Biology to Bedside: Mining Molecular Data for Cancer Prognosis
Targeted News Service
How will any one patient fare with their cancer treatment program? It's a question with no easy answer.
By mining the readily available data from The Cancer Genome Atlas (TCGA), scientists hope to answer this question by examining both molecular information and traditional clinical variables such as age or tumor stage. Their goal is nothing short of translating biological data into clinical use.
The Cancer Genome Atlas is a research program supported by the
Liang conducted a multi-institutional study of TCGA data, including 953 samples from four cancer types - lung, ovarian, brain and kidney. An article detailing his findings, "Assessing the Clinical Utility of Cancer Genomic and Proteomic Data Across Tumor Types," is published in the June online version of the journal Nature Biotechnology. Liang's study revealed that molecular profiling, while still in its infancy, may eventually hold promise for cancer patient prognosis.
"We hope that molecular profiling of tumors will one day advance the clinical management of cancer, but the benefits of integrating molecular and clinical data have not been studied in depth," said Liang. "The true value of our study is to serve as a starting point for building future prognostic and therapeutic strategies based on molecular profiling."
The overall goal for the study was to address how and to what extent TCGA molecular data could impact cancer treatment he said.
Focusing on the four cancers, Liang looked at the potential for predicting patient survival by evaluating molecular data from multiple tumor incidences, both alone or in combination with clinical variables. He then developed an open-access platform allowing researchers to build and evaluate cancer survival prediction models based on the data.
"By analyzing data from multiple cancer types, we were able to evaluate prognostic models and identify gene alterations that led to tumor formation," he said. "This would have not been obtained by looking at tumor data from just one cancer type."
By combining molecular data and clinical variables, Liang observed a better prediction of cancer prognosis in three of the four cancers: kidney, ovarian and lung. He cautions however that further analysis is needed.
While using large data sets to measure something as unpredictable as cancer survivability is very much in the early stages, Liang believes it is a good start.
"In contrast to previous studies driven by a single cancer or data type, we could evaluate patient survival prediction from different molecular data and describe the potential prognostic and/or therapeutic relevance across multiple cancers. This raised several important issues related to the potential clinical use of such large-scale molecular data," said Liang.
Other MD Anderson collaborators included
The study was funded by grants from the
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