed with high grade serous ovarian cancer, the non-transitory storage medium storing instructions executable by a digital processing device to perform the method of any one of claims 4 to 6.

9. A computer program for determining the prognosis of a subject diagnosed with high grade serous ovarian cancer comprising program code means for causing a digital processing apparatus to carry out the method of any one of claims 1 to 7 or for identifying a subject diagnosed with high grade serous ovarian cancer that would be suitable for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy, when the computer program is run on a digital processing apparatus, the computer program comprising program code means for causing the digital processing apparatus to carry out the method of any one of claims 4 to 6.

10. A kit for determining the prognosis of a subject diagnosed with high grade serous ovarian cancer or for identifying a subject diagnosed with high grade serous ovarian cancer who would be suitable for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy, the kit comprising:

means for measuring the expression level of six or more target genes of each of at least two cellular signaling pathways including the PI3K pathway and the NFkB pathway in a sample of the subject, wherein the means comprises primers and probes for determining the expression level of the six or more target genes for each cellular signaling pathway, and wherein,

the six or more PI3K target genes are selected from the group consisting of: AGRP, BCL2L11, BCL6, BNIP3, BTG1, CAT, CAV1, CCND1, CCND2, CCNG2, CDK1A, CDK1B, ESRl, FASLG, FBX032, GADD45A, INSR, MXIl, NOS3, PCKl, POMC, PPARGCIA, PRDX3, RBL2, SOD2, and TNFSF10, or selected from the group comprising: ATP8a1, BCL2L11, BNIP3, BTGl, C10orf10, CAT, CBLB, CCNDl, CCND2, CDKNIB, DDB1, DYRK2, ERBB3, EREG, ESRl, EXT1, FASLG, FGFR2, GADD45A, IGF1R, IGFBP1, IGFBP3, INSR, LGMN, MXI1, PPM1D, SEMA3C, SEPP1, SESN1, SLC5a3, SMAD4, SOD2, TLE4, and TNFSF10, or selected from the group comprising: SOD2, BNIP3, MXI1, PCK1, PPARGC1A and CAT, and

the six or more NFkB target genes are selected from the group comprising:

BCL2L1, BIRC3, CCL2, CCL3, CCL4, CCL5, CCL20, CCL22, CX3CL1, CXCL1, CXCL2, CXCL3, ICAM1, IL1B, IL6, IL8, IRF1, MMP9, NFKB2, NFKBIA, NFKBIE, PTGS2, SELE, STAT5A, TNF, TNFAIP2, TNIP1, TRAF1, and VCAM1, and optionally, an apparatus according to claim 7, a non-transitory storage medium according to claim 8, or a computer program according to claim 9.

11. Use of a kit according to claim 10 in performing a method according to any one of claims 1 to 6.

Technical Field

The subject matter described herein relates generally to the fields of bioinformatics, genomic processing, proteomic processing, and related fields. More particularly, the present invention relates to methods for determining the prognosis of a subject diagnosed with high grade serous ovarian cancer, and methods for identifying a subject diagnosed with high grade serous ovarian cancer that would be suitable for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy. The invention also relates to an apparatus, a non-transitory storage medium and a computer program for determining the prognosis of a subject diagnosed with high grade serous ovarian cancer or for identifying a subject diagnosed with high grade serous ovarian cancer that would be suitable for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy. The invention further relates to a kit for determining the prognosis of a subject diagnosed with high grade serous ovarian cancer or for identifying a subject diagnosed with high grade serous ovarian cancer that would be suitable for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy. Finally, the invention relates to the use of said kit in performing any of the above-mentioned methods. The prognosis and the identification are performed based on a combination of cell signaling pathway activities.

Background

Ovarian cancer (OVC) is the most lethal of gynecological malignancies and is also one of the most common causes of cancer death in women worldwide. The most common and deadliest subtype of ovarian cancer is high-grade serous ovarian cancer (HGSOC), which accounts for approximately 75% of ovarian cancers. For the treatment of high-grade serous ovarian cancer, standard chemotherapy is used in addition to surgical tumor reduction (where the majority of the tumor burden is taken away). Chemotherapy regimens typically comprise cisplatin or carboplatin, but many patients have been found to exhibit resistance to these compounds.

Disease-free survival (DFS) of high-grade serous ovarian cancer is highly variable following standard chemotherapy following tumor-reducing surgery, and there is currently no way to differentiate patients with poor prognosis and short disease-free survival from patients with better prognosis and longer disease-free survival.

Disclosure of Invention

According to one aspect, the invention relates to a method for determining a prognosis for a subject diagnosed with high grade serous ovarian cancer, wherein the method comprises:

wherein the cell signaling pathway activity is based on the expression level of three or more target genes directed to the cell signaling pathway, and wherein,

the three or more NFkB target genes are selected from the group comprising: BCL2L1, BIRC3, CCL2, CCL3, CCL4, CCL5, CCL20, CCL22, CX3CL1, CXCL1, CXCL2, CXCL3, ICAM1, IL1B, IL6, IL8, IRF1, MMP9, NFKB2, NFKBIA, NFKBIE, PTGS2, SELE, STAT5A, TNF, TNFAIP2, TNIP1, TRAF1, and VCAM1, and

wherein, when the activity of PI3K pathway is low and the activity of NFkB pathway is high, the prognosis is good, and

wherein when the activity of PI3K pathway is high and the activity of NFkB pathway is low, the prognosis is poor, and

wherein the prognosis is intermediate when the PI3K pathway activity is low and the NFkB pathway activity is low or when the PI3K pathway activity is high and the NFkB pathway activity is high.

The invention is based on the following insight of the inventors: analysis of the activity of the cellular signaling pathway, including the activity of the PI3K pathway and the NFkB pathway, can be used to characterize high-grade serous ovarian cancer. In particular, pathway activity was found to be suitable for determining the prognosis of a subject diagnosed with high-grade serous ovarian cancer, and/or for identifying a subject diagnosed with high-grade serous ovarian cancer who would be suitable for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy (see below).

As used herein, the term "prognosis" refers to the likelihood or expectation of a clinical outcome, such as disease recurrence, disease progression, disease onset, and death due to disease, including but not limited to whether signs and symptoms will improve or worsen (and how quickly) or remain stable over time or whether the subject is alive.

In some embodiments, the prognosis is defined in a quantitative manner in several ways, such as: "time to relapse (of disease)", "time to progression (of disease)", "(time to disease) occurrence" or "time to death (disease)".

In other embodiments, the prognosis is defined in a qualitative manner, such as: "good", "moderate" or "bad". The clinical outcome can be good, medium, or poor, whether in absolute terms, i.e., for example, greater than or less than or about equal to the survival period for a certain period of time (e.g., months or years), or relative to another clinical condition, can be good, medium, or poor compared to its clinical outcome.

In some embodiments, the prognosis is the likelihood or expectation of disease-free survival. The likelihood or expectation of disease-free survival can be quantitatively defined, for example: the time period between the last treatment and disease recurrence (e.g., in months or years), or in a qualitative manner, such as: "good", "medium" or "bad" in an absolute or relative environment as described above.

In some embodiments, the prognosis is the likelihood or expectation of disease-specific survival in general. The likelihood or expectation of overall disease-specific survival may be defined in a quantitative manner, for example: time period of survival (e.g., months or years), or in a qualitative manner, e.g.: "good", "medium" or "bad" in an absolute or relative environment as described above.

As used herein, the term "subject" refers to any organism. In some embodiments, the subject is an animal, preferably a mammal. In some embodiments, the object is a human, preferably a medical object. In some embodiments, the subject is a human who has been diagnosed with high-grade serous ovarian cancer.

The "sample" may be an extracted sample, i.e. a sample extracted from a subject. Examples of samples include, but are not limited to, tissues, cells, blood, and/or bodily fluids, such as bronchial aspirates, bone marrow aspirates, or samples drawn from a body cavity of a subject.

As used herein, the term "activity of a" (specific) pathway "refers to the activity of a Transcription Factor (TF) element associated with a cellular signaling pathway, a TF element controlling transcription of a target gene in a sample in driving expression in the target gene, i.e., the rate of transcription of the target gene, e.g., with respect to high activity (i.e., high rate, or rate above a specific rate) or low activity (i.e., low rate, or rate below a certain rate), or a corresponding score, value, or parameter associated with such activity. Transcription factor activity is a readout for the activity of the associated pathway. Pathway activity can be represented by, for example, activity level. The activity of each pathway can be determined quantitatively as a numerical value or qualitatively as, for example, "high" or "low". A high (or low) activity of a pathway may refer to an activity above (or below) a defined threshold or above (or below) an activity determined in a sample of a healthy subject or a subject with a particular clinical condition.

The Transcription Factor (TF) element of the NFkB pathway preferably comprises a protein complex comprising at least one of the NFkB members (NFkB1 or p50/p105, NFkB2 or p52/p100, RELA or p65, REL, and RELB) or dimers thereof, which is capable of binding to a specific DNA sequence, thereby controlling transcription of a target gene.

The Transcription Factor (TF) element of the PI3K pathway preferably comprises at least one FOXO family member. Since the PI3K Pathway negatively regulates tumor suppressor FOXO transcription factors, the Activity of the FOXOTF element is essentially negatively or inversely related to the Activity of the PI3K Pathway (in the absence of oxidative stress (see, e.g., "Assessment of Functional photophatically linked Activity 3-Kinase Pathway Activity in Cancer Tissue Using Forkhead Box-O Target Gene Expression in a Knowledge-Based comparative Model", American Journal of Pathology, Vol. 188, No. 9, p. 2018, p. 1956 to 1972)).

The determination of the prognosis may be performed by means of a mathematical model, in particular a calibrated mathematical model, or by means of a decision model as exemplified in table 1. The determination of the prognosis may comprise (i) receiving the activity of the pathway and (ii) determining the prognosis, based on evaluating a (calibrated) mathematical model that relates the activity of the pathway to a score indicative of the prognosis, or based on a decision model as shown in table 1.

The method of the first aspect of the present application may be a computer-implemented method.

Preferably, the prognosis is good when the activity of the PI3K pathway is low and the activity of the NFkB pathway is high.

It is also preferred that when PI3K pathway activity is high and NFkB pathway activity is low, prognosis is poor.

Still further, it is preferable that the prognosis is medium when PI3K pathway activity is low and NFkB pathway activity is low or when PI3K pathway activity is high and NFkB pathway activity is high.

In some embodiments, the determination of prognosis is based on the activity of the PI3K pathway and the activity of the NFkB pathway. In other embodiments, the determination of prognosis is based on the activity of the PI3K pathway, the activity of the NFkB pathway and the activity of the additional cell signaling pathway or the activity of the still additional cell signaling pathway.

The activity of the pathway, e.g., in a cell or tissue sample isolated from a subject, can be determined by pathway analysis.

Pathway analysis enables quantitative measurement of pathway activity in a subject sample based on inferring the activity of cellular signaling pathways from the measurement of mRNA levels of target genes of transcription factors associated with the respective cellular signaling pathways (see, e.g., "Selection of qualified pathway therapy pathway activities" by verhaeghw. et al, Cancer research, volume 74, volume 11, year 2014 6, pages 2936 to 2945, and "Knowledge-based signaling pathways" by verhaeghw. and volumetric storage a., volume 5, volume 14, year 20147, pages 5196 and 5197).

Determination of the activity of the PI3K pathway or NFkB pathway can be performed as described, for example, in the following documents, each of which is incorporated herein by reference in its entirety: published International patent applications WO2013/011479 (entitled "Association of cellular signalling using basic modulation of target gene expression"), WO2014/102668 (entitled "Association of cellular signalling using line association of target gene expression"), WO2015/101635 (entitled "Association of PI3K cellular signalling using specific modulation of target gene expression"), WO2016/062892 (entitled "association of cellular signalling and signalling using cellular signalling gene expression"), WO 2016/medial expression, WO2016/062893 (entitled "Medical diagnosis and prediction of multiple cellular signalling activities"), WO2017/029215 (entitled "assessment of NFkB cellular signalling packet activity using modulation of target gene expression"), and WO2018/096076 (entitled "Method differential to reactive food reactivity fragment").

Suitable target genes for determining pathway activity are indicated in the above references. In this regard, reference is also made specifically to the sequence listing of the target genes provided with the above references.

Thus, three or more PI3K target genes, e.g., three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen or more, are preferably selected from the group comprising: AGRP, BCL2L11, BCL6, BNIP3, BTG1, CAT, CAV1, CCND1, CCND2, CCNG2, CDK1A, CDK1B, ESRl, FASLG, FBX032, GADD45A, INSR, MXIl, NOS3, PCKl, POMC, rgcia, PRDX3, RBL2, SOD2, and TNFSF10(WO 2015/101635), or selected from the group consisting of: AATP8a1, BCL2L11, BNIP3, BTGl, C10orf10, CAT, CBLB, CCNDl, CCND2, CDKNIB, DDB1, DYRK2, ERBB3, EREG, ESRl, EXT1, FASLG, FGFR2, GADD45A, IGF1R, IGFBP1, IGFBP3, INSR, LGMN, MXI1, PPM1D, SEMA3C, SEPP1, SESN1, SLC5a3, SMAD4, SOD2, TLE4, and TNFSF10(WO 2016/062892, WO 2016/062893), or a group comprising: SOD2, BNIP3, MXI1, PCK1, PPARGC1A and CAT (WO2018/096076),

and/or

Three or more NFkB target genes, e.g. three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen or more, preferably selected from the group comprising: BCL2L1, BIRC3, CCL2, CCL3, CCL4, CCL5, CCL20, CCL22, CX3CL1, CXCL1, CXCL2, CXCL3, ICAM1, IL1B, IL6, IL8, IRF1, MMP9, NFKB2, NFKBIA, NFKBIE, PTGS2, SELE, STAT5A, TNF, TNFAIP2, TNIP1, TRAF1, and VCAM1(WO 2017/029215).

For use in pathway analysis, three or more (e.g., three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen or more) target genes of each assessed cellular signaling pathway can be analyzed to determine pathway activity.

Preferably, the activity of at least two cell signalling pathways in the sample is inferred or inferable by a method comprising:

receiving expression levels of three or more (e.g., three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen or more) target genes of each of the respective cell signaling pathways measured in the sample,

determining a level of activity of a Transcription Factor (TF) element associated with a cellular signaling pathway that controls transcription of three or more target genes, the determining based on evaluating a calibrated mathematical pathway model that relates expression levels of the three or more target genes to the level of activity of the TF element, and,

inferring an activity of the corresponding cellular signaling pathway based on the determined level of activity of the TF element associated with the cellular signaling pathway.

The three or more target genes are preferably selected from the group listed above.

Preferably, the calibrated mathematical pathway model is a probabilistic model, preferably a bayesian network model, based on conditional probabilities relating the activity level of the TF element to the expression levels of the three or more target genes, or the mathematical pathway model is based on one or more linear combinations of the expression levels of the three or more target genes.

This is described in published international patent applications WO2013/011479 ("Association of cellular signaling using basic modeling of target gene expressions") and WO2014/102668 ("Association of cellular signaling using linear combinations(s) of target gene expressions"), the contents of which are incorporated herein in their entirety. More details on the use of mathematical models of target gene expression to infer cell signaling pathway activity can be found in "Selection of qualified tissue therapy through the use of knowledge-based functional models of tissue transduction pathways" by VerhaeghW. et al, Cancer Research, volume 74, No. 11, 2014, pages 2936 to 2945.

As mentioned above, definitions and embodiments relating to "prognosis", "subject", "sample", "pathway activity" and "inferred (PI3K or NFkB) pathway activity" apply to other aspects of the invention.

According to a second aspect, the present invention relates to a method for identifying a subject diagnosed with high-grade serous ovarian cancer to be suitable for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy, wherein the method comprises:

identifying the subject based on activity in at least two cellular signaling pathways in a sample from the subject, the at least two cellular signaling pathways comprising a phosphatidylinositol 3-kinase (PI3K) pathway and a nuclear factor-kappa B (NFkB or NF κ B) pathway,

wherein the cell signaling pathway activity is based on the expression level of three or more target genes directed to the cell signaling pathway, and wherein,

As used herein, the term "PI 3K pathway-targeted therapy" refers to a therapy that targets PI3K or FOXO pathway or a member of the FOXO family and mediates the activity of PI3K or FOXO pathway through a drug.

As used herein, the term "NFkB pathway-targeted therapy" refers to a therapy that targets the NFkB pathway and mediates or, in particular, inhibits the activity of the NFkB pathway by a drug.

The subject of this aspect of the invention has been diagnosed with high grade serous ovarian cancer, but will have a good prognosis if the subject is provided with PI3K pathway-targeted therapy or NFkB pathway-targeted therapy, or, in particular, if a PI3K pathway-targeted drug or a NFkB pathway-targeted drug is administered to the subject, or if in addition to said therapy, an additional therapy is provided to the subject.

Targeting the PI3K pathway induces cell circulation and in this way sensitizes tumor cells to other therapies, such as chemotherapy, e.g., cisplatin, or radiation, which require dividing cells to be effective in cancer therapy. NFkB pathway-targeted therapies may have similar effects of blocking apoptosis, e.g., increasing sensitivity to radiation or chemotherapy. PI3K pathway-targeted therapy is preferred because it is more effective.

The determination of the prognosis can be performed by means of a mathematical model, in particular a calibrated mathematical model, or by means of a decision model. The identification of the object may include (i) receiving the activity of the path and (ii) identifying the object based on evaluating a (calibrated) mathematical model that correlates the activity of the path with a score that indicates that the object will be suitable for PI3K path-targeted therapy or NFkB path-targeted therapy, or based on a decision model.

The method of the second aspect of the present application may be a computer-implemented method.

Preferably, the subject is identified as suitable for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy when the activity of the PI3K pathway is low and the activity of the NFkB pathway is high.

In some embodiments, identification of subjects eligible for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy is based on the activity of the PI3K pathway and the activity of the NFkB pathway. In other embodiments, identification of a subject suitable for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy is based on the activity of the PI3K pathway, the activity of the NFkB pathway and the activity of an additional cellular signaling pathway or the activity of yet an additional cellular signaling pathway.

Preferably, the method further comprises:

providing PI3K pathway-targeted therapy or NFkB pathway-targeted therapy to the identified subject.

Preferably, the activity of at least two cell signalling pathways in the sample is inferred or inferable by a method comprising:

determining a level of activity of a Transcription Factor (TF) element associated with a cellular signaling pathway that controls transcription of three or more target genes, the determining based on evaluating a calibrated mathematical pathway model that relates expression levels of the three or more target genes to the level of activity of the TF element, and,

inferring an activity of the corresponding cellular signaling pathway based on the determined level of activity of the TF element associated with the cellular signaling pathway.

The three or more target genes are preferably selected from the group listed above.

According to a third aspect, the present invention relates to an apparatus for determining the prognosis of a subject diagnosed with high grade serous ovarian cancer, the apparatus comprising a digital processor configured to perform the method of the first aspect of the invention, or for identifying a subject diagnosed with high grade serous ovarian cancer who will be eligible for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy, the digital processor comprising a digital processor configured to perform the method according to the second aspect of the invention.

According to a fourth aspect, the present invention relates to a non-transitory storage medium storing instructions executable by a digital processing apparatus to perform the method of the first aspect of the present invention, or to identify a subject diagnosed with high grade serous ovarian cancer who is to be eligible for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy, for determining a prognosis of the subject diagnosed with high grade serous ovarian cancer, the non-transitory storage medium storing instructions executable by the digital processing apparatus to perform the method according to the second aspect of the present invention. The non-transitory storage medium may be a computer-readable storage medium, such as a hard disk drive or other magnetic storage medium, an optical disk or other optical storage medium, Random Access Memory (RAM), Read Only Memory (ROM), flash memory or other electronic storage medium, a network server, or the like. The digital processing device may be a handheld device (e.g., a personal data assistant or smart phone), a laptop computer, a desktop computer, a tablet computer or device, a remote network server, or the like.

According to a fifth aspect, the present invention relates to a computer program for determining the prognosis of a subject diagnosed with high grade serous ovarian cancer, comprising program code means for causing a digital processing apparatus to carry out the method according to the first aspect, or for identifying a subject diagnosed with high grade serous ovarian cancer who will be eligible for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy, when the computer program is run on a digital processing apparatus, comprising program code means for causing the digital processing apparatus to carry out the method according to the second aspect of the present invention. The digital processing device may be a handheld device (e.g., a personal data assistant or smart phone), a laptop computer, a desktop computer, a tablet computer or device, a remote network server, or the like.

According to a sixth aspect, the present invention relates to a kit for determining the prognosis of a subject diagnosed with high grade serous ovarian cancer or for identifying a subject diagnosed with high grade serous ovarian cancer who would be suitable for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy, the kit comprising:

means for measuring the expression level of six or more (e.g., six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen or more) target genes of each of at least two cellular signaling pathways including the PI3K pathway and the NFkB pathway in a sample of a subject, wherein the means comprises primers and probes for determining the expression level of the six or more target genes for each cellular signaling pathway, and wherein,

the six or more PI3K target genes (e.g., six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen or more) are selected from the group consisting of: AGRP, BCL2L11, BCL6, BNIP3, BTG1, CAT, CAV1, CCND1, CCND2, CCNG2, CDK1A, CDK1B, ESRl, FASLG, FBX032, GADD45A, INSR, MXIl, NOS3, PCKl, POMC, PPARGCIA, PRDX3, RBL2, SOD2, and TNFSF10, or selected from the group comprising: ATP8a1, BCL2L11, BNIP3, BTGl, C10orf10, CAT, CBLB, CCNDl, CCND2, CDKNIB, DDB1, DYRK2, ERBB3, EREG, ESRl, EXT1, FASLG, FGFR2, GADD45A, IGF1R, IGFBP1, IGFBP3, INSR, LGMN, MXI1, PPM1D, SEMA3C, SEPP1, SESN1, SLC5a3, SMAD4, SOD2, TLE4, and TNFSF10, or selected from the group comprising: SOD2, BNIP3, MXI1, PCK1, PPARGC1A and CAT, and

the six or more NFkB target genes (e.g., six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen or more) are selected from the group consisting of:

the apparatus according to the third aspect of the invention, the non-transitory storage medium according to the fourth aspect of the invention or the computer program according to the fifth aspect of the invention.

The three or more target genes are preferably selected from the group listed above.

According to a seventh aspect, the invention relates to the use of a kit according to the sixth aspect of the invention for performing the method according to any one of the first or second aspects of the invention.

The means for measuring the expression levels of three or more target genes of the respective cell signaling pathway may be selected from the group comprising: DNA array chips, oligonucleotide array chips, protein array chips, antibodies, a plurality of probes, such as labeled probes, a set of RNA reverse transcriptase sequencing components, and/or RNA or DNA, including cDNA, amplification primers.

In some embodiments, the kit comprises a set of (labeled) probes directed against a portion of the mRNA or cDNA sequences of three or more target genes as described above.

In some embodiments, the kit comprises a set of primers and probes directed against a portion of the mRNA or cDNA sequences of three or more target genes as described above.

Further advantages will become apparent to those of ordinary skill in the art upon reading and understanding the attached drawings, the following description, and particularly the detailed examples provided below.

In the method of identifying a subject diagnosed with high-grade serous ovarian cancer who is eligible for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy, the activity of at least two cell signaling pathways in a sample may be inferred or capable of being inferred by the same method as for determining the prognosis of a subject diagnosed with high-grade serous ovarian cancer, and the same target genes as described above may be used.

One advantage of the present invention resides in a clinical decision support system configured to determine a prognosis, e.g., likelihood or expectation of disease recurrence, disease progression, disease occurrence, and death due to disease, of a subject diagnosed with high grade serous ovarian cancer, based on a combination of pathway activities as described herein.

Another advantage of the present invention resides in a clinical decision support system configured to identify a subject diagnosed with high grade serous ovarian cancer that will be suitable for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy based on a combination of pathway activities as described herein.

It shall be understood that the methods according to the first and second aspects, the apparatus according to the third aspect, the non-transitory storage medium according to the fourth aspect, the computer program according to the fifth aspect, the kit according to the sixth aspect and the kit according to the seventh aspect use similar and/or identical preferred embodiments, in particular as defined in the dependent claims.

It shall be understood that preferred embodiments of the invention may also be any combination of the dependent claims or the above embodiments with the respective independent claims.

These and other aspects of the invention are apparent from and will be elucidated with reference to the embodiments described herein after.

Drawings

In the following drawings:

FIG. 1 shows the Kaplan-Meier curves for disease-free survival (DFS) for cluster 1 and 2 high-grade serous ovarian cancers. Only patients with DFS <12 months or DFS >24 months were included. The upper solid line indicates C1 patients (high FOXO/low PI3K activity, high NFkB activity) and the lower dashed line indicates C2 patients (low FOXO/high PI3K activity, low NFkB activity). Each "+" sign indicates a right-tailed patient, meaning that follow-up had ceased (since the end of the study period had been reached). Provided below the chart are numbers at risk (upper row represents C1 patients, lower row represents C2 patients).

FIG. 2 shows the Kaplan-Meier curves for disease-free survival (DFS) for cluster 1 and 2 high-grade serous ovarian cancers. Including all patients with known DFS. The upper line indicates C1 patients (high FOXO/low PI3K activity, high NFkB activity) and the lower line indicates C2 patients (low FOXO/high PI3K activity, low NFkB activity). Each "+" sign indicates a right-tailed patient, meaning that follow-up had ceased (since the end of the study period had been reached). Provided below the chart are numbers at risk (upper row represents C1 patients, lower row represents C2 patients).

Figures 3 and 4 show the correlation of FOXO activity score (ordinate) and NFkB activity score (abscissa) (both as log2odds) in short-term and long-term high-grade serous ovarian cancer disease-free survival (DFS). Lines were drawn to distinguish the groups. FIG. 3: cluster groups 1 and 2. FIG. 4: results for groups 1 to 3 according to cluster name. (in both figures, black circles represent C1 patients, white circles represent C2 patients).

Fig. 5 schematically illustrates a Clinical Decision Support (CDS) system configured to determine prognosis of a subject diagnosed with high grade serous ovarian cancer, or to identify a subject diagnosed with high grade serous ovarian cancer that would be eligible for pathway-targeted therapy of PI3K or NFkB pathway-targeted therapy.

Detailed Description

The following examples illustrate only particularly preferred methods and selected aspects related thereto. The teachings provided herein can be used to construct a number of tests and/or kits. The following examples should not be construed as limiting the scope of the invention.

Example (b):

a publicly available data set GSE9891(238 High Grade Serous (HGS), 14 High Grade Endometrioid (HGE), 11 low grade serous or endometrioid (LG) and 18 Low Malignancy Potential (LMP) ovarian cancers; see Tothill RW et al, "Novel molecular weights of individuals and endometrically acquired Cancer linked to Clinical output", Clinical Cancer Research, Vol.14, No. 16, 8.2008, pp.5198 to 5208 was used.

From this data set, only sample data from patients with high-grade serous ovarian cancer was selected. All samples were obtained prior to treatment and had clinical notes on disease-free survival.

Using a pathway analysis of these Affymetrix HG-U133 plus2.0 data, pathway activity scores for the NFkB pathway and for the FOXO transcription factor were measured and read in combination for individual patient samples. For this purpose, long clustering of PI3K and NFkB target genes was used, using a bayesian pathway activity model as described in the above published international patent applications.

Subsequently, two subgroups of patients were selected from the dataset, which had (1) short time to Disease Free Survival (DFS) <12 months, and (2) longest DFS >24 months (n ═ 81 in total). 59 sample data from intermediate DFS patients were kept separately (subgroup 3).

K-means clustering was performed and two stable clusters were generated: c1 with low PI3K pathway activity (high FOXO transcription factor activity score) and high NFkB pathway score (n-43 patient sample data), and C2 with high PI3K pathway activity (low FOXO transcription factor activity score) and low NFkB pathway activity (n-38). C1 is associated with a relatively good prognosis, reflected in a longer disease-free survival in Kaplan-Meier curve analysis, and C2 is associated with a poorer prognosis (p 0.011; FIG. 1)

Subsequently, the best-fit clusters (C1 or C2) of the samples in group 3 were determined and these were added to the Kaplan-Meier curve analysis. In this way, 73C 1 samples and 67C 2 samples were available for Kaplan-Meier analysis. Again, C1 appeared as the longest subgroup of DFS and therefore the best relative prognosis, and C2 was the shortest group of DFS (p ═ 0.036; fig. 2) as can be seen from fig. 3 and 4, the correlation curves between FOXO activity score (ordinate) and NFkB activity score (abscissa) (both as log2odds) and disease-free survival (short-long) indicated the same results. FIG. 3: cluster groups 1 and 2. FIG. 4: results for groups 1 to 3 according to cluster name. (in both figures, black circles represent C1 patients, white circles represent C2 patients.)

These results indicate that the combined activity of FOXO transcription factor and NFkB pathway correlates with a good prognosis, possibly due to a lower growth rate, e.g., associated with apoptosis, as suggested by NFkB-FOXO. FOXO3A is involved in the regulation of apoptosis, which is important for the tumor suppressive effects of these transcription factors. Thus, apoptosis is causally related to less aggressive tumor growth and a more favorable prognosis. In contrast, the PI3K pathway is known to be a growth factor pathway that functions as a "survival pathway" in various cancers, dividing cells, and amplifying the role of other active oncogenic signaling pathways in cells.

Based on these results, a decision model can be derived for determining the prognosis of a subject diagnosed with high-grade serous ovarian cancer. For example, as shown in table 1A, when the activity of the PI3K pathway is low (FOXO transcription factor activity is high) and the activity of the NFkB pathway is high, the prognosis can be determined to be good. In contrast, when the PI3K pathway activity is high (FOXO transcription factor activity is low) and the NFkB pathway activity is low, poor prognosis can be determined. When the activity of the PI3K pathway is low (FOXO transcription factor activity is high) and the NFkB pathway activity is low or the PI3K pathway activity is high (FOXO transcription factor activity is low) and the NFkB pathway activity is low, the prognosis can be determined to be moderate.

The pathway and/or transcription factor activity provided by the pathway analysis can be expressed in a quantitative manner as a numerical value (e.g., as a level or score), and whether the activity is high or low can be determined by comparing the numerical value to a suitably defined threshold. For example, in table 1A, the activity of the NFkB pathway and FOXO transcription factors is represented by log2odds scores. Activity of the NFkB pathway is considered high if the score exceeds a threshold of 8log2odds, and low if the score is below the threshold. In this example, FOXO transcription factor activity is considered high if the score exceeds a threshold of 3.5log2odds, and low if the score is below the threshold.

Alternatively, the NFkB pathway activity score and FOXO transcription factor activity score may be added together. If the combined activity of NFkB + FOXO is high, a good prognosis can be determined. For example, considering the log2odds scores of table 1A, the prognosis may be determined to be good if the sum of the scores exceeds a suitably defined upper threshold, e.g., 22, poor if the sum of the scores is below another suitably defined lower threshold, e.g., 2.5, and medium if the sum of the scores is between the upper and lower thresholds. The intermediate prognosis can also be made more quantitative by calculating a probability value from the sum of the scores based on a linear interpolation between the upper and lower thresholds. The probability value may then indicate whether the intermediate prognosis is more likely to be favorable or unfavorable and within what range, e.g., 10%, 40%, 80%, etc., it is likely to be favorable or unfavorable.

Table 1A-decision model for determining prognosis.

Other ways of determining prognosis based on a combination of pathway activities as described herein are also contemplated, for example:

1. a cluster analysis may be performed as described above, defining two clusters, a high NFkB pathway activity score/FOXO transcription factor activity score or a low NFkB pathway activity score/FOXO transcription factor activity score, and a centroid of activity may be determined (table 1B). To determine a prediction of the object, the distance to each of the centroids (in two-dimensional space) may be determined and the prognosis may be determined based on which centroid is closest. For example, if C1 is a cluster with a relatively good prognosis, C2 is a cluster with a relatively poor prognosis, C1 and C2 are centroids of the clusters, respectively, and d1 and d2 are distances to the centroids C1 and C2, it can be determined that the prognosis is good if d1< d2, and the prognosis is poor if d2< d 1.

2. An alternative to this approach is to assign the probability of favorable prognosis as d2/(d1+ d2) and the corresponding probability of unfavorable prognosis as d1/(d1+ d 2).

Table 1B-cluster analysis results, defining two clusters, high NFkB pathway activity score/FOXO transcription factor activity score or low NFkB pathway activity score/FOXO transcription factor activity score, indicating log2odds values of activity in the centroid. The number of cases in cluster 1 was 43 and the number of cases in cluster 2 was 38. All 81 cases are valid, and none are missing. Based on these data, a computational model can be built that determines the likelihood of favorable prognosis.

We describe two clusters of cell signaling pathway activity in high-grade serous ovarian cancer, with differences in DFS. A well-clustered low PI3K pathway activity (high FOXO transcription factor activity) and high NFkB pathway activity in prognosis may indicate apoptosis, while a poorly-clustered high PI3K pathway activity (low FOXO transcription factor activity) and low NFkB pathway activity in prognosis may indicate high cell division. Patients with high PI3K pathway activity may benefit from PI3K pathway inhibition therapy or (along with) potential chemotherapy.

CDS application

Referring to fig. 5, which diagrammatically illustrates a Clinical Decision Support (CDS) system configured to determine prognosis of a subject diagnosed with high-grade serous ovarian cancer, or to identify a subject diagnosed with high-grade serous ovarian cancer that would be suitable for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy, as described herein, a Clinical Decision Support (CDS) system 10 is implemented as a suitably configured computer 12. The computer 12 may be configured to operate by executing suitable software, firmware, or other instructions stored on a non-transitory storage medium (not shown), such as a hard disk drive or other magnetic storage medium, an optical disk or other optical storage medium, Random Access Memory (RAM), Read Only Memory (ROM), flash memory or other electronic storage medium, a network server, or the like. Although the illustrative CDS system 10 is embodied by an illustrative computer 12, more generally, the CDS system may be embodied by a digital processing device or apparatus that includes a digital processor configured to perform the clinical decision support method as described herein. For example, the digital processing device may be a handheld device (e.g., a personal data assistant or smart phone running a CDS application), a laptop, a desktop, a tablet or device, a remote web server, or the like. The computer 12 or other digital processing device typically includes or is operatively connected to a display device 14 via which information including clinical decision support recommendations is displayed to medical personnel 14. The computer 12 or other digital processing device also typically includes or is operatively connected to one or more user input devices, such as an illustrative keyboard 16, or a mouse, trackball, touch pad, touch sensitive screen (possibly integrated with the display device 14), or another pointer-based user input device via which medical personnel can input information, such as operating commands for controlling the CDS system 10, data for use by the CDS system 10, and the like.

CDS system 10 receives input information about a subject (e.g., an inpatient, or an outpatient treated by an oncologist, physician, or other medical personnel, or a person receiving a cancer screen or some other medical diagnosis that has been diagnosed with high-grade serous ovarian cancer). CDS system 10 applies various data analysis algorithms to the input information in order to generate clinical decision support recommendations, which are presented to medical personnel via display device 14 (or via a speech synthesizer or other device that provides a human perceptible output). In some embodiments, the algorithms may include applying clinical guidelines to the patient. A clinical guideline is a set of stored standard or "canonical" treatment recommendations, typically constructed based on recommendations of a medical expert panel, and optionally formatted in the form of a clinical "flowchart" to facilitate browsing of the clinical guideline. In various embodiments, the data processing algorithms of the CDS 10 may additionally or alternatively include various diagnostic or clinical testing algorithms, such as the machine learning methods disclosed herein, that are executed on the input information to extract clinical decision recommendations.

In the illustrative CDS systems disclosed herein (e.g., CDS system 10), the CDS data analysis algorithms include one or more diagnostic or clinical testing algorithms that are executed on input genomic and/or proteomic information collected by one or more medical laboratories 18. These laboratories may be located "on-site," i.e., a hospital or other location where the subject is receiving medical examination and/or treatment, or "off-site," e.g., a specialized and centralized laboratory that receives a sample of the subject taken from the subject (via mail or other delivery service).

The sample is processed by the laboratory to generate genomic or proteomic information. For example, a sample can be processed using a microarray (also variously referred to in the art as a gene chip, DNA chip, biochip, etc.) or by a quantitative polymerase chain reaction (qPCR) process to measure genomic or proteomic information examined, e.g., the expression level of a gene of interest, e.g., in the form of the level of messenger ribonucleic acid (mRNA) transcribed from the gene, or the level of protein translated from mRNA transcribed from the gene. As another example, a sample may be processed by a gene sequencing laboratory to generate a sequence of deoxyribonucleic acid (DNA), or to generate an RNA sequence, copy number variation, methylation, or the like. Other contemplated measurement methods include Immunohistochemistry (IHC), cytology, Fluorescence In Situ Hybridization (FISH), proximity ligation assays, etc. performed on pathology slides. Other information that can be generated by microarray processing, mass spectrometry, gene sequencing, or other laboratory techniques includes methylation information. Various combinations of such genomic and/or proteomic measurements can also be made.

In some embodiments, medical laboratory 18 performs a number of standardized data acquisitions on a sample of a subject to generate a number of genomic and/or proteomic data. For example, a standardized data acquisition technique may generate (optionally aligned) DNA sequences for one or more chromosomes or chromosome portions or the entire genome. Thousands or tens of thousands of data items, such as expression levels for a large number of genes, various methylation data, and the like, can be generated using standard microarrays. Likewise, PCR-based measurements can be used to measure the expression level of a selected gene. Such large amounts of genomic and/or proteomic data, or selected portions thereof, are input to CDS system 10 for processing in order to develop clinically useful information for making clinical decision support recommendations.

The disclosed CDS systems and related methods relate to processing genomic and/or proteomic data to assess activity of the cellular signaling pathways, including the PI3K pathway and the NFkB pathway, and determine prognosis of a subject diagnosed with high grade serous ovarian cancer, or identify a subject diagnosed with high grade serous ovarian cancer that would be suitable for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy disclosed herein. However, it should be understood that the disclosed CDS system (e.g., CDS system 10) may optionally further include various additional capabilities, such as generating clinical decision support recommendation data, patient history data, patient demographic data (e.g., gender, age, etc.), patient medical imaging data, etc., based on various patient data (e.g., vital signs monitoring) from stored clinical guidelines. Alternatively, in some embodiments, the capabilities of CDS system 10 may be limited to performing only genomic and/or proteomic data to assess the activity of cell signaling pathways, including the PI3K pathway and the NFkB pathway, and to determine the prognosis of a subject diagnosed with high grade serous ovarian cancer, or to identify a subject diagnosed with high grade serous ovarian cancer that would be suitable for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy disclosed herein.

With continued reference to exemplary fig. 5, CDS system 10 infers 22 the activity (P) of at least two cell signaling pathways, including the PI3K pathway and the NFkB pathway, in the subject sample_PI3K，P_NFkB) Based on, but not limited to, the expression levels of three or more target genes in the cell signaling pathway measured in a sample of the subject 20.

Measurement of the mRNA expression level of a gene encoding a protein that is modulated for a cellular signaling pathway, such as an intermediate protein that is part of a protein cascade that forms a cellular signaling pathway, is an indirect measure of the level of expression of the regulatory protein and may or may not be strongly correlated with the actual level of expression of the regulatory protein, let alone with the overall activity of the cellular signaling pathway. The cellular signaling pathway directly regulates the transcription of target genes-thus, the expression level of mRNA transcribed from a target gene is a direct consequence of this regulatory activity. Thus, CDS system 10 infers the activity of at least two cell signaling pathways based on the expression levels (mRNA or protein levels as an alternative measure) of three or more target genes of the cell signaling pathways. This ensures that CDS system 10 infers the activity of the pathway based on direct information provided by the measured expression level of the target gene(s).

The inferred pathway activity is then used to determine 24 the prognosis of the subject diagnosed with high grade serous ovarian cancer. The determination of the prognosis may be based on a decision model, as exemplarily described above.

Based on the determined prognosis, in this example, CDS system 10 assigns 26 the object to at least one of a plurality of prognostic groups, such as: "good", "moderate" or "poor" prognosis.

CDS system 10 may also be adapted to identify 24 subjects diagnosed as high grade serous ovarian cancer suitable for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy. The inferred pathway activity for the subject is used in the recognition. If a subject is identified as suitable for PI3K pathway-targeted therapy or NFkB pathway-targeted therapy, an oncologist, physician, or other medical professional may provide 28PI3K pathway-targeted therapy or NFkB pathway-targeted therapy to the identified subject.

This document describes several preferred embodiments. Modifications and alterations will occur to others upon reading and understanding the preceding detailed description. It is intended that the disclosure be construed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

In the claims, the word "comprising" does not exclude other elements or steps, and the word "a" or "an" does not exclude a plurality.

A single unit or device may fulfill the functions of several items recited in the claims. Although specific measures are recited in mutually different dependent claims, this does not indicate that a combination of these measures cannot be used to advantage.

The calculations like determining the prognosis performed by one or more units or devices may be performed by any other number of units or devices.

It shall be understood that preferred embodiments of the invention may also be any combination of the dependent claims or the above embodiments with the respective independent claims.

These and other aspects of the invention are apparent from and will be elucidated with reference to the embodiments described herein after.

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Method for prognosis of high-grade serous ovarian cancer

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