Prediction of adjuvant chemotherapy response in triple negative breast cancer with discovery and targeted proteomics

BACKGROUND

Triple-negative breast cancer (TNBC) accounts for 15-20% of all breast cancers and usually requires the administration of adjuvant chemotherapy after surgery but even with this treatment many patients still suffer from a relapse. The main objective of this study was to identify proteomics-based biomarkers that predict the response to standard adjuvant chemotherapy, so that patients at are not going to benefit from it can be offered therapeutic alternatives.

METHODS

We analyzed the proteome of a retrospective series of formalin-fixed, paraffin-embedded TNBC tissue applying high-throughput label-free quantitative proteomics. We identified several protein signatures with predictive value, which were validated with quantitative targeted proteomics in an independent cohort of patients and further evaluated in publicly available transcriptomics data.

RESULTS

Using univariate Cox analysis, a panel of 18 proteins was significantly associated with distant metastasis-free survival of patients (p<0.01). A reduced 5-protein profile with prognostic value was identified and its prediction performance was assessed in an independent targeted proteomics experiment and a publicly available transcriptomics dataset. Predictor P5 including peptides from proteins RAC2, RAB6A, BIEA and IPYR was the best performance protein combination in predicting relapse after adjuvant chemotherapy in TNBC patients.

CONCLUSIONS

This study identified a protein combination signature that complements histopathological prognostic factors in TNBC treated with adjuvant chemotherapy. The protein signature can be used in paraffin-embedded samples, and after a prospective validation in independent series, it could be used as predictive clinical test in order to recommend participation in clinical trials or a more exhaustive follow-up.

Combined Label-Free Quantitative Proteomics and microRNA Expression Analysis of Breast Cancer Unravel Molecular Differences with Clinical Implications

Better knowledge of the biology of breast cancer has allowed the use of new targeted therapies, leading to improved outcome. High-throughput technologies allow deepening into the molecular architecture of breast cancer, integrating different levels of information, which is important if it helps in making clinical decisions. microRNA (miRNA) and protein expression profiles were obtained from 71 estrogen receptor-positive (ER(+)) and 25 triple-negative breast cancer (TNBC) samples. RNA and proteins obtained from formalin-fixed, paraffin-embedded tumors were analyzed by RT-qPCR and LC/MS-MS, respectively. We applied probabilistic graphical models representing complex biologic systems as networks, confirming that ER(+) and TNBC subtypes are distinct biologic entities. The integration of miRNA and protein expression data unravels molecular processes that can be related to differences in the genesis and clinical evolution of these types of breast cancer. Our results confirm that TNBC has a unique metabolic profile that may be exploited for therapeutic intervention.

The Long-HER study: clinical and molecular analysis of patients with HER2+ advanced breast cancer who become long-term survivors with trastuzumab-based therapy

Background

Trastuzumab improves survival outcomes in patients with HER2+ metastatic breast cancer. The Long-Her study was designed to identify clinical and molecular markers that could differentiate long-term survivors from patients having early progression after trastuzumab treatment.

Methods

Data were collected from women with HER2-positive metastatic breast cancer treated with trastuzumab that experienced a response or stable disease during at least 3 years. Patients having a progression in the first year of therapy with trastuzumab were used as a control. Genes related with trastuzumab resistance were identified and investigated for network and gene functional interrelation. Models predicting poor response to trastuzumab were constructed and evaluated. Finally, a mutational status analysis of selected genes was performed in HER2 positive breast cancer samples.

Results

103 patients were registered in the Long-HER study, of whom 71 had obtained a durable complete response. Median age was 58 years. Metastatic disease was diagnosed after a median of 24.7 months since primary diagnosis. Metastases were present in the liver (25%), lungs (25%), bones (23%) and soft tissues (23%), with 20% of patients having multiple locations of metastases. Median duration of response was 55 months. The molecular analysis included 35 patients from the group with complete response and 18 patients in a control poor-response group. Absence of trastuzumab as part of adjuvant therapy was the only clinical factor associated with long-term survival. Gene ontology analysis demonstrated that PI3K pathway was associated with poor response to trastuzumab-based therapy: tumours in the control group usually had four or five alterations in this pathway, whereas tumours in the Long-HER group had two alterations at most.

Conclusions

Trastuzumab may provide a substantial long-term survival benefit in a selected group of patients. Whole genome expression analysis comparing long-term survivors vs. a control group predicted early progression after trastuzumab-based therapy. Multiple alterations in genes related to the PI3K-mTOR pathway seem to be required to confer resistance to this therapy.

Abstract P4-05-04: Proteomic patterns unravel a new luminal-A breast cancer molecular subgroup with prognostic value

Background: Breast cancer is a heterogeneous disease including a variety of entities with different genetic background. Estrogen receptor-positive, HER2 negative tumors (ER+) usually have a favorable outcome, although some patients eventually relapse, which suggests some heterogeneity within this category.

In the last years, proteomic approaches have been incorporated to the study of clinical samples as a way to complement the information provided by gene analysis. Shotgun proteomics allows measuring over 1,000 proteins in clinical samples. In the present study, we combined genomic and proteomic techniques to characterize a set of breast tumors.

Methods: The study population consisted of 102 patients with lymph-node positive breast cancer who had received anthracycline-based adjuvant chemotherapy. Protein extracts from FFPE samples were prepared in 2% SDS buffer and digested with trypsin. SDS was removed from digested lysates, and resulting peptides were analyzed in an Orbitrap Velos. Protein abundance was calculated on the basis of the normalized spectral protein intensity (LFQ intensity) using MaxQuant. A prognostic protein signature was built. Findings were verified using whole genome gene expression data from 1,141 patients included in public repositories. To this purpose, the protein signature was converted to a gene signature. Data analysis was done using MeV, BRBArray Tools, R and Cytoscape software suites and Uniprot (http://www.uniprot.org/) and DAVID (http://david.abcc.ncifcrf.gov) webtools.

Results: We identified 3,000 protein groups in FFPE breast cancer samples and selected 1,000 that were identified at least in 75% of the samples. Significance Analysis for Microarrays analysis revealed 224 protein groups differentially expressed between ER+ and triple-negative (TN) samples (False Discovery Rate set at &lt;0.001). Hierarchical clustering analyses of protein expression showed that some ER+ samples had a protein expression profile similar to that of TN samples: patients with TN-like tumors had a clinical outcome similar to those with TN disease. Gene ontology analyses unraveled a reduced expression of proteins related with cellular adhesion in the TN-like and the TN groups. A TN-like predictive protein signature was built, converted to a gene signature and evaluated in the whole-genome expression data. The signature had prognostic value in patients with luminal-A breast cancer. This prognostic information was independent from that provided by standard genomic tests for breast cancer, such as MammaPrint, OncoType Dx and the 8-gene Score.

Conclusions: Proteomic profiling showed that cellular adhesion is a differential process between ER+ and TN breast cancer, and is reduced in the TN tumors. A group of ER+ breast tumors with reduced cellular adhesion was identified (TN-like). Patients with this luminal-A, TN-like breast cancer type had a poor outcome. This prognostic information was complementary to that offered by genomic tests such as OncoType.

Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P4-05-04.