Research Grants Awarded
Predicting Outcome in Node-Negative Breast Cancer by Integrated Genomics and Proteomics
Detection, Diagnosis and Prognosis
Node-negative breast cancer is challenging to treat, as most patients are cured without adjuvant chemotherapy, which adds a small survival benefit (<10%), in the face of significant toxicity and financial, social and emotional costs. An assay that accurately predicts survival could change clinical practice, by avoiding chemotherapy for patients who are likely to be cured with local therapy with or without anti-hormonal therapy. A recent study reported 16 genes that by RT-PCR predict survival in node-negative, tamoxifen treated patients. Here we propose to create a different assay for this clinical problem by starting with a novel analytical approach to looking at differences between patient samples via analysis of regulatory links between genes rather than individual gene expression. In our preliminary work analyzing differences between transcriptomes of node-negative survivors and non-survivors, we constructed transcriptional networks of genes and their activating transcription factors, and found that the differentiating genes tend to be concentrated in neighborhoods on regulatory networks. This suggests a tight regulatory association between these genes, i.e., they share common direct or indirect regulators that are likely to be of prognostic value. We will employ these networks to identify pairs of transcription factors and regulated genes that are activated in patients with poor outcome, using publicly available cDNA microarray datasets. We will examine expression of these transcription factors and their targets at the protein level as well as the 16 above-mentioned markers, using large cohort node-negative tissue microarrays, in order to identify a subset of markers that accurately predict survival. We will analyze samples from the era that predated routine use of adjuvant therapy for node-negative breast cancer, which enable us to study the natural history of the disease. We will use a new objective method of in-situ protein measurement, which is simple and practical to implement in widespread practice. Our purpose is to create a new assay that confers a number of advantages: it does not require mRNA integrity, our output does not include stromal elements, and is based on a broader (and functional) approach to marker selection. We will validate these results on more recent samples from patients receiving newer anti-hormonal agents, with the goal of finding a subset of proteins that collectively predict outcome in node-negative breast cancer.
With improvements in screening for breast cancer over the past decades, more patients are being diagnosed at earlier stages of disease. Patients whose cancer has not spread to lymph nodes (node-negative) often pose a therapeutic dilemma, as the majority will be cured without chemotherapy. Chemotherapy confers a small survival benefit (<10%), in the face of significant toxicity and financial, social and emotional costs. A test that accurately predicts survival could change clinical practice, by avoiding chemotherapy for patients who are likely to be cured with surgery with or without radiation and possibly anti-hormonal therapy. A recent study reported 16 genes that collectively predict outcome in node-negative patients treated with tamoxifen. We propose to develop a different test, which will hopefully be technically easier to perform (as it will study expression of proteins, which are more stable than RNA used in the test mentioned above), and will be applicable to a broader population of patients (not only tamoxifen treated). We will start with a novel computational approach to identifying potential genes or proteins that can differentiate between node-negative survivors and non-survivors. In our preliminary work, we constructed networks of genes and the proteins (transcription factors) that activate or suppress these genes in node-negative breast cancer. We identified regions of these networks, which are altered in patients with poor outcome. Therefore, rather than merely looking at expression of genes, we will assess pairs of genes and protein regulators, that are abnormally activated or suppressed in breast cancer. We will study the protein levels of these pairs, as well as levels of the protein products of the 16 genes in the above-mentioned study, on a large number of breast cancer specimens from patients treated prior to the routine use of chemotherapy for node-negative breast cancer. We will thus have the rather unique opportunity to assess the association between these proteins and clinical outcome, in a manner that is not confounded by treatment effects. We will use a newly developed method of automated, quantitative analysis of protein levels, which is simple to perform, and practical to implement in wide-spread practice. We will validate these results on more recent samples from patients receiving newer anti-hormonal agents, with the goal of finding a smaller subset of proteins that collectively predict outcome in node-negative breast cancer.