Genetic analysis of lobular carcinoma in situ and associated invasive lobular cancer

Objectives

This is a pilot study to assess the feasibility of performing SNP-loss of heterozygosity (LOH) on micro-dissected formalin-fixed paraffin-embedded (FFPE) lobular carcinoma in situ (LCIS) tissue and to examine the genetic relationship between LCIS and associated invasive lobular carcinomas (ILC).

Introduction

LCIS is a risk factor for the development of subsequent invasive breast carcinoma in either breast. Approximately 50 to 70% of these subsequent cancers are ILC. Not all LCIS progresses to invasive disease, and at present there are no biomarkers that predict which cases will develop ILC.

Methods

LCIS and ILC samples were microdissected from FFPE tissue blocks. Genetic changes were studied using SNP-LOH (Goldengate Assay; Illumina) to assess CN-LOH and copy number changes. Copy number changes were confirmed using 32k BAC arrays. Ploidy was assessed using Feulgen staining.

Results

SNP-LOH was successful in 31/35 samples. LOH events were more common in classical LCIS with associated ILC compared with pure LCIS. Commonest changes were on 16q and 1q. Other areas of LOH were more common in LCIS associated with ILC but did not reach statistical significance. Patterns of genetic change were maintained in ILC compared with the associated LCIS and 3/5 cases acquired additional genetic changes. Copy number changes were confirmed in three cases using BAC arrays. Concordance for copy number gain and loss was 50% and 75%, respectively. Concordance for copy neutral LOH was 100%. Ploidy studies on 20 cases revealed that all cases of classical LCIS were diploid whereas 5/9 pleomorphic LCIS/ILC samples were tetraploid or aneuploid.

Conclusions

It is feasible to perform SNP-LOH on small amounts of micro-dissected FFPE LCIS tissue. The pattern of genetic changes confirms the findings of others that LCIS is a likely precursor of ILC. Further investigation of genetic changes in LCIS associated with ILC is expected to lead to the identification of biomarkers that predict for subsequent invasive transformation.