Molecular margin analysis predicts local recurrence after sublobar resection of lung cancer

Brett G. Masasyesva, Betty C. Tong, Malcolm V. Brock, Thomas Pilkington, David Goldenberg, David Sidransky, Susan Harden, William H. Westra, Joseph Califano

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41 Scopus citations


Sublobar resection for early-stage lung cancer has been used for patients who are not candidates for lobar resection. However, sublobar resection is associated with high local recurrence rates in the context of tumor-free parenchymal margins. The mechanism underlying this high recurrence rate is not well understood. We hypothesized that this elevated risk of local recurrence is due to undetected tumor cells present at parenchymal margins thought to be negative by conventional light microscopy. Thirteen of 44 patients who underwent sublobar resection for lung cancer were found to have a k-ras mutation at codon 12.1. A novel fluorescence-based assay for detection of rare copies of mutant DNA in a background of wild-type DNA, fluorescent gap ligase chain reaction, was used to quantitate the mutant/wild-type DNA in a range of 1 to 1/10,000 in histologically normal margins from these resections. Nine of 13 patients had at least one margin with the number of mutant cells over or equal to a threshold of 1/5,000, and of these, 6/9 (67%) recurred locally. None of the remaining 4 patients without mutant DNA in any surgical margin had evidence of recurrence. The higher rate of local recurrence associated with sublobar resection of lung cancer is likely due to the occult presence of tumor cells at resection margins. These occult tumor cells can be quantitated using a novel fluorescence-based assay and define a group of patients at high risk for local recurrence who are candidates for adjuvant therapy or more extensive resection. This methodology may be adaptable to a real-time format for intraoperative use.

Original languageEnglish (US)
Pages (from-to)1022-1025
Number of pages4
JournalInternational Journal of Cancer
Issue number6
StatePublished - Mar 1 2005

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research


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