Protein subcellular localization profiling of breast cancer cells by dissociable antibody microarray staining

KPNA2 is a nuclear export protein that contributes to aberrant localisation of key proteins and poor prognosis of breast cancer

Background:

It is recognised that modulations of the nuclear import of macromolecules have a role in changing cellular phenotypes and carcinogenesis. We and others have noticed that aberrant subcellular localisation of DNA damage response (DDR) proteins in breast cancer (BC) is associated with loss-of-function phenotype. This study aims to investigate the biological and clinical significance of the nucleocytoplasmic transport protein karyopherin α-2 (KPNA2), and its role in controlling DDR proteins subcellular localisation in BC.

Methods:

A large (n=1494) and well-characterised series of early-stage invasive BC with a long-term follow-up was assessed for KPNA2 protein by using immunohistochemistry.

Results:

KPNA2 expression was associated with the subcellular localisation of key DDR proteins that showed cytoplasmic expression including BRCA1, RAD51, SMC6L1, γH2AX, BARD1, UBC9, PIAS1 and CHK1. High level of KPNA2 was associated not only with cytoplasmic localisation of these proteins but also with their low/negative nuclear expression. Positive KPNA2 expression was associated with negative oestrogen receptor and triple-negative phenotype. Survival analysis showed that KPNA2 was associated with poor outcome (P<0.0001), but this effect was not independent of other prognostic variables.

Conclusions:

This study provides further evidence for the complexity of DDR mechanism in BC, and that KNPA2 has a role in the aberrant subcellular localisation of DDR proteins with subsequent impaired function.

Reinventing diagnostics for personalized therapy in oncology

Human cancers are still diagnosed and classified using the light microscope. The criteria are based upon morphologic observations by pathologists and tend to be subject to interobserver variation. In preoperative biopsies of non-small cell lung cancers, the diagnostic concordance, even amongst experienced pulmonary pathologists, is no better than a coin-toss. Only 25% of cancer patients, on average, benefit from therapy as most therapies do not account for individual factors that influence response or outcome. Unsuccessful first line therapy costs Canada CAN$1.2 billion for the top 14 cancer types, and this extrapolates to $90 billion globally. The availability of accurate drug selection for personalized therapy could better allocate these precious resources to the right therapies. This wasteful situation is beginning to change with the completion of the human genome sequencing project and with the increasing availability of targeted therapies. Both factors are giving rise to attempts to correlate tumor characteristics and response to specific adjuvant and neoadjuvant therapies. Static cancer classification and grading systems need to be replaced by functional classification systems that not only account for intra- and inter- tumor heterogeneity, but which also allow for the selection of the correct chemotherapeutic compounds for the individual patient. In this review, the examples of lung and breast cancer are used to illustrate the issues to be addressed in the coming years, as well as the emerging technologies that have great promise in enabling personalized therapy.