Genomic profiling is critical for the identification of treatment options for patients with metastatic cancer, but it remains unclear how frequently this procedure should be repeated during the course of the disease. To address this, we analyzed whole-genome sequencing (WGS) data of 250 biopsy pairs, longitudinally collected over the treatment course of 231 adult patients with a representative variety of metastatic solid malignancies. Within the biopsy interval (median, 6.4 months), patients received one or multiple lines of (mostly) standard-of-care (SOC) treatments, with all major treatment modalities being broadly represented. SOC biomarkers and biomarkers for clinical trial enrollment could be identified in 23% and 72% of biopsies, respectively. For SOC genomic biomarkers, we observed full concordance between the first and the second biopsy in 99% of pairs. Of the 219 biomarkers for clinical trial enrollment that were identified in the first biopsies, we recovered 94% in the follow-up biopsies. Furthermore, a second WGS analysis did not identify additional biomarkers for clinical trial enrollment in 91% of patients. More-frequent genomic evolution was observed when considering specific genes targeted by small-molecule inhibitors or hormonal therapies (21% and 22% of cases, respectively). Together, our data demonstrate that there is limited evolution of the actionable genome of treated metastases. A single WGS analysis of a metastatic biopsy is generally sufficient to identify SOC genomic biomarkers and to identify investigational treatment opportunities.

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Genome-wide mRNA expression measurements can identify molecular signatures of cancer and are anticipated to improve patient management. Such expression profiles are currently being critically evaluated based on an apparent instability in gene composition and the limited overlap between signatures from different studies. We have recently identified a primary tumor signature for detection of lymph node metastasis in head and neck squamous cell carcinomas. Before starting a large multicenter prospective validation, we have thoroughly evaluated the composition of this signature. A multiple training approach was used for validating the original set of predictive genes. Based on different combinations of training samples, multiple signatures were assessed for predictive accuracy and gene composition. The initial set of predictive genes is a subset of a larger group of 825 genes with predictive power. Many of the predictive genes are interchangeable because of a similar expression pattern across the tumor samples. The head and neck metastasis signature has a more stable gene composition than previous predictors. Exclusion of the strongest predictive genes could be compensated by raising the number of genes included in the signature. Multiple accurate predictive signatures can be designed using various subsets of predictive genes. The absence of genes with strong predictive power can be compensated by including more genes with lower predictive power. Lack of overlap between predictive signatures from different studies with the same goal may be explained by the fact that there are more predictive genes than required to design an accurate predictor. (Cancer Res 2006; 66(4): 2361-6)@en