Method for predicting the response of a tumor in a patient suffering from or at risk of developing recurrent gynecologic cancer towards a chemotherapeutic agent

FIELD OF THE INVENTION

The present invention relates to methods for predicting the response of a tumor in a patient suffering from or at risk of developing recurrent gynecologic cancer towards a chemotherapeutic agent.

BACKGROUND OF THE INVENTION

Every fourth cancer finding in women is breast cancer. Therewith, breast cancer is the most common cancer and the second most common cause of death among women in western industrial nations (Jemal et al., 2007)¹. It is estimated that every eighth to tenth woman will develop breast cancer during her lifetime. With a total share of 10% it is the third most common cancer worldwide (Veronesi et al., 2005)². With an incidence of 130 cases per 100000 women there are about 55000 new cases in Germany annually from which 18000 cases will cause death (GEKID, 2006)³.

The mamma carcinoma is a very heterogonous disease with many subtypes. Therefore, even pathologically similar tumors show a different clinical development towards the same therapy. For this reason, the current histopathological markers can not predict the clinical response adequate. Therefore, it is very difficult to perform an optimized therapy. Hence, a therapy often will be chosen due to empirical experiences, and most of the women will be treated systemically as a precaution (Bast et al., 2001; Goldhirsch et al., 2005)^4,5.

Usually, a combination of several agents will be used since due to the different therapeutic mechanisms of the single chemotherapeutic agents the development of crossresistances is unlikely. A very common combination therapy used already many years as standard therapy is CMF (Cyclophosphamid, Methotrexat and 5-Fluorouracil). These three chemotherapeutic agents have different molecular mechanisms of action. 5-Fluorouracil inhibits for instance the Thymidylate Synthetase irreversible and therewith the DNA synthesis (Longley et al., 2003)⁶.

Over the years new chemotherapeutic agents were identified, which might have an advantage for the treatment of patients. In clinical practice there exists the problem that a new substance can not be tested independently from other agents since they are added to an already established combination therapy. With the results from this therapy it can be determined, whether there could arise an improvement over the standard therapy.

Thus, after the discovery of anthracyclines it could be demonstrated in clinical studies that for instance the combination therapy EC/AC (Epirubicin and Doxorubicin, respectively, and Cyclophosphamid) has an advantage over the CMF therapy. Anthracyclines are intercalators, which can incorporate into the DNA, dissolve their structure and inhibit the topoisomerase II (Capranico et al., 1989)⁷. The administration of an anthracycline leads to a reduction of recurrent incidences about 12% and to a reduction of the death rate about 11% in comparison to a CMF therapy (Misset et al., 1996)⁸.

In current clinical studies it could be demonstrated that the completion of an anthacycline based chemotherapy with a taxane leads to an additional advantage of survival (Nabholtz et al., 1999; Henderson et al., 2003; Bishop et al., 1999)^9,10,11. The agent Paclitaxel was the first taxane, which was used for breast cancer therapy. Paclitaxel binds to the beta-tubuli-unities of the mikrotubuli and stabilizes them (Parness and Horwitz, 1981)¹².

Newly, platin derivatives (Carboplatin, Cisplatin) are used for the treatment of the mamma carcinoma. The cytotoxic effect of the platin derivatives is caused by a cross-linking of DNA single strands and double strands, which are disabled thereby.

One problem of chemotherapy is development of resistances of some tumors towards single or several agents which can inhibit the success of a chemotherapy. To date, only a few resistance mechanisms are known. Furthermore, the order of the application seems to be important regarding crossresistances (Paridaens et al., 2000)¹³. Thus, the identification at an early stage of those resistances is very important to change a therapy in good time. However, it would be optimal to evaluate possible resistances already before a treatment.

Another problem of chemotherapy is occurrence of adverse effects that might be life threatening or severely impairing the quality of life.

In the last years the heterogeneity and complexity of mamma carcinoma have been analyzed on molecular level to analyze which genes are expressed in these tumors.

An already known approach for the identification of predictive gene signatures is the cultivation of cell lines with the purpose to compare the gene expression of resistant and sensitive breast cancer tumor cells. Different genes could be identified which could be appropriate for the prediction of a tumor response towards single chemotherapeutic agents (Villeneuve et al., 2006; Gyorffy et al., 2006)^14,15. Since the expression profile of cell lines in the in vitro situation is much different from the in vivo situation of primary tumor tissues (Ross and Perou, 2001)¹⁶, it has to be evaluated, to which extent the identified genes could be appropriate for prediction in clinical practice.

In research neoadjuvant chemotherapy is very important as well since breast tumor response towards chemotherapeutic agents can be directly analyzed via the tumor reduction status.

A common approach is to isolate RNA from primary tumor tissues for gene expression analysis before neoadjuvant chemotherapy. The chemotherapeutic success can be directly evaluated via tumor reduction and correlated with the gene expression data. In several neoadjuvant studies predictive gene signatures could be identified (Ayers et al., 2004; Hess et al., 2006; Gianni et al., 2005; Thuerigen et al., 2006)^17,18,19,20. In most of the studies neoadjuvant combination therapies instead of monotherapies have been analyzed. Thus, it is difficult to identify the cause of resistances as well as to transfer the identified gene signatures upon other combination therapies.

To date, there exists no reliable predictive marker which can predict the response towards a chemotherapeutic agent in breast cancer. To date, no gene signature could be validated in an independent dataset.

Therefore, a need exists to identify those patients who are likely to respond to a chemotherapeutic agent by providing predictive information. There is a great need for predictive methods that can assist the practicing physician to make treatment choices based on reliable analysis.

DEFINITIONS

Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

The term “prediction”, as used herein, relates to an individual assessment of the malignancy of a tumor, or to the expected survival rate (DFS, disease free survival) of a patient, if the tumor is treated with a given therapy.

“Prediction of the response to chemotherapy”, within the meaning of the invention, shall be understood to be the act of determining a likely outcome of a chemotherapy in a patient inflicted with cancer. The prediction of a response is preferably made with reference to probability values for reaching a desired or non-desired outcome of the chemotherapy. The predictive methods of the present invention can be used clinically to make treatment decisions by choosing the most appropriate treatment modalities for any particular patient.

The “response of a tumor to chemotherapy”, within the meaning of the invention, relates to any response of the tumor to chemotherapy, preferably to a change in tumor mass and/or volume after initiation of neoadjuvant chemotherapy. Tumor response may be assessed in a neoadjuvant situation where the size of a tumor after systemic intervention can be compared to the initial size and dimensions as measured by CT, PET, mammogram, ultrasound or palpation, usually recorded as “clinical response” of a patient. Response may also be assessed by caliper measurement or pathological examination of the tumor after biopsy or surgical resection. Response may be recorded in a quantitative fashion like percentage change in tumor volume or in a qualitative fashion like “no change” (NC), “partial remission” (PR), “complete remission” (CR) or other qualitative criteria. Assessment of tumor response may be done early after the onset of neoadjuvant therapy e.g. after a few hours, days, weeks or preferably after a few months. A typical endpoint for response assessment is upon termination of neoadjuvant chemotherapy or upon surgical removal of residual tumor cells and/or the tumor bed. This is typically three month after initiation of neoadjuvant therapy.

The term “response marker” relates to a marker which can be used to predict the clinical response of a patient towards a given treatment.

The terms “cancer” and “cancerous” refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth. The term “cancer” as used herein includes carcinomas, (e.g., carcinoma in situ, invasive carcinoma, metastatic carcinoma) and pre-malignant conditions, neomorphic changes independent of their histological origin. The term “cancer” is not limited to any stage, grade, histomorphological feature, invasiveness, aggressiveness or malignancy of an affected tissue or cell aggregation. In particular stage 0 cancer, stage I cancer, stage II cancer, stage III cancer, stage IV cancer, grade I cancer, grade II cancer, grade III cancer, malignant cancer and primary carcinomas are included.

As used herein, the term “gynecologic cancers” refers to cancer which are diagnosed in female breast and reproductive organs that include the uterus, ovaries, cervix, fallopian tubes, vulva, and vagina. Examples of gynecologic cancers include, but are not limited to breast cancer, ovarian cancer, vulvar cancer, vaginal cancer, tubal cancer, endometrian cancer and/or cervical cancer. As used herein, the term “endometrian cancer”, also called endometrial cancer or uterine cancer, includes malignant growth of cells in the endometrium, the lining of the uterus.

The term “tumor” as used herein, refers to all neoplastic cell growth and proliferation, whether malignant or benign, and all precancerous and cancerous cells and tissues.

The term “determining the status” as used herein, refers to a measurable property of a gene and its products, especially on the nucleotide level and the gene level including mutation status and gene expression status. A number of parameters to determine the status of a gene and its products can be used including, but not limited to, determining the level of protein expression, the amplification or expression status on RNA level or DNA level, of polynucleotides and of polypeptides, and the analysis of haplotype or the mutation status of the gene. An exemplary determinable property correlated with the status of estrogen receptor or progesterone receptor is the amount of the estrogen receptor or progesterone receptor RNA, DNA or other polypeptide in the sample or the presence of nucleotide polymorphisms.

The term “biological sample”, as used herein, refers to a sample obtained from a patient. The sample may be of any biological tissue or fluid. Such samples include, but are not limited to, sputum, blood, serum, plasma, blood cells (e.g., white cells), tissue, core or fine needle biopsy samples, cell-containing body fluids, free floating nucleic acids, urine, peritoneal fluid, and pleural fluid, or cells there from. Biological samples may also include sections of tissues such as frozen or fixed sections taken for histological purposes or microdissected cells or extracellular parts thereof. A biological sample to be analyzed is tissue material from neoplastic lesion taken by aspiration or punctuation, excision or by any other surgical method leading to biopsy or resected cellular material. Such biological sample may comprise cells obtained from a patient. The cells may be found in a cell “smear” collected, for example, by a nipple aspiration, ductal lavarge, fine needle biopsy or from provoked or spontaneous nipple discharge. In another embodiment, the sample is a body fluid. Such fluids include, for example, blood fluids, serum, plasma, lymph, ascitic fluids, gynecological fluids, or urine but not limited to these fluids.

By “array” or “matrix” is meant an arrangement of addressable locations or “addresses” on a device. The locations can be arranged in two dimensional arrays, three dimensional arrays, or other matrix formats. The number of locations can range from several to at least hundreds of thousands. Most importantly, each location represents a totally independent reaction site. Arrays include but are not limited to nucleic acid arrays, protein arrays and antibody arrays. A “nucleic acid array” refers to an array containing nucleic acid probes, such as oligonucleotides, polynucleotides or larger portions of genes. The nucleic acid on the array is preferably single stranded. Arrays wherein the probes are oligonucleotides are referred to as “oligonucleotide arrays” or “oligonucleotide chips.” A “microarray,” herein also refers to a “biochip” or “biological chip”, an array of regions having a density of discrete regions of at least about 100/cm², and preferably at least about 1000/cm². The regions in a microarray have typical dimensions, e.g., diameters, in the range of between about 10-250 μm, and are separated from other regions in the array by about the same distance. A “protein array” refers to an array containing polypeptide probes or protein probes which can be in native form or denatured. An “antibody array” refers to an array containing antibodies which include but are not limited to monoclonal antibodies (e.g. from a mouse), chimeric antibodies, humanized antibodies or phage antibodies and single chain antibodies as well as fragments from antibodies.

The terms “regulated” or “regulation” as used herein refer to both upregulation [i.e., activation or stimulation (e.g., by agonizing or potentiating] and down regulation [i.e., inhibition or suppression (e.g., by antagonizing, decreasing or inhibiting)].

The term “transcriptome” relates to the set of all messenger RNA (mRNA) molecules, or “transcripts”, produced in one or a population of cells. The term can be applied to the total set of transcripts in a given organism, or to the specific subset of transcripts present in a particular cell type. Unlike the genome, which is roughly fixed for a given cell line (excluding mutations), the transcriptome can vary with external environmental conditions. Because it includes all mRNA transcripts in the cell, the transcriptome reflects the genes that are being actively expressed at any given time, with the exception of mRNA degradation phenomena such as transcriptional attenuation. The discipline of transcriptomics examines the expression level of mRNAs in a given cell population, often using high-throughput techniques based on DNA microarray technology.

The term “expression levels” refers, e.g., to a determined level of gene expression. The term “pattern of expression levels” refers to a determined level of gene expression compared either to a reference gene (e.g. housekeeper or inversely regulated genes) or to a computed average expression value (e.g. in DNA-chip analyses). A pattern is not limited to the comparison of two genes but is more related to multiple comparisons of genes to reference genes or samples. A certain “pattern of expression levels” may also result and be determined by comparison and measurement of several genes disclosed hereafter and display the relative abundance of these transcripts to each other.

Alternatively, a differentially expressed gene disclosed herein may be used in methods for identifying reagents and compounds and uses of these reagents and compounds for the treatment of cancer as well as methods of treatment. The differential regulation of the gene is not limited to a specific cancer cell type or clone, but rather displays the interplay of cancer cells, muscle cells, stromal cells, connective tissue cells, other epithelial cells, endothelial cells of blood vessels as well as cells of the immune system (e.g. lymphocytes, macrophages, killer cells).

A “reference pattern of expression levels”, within the meaning of the invention shall be understood as being any pattern of expression levels that can be used for the comparison to another pattern of expression levels. In a preferred embodiment of the invention, a reference pattern of expression levels is, e.g., an average pattern of expression levels observed in a group of healthy or diseased individuals, serving as a reference group.

“Primer pairs” and “probes”, within the meaning of the invention, shall have the ordinary meaning of this term which is well known to the person skilled in the art of molecular biology. In a preferred embodiment of the invention “primer pairs” and “probes” shall be understood as being polynucleotide molecules having a sequence identical, complementary, homologous, or homologous to the complement of regions of a target polynucleotide which is to be detected or quantified. In yet another embodiment, nucleotide analogues are also comprised for usage as primers and/or probes.

The term “marker” refers to a biological molecule, e.g., a nucleic acid, peptide, protein, hormone, etc., whose presence or concentration can be detected and correlated with a known condition, such as a disease state.

The term “marker gene,” as used herein, refers to a differentially expressed gene whose expression pattern may be utilized as part of a predictive, prognostic or diagnostic process in malignant neoplasia or cancer evaluation, or which, alternatively, may be used in methods for identifying compounds useful for the treatment or prevention of malignant neoplasia and head and neck, colon or breast cancer in particular. A marker gene may also have the characteristics of a target gene.

The term “expression level”, as used herein, relates to the process by which a gene's DNA sequence is converted into functional protein (i.e. ligands) and particularly to the amount of said conversion.

When used in reference to a single-stranded nucleic acid sequence, the term “substantially homologous” refers to any probe that can hybridize (i.e., it is the complement of) the single-stranded nucleic acid sequence under conditions of low stringency as described above.

As used herein, the term “hybridization” is used in reference to the pairing of complementary nucleic acids.

The term “hybridization based method”, as used herein, refers to methods imparting a process of combining complementary, single-stranded nucleic acids or nucleotide analogues into a single double stranded molecule. Nucleotides or nucleotide analogues will bind to their complement under normal conditions, so two perfectly complementary strands will bind to each other readily. In bioanalytics, very often labeled, single stranded probes are in order to find complementary target sequences. If such sequences exist in the sample, the probes will hybridize to said sequences which can then be detected due to the label. Other hybridization based methods comprise microarray and/or biochip methods. Therein, probes are immobilized on a solid phase, which is then exposed to a sample. If complementary nucleic acids exist in the sample, these will hybridize to the probes and can thus be detected. These approaches are also known as “array based methods”. Yet another hybridization based method is PCR, which is described below. When it comes to the determination of expression levels, hybridization based methods may for example be used to determine the amount of mRNA for a given gene.

The term “a PCR based method” as used herein refers to methods comprising a polymerase chain reaction (PCR). This is an approach for exponentially amplifying nucleic acids, like DNA or RNA, via enzymatic replication, without using a living organism. As PCR is an in vitro technique, it can be performed without restrictions on the form of DNA, and it can be extensively modified to perform a wide array of genetic manipulations. When it comes to the determination of expression levels, a PCR based method may for example be used to detect the presence of a given mRNA by (1) reverse transcription of the complete mRNA pool (the so called transcriptome) into cDNA with help of a reverse transcriptase enzyme, and (2) detecting the presence of a given cDNA with help of respective primers. This approach is commonly known as reverse transcriptase PCR (rtPCR)

The term “determining the protein level”, as used herein, refers to methods which allow the quantitative and/or qualitative determination of one or more proteins in a sample. These methods include, among others, protein purification, including ultracentrifugation, precipitation and chromatography, as well as protein analysis and determination, including the use protein microarrays, two-hybrid screening, blotting methods including western blot, one- and two dimensional gelelectrophoresis, isoelectric focusing and the like.

The term “anamnesis” relates to patient data gained by a physician or other healthcare professional by asking specific questions, either of the patient or of other people who know the person and can give suitable information (in this case, it is sometimes called heteroanamnesis), with the aim of obtaining information useful in formulating a diagnosis and providing medical care to the patient. This kind of information is called the symptoms, in contrast with clinical signs, which are ascertained by direct examination.

The term “etiopathology” relates to the course of a disease, that is its duration, its clinical symptoms, and its outcome.

OBJECT OF THE INVENTION

It is one object of the present invention to provide an improved method for the prediction of a response of a tumor in a patient suffering from gynecologic cancer towards a chemotherapeutic agent to current status tests.

The present invention provides new diagnostic criteria for the treatment of gynecologic cancer and an optimal predictive gene signature for different chemotherapeutic agents.

These objects are met with methods and means according to the independent claims of the present invention. The dependent claims are related to preferred embodiments. It is yet to be understood that value ranges delimited by numerical values are to be understood to include the said delimiting values.

SUMMARY OF THE INVENTION

Before the invention is described in detail, it is to be understood that this invention is not limited to the particular component parts of the devices described or process steps of the methods described as such devices and methods may vary. It is also to be understood that the terminology used herein is for purposes of describing particular embodiments only, and is not intended to be limiting. It must be noted that, as used in the specification and the appended claims, the singular forms “a,” “an” and “the” include singular and/or plural referents unless the context clearly dictates otherwise. It is moreover to be understood that, in case parameter ranges are given which are delimited by numeric values, the ranges are deemed to include these limitation values.

According to the invention, a method is provided for predicting a response of a tumor in a patient suffering from or at risk of developing recurrent gynecologic cancer, preferably breast cancer, towards a chemotherapeutic agent. Said method comprises the steps of:

- a) obtaining a biological sample from said patient;
- b) determining the pattern of expression level of at least one gene of the group comprising AKR1C1, MLPH, ESR1, PGR, COMP, DCN, IGKC, CCL5, FBN1 and/or UBE2C, or of genes coregulated therewith, in said sample;
- c) comparing the pattern of expression levels determined in (b) with one or several reference pattern(s) of expression levels;
- d) identifying at least one marker gene;
- e) determining a molecular subtype for said sample on the basis of (d); and
- f) predicting from said molecular subtype response of a tumor for a chemotherapeutic agent,
  
  wherein the molecular subtype is selected from the group comprising the subtypes basal, stromal-high, stromal-low, lu-minal A, immune system-high, immune system-low, proliferation-high and/or proliferation-low.

The molecular subtypes are divided into said groups based on the gene expression of the tumor.

It is vital to the present invention that, in addition to the genes enumerated under item b), one or more genes coregulated with at least one of these genes may be used in addition, or in replacement, of the said genes. Replacement of the said genes with one or more genes coregulated therewith is advantageous in cases, where determining the expression level of the genes enumerated under item b) is critical. Further, an additional use of coregulated genes increases the specificity of said method.

Such genes may be selected from the following table.

TABLE 1

Coregulated genes of Interest

Probeset

ID
Pearson r
Gene Name
Description

218211_s_at
1
MLPH
melanophilin

204667_at
0.874
FOXA1
forkhead box A1

209173_at
0.838
AGR2
anterior gradient 2 homolog

(Xenepus laevis)

209604_s_at
0.819
GATA3
GATA binding protein 3

214404_x_at
0.814
PDEF
prostate epithelium-specific Ets

transcription factor

213441_x_at
0.807
PDEF
prostate epithelium-specific Ets

transcription factor

215867_x_at
0.802

adaptor-related protein complex

1, gamma 1 subunit

220192_x_at
0.802
PDEF
prostate epithelium-specific Ets

transcription factor

209603_at
0.801
GATA3
GATA binding protein 3

200670_at
0.796
XBP1
X-box binding protein 1

203963_at
0.794
CA12
carbonic anhydrase XII

214164_x_at
0.791
FLJ20151
hypothetical protein FLJ20151

212956_at
0.782
KIAA0882
KIAA0882 protein

204508_s_at
0.766
FLJ20151
hypothetical protein FLJ20151

209602_s_at
0.763
GATA3
GATA binding protein 3

205225_at
0.761
ESR1
estrogen receptor 1

204623_at
0.759
TFF3
trefoil factor 3 (intestinal)

203453_at
0.748
SCNN1A
sodium channel, nonvoltage-gated

1 alpha

212099_at
0.747

Human HepG2 3′ region cDNA,

clone hmd1f06.

209459_s_at
0.739
NPD009
NPD009 protein

201596_x_at
0.736
KRT18
keratin 18

209696_at
0.736
FBP1
fructose-1,6-bisphosphatase 1

218471_s_at
0.73
BBS1
Bardet-Biedl syndrome 1

218966_at
0.728
MYO5C
myosin 5C

51158_at
0.728

Homo sapiens, clone

IMAGE: 4816940, mRNA

218313_s_at
0.724
GALNT7
UDP-N-acetyl-alpha-D-

galactosamine:polypeptide

N-

acetylgalactosaminyltransferase

7 (GalNAc-T7)

211621_at
0.723
AR
androgen receptor (dihydrotestosterone

receptor;

testicular feminization; spinal

and bulbar muscular

atrophy; Kennedy disease)

212692_s_at
0.723
LRBA
LPS-responsive vesicle trafficking,

beach and anchor

containing

216092_s_at
0.721
SLC7A8
solute carrier family 7 (cationic

amino acid transporter,

y+ system), member 8

200810_s_at
0.716
CIRBP
cold inducible RNA binding protein

215304_at
0.711

Human clone 23948 mRNA sequence

205009_at
0.701
TFF1
trefoil factor 1 (breast cancer,

estrogen-inducible

sequence expressed in)

205225_at
1
ESR1
estrogen receptor 1

209603_at
0.85
GATA3
GATA binding protein 3

209604_s_at
0.844
GATA3
GATA binding protein 3

212956_at
0.834
KIAA0882
KIAA0882 protein

215867_x_at
0.828

adaptor-related protein complex

1, gamma 1 subunit

203963_at
0.826
CA12
carbonic anhydrase XII

214164_x_at
0.822
FLJ20151
hypothetical protein FLJ20151

209602_s_at
0.814
GATA3
GATA binding protein 3

202088_at
0.795
LIV-1
LIV-1 protein, estrogen regulated

215304_at
0.795

Human clone 23948 mRNA sequence

218195_at
0.786
FLJ12910
hypothetical protein FLJ12910

204508_s_at
0.781
FLJ20151
hypothetical protein FLJ20151

218211_s_at
0.761
MLPH
melanophilin

212496_s_at
0.754
KIAA0876
KIAA0876 protein

204667_at
0.752
FOXA1
forkhead box A1

212960_at
0.75
KIAA0882
KIAA0882 protein

209459_s_at
0.742
NPD009
NPD009 protein

214053_at
0.74

Homo sapiens clone 23736 mRNA

sequence

205186_at
0.737
DNALI1
dynein, axonemal, light intermediate

polypeptide 1

209173_at
0.736
AGR2
anterior gradient 2 homolog

(Xenepus laevis)

200670_at
0.731
XBP1
X-box binding protein 1

203929_s_at
0.731
MAPT
microtubule-associated protein

tau

41660_at
0.731
CELSR1
cadherin, EGF LAG seven-pass G-

type receptor 1

(flamingo homolog, Drosophila)

211712_s_at
0.73
ANXA9
annexin A9

219197_s_at
0.721
SCUBE2
signal peptide, CUB domain, EGF-

like 2

218976_at
0.718
JDP1
J domain containing protein 1

212195_at
0.712

Homo sapiens mRNA; cDNA

DKFZp564F053

(from clone DKFZp564F053)

205471_s_at
0.706
DACH
dachshund homolog (Drosophila)

205009_at
0.703
TFF1
trefoil factor 1 (breast cancer,

estrogen-inducible

sequence expressed in)

205713_s_at
1
COMP
cartilage oligomeric matrix protein

(pseudoachondroplasia,

epiphyseal deysplasia 1, multiple)

201792_at
0.741
AEBP1
AE binding protein 1

205422_s_at
0.74
ITGBL1
integrin, beta-like 1 (with EGF-

like repeat domains)

213909_at
0.723

Homo sapiens cDNA FLJ12280 fis,

clone MAMMA1001744.

217428_s_at
0.716
COL10A1
—

205941_s_at
0.715
COL10A1
collagen, type X, alpha 1(Schmid

metaphyseal

chondrodysplasia)

209758_s_at
0.712
MAGP2
Microfibril-associated glycoprotein-2

213764_s_at
0.706

Homo sapiens mRNA; cDNA

DKFZp666A038

(from clone DKFZp666A038)

209335_at
1
DCN
decorin

212764_at
0.846
TCF8
transcription factor 8

(represses interleukin 2 expression)

201744_s_at
0.826
LUM
lumican

213891_s_at
0.824

Homo sapiens cDNA FLJ11918 fis,

clone HEMBB1000272.

204619_s_at
0.82
CSPG2
chondroitin sulfate proteoglycan

2 (versican)

207173_x_at
0.812
CDH11
cadherin 11, type 2, OB-cadherin

(osteoblast)

203131_at
0.81
PDGFRA
platelet-derived growth factor

receptor,

alpha polypeptide

211161_s_at
0.809

—

202450_s_at
0.807
CTSK
cathepsin K (pycnodysostosis)

206101_at
0.806
ECM2
extracellular matrix protein 2,

female organ and adipocyte specific

202766_s_at
0.805
FBN1
fibrillin 1 (Marfan syndrome)

213800_at
0.804
HF1
H factor 1 (complement)

221541_at
0.802
DKFZP434B044
hypothetical protein

DKFZp434B044

212667_at
0.798
SPARC
secreted protein, acidic, cysteine-

rich (osteonectin)

201893_x_at
0.797
DCN
decorin

213994_s_at
0.794
SPON1
spondin 1, (f-spondin) extracellular

matrix protein

209550_at
0.792
NDN
necdin homolog (mouse)

201069_at
0.791
MMP2
matrix metalloproteinase 2 (gelatinase

A,

72 kDa gelatinase, 72 kDa type IV

collagenase)

205407_at
0.788
RECK
reversion-inducing-cysteine-rich

protein

with kazal motifs

202994_s_at
0.783
E46L;
fibulin 1

DKFZP586H2219

201185_at
0.781
PRSS11
protease, serine, 11 (IGF binding)

217525_at
0.778

Homo sapiens, Similar to HNOEL-

iso protein,

clone IMAGE: 5229159, mRNA

221731_x_at
0.767
CSPG2
chondroitin sulfate proteoglycan

2 (versican)

213429_at
0.765

Homo sapiens mRNA; cDNA

DKFZp564B222

(from clone DKFZp564B222)

219087_at
0.764
ASPN
asporin (LRR class 1)

213068_at
0.758
DPT
dermatopontin

221729_at
0.754
COL5A2
collagen, type V, alpha 2

204359_at
0.752
FLRT2
fibronectin leucine rich transmembrane

protein 2

215076_s_at
0.749
COL3A1
collagen, type III, alpha 1

(Ehlers-Danlos syndrome type IV,

autosomal dominant)

212386_at
0.747

Homo sapiens cDNA FLJ11918 fis,

clone HEMBB1000272.

205422_s_at
0.744
ITGBL1
integrin, beta-like 1 (with EGF-

like repeat domains)

212865_s_at
0.744
COL14A1
collagen, type XIV, alpha 1 (undulin)

205907_s_at
0.742
OMD
osteomodulin

211896_s_at
0.741
DCN
decorin

212419_at
0.741
FLJ90798
hypothetical protein FLJ90798

206580_s_at
0.739
EFEMP2
EGF-containing fibulin-like extracellular

matrix

protein 2

201505_at
0.737
LAMB1
laminin, beta 1

203083_at
0.735
THBS2
thrombospondin 2

209596_at
0.735
DKFZp564I1922
adlican

212813_at
0.735
JAM3
junctional adhesion molecule 3

204052_s_at
0.732
SFRP4
secreted frizzled-related protein 4

221447_s_at
0.732
LOC83468
gycosyltransferase

205226_at
0.731
PDGFRL
platelet-derived growth factor

receptor-like

209687_at
0.731

chemokine (C—X—C motif) ligand

12

(stromal cell-derived factor 1)

201438_at
0.73
COL6A3
collagen, type VI, alpha 3

204620_s_at
0.728
CSPG2
chondroitin sulfate proteoglycan

2 (versican)

213071_at
0.725
DPT
dermatopontin

205559_s_at
0.724
PCSK5
proprotein convertase subtilisin/

kexin type 5

210517_s_at
0.724
AKAP12
A kinase (PRKA) anchor protein

(gravin) 12

209436_at
0.721
SPON1
spondin 1, (f-spondin) extracellular

matrix protein

219778_at
0.721
ZFPM2
zinc finger protein, multitype 2

204955_at
0.72
SRPX
sushi-repeat-containing protein,

X chromosome

213652_at
0.72

Homo sapiens cDNA FLJ13034 fis,

clone NT2RP3001232.

202404_s_at
0.719
COL1A2
collagen, type I, alpha 2

209496_at
0.719
RARRES2
retinoic acid receptor responder

(tazarotene induced) 2

213069_at
0.719
KIAA1237
KIAA1237 protein

212077_at
0.718
CALD1
caldesmon 1

202202_s_at
0.717
LAMA4
laminin, alpha 4

218656_s_at
0.717
LHFP
lipoma HMGIC fusion partner

204457_s_at
0.716
GAS1
growth arrest-specific 1

201163_s_at
0.715
IGFBP7
insulin-like growth factor binding

protein 7

204682_at
0.711
LTBP2
latent transforming growth factor

beta binding protein 2

214247_s_at
0.71
RIG
regulated in glioma

202283_at
0.705
SERPINF1
serine (or cysteine) proteinase

Inhibitor, clade F

(alpha-2 antiplasmin, pigment

epithelium derived

factor), member 1

222288_at
0.704

ESTs, Weakly similar to hypothetical

protein

FLJ20489 [Homo sapiens]

[H. sapiens]

204517_at
0.702
PPIC
peptidylprolyl isomerase C

(cyclophilin C)

219773_at
0.702
NOX4
NADPH oxidase 4

214927_at
0.701

Homo sapiens cDNA FLJ90655 fis,

clone PLACE1004630.

214669_x_at
1
IGKC
immunoglobulin kappa constant

214836_x_at
0.952
IGKC
immunoglobulin kappa constant

211644_x_at
0.948
IGKC
immunoglobulin kappa constant

215121_x_at
0.945
IGLJ3
immunoglobulin lambda joining 3

215176_x_at
0.943
IGKC
immunoglobulin kappa constant

215379_x_at
0.939
IGLJ3
immunoglobulin lambda joining 3

211645_x_at
0.93
IGKC
immunoglobulin kappa constant

209138_x_at
0.926
IGLJ3
immunoglobulin lambda joining 3

211643_x_at
0.924
IGKC
immunoglobulin kappa constant

221651_x_at
0.92
IGKC
immunoglobulin kappa constant

216576_x_at
0.918

—

216207_x_at
0.916
IGKV1D-13
immunoglobulin kappa variable

1D-13

221671_x_at
0.915
IGKC
immunoglobulin kappa constant

217157_x_at
0.913

immunoglobulin kappa constant

217148_x_at
0.891
IGLJ3
immunoglobulin lambda joining 3

214677_x_at
0.888
IGLJ3
immunoglobulin lambda joining 3

215946_x_at
0.887
LOC91316
similar to bK246H3.1 (immunoglobulin

lambda-like

polypeptide 1, pre-B-cell specific)

217378_x_at
0.887
IGKV1OR2-
—

108; IGO1;

IGKV1OR2108;

IGKV1/OR2-

108

214768_x_at
0.885
IGKC
immunoglobulin kappa constant

214777_at
0.883
IGKC
immunoglobulin kappa constant

217480_x_at
0.882
IGKV1OR15-
—

118;

IGKVP2;

IGKV1OR118;

IGKV1/OR-

118;

IGKV1/OR15-

118

216401_x_at
0.879
IGKV
—

211430_s_at
0.877
IGHG3
immunoglobulin heavy constant

gamma 3

(G3m marker)

213502_x_at
0.872
IGLL3;
similar to bK246H3.1 (immunoglobulin

16.1
lambda-like

polypeptide 1, pre-B-cell specific)

216984_x_at
0.867
IGLJ3
immunoglobulin lambda joining 3

211798_x_at
0.852
IGLJ3
immunoglobulin lambda joining 3

209374_s_at
0.845
IGHM
immunoglobulin heavy constant mu

215214_at
0.834
IGL@
immunoglobulin lambda locus

211634_x_at
0.83
IGHM
immunoglobulin heavy constant

gamma 3 (G3m marker)

217281_x_at
0.828
IGHG3
immunoglobulin heavy constant

gamma 3 (G3m marker)

205267_at
0.819
POU2AF1
POU domain, class 2, associating

factor 1

211881_x_at
0.819
IGLJ3
immunoglobulin lambda joining 3

217258_x_at
0.819

—

217179_x_at
0.817
IGL@
immunoglobulin lambda locus

217235_x_at
0.808
IGLJ3
immunoglobulin lambda joining 3

216365_x_at
0.796

immunoglobulin lambda joining 3

217227_x_at
0.792
IGL@
immunoglobulin lambda locus

212592_at
0.791
IGJ
immunoglobulin J polypeptide,

linker protein for

immunoglobulin alpha and mu polypeptides

216560_x_at
0.786

immunoglobulin lambda locus

206641_at
0.785
TNFRSF17
tumor necrosis factor receptor

superfamily, member 17

211868_x_at
0.784

—

211637_x_at
0.78
IGHM
immunoglobulin heavy constant mu

214973_x_at
0.78
IGVH3
immunoglobulin heavy constant

gamma 3 (G3m marker)

216491_x_at
0.773
V4-4
immunoglobulin heavy constant mu

211635_x_at
0.761
IGHM
immunoglobulin heavy constant

gamma 3 (G3m marker)

217022_s_at
0.761
IGHM
hypothetical protein MGC27165

216510_x_at
0.75
IgH VH
—

216853_x_at
0.75
IGLJ3
immunoglobulin lambda joining 3

211650_x_at
0.746
IGHM
immunoglobulin heavy constant mu

211908_x_at
0.745
IGHM
immunoglobulin heavy constant mu

212311_at
0.731
KIAA0746
KIAA0746 protein

207734_at
0.729
LAX
hypothetical protein FLJ20340

210915_x_at
0.718
TRB@
T cell receptor beta locus

211633_x_at
0.712
ICAP-1A
integrin cytoplasmic domain-

associated protein 1

206150_at
0.701
TNFRSF7
tumor necrosis factor receptor

superfamily, member 7

216557_x_at
0.701
A1VH3
—

213193_x_at
0.7
TRB@
T cell receptor beta locus

1405_i_at
1
CCL5
chemokine (C-C motif) ligand 5

204655_at
0.923
CCL5
chemokine (C-C motif) ligand 5

205831_at
0.853
CD2
CD2 antigen (p50), sheep red

blood cell receptor

213539_at
0.847
CD3D
CD3D antigen, delta polypeptide

(TiT3 complex)

205569_at
0.808
LAMP3
lysosomal-associated membrane

protein 3

213193_x_at
0.807
TRB@
T cell receptor beta locus

210915_x_at
0.806
TRB@
T cell receptor beta locus

204891_s_at
0.799
LCK
lymphocyte-specific protein tyrosine

kinase

202270_at
0.797
GBP1
guanylate binding protein 1, interferon-

inducible, 67 kDa

211796_s_at
0.79
TRB@
T cell receptor beta locus

206666_at
0.789
GZMK
granzyme K (serine protease,

granzyme 3; tryptase II)

204912_at
0.786
IL10RA
interleukin 10 receptor, alpha

203915_at
0.783
CXCL9
chemokine (C—X—C motif) ligand 9

212588_at
0.781
PTPRC
protein tyrosine phosphatase,

receptor type, C

205798_at
0.769
IL7R
interleukin 7 receptor

204533_at
0.768
CXCL10
chemokine (C—X—C motif) ligand

10

202269_x_at
0.767
GBP1
guanylate binding protein 1, interferon-

inducible, 67 kDa

203416_at
0.766
CD53
CD53 antigen

203879_at
0.766
PIK3CD
phosphoinositide-3-kinase, catalytic,

delta polypeptide

38149_at
0.766
KIAA0053
KIAA0053 gene product

205488_at
0.762
GZMA
granzyme A (granzyme 1,

cytotoxic T-lymphocyte-

associated serine esterase 3)

211368_s_at
0.76
CASP1
caspase 1, apoptosis-related

cysteine protease

(interleukin 1, beta, convertase)

204882_at
0.756
KIAA0053
KIAA0053 gene product

200905_x_at
0.755
HLA-E
major histocompatibility complex,

class I, E

204118_at
0.753
CD48
CD48 antigen (B-cell membrane

protein)

204279_at
0.752
PSMB9
proteasome (prosome, macropain)

subunit, beta type,

9 (large multifunctional protease

2)

219386_s_at
0.752
BLAME
B lymphocyte activator macrophage

expressed

209083_at
0.75
CORO1A
coronin, actin binding protein,

1A

209670_at
0.75
TRA@
T cell receptor alpha locus

64064_at
0.75
IAN4L1
immune associated nucleotide 4

like 1 (mouse)

214567_s_at
0.749
XCL1
chemokine (C motif) ligand 2

210031_at
0.746
CD3Z
CD3Z antigen, zeta polypeptide

(TiT3 complex)

213603_s_at
0.746
RAC2
ras-related C3 botulinum toxin

substrate 2 (rho family,

small GTP binding protein Rac2)

207651_at
0.744
H963
platelet activating receptor homolog

202644_s_at
0.743
TNFAIP3
tumor necrosis factor, alpha-

induced protein 3

205890_s_at
0.743
UBD
ubiquitin D

209970_x_at
0.743
CASP1
caspase 1, apoptosis-related

cysteine protease

(interleukin 1, beta, convertase)

204198_s_at
0.74
RUNX3
runt-related transcription factor 3

211339_s_at
0.74
ITK
IL2-inducible T-cell kinase

210116_at
0.738
SH2D1A
SH2 domain protein 1A, Duncan's

disease

(lymphoproliferative syndrome)

34210_at
0.738
CDW52
CDW52 antigen (CAMPATH-1 antigen)

206337_at
0.735
CCR7
chemokine (C-C motif) receptor 7

204205_at
0.732
APOBEC3G
apolipoprotein B mRNA editing

enzyme,

catalytic polypeptide-like 3G

212314_at
0.732
KIAA0746
KIAA0746 protein

202307_s_at
0.73
TAP1
transporter 1, ATP-binding cassette,

sub-family B (MDR/TAP)

202524_s_at
0.729
SPOCK2
sparc/osteonectin, cwcv and kazal-

like domains

proteoglycan (testican) 2

205159_at
0.728
CSF2RB
colony stimulating factor 2 receptor,

beta, low-affinity

(granulocyte-macrophage)

205269_at
0.728
LCP2
lymphocyte cytosolic protein 2

(SH2 domain containing

leukocyte protein of 76 kDa)

207339_s_at
0.728
LTB
lymphotoxin beta (TNF super-

family, member 3)

213975_s_at
0.728
LYZ
lysozyme (renal amyloidosis)

204820_s_at
0.726
BTN3A3
butyrophilin, subfamily 3, member

A3

210164_at
0.726
GZMB
granzyme B (granzyme 2, cytotoxic

T-lymphocyte-associated serine

esterase 1)

203508_at
0.724
TNFRSF1B
tumor necrosis factor receptor

superfamily, member 1B

206134_at
0.722
ADAMDEC1
ADAM-like, decysin 1

200904_at
0.72
HLA-E
major histocompatibility complex,

class I, E

204834_at
0.72
FGL2
fibrinogen-like 2

205821_at
0.72
D12S2489E
DNA segment on chromosome 12

(unique) 2489

expressed sequence

213915_at
0.72
NKG7
natural killer cell group 7 sequence

201859_at
0.716
PRG1
proteoglycan 1, secretory granule

204116_at
0.716
IL2RG
interleukin 2 receptor,

gamma (severe combined immunodeficiency)

210163_at
0.714
CXCL11
chemokine (C—X—C motif) ligand

11

201487_at
0.712
CTSC
cathepsin C

204220_at
0.712
GMFG
glia maturation factor, gamma

205671_s_at
0.711
HLA-DOB
major histocompatibility complex,

class II, DO beta

204057_at
0.71
ICSBP1
interferon consensus sequence

binding protein 1

210538_s_at
0.709
BIRC3
baculoviral IAP repeat-

containing 3

213888_s_at
0.709
ADORA2BP
—

206978_at
0.707
CCR2
chemokine (C-C motif) receptor 2

206715_at
0.705
TFEC
transcription factor EC

207375_s_at
0.705
IL15RA
interleukin 15 receptor, alpha

203868_s_at
0.702
VCAM1
vascular cell adhesion molecule 1

211742_s_at
0.702
EVI2B
ecotropic viral integration site

2B

205267_at
0.701
POU2AF1
POU domain, class 2, associating

factor 1

211795_s_at
0.701
FYB
FYN binding protein (FYB-

120/130)

202766_s_at
1
FBN1
fibrillin 1 (Marfan syndrome)

221731_x_at
0.876
CSPG2
chondroitin sulfate proteoglycan

2 (versican)

221729_at
0.871
COL5A2
collagen, type V, alpha 2

204620_s_at
0.848
CSPG2
chondroitin sulfate proteoglycan

2 (versican)

201438_at
0.845
COL6A3
collagen, type VI, alpha 3

211161_s_at
0.845

—

207173_x_at
0.84
CDH11
cadherin 11, type 2, OB-cadherin

(osteoblast)

204619_s_at
0.836
CSPG2
chondroitin sulfate proteoglycan

2 (versican)

215446_s_at
0.836
LOX
lysyl oxidase

212667_at
0.828
SPARC
secreted protein, acidic, cysteine-

rich (osteonectin)

215076_s_at
0.827
COL3A1
collagen, type III, alpha 1

(Ehlers-Danlos syndrome

type IV, autosomal dominant)

202404_s_at
0.823
COL1A2
collagen, type I, alpha 2

202450_s_at
0.823
CTSK
cathepsin K (pycnodysostosis)

212488_at
0.82
COL5A1
collagen, type V, alpha 1

212764_at
0.819
TCF8
transcription factor 8 (re-

presses interleukin 2 expression)

221541_at
0.816
DKFZP434B044
hypothetical protein

DKFZp434B044

213790_at
0.815

Homo sapiens cDNA FLJ31066 fis,

clone HSYRA2001153.

221730_at
0.815
COL5A2
collagen, type V, alpha 2

202403_s_at
0.812
COL1A2
collagen, type I, alpha 2

201792_at
0.805
AEBP1
AE binding protein 1

209335_at
0.805
DCN
decorin

203083_at
0.803
THBS2
thrombospondin 2

219087_at
0.8
ASPN
asporin (LRR class 1)

201185_at
0.798
PRSS11
protease, serine, 11 (IGF binding)

201852_x_at
0.798
COL3A1
collagen, type III, alpha 1

(Ehlers-Danlos syndrome

type IV, autosomal dominant)

209955_s_at
0.793
FAP
fibroblast activation protein,

alpha

213909_at
0.791

Homo sapiens cDNA FLJ12280 fis,

clone MAMMA1001744.

200665_s_at
0.79
SPARC
secreted protein, acidic, cysteine-

rich (osteonectin)

201744_s_at
0.781
LUM
lumican

201069_at
0.778
MMP2
matrix metalloproteinase 2 (gelatinase

A, 72 kDa

gelatinase, 72 kDa type IV collagenase)

209596_at
0.771
DKFZp564I1922
adlican

213891_s_at
0.771

Homo sapiens cDNA FLJ11918 fis,

clone HEMBB1000272.

208782_at
0.77
FSTL1
follistatin-like 1

219773_at
0.769
NOX4
NADPH oxidase 4

203325_s_at
0.767
COL5A1
collagen, type V, alpha 1

204114_at
0.767
NID2
nidogen 2 (osteonidogen)

205941_s_at
0.766
COL10A1
collagen, type X, alpha 1(Schmid

metaphyseal

chondrodysplasia)

201505_at
0.765
LAMB1
laminin, beta 1

202310_s_at
0.762
COL1A1
collagen, type I, alpha 1

213994_s_at
0.762
SPON1
spondin 1, (f-spondin) extracellular

matrix protein

219179_at
0.762
DACT1
dapper homolog 1, antagonist of

beta-catenin (xenopus)

202283_at
0.758
SERPINF1
serine (or cysteine) proteinase

inhibitor, clade F

(alpha-2 antiplasmin,

pigment epithelium derived factor),

member 1

217428_s_at
0.758
COL10A1
—

221447_s_at
0.757
LOC83468
glycosyltransferase

213069_at
0.755
KIAA1237
KIAA1237 protein

201893_x_at
0.752
DCN
decorin

210809_s_at
0.752
OSF-2
osteoblast specific factor 2

(fasciclin I-like)

212489_at
0.751
COL5A1
collagen, type V, alpha 1

213001_at
0.748
ANGPTL2
angiopoietin-like 2

205422_s_at
0.743
ITGBL1
integrin, beta-like 1 (with EGF-

like repeat domains)

209436_at
0.743
SPON1
spondin 1, (f-spondin) extracellular

matrix protein

221900_at
0.743
COL8A2
collagen, type VIII, alpha 2

222288_at
0.743

ESTs, Weakly similar to hypothetical

protein FLJ20489

[Homo sapiens] [H. sapiens]

212386_at
0.742

Homo sapiens cDNA FLJ11918 fis,

clone HEMBB1000272.

202952_s_at
0.741
ADAM12
a disintegrin and metalloproteinase

domain 12

(meltrin alpha)

209496_at
0.739
RARRES2
retinoic acid receptor responder

(tazarotene induced) 2

201261_x_at
0.734
BGN
biglycan

200897_s_at
0.733
KIAA0992
palladin

201431_s_at
0.73
DPYSL3
dihydropyrimidinase-like 3

205499_at
0.728
SRPUL
sushi-repeat protein

219778_at
0.727
ZFPM2
zinc finger protein, multitype 2

37408_at
0.725
KIAA0709
mannose receptor, C type 2

211896_s_at
0.724
DCN
decorin

202994_sat
0.723
E46L;
fibulin 1

DKFZP586H2219

213004_at
0.723
ANGPTL2
angiopoietin-like 2

202311_s_at
0.722

collagen, type I, alpha 1

209210_s_at
0.721
MIG2
mitogen inducible 2

209758_s_at
0.721
MAGP2
Microfibril-associated glycoprotein-2

203131_at
0.719
PDGFRA
platelet-derived growth factor

receptor,

alpha polypeptide

213764_s_at
0.717

Homo sapiens mRNA; cDNA

DKFZp666A038

(from clone DKFZp666A038)

202202_s_at
0.714
LAMA4
laminin, alpha 4

213765_at
0.711

Homo sapiens mRNA; cDNA

DKFZp666A038

(from clone DKFZp666A038)

206580_s_at
0.71
EFEMP2
EGF-containing fibulin-like extracellular

matrix protein 2

212077_at
0.71
CALD1
caldesmon 1

213068_at
0.71
DPT
dermatopontin

204464_s_at
0.709
EDNRA
endothelin receptor type A

206101_at
0.709
ECM2
extracellular matrix protein 2,

female organ and adipocyte specific

209550_at
0.707
NDN
necdin homolog (mouse)

203886_s_at
0.705
FBLN2
fibulin 2

202363_at
0.704
SPOCK
sparc/osteonectin, cwcv and kazal-

like domains

proteoglycan (testican)

202238_s_at
0.702
NNMT
nicotinamide N-methyltransferase

205226_at
0.702
PDGFRL
platelet-derived growth factor

receptor-like

202954_at
1
UBE2C
ubiquitin-conjugating enzyme E2C

209408_at
0.84
KNSL6
kinesin family member 2C

202705_at
0.835
CCNB2
cyclin B2

202870_s_at
0.832
CDC20
CDC20 cell division cycle 20 homolog

(S. cerevisiae)

208079_s_at
0.831
STK6
serine/threonine kinase 6

210052_s_at
0.831
C20orf1
chromosome 20 open reading frame 1

204092_s_at
0.825
STK6
serine/threonine kinase 6

202580_x_at
0.819
FOXM1
forkhead box M1

214710_s_at
0.819
CCNB1
cyclin B1

204962_s_at
0.818
CENPA
centromere protein A, 17 kDa

38158_at
0.815
KIAA0165
extra spindle poles like 1 (S. cerevisiae)

218542_at
0.811
C10orf3
chromosome 10 open reading frame 3

202095_s_at
0.808
BIRC5
baculoviral IAP repeat-

containing 5 (survivin)

218009_s_at
0.808
PRC1
protein regulator of cytokinesis 1

203554_x_at
0.803
PTTG1
pituitary tumor-transforming 1

218355_at
0.803
KIF4A
kinesin family member 4A

203764_at
0.798
DLG7
discs, large homolog 7 (Drosophila)

207828_s_at
0.798
CENPF
centromere protein F, 350/400ka

(mitosin)

221520_s_at
0.795
FLJ10468
hypothetical protein FLJ10468

218039_at
0.79
ANKT
nucleolar protein ANKT

209642_at
0.787
BUB1
BUB1 budding uninhibited by benzimidazoles 1

homolog (yeast)

204825_at
0.781
MELK
maternal embryonic leucine zipper

kinase

219918_s_at
0.78
ASPM
asp (abnormal spindle)-like, microcephaly

associated

(Drosophila)

205046_at
0.771
CENPE
centromere protein E, 312 kDa

218726_at
0.771
DKFZp762E1312
hypothetical protein

DKFZp762E1312

204033_at
0.77
TRIP13
thyroid hormone receptor interactor

13

218115_at
0.765
ASF1B
anti-silencing function 1B

218755_at
0.765
KIF20A
kinesin family member 20A

222077_s_at
0.755
RACGAP1
Rac GTPase activating protein 1

204026_s_at
0.753
ZWINT
ZW10 interactor

204641_at
0.752
NEK2
NIMA (never in mitosis gene a)-

related kinase 2

203145_at
0.749
SPAG5
sperm associated antigen 5

203755_at
0.749
BUB1B
BUB1 budding uninhibited by benzimidazoles 1

homolog beta (yeast)

209773_s_at
0.745
RRM2
ribonucleotide reductase M2 polypeptide

212949_at
0.743
BRRN1
barren homolog (Drosophila)

206102_at
0.741
KIAA0186
KIAA0186 gene product

222039_at
0.738
LOC146909
hypothetical protein LOC146909

202107_s_at
0.736
MCM2
MCM2 minichromosome maintenance

deficient 2,

mitotin (S. cerevisiae)

204267_x_at
0.736
PKMYT1
membrane-associated tyrosine-

and threonine-specific

cdc2-inhibitory kinase

201710_at
0.735
MYBL2
v-myb myeloblastosis viral oncogene

homolog

(avian)-like 2

206364_at
0.733
KIF14
kinesin family member 14

218308_at
0.731
TACC3
transforming, acidic coiled-coil

containing protein 3

221436_s_at
0.73
TOME-1
likely ortholog of mouse gene

rich cluster, C8 gene

204170_s_at
0.729
CKS2
CDC28 protein kinase regulatory

subunit 2

205436_s_at
0.725
H2AFX
H2A histone family, member X

207165_at
0.722
HMMR
hyaluronan-mediated motility receptor

(RHAMM)

204603_at
0.721
EXO1
exonuclease 1

218663_at
0.719
HCAP-G
chromosome condensation protein G

203362_s_at
0.718
MAD2L1
MAD2 mitotic arrest deficient-

like 1 (yeast)

218662_s_at
0.718
HCAP-G
chromosome condensation protein G

204649_at
0.716
TROAP
trophinin associated protein

(tastin)

212022_s_at
0.714
MKI67
antigen identified by monoclonal

antibody Ki-67

213226_at
0.714
PMSCL1
polymyositis/scleroderma autoantigen

1, 75 kDa

219105_x_at
0.714
ORC6L
origin recognition complex,

subunit 6 homolog-like (yeast)

201890_at
0.713
RRM2
ribonucleotide reductase M2 polypeptide

219148_at
0.713
TOPK
T-LAK cell-originated protein

kinase

210559_s_at
0.711
CDC2
cell division cycle 2, G1 to S

and G2 to M

203358_s_at
0.71
EZH2
enhancer of zeste homolog 2

(Drosophila)

204822_at
0.706
TTK
TTK protein kinase

205024_s_at
0.705
RAD51
RAD51 homolog (RecA homolog, E. coli)

(S. cerevisiae)

204444_at
0.704
KIF11
kinesin family member 11

203213_at
0.703
CDC2
cell division cycle 2, G1 to S

and G2 to M

219306_at
0.702
KNSL7
kinesin-like 7

204151_x_at
1
AKR1C1
aldo-keto reductase family 1,

member C1

(dihydrodiol dehydrogenase 1;

20-alpha (3-alpha)-

hydroxysteroid dehydrogenase)

209699_x_at
0.954
AKR1C2
aldo-keto reductase family 1,

member C2

(dihydrodiol dehydrogenase 2;

bile acid binding protein;

3-alpha hydroxysteroid dehydrogenase,

type III)

211653_x_at
0.934
AKR1C-
pseudo-chlordecone reductase

pseudo

216594_x_at
0.957
AKR1C1
aldo-keto reductase family 1,

member C1

(dihydrodiol dehydrogenase 1;

20-alpha (3-alpha)-

hydroxysteroid dehydrogenase)

However, other genes not mentioned in the above table, which are yet coregulated with at least one of the genes enumerated under item b), do also fall under the scope of the present invention. The teaching presented herein will make it obvious to the person skilled in the art to find such coregulated genes in literature, in databases or the like, without the need of inventive step.

In a preferred embodiment, said method comprises the further steps of

- g) selecting at least one chemotherapeutic agent which is predicted to be successful,
  
  wherein the chemotherapeutic agent is selected from the group comprising Epirubicin, Paclitaxel, 5-Fluorouracil and/or Carboplatin.

The chemotherapeutics may be selected from the group consisting of Cyclophosphamid (Endoxan®, Cyclostin®). Adriamycin (Doxorubicin) (Adriblastin®), BCNU (Carmustin) (Carmubris®), Busulfan (Myleran®), Bleomycin (Bleomycin®), Carboplatin (Carboplat®), Chlorambucil (Leukeran®), Cis-Platin (Cisplatin®), Platinex (Platiblastin®), Dacarbazin (DTIC®; Detimedac®), Docetaxel (Taxotere®), Epirubicin (Farmorubicin®), Etoposid (Vepesid®), 5-Fluorouracil (Fluroblastin®, Fluorouracil®), Gemcitabin (Gemzar®), Ifosfamid (Holoxan®), Interferon alpha (Roferon®), Irinotecan (CPT 11, Campto®), Melphalan (Alkeran®), Methotrexat (Methotrexat®, Farmitrexat®), Mitomycin C (Mitomycin®), Mitoxantron (Novantron®), Oxaliplatin (Eloxatine®), Paclitaxel (Taxol®), Prednimustin (Sterecyt®), Procarbazin (Natulan®), Ralitrexed (Tomudex®), Trofosfamid (Ixoten®), Vinblastin (Velbe®), Vincristin (Vincristin®), Vindesin (Eldisine®), Vinorelbin (Navelbine®).

In a first step for the identification of an optimal predictive gene signature for single chemotherapeutic agents four different molecular subtypes (basal, luminal A, stromal-high and stromal-low) whose tumors differ in their response towards chemotherapy have been identified via the identified marker genes MLPH, ESR1, PGR and COMP.

The basal subtype is

- hormone receptor negative,
- progesterone receptor negative,
- Her2 receptor negative,
- sensitive towards most of the chemotherapeutic agents, and
- exhibits the gene MLPH as differentially expressed gene (down-regulated) and marker gene.

The luminal A subtype is

- resistant towards most of the chemotherapeutic agents, and
- exhibits the genes ESR1 and PGR as differentially expressed genes (up-regulated) and marker gene.

The stromal-high subtype is

- resistant towards most of the chemotherapeutic agents, and
- exhibits the gene COMP as differentially expressed gene (up-regulated) and marker gene.

The stromal-low subtype is

- sensitive towards most of the chemotherapeutic agents, and
- exhibits the gene COMP as differentially expressed gene (down-regulated) and marker gene.

In a second step for the identification of an optimal predictive gene signature in vitro chemosensitivity assays of primary tumors are performed to determine the response of a tumor towards a single chemotherapeutic agent. In said in vitro chemosensitivity assays the primary tumors were cultivated in different assays with increasing concentrations of the agents. After 6 days of incubation the vitality of the tumor cells were determined with an ATP-measurement. Hereby, the growing inhibition for the different agent concentrations could be determined and a dose-response curve could be provided. For each tumor the Area under the dose-response curve (AUC) could be determined for the different agents. The AUC is used to evaluate the response of a tumor towards a chemotherapeutic agent. The bigger the AUC, the more sensitive is the tumor towards the agent. The tumor samples were classified according to their sensitivity towards the agents into three classes (resistant, intermediate, sensitive) via the tertiles of the AUC arrangement.

For the expression analyses isolated RNA from the tumor tissues was used for molecular profiling with microarrays. Unsupervised hierarchical clustering and principal component analysis identified the molecular subtypes.

In a quantitative diagnostic assay it is a common practice to use a cut-off score which distinguishes between two different test results (up-regulated expression and down-regulated expression). The following cut off values relate to gene expression values determined by HG-U133a arrays of Affymetrix using MAS5.0 software with target intensity settings of 500.

Tumor tissues with an expression MLPH<2000 (cut-off score) have been characterized as the basal molecular subtype. In case the expression of the genes ESR1 and PGR were >6000 and 160, respectively, the tumor tissues have been characterized as the subtype luminal A. The remaining tumor tissues have been divided into two different subtypes via the stromal gene COMP (cut-off score 300).

The response of these molecular subtypes differs significantly towards single agents.

To improve separation between resistant and sensitive tumors for Epirubicin, Paclitaxel, 5-Fluorouracil and Carboplatin, additional biological motives were included in the analysis. Predictive gene signatures were defined for the single agents (FIGS. 1-4). See Table 2 for an overview regarding the different molecular subtypes and their predictive gene expression signatures.

TABLE 2

Overview regarding the different molecular subtypes

and their predictive gene expression signatures

MLPH
ESR1
PGR
COMP
DCN
IGKC
CCL5
FBN1
UBE2C

basal
↓
↓
↓

stromal ↑

↑
↑

↑

stromal ↓

↓
↓

↓

luminal A

↑
↑

immune system ↑

↑
↑

immune system ↓

↓
↓

proliferation ↑

↑

proliferation ↓

↓

+ positive

− negative

↑ up-regulated

↓ down-regulated

In a preferred embodiment

- a) the basal molecular subtype predictive for Epirubicin sensitivity is characterized by down-regulated MLPH-expression, the luminal A molecular subtype predictive for Epirubicin resistance is characterized by up-regulated ESR1 and PGR expression, the immune system-high molecular subtype predictive for Epirubicin sensitivity is characterized by up-regulated IGKC and CCL5 expression, and/or the immune system-low molecular subtype predictive for Epirubicin resistance is characterized by down-regulated IGKC and CCL5 expression;
- b) the basal molecular subtype predictive for Paclitaxel sensitivity is characterized by down-regulated MLPH expression, the luminal A molecular subtype predictive for Paclitaxel resistance is characterized by up-regulated ESR1 and PGR expression, the stromal-low molecular subtype predictive for Paclitaxel sensitivity is characterized by down-regulated DCN expression, and/or the stromal-high molecular subtype predictive for Paclitaxel resistance is characterized by up-regulated DCN expression;
- c) the stromal-low molecular subtype predictive for 5-Fluorouracil sensitivity is characterized by down-regulated FBN1 expression, and/or the stromal-high molecular subtype predictive for 5-Fluorouracil resistance is characterized by up-regulated FBN1 expression; and/or
- d) the stromal/proliferation-low molecular subtype predictive for Carboplatin sensitivity is characterized by down-regulated FBN1/UBE2C expression ratio, and/or the stromal/proliferation-high molecular subtype predictive for Carboplatin resistance is characterized by up-regulated FBN1/UBE2C expression ratio.

Since the tumors of the basal and luminal A subtype are particularly sensitive and resistant, respectively, towards the chemotherapeutic agents Epirubicin and Paclitaxel, the marker genes MLPH and ESR1 and PGR, respectively, are used for the prediction towards said agents.

In favor of the stromal subtypes the marker gene DCN is used for the prediction towards Paclitaxel (p=0.0188) (FIG. 2). For the agent Epirubicin the tumors, which can not be classified as luminal A and basal subtypes, are divided via the expression average of the immune system genes IGKC (normalized B-cell) and CCL5 (normalized T-cell) into a resistant and sensitive group (p=0.0341) (FIG. 1). In case of signal intensities obtained by MAS5.0 software from HG-U133a arrays using scaling to a target intensity of 500, IGKC values are divided by 2338 and CCL5 values are divided by 399.5, corresponding to their respective median expression values in a reference cohort. In order to get an immune system score, both normalized values are added and divided by two.

For 5-Fluorouracil all tumors are divided into two groups via the stromal gene FBN1 (p=0.0005) (FIG. 3). And regarding Carboplatin the proliferation gene UBE2C and the stromal gene FBN1 are used for classification. Due to the inverse correlation of said motives regarding chemosensitivity the ratio between UBE2C and FBN1 is calculated (FIG. 4). The tumors with high expressed proliferation genes and with low expressed stromal genes differ significantly (p=0.0029) to the tumors with an inverse ratio.

The defined cut-off score for MLPH predictive for Epirubicin is 2000, the defined score for ESR1 predictive for Epirubicin is 6000, the defined score for PGR predictive for Epirubicin is 160 and the defined score for the immune system score (average of IGKC and CCL5) predictive for Epirubicin is 1.5. An overview about the predictive gene (expression) signature for Epirubicin in breast cancer is demonstrated in Table 3.

TABLE 3

Predictive gene (expression) signature for Epirubicin

in breast cancer

IGKC +

MLPH
ESR1
PGR
CCL5

basal
↓
−
−

luminal A

↑
↑

immune system↑

↑

immune system↓

↓

+ positive

− negative

↑ up-regulated

↓ down-regulated

The defined cut-off score for MLPH predictive for Paclitaxel is 2000, the defined score for ESR1 predictive for Paclitaxel is 6000, the defined score for PGR predictive for Paclitaxel is 160 and the defined score for DCN predictive for Paclitaxel is 1500. An overview about the predictive gene (expression) signature for Paclitaxel in breast cancer is demonstrated in Table 4.

TABLE 4

Predictive gene (expression) signature for Paclitaxel

in breast cancer

MLPH
ESR1
PGR
DCN
COMP

basal
↓
−
−

luminal A

↑
↑

stromal↑

↑
↑

stromal↓

↓
↓

+ positive

− negative

↑ up-regulated

↓ down-regulated

The defined cut-off score for FBN1 predictive for 5-Fluorouracil is 3500. An overview about the predictive gene (expression) signature for 5-Fluorouracil in breast cancer is demonstrated in Table 5.

TABLE 5

Predictive gene (expression) signature for 5-

Fluorouracil in breast cancer

FBN1
COMP

stromal↑
↑
↑

stromal↓
↓
↓

+ positive

− negative

↑ up-regulated

↓ down-regulated

The defined cut-off score for the ratio between FBN1 and UBE2C predictive for Carboplatin is 1. An overview about the predictive gene (expression) signature for Carboplatin in breast cancer is demonstrated in Table 6.

TABLE 6

Predictive gene (expression) signature for Carboplatin

in breast cancer

FBN1/UBE2C

stromal/proliferation↓
↓

stromal/proliferation↑
↑

+ positive

− negative

↑ up-regulated

↓ down-regulated

In another preferred embodiment of the present invention, Paclitaxel resistance in the basal molecular subtype is characterized by up-regulated AKR1C1 expression.

It is particularly preferred that, in the method according to the invention, the said expression level is determined by

- a) a hybridization based method;
- b) a PCR based method;
- c) determining the protein level, and/or by
- d) an array based method.

In yet another preferred embodiment of the present invention, it is provided that the expression level of at least one of the said genes is determined with rtPCR (reverse transcriptase polymerase chain reaction) of the gene related mRNA.

In another preferred embodiment of the present invention, it is provided that the expression level of at least one of the said genes is determined in formalin and/or paraffin fixed tissue samples.

Routinely, in tumor diagnosis tissue samples are taken as biopsies from a patient and undergo diagnostic procedures. For this purpose, the samples are fixed in formaline and/or paraffin and are then examined with immunohistochemistry methods. The formaline treatment leads to the inactivation of enzymes, as for example the ubiquitous RNA-digesting enzymes (RNAses). For this reason, the mRNA status of the tissue (the so called transcriptome), remains undigested.

However, the formaline treatment leads to partial depolymerization of the individual mRNA molecules. For this reason, the current doctrine is that formaline fixed tissue samples can not be used for the analysis of the transcriptome of said tissue.

For this reason, it is provided in a preferred embodiment of the present invention that after lysis, the samples are treated with silica-coated magnetic particles and a chaotropic salt, in order to purify the nucleic acids contained in said sample for further determination.

Collaborators of the inventors of the present invention have developed an approach which however allows successful purification of mRNA out of tissue samples fixed in such manner, and which is disclosed, among others, in WO03058649, WO2006136314A1 and DE10201084A1, the content of which is incorporated herein by reference.

Said method comprises the use of magnetic particles coated with silica (SiO₂). The silica layer is closed and tight and is characterized by having an extremely small thickness on the scale of a few nanometers. These particles are produced by an improved method that leads to a product having a closed silica layer and thus entail a highly improved purity. The said method prevents an uncontrolled formation of aggregates and clusters of silicates on the magnetite surface whereby positively influencing the additional cited properties and biological applications. The said magnetic particles exhibit an optimized magnetization and suspension behavior as well as a very advantageous runoff behavior from plastic surfaces. These highly pure magnetic particles coated with silicon dioxide are used for isolating nucleic acids, including DNA and RNA, from cell and tissue samples, the separating out from a sample matrix ensuing by means of magnetic fields. These particles are particularly well-suited for the automatic purification of nucleic acids, mostly from biological body samples for the purpose of detecting them with different amplification methods.

The selective binding of these nucleic acids to the surface of said particles is due to the affinity of negatively charged nucleic acids to silica containing media in the presence of chaotropic salts like guanidinisothiocyanate. Said binding properties are known as the so called “boom principle”. They are described in the European patent EP819696.

The said approach is particularly useful for the purification of mRNA out of formaline and/or paraffine fixed tissue samples. In contrast to most other approaches, which leave very small fragments behind that are not suitable for later determination by PCR and/or hybridization technologies, the said approach creates mRNA fragments which are large enough to allow specific primer hybridzation and/or specific probe hybridization. A minimal size of at least 100 bp, more preferably 200 base pairs is needed for specific and robust detection of target gene expression. Moreover it is also necessary to not have too many inter-sample variations with regard to the size of the RNA fragments to guarantee comparability of gene expression results. Other issues of perturbance of expression data by sample preparation problems relate to the contamination level with DNA, which is lower compared to other bead based technologies. This of particular importance, as the inventors have observed, that DNAse treatment is not efficient in approximately 10% of FFPE samples generated by standard procedures and stored at room temperature for some years before cutting and RNA extraction.

The said approach thus allows a highly specific determination of candidate gene expression levels with one of the above introduced methods, particularly with hybridization based methods, PCR based methods and/or array based methods, even in formaline and/or paraffine fixed tissue samples, and is thus extremely beneficial in the context of the present invention, as it allows the use of tissue samples fixed with formaline and/or paraffine, which are available in tissue banks and connected to clinical databases of sufficient follow-up to allow retrospective analysis.

However, other methods are appropriate for the purification of mRNA out of formaline and/or paraffine fixed tissue samples as well.

In a yet preferred embodiment of the present invention said gynecologic cancer is selected from the group comprising breast cancer, ovarian cancer, vulvar cancer, vaginal cancer, tubal cancer, endometrian cancer and/or cervical cancer.

In an especially preferred embodiment of the present invention said gynecologic cancer is breast cancer. The method according to the invention may be used for the analysis of a wide variety of neoplastic cell growth and proliferation of the breast tissues including, but not limited to ductal carcinoma in situ, lobular carcinoma, colloid carcinoma, tubular carcinoma, medullary carcinoma, metaplastic carcinoma, intraductal carcinoma in situ, lobular carcinoma in situ and papillary carcinoma in situ.

Furthermore, a kit useful for carrying out one of the said methods, comprising at least

- a) a primer pair and/or a probe each having a sequence sufficiently complementary to a marker gene according to the present invention; and/or
- b) at least an antibody directed against a marker according to the present invention
  
  is provided.

In yet another embodiment of the invention a method for correlating the clinical outcome of a patient suffering from or at risk of developing recurrent gynecologic cancer, preferably breast cancer, with the presence or non-presence of a defect in marker gene expression is provided, said method comprising the steps of:

- a) obtaining a biological sample from said patient;
- b) determining the expression level of at least one marker gene according to the present invention in said patient, and
- c) correlating the pattern of expression levels determined in (b) with said patient's data, said data being selected from the group consisting of etiopathology data, clinical symptoms, anamnesis data and/or data concerning the therapeutic regimen.

In yet another embodiment of the invention a nucleic acid molecule is provided, said nucleic acid molecule selected from the group comprising

- a) the nucleic acid molecule presented as SEQ ID NO: 1-30,
- b) a nucleic acid molecule having a length of 4-80 nucleotides, preferably 18-30 nucleotides, the sequence of which corresponds to the sequence of a single stranded fragment of a gene encoding for a marker from the group comprising MLPH, ESR1, PGR, COMP, DCN, IGKC, CCL5, FBN1, UBE2C and/or AKR1C1,
- c) a nucleic acid molecule that is a fraction, variant, homologue, derivative, or fragment of the nucleic acid molecule presented as SEQ ID NO: 1-30,
- d) a nucleic acid molecule that is capable of hybridizing to any of the nucleic acid molecules of a)-c) under stringent conditions,
- e) a nucleic acid molecule that is capable of hybridizing to the complement of any of the nucleic acid molecules of a)-d) under stringent conditions,
- f) a nucleic acid molecule that is capable of hybridizing to the complement of a nucleic acid molecule of e),
- g) a nucleic acid molecule having a sequence identity of at least 95% with any of the nucleic acid molecules of a)-f),
- h) a nucleic acid molecule having a sequence identity of at least 70% with any of the nucleic acid molecules of a)-f),
- i) a complement of any of the nucleic acid molecules of a)-h), and/or
- j) a nucleic acid molecule that comprises any nucleic acid molecule of a)-i).

Genes of interest are listed in Table 7, and the sequence listing is depicted in Table 8. These nucleic acids are being used either as primers for a polymerase chain reaction protocol, or as detectable probes for monitoring the said process.

Furthermore it is provided that the said nucleic acid is selected from the group comprising DNA, RNA, PNA, LNA and/or Morpholino. The nucleic acid may, in a preferred embodiment, be labelled with at least one detectable marker. This feature is applicable particularly for those nucleic acids which serve as detectable probes for monitoring the polymerase chain reaction process

Such detectable markers may for example comprise at least one label selected from the group consisting of fluorescent molecules, luminescent molecules, radioactive molecules, enzymatic molecules and/or quenching molecules.

In a particularly preferred embodiment, the said detectable probes are labeled with a fluorescent marker at one end and a quencher of fluorescence at the opposite end of the probe. The close proximity of the reporter to the quencher prevents detection of its fluorescence; breakdown of the probe by the 5′ to 3′ exonuclease activity of the taq polymerase breaks the reporter-quencher proximity and thus allows unquenched emission of fluorescence, which can be detected. An increase in the product targeted by the reporter probe at each PCR cycle therefore causes a proportional increase in fluorescence due to the breakdown of the probe and release of the reporter.

In another preferred embodiment of the present invention, a kit of primers and/or detection probes is provided, comprising at least one of the nucleic acids according to the above enumeration and/or their fractions, variants, homologues, derivatives, fragments, complements, hybridizing counterparts, or molecules sharing a sequence identity of at least 70%, preferably 95%.

Said kit may, in another preferred embodiment, comprise at least one of the nucleic acid molecules presented as SEQ ID NO: 1-30, and/or their fractions, variants, homologues, derivatives, fragments, complements, hybridizing counterparts, or molecules sharing a sequence identity of at least 70%, preferably 95%, for the detection of at least one marker gene according to the present invention.

Furthermore, the use of a nucleic acid according as recited above, or of a kit as recited above for the prediction of a clinical response of a patient suffering from or at risk of developing recurrent gynecologic cancer, preferably breast cancer, towards a chemotherapeutic agent is provided.

BRIEF DESCRIPTION OF THE EXAMPLES AND DRAWINGS

Additional details, features, characteristics and advantages of the object of the invention are disclosed in the subclaims, and the following description of the respective figures and examples, which, in an exemplary fashion, show preferred embodiments of the present invention. However, these drawings should by no means be understood as to limit the scope of the invention.

Example 1

The predictive gene signatures for the chemotherapeutic agents Epirubicin, 5-Fluorouracil and Paclitaxel have been validated in neoadjuvant studies via the defined cutoff scores. The Epirubicin prediction markers have been tested, to which extent they can predict the relative tumor reduction towards a neoadjuvant EC (Epirubin, Cyclophosphamid) combination therapy in 86 patients. Furthermore, the predictive genes for Epirubicin and 5-Fluorouracil were tested in a study, in which 39 patients received a neoadjuvant FEC (5-Fluorouracil, Epirubicin and Cyclophosphamid) therapy.

Via the predictive gene signature for Epirubicin the tumors of the neoadjuvant studies were divided into four prediction classes. Most of the tumors being sensitive towards the chemotherapeutic agents were classified as basal subtype and the class with a high expression of the immune system genes. The resistant tumors can be mainly found in the luminal A subtype and in the group with a low expressed immune system. In the EC study the four molecular prediction classes differ among each other significantly (p=0.0008). In particular noticeable is the difference, which occurs for the classification with the immune system markers (p=0.0038) (FIG. 5). In the classification of the FEC study with the Epirubicin prediction markers only the basal subtype and the group with the low expression of the immune system markers diverged significantly (p=0.0207). In contrast, between the two immune system subtypes there is a trend to statistical significance (p=0.0848) (FIG. 6). The 5-Fluorouracil prediction markers divide the tumors into two prediction classes, which differ in regard to the tumor reduction with a trend to statistical significance (FIG. 7).

For the validation of the predictive Paclitaxel gene signatures two different neoadjuvant studies could be used, wherein the clinical response towards TAC (Rody et al., 2006) 21 and T-FAC combination (Hess et al., 2006)¹⁸was analyzed.

Since no data have been provided for the percental tumor reduction, it could be only performed a classification via the provided clinical information. For each prediction class of the Paclitaxel prediction markers the percentage of the (pathological) complete and incomplete remission (pCR/CR), respectively, has been determined.

In the neoadjuvant TAC study with the Paclitaxel marker genes all patients with a complete remission (in each case the bar on the left side) were classified into the basal subtype and the subtype with low expression of the stromal motive, respectively (FIG. 8). In the second validation study most of the tumors showing a pathological complete remission (in each case the bar on the left side) towards a T-FAC combination therapy have been classified into these two subtypes as well. A little percentage has been classified into the subtype with a high expression of the stromal genes (FIG. 9).

Discussion

The inventors of the present invention described for the first time methods for predicting the response of a tumor in a patient suffering from or at risk of developing recurrent breast cancer towards single chemotherapeutic agents.

FIGS. 10 to 13 present an overview via a general classification schema using the preferred marker genes. Cut off values are derived after expression analysis using HG-U133a arrays and MAS5.0 software using global scaling and target intensity settings of TGT=500. In case two or more genes are used for a classification step, the expression values of the individual genes might be combined by first normalising each individual value and then combining the normalised values into a single meta-value. For example, the ESR1 expression value might be divided by e.g. 5000, the PGR expression value by e.g. 150, and the normalised values are added to a combined ESR1-PGR score. The expression value of IGKC (214669_s_at) might be divided by e.g. 2338, the value of CCL5 might be divided by e.g. 399 and both normalised values might then be added and subsequently divided by two in order to obtain an immune system metagen score. In addition, the ratio between stromal and proliferation might be deduced from normalised expression values of FBN1 and UBE2C by e.g. dividing the FBN1 value by e.g. 3366 and dividing the value of UBE2C by 973 and subsequently calculating a ratio between the normalised values. The constant used for normalisation are typically derived as the median expression value of that gene found in samples collected from a representative patient cohort. A representative patient cohort is a population of breast cancer patient of a) sufficient size, e.g. preferentially more than 30 breast cancer patients and b) preferentially containing a proportion of grade 1, 2, 3, as well as estrogen receptor positive and estrogen receptor negative tumors of at least 5%.

However, the TAC study and the T-FAC study differ too much compared to the in vitro study. Therefore, the Paclitaxel prediction markers should be analyzed in further studies.

The results of the validations demonstrate that, although the validation studies included complex agent combinations and the effect of the other agents could not be analyzed, the predictive gene signatures for the single agents can classify sensitive and resistant tumors in a neoadjuvant combination therapy as well.

The question, which meaning the identified signatures might have for the clinical practice, has to be analyzed first in larger studies.

Example 2

To prove the effect of the expression of AKR1C1 in cell lines, the study NCI 60 (Staunton et al., 2001)²²was used. In this study several tumor cell lines of different cancer types were tested for their sensitivity towards several chemotherapeutic agents. The GI-50 value (concentration, where half of the tumor cells are inhibited regarding growing) for Paclitaxel was used to test, whether AKR1C1 is appropriate for a prediction of a Paclitaxel sensitivity in cell lines. The analysis demonstrates that in case of a high expression of AKR1C1 the cell lines mainly are resistant towards the agent Paclitaxel. The defined cut-off score (5000) allowed a significant classification (p=0.0049) of the resistant and sensitive tumor cells of the NCI 60 cell lines (FIG. 14).

In primary breast cancer tissues it was analyzed, which effect AKR1C1 has as predictive gene marker for Paclitaxel in the different subtypes defined in the in vitro study. Since in the in vitro study not enough tumor samples exist in the single subtypes to enable an appropriate analysis, the largest available validation dataset was used. The tumors of the neoadjuvant T-FAC study (Hess et al., 2006)¹⁸have been classified via the defined predictive gene signatures for Paclitaxel into four subtypes. Subsequently, it could be analyzed to what extent the AKR1C1 gene expression in the subtypes differs in the patients, who received a complete remission due to the neoadjuvant therapy, compared to the rest.

Within the basal subtype tumors with higher expression of AKR1C tended to receive an incomplete remission (trend for significance p=0.09; FIG. 15).

Discussion

Only for the basal subtype it was possible regarding the expression of AKR1C1 to differ between resistant and sensitive tumors with a significant trend. This observation should be verified in subsequent studies.

In case it is possible to identify with this gene for the basal subtype tumors, which do not respond to Paclitaxel, this would be very important for a therapy. Patients, whose tumors belong to the basal subtype, have very often a poor clinical prediction since this tumor entity is very aggressive. At the same time, this tumor subtype responds very strong towards chemotherapeutic agents. Therefore, especially for this subtype it is important to elucidate resistance mechanisms to improve the life span via an optimal treatment.

FIGURES

FIGS. 1-4 demonstrate the chemosensitivity of the tumors towards the chemotherapeutic agents Epirubicin (FIG. 1), Paclitaxel (FIG. 2), 5-Fluorouracil (FIG. 3) and Carboplatin (FIG. 4) after classification with predictive gene signatures. The results are depicted via a Box-Whisker-Plot of the AUC (Area under the dose-response curve) of the prediction classes for Epirubicin (basal, immune system-high, immune system-low, luminal A), Paclitaxel (basal, stromal-low, stro-mal-high, luminal A), 5-Fluorouracil (stromal-low, stromal-high) and Carboplatin (ratio stromal/proliferation-low, ratio stromal/proliferation-high) (43 tumors for the agents Paclitaxel/Epirubicin and 34 tumors for 5-Fluorouracil/Carboplatin). Significant results of a nonparametric test are demonstrated for the comparison of all subtypes adjacent the Figures and for the pairwise comparison of the subtypes within the Figures (*: p-score <0.05, **: p-score <0.01, ***: p-score <0.001) (Tab. 9).

FIG. 5 demonstrates the relative reduction of 86 tumors towards a neoadjuvant EC combination therapy. The results are depicted via a Box-Whisker-Plot of the relative tumor reduction of the Epirubicin prediction classes (basal, immune system-high, immune system-low and luminal A). Significant results of a nonparametric test are demonstrated for the comparison of all subtypes adjacent the Figure and for the pair-wise comparison of the subtypes within the Figure (*: p-score <0.05, **: p-score <0.01, ***: p-score <0.001) (Tab. 9).

FIGS. 6 and 7 demonstrate the relative reduction of 39 tumors towards a neoadjuvant FEC combination therapy. The results are depicted via a Box-Whisker-Plot of the relative tumor reduction of the prediction classes, which are defined via the Epirubicin (FIG. 6) and 5-Fluorouracil (FIG. 7) prediction markers. Significant results of a nonparametric test are demonstrated for the comparison of all subtypes adjacent the Figures and for the pairwise comparison of the subtypes within the Figures (*: p-score <0.05) (Tab. 9).

FIGS. 8 and 9 demonstrate prediction classes of the Paclitaxel prediction markers in neoadjuvant studies. The percentage of the tumors with “(pathological) complete remission” (pCR/CR) (in each case the bar on the left side) and with incomplete remission (in each case the bar on the right side) in a neoadjuvant TAC (FIG. 8) and T-FAC (FIG. 9) study is demonstrated.

FIGS. 10 to 13 demonstrate the general classification schema using preferred marker genes for the different chemotherapeutic agents.

FIG. 14 demonstrates the chemosensitivity of different cell lines towards Paclitaxel. NCI 60 cell lines were classified with the marker gene AKR1C1 (cut-off score 5000). Onto the ordinate the concentration of Palitaxel is depicted, which is needed for the inhibition of half of the cells (GI-50). Altogether for Paclitaxel 54 GI-50 values for the cell lines were provided.

FIG. 15 demonstrates the expression of the gene AKR1C1 in a neoadjuvant T-FAC study for the molecular subtypes basal, stromal-low, stromal-high and luminal A. For each group there occurred a classification of the tumors, which received a pathological complete remission (depicted in each case on the right side) or an incomplete remission (depicted in each case on the left side) towards the neoadjuvant therapy.

The figures are described in the context of the respective examples.

Disclaimer

To provide a comprehensive disclosure without unduly lengthening the specification, the applicant hereby incorporates by reference each of the patents and patent applications referenced above.

The particular combinations of elements and features in the above detailed embodiments are exemplary only; the inter-changing and substitution of these teachings with other teachings in this and the patents/applications incorporated by reference are also expressly contemplated. As those skilled in the art will recognize, variations, modifications, and other implementations of what is described herein can occur to those of ordinary skill in the art without departing from the spirit and the scope of the invention as claimed. Accordingly, the foregoing description is by way of example only and is not intended as limiting. The invention's scope is defined in the following claims and the equivalents thereto. Furthermore, reference signs used in the description and claims do not limit the scope of the invention as claimed.

TABLE 7

Genes of Interest

Affymetrix Probeset

ID
Gene symbol
Gene name

218211_s_at
MLPH
melanophilin

205225_at
ESR1
estrogen receptor 1

208305_at
PGR
progesterone receptor

205713_s_at
COMP
cartilage oligomeric matrix

protein

214669_x_at
IGKC
Immunoglobulin kappa variable

1-5

1405_i_at
CCL5
chemokine (C-C motif)

ligand 5

209335_at
DCN
decorin

202766_s_at
FBN1
fibrillin 1 (Marfan syndrome)

202954_at
UBE2C
ubiquitin-conjugating enzyme

E2C

204151_x_at
AKR1C1
aldo-keto reductase family

1, member C1

(dihydrodiol dehydrogenase

1)

(20-alpha (3-alpha)-

hydroxysteroid dehydro-

genase)

TABLE 8

Primer sequences and probe sequences used in

accordance with the present invention

Gene
Oligonu-

SEQ

symbol
cleotide
Sequence
ID

MLPH
Probe
CCAAATGCAGACCCTTCAAGTGAGGC
1

for-
TCGAGTGGCTGGGAAACTTG
2

Primer

rev-
AGATAGGGCACAGCCATTGC
3

Primer

ESR1
Probe
ATGCCCTTTTGCCGATGCA
4

for-
GCCAAATTGTGTTTGATGGATTAA
5

Primer

rev-
GACAAAACCGAGTCACATCAGTAATAG
6

Primer

PGR
Probe
TTGATAGAAACGCTGTGAGCTCGA
7

for-
AGCTCATCAAGGCAATTGGTTT
8

Primer

rev-
ACAAGATCATGCAAGTTATCAAGAAGTT
9

Primer

COMP
Probe
CAGGAGAACATCATCTGGGCCAACCTG
10

for-
GACAGCAACGTGGTCTTGGA
11

Primer

rev-
ATGGTGTCATTGCAGCGGTAA
12

Primer

IGKC
Probe
AGCAGCCTGCAGCCTGAAGATTTTGC
13

for-
GATCTGGGACAGAATTCACTCTCA
14

Primer

rev-
GCCGAACGTCCAAGGGTAA
15

Primer

CCL5
Probe
CTCGGACACCACACCCTGCTGCT
16

for-
CTGCATCTGCCTCCCCATA
17

Primer

rev-
AGTGGGCGGGCAATGTAG
18

Primer

DCN
Probe
TCTTTTCAGCAACCCGGTCCA
19

for-
AAGGCTTCTTATTCGGGTGTGA
20

Primer

rev-
TGGATGGCTGTATCTCCCAGTA
21

Primer

FBN1
Probe
CTCAGTGGCCAGAGGATCACCAGTGC
22

for-
GTCTGGGAGGACCAGGAAACA
23

Primer

rev-
TGCACATGCTGTGATGAAGGA
24

Primer

UBE2C
Probe
TGAACACACATGCTGCCGAGCTCTG
25

for-
CTTCTAGGAGAACCCAACATTGATAGT
26

Primer

rev-
GTTTCTTGCAGGTACTTCTTAAAAGCT
27

Primer

AKR1C1
Probe
CCTATGCGCCTGCAGAGGTTCCTAAAAG
28

for-
CATGCCTGTCCTGGGATTTG
29

Primer

rev-
AATTTGGTGGCCTCTAAAGCTTT
30

Primer

TABLE 9

Molecular subtypes

5-

Epirubicin
Paclitaxel
Fluorouracil
Carboplatin

p-score
p-score
p-score
p-score

Kruskal-Wallis test
0.002*
0.002*
0.039*
0.025*

basal vs. stromal
0.023*
0.149
0.054
0.281

(low)

stromal (high) vs.
0.011*
0.054
0.004*
0.076

stromal (low)

stromal (high) vs.
0.005*
0.075
0.511
0.313

luminal A

basal vs. luminal A
0.002*
0.004*
1.000
0.101

basal vs. stromal
0.705
0.006*
0.342
0.007*

(high)

stromal (low) vs.
0.365
0.024*
0.282
0.345

luminal A

in vitro
EC-
FEC-

study
validation
validation

Epirubicin predictor:
p-score
p-score
p-score

Kruskal-Wallis test
0.0028*
0.0008*
0.0343*

basal vs. immune
0.7715
0.4025
0.1672

(high)

immune (high) vs.
0.0341*
0.0038*
0.0848

immune (low)

immune (low) vs.
0.4758
0.1368
0.5657

luminal A

basal vs. luminal A
0.0022*
0.0390*
0.1333

basal vs. immune
0.0327*
0.0029*
0.0207*

(low)

immune (high) vs.
0.0015*
0.0328*
0.1455

luminal A

in vitro

study

Paclitaxel predictor:
p-score

Kruskal-Wallis
0.0014*

test

basal vs. stromal
0.0759

(low)

stromal (high) vs.
0.0188*

stromal (low)

stromal (high) vs.
0.0962

luminal A

basal vs. luminal A
0.0037*

basal vs. stromal
0.0066*

(high)

stromal (low) vs.
0.0241*

luminal A

in vitro
FEC-

5-Fluorouracil
study
validation

predictor:
p-score
p-score

stromal (low) vs.
0.0005*
0.0837

stromal (high)

in vitro

study

Carboplatin predictor:
p-score

stromal (low) vs.
0.0029*

stromal (high)

cell culture
Cal 51
HCC 1954
MCF 7

study:
p-score
p-score
p-score

AKR1C1 resistant-
0.004*
0.001*
0.016*

sensitive:

significant results (p < 0.05) are marked with an asterisk*

REFERENCES

1. Jemal A et al., CA Cancer J Clin 2007; 57:43-66.

2. Veronesi U et al., Lancet 2005; 365:1727-1741.

3. Gesellschaft der epidemiologischen Krebsregister in Deutschland e.V. (GEKID) and RKI, Krebs in Deutschland 2006; 5. Auflage, Saarbrücken 2006.

4. Bast R C JR et al., J Clin Oncol 2001; 19: 1865-1878.

5. Goldhirsch A et al., Ann Oncol 2005; 16: 1569-83.

6. Longley D B et al., Nat Rev Cancer 2003; 3: 330-338.

7. Capranico G et al., Cancer Res 1989; 49: 2022-2027.

8. Misset J L et al., J Clin Oncol 1996; 14: 1136-1145.

9. Nabholtz J M et al., J Clin Oncol 1999; 17: 1413-1424.

10. Henderson I C et al., J Clin Oncol 2003; 21: 976-83.

11. Bishop J F et al., J Clin Oncol 1999; 17: 2355-64.

12. Parness J and Horwitz S B J Cell Biol 1981; 191: 479-87.

13. Paridaens R et al., J Clin Oncol 2000; 18: 724-733.

14. Villeneuve D J et al., Breast Cancer Res Treat 2006; 96:17-39.

15. Gyorffy B et al., Int J Cancer 2006; 118:1699-712.

16. Ross D T and Perou C M, Dis Markers 2001; 17:99-109.

17. Ayers M et al., J Clin Oncol 2004; 22:2284-2293.

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Number	Date	Country	Kind
10 2007 043 455.5	Sep 2007	DE	national
07122919.9	Dec 2007	EP	regional

Method for predicting the response of a tumor in a patient suffering from or at risk of developing recurrent gynecologic cancer towards a chemotherapeutic agent

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