Abstract
BACKGROUND:
Computational algorithms, such as NHS PREDICT, have been developed using cancer registry data to guide decisions regarding adjuvant chemotherapy. They are limited by biases of the underlying data. Recent breakthroughs in molecular biology have aided the development of genomic assays which provide superior clinical information. In this study, we compared the performance in risk stratification of EndoPredict Clinical (EPClin, a composite of clinical data and EndoPredict) and PREDICT in a cohort of patients with breast cancer considered potential candidates for chemotherapy by the clinicians.
MATERIALS AND METHODS:
One hundred and twenty patients with biopsy-proven oestrogen receptor positive (ER+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer who underwent surgery were included. EPClin and PREDICT were determined for every tumour, and the results were compared.
RESULTS:
Using EPClin scores performed on 120 tumours, the cohort was stratified into low- (n=60) and high-risk (n=60) groups leading to 50% reduction in total chemotherapy prescriptions. PREDICT differentiated the patients into low- (n=45), intermediate- (n=33), and high-risk groups (n=42). Discordance between scores was demonstrated for 50 (41.66%) tumours. Nine (20%) out of 45 patients with low PREDICT scores had high EPClin scores and would otherwise not have received chemotherapy if the NHS PREDICT tool had been used alone. Eight (19%) out of 42 patients at high risk by PREDICT were reclassified as being at low risk by EPClin and avoided adjuvant chemotherapy. The sensitivity, specificity, positive predictive value and negative predictive value for NHS PREDICT to predict the potential need for chemotherapy as determined by EPClin were 85%, 51%, 68% and 80%, respectively.
CONCLUSION:
To our knowledge, this is the first clinical study to compare EPClin and PREDICT. The data indicate that computational algorithms such as NHS PREDICT may not accurately predict the need for chemotherapy leading to overtreatment, undertreatment or uncertainty and anxiety in a significant proportion of patients. This underscores the importance of more personalized prognostic tools.

In 2004 a 9 year old girl with a unilateral
phaeochromocytoma was referred to Lab A
for VHL mutation analysis. Sanger sequencing
on a gel-based ABI377 DNA sequencer did
not detect a mutation and nor did subsequent
capillary sequencing of RET, SDHB, SDHC,
SDHD, SDHAF2, TMEM127 and MAX. In
2013, aged 18 years, she developed a
paraganglioma. The VHL gene was resequenced
in Lab B using ABI3730 capillary
sequencing and a mosaic c.250G>T
(p.Val84Leu) mutation was identified.

A metastatic phenotype can be induced in benign rat mammary cells (Rama 37 cells) by transfecting them with metastasis-inducing DNAs (Met-DNAs). Stable transfection of Met-DNAs increases the level of the metastasis-associated protein, osteopontin. Randomly picked clonal cell lines have been established from the pool of Rama 37 cells transfected with one metastasis-inducing DNA, C9-Met-DNA. In these cell lines, moderate correlation is observed between the copy number of C9-Met-DNA and their metastatic potential (linear regression coefficient, R(2)=0.48). A very close correlation is observed between the cell lines' metastatic potential in vivo and the osteopontin mRNA levels in vitro (R(2)=0.74), but not with another metastasis-associated protein in this system, S100A4 (R(2)=0.21). A close correlation is also observed between osteopontin mRNA levels and the adhesive potential (R(2)=0.91) of the cells, but not with their growth rate in vitro (R(2)=0.03). These observations support the previous suggestion that osteopontin is the direct effector of C9-Met-DNA and that the presence of C9-Met-DNA is necessary, if not sufficient, for the induction of metastasis in vivo in this system. Additionally, these results suggest that Rama 37 cells with increased osteopontin mRNA levels become metastatic not through an increased growth rate, but through an increase in cellular adhesiveness.