A prototype methodology combining surface-enhanced laser desorption/ionization protein chip technology and artificial neural network algorithms to predict the chemoresponsiveness of breast cancer cell lines exposed to Paclitaxel and Doxorubicin under in vitro conditions.
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.
Mian
Shahid
S
Interdisciplinary Biomedical Research Building, Faculty of Science and Land-based Studies, Nottingham Trent University, Nottingham, UK.
Ball
Graham
G
Hornbuckle
Jo
J
Holding
Finn
F
Carmichael
James
J
Ellis
Ian
I
Ali
Selman
S
Li
Geng
G
McArdle
Stephanie
S
Creaser
Colin
C
Rees
Robert
R
D000465:Algorithms; D000970:Antineoplastic Agents / Q000627:therapeutic use / Q000633:toxicity*; D001185:Artificial Intelligence; D015415:Biomarkers / Q000737:chemistry; D001943:Breast Neoplasms / Q000188:drug therapy*; D045744:Cell Line, Tumor; D004317:Doxorubicin / Q000627:therapeutic use / Q000633:toxicity*; D004353:Drug Evaluation, Preclinical / Q000379:methods; D019008:Drug Resistance, Neoplasm; D005260:Female; D006801:Humans; D016571:Neural Networks, Computer; D017239:Paclitaxel / Q000627:therapeutic use / Q000633:toxicity*; D011237:Predictive Value of Tests; D040081:Protein Array Analysis / Q000379:methods*; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*
D000970:Antineoplastic Agents; D015415:Biomarkers; D004317:Doxorubicin; D017239:Paclitaxel