METHOD OF PREDICTING THERAPEUTIC RESPONSE AND PROGNOSIS OF METASTATIC BREAST CANCER TO CHEMOTHERAPEUTIC AGENTS, AND TREATING METASTATIC BREAST CANCER

Abstract

The present disclosure relates to a method of predicting therapeutic response or prognosis of an anticancer drug for metastatic breast cancer, and treating HR+/HER2− metastatic breast cancer. When the biomarker of an embodiment of the present disclosure is used as a marker for predicting therapeutic response or prognosis of an anticancer drug for metastatic breast cancer of a specific type, it is possible to predict therapeutic response or prognosis of an anticancer drug, and accordingly, a therapeutic method suitable for a patient may be applied to maximize the treatment effect.

Claims

1. A method of predicting therapeutic response or prognosis of an anticancer drug for HR (hormone-receptor) positive and HER2 negative (HR+/HER2−) metastatic breast cancer (MBC), and treating HR+/HER2− metastatic breast cancer, the method including: (a) measuring at least one type of mutation selected from the group consisting of NF2, FAT3, LRP1B, PTEN and RAD50 in a biological sample isolated from a subject; (b) comparing the mutation of a gene measured in the sample with a control sample; (c) when the mutation exists, determining that the subject has poor response to a first anticancer drug or poor therapeutic prognosis; and (d) treating the HR+/HER2− metastatic breast cancer by administering an effective amount of a second anticancer drug for breast cancer to the subject determined to have poor response to the first anticancer drug or poor therapeutic prognosis.

2. The method of claim 1, wherein the biological sample is at least one selected from the group consisting of saliva, biopsy, blood, serum, plasma, lymph, cerebrospinal fluid, ascites, skin tissue, liquid culture, feces and urine.

3. The method of claim 1, wherein the phase (a) is a phase of measuring mutations of: any one or more of NH2 or FAT3; and LRP1B, PTEN, and RAD50.

4. The method of claim 1, wherein the mutation is one or more types of variations selected from the group consisting of single nucleotide variation (SNV), insertion/deletion variation (Indel), copy number variation (CNV), deletion and inversion.

5. The method of claim 1, wherein the first anticancer drug is capecitabine or 5-fluorouracil.

6. The method of claim 1, wherein the HR+/HER2− metastatic breast cancer is developed before menopause.

7. The method of claim 1, wherein the second anticancer drug is different from the first anticancer drug.

8. The method of claim 1, wherein the second anticancer drug is a CDK4/6 inhibitor or cytotoxic chemotherapy.

9. The method of claim 8, wherein the CDK4/6 inhibitor is palbociclib.

10. The method of claim 8, wherein the cytotoxic chemotherapy is paclitaxel.

11. A method of predicting therapeutic response or prognosis of an anticancer drug for HR (hormone-receptor) positive and HER2 negative (HR+/HER2−) metastatic breast cancer (MBC), and treating HR+/HER2− metastatic breast cancer, the method including: (a) measuring at least one type of mutation selected from the group consisting of NF2, FAT3, LRP1B, PTEN and RAD50 in a biological sample isolated from a subject; (b) comparing the mutation of a gene measured in the sample with a control sample; (c) when the mutation does not exist, determining that the subject has good response to a first anticancer drug or good therapeutic prognosis; and (d) treating the HR+/HER2− metastatic breast cancer by administering an effective amount of the first anticancer drug for breast cancer to the subject determined to have good response to the first anticancer drug or good therapeutic prognosis.

12. The method of claim 11, wherein the first anticancer drug is capecitabine or 5-fluorouracil.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0073] FIG. 1 shows the results according to a combination of five selected biomarker genes associated with drug therapeutic response and prognosis of premenopausal HR+/HER2− metastatic breast cancer.

[0074] FIG. 2 is a PFS analysis result using IHC classification.

[0075] FIG. 3 is a PFS analysis result using five types of marker combinations selected in an embodiment of the present disclosure.

DETAILED DESCRIPTION

[0076] Hereinafter, the examples are only for explaining the present disclosure in more detail, and it will be apparent to those skilled in the art to which the present disclosure pertains that the scope of the present disclosure is not to be construed as being limited by these examples according to the gist of the present disclosure.

Example 1. Selection of Biomarker Genes Associated with Drug Therapeutic Response and Prognosis of Premenopausal HR+/HER2− Metastatic Breast Cancer

[0077] The present inventors attempted to discover biomarkers capable of predicting therapeutic response and prognosis of a specific drug for patients with premenopausal HR+/HER2− [HR (hormone-receptor)-positive and HER2− negative] metastatic breast cancer (MBC), who exhibited different therapeutic methods (response) and prognosis than early breast cancer patients.

[0078] In this regard, through the next generation sequencing (NGS, DNA/RNA) of patients treated with capecitabine (Xeloda®), a specific gene mutation associated with the survival of breast cancer patients, and CNV (gene copy number variation) were identified. In addition, a model was constructed through survival analysis after combining the follow-up survival data of the patients who received the drug (Median follow-up=17.7 months) with the NGS results.

[0079] The target patients from whom the breast cancer samples were collected agreed to the use of the clinical sample tissues for the purpose of the study according to an embodiment of the present disclosure, and the histologic classification and tumor stage of the target patients from whom the breast cancer samples were collected were reviewed by a pathologist.

[0080] More details follow:

[0081] Among 141 premenopausal HR+/HER2− MBC patients, a tumor sample was isolated from a group (n=62) administered with capecitabine, and sequencing was performed on the sample. CancerSCAN™ targeted panel sequencing was performed to detect 375 cancer-related gene variations, and transcriptome analysis was performed to detect overall gene expression patterns. Genomic differences related to drug response in PFS in patients with poor prognosis and patients with good prognosis using gene variation and gene expression were examined.

[0082] A univariate Cox proportional hazard model was analyzed for each genetic variation/CNV, the p-value derived by the log-rank test was defined as a p-value cutoff of 0.05 as the criterion for a statistically significant difference, and 30 biomarkers with p-value<0.05 were selected as candidate markers. Based thereon, the final five genes, in other words, NF2 (moesin-ezrin-radixin like (MERLIN) tumor suppressor), FAT3 (FAT atypical cadherin 3), LRP1B (LDL receptor related protein 1B), PTEN (phosphatase and tensin homolog) and RAD50 (RAD50 double strand break repair protein), were derived as biomarkers through the stepwise variable selection process of multivariate Cox proportional hazard model analysis. Among them, in the case of the NF2 and FAT3 genes included in the Hippo pathway gene, the number of individuals with mutations is small and these genes belong to the gene group that performs the same function in relation to the Hippo pathway. Hence, survival analysis was performed by bundling the two genes and considering them as one marker. In other words, the description of “NF2+FAT3” includes the case where there exists a mutation in either NF2 or FAT3 gene.

[0083] The multivariate Cox proportional model analysis results integrating all of the aforementioned gene mutations are shown in FIG. 1.

[0084] As shown in FIG. 1, when FAT3+NF2 mutation (35.5%), LRP1B mutation (14.5%), PTEN mutation (4.8%), and RAD50 mutation (12.9%) exist in young premenopausal metastatic breast cancer patients of HR+/HER2−[HR (hormone-receptor)-positive and HER2-negative], the prognosis was found to be poor.

[0085] In other words, generic modifications such as NF2 mutation, FAT3 mutation, LRP1B mutation, PTEN mutation, and RAD50 mutation found in the HR+/HER2− premenopausal MBC group were significantly associated with progression-free survival (PFS) and capecitabine resistance in patients.

[0086] In addition, the results of analyzing the ratio of patients with mutations among patients used in this analysis and the ratio of patients with corresponding mutations among normal people are shown in Table 1, and the types of mutations possessed by patients used in this analysis are shown in Table 2.

TABLE-US-00001 TABLE 1 Gene YoungPEARL_Capecitabine 1000Genome NF2  2% 0% FAT3 34% 2% LRP1B 15% 0% PTEN  5% 0% RAD50 13% 0%

[0087] In Table 1, 1000Genome is a database that analyzes the mutation frequency of normal population (https://www.internationalgenome.org/). It was identified that normal people possess no mutation in the gene selected in an embodiment of the present disclosure or possess only a very low ratio.

TABLE-US-00002 TABLE 2 Gene cDNA Ref 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 indicates data missing or illegible when filed

Example 2. Verification of Selected Biomarker Genes Related to Drug Therapeutic Response and Prognosis of Premenopausal HR+/HER2− Metastatic Breast Cancer

[0088] As identified in Example 1, in order to prove that five types of marker combinations associated with NF2 mutation, FAT3 mutation, LRP1B mutation, PTEN mutation, and RAD50 mutation, may be applied as an important indicator for determining the prognosis of premenopausal HR+/HER2− metastatic breast cancer and/or response to capecitabine, the present inventors performed a PFS analysis using the existing IHC model, and performed a comparative analysis to see if it had a significant result in predicting prognosis.

[0089] First, Cox proportional hazards analysis was used to identify statistical significance whether it is more significant than the clinical information-based prognostic evaluation model. For clinical information using IHC classification, individual markers, and each of the five types of marker combinations selected in Example 1, the performance of the predictive model was calculated by C-index and compared. In general, when the C-index is greater than 0.7, it may be determined that the diagnostic marker performance evaluation index, AUC, corresponds to a value greater than 0.7, and that the performance of the predictive model is acceptable. The results are shown in Table 3.

TABLE-US-00003 TABLE 3 C-index (Cox model performance) Variable type Variable list Univiariate Multivariate Clinical variable IHC.type 0.514 (0.44, 0.589) Genomic variable FAT3 + NF2 0.577 (0.489, 0.665) LRP1B 0.583 (0.515, 0.65) PTEN 0.537 (0.485, 0.59) RAD50 0.584 (0.518, 0.65) FAT3 + NF2, 0.737 (0.665, LRP1B, PTEN, 0.81) RAD50

[0090] As shown in Table 3, the C-index was 0.514 in the group using IHC type, which is clinical information, and 0.537 to 0.584 for individual markers, making it difficult to say that the performance of the predictive model was excellent. In the group using five types of marker combinations according to an embodiment of the present disclosure, the C-index value was identified to be 0.737, which identified that it had acceptable discrimination performance.

[0091] In addition, the results of PFS analysis using clinical information using IHC classification and five types of marker combinations selected in Example 1 are shown in FIGS. 2 and 3 by applying Kaplan Meier analysis.

[0092] As shown in FIG. 2, in the case of using the IHC classification, it was not possible to identify a significant difference in PFS according to each group, and it was identified that the prognosis analysis was impossible accordingly.

[0093] However, as shown in FIG. 3, as a result of comparative analysis of patients in whom mutations were not detected for all of the five genes as WT (normal group) and the mutant group, when the marker combination according to an embodiment of the present disclosure was used, it was identified that the mutant group had a relatively poor prognosis as the risk was about 5 times higher than that of the normal group and the median PFS was short at 12 months.

[0094] Accordingly, through the above analysis results, it was identified that in the case of using the biomarker set selected in an embodiment of the present disclosure, it was possible to predict drug therapeutic response and prognosis of premenopausal HR+/HER2− metastatic breast cancer, through which the selection of a therapeutic method was able to be optimized and the therapeutic effect was able to be increased.

[0095] Although the present disclosure has been described in detail with reference to the specific features, it will be apparent to those skilled in the art that this description is only for a preferred embodiment and does not limit the scope of the present disclosure. Thus, the substantial scope of the present disclosure will be defined by the appended claims and equivalents thereof.