Pharmacogenetics of Breast Cancer: Towards the Individualization of Therapy
Pharmacogenetics of Breast Cancer: Towards the Individualization of Therapy (Translational Medicine)
By Brian Leyland-Jones
* Publisher: Informa
* Number Of Pages: 352
* Publication Date: 2008-05-19
* ISBN-10 / ASIN: 1420052934
Product Description:
Pharmacogenetics, generally referred to as the study of genetic variation that gives rise to differing response todrugs , is becoming more relevant in the diagnosis , treatment , and recovery of cancer patients. The problem faced when treating cancer is the outstanding varied efficacy between success and failure. Unpredictability between a population of patients who are affected with the same occurring malignancy can show varying associated toxicities in drug treatment ranging from zero effect through lethal doses. Since the chemotherapeutic agent is often developed to fit the average patient the unfortunate result is that approximately 40% of patients may be receiving the wrong drug. However, using pharmacogenetics there are promising advancements in the development of effective agents which will enable ‘personalized cancer chemotherapy’ to become routine for the clinical practice. This individualization is most advanced in the field of breast cancer; while many breast oncologists are individualizing techniques based on specific profiles, within 10 years it is likely all breast oncologists will be using RT-PCR, FISH-based multiplexed arrays, or a similar testing regime for patient individualization therapy.
Towards the Individualization of Therapy
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INTRODUCTION
Towards the Individualization of Therapy
Pharmacogenetics, generally referred to as the study of genetic variation that gives rise to differing response to
Towards the Individualization of Therapy
READ THIS EBOOK
INTRODUCTION
Mortality from breast cancer results from the ability of some tumors to metastasize to distant sites. Selecting patients with micrometastases at diagnosis is crucial for clinicians in deciding who should, and who should not, receive toxic and expensive adjuvant chemotherapy to eradicate these metastatic cells. Although many individual biomarkers were originally attractive, over the years most have failed to become clinically useful. In addition, the management of breast cancer has changed, with the majority of node-negative patients now undergoing systemic adjuvant therapy because we cannot precisely determine an individual’s risk of recurrence. A majority of node-negative patients are being unnecessarily overtreated because if left systemically untreated, only about 25% of node-negative patients would ever develop recurrence.
There is therefore a critical need to identify patients with sufficiently low risk of breast cancer
recurrence so as to avoid further treatment. In addition, in patients at risk of recurrence and in need of therapy, optimal therapeutic selection is an increasingly important objective. Recent developments in applying microarray technologies to breast tumor samples suggest that these new techniques may provide for the transition of molecular biological discoveries to clinical application and will generate clinically useful genomic profiles that more accurately predict the
long-term outcome of individual breast cancer patients.
BACKGROUND
Until recently, evaluations of prognostic and predictive factors have considered one factor at a time or have used small panels of markers. However, with the advent of new genomic technologies such as microarrays, capable of simultaneously measuring thousands of genes or gene products, we are beginning to construct molecular fingerprints of individual tumors so that accurate prognostic and predictive assessments of each cancer may be made. Clinicians may one day base clinical management on each woman’s personal prognosis and predict the best individual therapies according to the genetic fingerprint of each individual cancer.
Breast cancer is characterized by a very heterogeneous clinical course. A major goal of recent studies is to determine whether RNA microarray expression profiling or DNA array gene amplification or gene loss patterns can accurately predict an individual’s long-term potential for recurrence of breast cancer, so that appropriate treatment decisions can be made. Microarrays can be used to measure the mRNA expression of thousands of genes at one time or survey genomic alterations that may distinguish molecular phenotypes associated with long-term, recurrence-free survival or clinical response to treatment. These new technologies have been successfully applied to primary breast cancers and may eventually outperform currently used clinical parameters in predicting disease outcome.
Since RNA expression microarray technology provided a method for monitoring the RNA expression of many thousands of human genes at a time, there was considerable anticipation that it would quickly and easily revolutionize our approaches to cancer diagnosis, prognosis, and treatment. The reality remains extremely promising but is also complex. A potential complication in the application of microarray technology to primary human breast tumor samples is the presence of variable numbers of normal cells, such as stroma, blood vessels, and lymphocytes, in the tumor. Indeed, it has been demonstrated using gross analysis of human breast cancer specimens compared with breast cancer cell lines that the tumors expressed sets of genes in common not only with these cell lines but also with cells of hematopoietic lineage and stromal origin (1,2). Laser capture microdissection has also been successfully used to isolate pure-cell populations from primary breast cancers for array profiling (3). Sgroi et al. (3) utilized laser capture microdissection to isolate morphologically “normal” breast epithelial cells, invasive breast cancer cells, and metastatic lymph node cancer cells from one patient and were able to demonstrate the feasibility of using microdissected samples for array profiling as well as following potential progression of cancer in this patient. However, with the emerging data supporting important roles for the surrounding stroma in breast cancer progression and the labor-intensive and technically challenging nature of laser capture technology with subsequent amplification of RNA for quantitation, most published investigations to date have evaluated total gene expression to identify prognostic profiles, as will be described in the next section.
MOLECULAR CLASSIFICATION OF BREAST CANCER
A study of sporadic breast tumor samples by Perou et al. (2) was the first to show that breast tumors could be classified into subtypes distinguished by differences in their expression profiles. Using 40 breast tumors and 20 matched pairs of samples before and after doxorubicin treatment, an “intrinsic gene set” of 476 genes was selected that was more variably expressed between the 40 sporadic tumors than between the paired samples. This intrinsic gene set was then used to cluster and segregate the tumors into four major subgroups: a “luminal cell-like”
group expressing the estrogen receptor (ER); a “basal cell-like” group expressing keratins 5 and 17, integrinb4, and laminin, but lacking ER expression; an “Erb-B2-positive” group; and a “normal” epithelial group (Fig. 1). In a subsequent study with 38 additional cancers, the investigators found the same subgroups as before (4), except that the luminal, ER-positive group was further subdivided into subsets with distinctive gene expression profiles. In univariate survival analysis, performed on the 49 patients diagnosed with locally advanced disease but without evidence of distant metastasis, ER positivity was not a significant prognostic factor on its own, but the luminal-type group enjoyed a more favorable survival compared with the other groups. Conversely, the basal-like group had a significantly poorer prognosis. Although small and
exploratory, this study suggests that important differences in outcome can be ascertained from microarray expression profiling.
An interesting study was reported by Gruvberger et al. (5), who profiled 58 grossly dissected primary invasive breast tumors and used artificial neural network analysis to predict the ER status of the tumors on the basis of their gene expression patterns. They then determined which specific genes were themost important for ER classification. By comparing to Serial Analysis of Gene Expression (SAGE) data from estradiol-stimulated breast cancer cells, they determined that only a few genes of the many genes that were associated with ER expression in tumors were indeed estrogen responsive in cell culture. This observation lent further support to the hypothesis developed by Perou et al. (1) that basic cell lineages, such as the luminal ER-positive cell type, can be partly explained by observed genomic gene expression patterns rather than downstream effectors of only one pathway, such as the ER.
There is therefore a critical need to identify patients with sufficiently low risk of breast cancer
recurrence so as to avoid further treatment. In addition, in patients at risk of recurrence and in need of therapy, optimal therapeutic selection is an increasingly important objective. Recent developments in applying microarray technologies to breast tumor samples suggest that these new techniques may provide for the transition of molecular biological discoveries to clinical application and will generate clinically useful genomic profiles that more accurately predict the
long-term outcome of individual breast cancer patients.
BACKGROUND
Until recently, evaluations of prognostic and predictive factors have considered one factor at a time or have used small panels of markers. However, with the advent of new genomic technologies such as microarrays, capable of simultaneously measuring thousands of genes or gene products, we are beginning to construct molecular fingerprints of individual tumors so that accurate prognostic and predictive assessments of each cancer may be made. Clinicians may one day base clinical management on each woman’s personal prognosis and predict the best individual therapies according to the genetic fingerprint of each individual cancer.
Breast cancer is characterized by a very heterogeneous clinical course. A major goal of recent studies is to determine whether RNA microarray expression profiling or DNA array gene amplification or gene loss patterns can accurately predict an individual’s long-term potential for recurrence of breast cancer, so that appropriate treatment decisions can be made. Microarrays can be used to measure the mRNA expression of thousands of genes at one time or survey genomic alterations that may distinguish molecular phenotypes associated with long-term, recurrence-free survival or clinical response to treatment. These new technologies have been successfully applied to primary breast cancers and may eventually outperform currently used clinical parameters in predicting disease outcome.
Since RNA expression microarray technology provided a method for monitoring the RNA expression of many thousands of human genes at a time, there was considerable anticipation that it would quickly and easily revolutionize our approaches to cancer diagnosis, prognosis, and treatment. The reality remains extremely promising but is also complex. A potential complication in the application of microarray technology to primary human breast tumor samples is the presence of variable numbers of normal cells, such as stroma, blood vessels, and lymphocytes, in the tumor. Indeed, it has been demonstrated using gross analysis of human breast cancer specimens compared with breast cancer cell lines that the tumors expressed sets of genes in common not only with these cell lines but also with cells of hematopoietic lineage and stromal origin (1,2). Laser capture microdissection has also been successfully used to isolate pure-cell populations from primary breast cancers for array profiling (3). Sgroi et al. (3) utilized laser capture microdissection to isolate morphologically “normal” breast epithelial cells, invasive breast cancer cells, and metastatic lymph node cancer cells from one patient and were able to demonstrate the feasibility of using microdissected samples for array profiling as well as following potential progression of cancer in this patient. However, with the emerging data supporting important roles for the surrounding stroma in breast cancer progression and the labor-intensive and technically challenging nature of laser capture technology with subsequent amplification of RNA for quantitation, most published investigations to date have evaluated total gene expression to identify prognostic profiles, as will be described in the next section.
MOLECULAR CLASSIFICATION OF BREAST CANCER
A study of sporadic breast tumor samples by Perou et al. (2) was the first to show that breast tumors could be classified into subtypes distinguished by differences in their expression profiles. Using 40 breast tumors and 20 matched pairs of samples before and after doxorubicin treatment, an “intrinsic gene set” of 476 genes was selected that was more variably expressed between the 40 sporadic tumors than between the paired samples. This intrinsic gene set was then used to cluster and segregate the tumors into four major subgroups: a “luminal cell-like”
group expressing the estrogen receptor (ER); a “basal cell-like” group expressing keratins 5 and 17, integrinb4, and laminin, but lacking ER expression; an “Erb-B2-positive” group; and a “normal” epithelial group (Fig. 1). In a subsequent study with 38 additional cancers, the investigators found the same subgroups as before (4), except that the luminal, ER-positive group was further subdivided into subsets with distinctive gene expression profiles. In univariate survival analysis, performed on the 49 patients diagnosed with locally advanced disease but without evidence of distant metastasis, ER positivity was not a significant prognostic factor on its own, but the luminal-type group enjoyed a more favorable survival compared with the other groups. Conversely, the basal-like group had a significantly poorer prognosis. Although small and
exploratory, this study suggests that important differences in outcome can be ascertained from microarray expression profiling.
An interesting study was reported by Gruvberger et al. (5), who profiled 58 grossly dissected primary invasive breast tumors and used artificial neural network analysis to predict the ER status of the tumors on the basis of their gene expression patterns. They then determined which specific genes were themost important for ER classification. By comparing to Serial Analysis of Gene Expression (SAGE) data from estradiol-stimulated breast cancer cells, they determined that only a few genes of the many genes that were associated with ER expression in tumors were indeed estrogen responsive in cell culture. This observation lent further support to the hypothesis developed by Perou et al. (1) that basic cell lineages, such as the luminal ER-positive cell type, can be partly explained by observed genomic gene expression patterns rather than downstream effectors of only one pathway, such as the ER.
Towards the Individualization of Therapy
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