Recently, trials of trastuzumab in the adjuvant setting have been completed in both the US (NCCTG/NSABP) and in Europe (the HERA trial) that have shown this drug can decrease the recurrence rate in patients with HER2 positive breast cancer. However in both trials, there are still a significant percentage of patients that recur on the drug. Similarly, numerous earlier trials in patients with metastatic cancer also showed a lack of response even though they had HER2 expressing tumors. These facts, combined with the facts that new drugs are now available that target HER family signaling pathways, suggest that new, more specific, companion diagnostics could be developed for trastuzumab that increase the specificity of selection of patients for this therapy. The underlying hypothesis of this proposal is that an optimal predictor of outcome for patients on trastuzumab can be achieved by combining multiple markers which predict response. We propose that using a set of novel techniques to interrogate HER2 tumors being treated with trastuzumab and chemotherapy, we can define the optimal predictor and validate this classifier in a prospective trial. The techniques will include assessment of DNA, RNA and protein to determine the best modality for optimal prediction. Protein will be assessed using multiplexed immunofluoresence, RNA will be assessed using transcriptional microarray profiling and DNA will be assessed in the form of copy number analysis using high density SNP arrays. Each of these assays has the potential to be translated into a usable companion diagnostic assay for breast cancer patients. In this revised 2 year version. of this grant we propose to keep the analysis of all 3 modalities, but to only complete the training set aspects of the grant. We envision a follow-up submission in 18-24 months that shows the model resulting for the efforts of this project, then proposing validation of the model(s) on the CALGB 40601 cohort or similar. The scaled aims include:
AIM 1) To develop an optimal multiplexed predictive model that uses HER pathway related proteins, downstream signaling proteins and hetero- or homodimerization state of HER2.
This aim will use the quantitative multiplexing technology (called AQUA) for accurate in situ measurement of protein expression on a series of 10-25 HER2 pathway related proteins to construct a series of models that predict response to trastuzumab in the CALGB 9840 trial (A trial of taxol and trastuzumab in in the first line metastatic setting) AIM 2) To use Ilumina DASL-based Gene Expression Profiling to develop an optimal multiplexed predictive model.
This aim will assess gene expression using a custom gene set on the Illumina DASL custom array (1536 genes) Platform in the CALGB 9840 cohort to identify candidate predictors for the multiplexed predictive model. Specifically, candidate amplicons, and genes associated with these candidate amplicons, which are associated with response to trastuzumab will be identified for inclusion in the model.
AIM 3) To do computational modeling of the combined data from Aim I and 2 to discover the best 3-5 models that fit the training set (CALGB 9840) data.
This aim will create a series of optimal models that best select responders from non-responders in the CALGB training set. Validation of the models will be done by Leave One Out Cross Validation methods in anticipation of future more robust validation in an independent cohort in a subsequent study.
The concept of the selection of a therapeutic that is matched to a specific disease class is the underlying principal of companion diagnostics. The paradigm for this concept is the Herceptin/Herceptest pair. However, a series of facts and recent events have occurred that suggest re-examination of this aging diagnostic test. Even at its best, the companion diagnostics for Herceptin, including IHC and FISH were only able to predict response in about half the cases with a positive test. Recently a second therapeutic option (lapatinib) has become available that targets the same pathway. Thus a better test, that can selectively distinguish the best choice for each patient is now needed. It has been estimated that only 15-25 percent of cases of breast cancer will respond to anti-HER2 therapy. However, in the US alone, that may be over 50,000 cases per year. Furthermore, recent data has shown this subgroup of patients has the most aggressive type of cancer, and though treatment can be highly effective, there is a population of patients (nearly half of those with current positive tests) that will not benefit, and should be triaged to an alternative therapy. The adoption of a second generation test for HER2 pathway therapeutics is likely to be rapid and widespread. There is already a strong precedent for use of companion diagnostics in this class of tumor, and there are already established companies and billing codes that could readily license the results of this study to produce a second generation test. The key barrier to adoption of new diagnostics is often the lack of compelling data from large scale clinical trials. This scaled down version of this proposal will allow us to produce a new assay, but not yet validate it. The will enable the collection of data to justify a follow-on grant to fund the testing of tissue from CALGB 40601 which will enroll 400 patients and has a near term measurable endpoint for response (pathologic complete response). Analogous to a phase 3 trial required for FDA approval of a drug, 40601 is like a phase 3 trial for our optimized diagnostic test. Thus it is reasonable to project that the results of this proposal may broadly impact patients with breast cancer within the next 5-7 years. Broad adoption, in the best cases scenario, could lead to 10,000 fewer deaths from HER2 family positive tumors per year in the US alone.
