An algorithm to discover gene signatures with predictive potential

Clinicians are commonly faced with two important decisions when treating cancer patients: whether or not adjuvant chemotherapy is required, and selecting the most appropriate treatment. Traditionally, several histopathological characteristics of the tumor are taken into consideration when deciding on the best treatment[1]. However, it has been reported that 70-80% of breast cancer patients do not benefit from the use of chemotherapy, but are still exposed to the deleterious side effects of these drugs[2]. Therefore additional prediction methods are needed to improve the quality of life for breast cancer patients. One of these methods relies on gene expression profiling based predictors, which can be used to further inform the decision making process and increase a clinician's ability to successfully treat cancer patients [3]. Recently, researchers developed a 70-gene signature that can correctly separate patients into good- and poor-prognosis groups, and identified patients who can be spared unnecessary chemotherapy [2, 4]. However, constructing such a signature requires the use of various clustering and classification algorithms, which in turn require specialized software and bioinformatics training. Consequently, the need arises for strategies that can be used to generate predictive gene signatures, which are amenable to the software and skill sets available to the cancer biologist.

Typically gene expression based predictors are "trained" on a cohort of samples whose gene expression profiles are known, and for which at least one biological characteristic has been measured[5]. After the "training" of a predictor it must be validated on a set of samples, which were not used to initially "train" the algorithm. Predictors should in turn be able to accurately forecast the biological characteristic of samples of interest.

For our purposes we used a data set consisting of whole tumor gene expression profiles derived from 295 primary human breast tumors, as well as clinical data relating to the patients survival and occurrence of metastasis [2]. We then coarsely grained the expression data into high, average and low expression levels, and ranked genes based on the extent of their expression in patients who either survived or succumbed to breast cancer. In this fashion we were able to find genes whose transcripts generally had high and low expression in patients who succumbed and survived, respectively, and vice versa. By combining the top ranked candidates from a 144 patient training dataset we were able to create a 20 gene signature which performed well on a 151 patient validation dataset.

Our analyses establish an effective method to obtain gene expression based predictors that clearly separate human breast cancer patients into distinguishable prognosis groups with statistically significant differences in survival.

We sought to generate an algorithm with the following properties: (i) simple implementation with straight forward methodology, and (ii) high predictive accuracy. The reasons for this were to facilitate non-bioinformatic expert biologist development of valuable and biologically useful gene expression based prediction models. Importantly, we completed all steps of our algorithm using Microsoft Excel™ 2007, and will share the template files used for these analyses with interested researchers. This software is widely (if not universally) accessible to and used by the biological research community, suggesting that implementation of this technique will not be hampered by lack of software or training. As mentioned previously, most other feature selection techniques require the use of sophisticated clustering and classification algorithms, whose use requires specialized software and software based training.

To confirm that our algorithm produced a prediction model with comparable predictive power to other techniques in feature selection we compared its predictive power with that of an Aurora kinase A expression model as well as the 70-gene signature MammaPrint™ model. The Aurora kinase A model was previously shown to have comparable predictive accuracy to several feature selection techniques at predicting breast cancer patient survival, and can be used to make comparisons between feature selection techniques [8]. Additionally, the 70-gene signature has previously been tested on the NKI dataset, which allowed us to make model comparisons on the same patients. The 70-gene signature is also used clinically and thus represents a "gold standard" against which to compare predictive accuracy of gene signatures which predict breast cancer patient outcome [9]. We observed that our model had a slightly higher overall predictive accuracy than either the Aurora kinase A expression model or the 70-gene signature, and all three models had comparable specificities and positive predictive values (Table 2). Importantly, these observations demonstrate that our algorithm produces prediction models with comparable accuracy to other feature selection techniques while having generally better accessibility and useability for biological research scientists. To this end, we've begun using our algorithm to generate gene expression based prediction models of breast cancer cell sensitivity to commonly used anti-cancer therapies.

Here, we present an algorithm to generate gene signatures with predictive potential. It is noteworthy that our algorithm was developed using Microsoft Excel™ and tested using GraphPad Prism5™, commonly available software that should significantly increase its use. Importantly, the signature developed using our method had comparable predictive accuracy to either the Aurora kinase A expression or 70-gene MammaPrint™ models [2, 8]. Our methods represent a novel and broadly applicable technique to generate predictive gene signatures that we anticipate will prove useful to the molecular biological research community.

The authors declare that they have no competing interests.