NEJM: Tumor heterogeneity present challenges to personalized medicine
Analysis of renal cancer biopsies and metastases showed intratumour heterogeneity, which could interfere with the delivery of personalized medicine, when strategies utilize a single tumor biopsy sample, according to a study published March 8 in New England Journal of Medicine.

Previous research has indicated that intratumor heterogeneity may contribute to treatment failure and drug resistance. However, studies had not evaluated intratumour heterogeneity by next-generation sequencing.

Marco Gerlinger, MD, from the Cancer Research UK London Research Institute, and colleagues sought to examine tumor heterogeneity and conducted exome sequencing, chromosome aberration analysis and ploidy profiling on spatially separated samples obtained from primary renal carcinomas and metastatic sites in four patients.

The researchers found substantial heterogeneity, with 63 to 69 percent of somatic mutations not detected across every tumor region. Tumor-suppressor genes underwent various mutations within a single tumor, which suggested convergent phenotypic evolution, according to the researchers. “Gene-expression signatures of good and poor prognosis were detected in different regions of the same tumor,” wrote Gerlinger et al.

According to the researchers, a single biopsy contained an average of 70 mutations. However, this represents only 55 percent of mutations in the tumor. And only 34 percent of all mutations were present in all regions, “indicating that a single biopsy was not representative of the mutational landscape of the entire tumor bulk.”  A single sample shows only a minority of genetic aberrations in the entire tumor.

“Intratumour heterogeneity may explain the differences encountered in the validation of oncology biomarkers owing to sampling bias, contribute to Darwinian selection of preexisting drug-resistant clones and predict therapeutic response,” Gerlinger and colleagues concluded.

Going forward, they suggested reconstructing tumor clonal architectures and identifying common mutations could help develop more robust biomarkers and therapeutic approaches.