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Genomics of lung cancer progression varies widely among patients; offers clues to predictive biomarkers

Date:
June 3, 2014
Source:
Mayo Clinic
Summary:
Tumor sequencing of several different lung cancers and their surrounding tissue complicates the prevailing theory of linear lung cancer progression and offers new insights for management of this deadly cancer, according to a new study. Sequencing results provide, for the first time, strong molecular evidence of progression from phenotypically indolent components to more aggressive disease and also show that both components can progress independently, even if they arise from the same precursor, according to the study.
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Tumor sequencing of several different lung cancers and their surrounding tissue complicates the prevailing theory of linear lung cancer progression and offers new insights for management of this deadly cancer, according to a new Mayo Clinic study. Sequencing results provide, for the first time, strong molecular evidence of progression from phenotypically indolent components to more aggressive disease and also show that both components can progress independently, even if they arise from the same precursor, according to the study. The paper appears online in Cancer Research.

“This study sheds light on potential changes in our understanding of both the molecular pathogenesis and best treatment of lung adenocarcinoma,” says George Vasmatzis, Ph.D., senior author of the study and co-director of the Biomarker Discovery Program in the Mayo Clinic Center for Individualized Medicine. “The heterogeneity of lung cancer tells us repeatedly that the natural history of tumors and the roads to progression vary among cases, and multiple models are possible in certain cancers.”

Lung cancer accounts for nearly 160,000 deaths every year in the United States, more than the next three most-common cancers combined, according to the American Lung Association. Treatment of early-stage cancers may be tailored according to the type of genomic alterations observed, says Dr. Vasmatzis. In some cases, this could mean less- aggressive treatment and periods of close observation, while other situations may call for more immediate interventions, such as surgery or radiation.

“As suggested by clinical studies demonstrating improved disease-free and overall survival for treatment of lesions containing components of adenocarcinoma in situ [noninvasive lung cancer], it may be that this represents a distinct clinical entity that can be treated less aggressively by either sub-lobar resection or even periods of watchful waiting with close imaging follow-up prior to any treatment,” says Dr. Vasmatzis.

Future studies of lung cancer genomics and tumor progression are underway from Dr. Vasmatzis’ team in the Biomarker Discovery Program. Their goal is to develop a series of predictive biomarkers that can help patients and physicians separate potentially aggressive and life-threatening lung cancers from indolent ones based on the molecular signatures found within the individual patient’s tissue.


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Materials provided by Mayo Clinic. Note: Content may be edited for style and length.


Journal Reference:

  1. S. J. Murphy, D. A. Wigle, J. F. Lima, F. R. Harris, S. H. Johnson, G. Halling, M. K. Asiedu, C. T. Seto, S. Terra, F. Kosari, T. Peikert, P. Yang, M.-C. Aubry, G. Vasmatzis. Genomic Rearrangements Define Lineage Relationships between Adjacent Lepidic and Invasive Components in Lung Adenocarcinoma. Cancer Research, 2014; 74 (11): 3157 DOI: 10.1158/0008-5472.CAN-13-1727

Cite This Page:

Mayo Clinic. "Genomics of lung cancer progression varies widely among patients; offers clues to predictive biomarkers." ScienceDaily. ScienceDaily, 3 June 2014. <www.sciencedaily.com/releases/2014/06/140603114127.htm>.
Mayo Clinic. (2014, June 3). Genomics of lung cancer progression varies widely among patients; offers clues to predictive biomarkers. ScienceDaily. Retrieved December 21, 2024 from www.sciencedaily.com/releases/2014/06/140603114127.htm
Mayo Clinic. "Genomics of lung cancer progression varies widely among patients; offers clues to predictive biomarkers." ScienceDaily. www.sciencedaily.com/releases/2014/06/140603114127.htm (accessed December 21, 2024).

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