CureMatch Announces New Advances In Using Metabolomic Profiling To Distinguish Among Cancer Subtypes
The Discovery is Expected to Pave the Way for Metabolomics in the Monitoring and Treatment of Cancer
SAN DIEGO, CA – February 22, 2017 – CureMatch, the developer of a decision support platform for combination therapy in cancer, revealed in the 2nd Annual Biomarker Conference new discoveries using metabolomics as a tool for clinical cancer research and care.
Metabolomic profiling is an emerging field of scientific research for genomics, biomarker discovery, and cancer. While various molecular profiling techniques, such as DNA sequencing, have enabled significant advances in precision oncology, metabolomics has not yet been widely incorporated into this growing field.
In separate studies, metabolites were analyzed to elucidate differentially expressed metabolic pathways in the four subtypes of lung cancer (adenocarcinoma, squamous cell carcinoma, large cell lung cancer, small cell lung cancer) and the four subtypes of breast cancer (basal-like, luminal A, luminal B, HER-2 positive). In the lung cancer study, it was found that five metabolic pathways were selective for adenocarcinoma and fourteen for small cell lung cancer. In the breast cancer study, it was found that each of the four cancer subtypes could be distinguished by at least one metabolic pathway.
“The results of the studies indicate that our metabolomic profiling technique can potentially be used as an accurate and inexpensive way to distinguish among different cancer subtypes,” said Valentina Kouznetsova, Ph.D., lead author of the studies and research scientist at the UC San Diego Supercomputer Center (SDSC) and Moores Cancer Center (MCC).
“Our CureMatch decision support platform already incorporates genomic, transcriptomic, and proteomic data to allow oncologists to determine the best treatment plan for their patients,” said Stephane Richard, Ph.D., President and COO of CureMatch, Inc., “We believe that this is the first step in adding metabolomics data into our platform, and will enable a broader range of genes to be analyzed and additional drug therapy options to be provided to the oncologist.”
“I need to note,” added Igor Tsigelny, Ph.D., senior author of the studies, research scientist at the SDCS, MCC and CTO of CureMatch Inc., “that using metabolomics profiling and recognition strategies can help in elucidation of genes that are activated or deactivated based on the metabolic responses of the organism.”
The study was performed in collaboration with the San Diego Supercomputer Center and Moores Cancer Center at UC San Diego. Dr. Valentina Kouznetsova, Katherine Zhuo, Matthew Bronars, Dr. Stephane Richard, and Dr. Igor Tsigelny are co-authors on the studies, entitled “Combined Metabolomics and Genomic Analyses Pave the Way to Machine-Learning Methods for Lung Cancer Diagnosis” and “Metabolomic Profiling to Distinguish Between Subtypes of Breast Cancer.” The studies were presented at the 2nd Annual Biomarker Conference in San Diego on 6–7 February 2017. More information about the conference is available at http://www.mnmconferences.com/The-Biomarker-Conference
SAN DIEGO, CA – February 22, 2017 – CureMatch, the developer of a decision support platform for combination therapy in cancer, revealed in the 2nd Annual Biomarker Conference new discoveries using metabolomics as a tool for clinical cancer research and care.
Metabolomic profiling is an emerging field of scientific research for genomics, biomarker discovery, and cancer. While various molecular profiling techniques, such as DNA sequencing, have enabled significant advances in precision oncology, metabolomics has not yet been widely incorporated into this growing field.
In separate studies, metabolites were analyzed to elucidate differentially expressed metabolic pathways in the four subtypes of lung cancer (adenocarcinoma, squamous cell carcinoma, large cell lung cancer, small cell lung cancer) and the four subtypes of breast cancer (basal-like, luminal A, luminal B, HER-2 positive). In the lung cancer study, it was found that five metabolic pathways were selective for adenocarcinoma and fourteen for small cell lung cancer. In the breast cancer study, it was found that each of the four cancer subtypes could be distinguished by at least one metabolic pathway.
“The results of the studies indicate that our metabolomic profiling technique can potentially be used as an accurate and inexpensive way to distinguish among different cancer subtypes,” said Valentina Kouznetsova, Ph.D., lead author of the studies and research scientist at the UC San Diego Supercomputer Center (SDSC) and Moores Cancer Center (MCC).
“Our CureMatch decision support platform already incorporates genomic, transcriptomic, and proteomic data to allow oncologists to determine the best treatment plan for their patients,” said Stephane Richard, Ph.D., President and COO of CureMatch, Inc., “We believe that this is the first step in adding metabolomics data into our platform, and will enable a broader range of genes to be analyzed and additional drug therapy options to be provided to the oncologist.”
“I need to note,” added Igor Tsigelny, Ph.D., senior author of the studies, research scientist at the SDCS, MCC and CTO of CureMatch Inc., “that using metabolomics profiling and recognition strategies can help in elucidation of genes that are activated or deactivated based on the metabolic responses of the organism.”
The study was performed in collaboration with the San Diego Supercomputer Center and Moores Cancer Center at UC San Diego. Dr. Valentina Kouznetsova, Katherine Zhuo, Matthew Bronars, Dr. Stephane Richard, and Dr. Igor Tsigelny are co-authors on the studies, entitled “Combined Metabolomics and Genomic Analyses Pave the Way to Machine-Learning Methods for Lung Cancer Diagnosis” and “Metabolomic Profiling to Distinguish Between Subtypes of Breast Cancer.” The studies were presented at the 2nd Annual Biomarker Conference in San Diego on 6–7 February 2017. More information about the conference is available at http://www.mnmconferences.com/The-Biomarker-Conference