Pre-clinical validation of early molecular markers of sensitivity to aromatase inhibitors in a mouse model of post-menopausal hormone-sensitive breast cancer

Biomarkers of Response and Resistance to Palbociclib Plus Letrozole in Patients With ER+/HER2- Breast Cancer

PURPOSE

To determine (i) the relationship between candidate biomarkers of the antiproliferative (Ki67) response to letrozole and palbociclib alone and combined in ER⁺/HER2^- breast cancer; and (ii) the pharmacodynamic effect of the agents on the biomarkers.

EXPERIMENTAL DESIGN

307 postmenopausal women with ER⁺/HER2^- primary breast cancer were randomly assigned to neoadjuvant treatment with letrozole for 14 weeks; letrozole for 2 weeks, then letrozole+palbociclib to 14 weeks; palbociclib for 2 weeks, then letrozole+palbociclib to 14 weeks; or letrozole+palbociclib for 14 weeks. Biopsies were taken at baseline, 2 and 14 weeks and surgery at varying times after stopping palbociclib. Immunohistochemical analyses were conducted for Ki67, c-PARP, ER, PgR, RB1, CCNE1, and CCND1.

RESULTS

Higher baselines ER and PgR were significantly associated with a greater chance of complete cell-cycle arrest (CCCA: Ki67 <2.7%) at 14 weeks and higher baseline Ki67, c-PARP, and CCNE1 with a lower chance. The interaction with treatment was significant only for c-PARP. CCND1 levels were decreased c.20% by letrozole at 2 and 14 weeks but showed a tendency to increase with palbociclib. CCNE1 levels fell 82% (median) in tumors showing CCCA but were unchanged in those with no CCCA. Only 2/9 tumors showed CCCA 3-9 days after stopping palbociclib. ESR1 mutations were found in 2/4 tumors for which surgery took place ≥6 months after starting treatment.

CONCLUSIONS

High CCNE1 levels were confirmed as a biomarker of resistance to letrozole+palbociclib. Ki67 recovery within 3-9 days of discontinuing palbociclib indicates incomplete suppression of proliferation during the "off" week of its schedule.

Decreased expression of ABAT and STC2 hallmarks ER-positive inflammatory breast cancer and endocrine therapy resistance in advanced disease

BACKGROUND

Patients with Estrogen Receptor α-positive (ER+) Inflammatory Breast Cancer (IBC) are less responsive to endocrine therapy compared with ER+ non-IBC (nIBC) patients. The study of ER+ IBC samples might reveal biomarkers for endocrine resistant breast cancer.

MATERIALS & METHODS

Gene expression profiles of ER+ samples from 201 patients were explored for genes that discriminated between IBC and nIBC. Classifier genes were applied onto clinically annotated expression data from 947 patients with ER+ breast cancer and validated with RT-qPCR for 231 patients treated with first-line tamoxifen. Relationships with metastasis-free survival (MFS) and progression-free survival (PFS) following adjuvant and first-line endocrine treatment, respectively, were investigated using Cox regression analysis.

RESULTS

A metagene of six genes including the genes encoding for 4-aminobutyrate aminotransferase (ABAT) and Stanniocalcin-2 (STC2) were identified to distinguish 22 ER+ IBC from 43 ER+ nIBC patients and remained discriminatory in an independent series of 136 patients. The metagene and two genes were not prognostic in 517 (neo)adjuvant untreated lymph node-negative ER+ nIBC breast cancer patients. Only ABAT was related to outcome in 250 patients treated with adjuvant tamoxifen. Three independent series of in total 411 patients with advanced disease showed increased metagene scores and decreased expression of ABAT and STC2 to be correlated with poor first-line endocrine therapy outcome. The biomarkers remained predictive for first-line tamoxifen treatment outcome in multivariate analysis including traditional factors or published signatures. In an exploratory analysis, ABAT and STC2 protein expression levels had no relation with PFS after first-line tamoxifen.

CONCLUSIONS

This study utilized ER+ IBC to identify a metagene including ABAT and STC2 as predictive biomarkers for endocrine therapy resistance.

Can predictive biomarkers in breast cancer guide adjuvant endocrine therapy?

Personalized medicine for oestrogen receptor-α (ERα)-positive breast cancer requires predictive biomarkers for broad endocrine resistance as well as biomarkers capable of predicting resistance to a specific agent. In addition, biomarkers could be used to select patients that might benefit from the addition of treatments that do not target ERα. However, biomarker identification studies seem to be far from consistent and identified biomarkers seldom face an introduction into clinical practice. Importantly, most of the studies that seek to identify biomarkers have been performed using material from consecutive series of patients treated with tamoxifen (the most commonly prescribed ERα antagonist). Consequently, the predictive value of any biomarker identified is confounded by its prognostic value. Another important issue is the lack of differentiation between premenopausal and postmenopausal patients with breast cancer. The hormonal environment of a tumour in patients who are premenopausal is intrinsically distinct from those arising in postmenopausal women. Biomarkers of different biological mechanisms might enable the prediction of either broad endocrine resistance or resistance to a specific agent in each of these patient subtypes. Ultimately, improvements to study design are needed to establish the clinical validity of the most promising biomarkers to predict benefit from endocrine therapy.

GenDrux: a biomedical literature search system to identify gene expression-based drug sensitivity in breast cancer

Background

This paper describes the development of a web-based tool, GenDrux, which extracts and presents (over the Internet) information related to the disease-gene-drug nexus. This information is archived from the relevant biomedical literature using automated methods. GenDrux is designed to alleviate the difficulties of manually processing the vast biomedical literature to identify disease-gene-drug relationships. GenDrux will evolve with the literature without additional algorithmic modifications.

Results

GenDrux, a pilot system, is developed in the domain of breast cancer and can be accessed at http://www.microarray.uab.edu/drug_gene.pl. GenDrux can be queried based on drug, gene and/or disease name. From over 8,000 relevant abstracts from the biomedical literature related to breast cancer, we have archived a corpus of more than 4,000 articles that depict gene expression-drug activity relationships for breast cancer and related cancers. The archiving process has been automated.

Conclusions

The successful development, implementation, and evaluation of this and similar systems when created may provide clinicians with a tool for literature management, clinical decision making, thus setting the platform for personalized therapy in the future.

Chromatin immunoprecipitation to analyze DNA binding sites of HMGA2

Background

HMGA2 is an architectonic transcription factor abundantly expressed during embryonic and fetal development and it is associated with the progression of malignant tumors. The protein harbours three basically charged DNA binding domains and an acidic protein binding C-terminal domain. DNA binding induces changes of DNA conformation and hence results in global overall change of gene expression patterns. Recently, using a PCR-based SELEX (Systematic Evolution of Ligands by Exponential Enrichment) procedure two consensus sequences for HMGA2 binding have been identified.

Methodology/Principal Findings

In this investigation chromatin immunoprecipitation (ChIP) experiments and bioinformatic methods were used to analyze if these binding sequences can be verified on chromatin of living cells as well.

Conclusion

After quantification of HMGA2 protein in different cell lines the colon cancer derived cell line HCT116 was chosen for further ChIP experiments because of its 3.4-fold higher HMGA2 protein level. 49 DNA fragments were obtained by ChIP. These fragments containing HMGA2 binding sites have been analyzed for their AT-content, location in the human genome and similarities to sequences generated by a SELEX study. The sequences show a significantly higher AT-content than the average of the human genome. The artificially generated SELEX sequences and short BLAST alignments (11 and 12 bp) of the ChIP fragments from living cells show similarities in their organization. The flanking regions are AT-rich, whereas a lower conservation is present in the center of the sequences.