Periodic Reporting for period 1 - breast cancer dormancy (Molecular characterisation of a clinical model of estrogen receptor-positive breast cancer dormancy)
Reporting period: 2015-09-01 to 2017-08-31
We aimed to characterise ER+ breast cancer dormancy and letrozole resistance by combing expression profiling technologies with a unique series of patient-matched sequential samples collected in Edinburgh. Genome- and proteome-wide expression data after extended letrozole treatment was analysed and compared with data from the same patients at diagnosis according to clinical outcomes. Our study is the first to characterise extended growth suppression in letrozole-treated dormant-state breast cancer. Objectives of the study were 1) to determine genome- and proteome-wide expression profiles in ER+ breast cancer patients that received extended neoadjuvant endocrine therapy; and 2) to characterise ""predictive molecular signature"" of ER+ breast cancer dormancy and letrozole resistance through integrated analysis of genome- and proteome-wide expression data.
Differentially expressed genes and enriched pathways in dormant and acquired resistant tumours under long-term neoadjuvant letrozole treatment were identified. In addition, a subset of the genes including histone genes significantly separating 12 (out of 20) acquired resistant from dormant tumours after long-term treatment were determined. The challenge in defining a clear separation between all resistant and dormant patients was mainly due to the heterogeneity amongst resistance patients as some of them share changes with dormant patients.
In conclusion, these results will contribute to extending ER+ breast cancer patients' survival and quality of life by preventing metastasis and contribute to the economy and society by introducing an individualised therapy that is tailored in accordance with cancer patients’ expression profiles. This multidisciplinary project combined both clinical and academic aspects provided the fellow with great competence in cancer genomics through training in advanced integrative bioinformatics analysis of high-throughput data.
The fellow completed specific training for RNA/DNA/protein extractions, processing and analysis of clinical samples in the first 6 months of the project. A previously generated small transcriptomic dataset (13 patients) was analysed while new data was being generated to improve the fellow’s bioinformatics expertise. Relevant tissue blocks had been identified and the process of sectioning and extracting mRNA, DNA and proteins for further analysis was started.
Whole transcriptome data on 83 sequential samples from 30 patients was generated in the second 6 months of the project. A trial reverse-phase protein array (RPPA) experiment has been performed to determine the best method to purify protein. In addition to RPPA, optimization experiments with a more high-throughput approach, protein mass spectroscopy, was also performed in parallel.
Transcriptomics analysis performed using R and BioConductor packages in 12-18 months of the project. Transcriptomics data was pre-processed and successfully combined with previously generated transcriptomic dataset, bringing the sample numbers up to 167. Differentially expressed genes in dormant and acquired resistant patients under long-term treatment have been determined. Proteomics analysis of a total of 62 samples was performed using label-free quantitation method in mass spectrometry.
In the last 6 months of the project, comparative analysis of gene expression and proteomics data from neoadjuvant (letrozole)-treated dormant tumours with recurrence samples under long-term adjuvant therapy has been performed using different approaches. Features significantly differentially expressed in both omics approaches significantly separated a subset of acquired resistant patients and dormant patients both after long-term and early-on (first 4 months) treatment.
Overview and dissemination of the results
This is the first patient-matched gene expression study to look at long-term aromatase inhibitor-induced dormancy and acquired resistance in breast cancer. We have identified transcriptome- and proteome-wide expression profiles in dormant and acquired resistant tumours after extended-neoadjuvant letrozole therapy (Objective 1). The challenge in analysing this unique dataset to identify “predictive molecular signature” of dormancy (Objective 2) was mainly due to the heterogeneity of response and resistance amongst patients. To overcome this, we have utilized machine learning and successfully identified genes that differentiate a subset of acquired resistant tumours from dormant tumours after long-term treatment and also in the first 4 months of therapy. Three peer-reviewed research articles have been published in high-quality journals, one has recently been submitted and another is currently in preparation.
Our study generated new insights into the molecular changes, state and activity of dormant breast cancer cells. There are important clinical implications as most metastatic recurrences develop from dormant oestrogen receptor positive breast cancers. The identified genes that differentiate oestrogen receptor positive dormant and resistant breast tumours may help to cure more women of their breast cancer. The results also have the potential to identify new classes of treatments that may overcome breast cancer recurrence, treatment resistance and to decrease treatment costs by individualized treatment of breast cancer.