Abstract PL01-03: The therapeutic implications of genome sequencing and expression analyses for breast cancer.

It is now accepted that breast cancer is not a single disease, but instead is a spectrum of tumor subtypes with distinct cellular origins, somatic changes and somewhat predictable clinical behaviors. Prognostic factors such as stage and grade, and predictive factors such as hormone receptors and HER2 are the main features upon which clinical decision-making has traditionally been based. Gene expression data coming from DNA microarrays, and now sequencing data, have provided additional insights into the biology of breast cancer and has resulted in the development of a number of clinically useful assays. Our work on breast tumors using genomic analyses has led to a new molecular taxonomy that identifies at least five subtypes of breast cancers (Luminal A, Luminal B, HER2-enriched, Basal-like and Claudin-low). Known as the “intrinsic subtypes," these groups have revealed critical differences in incidence, survival, metastatic site specificity and response to treatment. Importantly, the information provided by the intrinsic subtypes complements the information provided by classical clinical-pathological markers, and adds value beyond estrogen receptor (ER) status, HER2 status and stage. In addition to the intrinsic subtypes, a number of other prognostic and predictive gene expression-based assays have been developed including the two most widely used clinical assays that are OncotypeDX and Mammaprint. Over the last year, at least six broad-scale papers have appeared that describe the application of massively parallel sequencing to hundreds of breast tumors in order to provide a comprehensive set of somatic mutations, methylation patterns, and DNA copy number changes. When these data are combined together, a highly complex picture of the somatic genetic events driving breast cancer pathogenesis have been revealed. Most importantly, new disease entities have been identified, and the genetic causes of most of the previously known breast cancer disease subtypes identified. Among the many examples of mutations possibly causally associated with a given subtype include somatic mutations highly associated with the Luminal A subtype; specific examples include 25/27 PIK3CA E545K mutations being seen in Luminal A tumors, as well as 13/13 intron 4 CA deletion mutants in GATA3. Alternatively, a number of mutations (TP53 and RB1) and copy number changes are uniquely associated with the Luminal B subtype including GATA3 exon 5 frameshift mutations. Some of these somatic mutations (AKT1, PIK3CA, PIK3R1) and DNA copy number changes (HER2, FGFRs, PTEN, INPP4B and MDM2) may also be predictive of response to targeted therapeutics, with most of these alterations being either retrospectively, and/or prospectively, tested in the clinic. Other genomics findings with therapeutic potential include the discovery that a subtype of breast cancer (i.e. the Basal-like subtype) shows significant genetic and mRNA expression similarities with Serous Ovarian Cancers; these similarities include TP53 mutation (>85% with almost no other commonly mutated genes), wide spread genomic instability, RB1-deficiency or Cyclin E1 amplification, BRCA1 inactivation, cMYC amplification, and high expression of AKT3. These common genetic/genomic features suggest that drug regimens that are used for Ovarian Cancer (i.e. a taxane and cis/carboplatin regimen) may also be effective on Basal-like breast cancers, which is being tested at a number of sites worldwide. Two additional “omic” findings of note include a subset of Luminal B cancers that show a hyper-methylated phenotype and an under representation of PIK3CA mutations, and a strong multi-platform signature of HER2 signaling within the HER2-enriched expression subtype that spans technologies from DNA copy number, to mRNA, to protein, to phopho-protein expression. A clear implication of all these “omic” data is that we are now in the era of personalized medicine, which is posing many new challenges. First and foremost of these challenges is to make biological sense, and then clinical sense, of the very large number of variants seen within any given individual, or any tumor. It is likely that the vast majority of these variants are functionally insignificant, and thus a current computational challenge is the automatic annotation, and functional prioritization, of the variants that are coming from any sequenced cancer genome (be it tumor or normal). A second challenge is what “defines a clinically actionable variant," which typically differs from physician to physician. A third challenge is how does one integrates the findings across multiple “omic” platforms as we know that no platform provides the complete cancer genome portrait. For example, it is still likely that the cell type of origin matters, as the consequences of a BRAF mutation in a melanoma are not the same as this same mutation in a colon cancer. Thus, in the long run, the best diagnostics for personalized medicine will likely be a combination of “omics-based” technologies including gene/protein expression, somatic mutations and DNA copy number changes, which will be synthesized together into objective and predictive computational models. Citation Format: Charles M. Perou. The therapeutic implications of genome sequencing and expression analyses for breast cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr PL01-03. doi:10.1158/1538-7445.AM2013-PL01-03