DNA arrays as predictors of efficacy of adjuvant/neoadjuvant chemotherapy in breast cancer patients: current data and issues on study design

Changes in taste and food preferences in breast cancer patients receiving chemotherapy: a pilot study

PURPOSE

Cancer treatment may relate to appetite reduction and malnutrition. We investigated taste alterations and dish-type preferences during chemo- and/or radiation therapy in breast cancer patients.

METHODS

Breast cancer patients (BC, n = 59) scheduled to receive cancer therapy and healthy subjects (control group or CTRL, n = 49) were voluntarily recruited. Taste detection thresholds (DTs) and recognition thresholds (RT) were compared between pre-treatment BC patients and CTRL for sweet (sucrose), salty (NaCl), bitter (caffeine), and sour (citric acid) solutions. Changes in taste thresholds and dish preferences during treatment were monitored in the BC group. Blood chemistry and anthropometric data were collected.

RESULTS

At baseline, BC patients demonstrated lower sweet and salty DTs and RTs and a higher sour RT compared to CTRL. Bitter DT and RT were similar in both groups. Mild/soft dishes were preferred over fried/oily dishes by BC patients. Throughout treatment in BC patients, sweet thresholds significantly declined, while salty, bitter, and sour DTs and RTs were not affected, and there was no increase in preference for a dish. However, preference towards mild/soft dishes remained. While sweet-sour fruits and sweetened nuts were not favored during therapy.

CONCLUSIONS

Sensitivities to sweet, salty, and sour but not bitter tastes differed between BC patients and CTRL. During treatment, sweet taste sensitivity increased while other tastes were unaffected. BC patients preferred mild/soft dishes over fried and sweetened dishes compared to CTRL. Our findings may contribute to developing dishes for breast cancer patients to increase food intake and thereby lower the risk of malnutrition.

Molecular profiling for personalized cancer care

The pace of genomic discoveries in the field of cancer is revolutionizing our understanding of the biological dynamics of cancerous growth and, at the same time, fueling research for newer and smarter cancer therapies to reverse the effects of these alterations. These dynamics are driving a tremendous paradigm shift in cancer diagnostics, drug development and clinical trial design with the hope of eliminating the current structure and approach of cancer care, to one which is driven by the underlying biology of the tumor and, thus highly personalized. Much of this paradigm shift has been fueled by the current availability of novel technologies, platforms and bioinformatic tools. Today, therapies are being rationally designed to target the precise genetic alterations with better clinical outcomes with reduced morbidity. Therefore, molecular profiling of tumors to identify the multiplicity of alterations in a tumor is an essential and necessary companion for targeted therapies.

Gene expression pathway analysis to predict response to neoadjuvant docetaxel and capecitabine for breast cancer

Neoadjuvant chemotherapy has been shown to be equivalent to post-operative treatment for breast cancer, and allows for assessment of chemotherapy response. In a pilot trial of docetaxel (T) and capecitabine (X) neoadjuvant chemotherapy for Stage II/III BC, we assessed correlation between baseline gene expression and tumor response to treatment, and examined changes in gene expression associated with treatment. Patients received four cycles of TX. Tumor tissue obtained from Mammotome core biopsies pretreatment (BL) and post-cycle 1 (C1) of TX was FLash frozen and stored at -70 degrees C until processing. Gene expression analysis utilized Affymetrix HG-U133 Plus 2.0 GeneChip arrays. Statistical analysis was performed using BRB Array Tools after RMA normalization. Gene ontology (GO) pathway analysis used random variance t tests with a significance level of P\0.005. For gene categories identified byGO pathway analysis as significant, expression levels of individual genes within those pathways were compared between classes using univariate t tests; those genes with significance level of P\0.05 were reported. PAM50 analyses were performed on tumor samples to investigate biologic subtype and risk of relapse (ROR). Using GO pathway analysis, 39 gene categories discriminated between responders and non-responders,most notably genes involved in microtubule assembly and regulation. When comparing pre- and post-chemotherapy specimens, we identified 71 differentially expressed gene categories, including DNA repair and cell proliferation regulation. There were 45 GO pathways in which the change in expression after one cycle of chemotherapy was significantly different among responders and nonresponders. The majority of tumor samples fell into the basal like and luminal B categories. ROR scores decreased in response to chemotherapy; this change was more evident in samples from patients classified as responders by clinical criteria. GO pathway analysis identified a number of gene categories pertinent to therapeutic response, and may be an informative method for identifying genes important in response to chemotherapy. Larger studies using the methods described here are necessary to fully evaluate gene expression changes in response to chemotherapy.

Genetic characterization of breast cancer and implications for clinical management

Breast cancer is a genetic disease caused by the accumulation of mutations in neoplastic cells. In the last few years, high-throughput microarray-based molecular analysis has provided increasingly more coherent information about the genetic aberrations in breast cancer. New biomarkers and molecular techniques are slowly becoming part of the diagnostic and prognostic armamentarium available for pathologists and oncologists to tailor the therapy for breast cancer patients. In this review, we will focus on the contribution of breast cancer somatic genetics to our understanding of breast cancer biology and its impact on breast cancer patient management.

[Genome-wide expression profiling as a clinical tool: are we there yet?]

Breast cancer is a heterogeneous disease, encompassing a plethora of histological types and clinical courses. Current histopathological classification systems for breast cancer are based on descriptive entities that are of prognostic significance. Few prognostic markers beyond those offered by histopathological analysis are available. Furthermore, a very limited armamentarium of predictive biomarkers has been introduced in clinical practice. High throughput molecular technologies are reshaping our understanding of breast cancer, of which microarray-based gene expression has received the most attention. This method has been successfully used to derive a molecular taxonomy for breast cancer, which has provided interesting insights into the biology of the disease. Microarray-based class prediction studies have generated a multitude of prognostic/predictive signatures. Although these signatures have not been fully translated to clinical practice as yet, they herald the promise of an improvement in breast cancer treatment decision-making. It should be noted, however, that most of the signatures developed to date seem to have discriminatory power almost restricted to oestrogen receptor-positive disease. This review addresses the contribution of gene expression profiling to our understanding of breast cancer and its clinical management and what has yet to be done for these classifiers to be incorporated in clinical practice.

Mechanisms of drug combinations: interaction and network perspectives

Understanding the molecular mechanisms underlying synergistic, potentiative and antagonistic effects of drug combinations could facilitate the discovery of novel efficacious combinations and multi-targeted agents. In this article, we describe an extensive investigation of the published literature on drug combinations for which the combination effect has been evaluated by rigorous analysis methods and for which relevant molecular interaction profiles of the drugs involved are available. Analysis of the 117 drug combinations identified reveals general and specific modes of action, and highlights the potential value of molecular interaction profiles in the discovery of novel multicomponent therapies.

Evaluation of biological pathways involved in chemotherapy response in breast cancer

Introduction

Our goal was to examine the association between biological pathways and response to chemotherapy in estrogen receptor-positive (ER⁺) and ER-negative (ER^-) breast tumors separately.

Methods

Gene set enrichment analysis including 852 predefined gene sets was applied to gene expression data from 51 ER^- and 82 ER⁺ breast tumors that were all treated with a preoperative paclitaxel, 5-fluoruracil, doxorubicin, and cyclophosphamide chemotherapy.

Results

Twenty-seven (53%) ER^- and 7 (9%) ER⁺ patients had pathologic complete response (pCR) to therapy. Among the ER^- tumors, a proliferation gene signature (false discovery rate [FDR] q = 0.1), the genomic grade index (FDR q = 0.044), and the E2F3 pathway signature (FDR q = 0.22, P = 0.07) were enriched in the pCR group. Among the ER⁺ tumors, the proliferation signature (FDR q = 0.001) and the genomic grade index (FDR q = 0.015) were also significantly enriched in cases with pCR. Ki67 expression, as single gene marker of proliferation, did not provide the same information as the entire proliferation signature. An ER-associated gene set (FDR q = 0.03) and a mutant p53 gene signature (FDR q = 0.0019) were enriched in ER⁺ tumors with residual cancer.

Conclusion

Proliferation- and genomic grade-related gene signatures are associated with chemotherapy sensitivity in both ER^- and ER⁺ breast tumors. Genes involved in the E2F3 pathway are associated with chemotherapy sensitivity among ER^- tumors. The mutant p53 signature and expression of ER-related genes were associated with lower sensitivity to chemotherapy in ER⁺ breast tumors only.

Prediction of the outcome of preoperative chemotherapy in breast cancer using DNA probes that provide information on both complete and incomplete responses

Background

DNA microarray technology has emerged as a major tool for exploring cancer biology and solving clinical issues. Predicting a patient's response to chemotherapy is one such issue; successful prediction would make it possible to give patients the most appropriate chemotherapy regimen. Patient response can be classified as either a pathologic complete response (PCR) or residual disease (NoPCR), and these strongly correlate with patient outcome. Microarrays can be used as multigenic predictors of patient response, but probe selection remains problematic. In this study, each probe set was considered as an elementary predictor of the response and was ranked on its ability to predict a high number of PCR and NoPCR cases in a ratio similar to that seen in the learning set. We defined a valuation function that assigned high values to probe sets according to how different the expression of the genes was and to how closely the relative proportions of PCR and NoPCR predictions to the proportions observed in the learning set was. Multigenic predictors were designed by selecting probe sets highly ranked in their predictions and tested using several validation sets.

Results

Our method defined three types of probe sets: 71% were mono-informative probe sets (59% predicted only NoPCR, and 12% predicted only PCR), 25% were bi-informative, and 4% were non-informative. Using a valuation function to rank the probe sets allowed us to select those that correctly predicted the response of a high number of patient cases in the training set and that predicted a PCR/NoPCR ratio for validation sets that was similar to that of the whole learning set. Based on DLDA and the nearest centroid method, bi-informative probes proved more successful predictors than probes selected using a t test.

Conclusion

Prediction of the response to breast cancer preoperative chemotherapy was significantly improved by selecting DNA probe sets that were successful in predicting outcomes for the entire learning set, both in terms of accurately predicting a high number of cases and in correctly predicting the ratio of PCR to NoPCR cases.

Research Issues Affecting Preoperative Systemic Therapy for Operable Breast Cancer

Preoperative systemic therapy (PST) in operable breast cancer allows a small increase in breast conservation rates and has significant potential as a research platform. PST offers the ability to discern treatment effect in vivo, and may allow smaller trials targeting specific breast cancer subtypes and making more efficient use of resources. Early observations of a specific outcome of interest in individual patient subgroups may improve the design of larger definitive randomized adjuvant trials using survival as a main outcome. PST offers the potential for therapeutic adjustments midcourse, which assumes the existence of validated intermediate end points and effective alternative therapies. This article reviews critical research issues affecting the design of PST trials, including the appropriate selection of trial end points and markers for long-term outcome, baseline marker expression as a predictor of response, and statistical considerations using novel trial designs. Key issues regarding optimal tumor subtype selection for individual trials, novel approaches using nontherapeutic window trial designs, and ethical and advocacy considerations are also discussed. PST requires an experienced and cohesive multidisciplinary team for it to fulfill its potential in both research and clinical care.