Purpose

To explore the genetic landscape of tumors from patients enrolled on the BOLERO-2 trial to identify potential correlations between genetic alterations and efficacy of everolimus treatment. The BOLERO-2 trial has previously demonstrated that the addition of everolimus to exemestane prolonged progression-free survival by more than twofold in patients with hormone receptor-positive, human epidermal growth factor receptor 2-negative, advanced breast cancer previously treated with nonsteroidal aromatase inhibitors.

Patients and methods

Next-generation sequencing was used to analyze genetic status of cancer-related genes in 302 archival tumor specimens from patients representative of the BOLERO-2 study population. Correlations between the most common somatic alterations and degree of chromosomal instability, and treatment effect of everolimus were investigated.

Results

Progression-free survival benefit with everolimus was maintained regardless of alteration status of PIK3CA, FGFR1, and CCND1 or the pathways of which they are components. However, quantitative differences in everolimus benefit were observed between patient subgroups defined by the exon-specific mutations in PIK3CA (exon 20 v 9) or by different degrees of chromosomal instability in the tumor tissues.

Conclusion

The data from this exploratory analysis suggest that the efficacy of everolimus was largely independent of the most commonly altered genes or pathways in hormone receptor-positive, human epidermal growth factor receptor 2-negative breast cancer. The potential impact of chromosomal instabilities and low-frequency genetic alterations on everolimus efficacy warrants further investigation.

Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.