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. Pat. No. 7,736,905, the content of which is incorporated by reference herein, which describes the test in detail. In brief, 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”. 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. The complementary patient population, associated with the class label of Good, is likely to benefit depending on the details of the indication. In the absence of treatment, 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). The following patents documents of the applicant's assignee describe further background information concerning the VeriStrat test and its applications: U.S. Pat. Nos. 8,024,282; 7,906,342; 7,879,620; 7,867,775; 7,858,390; 7,858,389 and 7,736,905.
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.
It has been known for many years that changing the hormonal balance of a patient with breast cancer could lead to changes in tumor growth and regression of metastatic disease. Estrogen in particular can promote the growth of breast cancer cells. Accordingly, while treatment of breast cancer can follow several avenues, including surgery and chemotherapy, so-called endocrine therapies that are designed to block the generation or uptake of estrogen are commonly used in treatment of breast cancer. See generally A. Goldhirsch et al. [1]. Currently, one of the most promising avenues of endocrine therapy takes the form of administration of drugs that modulate estrogen synthesis and inhibit estrogen receptor pathways.
Agents targeting estrogen receptors include selective estrogen receptor modulators (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. In post-menopausal women tamoxifen is also used, as well as some other antagonists, such as Fulvestrant (a SERD) and toremifine (a SERM). 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 non-reversible, 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. In post-menopausal settings letrozole, anastrozole, and exemestane are aromatase inhibitors (AIs) that are used most frequently.
Many breast cancer patients have a primary resistance or develop tumor resistance to endocrine therapy despite detected hormone receptor (HR)-positive status. The art has recognized a variety of methods for attempting to predict resistance to endocrine therapy in breast cancer patients. See U.S. Pat. Nos. 7,217,533; 7,642,050; 7,504, 214; 7,402,402; 7,537,891, 7,504,211; 5,693,463 and the article of Ma et al [2]. These methods typically involve either determining whether breast cancer cells express certain gene expression products or profiles, or analyzing certain ratios of certain gene expression products.