Use of cyp2a6 genotype as a marker for predicting the efficacy of neoadjuvant chemotherapy for breast cancer

Abstract

The application provides use of CYP2A6 genotype as a marker for predicting the efficacy of neoadjuvant chemotherapy combined with neoadjuvant endocrine therapy for breast cancer, patients with CYP2A6 intermediate / slow metabolism type are more likely to achieve pCR than patients with normal metabolism type, CYP2A6 rs61663607 T>C TT type is more likely to achieve pCR than TC and CC types, in patients with neoadjuvant chemotherapy combined with or without letrozole, CYP2A6 rs7250713 G>C CC type is more likely to achieve pCR than CC and CG types. The application also provides use of CYP2A6 genotype in preparation of a reagent for diagnosing and predicting the efficacy of neoadjuvant chemotherapy combined with neoadjuvant endocrine therapy for breast cancer. The application is used for auxiliary diagnosis of the efficacy of neoadjuvant chemotherapy combined with or without letrozole treatment for early hormone receptor positive breast cancer patients receiving neoadjuvant therapy and surgery.

Description

Technical fields:

[0001] This invention relates to the field of biological detection, specifically to the use of the CYP2A6 genotype as a marker for predicting the efficacy of neoadjuvant chemotherapy in breast cancer. Background technology:

[0002] Breast cancer is currently the most common malignant tumor, with 60-75% being hormone receptor-positive breast cancer. Letrozole, a third-generation aromatase inhibitor, is also one of the standard endocrine therapy drugs for postmenopausal patients with hormone receptor-positive early breast cancer. Compared with tamoxifen, it can significantly reduce the risk of breast cancer recurrence and death.

[0003] The classic CYP2A6 genotype grouping is based on the recognized criteria reported in the literature (Source: PMID:26662855). According to the effect of CYP2A6 alleles on enzyme activity, they are divided into "reduced function (D)" alleles [CYP2A6*9 i.e. rs28399433 and CYP2A6*1A(51A) i.e. rs1137115] and "loss of function (L)" alleles (CYP2A6*4): (1) "Normal metabolizers" are defined as having neither D nor L alleles (CYP2A6 wild type, i.e. *1 / *1); (2) "Intermediate metabolizers" are defined as having only one D allele [i.e. *1A(51A) / *1 or *9 / *1]; (3) "Slow metabolizers" have one L allele or two D alleles [e.g. *1 / *4 or *1A(51A) / *1A(51A)].

[0004] Letrozole's main pharmacological action is to inhibit aromatase activity in the liver, muscles, fat, and bones of postmenopausal patients, thereby inhibiting the conversion of androgens to estrogens and reducing circulating estrogen levels. Further studies have found that letrozole can also inhibit aromatase activity in hormone receptor-positive breast cancer cells, thus reducing estrogen concentrations not only in the circulating blood but also in the local microenvironment of breast cancer. Letrozole can reduce estrogen levels to undetectable levels in most patients, blocking the estrogen receptor signaling pathway at its source and inhibiting tumor proliferation, thus achieving a certain therapeutic effect. However, some patients still experience disease recurrence and progression.

[0005] The FACE study enrolled 4136 postmenopausal patients with hormone receptor-positive, lymph node-positive early breast cancer, who were randomly assigned to receive either oral letrozole (2.5 mg, qd) or anastrozole (1 mg, qd). The primary endpoint was 5-year disease-free survival. Results showed no significant difference in 5-year disease-free survival between the letrozole and anastrozole groups (HR = 0.93; 95 CI, 0.80–1.07, P = 0.3150). However, we found that after 5 years of follow-up, 25.1% of patients in the letrozole group experienced disease recurrence.

[0006] In neoadjuvant endocrine therapy, the P024 clinical trial treated 324 postmenopausal patients with hormone receptor-positive locally advanced breast cancer with either letrozole or tamoxifen for 4 months. The primary endpoint was clinical response rate assessed by breast palpation. Results showed that the overall response rate in the letrozole group was superior to that in the tamoxifen group (55% vs 36%, P<0.001), and the breast-conserving rate in the letrozole group was higher than that in the tamoxifen group (45% vs 35%, P=0.022). These studies suggest that letrozole treatment can achieve good efficacy. Currently, the mechanism underlying the differences in efficacy among patients remains unclear. Some studies have shown that rs4646 CYP19A1 gene polymorphism is associated with steroid concentration and also with the efficacy of letrozole treatment. However, the selection of suitable patients for letrozole treatment is still in the exploratory stage, and there is an urgent need to find other potential biomarkers to predict the efficacy of letrozole, achieve precise endocrine therapy with letrozole, and further improve efficacy.

[0007] Drug-metabolizing enzymes participate in the biotransformation of drugs in the body, thereby enabling drugs to exert important pharmacological effects. The pharmacological activity of a drug changes after being transformed by metabolic enzymes. Most drugs lose their activity in this process, a process known as inactivation. Letrozole is mainly metabolized through the CYP enzyme system, specifically in the liver by the human cytochrome P450 isoenzyme CYP2A6, which catalyzes its metabolism into the inactive product CGP 446453. The CYP2A6 enzyme is encoded by the CYP2A6 gene located on human chromosome 19q13.2. CYP2A6 gene polymorphisms, such as single nucleotide polymorphisms (SNPs) or deletion mutations, can alter the expression or activity of the CYP2A6 metabolic enzyme. Studies have shown that CYP2A6 gene polymorphisms are related to letrozole blood concentrations; patients in the CYP2A6 slow metabolizer group have significantly higher letrozole blood concentrations than those in the CYP2A6 normal metabolizer group. However, the relationship between CYP2A6 gene polymorphism and letrozole efficacy has not yet been reported and warrants further investigation.

[0008] Neoadjuvant therapy is an important component of systemic treatment for breast cancer, not only allowing for the assessment of drug efficacy during treatment but also predicting patient prognosis. Patients with locally advanced hormone receptor-positive breast cancer who are eligible for neoadjuvant therapy may theoretically benefit from neoadjuvant chemotherapy combined with neoadjuvant endocrine therapy, but this remains controversial. A two-arm randomized clinical trial enrolled 101 postmenopausal patients with locally advanced breast cancer aged 50-83 years, comparing the efficacy and safety of neoadjuvant chemotherapy versus neoadjuvant chemotherapy combined with neoadjuvant letrozole (2.5 mg / qd). The results showed that the pathological complete response (pCR) rate in the neoadjuvant chemotherapy plus neoadjuvant letrozole group was significantly higher than that in the chemotherapy-only group, at 25.2% and 10.2%, respectively (P = 0.049). However, another small phase II clinical trial enrolled 28 patients with stage II-III estrogen receptor-positive invasive breast cancer, comparing neoadjuvant chemotherapy with or without endocrine therapy (goserelin for premenopausal patients; aromatase inhibitors for postmenopausal patients). The results showed no significant difference in pCR rate between the two groups (12.5% ​​vs 8.3%).

[0009] For the reasons mentioned above, it is necessary to conduct more research and validation on the population that can benefit from neoadjuvant chemotherapy combined with neoadjuvant endocrine therapy, in order to achieve precision treatment and improve treatment outcomes. Summary of the Invention:

[0010] To address the deficiencies in the existing technology, this invention provides the use of CYP2A6 genotype as a marker for predicting the efficacy of neoadjuvant chemotherapy in breast cancer. This use aims to solve the technical problem of poor efficacy of existing drugs in treating breast cancer.

[0011] This invention provides the use of CYP2A6 genotype as a marker for predicting the efficacy of neoadjuvant chemotherapy combined with neoadjuvant endocrine therapy in breast cancer. Patients with CYP2A6 intermediate / slow metabolizer genotypes are more likely to achieve pCR than patients with normal metabolizer genotypes. The CYP2A6 rs61663607T>C TT genotype is more likely to achieve pCR than the TC and CC genotypes. In patients receiving neoadjuvant chemotherapy with or without letrozole, the CYP2A6 rs7250713G>C CC genotype is more likely to achieve pCR than the CC and CG genotypes. pCR is defined as the absence of invasive carcinoma in the breast sample obtained at the time of surgery.

[0012] Furthermore, the breast cancer described is hormone receptor positive.

[0013] This invention also provides the use of CYP2A6 genotypes in the preparation of reagents for diagnosing and predicting the efficacy of neoadjuvant chemotherapy combined with neoadjuvant endocrine therapy in breast cancer. Patients with CYP2A6 intermediate / slow metabolizers are more likely to achieve pCR than patients with normal metabolizers. The CYP2A6 rs61663607T>C TT genotype is more likely to achieve pCR than the TC and CC genotypes. In patients receiving neoadjuvant chemotherapy with or without letrozole, the CYP2A6 rs7250713G>C CC genotype is more likely to achieve pCR than the CC and CG genotypes. pCR is defined as the absence of invasive carcinoma in the breast sample obtained at the time of surgery.

[0014] Furthermore, the sequence of the specific amplification primer pair used to detect rs8192728 is as shown in SEQ ID.

[0015] The nucleotide sequences of the single-base extension primers are shown in NO:10 and SEQ ID NO:11; the nucleotide sequences of the single-base extension primers are shown in SEQ ID NO:1.

[0016] As shown;

[0017] The sequences of the specific amplification primer pairs used to detect rs7250713 are shown in SEQ ID NO:12 and SEQ ID NO:13; the nucleotide sequences of the single-base extension primers are shown in SEQ ID NO:2.

[0018] The sequences of the specific amplification primer pairs used to detect rs28399433 are shown in SEQ ID NO:14 and SEQ ID NO:15; the nucleotide sequences of the single-base extension primers are shown in SEQ ID NO:3.

[0019] The sequences of the specific amplification primer pairs used to detect rs56113850 are shown in SEQ ID NO:16 and SEQ ID NO:17; the nucleotide sequences of the single-base extension primers are shown in SEQ ID NO:4.

[0020] The sequences of the specific amplification primer pairs used to detect rs7256108 are shown in SEQ ID NO:18 and SEQ ID NO:19; the nucleotide sequences of the single-base extension primers are shown in SEQ ID NO:5.

[0021] The sequences of the specific amplification primer pairs used to detect rs61663607 are shown in SEQ ID NO:20 and SEQ ID NO:21; the nucleotide sequences of the single-base extension primers are shown in SEQ ID NO:6.

[0022] The sequences of the specific amplification primer pairs used to detect rs1137115 are shown in SEQ ID NO:22 and SEQ ID NO:23; the nucleotide sequences of the single-base extension primers are shown in SEQ ID NO:7.

[0023] The sequences of the specific amplification primer pairs used to detect rs8102683 are shown in SEQ ID NO:24 and SEQ ID NO:25; the nucleotide sequences of the single-base extension primers are shown in SEQ ID NO:8.

[0024] The sequences of the specific amplification primer pairs used to detect rs8192720 are shown in SEQ ID NO:26 and SEQ ID NO:27; the nucleotide sequences of the single-base extension primers are shown in SEQ ID NO:9.

[0025] The sequences of the first specific amplification primer pair used to detect the CYP2A6*4del site are shown in SEQ ID NO:28 and SEQ ID NO:29; the sequences of the second specific amplification primer pair used to detect the CYP2A6*4del site are shown in SEQ ID NO:30, SEQ ID NO:31 and SEQ ID NO:32.

[0026] This invention also provides the use of a reagent in diagnosing and predicting the efficacy of neoadjuvant chemotherapy for breast cancer, for detecting the CYP2A6 genotype.

[0027] The sequences of the specific amplification primer pairs used to detect rs8192728 are shown in SEQ ID NO:10 and SEQ ID NO:11; the nucleotide sequences of the single-base extension primers are shown in SEQ ID NO:1.

[0028] The sequences of the specific amplification primer pairs used to detect rs7250713 are shown in SEQ ID NO:12 and SEQ ID NO:13; the nucleotide sequences of the single-base extension primers are shown in SEQ ID NO:2.

[0029] The sequences of the specific amplification primer pairs used to detect rs28399433 are shown in SEQ ID NO:14 and SEQ ID NO:15; the nucleotide sequences of the single-base extension primers are shown in SEQ ID NO:3.

[0030] The sequences of the specific amplification primer pairs used to detect rs56113850 are shown in SEQ ID NO:16 and SEQ ID NO:17; the nucleotide sequences of the single-base extension primers are shown in SEQ ID NO:4.

[0031] The sequences of the specific amplification primer pairs used to detect rs7256108 are shown in SEQ ID NO:18 and SEQ ID NO:19; the nucleotide sequences of the single-base extension primers are shown in SEQ ID NO:5.

[0032] The sequences of the specific amplification primer pairs used to detect rs61663607 are shown in SEQ ID NO:20 and SEQ ID NO:21; the nucleotide sequences of the single-base extension primers are shown in SEQ ID NO:6.

[0033] The sequences of the specific amplification primer pairs used to detect rs1137115 are shown in SEQ ID NO:22 and SEQ ID NO:23; the nucleotide sequences of the single-base extension primers are shown in SEQ ID NO:7.

[0034] The sequences of the specific amplification primer pairs used to detect rs8102683 are shown in SEQ ID NO:24 and SEQ ID NO:25; the nucleotide sequences of the single-base extension primers are shown in SEQ ID NO:8.

[0035] The sequences of the specific amplification primer pairs used to detect rs8192720 are shown in SEQ ID NO:26 and SEQ ID NO:27; the nucleotide sequences of the single-base extension primers are shown in SEQ ID NO:9.

[0036] The sequences of the first specific amplification primer pair used to detect the CYP2A6*4del site are shown in SEQ ID NO:28 and SEQ ID NO:29; the sequences of the second specific amplification primer pair used to detect the CYP2A6*4del site are shown in SEQ ID NO:30, SEQ ID NO:31 and SEQ ID NO:32.

[0037] This invention found that in patients receiving neoadjuvant chemotherapy combined with letrozole, those with intermediate / slow CYP2A6 metabolites were more likely to achieve pCR (pathological complete response) than those with normal metabolites. The area under the curve (AUC) of the pCR prediction model based on CYP2A6 metabolite type, presence or absence of letrozole, and clinicopathological characteristics was 0.823, which was superior to the prediction model without CYP2A6 metabolite type (AUC = 0.801). For patients with intermediate / slow CYP2A6 metabolites, neoadjuvant chemotherapy combined with letrozole improved disease-free survival compared to neoadjuvant chemotherapy alone.

[0038] Furthermore, different models were used to analyze SNP sites whose nature is currently unknown. This invention found that in patients receiving neoadjuvant chemotherapy combined with letrozole, the CYP2A6 rs61663607T>C TT type was more likely to achieve pCR than the TC and CC types. In patients receiving neoadjuvant chemotherapy combined with or without letrozole, the CYP2A6 rs7250713G>C CC type was more likely to achieve pCR than the CC and CG types.

[0039] Compared with existing technologies, the technical effects of this invention are positive and significant. This invention provides an auxiliary diagnostic tool for the efficacy of neoadjuvant chemotherapy with or without letrozole in patients with early-stage hormone receptor-positive breast cancer who have undergone neoadjuvant therapy and surgery. Attached Figure Description

[0040] Figure 1 Subgroup analysis showing the correlation between pCR and CYP2A6 metabolotype.

[0041] Figure 2 A pCR prediction model based on the CYP2A6 metabolite was established and evaluated.

[0042] Figure 3 This study demonstrates the establishment and evaluation of a pCR prediction model based on the CYP2A6 metabolite in the neoadjuvant chemotherapy plus letrozole group.

[0043] Figure 4 The disease-free survival of different CYP2A6 metabolites in the overall group was shown.

[0044] Figure 5 Subgroup analysis showing the correlation between DFS and CYP2A6 metabolotype.

[0045] Figure 6 The disease-free survival of patients in different neoadjuvant therapy groups within different metabolotype subgroups is shown in Figure A; patients with intermediate / slow metabolites are shown in Figure B. Detailed implementation method:

[0046] Example 1

[0047] 1. Materials and Methods

[0048] 1.1 Collect multiple breast cancer patients who received neoadjuvant chemotherapy with or without letrozole endocrine therapy. Before neoadjuvant therapy, collect about 5 ml of whole blood from the patients and store it in EDTA anticoagulant tubes at 4°C.

[0049] 1.2 All clinical data were prospectively collected at patient enrollment. Body mass index (BMI) was calculated as weight (kg) divided by the square of height (m), with a cutoff value of 25. All tissues were histologically diagnosed by the Department of Pathology, Renji Hospital, Shanghai Jiao Tong University School of Medicine. ER, PR, Ki67, and HER2 were measured in paraffin-embedded tumor samples obtained after core needle biopsy prior to neoadjuvant therapy. Hormone receptor (HR) positivity was defined as tumor cell nuclei stained with ≥1% ER and / or PR immunohistochemical (IHC) staining. The cutoff values ​​for ER and PR expression levels were set at 10%, with <10% considered low expression and ≥10% considered high expression. HER2 positivity was defined as IHC 3+ or fluorescence in situ hybridization amplification. The cutoff value for Ki67 was defined as 20%, with <20% considered low expression and ≥20% considered high expression.

[0050] 1.3 Whole blood was collected before processing and stored at -80°C. DNA extraction was performed using the TIANamp Genomic DNA Kit (Tiangen Biotech Co., Ltd., Beijing, China).

[0051] DNA extraction steps:

[0052] (1) Add 20 μl of Proteinase K solution to the blood sample (200 μl) and mix well.

[0053] (2) Add 200 μl of buffer GB to the mixture and incubate at 70℃ for 10 min.

[0054] (3) Add 200 μL of 100% ethanol to the mixture obtained in the previous step and shake for 15 s.

[0055] (4) Add the above solution to an adsorption column CB3, centrifuge at 12000 rpm for 30 s, discard the waste liquid, and place the adsorption column CB3 in a collection tube.

[0056] (5) Add 500 μl of buffer solution to the adsorption column CB3, centrifuge at 12000 rpm for 30 s, discard the waste liquid after centrifugation, and place the adsorption column CB3 in the collection tube.

[0057] (6) Add 600 μl of washing buffer PW to the adsorption column CB3 and centrifuge at 12000 rpm for 30 s. After centrifugation, discard the waste liquid and place the adsorption column CB3 in the collection tube.

[0058] (7) Repeat step (6).

[0059] (8) Place the adsorption column CB3 back into the collection tube, centrifuge at 12000 rpm for 2 min, and discard the waste liquid after centrifugation. Place the adsorption column CB3 at room temperature for a few minutes to dry.

[0060] (9) Place the adsorption column CB3 in another clean centrifuge tube, add 100 μl of elution buffer TE to the middle of the adsorption membrane, place at room temperature for 2-5 min, centrifuge at 12000 rpm for 2 min, and collect the solution in a centrifuge tube after centrifugation.

[0061] 1.4 After reviewing public databases (1000 Genomes Project) and literature, genotypes with an allele frequency >0.1 in the Han Chinese population and associated with CYP2A6 metabolites were screened. Ultimately, three genotypes associated with the loss or reduction of CYP2A6 metabolic enzyme activity were detected [CYP2A6*4, CYP2A6*1A(51A) i.e. rs1137115, CYP2A6*9 i.e. rs28399433], and seven genotypes whose association with CYP2A6 metabolic enzyme activity was not determined (rs61663607, rs8192720, rs56113850, rs7250713, rs7256108, rs8102683, rs8192728). Among them, CYP2A6*4 is the CYP2A6 gene deletion type, and the detection method is described in 1.9. The detection methods for the other genotypes are described in 1.5-1.8.

[0062] 1.5 Polymerase Chain Reaction Analysis

[0063] (1) Compile a 384-well reaction table based on the extracted samples, and label the DNA sample number and primers used for each well.

[0064] (2) Add 1 μL of DNA template to each well of the 384-well plate according to the table, attach the membrane, centrifuge at 2000 rpm for 10 seconds and set aside.

[0065] (3) Prepare the PCR reaction solution according to the table below:

[0066]

[0067] (4) Take a row of 12 tubes, add 133 μL of the prepared PCR reaction solution to each well, and centrifuge briefly before use.

[0068] (5) Use a 10ul pipette to take 4μl of PCR reaction solution from the 12-tube strip and add it to a 384-well plate containing 1μl of DNA, so that the final volume of each well is 5μl (note that the pipette tip should not touch the DNA sample in the 384-well plate; if it does, the pipette tip should be replaced immediately).

[0069] (6) Briefly centrifuge the 384-well plate containing 5 μl of reaction solution, then place it in a PCR instrument and run the reaction program named "PCR". The PCR program is as follows:

[0070]

[0071]

[0072] After the PCR reaction is completed, briefly centrifuge the 384-well plate and store it at 4°C for later use.

[0073] SAP processing

[0074] (1) Prepare the SAP reaction solution according to the following order:

[0075]

[0076] (2) Take a row of 12 tubes, dispense the SAP reaction solution into each well at 66 μL, centrifuge briefly, then dispense 2 μL into each well of a 384-well PCR reaction plate using a 10 μL pipette, seal the plate and centrifuge.

[0077] (3) Place the 384-well plate containing the SAP reaction solution into the PCR instrument and run the reaction program named "SAP".

[0078] SAP procedure: 37℃, 40min → 85℃, 5min → 4℃, forever

[0079] (4) After the reaction is complete, remove the 384-well plate and centrifuge briefly for later use.

[0080] 1.6 Extension reaction

[0081] (1) Preparation of iPlex reaction reagents

[0082]

[0083]

[0084] (2) Take a row of 12 tubes, dispense iPlex reaction solution into each well at 66 μL, centrifuge briefly, then dispense 2 μL into each well of a 384-well PCR reaction plate using a 10 μL multi-channel pipette, seal and centrifuge.

[0085] (3) Place the 384-well plate containing the iPlex reaction solution into the PCR instrument and run the reaction program named "extension".

[0086] The procedure is shown in the table below:

[0087]

[0088] (4) After the reaction is complete, remove the 384-well plate and centrifuge briefly for later use.

[0089] 1.7 Product Purification

[0090] (1) Take 6 mg of resin and spread it evenly on a 384-well resin scraper. Scrape off the excess resin and let it stand for 20 minutes.

[0091] (2) Centrifuge the 384-well plate at 1000 rpm for 1 min after the reaction is complete. Add 25 μL of deionized water to each well and invert it on top of the resin plate (make sure it is fixed and does not move). Then invert the plate and place the resin plate on the 384-well plate. Tap the plate to make the resin fall into the 384-well plate and seal it.

[0092] (3) Using the long axis of the 384-well plate as the axis, rotate the 384-well plate for 20 minutes, centrifuge at 3500 rpm for 5 minutes and then set aside.

[0093] 1.8 Detection

[0094] (1) Nanodispenser SpectroCHIP chip sampling

[0095] The test sample was transferred from the 384-well reaction plate to the MassARRAY SpectroCHIP chip with a surface-coated matrix.

[0096] (2) Mass spectrometry detection by MassARRAY Analyzer Compac

[0097] After transferring the sample to the SpectroCHIP chip, it can be placed in the mass spectrometer for detection.

[0098] (3) The experimental results were analyzed using TYPER software to obtain CYP2A6 genotyping data.

[0099] Table 1 Primer sequences

[0100]

[0101]

[0102] The following are genotypes related to CYP2A6 metabolites:

[0103] Genotype rs8192728 G > T rs7250713 G > C rs28399433 A > C rs56113850 T > C rs7256108 T > G rs61663607 T > C rs1137115 T > C rs8102683 T > C rs8192720 G > A

[0104] Below are the sequences of CYP2A6 metabolotype-related genotypes (uppercase letters indicate PCR primer positions, and SNP site positions are indicated by square brackets):

[0105]

[0106]

[0107]

[0108] 1.9 PCR detection of CYP2A6*4del site (PCR detection of CYP2A6*4del site was performed according to the method referenced in the literature (Literature source: PMID:10217419))

[0109] Primer information

[0110] PCR1: Approximately 2100 bp, the first step of amplification, used to amplify the target fragment.

[0111] 2Aex7F: 5P-GGCCAAGATGCCCTACATG-3P (SEQ ID NO.28)

[0112] 2A6R3:5P-GGAATAGGTGCTTTTTAAGAATC-3P(SEQ ID NO.29)

[0113] PCR2: Approximately 1300 bp, the second amplification step, used to distinguish between CYP2A6*1 and CYP2A6*4.

[0114] 2A6ex8F:5P-CACTTCCTGAATGAG-3P(SEQ ID NO.30)

[0115] 2A7ex8F:5P-CATTTCCTGGATGAC-3P(SEQ ID NO.31)

[0116] 2A6R2: 5P-AAAATGGGCATGAACGCCC-3P (SEQ ID NO. 32).

[0117] The first step used 2Aex7F (forward primer) and 2A6R3 (reverse primer); the second step used 3 primers: PCR2-1 used primers 2A6ex8F (forward primer) and 2A6R2 (reverse primer), and the specific band amplified was CYP2A6*1; PCR2-2 used primers 2A7ex8F (forward) and 2A6R2 (reverse primer), and the specific band amplified was CYP2A6*4.

[0118] Electrophoresis band interpretation:

[0119] If only PCR2-1 shows an amplification band, then it is a homozygous CYP2A6*1;

[0120] If only PCR2-2 shows an amplification band, then it is a homozygous CYP2A6*4;

[0121] If PCR2-1 and PCR2-2 show amplification bands, then it is a CYP2A6*1 / CYP2A6*4 heterozygous type;

[0122] If neither PCR2-1 nor PCR2-2 shows a band, then the amplification has failed.

[0123] Electrophoresis conditions: 1.5% agarose gel electrophoresis, 20 min, 3 μl PCR product plus 2 μl buffer for a total of 5 μl for electrophoresis.

[0124] System and reaction conditions

[0125] The reaction system is shown in the table below:

[0126]

[0127] Note: The DNA template for PCR2-1 / PCR2-2 is the amplification product of PCR1.

[0128] Reaction conditions for PCR1 in the reaction system:

[0129]

[0130] Reaction conditions for PCR2-1 and PCR2-2:

[0131]

[0132] 1.10 Determining the CYP2A6 genotype

[0133] The mRNA sequence of CYP2A6 is as follows:

[0134]

[0135] The CYP2A6 genotype grouping was based on the generally accepted criteria reported in the literature (Source: PMID: 26662855). Based on the effect of CYP2A6 alleles on enzyme activity, they were divided into "reduced function (D)" alleles [CYP2A6*9 and *1A(51A)] and "loss of function (L)" alleles (CYP2A6*4): (1) "Normal metabolizers" were defined as those without either D or L alleles (CYP2A6 wild-type, i.e., *1 / *1); (2) "Intermediate metabolizers" were defined as those with only one D allele [i.e., *1A(51A) / *1 or *9 / *1]; (3) "Slow metabolizers" had one L allele or two D alleles [e.g., *1 / *4 or *1A(51A) / *1A(51A)]. In this analysis, different models were used to analyze SNP sites whose nature was currently unknown. For example, for CYP2A6 rs56113850, C is a minor allele compared to T. The specific model settings are: dominant model comparing TT vs TC+CC; recessive model comparing TC+TT vs CC; additive model comparing CC vs TC vs TT.

[0136] 1.11 Study Endpoint

[0137] pCR was defined as the absence of invasive carcinoma (ypT0 / is) in a breast sample obtained at the time of surgery; disease-free survival (DFS) was defined as the time from surgery to local recurrence, distant metastasis, second primary malignancy, or patient death. Adverse events (AEs) were assessed during the study and graded according to the Common Terminology Criteria for Adverse Events (CTCAE) version 4.01.

[0138] 1.12 Statistical Analysis

[0139] Chi-square tests were performed to compare categorical variables. Fisher's exact test was used to examine the bias from Hardy-Weinberg equilibrium. Univariate and multivariate logistic regressions were used to calculate the relationship between CYP2A6 metabolotype and pCR in the overall and subgroups, adjusting for age, tumor size, ER, PR, HER2, BMI, and neoadjuvant therapy regimen. To provide a quantitative tool to predict the probability of achieving pCR, nomograms were constructed based on multivariate logistic analysis of all patients in this study. The accuracy of the nomograms was assessed using calibration curves, and receiver operating characteristic (ROC) curves were generated to further clarify whether the model incorporating CYP2A6 metabolotype improved the ability to predict sensitivity to neoadjuvant chemotherapy combined with endocrine therapy compared to the clinicopathological model. Median follow-up time was calculated using the reverse Kaplan-Meier method, Kaplan-Meier curves were used to compare DFS between the two groups, and a log-rank test was performed. 95% confidence intervals (CI) and hazard ratios (HRs) were calculated using a Cox proportional hazards regression model, with adjustments made for age, tumor size, ER, PR, HER2BMI, and neoadjuvant therapy. All statistical analyses were performed using R version 3.6.1 (www.r-project.org). All analyses were two-tailed, and p < 0.05 was considered statistically significant.

[0140] 2. Experimental Results

[0141] 2.1 Patient baseline characteristics

[0142] A total of 114 patients met the criteria for this study (Table 2). Of these 114 patients, 58 (50.9%) received neoadjuvant chemotherapy combined with letrozole endocrine therapy, and 47 (41.2%) were HER2-positive breast cancer patients who received trastuzumab targeted therapy. Except for rs7256108 and rs8102683, the genotype frequencies of all SNPs were in Hardy-Weinberg equilibrium. Based on the influence of CYP2A6 alleles [CYP2A6*4, *9, *1A (51A)] on enzyme activity, we divided the patients into three metabolites (Table 3). Among all patients, 30 (26.3%) were normal metabolites, 56 (49.1%) were intermediate metabolites, and 26 (22.8%) were slow metabolites; Table 2. No significant correlation was found between CYP2A6 metabolite type and clinicopathological features such as age, tumor size, and lymph node status (Table 4).

[0143]

[0144]

[0145]

[0146]

[0147]

[0148] 2.2 Relationship between CYP2A6 metabolites and pCR

[0149] Of the 114 patients in this study, 33 (26.8%) achieved pathological complete remission (pCR). There was no significant difference in pCR rate between intermediate / slow CYP2A6 metabolizers and normal metabolizers (27.4% vs 33.3%; OR = 0.894, 95% CI 0.300–2.669; P = 0.841; Table 5).

[0150] In patients receiving neoadjuvant chemotherapy combined with letrozole, univariate analysis showed no difference in pCR rate between patients with intermediate / slow CYP2A6 metabolites and those with normal CYP2A6 metabolites (29.17% vs 12.5%; OR = 2.882, 95% CI 0.324–25.646, P = 0.342). In multivariate analysis adjusted for age, ER, PR, HER2, tumor size, and BMI, the pCR rate was significantly higher in patients with intermediate / slow CYP2A6 metabolites compared to those with normal metabolites (OR = 33.168, 95% CI 1.064–1033.970, P = 0.046; Table 6). Furthermore, ER expression level (OR = 0.963, 95% CI 0.934–0.994, P = 0.019) and HER2 status (OR = 9.695, 95% CI 1.561–60.213, P = 0.015) were also independent predictors of pCR in patients receiving neoadjuvant chemotherapy combined with letrozole (Table 7). Multivariate regression analysis between other CYP2A6 genotypes and pCR showed that, in the entire patient group, CYP2A6 rs7250713G>C was associated with a higher pCR rate in the recessive model (OR = 4.124, 95% CI 1.408–12.073, P = 0.01). In patients in the neoadjuvant chemotherapy plus letrozole group, CYP2A6 rs61663607T>C was associated with a lower pCR rate in the dominant model (OR = 0.119, 95% CI 0.014–0.991, P = 0.049) (Table 8).

[0151] We observed a significant interaction between different treatment modalities (whether or not letrozole was used in combination with neoadjuvant therapy) and different CYP2A6 metabolite status in predicting pCR in neoadjuvant therapy (P = 0.031). Figure 1 In subgroup analyses of different neoadjuvant therapy modalities, we found that the pCR rate in patients with intermediate / slow CYP2A6 metabolism (25.0%) was similar to that in patients with normal metabolism (40.9%) who received neoadjuvant chemotherapy alone (OR = 0.428, 95% CI 0.120-1.532, P = 0.192). Figure 1 However, in patients receiving neoadjuvant chemotherapy combined with letrozole, the pCR rate in patients with intermediate / slow CYP2A6 metabolism (29.2%) was higher than that in patients with normal metabolism (12.5%; OR = 33.168, 95% CI 1.064-1033.970, P = 0.046). Figure 1 ).

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[0161] Based on the interaction between CYP2A6 metabolotype and neoadjuvant therapy regimen, a nomogram was constructed and eight variables, including CYP2A6 metabolotype, neoadjuvant therapy regimen (neoadjuvant chemotherapy with or without letrozole endocrine therapy), and clinicopathological characteristics, were incorporated to predict the neoadjuvant therapy outcome in patients. Calibration curves showed strong agreement between the model's predicted probability and observed pCR results. Figure 2Furthermore, we used ROC curves to compare the accuracy of different models to evaluate the predictive value of CYP2A6 metabolite status for pCR. Model 1 is a clinicopathological treatment model: including clinicopathological features + neoadjuvant therapy, with an area under the curve (AUC) of 0.801; Model 2 is a clinicopathological treatment CYP2A6 model: including clinicopathological features + neoadjuvant therapy + CYP2A6 metabolic status, with an AUC of 0.823, numerically superior to the clinicopathological treatment model (Model 1) that did not include CYP2A6 metabolic status.

[0162] Nonographs were also established in the chemotherapy + letrozole subgroup, and the accuracy of different models in this subgroup was evaluated using ROC curves to assess the predictive value of CYP2A6 metabolite for pCR. The clinicopathological model, which included clinicopathological features, had an area under the curve (AUC) of 0.878; the clinicopathological CYP2A6 model, which included both clinicopathological features and CYP2A6 metabolic status, had an AUC of 0.907, numerically superior to the clinicopathological model that did not include CYP2A6 metabolic status. Figure 3 ).

[0163] 2.3 Relationship between CYP2A6 metabolite and survival

[0164] The median follow-up time for all patients was 43 (37-49) months. Univariate analysis showed no significant association between CYP2A6 metabolite and DFS (HR = 1.630, 95% CI 0.541-4.912, P = 0.385). Figure 4 Multivariate analysis also revealed no significant difference in DFS among different CYP2A6 metabolites (HR = 2.338, 95% CI 0.715–7.650, P = 0.16; Table 10). However, DFS survival analysis revealed a significant interaction between CYP2A6 metabolite type and whether neoadjuvant chemotherapy was combined with letrozole (P = 0.021). Figure 5 ).

[0165] In the CYP2A6 intermediate / slow metabolizer subgroup (n=84), neoadjuvant chemotherapy combined with letrozole was observed to significantly improve DFS compared with neoadjuvant chemotherapy alone (HR=0.216, 95% CI 0.062-0.749, P=0.016). Figure 4-6 In the CYP2A6 normal metabolizer subgroup (n=30), neoadjuvant therapy combined with letrozole did not significantly improve DFS compared with neoadjuvant chemotherapy alone (HR=9.435, 95% CI 0.284-312.924, P=0.209). Figure 6 ).

[0166] In the subgroup receiving neoadjuvant chemotherapy combined with letrozole, disease-free survival (DFS) was similar in patients with intermediate / slow CYP2A6 metabolites to that of patients with normal metabolites (HR = 0.390, 95% CI 0.038–4.024, P = 0.429). In patients receiving neoadjuvant chemotherapy, no significant difference in DFS was found between different CYP2A6 metabolites (HR = 4.598, 95% CI 0.969–21.818, P = 0.055). Furthermore, no other associations were found between CYP2A6 genotypes and survival outcomes.

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[0168] 4.4 Relationship between CYP2A6 metabolites and safety

[0169] This study collected adverse event (AE) information from 89 patients (78.1%), including 55 patients in the neoadjuvant chemotherapy and letrozole group and 34 patients in the neoadjuvant chemotherapy group. Adverse events (AEs) occurring in different metabolic states are shown in Tables 10 and 11.

[0170] In the subgroup receiving neoadjuvant chemotherapy combined with letrozole, the most common adverse events (AEs) in patients with normal and intermediate / slow metabolites were alopecia (71.4% vs 87.5%), nausea (71.4% vs 72.9%), fatigue (52.1% vs 57.1%), increased total bilirubin (42.9% vs 27.1%), hyperlipidemia (40.0% vs 46.2%), elevated AST (28.6% vs 39.6%), elevated ALT (14.3% vs 20.8%), and hypercholesterolemia (17.9% vs 10.5%). The most common grade 3 or 4 AEs in patients with normal and intermediate / slow metabolites were fatigue (0.0% vs 6.3%) and hyperlipidemia (0.0% vs 5.1%).

[0171] For the neoadjuvant chemotherapy subgroup, the most common adverse events (AEs) (>10% of patients) were hyperlipidemia (85.7% vs 50.0%), alopecia (50.0% vs 50.0%), nausea (42.9% vs 45.0%), increased total bilirubin (42.9% vs 45.0%), increased AST (35.7% vs 55.0%), fatigue (28.6% vs 35.0%), elevated ALT (14.2% vs 1.5%), and hypercholesterolemia (0.0% vs 16.6%). The most common grade 3 or 4 AE report was fatigue (7.0% vs 0.0%).

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[0174] Advantages:

[0175] Elucidating that CPY2A6 gene polymorphism may be a novel biomarker for predicting and prognostic outcomes in postmenopausal hormone receptor-positive breast cancer patients undergoing neoadjuvant chemotherapy combined with letrozole treatment has certain clinical guiding significance for guiding individualized precision endocrine therapy in patients with hormone receptor-positive breast cancer.

Claims

1. Use of reagents for detecting CYP2A6 genotypes in the preparation of kits predicting pCR in patients with hormone receptor-positive breast cancer receiving neoadjuvant chemotherapy combined with letrozole. Hormone receptor positivity is defined as ≥1% of tumor cell nuclei stained by immunohistochemistry for ER and / or PR. In patients receiving neoadjuvant chemotherapy combined with letrozole, patients with intermediate / slow metabolizers of CYP2A6 are more likely to achieve pCR than those with normal metabolizers. Intermediate metabolizers are defined as having one D allele, i.e., *1A(51A) / *1 or *9 / *1; slow metabolizers are defined as having one L allele or two D alleles, i.e., *1 / *4 or *1A(51A) / *1A(51A); normal metabolizers are defined as having neither D nor L alleles. pCR is defined as the absence of invasive carcinoma in the breast sample obtained at the time of surgery. The CYP2A6 metabolizer-related genotypes are: CYP2A6*9, CYP2A6*1A(51A), and CYP2A6*4 del; among which, CYP2A6*9 means rs28399433 A>C; CYP2A6*1A (51A) means rs1137115T>C.

2. The use according to claim 1, characterized in that: The sequences of the specific amplification primer pairs used to detect rs28399433 are shown in SEQ ID NO:14 and SEQ ID NO:15; the nucleotide sequences of the single-base extension primers are shown in SEQ ID NO:

3. The sequences of the specific amplification primer pairs used to detect rs1137115 are shown in SEQ ID NO:22 and SEQ ID NO:23; the nucleotide sequences of the single-base extension primers are shown in SEQ ID NO:

7. The sequences of the first specific amplification primer pair used to detect the CYP2A6*4 del site are shown in SEQ ID NO:28 and SEQ ID NO:29; the sequences of the second specific amplification primer pair used to detect the CYP2A6*4 del site are shown in SEQ ID NO:30, SEQ ID NO:31 and SEQ ID NO:32.

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