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Copy Number Profiles Classify Lung Cancer Patients as Sensitive or Resistant to Therapy

NEW YORK (GenomeWeb) – University of Manchester researchers have developed a classifier to gauge whether small-cell lung cancer patients will be sensitive or resistant to initial chemotherapy treatment.

As they reported this week in Nature Medicine, the researchers found that by analyzing copy-number changes within circulating tumor cells, they could distinguish chemosensitive and chemorefractory patients even before they had received treatment. When the researchers applied the classifier they developed to another set of patients, they reported that they could correctly assign 83.3 percent of patients to the chemosensitive or chemorefractory groups.

"Our study reveals how blood samples could be used to anticipate how lung cancer patients may respond to treatments," study author Caroline Dive from the Cancer Research UK Manchester Institute said in a statement.

Tumors from most small-cell lung cancer patients initially respond to chemotherapy before becoming resistant to it. But a small group of patients experience disease progression during or shortly after first-line therapy.

As biopsies are difficult to perform for this cancer, the researchers chose to examine circulating tumor cells. Dive and her colleagues took samples from 13 patients with SCLC — six of whom had been determined clinically to be chemorefractory and seven of whom were chemosensitive — and searched for genetic alterations that distinguished the groups.

Sequencing 13 genes known to be mutated in SCLC within these patients didn't reveal any differences in them that correlated with treatment sensitivity. However, the researchers also performed a copy-number analysis, uncovering 2,281 loci with copy-number changes that differed between the sensitive and refractory groups. Grouping these changes by chromosomal location and copy-number aberration (CNA) status resulted in 16 CNAprofiles. Clustering based on baseline CTC sample profiles segregated these profiles based on whether the tumors were sensitive to treatment or not.

Based on these profiles, Dive and her colleagues developed a predictive model using a radial basis support vector machine. After validating their tool on those initial 13 training samples, they conducted a test on an independent set of 18 SCLC patents for which CTC samples and follow-up clinical data were available.

The model was able to classify 15 of the 18 patients, or 83.3 percent, correctly, the researchers reported. In addition, across different CTC samples from the same patients, the researchers noted that most exhibited homogeneity in their classification, though a little more than a third exhibited classification heterogeneity.

The researchers further tested their classifier using a panel of nine CTC-derived explant tumors (CDX) from six SCLC patients. Here, the classifier grouped five of the six CDX tumors correctly. Combined with the other test set and the training set, the classifier had an overall accuracy of 89.2 percent, the researchers reported.

Dive and her colleagues added that when their classifier was used, Kaplan-Meier survival analysis indicated a statistically significant difference in progression-free survival for patients it determined to be chemosensitive or chemorefractory.

"By identifying differences in the patterns of genetic faults between patients, we now have a starting point to begin to understand more about how drug resistance develops in patients with this aggressive form of lung cancer," Dive said.

She and her colleagues also examined the CTC CNA profiles of a set of patients who were initially chemosensitive, but who later became resistant. After becoming resistant, their CNA profiles didn't match those of patients who started off as treatment resistant, suggesting to the researchers that there is a difference in the underlying genetic basis for initial versus acquired chemoresistance.