WO2012102829A1 - Predictive test for selection of metastatic breast cancer patients for hormonal and combination therapy - Google Patents
Predictive test for selection of metastatic breast cancer patients for hormonal and combination therapy Download PDFInfo
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- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
- G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
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- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
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- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
- G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
- G01N33/57407—Specifically defined cancers
- G01N33/57415—Specifically defined cancers of breast
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- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
- G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
- G01N33/6848—Methods of protein analysis involving mass spectrometry
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- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
- G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
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- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
- G16B40/20—Supervised data analysis
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- G—PHYSICS
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- G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
- G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
- G01N2333/475—Assays involving growth factors
- G01N2333/485—Epidermal growth factor [EGF] (urogastrone)
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
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- G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
- G01N2333/705—Assays involving receptors, cell surface antigens or cell surface determinants
- G01N2333/71—Assays involving receptors, cell surface antigens or cell surface determinants for growth factors; for growth regulators
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
- G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
- H01J49/00—Particle spectrometers or separator tubes
Definitions
- This application relates generally to the field of treatment of breast cancer and more particularly to a predictive test for determining, in advance of treatment, whether a breast cancer patient is a member of a class of patients that would be likely to benefit from a combination of certain anti-cancer drugs.
- the application also relates to a predictive test for determining, in advance of treatment, whether a breast cancer patient is a member of a class of patients that would not be likely to benefit from endocrine therapy alone, including for example an aromatase inhibitor such as letrozole.
- the applicant's Assignee Biodesix, Inc. has developed a predictive test for determining whether certain cancer patients would be likely to benefit from anti-cancer drugs or combinations of drugs.
- the commercial version of the test known as VERISTRAT ®, is a MALDI-ToF mass spectrometry serum-based test that has clinical utility in the selection of specific targeted therapies in solid epithelial tumors. See U.S. patent 7,736,905, the content of which is incorporated by reference herein, which describes the test in detail.
- a mass spectrum of a serum sample of a patient is obtained. After certain pre-processing steps are performed on the spectrum, the spectrum is compared with a training set of class-labeled spectra of other cancer patients with the aid of a classifier.
- the class-labeled spectra are associated with two classes of patients: those that benefitted from epidermal growth factor receptor inhibitors (EGFRIs), class label of "Good”, and those that did not, class label of "Poor”.
- EGFRIs epidermal growth factor receptor inhibitors
- the classifier assigns a class label to the spectrum under test.
- the class label for the sample under test is either "Good” or "Poor,” or in rare cases where the classification test fails the class label for the sample is deemed “undefined.”
- Patients whose sample is identified by the test as Poor are identified as members of a group or class of patients which appear to be unlikely to obtain clinical benefit from treatment with epidermal growth factor receptor inhibitors (EGFRIs) such as gefitinib (Iressa®), erlotinib (Tarceva®), and cetuximab (Erbitux®) in the treatment of solid epithelial tumors.
- EGFRIs epidermal growth factor receptor inhibitors
- the complementary patient population, associated with the class label of Good is likely to benefit depending on the details of the indication.
- the VeriStrat test has a strong prognostic component, meaning that "Poor” patients perform significantly worse than "Good” patients.
- the VeriStrat Poor signature has been found in a variety of solid tumors including non-small cell lung cancer (NSCLC), colorectal cancer (CRC), and squamous cell cancer of the head and neck (SCCHN or, alternatively, H&N).
- NSCLC non-small cell lung cancer
- CRC colorectal cancer
- SCCHN squamous cell cancer of the head and neck
- breast cancer is the leading form of cancer in women and the second leading cause of cancer death in women, after lung cancer.
- the development of breast cancer is believed to be a multi-step process of genetic alteration that transforms normal cells into highly malignant derivatives.
- Agents targeting estrogen receptors include selective estrogen receptor modulators I (SERMs) and selective estrogen receptor downregulators (SERDs). Both SERDs and SERMs are in use in treatment of breast cancer.
- Tamoxifen a most often used agent in pre- menopausal setting, is an estrogen receptor antagonist in breast tissue, but acts as an agonist in some other tissues, hence it belongs to the SERM class.
- a SERD Fulvestrant
- a SERM toremifine
- Tamoxifen a non-steroidal antiestrogen, is thought to inhibit breast cancer growth by competitively blocking estrogen receptor (ER), thereby inhibiting estrogen- induced growth.
- ER is a ligand-dependent transcription factor activated by estrogen. Upon interaction with the hormone it enters the nucleus, binds to specific DNA sequences and activates ER-regulated genes, mediating most biological effects of estrogens on normal cells and estrogen -dependent tumors.
- Endocrine therapy drugs also include a class of drugs known as aromatase inhibitors, including selective and nonselective aromatase inhibitors.
- Selective aromatase inhibitors include letrozole, as well as anastrozole (arimidex); another similar acting, however nonreversible, agent is exemestane (aromasin).
- Aromatase is an enzyme that synthesizes estrogen in the body by converting the hormone androgen into estrogen.
- Aromatase inhibitors stop the production of estrogen by blocking the aromatase.
- Administration of aromatase inhibitors thus reduces the amount of estrogen which is available to stimulate the growth of hormone receptor-positive breast cancer cells.
- letrozole, anastrozole, and exemestane are aromatase inhibitors (AIs) that are used most frequently.
- This method involves a) obtaining a mass spectrum from a blood-based sample from the patient; b) performing one or more predefined pre-processing steps on the mass spectrum obtained in step a); c) obtaining values of selected features in the mass spectrum at one or more predefined m/z ranges after the pre-processing steps on the mass spectrum in step b) have been performed; and d) using the values obtained in step c) in a classification algorithm using a training set comprising class-labeled spectra produced from samples from other cancer patients and obtaining a class label for the sample.
- the class label assigned to the mass spectrum by the classification algorithm predicts whether the breast cancer patient is likely to benefit. In particular, if the class label obtained in step d) is "Poor" or the equivalent, the patient is identified as being unlikely to benefit from the endocrine therapy drug.
- Our method includes the steps of a) obtaining a mass spectrum from a blood-based sample from the patient; b) performing one or more predefined pre-processing steps on the mass spectrum obtained in step a); c) obtaining values of selected features in said spectrum at one or more predefined m/z ranges after the pre-processing steps on the mass spectrum in step b) have been performed; and d) using the values obtained in step c) in a classification algorithm using a training set comprising class-labeled spectra produced from samples from other cancer patients and obtaining a class label for the sample. If the class label obtained in step d) is "Poor" or the equivalent then the patient is identified as being likely to benefit from the combination treatment.
- VeriStrat test results for 1046 of these patients, of which 961 were classified as VeriStrat Good, 80 were VeriStrat Poor, and 5 were VeriStrat Indeterminate (Undefined) (patients for whom 3 replicate spectra produced discrepant results); 1 17 samples were not evaluable due to hemolysis and we could not assign a VeriStrat label to one patient due to data inconsistencies between available samples.
- EGF30008 As a result of the analysis of EGF30008 we have made several observations leading to the present inventive methods. One of which is that, for those patients having a mass spectral signature that is classified as “poor” using the training set, that class label identifies those patients that are not likely to benefit from administration of endocrine therapy alone, regardless of their HER2 status. Such patients can be characterized as "endocrine resistant", i.e., resistant to endocrine therapy drugs. Patients with hormone-receptor positive status are considered to be sensitive to endocrine therapy, however up to 40-50 % of them do not respond to it from the beginning of treatment or stop responding at some point in the course of treatment.
- estrogen is produced mainly in the ovaries, hence, the treatment strategy for the HR-positive breast cancer in this population involves ovarian suppression usually in combination with ER modulator, tamoxifen.
- ovarian function has ceased and estrogen is synthesized in smaller quantities from androgens.
- Aromatase plays a key role in this process, providing a biological rationale for using aromatase inhibitors (AIs) for treatment of HR-positive breast cancer in postmenopausal women.
- AIs aromatase inhibitors
- Both ER modulators (tamoxifen) and aromatase inhibitors show effectiveness in post-menopausal women.
- letrozole was significantly superior to anastrozole in the overall response rate (ORR), however there were no significant differences between the treatment arms in the rate of clinical benefit, median duration of response, duration of clinical benefit, time to treatment failure, or overall survival.
- ORR overall response rate
- Similarity of the mechanisms of action as well as of clinical outcomes in clinical trials with different AIs give us a reason to expect that separation of breast cancer patients by VeriStrat test with respect to clinical benefit observed with letrozole is likely to be similar to other AIs.
- tamoxifen's and AIs' therapeutic effects are based on the reduction of activated hormone - ER receptor complexes in the cell, either through the inhibition of estrogen synthesis or minimization of number of receptors available for Iigand binding, one can hypothesize that the effect observed in the study with one of AIs (letrozole) is likely to be similar in the case of treatment with an estrogen modulator tamoxifen.
- the VeriStrat test may be of significant clinical utility in various types of hormonal therapy of breast cancer.
- HER2 test If a patient's VeriStrat status were VeriStrat Good, one could then perform a HER2 test to decide whether the addition of lapatinib would be appropriate. Alternatively, if the HER2 status were known to be HER2-negative , one can perform the VeriStrat test to decide whether the patient belongs to the VeriStrat Poor subgroup and may benefit from the addition of lapatinib.
- Fig. 1 is a block diagram showing a mass-spectrometry based test for predicting breast cancer patient response to certain drugs or combination of drugs for use in the methods of this disclosure.
- Figures 2-13 show data resulting from our retrospective analysis of the phase III EGF 30008 trial, and in particular:
- Fig. 2 is a Kaplan-Meier plot of Progression Free Survival (PFS) for overall population by VeriStrat classification and treatment arm.
- Fig. 2 shows that patients have similar outcomes on the combination of lapatinib and letrozole regardless of their VeriStrat status, but not on letrozole alone.
- Fig. 2 shows that, for those patients treated with letrozole alone, patients identified as "Poor” do much worse on letrozole alone than those patients identified as "Good.”
- Fig. 2 also shows that patients whose serum was classified as "Poor” showed improved progression free survival (PFS) with the addition of lapatinib to letrozole.
- Fig. 3 is a Kaplan-Meier plot of PFS for the letrozole arm by VeriStrat classification. Fig. 3 demonstrates that our VeriStrat test identifies a group of patients with poor outcomes on letrozole alone.
- Fig. 4 is a Kaplan-Meier plot of PFS for "Good” patients by treatment arm.
- Fig. 5 is a Kaplan-Meier plot of PFS for "Poor” patients by treatment arm.
- Fig. 5 illustrates that patients whose serum is classified as “Poor” benefit significantly more with combination treatment (lapatinib and letrozole) than those receiving letrozole alone; the median PFS is greater by 8.2 months with combination treatment. The significance of the difference in benefit is demonstrated in the multivariate analysis with the interaction term included.
- Fig. 6 is a Kaplan-Meier plot of PFS for by VeriStrat classification and treatment arm for HER2- population.
- Fig. 7 is a Kaplan-Meier plot of PFS for the letrozole arm by VeriStrat classification for HER2- patients.
- Fig. 8 is a Kaplan-Meier plot of PFS for the letrozole arm by VeriStrat classification for HER2+ patients. Figs. 7 and 8 show that our test identifies patients with poor outcomes on letrozole alone, independent of HER2 status.
- Fig. 9 is a Kaplan-Meier plot of PFS for VeriStrat Good patients by treatment arm for HER2- patients.
- Fig. 10 is a Kaplan-Meier plot of PFS for VeriStrat Poor patients by treatment arm for HER2- patients.
- Fig. 10 demonstrates that HER2- patients whose serum is classified as "Poor” showed a trend for improved PFS with the addition of lapatinib to letrozole as compared to treatment by letrozole alone.
- Fig. 1 1 is a Kaplan-Meier plot of PFS for F1ER2+ patients by VeriStrat classification and treatment arm. It shows that patients have similar outcomes with lapatinib plus letrozole treatment regardless of their VeriStrat classification.
- Fig. 12 is a Kaplan-Meier plot of PFS for VeriStrat "Good” patients by treatment arm for HER2+ patients.
- Fig 13 is a Kaplan-Meier plot of PFS for VeriStrat "Poor" patients by treatment arm for HER2+ patients.
- Figures 1 1-13 demonstrate that, within the population of HER2+ patients, patients have similar outcomes with lapatinib plus letrozole regardless of their VeriStrat classification.
- the classifier assigned a class label to the samples, either "Good” or “Poor” or in a few instances "undefined.”
- the class labels were assigned using a K-nearest neighbor (KN ) scoring algorithm based on a comparison of the spectra, after preprocessing and calculation of integrated intensity values at selected features in the spectra, with a training set of class- labeled spectra from other cancer patients.
- KN K-nearest neighbor
- the training set used by the classification algorithm used class-labeled spectra from a population of non-small cell lung cancer patients, with the class- label in the training set being "Good” if the associated spectra in the training set was assigned to a patient who benefitted from administration of an EGFR-I, whereas the class label "Poor” was assigned to spectra for patients who did not benefit from such drugs.
- This training set and the classifier was the subject of extensive validation studies. The method of conducting our mass-spectral testing and classification of blood-based samples is explained in further detail below.
- the method is described herein for determining whether a hormone receptor positive breast cancer patient, regardless of the patients' HER2 status, is unlikely to benefit from administration of an endocrine therapy drug alone for treatment of the cancer.
- the method includes the steps of: a) obtaining a mass spectrum from a blood-based sample from the patient; b) performing one or more predefined pre-processing steps on the mass spectrum obtained in step a); c) obtaining values of selected features in the spectrum at one or more predefined m/z ranges after the pre-processing steps on the mass spectrum in step b) have been performed; d) using the values obtained in step c) in a classification algorithm using a training set comprising class-labeled spectra produced from samples from other cancer patients and obtaining a class label for the patient's sample; and e) if the class label obtained in step d) is "Poor" or the equivalent, then the patient is identified as being unlikely to benefit from the treatment.
- a second practical test is described herein in the form of a method of determining whether a post-menopausal hormone receptor positive breast cancer patient with HER2- negative status is likely to benefit from administration of a combination treatment comprising administration of a targeted anti-cancer drug in addition to an endocrine therapy drug.
- the method involves the steps of: a) obtaining a mass spectrum from a blood-based sample from the patient; b) performing one or more predefined pre-processing steps on the mass spectrum obtained in step a); c) obtaining values of selected features in said spectrum at one or more predefined m/z ranges after the pre-processing steps on the mass spectrum in step b) have
- step d) using the values obtained in step c) in a classification algorithm using a training set comprising class-labeled spectra produced from samples from other cancer patients and obtaining a class label for the sample; and e) if the class label obtained in step d) is "Poor" or the equivalent then the patient is identified as being likely to benefit from the combination treatment.
- Cross-talk between pathways involved with estrogen receptors and HER2 and EGFR is considered as one of the primary mechanisms of this resistance to letrozole alone and constitutes the rationale for the combination of drugs (targeted therapies and endocrine therapies) used in our study, since inhibition of production of estrogen and at the same time of HER2 and EGFR signaling, stops these interactions and helps to prevent/overcome resistance.
- HER2-negative patients were not expected to gain benefit from the combination treatment, but we have been able to identify a subgroup of HER2 -negative patients that are likely to benefit from the combination treatment, which is a significant advance.
- I inhibitor, tamoxifen, other SERMs and SERDs alone, or alternatively to benefit from the addition of certain targeted therapies and endocrine therapy drugs
- a blood-based sample serum or plasma
- the class label assigned to the specimen indicates whether the patient is unlikely to benefit from the administration of i the endocrine therapy drug alone, or alternatively likely to benefit from the administration of a combination of a targeted therapy and an endocrine therapy drug.
- the test is illustrated in flow chart form in Figure 1 as a process 100.
- a serum or plasma sample is obtained from the patient.
- the serum samples are separated into three aliquots and the mass spectroscopy
- step 104 the sample (aliquot) is subject to mass spectroscopy.
- a preferred method of mass spectroscopy is matrix assisted laser desorption ionization (MALDI) time of flight (TOF) mass spectroscopy, but other methods are possible.
- MALDI matrix assisted laser desorption ionization
- TOF time of flight
- the samples are thawed and centrifuged at 1500 rpm for five minutes at four degrees Celsius.
- the serum samples may be diluted 1 :10, or 1 :5, in MilliQ water. Diluted samples may be spotted in randomly allocated positions on a MALDI plate in triplicate (i.e., on three different MALDI targets). After 0.75 ul of diluted serum is spotted on a MALDI plate, 0.75 ul of 35 mg/ml sinapinic acid (in 50 % acetonitrile and 0.1% trifluoroacetic acid (TFA)) may be added and mixed by pipetting up and down five times. Plates may be allowed to dry at room temperature. It should be understood that other techniques and procedures may be utilized for preparing and processing serum in accordance with the principles of the present invention.
- Mass spectra may be acquired for positive ions in linear mode using a Voyager DE- PRO or DE-STR MALDI TOF mass spectrometer with automated or manual collection of the spectra. Seventy five or one hundred spectra are collected from seven or five positions within each MALDI spot in order to generate an average of 525 or 500 spectra for each serum specimen. Spectra are externally calibrated using a mixture of protein standards (Insulin (bovine), thioredoxin (E. coli), and Apomyglobin (equine)).
- the spectra obtained in step 104 are subject to one or more pre-defined pre-processing steps.
- the pre-processing steps 106 are implemented in a general purpose computer using software instructions that operate on the mass spectral data obtained in step 104.
- the pre-processing steps 106 include background subtraction (step 108), normalization (step 1 10) and alignment (step 1 12).
- the step of background subtraction preferably involves generating a robust, asymmetrical estimate of background in the spectrum and subtracts the background from the spectrum.
- Step 108 uses the background subtraction techniques described in U.S 7,736,905, which is incorporated by reference herein.
- the normalization step 110 involves a normalization of the background subtracted spectrum.
- the normalization can take the form of a partial ion current normalization, or a total ion current normalization, as described in U.S. Patent 7,736,905.
- Step 1 12 aligns the normalized, background subtracted spectrum to a predefined mass scale, as described in U.S. 7,736,905, which can be obtained from investigation of the training set used by the classifier.
- the process 100 proceeds to step 1 14 of obtaining values of selected features (peaks) in the spectrum over predefined m/z ranges.
- the normalized and background subtracted amplitudes may be integrated over these m/z ranges and assigned this integrated value (i.e., the area under the curve between the width of the feature) to a feature.
- the integration range may be defined as the interval around the average m/z position of this feature with a width corresponding to the peak width at the current m/z position. This step is also disclosed in further detail in U.S. patent 7,736,905.
- step 114 as described in U.S. patent 7,736,905, the integrated values of features in the spectrum is obtained at one or more of the following m/z ranges:
- values are obtained at eight of these m/z ranges shown in Table 1 below. The significance, and methods of discovery of these peaks, is explained in the U.S. patent 7,736,905.
- the values obtained at step 1 14 are supplied to a classifier, which in the illustrated embodiment is a K-nearest neighbor (KNN) classifier.
- KNN K-nearest neighbor
- the classifier makes use of a training set of class labeled spectra from a multitude of other patients (which may be NSCLC cancer patients, or other solid epithelial cancer patients, e.g., HNSCC, Breast Cancer).
- the application of the KNN classification algorithm to the values at 1 14 and the training set is explained in U.S. patent 7,736,905.
- Other classifiers can be used, including a probabilistic KNN classifier or other classifier.
- the training set is in the form of class-labeled spectra from NSCLC patients that either did or did not benefit from administration of EGFR inhibitors, those that did benefit being labeled "Good” and those that did not labeled "Poor.”
- the classifier uses a. training set from patients that are not breast cancer patients, but the predictions made by the method are nevertheless valid.
- the reason for using the NSCLC training set for the present study is that it has been subject to extensive validation.
- the set of spectra we used in the EGF30008 study could be used to construct the training set and used in the classification of the test sample.
- Such an endeavor would have required substantial additional validation work which was not necessary in our case since the NSCLC training set used in the classifier performed so well.
- the classifier produces a label for the spectrum, either "Good", “Poor” or "Undefined".
- steps 104-1 18 are performed in parallel on the three separate aliquots from a given patient sample (or whatever number of aliquots are used).
- a check is made to determine whether all the aliquots produce the same class label. If not, an undefined (or Indeterminate) result is returned as indicated at step 122. If all aliquots produce the same label, the label is reported as indicated at step 124.
- those hormone receptor positive, HER2 -negative breast cancer patients labeled "Poor" in accordance with the VeriStrat test are likely to benefit from treatment in the form of an endocrine therapy drug, e.g., an aromatase inhibitor (letrozole) in combination with targeted therapy (e.g., lapatinib) in accordance with the present disclosure.
- an endocrine therapy drug e.g., an aromatase inhibitor (letrozole) in combination with targeted therapy (e.g., lapatinib) in accordance with the present disclosure.
- targeted therapy e.g., lapatinib
- steps 106, 114, 1 16 and 1 18 are typically performed in a programmed general purpose computer using software coding the pre-processing step 106, the obtaining of spectral values in step 1 14, the application of the KNN classification algorithm in step 1 16 and the generation of the class label in step 1 18.
- the training set of class labeled spectra used in step 1 16 is stored in memory in the computer or in a memory accessible to the computer.
- the method and programmed computer may be advantageously implemented at a laboratory test processing center as described in our prior patent application publication U.S. patent 7,736,905.
- Fig. 3 is a Kaplan-Meier plot of PFS for the letrozole + placebo arm of the EGF30008 study by VeriStrat classification.
- Fig. 3 demonstrates that our VeriStrat test identifies a group of patients with poor outcomes on letrozole alone.
- HR hazard ratio
- CI Confidence Interval
- Median PFS was 1 1.4 months for Good patients and 1 1.0 months for Poor patients.
- the effect on PFS with the addition of a targeted therapy (lapatinib) in addition to letrozole, separated by VeriStrat classification, is shown in Figs. 4, 5 and 6.
- VeriStrat "Good” patients are shown in Fig. 4 and "Poor” patients in Fig. 5.
- the median PFS was 1 1.4 months for the combination arm and 10.8 months for the letrozole+placebo arm.
- Fig. 2 is a Kaplan-Meier plot of Progression Free Survival (PFS) for overall population by VeriStrat classification and treatment arm.
- Fig.2 shows that patients have similar outcomes on the combination of lapatinib and letrozole regardless of their VeriStrat status, but not on letrozole alone.
- Fig. 2 shows that, for those patients treated with letrozole alone, patients identified as "Poor” do much worse on letrozole alone than those patients identified as "Good.”
- Fig. 2 also shows that for patients whose serum was classified as "Poor” showed improved progression free survival (PFS) with the addition of lapatinib to letrozole.
- Figs. 7 and 8 show our data of PFS for patients with known HER2 status receiving letrozole alone.
- PFS for HER2- patients is shown in Fig. 7
- PFS for HER2+ patients is shown in Fig. 8.
- HR 0.37 (95% CI: 0.21-0.64)
- log-rank p 0.0004.
- the median PFS was 13.6 months for VeriStrat Good patients and 3.1 months for VeriStrat Poor patients.
- the median PFS was 13.8 months for the combination arm and 13.6 months for the letrozole+placebo arm.
- the median PFS was 1 1.0 months in the combination arm and only 3.1 months in the letrozole+placebo arm.
- Fig. 6 is a Kaplan-Meier plot of Progression Free Survival (PFS) for overall population by VeriStrat classification and treatment arm for the F1ER2 -negative population.
- Fig. 6 shows that HER2- patients have similar outcomes on the combination of lapatinib and letrozole regardless of their VeriStrat status, but not on letrozole alone.
- Fig. 6 shows that, for those patients treated with letrozole alone, patients identified as VeriStrat Poor do much worse on letrozole alone than those patients identified as VeriStrat Good.
- Fig. 6 also shows that for those HER2- patients whose serum was classified as "Poor" showed a trend for improved progression free survival with the addition of lapatinib to letrozole.
- Figs. 11-13 Data showing the effect on PFS with the addition of lapatinib to letrozole in the HER2 positive (HER2+) population is shown in Figs. 11-13.
- each treatment arm was analyzed separately by VeriStrat classification.
- the data for VeriStrat Good patients is shown in Fug. 12.
- the data for VeriStrat Poor patients is shown in Fig. 13.
- the combined data for all HER2+ patients and both treatment arms is shown in Fig. 1 1.
- Figs. 11, 12 and 13 show that HER2+ patients have similar outcomes with lapatinib plus letrozole treatment regardless of VeriStrat classification.
- a Cox Proportional Hazard Model analysis was carried out including VeriStrat classification, treatment arm, and an interaction term between the two. The results are shown in Table 5. Treatment and VeriStrat classification were both significant, as was the interaction term, indicating that the difference in Hazard Ratio (HR) between VeriStrat Good and VeriStrat Poor patients is significantly different between the letrozole+placebo arm and the letrozole+lapatinib arm.
- HR Hazard Ratio
- EGF30008 study involved a single targeted therapy (lapatinib) and a single aromatase inhibitor (letrozole), there are several dual HER2 and EGFR inhibitors under investigations, e.g. neratinib, afatinib, ARRY-543 that are likely examples of other targeted therapies that could be used in the method. Also, the effect of the combination of EGFR inhibitors (eriotinib, gefitinib) and HER2 inhibitor (trastuzumab) may be similar to one of the dual inhibitors.
- Letrozole belongs to the class of selective reversible aromatase inhibitors, as well as Anastrozole (Arimidex); another similar acting, however non-reversible, agent is Exemestane (Aromasin).
- the methods of this disclosure may be used to predict HER2-, post-menopausal hormone receptor positive breast cancer patient benefit from the combination of targeted therapies and an aromatase inhibitor other than letrozole.
- endocrine therapy and “endocrine therapy drugs” should be interpreted to mean those drugs which influence the endocrine system by modulating estrogen synthesis and/or estrogen receptor pathways, including but not limited to SERDs, SERMs and aromatase inhibitors.
- targeted therapies should be interpreted to mean those drugs targeting specific pathways within the cell, including but not limited to EGFR-Is, HER2 inhibitors, lapatinib and combinations thereof.
- hormone receptor positive is intended to include estrogen (ER) and/or progesterone (PgR) receptors -positive breast cancer patients.
Abstract
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