WO2023099350A1 - In vitro method for predicting the response of patients suffering from her2+ breast cancer to a treatment with anti-her2 neoadjuvant therapy - Google Patents

Abstract

The present invention refers to an in vitro method for predicting the response of patients suffering from HER2+ breast cancer to a treatment with anti-HER2 neoadjuvant therapy, for deciding or recommending whether to treat patients suffering from HER2+ breast cancer with anti-HER2 neoadjuvant therapy, for classifying HER2+ breast cancer patients into responder or non-responder patients to anti-HER2 neoadjuvant therapy, or for the prognosis of patients suffering from HER2+ breast cancer.

Description

IN VITRO METHOD FOR PREDICTING THE RESPONSE OF PATIENTS SUFFERING FROM HER2+ BREAST CANCER TO A TREATMENT WITH ANTI¬

HERZ NEOADJUVANT THERAPY

FIELD OF THE INVENTION

The present invention refers to the medical filed. Particularly, the present invention refers to an in vitro method for predicting the response of patients suffering from HER2+ breast cancer to a treatment with anti-HER2 neoadjuvant therapy, for deciding or recommending whether to treat patients suffering from HER2+ breast cancer with anti-HER2 neoadjuvant therapy, for classifying HER2+ breast cancer patients into responder or non-responder patients to anti- HER2 neoadjuvant therapy, or for the prognosis of patients suffering from HER2+ breast cancer.

STATE OF THE ART

Breast cancer (BC) is the tumor with the highest incidence worldwide and the leading cause of cancer-related death in women. Its incidence varies according to sex (it is 100 times more frequent in women than in men) and geographical region (being more frequent in Europe, Australia and North America). BC is a heterogeneous disease, whose classic prognostic assessment has been based on clinical and histopathological parameters. The staging of cases has been based mainly on lymph node involvement, where relapse-free survival decreases as the number of affected nodes increases, and tumor size, the smaller the tumor the greater the disease-free survival. In addition, the histologic grade or degree of differentiation of the tumor cells is an important prognostic factor.

Historically, immunohistochemical (IHC) markers have allowed differentiation of various breast cancer subtypes with different prognoses: the expression of estrogen and progesterone receptors (ER and PR, respectively) allowed identification of patients with better prognosis and candidates for hormone therapy; measurement of HER2 membrane receptor overexpression subsequently allowed differentiation of a group of more aggressive tumors but candidates for targeted therapy. On the other hand, the tumor proliferation index, determined by the expression of molecules such as MIB-1 (ki67), has prognostic value; the greater the proliferative activity, the worse the prognosis. Advances in molecular biology techniques have made it possible to improve the classification of these tumors. Based on differential expression analysis by microarray, different molecular subtypes have been defined based on different genetic patterns with implications for both treatment and prognosis. The results demonstrated a great heterogeneity of BC and the following subtypes were differentiated: luminal A (25- 45% all breast tumors), luminal B (15-50%), HER2+ (5-15%) and basal-like or triple negative (10-20%).

In the case of patients with HER2+ subtype, neoadjuvant chemotherapy is the treatment of choice in locally advanced and inflammatory breast cancer. The goals of this treatment are to improve surgical options (to convert inoperable tumors into operable ones, as well as to obtain better cosmetic results), to determine the response to chemotherapy (pathologic complete response [pCR]) and to increase disease-free survival. Neoadjuvant chemotherapy is an ideal clinical situation to investigate molecular predictors of response, predict patients who will achieve a pCR and patients with a favorable prognosis, even if they do not achieve a pCR. The definitive way to evaluate the response to neoadjuvant treatment is the anatomopathological study of the surgical specimen. Although there are disparate criteria, in general terms we speak of pCR when there is evidence of complete disappearance of the infiltrating component in both the breast and the axilla (ypTo/is ypNO).

Biological treatments or targeted therapies are designed to act precisely on specific molecular processes that the tumor needs for its growth and progression. This is in contrast to "traditional chemotherapies", which affect all rapidly dividing cells, whether they are cancerous or healthy cells. Therefore, biologic treatments can be more controlled than other types of treatment and less harmful to healthy cells. Some targeted biologic treatments are available specifically for HER2 -positive breast cancer. These are monoclonal antibodies such as trastuzumab, which specifically targets the HER2 receptor.

Although anti-HER2 neoadjuvant therapy is a very important treatment for patients suffering from HER2+ breast cancer, there is an unmet medical need of finding reliable strategies aimed at predicting the response of patients suffering from HER2+ breast cancer to a treatment with anti-HER2 neoadjuvant therapy, deciding or recommending whether to treat patients suffering from HER2+ breast cancer with anti-HER2 neoadjuvant therapy, classifying HER2+ breast cancer patients into responder or non-responder patients to anti-HER2 neoadjuvant therapy, or prognosing patients suffering from HER2+ breast cancer.

The present invention is focused on solving this problem and a new strategy is herein described for predicting the response of patients suffering from HER2+ breast cancer to a treatment with anti-HER2 neoadjuvant therapy, for deciding or recommending whether to treat patients suffering from HER2+ breast cancer with anti-HER2 neoadjuvant therapy, for classifying HER2+ breast cancer patients into responder or non-responder patients to anti-HER2 neoadjuvant therapy, or for the prognosis of patients suffering from HER2+ breast cancer.

DESCRIPTION OF THE INVENTION

Brief description of the invention

Such as it is explained above, the present invention refers to an in vitro method for predicting the response of patients suffering from HER2+ breast cancer to a treatment with anti-HER2 neoadjuvant therapy, for deciding or recommending whether to treat patients suffering from HER2+ breast cancer with anti-HER2 neoadjuvant therapy, for classifying HER2+ breast cancer patients into responder or non-responder patients to anti-HER2 neoadjuvant therapy, or for the prognosis of patients suffering from HER2+ breast cancer.

Particularly, the results provided in the present invention significantly show a differential expression of the UDP-glucuronosyltransferase 2B15 gene (UGT2B15; p-value= 0.0060). In particular, the present invention demonstrates that there is an increased expression of UGT2B15 gene in those patients who do not respond to neoadjuvant therapy.

So, the first embodiment of the present invention refers to an in vitro method for predicting the response of patients suffering from HER2+ breast cancer to a treatment with anti-HER2 neoadjuvant therapy which comprises assessing, before treatment, the level of expression of UGT2B15 gene in a biological sample obtained from the patient, wherein if the level of expression of UGT2B 15 gene is higher than a pre-established threshold value, it is an indication that the patient does not respond to the treatment. The second embodiment of the present invention refers to an in vitro method for deciding or recommending whether to treat patients suffering from HER2+ breast cancer with anti-HER2 neoadjuvant therapy which comprises determining the level of expression of UGT2B15 gene in a biological sample obtained from the patient, wherein if the level of expression of UGT2B 15 gene is higher than a pre-established threshold value, it is an indication that the patient does not respond to the treatment and anti-HER2 neoadjuvant therapy is not recommended.

The third embodiment of the present invention refers to an in vitro method for classifying HER2+ breast cancer patients into responder or non-responder patients to anti-HER2 neoadjuvant therapy which comprises determining the level of expression of UGT2B15 gene in a biological sample obtained from the patient, wherein if the level of expression of UGT2B 15 gene is higher than a pre-established threshold value, it is an indication that the patient does not respond to the treatment.

The fourth embodiment of the present invention refers to an in vitro method for the prognosis of patients suffering from HER2+ breast cancer which comprises determining the level of expression of UGT2B15 gene in a biological sample obtained from the patient, wherein if the level of expression of UGT2B15 gene is higher than a pre-established threshold value, it is an indication of bad prognosis.

The fifth embodiment of the present invention refers to the in vitro use of UGT2B15 for predicting the response of patients suffering from HER2+ breast cancer to a treatment with anti-HER2 neoadjuvant therapy, for deciding or recommending whether to treat patients suffering from HER2+ breast cancer with anti-HER2 neoadjuvant therapy, for classifying HER2+ breast cancer patients into responder or non-responder patients to anti-HER2 neoadjuvant therapy, or for the prognosis of patients suffering from HER2+ breast cancer.

The sixth embodiment of the present invention refers to the in vitro use of a kit consisting of reagents for measuring the level of expression of UGT2B15 for predicting the response of patients suffering from HER2+ breast cancer to a treatment with anti-HER2 neoadjuvant therapy, for deciding or recommending whether to treat patients suffering from HER2+ breast cancer with anti-HER2 neoadjuvant therapy, for classifying HER2+ breast cancer patients into responder or non-responder patients to anti-HER2 neoadjuvant therapy, or for the prognosis of patients suffering from HER2+ breast cancer. The seventh embodiment of the present invention refers to anti-HER2 antibody or drug, or any pharmaceutical composition comprising thereof, optionally including pharmaceutically acceptable excipients or carriers, for use in the treatment of patients suffering from HER2+ breast cancer, wherein the patient is a responder patient characterized by a UGT2B15 level of expression lower than a pre-established threshold value.

In a preferred embodiment of the invention, the anti-HER2 neoadjuvant therapy is an anti- HER2 antibody.

In a preferred embodiment of the invention, the anti-HER2 neoadjuvant therapy is an anti- HER2 antibody comprising: trastuzumab or pertuzumab, or any combination thereof.

In a preferred embodiment of the invention, the anti-HER2 neoadjuvant therapy is an anti- HER2 antibody comprising: trastuzumab or pertuzumab, or any combination thereof, in combination with chemotherapy.

In a preferred embodiment of the invention, the biological sample is selected from the group comprising: tissue, blood, serum or plasma.

For the purpose of the present invention, the following terms are defined:

• The term "comprising" means including, but not limited to, whatever follows the word "comprising". Thus, the use of the term "comprising" indicates that the listed elements are required or mandatory but that other elements are optional and may or may not be present.

• The term "consisting of’ means including, and limited to, whatever follows the phrase “consisting of’. Thus, the phrase "consisting of’ indicates that the listed elements are required or mandatory and that no other elements may be present.

• The term “pre-established threshold level” typically refers to the level measured in responder patients. A "threshold value" or "cut-off value" can be determined experimentally, empirically, or theoretically. A “threshold value” can also be arbitrarily selected based upon the existing experimental and/or clinical conditions, as would be recognized by a person of ordinary skilled in the art. The “threshold value” has to be determined in order to obtain the optimal sensitivity and specificity according to the function of the test and the benefit/risk balance (clinical consequences of false positive and false negative). Preferably, the person skilled in the art may compare the expression level of the gene obtained according to the method of the invention with a defined “threshold value”. In one embodiment of the present invention, the “threshold value” is derived from the level of biomarker determined in a control sample derived from responder patients. Furthermore, retrospective measurement of the level of the biomarker in properly banked historical subject samples may be used in establishing these “threshold values”. Typically, the optimal sensitivity and specificity (and so the “threshold value”) can be determined using a Receiver Operating Characteristic (ROC) curve based on experimental data. For example, after determining the levels of the biomarker in a group of reference, one can use algorithmic analysis for the statistic treatment of the measured concentrations of biomarkers in biological samples to be tested, and thus obtain a classification standard having significance for sample classification. The full name of ROC curve is receiver operator characteristic curve, which is also known as receiver operation characteristic curve. It is mainly used for clinical biochemical diagnostic tests. ROC curve is a comprehensive indicator that reflects the continuous variables of true positive rate (sensitivity) and false positive rate (1 -specificity). It reveals the relationship between sensitivity and specificity with the image composition method. A series of different cut-off values (thresholds or critical values, boundary values between normal and abnormal results of diagnostic test) are set as continuous variables to calculate a series of sensitivity and specificity values. Then sensitivity is used as the vertical coordinate and specificity is used as the horizontal coordinate to draw a curve. The higher the area under the curve (AUC), the higher the accuracy of diagnosis. On the ROC curve, the point closest to the far upper left of the coordinate diagram is a critical point having both high sensitivity and high specificity values. The AUC value of the ROC curve is between 1.0 and 0.5. When AUC>0.5, the diagnostic result gets better and better as AUC approaches 1. When AUC is between 0.5 and 0.7, the accuracy is low. When AUC is between 0.7 and 0.9, the accuracy is moderate. When AUC is higher than 0.9, the accuracy is quite high. This algorithmic method is preferably done with a computer. Existing software or systems in the art may be used for the drawing of the ROC curve. Description of the figures

Figurel. Differentially expressed transcripts. Representation of a volcano plot showing transcripts that meet the selection criteria of a log fold change greater than 1.5 or less than -1.5 and a p-value = 0.05.

Figure 2. Expression data obtained on the Affymetrix Clariom D pico array microarray. The hybridization signals obtained for each of the samples analyzed are represented by a scatter plot. The microarray uses the probe TC1700012248.hg. l for HER2/ERBB2 and TC0400012924.hg. l for the UGT2B15 gene. Statistical differences were calculated by applying the Mann-Whitney U test. Y axis represents signal (log2).

Figure 3. Validation of the UGT2B15 gene by qPCR. The expression analysis of the HER2/ERBB2 and UGT2B15 genes was performed by qPCR. The data are presented with a scatter plot, where the expression of the aforementioned genes in patients who responded to neoadjuvant treatment (pCR; n=5) is compared with patients who did not respond (non-pCR; n=6). Normalization was performed with the endogenous microglobulin-P2 gene (B2M). Statistical differences were calculated by applying the Mann-Whitney U test.

Detailed description of the invention

The present invention is illustrated by means of the examples set below without the intention of limiting its scope of protection.

Example 1. Material and methods

Example 1.1. Patients and biological samples

The present study was performed in 30 patients who underwent surgery for breast cancer. Eighteen were used in the discovery cohort and 12 in the validation cohort. The patients were enrolled at the Hospital Universitario Virgen del Rocio in Seville. The clinical characteristics of the patients are summarized in Table 1. All patients were treated with chemotherapy plus targeted therapy against HER2 with trastazumab and in some cases with trastazumab and pertuzumab. Informed consent was obtained from all patients and the hospital ethics committee approved the study. Patients were stratified according to response to neoadjuvant treatment. Those patients who performed a pCR formed the group of responders, while the determination of residual disease following RECIST criteria after analysis of the surgical specimen was used as a reason for inclusion in the group of non-responders. Table 1

Clinical data of the patients included in the study Example 1.2. RNA isolation and hybridization with Clarion D microarray

Total RNA was extracted using the RecoverAll Total Nucleic Acid Isolation commercial kit (Ambion, Austin, TX, USA) following the manufacturer's instructions. RNA concentration was measured using the NanoDrop ND-1000 spectrophotometer (Nanodrop Tech, DE, USA). A total of 18 samples were labelled and hybridized with Clariom D pico Array microarray (Affymetrix, Santa Clara, CA, USA) following the manufacturer's instructions. Briefly, complementary double-stranded DNA (cDNA) and complementary RNA (cRNA) were synthesized from 30 ng of RNA, then, the biotinylated cDNA was hybridized for 16 hours in an Affymetrix GeneChip 645 hybridization oven at45°C. The arrays (microarray) were stained using GeneChip Fluidics Station 450. Subsequently, the chip was scanned with the GeneChip™ 3000 scanner. Affymetrix Clariom D .CEL files with intensity data were normalized to produce probe-level signal expression values and using transcriptome analysis console (TAC) software the expression pattern of genes, exons, splicing variants and related pathways involved in the response to neoadjuvant chemotherapy was analyzed.

Example 1.3. Analysis of UGT2B15 expression by qRT-PCR

Total RNA was extracted using the RecoverAll Total Nucleic Acid Isolation commercial kit (Ambion, Austin, TX, USA) following the manufacturer's instructions. RNA concentration was measured using the NanoDrop ND-1000 spectrophotometer (Nanodrop Tech, DE, USA). Expression analysis was performed in a three-step process. First, 50 ng of total RNA was used to perform retrotranscription with the High capacity cDNA reverse transcription kit (Applied Biosystems, CA, USA) according to the manufacturer's instructions. The reactions were incubated in a thermal cycler (Gene Technologies, Essex, UK) 10 min at 25°C, 120 min at 37°C, 5 min at 85°C and kept at 4°C. Secondly, cDNA preamplification was performed with TaqMan PreAmp Master Mix (Applied Biosystems, CA, USA). For this, 12.5 uL of the cDNA sample was mixed with 50 uL of TaqMan Pre Amp Master Mix solution and 12.5 uL of the probes of interest diluted in TE buffer. The reactions were incubated in a thermal cycler (Gene Technologies, Essex, UK) 10 min at 95°C followed by 14 cycles of 15 sec at 95°C and 4 min at 60°C, one cycle of 10 min at 99°C and kept at 4°C. Finally, 2 uL of the pre-amplified product was mixed with TaqMan Universal PCR master mix was amplified by qPCR using TaqMan® Assays expression probes. Samples were processed and analyzed on a ViiA 7 real-time PCR system (Applied Biosystems, CA, USA). The reactions were incubated 10 min at 95°C followed by 40 cycles of 15 sec at 95°C and 1 min at 60°C. Expression of the genes of interest (UGT2B15 and ERBB2) was calculated relative to the endogenous control beta-2 - microglobulin (B2M). Data are presented as expression of the gene of interest = 2(-ACt), where ACt = (Ct of the gene of interest - Ct of the endogenous control). Data were plotted using GraphPad Prism v9.0. Statistical differences were calculated by applying the Mann-Whitney U test.

Table 2

TaqMan Assays used in this study

Example 2. Results

Example 2.1. Identification of differentially expressed transcripts

The Clariom D assay enables broad and deep transcriptome analysis and biomarker discovery. In this work we have combined it with Applied Biosystems ™ Transcriptome Analysis Console (TAC 4.0) software. These microarrays allow us to obtain information on the expression of coding and non-coding RNA. In this study and with the aim of identifying those transcripts that showed differential expression between responders and non-responders, the following filters were established: that the logarithm of the expression change (fold change) was greater than 1.5 or less than -1.5 and that the p-value was less than 0.05. The application of the filters revealed a differential expression of 451 transcripts (Figure 1). In statistics, a volcano plot is a type of scatter-plot that is used to quickly identify changes in large datasets composed of replicate data. It plots significance versus fold-change on the y- and x-axes, respectively. The first (horizontal) dimension is the fold change between the two groups (on a log scale, so that up- and down regulation appear symmetric), and the second (vertical) axis represents the p- value for a t-test of differences between samples (most conveniently on a negative log scale - so smaller p-values appear higher up). The first axis indicates biological impact of the change and the second indicates the statistical evidence, or reliability of the change.) Example 2.2. TaqMan gene expression assays to verify Clariom D assay data

Among the detected mRNAs with significant expression differences (Example 2.1), UGT2B15 was detected at least in 50% of patients of both compared groups (pCR and no-pCR) (Figure 2). This value was considered to be representative of the general behaviour of the study population. Therefore, we decided to validate the data using an independent cohort but with similar clinical features.

To validate the data, expression of target mRNA UGT2B15 was normalized in relation to the expression of B2M. The samples were processed and analysed on a ViiA7 System. Cycle threshold (Ct) values were calculated using the QuantStudio Software VI.2.4, an automatic baseline settings and a threshold of 0.2. In addition, HER2/ERBB2 was used as a positive control, as their differential expression has been described previously. Relative quantification of both mRNA expression levels was calculated by the 2⁽~^ACt) method. The 2 -AACt method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. When we stratified the patients according to the response to treatment (pCR or responder vs no pCR or non-responder) and compared the expression levels, our data significantly show differential expression of the UDP-glucuronosyltransferase 2B15 gene (Figure 3; p-value= 0.0060). In particular, our data demonstrate that there is an increased expression in those patients who do not respond to neoadjuvant therapy. This gene codes for a glycosyltransferase involved in the metabolism and elimination of toxic substances.

Claims

1. In vitro method for predicting the response of patients suffering from HER2+ breast cancer to a treatment with anti-HER2 neoadjuvant therapy which comprises assessing the level of expression of UGT2B15 gene in a biological sample obtained from the patient, wherein if the level of expression of UGT2B15 gene is higher than a pre- established threshold value, it is an indication that the patient does not respond to the treatment.

2. In vitro method for deciding or recommending whether to treat patients suffering from HER2+ breast cancer with anti-HER2 neoadjuvant therapy which comprises determining the level of expression of UGT2B15 gene in a biological sample obtained from the patient, wherein if the level of expression of UGT2B15 gene is higher than a pre-established threshold value, it is an indication that the patient does not respond to the treatment and anti-HER2 neoadjuvant therapy is not recommended.

3. In vitro method for classifying HER2+ breast cancer patients into responder or nonresponder patients to anti-HER2 neoadjuvant therapy which comprises determining the level of expression of UGT2B15 gene in a biological sample obtained from the patient, wherein if the level of expression of UGT2B15 gene is higher than a pre-established threshold value, it is an indication that the patient does not respond to the treatment.

4. In vitro method for the prognosis of patients suffering from HER2+ breast cancer which comprises determining the level of expression of UGT2B15 gene in a biological sample obtained from the patient, wherein if the level of expression of UGT2B15 gene is higher than a pre-established threshold value, it is an indication of bad prognosis.

5. In vitro method, according to any of the previous claims, wherein the anti-HER2 neoadjuvant therapy is an anti-HER2 antibody.

6. In vitro method, according to any of the previous claims, wherein the anti-HER2 neoadjuvant therapy is an anti-HER2 antibody comprising: trastuzumab or pertuzumab, or any combination thereof.

7. In vitro method, according to any of the previous claims, wherein the anti-HER2 neoadjuvant therapy comprises at least an anti-HER2 antibody in combination with chemotherapy.

8. In vitro method, according to any of the previous claims, wherein the biological sample is selected from the group comprising: tissue, blood, serum or plasma. In vitro use of UGT2B15 for predicting the response of patients suffering from HER2+ breast cancer to a treatment with anti-HER2 neoadjuvant therapy, for deciding or recommending whether to treat patients suffering from HER2+ breast cancer with anti- HER2 neoadjuvant therapy, for classifying HER2+ breast cancer patients into responder or non-responder patients to anti-HER2 neoadjuvant therapy, or for the prognosis of patients suffering from HER2+ breast cancer. In vitro use of a kit consisting of reagents for measuring the level of expression of UGT2B15 for predicting the response of patients suffering from HER2+ breast cancer to a treatment with anti-HER2 neoadjuvant therapy, for deciding or recommending whether to treat patients suffering from HER2+ breast cancer with anti-HER2 neoadjuvant therapy, for classifying HER2+ breast cancer patients into responder or non-responder patients to anti-HER2 neoadjuvant therapy, or for the prognosis of patients suffering from HER2+ breast cancer. In vitro use, according to any of the claims 9 or 10, wherein neoadjuvant therapy is an anti-HER2 antibody. In vitro use, according to any of the claims 9 to 11, wherein the anti-HER2 neoadjuvant therapy is an anti-HER2 antibody comprising: trastuzumab or pertuzumab, or any combination thereof. In vitro use, according to any of the claims 9 to 12, wherein the anti-HER2 neoadjuvant therapy comprises at least an anti-HER2 antibody and chemotherapy. Anti-HER2 antibody, or any pharmaceutical composition comprising thereof, optionally including pharmaceutically acceptable excipients or carriers, for use in the treatment of patients suffering from HER2+ breast cancer, wherein the patient is a responder patient characterized by a UGT2B15 level of expression lower than a pre- established threshold value. Anti-HER2 antibody for use, according to claim 14, in combination with chemotherapy.