Gene expression profile predicts outcome after anthracycline-based adjuvant chemotherapy in early breast cancer

Comprehensive characterization of pre- and post-treatment samples of breast cancer reveal potential mechanisms of chemotherapy resistance

When locally advanced breast cancer is treated with neoadjuvant chemotherapy, the recurrence risk is significantly higher if no complete pathologic response is achieved. Identification of the underlying resistance mechanisms is essential to select treatments with maximal efficacy and minimal toxicity. Here we employed gene expression profiles derived from 317 HER2-negative treatment-naïve breast cancer biopsies of patients who underwent neoadjuvant chemotherapy, deep whole exome, and RNA-sequencing profiles of 22 matched pre- and post-treatment tumors, and treatment outcome data to identify biomarkers of response and resistance mechanisms. Molecular profiling of treatment-naïve breast cancer samples revealed that expression levels of proliferation, immune response, and extracellular matrix (ECM) organization combined predict response to chemotherapy. Triple negative patients with high proliferation, high immune response and low ECM expression had a significantly better treatment response and survival benefit (HR 0.29, 95% CI 0.10–0.85; p = 0.02), while in ER+ patients the opposite was seen (HR 4.73, 95% CI 1.51–14.8; p = 0.008). The characterization of paired pre-and post-treatment samples revealed that aberrations of known cancer genes were either only present in the pre-treatment sample (CDKN1B) or in the post-treatment sample (TP53, APC, CTNNB1). Proliferation-associated genes were frequently down-regulated in post-treatment ER+ tumors, but not in triple negative tumors. Genes involved in ECM were upregulated in the majority of post-chemotherapy samples. Genomic and transcriptomic differences between pre- and post-chemotherapy samples are common and may reveal potential mechanisms of therapy resistance. Our results show a wide range of distinct, but related mechanisms, with a prominent role for proliferation- and ECM-related genes.

Personalized medicine, genomics, and pharmacogenomics: a primer for nurses

Personalized medicine is the study of patients' unique environmental influences as well as the totality of their genetic code-their genome-to tailor personalized risk assessments, diagnoses, prognoses, and treatments. The study of how patients' genomes affect responses to medications, or pharmacogenomics, is a related field. Personalized medicine and genomics are particularly relevant in oncology because of the genetic basis of cancer. Nurses need to understand related issues such as the role of genetic and genomic counseling, the ethical and legal questions surrounding genomics, and the growing direct-to-consumer genomics industry. As genomics research is incorporated into health care, nurses need to understand the technology to provide advocacy and education for patients and their families.

Economic issues involved in integrating genomic testing into clinical care: the case of genomic testing to guide decision-making about chemotherapy for breast cancer patients

The use of taxanes to treat node-positive (N+) breast cancer patients is associated with heterogeneous benefits as well as with morbidity and financial costs. This study aimed to assess the economic impact of using gene expression profiling to guide decision-making about chemotherapy, and to discuss the coverage/reimbursement issues involved. Retrospective data on 246 patients included in a randomised trial (PACS01) were analyzed. Tumours were genotyped using DNA microarrays (189-gene signature), and patients were classified depending on whether or not they were likely to benefit from chemotherapy regimens without taxanes. Standard anthracyclines plus taxane chemotherapy (strategy AT) was compared with the innovative strategy based on genomic testing (GEN). Statistical analyses involved bootstrap methods and sensitivity analyses. The AT and GEN strategies yielded similar 5-year metastasis-free survival rates. In comparison with AT, GEN was cost-effective when genomic testing costs were less than 2,090€. With genomic testing costs higher than 2,919€, AT was cost-effective. Considering a 30% decrease in the price of docetaxel (the patent rights being about to expire), GEN was cost-effective if the cost of genomic testing was in the 0€-1,139€, range; whereas AT was cost-effective if genomic testing costs were higher than 1,891€. The use of gene expression profiling to guide decision-making about chemotherapy for N+ breast cancer patients is potentially cost-effective. Since genomic testing and the drugs targeted in these tests yield greater well-being than the sum of those resulting from separate use, questions arise about how to deal with extra well-being in decision-making about coverage/reimbursement.

[An economic perspective on targeted treatments]

In the current context of crisis in the field of drug research and patients' increasing demand for greater involvement in the decision-making about their treatment, the possibility of a more personal tailored approach to medicine is emerging. This raises questions about the clinical benefits of targeted therapeutic approaches versus the cost of determining which patients are likely to benefit the most, and protecting the others from the toxicity of inappropriate treatments. Although cost-effectiveness studies shed light on this issue, the conclusions to which they point tend to be obscured by interdependence between the pricing of these tests and the cost of the treatments involved. Investigating the societal willingness-to-pay for gains in QALY (quality adjusted life year) should help to disentangle this interdependence and clarify the conclusions of cost-effectiveness studies.

[Personalized medicine and breast cancer: anticipatory medicine, prognostic evaluation and therapeutic targeting]

Breast cancer is now considered as a large collection of distinct biological entities, the management of which is increasingly personalized. Personalized medicine - defined as a medicine, which uses molecular profiles, notably genetic profiles, from patients and/or tumors to tailor therapeutic decisions - is now introduced in the management of breast cancer at any stages: screening and prevention of hereditary forms, prognostic and predictive evaluation of early breast cancer, and, more recently, novel clinical trials in advanced breast cancer, where genetic characterization of tumor tissue based on genomics, including next-generation sequencing tools, is used to drive specific therapeutic targeting.

[Health care expenditures linked to the use of targeted therapies and diagnostic tests for cancer patients]

Recent advances in cancer research have led to the development of very expensive new drugs for cancer treatment: the targeted therapies. However, the introduction of these new therapeutic agents which costs are increasing could threaten the diffusion of these innovations. It is thus necessary to determine whether the use of targeted therapies yields clinical benefits that justify their increasing cost. The development of companion diagnosis tests to target drugs and thus to select those patients most likely to benefit from the treatment may provide a useful means of containing the progress of health care expenditures and improve the cost/benefit ratio. In this paper, we present current estimates of health care expenditures linked to the use of targeted therapies for cancer care. We also discuss some of the issues related to the regulatory decisions (pricing and reimbursement) concerning the test/drug couple.

[Genomics and clinical research for breast cancer]

Genomics of breast cancer is paving the way towards more and more tailored treatments. The number of molecularly targeted therapies under development is increasing. In parallel, the high-throughput analyses revealed the molecular heterogeneity of disease, and identified several very different molecular subtypes, numerous and sometimes very scarce molecular alterations, and multigenic signatures predictive for clinical outcome, some of which are being tested in prospective clinical trials. This molecular segmentation of breast cancer and the multitude of new drugs to be tested (alone and in combination) lead to develop clinical trials based on the molecular profile of tumors to guide the patient towards the most suitable drug.

Kinome expression profiling and prognosis of basal breast cancers

Background

Basal breast cancers (BCs) represent ~15% of BCs. Although overall poor, prognosis is heterogeneous. Identification of good- versus poor-prognosis patients is difficult or impossible using the standard histoclinical features and the recently defined prognostic gene expression signatures (GES). Kinases are often activated or overexpressed in cancers, and constitute targets for successful therapies. We sought to define a prognostic model of basal BCs based on kinome expression profiling.

Methods

DNA microarray-based gene expression and histoclinical data of 2515 early BCs from thirteen datasets were collected. We searched for a kinome-based GES associated with disease-free survival (DFS) in basal BCs of the learning set using a metagene-based approach. The signature was then tested in basal tumors of the independent validation set.

Results

A total of 591 samples were basal. We identified a 28-kinase metagene associated with DFS in the learning set (N = 73). This metagene was associated with immune response and particularly cytotoxic T-cell response. On multivariate analysis, a metagene-based predictor outperformed the classical prognostic factors, both in the learning and the validation (N = 518) sets, independently of the lymphocyte infiltrate. In the validation set, patients whose tumors overexpressed the metagene had a 78% 5-year DFS versus 54% for other patients (p = 1.62E-4, log-rank test).

Conclusions

Based on kinome expression, we identified a predictor that separated basal BCs into two subgroups of different prognosis. Tumors associated with higher activation of cytotoxic tumor-infiltrative lymphocytes harbored a better prognosis. Such classification should help tailor the treatment and develop new therapies based on immune response manipulation.