Predicting Response to Chemotherapy With Early-Stage Lung Cancer

Abstract

A recent meta-analysis of 11,107 patients with non-small cell lung cancer who had undergone surgical resection showed that the 5-year survival benefit of adjuvant chemotherapy was 4%, and that of adjuvant chemoradiotherapy was 5%. Two trials have shown a trend toward improved survival with adjuvant paclitaxel plus carboplatin. However, the benefit of adjuvant treatment remains suboptimal. We must distinguish between patients who will not relapse-and who can thus be spared adjuvant treatment-and those who will-for whom adjuvant treatment must be personalized. Several gene expression signatures, generally containing nonoverlapping genes, provide similar predictive information on clinical outcome, and a model combining several signatures did not perform better than did each of the signatures separately. The invasiveness gene signature, containing 186 genes, includes genes involved in the nuclear factor κB pathway, the RAS-mitogen-activated protein kinase pathway, and epigenetic control of gene expression. A 15-gene signature has identified JBR.10 patients who are more sensitive to adjuvant chemotherapy.

Systems biology approaches to develop innovative strategies for lung cancer therapy

“…With the aim of revealing candidate signaling networks that may predict outcome to certain CT/RT regimens, gene and miRNA expression profiling, DNA sequencing of LC genomes and different proteomic approaches have all been applied, hence allowing a tailored approach with old CT drugs to patients who will benefit the most. 15, 16 Along the same line, the necessity of predicting the relapse status as well as metastatic propensity of LC patients was stated, thereby directing CT for those with a high risk of relapse in which a personalized approach of CT application should be taken. 15, 16 In two studies, gene expression analyses was made on NCI 60 tumor cell lines and/or their corresponding xenografts in mice in relation to responsiveness of about 100 000 different compounds, including natural compounds from different sources, targeted agents and all currently applied CTs.…”

“…15, 16 Along the same line, the necessity of predicting the relapse status as well as metastatic propensity of LC patients was stated, thereby directing CT for those with a high risk of relapse in which a personalized approach of CT application should be taken. 15, 16 In two studies, gene expression analyses was made on NCI 60 tumor cell lines and/or their corresponding xenografts in mice in relation to responsiveness of about 100 000 different compounds, including natural compounds from different sources, targeted agents and all currently applied CTs. 37, 38 These studies generated large gene expression data sets that can be used in data mining and has enabled target prediction of compounds with an unknown mechanism of action.…”

“…Some of the generated profiles have indeed shown the ability to predict the prognosis of LC patients in an accurate way. 15, 16 …”

Cancer adjuvant chemotherapy strategic classification by artificial neural network with gene expression data: An example for non-small cell lung cancer

“…The log-rank test is used to determine the significant differences in survival between groups, treatments, etc. Several studies have used KM-plot to make sure their prediction results were significantly [8][9][10][11][12]15,16,18,19]. We use the mixed methodology K-fold cross-validation and KM-plot to verify the prediction results.…”

“…Because adjuvant chemotherapy has considerable toxicity, each patient needs a prudent assessment of the risks before ACT treatment. Recently, several studies have to investigate the survival benefit of adjuvant chemotherapy in NSCLC with other impact factors such as age, sex, stage and gene signature [8][9][10][11][12][13]. However, there is no proper to assess adjuvant chemotherapy benefit.…”