Predicting Outcome and Therapeutic Benefit from Molecular Profiles of Cancer

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Cancer patients with the same stage of disease can have markedly different treatment responses and overall outcomes. In addition to better systems of cancer classification and clarification of pathways that are altered in neoplastic cells, the advent of microarray technology has also prompted investigators to explore gene expression as a means of predicting the prognosis and response to chemothera-peutic decisions.116,117,119-122 Van't Veer and colleagues, for instance, have used microarray analysis to predict clinical outcomes of breast cancer. They identified a ''poor prognosis'' gene signature strongly predictive of distant metastases in

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Figure 11.1 Gene expression patterns for acute lymphoblastic leukemia (ALL) and acute myelogenous leukemia (AML). With permission from the New England Journal of Medicine.

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breast cancer patients without tumor cells in local lymph nodes (lymph node negative).116 This finding provided a strategy to select patients who would benefit from adjuvant therapy. This prognostic profile could provide a tool to tailor adjuvant systemic treatment that could greatly decrease the risk of adverse side effects and health care expenditure. The study of van de Vijver and co-workers describes a gene expression signature as a predictor for breast cancer survival among a cohort of patients younger than 53 years of age (range <40-53 years) and with either stage I or II breast cancer.117 The gene signature they identified is a powerful predictor of outcome in young patients. The data indicated that classification of patients into high-risk and low-risk subgroups according to their prognosis profile might be a useful guide to adjuvant therapy in patients with lymph node-positive breast cancer. Such an approach should also improve the selection of patients who would benefit from adjuvant systemic treatment, reducing the rate of both overtreatment and undertreatment.

Liu and colleagues examined the prognostic role of a gene signature from tumorigenic breast cancer cells.122 Differentially expressed genes were used to generate a 186-gene ''invasiveness gene signature'' (IGS) for its association with overall survival and metastasis-free survival in patients with breast cancer and other types of cancer. They found a significant association between the IGS and both overall and metastasis-free survival (p < 0.001 for both) in patients with breast cancer, which was independent of established clinical and pathological variables. The IGS was also associated with the prognosis in medulloblastoma (p = 0.004), lung cancer (p = 0.03), and prostate cancer (p = 0.01). This genetic signature of tumorigenic breast cancer cells was more strongly associated with clinical outcomes when combined with wound response signature in breast cancer. The ''wound response'' signature is a 512-gene signature that correlates with overall survival and metastasis-free survival in breast cancer patients. The IGS and WR signatures are representations of different biological phenomena and are based on nonoverlapping lists of genes.

Molecular profiling to characterize tumors and predict outcomes is a major effort to improve the control of lung cancer, one of the deadliest cancers in human populations. Molecular studies of lung tumors began with single or relatively small groups of potential prognostic markers and have progressed to microarray analysis of thousands of genes in large numbers of tissue analyses. The recent study of small cell lung cancer, the most common form of lung cancer, in Chinese (Taiwanese) subjects, identified a five-gene signature that is closely associated with survival of this form of lung cancer.123 Analysis of microarray data and risk scores of 185 frozen tissue specimens led to the identification of 16 genes that correlated with survival. Further analysis of five selected genes (DUSP6, MMD, STAT1, ERBB3, and LCK) that were submitted to decision-tree analysis showed them to be an independent predictor of relapse-free and overall survival. The model was validated with data from an independent cohort of 60 patients and with a set of published microarray data for 86 patients with small cell lung cancer.

Despite advances in the understanding of molecular pathways that are altered in cancerous cells, diagnosis and decisions regarding treatment still rely largely on classical histopathological and immunohistochemical techniques. Diagnosis and treatment would both benefit from a more quantitative approach. Molecular signatures of gene expression that correlate with the recurrence of breast cancer have been identified, but their clinical application has been limited by the requirement for fresh or snap frozen tissue and uncertainties about reproducibility, and few assays have been rigorously tested as prognostic or predictive in oncology. In 2004, Paik and colleagues resolved some of these issues by developing an assay to predict recurrence in patients with tamoxifen-treated, node-negative, breast cancer.124 The levels of expression of 16 cancer-related genes and 5 reference genes were used in the prospectively defined algorithm to calculate the recurrence score and to determine a risk group (low, intermediate, high) for each patient. The recurrence score was validated in a large multicenter clinical trial as quantifying the likelihood of distant recurrence in tamoxifen-treated patients with node-negative, estrogen-positive breast cancer. The rates of distant recurrence at 10 years in the low-risk, intermediate-risk, and high-risk groups were 6.8%, 14.3%, and 30.5%. In addition, the rate in the low-risk group was significantly lower than that in the high-risk group (p < 0.001), and was independent of age and tumor size. In an extension of this work, Paik et al. subsequently examined the relationship of the recurrence score (RS) to benefit from chemotherapy.125 The RS was measured in tumors from the tamoxifen-treated (227) and tamoxifen plus chemotherapy-treated patients (424) in the National Surgical Adjuvant Breast and Bowel Project (NSABP) B20 trial. Patients with high-RS tumors benefited greatly from chemotherapy whereas patients with low-RS tumors derived minimal, if any, benefit from chemotherapy. Patients with intermediate-RS tumors did not appear to derive a large benefit, but the uncertainty of the estimate could not exclude a clinically important benefit. The RS assay not only quantified the likelihood of breast cancer recurrence, but it also predicted the magnitude of the chemotherapy benefit. This methodology is now commercially available and is being used clinically.

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