Jack Yu

Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer

BACKGROUND Genome-wide measures of gene expression can identify patterns of gene activity that subclassify tumours and might provide a better means than is currently available for individual risk assessment in patients with lymph-node-negative breast cancer. METHODS We analysed, with Affymetrix Human U133a GeneChips, the expression of 22000 transcripts from total RNA of frozen tumour samples from 286 lymph-node-negative patients who had not received adjuvant systemic treatment. FINDINGS In a training set of 115 tumours, we identified a 76-gene signature consisting of 60 genes for patients positive for oestrogen receptors (ER) and 16 genes for ER-negative patients. This signature showed 93% sensitivity and 48% specificity in a subsequent independent testing set of 171 lymph-node-negative patients. The gene profile was highly informative in identifying patients who developed distant metastases within 5 years (hazard ratio 5.67 [95% CI 2.59-12.4]), even when corrected for traditional prognostic factors in multivariate analysis (5.55 [2.46-12.5]). The 76-gene profile also represented a strong prognostic factor for the development of metastasis in the subgroups of 84 premenopausal patients (9.60 [2.28-40.5]), 87 postmenopausal patients (4.04 [1.57-10.4]), and 79 patients with tumours of 10-20 mm (14.1 [3.34-59.2]), a group of patients for whom prediction of prognosis is especially difficult. INTERPRETATION The identified signature provides a powerful tool for identification of patients at high risk of distant recurrence. The ability to identify patients who have a favourable prognosis could, after independent confirmation, allow clinicians to avoid adjuvant systemic therapy or to choose less aggressive therapeutic options.

Novel Genes Associated with Malignant Melanoma but not Benign Melanocytic Lesions

Purpose: Cutaneous melanoma is a common, aggressive cancer with increasing incidence. The identification of melanoma-specific deregulated genes could provide molecular markers for lymph node staging assays and further insight into melanoma tumorigenesis. Experimental Design: Total RNA isolated from 45 primary melanoma, 18 benign skin nevi, and 7 normal skin tissue specimens were analyzed on an Affymetrix Hu133A microarray containing 22,000 probe sets. Results: Hierarchical clustering revealed a distinct separation of the melanoma samples from the benign and normal specimens. Novel genes associated with malignant melanoma were identified. Differential gene expression of two melanoma-specific genes, PLAB and L1CAM, were tested by a one-step quantitative reverse transcription-PCR assay on primary malignant melanoma, benign nevi, and normal skin samples, as well as on malignant melanoma lymph node metastasis and melanoma-free lymph nodes. The performance of the markers was compared with conventional melanoma markers such as tyrosinase, gp100, and MART1. Conclusion: Our study systematically identified novel melanoma-specific genes and showed the feasibility of using a combination of PLAB and L1CAM in a reverse transcription-PCR assay to differentiate clinically relevant samples containing benign or malignant melanocytes.

Gene Expression Signatures for Predicting Prognosis of Squamous Cell and Adenocarcinomas of the Lung

Non-small-cell lung cancers (NSCLC) compose 80% of all lung carcinomas with squamous cell carcinomas (SCC) and adenocarcinoma representing the majority of these tumors. Although patients with early-stage NSCLC typically have a better outcome, 35% to 50% will relapse within 5 years after surgical treatment. We have profiled primary squamous cell lung carcinomas from 129 patients using Affymetrix U133A gene chips. Unsupervised analysis revealed two clusters of SCC that had no correlation with tumor stage but had significantly different overall patient survival (P = 0.036). The high-risk cluster was most significantly associated with down-regulation of epidermal development genes. Cox proportional hazard models identified an optimal set of 50 prognostic mRNA transcripts using a 5-fold cross-validation procedure. Quantitative reverse transcription-PCR and immunohistochemistry using tissue microarrays were used to validate individual gene candidates. This signature was tested in an independent set of 36 SCC samples and achieved 84% specificity and 41% sensitivity with an overall predictive accuracy of 68%. Kaplan-Meier analysis showed clear stratification of high-risk and low-risk patients [log-rank P = 0.04; hazard ratio (HR), 2.66; 95% confidence interval (95% CI), 1.01-7.05]. Finally, we combined the SCC classifier with our previously identified adenocarcinoma prognostic signature and showed that the combined classifier had a predictive accuracy of 71% in 72 NSCLC samples also showing significant differences in overall survival (log-rank P = 0.0002; HR, 3.54; 95% CI, 1.74-7.19). This prognostic signature could be used to identify patients with early-stage high-risk NSCLC who might benefit from adjuvant therapy following surgery.

mRNA and microRNA expression profiles in circulating tumor cells and primary tumors of metastatic breast cancer patients

Purpose: Molecular characterization of circulating tumor cells (CTC) holds great promise. Unfortunately, routinely isolated CTC fractions currently still contain contaminating leukocytes, which makes CTC-specific molecular characterization extremely challenging. In this study, we determined mRNA and microRNA (miRNA) expression of potentially CTC-specific genes that are considered to be clinically relevant in breast cancer. Experimental Design: CTCs were isolated with the epithelial cell adhesion molecule–based CellSearch Profile Kit. Selected genes were measured by real-time reverse transcriptase PCR in CTCs of 50 metastatic breast cancer patients collected before starting first-line systemic therapy in blood from 53 healthy blood donors (HBD) and in primary tumors of 8 of the patients. The molecular profiles were associated with CTC counts and clinical parameters and compared with the profiles generated from the corresponding primary tumors. Results: We identified 55 mRNAs and 10 miRNAs more abundantly expressed in samples from 32 patients with at least 5 CTCs in 7.5 mL of blood compared with samples from 9 patients without detectable CTCs and HBDs. Clustering analysis resulted in 4 different patient clusters characterized by 5 distinct gene clusters. Twice the number of patients from cluster 2 to 4 had developed both visceral and nonvisceral metastases. Comparing transcript levels in CTCs with those measured in corresponding primary tumors showed clinically relevant discrepancies in estrogen receptor and HER2 levels. Conclusions: Our study shows that molecular profiling of low numbers of CTCs in a high background of leukocytes is feasible and shows promise for further studies on the clinical relevance of molecular characterization of CTCs. Clin Cancer Res; 17(11); 3600–18. ©2011 AACR.

Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer

BackgroundPublished prognostic gene signatures in breast cancer have few genes in common. Here we provide a rationale for this observation by studying the prognostic power and the underlying biological pathways of different gene signatures.MethodsGene signatures to predict the development of metastases in estrogen receptor-positive and estrogen receptor-negative tumors were identified using 500 re-sampled training sets and mapping to Gene Ontology Biological Process to identify over-represented pathways. The Global Test program confirmed that gene expression profilings in the common pathways were associated with the metastasis of the patients.ResultsThe apoptotic pathway and cell division, or cell growth regulation and G-protein coupled receptor signal transduction, were most significantly associated with the metastatic capability of estrogen receptor-positive or estrogen-negative tumors, respectively. A gene signature derived of the common pathways predicted metastasis in an independent cohort. Mapping of the pathways represented by different published prognostic signatures showed that they share 53% of the identified pathways.ConclusionWe show that divergent gene sets classifying patients for the same clinical endpoint represent similar biological processes and that pathway-derived signatures can be used to predict prognosis. Furthermore, our study reveals that the underlying biology related to aggressiveness of estrogen receptor subgroups of breast cancer is quite different.

Copy number alterations that predict metastatic capability of human breast cancer.

We have analyzed the DNA copy numbers for over 100,000 single-nucleotide polymorphism loci across the human genome in genomic DNA from 313 lymph node-negative primary breast tumors for which genome-wide gene expression data were also available. Combining these two data sets allowed us to identify the genomic loci and their mapped genes, having high correlation with distant metastasis. An estimation of the likely response based on published predictive signatures was performed in the identified prognostic subgroups defined by gene expression and DNA copy number data. In the training set of 200 patients, we constructed an 81-gene prognostic copy number signature (CNS) that identified a subgroup of patients with increased probability of distant metastasis in the independent validation set of 113 patients [hazard ratio (HR), 2.8; 95% confidence interval (95% CI), 1.4-5.6] and in an external data set of 116 patients (HR, 3.7; 95% CI, 1.3-10.6). These high-risk patients constituted a subset of the high-risk patients predicted by our previously established 76-gene gene expression signature (GES). This very poor prognostic group identified by CNS and GES was putatively more resistant to preoperative paclitaxel and 5-fluorouracil-doxorubicin-cyclophosphamide combination chemotherapy (P = 0.0048), particularly against the doxorubicin compound, while potentially benefiting from etoposide. Our study shows the feasibility of using copy number alterations to predict patient prognostic outcome. When combined with gene expression-based signatures for prognosis, the CNS refines risk classification and can help identify those breast cancer patients who have a significantly worse outlook in prognosis and a potential differential response to chemotherapeutic drugs.