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A target based approach identifies genomic predictors of breast cancer patient response to chemotherapy
© Hallet et al.; licensee BioMed Central Ltd. 2012
Received: 25 January 2012
Accepted: 20 April 2012
Published: 11 May 2012
The efficacy of chemotherapy regimens in breast cancer patients is variable and unpredictable. Whether individual patients either achieve long-term remission or suffer recurrence after therapy may be dictated by intrinsic properties of their breast tumors including genetic lesions and consequent aberrant transcriptional programs. Global gene expression profiling provides a powerful tool to identify such tumor-intrinsic transcriptional programs, whose analyses provide insight into the underlying biology of individual patient tumors. For example, multi-gene expression signatures have been identified that can predict the likelihood of disease reccurrence, and thus guide patient prognosis. Whereas such prognostic signatures are being introduced in the clinical setting, similar signatures that predict sensitivity or resistance to chemotherapy are not currently clinically available.
We used gene expression profiling to identify genes that were co-expressed with genes whose transcripts encode the protein targets of commonly used chemotherapeutic agents.
Here, we present target based expression indices that predict breast tumor response to anthracycline and taxane based chemotherapy. Indeed, these signatures were independently predictive of chemotherapy response after adjusting for standard clinic-pathological variables such as age, grade, and estrogen receptor status in a cohort of 488 breast cancer patients treated with adriamycin and taxotere/taxol.
Importantly, our findings suggest the practicality of developing target based indices that predict response to therapeutics, as well as highlight the possibility of using gene signatures to guide the use of chemotherapy during treatment of breast cancer patients.
KeywordsGene expression profiling Breast cancer Chemotherapy response Gene signatures Chemotherapy
Oncologists are faced with the challenging task of selecting the most effective therapies for individual cancer patients to achieve the best possible outcome. Indeed, the latter is the central goal of personalized cancer medicine. Trastuzumab is one of the best examples illustrating the importance of tailoring treatment to the characteristics of an individual’s tumor. In the absence of patient selection only 10% of breast cancer patients derive clinical benefit from trastuzumab treatment . However, when patients are selected for trastuzumab therapy based on ERBB2/HER2 gene amplification, their response rate rises to as high as 50%. The development of global gene expression profiling technologies, such as DNA microarrays, has provided additional avenues to identify the molecular features of tumors that are associated with clinical variables, such as tumor grade or outcome. In fact, sets of genes, commonly called gene signatures, have been identified that predict patient prognosis, and are already used in various clinical settings [2–8].
Many current studies focus on identifying similar gene signatures to guide the selection of appropriate chemotherapy regimens [9–12]. However, gene signatures that predict tumor sensitivity or resistance to chemotherapy are not currently clinically available. The development and clinical implementation of gene signatures that predict response to commonly used chemotherapeutic agents could facilitate selecting the most efficacious therapeutic regimen given the molecular characteristics of an individual’s tumor. Furthermore, therapy-predictive gene signatures could ensure patients do not receive ineffective and potentially deleterious chemotherapeutic regimens.
Measuring the inherent chemosensitivity of a tumor can be accomplished by assessment of pathological response following neoadjuvant treatment with a given treatment regimen. In this fashion, patients in which no invasive or metastatic breast cancer can be detected following treatment are classified as having achieved complete pathological response (pCR), whereas patients that fail to achieve pCR are classified as having residual disease (RD). Importantly, neoadjuvant chemotherapy has been found to be as efficacious as chemotherapy given in the adjuvant setting, and patients who achieve complete pathological response after neoadjuvant intervention generally have an excellent probability of experiencing long-term survival [13–15]. Taken together, these data suggest that response to neoadjuvant chemotherapy (pCR/RD) provides a relevant clinical model to develop and validate gene signature based predictors of breast tumor response to chemotherapy.
We sought to test whether TOP2A and β-tubulin transcript expression indices could predict response to commonly used chemotherapeutic agents, as the protein products of these genes represent the respective molecular targets of commonly used anthracycline- and taxane-related drugs [16–18]. We hypothesized that such target based expression indices would provide a biologically comprehensive measurement of either TOP2A or β-tubulin activity in a patient’s tumor, and thus its likely dependence on either of these targets. Importantly, these analyses establish an effective method for identifying predictive drug response signatures, and highlight the use of predictive gene signatures to guide the selection of anthracycline and taxane based chemotherapy regimens for breast cancer patients.
TOP2A and β-tubulin expression are associated with complete pathological response in breast cancer patients treated with chemotherapy
The TOP2A index is associated with complete pathological response in breast cancer patients treated with anthracyclines
Combining indices is predictive of response to multi-agent chemotherapy
TOP2A and β-tubulin indices are more accurate than a similarly derived proliferation index
Comparison of the TOP2A and β-tubulin indices with clinico-pathologic parameters
Logistic regression analysis of the GSE25055 & GSE25065 validation set
Odds ratio (95% CI)
1.57 (1.39, 1.77)
Age (/10 years)
0.84 (0.68, 1.04)
ER Status Positive
0.23 (0.14, 0.37)
5.28 (2.95, 9.46)
1.16 (0.72, 1.88)
Age (/10 years)
0.89 (0.70, 1.13)
ER Status Positive
0.48 (0.27, 0.86)
2.24 (1.16, 4.33)
0.95 (0.54, 1.67)
1.33 (1.14, 1.55)
Here we describe the identification of TOP2A and β-tubulin transcript expression indices that predict complete pathological response to neoadjuvant chemotherapy regimens containing anthracycline and taxane drugs. Complete pathological response represents an appropriate clinical endpoint for these studies as patients who experience pCR also experience improved survival compared to those patients who retain RD [13–15]. Notably, TOP2A or β-tubulin, the respective targets of anthracycline and taxane drugs, have been linked to anthracycline and taxane response in previous studies, respectively [17, 18, 21, 42–44]. However, the expression of either of these genes has failed to become a useful clinical predictor of anthracycline or taxane response. We hypothesized that measurement of target-associated transcripts in a tumor sample might provide a more comprehensive measure of molecular target activity, and thus the tumor’s likelihood of response to therapy. Indeed, based on the datasets explored for the studies presented here, this appears to be the case. Moreover, a combination index derived from the TOP2A and β-tubulin expression indices was statistically significantly related to pathological response in a multivariate model that also included age, nodal status, tumor grade and estrogen receptor status in a group of 488 patients treated with anthracycline and taxane based chemotherapy.
From a clinical standpoint, predicting response to anthracycline and taxane based chemotherapy may be useful to identify breast cancer patients who have a high likelihood of benefiting from such regimens. Conversely, patients predicted to be resistant to anthracycline- and taxane-based chemotherapy may benefit from enrollment in clinical trials investigating the efficacy of novel treatments . Many issues remain to be addressed to confirm the clinical utility of the TOP2A and β-tubulin indices. In this study our conclusions are based on the analysis of retrospective data, which limits its clinical value. Moreover, we did not establish or optimize a threshold that would serve to separate patients predicted likely to respond to therapy from those likely to be resistant. Additionally, we did not test the capacity of the TOP2A index to predict response to neoadjuvant chemotherapy that consisted of only of an anthracycline, suggesting that the TOP2A index may be predictive of general chemotherapy response. Athough we did observe that the TOP2A index was not predictive of patient response ot docetaxel. Based on our multivariate analysis, our data suggests the TOP2A and β-tubulin indices remain predictive even after adjusting for clinical parameters such as tumor grade and estrogen receptor status, indicating that these indices likely have clinical value. Strictly speaking however, a true estimate of the usefulness of these indices would require a prospective clinical trial comparing randomly selected with index selected chemotherapy regimens.
An advantage of the approach taken here is our use of publicly available data, as well as the efficient use of patient samples for validation purposes. For example, the traditional approach for gene signature identification [2, 6, 7, 9, 46], commonly called the top-down approach, multiple datasets are required that comprise both tumor gene expression profiles as well as knowledge of the clinical variables under investigation, for the purposes of signature identification and subsequent independent validation. Other approaches, such as large-scale functional based RNA interference screens, have also yielded predictive signatures, although these experiments are relatively labour intensive and expensive . Here, we calculated target indices using datasets for which response to chemotherapy is not known. In this fashion, we maintained the independence of datasets for which response to neoadjuvant chemotherapy was measured as a clinical variable, thus maintaining the availability of multiple independent datasets for validation.
The identification of gene signatures that predict response to chemotherapy also have potential to offer new insights into the biology of breast tumors, particularly the transcriptional programs that govern therapy response. In this regard, it may be possible to identify molecular signaling pathways that either augment chemotherapy resistance or enhance sensitivity. Indeed, the latter strategy provides a rational approach to identifying new drug regimens, where a signaling pathway inhibitor/activator is included with the original chemotherapy regimen. In this fashion, tumors predicted to be therapy resistant might be rendered sensitive to the original therapy and treatment efficacy could be increased.
Another important implication of this study is that it highlights the identification of target based expression indices as a means to predict response to therapeutics. For example, it might be possible to generate a target based expression index for additional molecular targets, such as the HER2/Neu receptor tyrosine kinase, which is the molecular target of the humanized monoclonal antibody trastuzumab  as well as the small molecule Her2/Neu kinase inhibitor, lapatinib [48, 49]. Using such an approach, therapeutic response to the latter agents might then be predicted using transcriptional target based signatures. Indeed, this approach could be tested for multiple new experimental molecularly targeted therapies.
Importantly, these findings suggest the practicality of developing and testing target based indices that predict response to therapeutics. Moreover, our data highlights the possibility of using gene signatures to guide the use of chemotherapy during treatment of breast cancer patients.
Patients and Samples
Summary of samples used to identify and validate target indices
Total arrays: 811
Total arrays: 800
Characteristics of the validation cohorts
The raw intensity files (.CEL) comprising each dataset were download and normalised using the Robust Multichip Algorithm (RMA)to generate probe set intensities .
Identification of Target Related Genes
Target index genes were identified by their co-expression with either TOP2A (TOP2A, 201292_at), β-tubulin (TUBB, 212320_at), or E2F1 (204947_at) based on a Pearson distance function . We filtered these results such that only probe sets appearing in the most and least 1% of co- expressed probe sets within each identification cohort were included in the target index. The final TOP2A index comprised 86 probe sets with positive and 38 probe sets with negative correlation to TOP2A transcript levels (Additional file 1: Table S1). The β-tubulin index comprised 28 probe sets with positive and 14 probe sets with negative correlation to β-tubulin transcript levels (Additional file 1: Table S2).
Where x is the transformed expression, n is the number of probe sets, P is the set of probes with reported positive correlation to the target probe set, and N is the set of probes with reported negative correlation to the target probe set [46, 52].
pCR or RD following treatment with neoadjuvant chemotherapy was used as the clinical endpoints for this study. The predictive capacities of the target indices were evaluated using receiver- operator characteristic curve (ROC) analysis and both univariate and multivariate logistic regression. T- tests were used to compare indices between responders and non-responders. Welch’s correction was used when the variance of the index was unequal in these two patient groups. All tests were two-sided and a p-value of 0.05 or less was considered statistically significant. ANOVA and Tukeys multitple comparison test was used to test for differences between multiple groups (n > 2), and p-values of 0.05 `or less were considered significant.
Study design and conception of project, RMH and JAH. Completion of research, RMH. Statistical analysis, RMH and GP. Writing of manuscript RMH, GP and JAH. All authors read and approved the final manuscript.
This work was generously supported by grants from the Canadian Stem Cell Network, the Ontario Institute for Cancer Research and the Canadian Breast Cancer Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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