[show abstract][hide abstract] ABSTRACT: An ability to predict the likelihood of cellular response towards particular chemotherapeutic agents based upon protein expression patterns could facilitate the identification of biological molecules with previously undefined roles in the process of chemoresistance/chemosensitivity, and if robust enough these patterns might also be exploited towards the development of novel predictive assays. To ascertain whether proteomic based molecular profiling in conjunction with artificial neural network (ANN) algorithms could be applied towards the specific recognition of phenotypic patterns between either control or drug treated and chemosensitive or chemoresistant cellular populations, a combined approach involving MALDI-TOF matrix-assisted laser desorption/ionization-time of flight mass spectrometry, Ciphergen protein chip technology and ANN algorithms have been applied to specifically identify proteomic 'fingerprints' indicative of treatment regimen for chemosensitive (MCF-7, T47D) and chemoresistant (MCF-7/ADR) breast cancer cell lines following exposure to Doxorubicin or Paclitaxel. The results indicate that proteomic patterns can be identified by ANN algorithms to correctly assign 'class' for treatment regimen (e.g. control/drug treated or chemosensitive/chemoresistant) with a high degree of accuracy using boot-strap statistical validation techniques and that biomarker ion patterns indicative of response/non-response phenotypes are associated with MCF-7 and MCF-7/ADR cells exposed to Doxorubicin. We have also examined the predictive capability of this approach towards MCF-7 and T47D cells to ascertain whether prediction could be made based upon treatment regimen irrespective of cell lineage. Models were identified that could correctly assign class (control or Paclitaxel treatment) for 35/38 samples of an independent dataset. A similar level of predictive capability was also found (> 92%; n = 28) when proteomic patterns derived from the drug resistant cell line MCF-7/ADR were compared against those derived from MCF-7 and T47D as a model system of drug resistant and drug sensitive phenotypes. This approach might offer a potential methodology for predicting the biological behaviour of cancer cells towards particular chemotherapeutics and through protein isolation and sequence identification could result in the identification of biological molecules associated with chemosensitive/chemoresistance tumour phenotypes.
[show abstract][hide abstract] ABSTRACT: MOTIVATION: MALDI mass spectrometry is able to elicit macromolecular expression data from cellular material and when used in conjunction with Ciphergen protein chip technology (also referred to as SELDI-Surface Enhanced Laser Desorption/Ionization), it permits a semi-high throughput approach to be taken with respect to sample processing and data acquisition. Due to the large array of data that is generated from a single analysis (8-10000 variables using a mass range of 2-15 kDa-this paper) it is essential to implement the use of algorithms that can detect expression patterns from such large volumes of data correlating to a given biological/pathological phenotype from multiple samples. If successful, the methodology could be extrapolated to larger data sets to enable the identification of validated biomarkers correlating strongly to disease progression. This would not only serve to enable tumours to be classified according to their molecular expression profile but could also focus attention upon a relatively small number of molecules that might warrant further biochemical/molecular characterization to assess their suitability as potential therapeutic targets. RESULTS: Using a multi-layer perceptron Artificial Neural Network (ANN) (Neuroshell 2) with a back propagation algorithm we have developed a prototype approach that uses a model system (comprising five low and seven high-grade human astrocytomas) to identify mass spectral peaks whose relative intensity values correlate strongly to tumour grade. Analyzing data derived from MALDI mass spectrometry in conjunction with Ciphergen protein chip technology we have used relative importance values, determined from the weights of trained ANNs (Balls et al., Water, Air Soil Pollut., 85, 1467-1472, 1996), to identify masses that accurately predict tumour grade. Implementing a three-stage procedure, we have screened a population of approximately 100000-120000 variables and identified two ions (m/z values of 13454 and 13457) whose relative intensity pattern was significantly reduced in high-grade astrocytoma. The data from this initial study suggests that application of ANN-based approaches can identify molecular ion patterns which strongly associate with disease grade and that its application to larger cohorts of patient material could potentially facilitate the rapid identification of validated biomarkers having significant clinical (i.e. diagnostic/prognostic) potential for the field of cancer biology. AVAILIBILITY: Neuroshell 2 is commercially available from ward systems.