The invasive or metastatic potential of a malignant neoplasm and the growth-inhibition of tumor cells by a therapeutic agent are two common denominators of patient survival in cancer systemic therapy. While cell line models have been used to predict treatment response or patient survival by genes associated with drug sensitivity in the cancer cell lines [46–49], these studies have not considered the varying metastasis potential of a tumor. To weigh in the interplay of both factors directly, our pre-clinical gene signature discovery method features the co-integration of invasion phenotypes and compound-sensitity profiles with gene expression at the full genome scale.
Our approach is based on the observation that invasion ability and drug sensitivity are both phenotypes of the cell lines available for study. Each phenotype is naturally associated with its own set of molecular determinants. We hypothesize the potential overlap between the set for invasion ability phenotype and the set for drug responsiveness phenotype. By identifying these common determinants, then we may use these shared determinants to estimate the overall invasion potential of the cancer cells in a tumor and also use it to predict the drug response at the same time. However, because the tumor microenvironment in a patient is different from the growth environment of cancer cell lines monitored in a lab, the robustness of an invasion molecular marker becomes an important factor for increasing the chance of success in clinical applications.
An alternative strategy of analyzing the three-way interaction of invasion, gene expression and drug response would be to correlate invasion with drug response first. Once the most correlated drugs are identified, then genes correlated with response to these drugs can be used to predict the drug sensitivity. However, we did not pursue this line of analysis further because the phenotye-phenotype correlation is often weaker than phenotype-genomic determinant correlation. As a matter of fact, our data show that most drug-invasion correlations appear weak; only four drugs pass the statistical significance and the best two correlations are only 0.39 and −0.35 (Additional file 1: Table S6). Our approach overcomes the limitations of weak phenotype-phenotype correlation by looking for statistical evidence of correlations directly from the genomic determinant. This helps improve the robustness of the genetic marker thus obtained.
Previously, without considering drug sensitivity, our team performed invasion profiling for the nine lung cancer cell lines of NCI-60 to obtain a four gene signature for clinical outcome prediction . We find that among the four genes ANKRD49 and LPHN1 are in the IA gene list and only ANKRD49 has a significant correlation with paclitaxel and docetaxel. The four-gene signature failed to predict the survival outcome for the two validation cohorts receiving adjuvant chemotherapy (Additional file 1: Figure S7).
To gain robustness of our gene signature, instead of using different panels of tissue origins in NCI-60 to obtain different sets of IA genes for different types of cancer, we used the invasion data from all 53 NCI60 solid tumor cell lines and obtained 633 IA genes. Then a series of statistical analyses were designed to increase the robustness of the final eight-gene signature in predicting drug sensitivity for the selected compounds. The eight-gene score differed between drug-resistant and drug-sensitive cell lines (Figure 6). We succeeded in applying our gene signature to one lung cancer cohort and one breast cancer cohort, of which the patients received a regimen containing an anti-microtubule agent. The success in using the same signature to predict patient outcome for different types of cancer showed the robustness of this gene signature.
The eight-gene signature showed a positive correlation with the sensitivity of targeted therapy compounds and a negative correlation with the sensitivity of anti-microtubule compounds (Figure 5B). Because high values of the eight-gene signature correlate with high invasion potential in cancer cells, this suggests that the direct correlation between the invasion profile and the sensitivity profiles of anti-MT drugs may be negative. This is indeed the case, but the correlation is weak (Additional file 1: Table S6, correlation = −0.16, -0.28 for paclitaxel and docetaxel, respectively). On the other hand, the correlation between the eight-gene score and the drug sensitivity is stronger (−0.41, -0.54, respectively). Similarly, the correlation between the eight-gene score and the sensitivity for erlotinib, dasatinib and everolimus is 0.46, 0.52, 0.44, respectively, which is again stronger than the correlation between invasion and drug sensitivity (0.26, 0.24, 0.09, respectively). Therefore, despite the weak correlation between the invasion phenotype and the drug sensitivity phenotype, the eight-gene signature is an effective genomic marker for invasion potential and it can be used to predict the drug’s differential growth-inhibition efficacy that varies between cancer cells of higher invasion potential and those of lower invasion potential.
When applying to the patient’s tumor specimen, the eight-gene signature provides an averaged profile of the gene expression by individual cells with varying invasion potential. A low eight-gene score indicates that the overall invasion potential of the tumor is low and the chance of the patient’s favorable response to regimens containing anti-microtubule compounds increases. On the other hand, a high eight-gene score predicts the abundance of the cells of higher invasion potential, which are harder to eradicate by anti-microtubule compounds, but may be more likely to succumb to the said targeted therapy. This suggests the combined use of targeted therapy like dasatinib or erlotinib with anti-microtubule agents to increase the regimen efficacy of chemotherapy alone. There have been several studies on augmenting the anticancer effect of chemotherapy with targeted therapy. Erlotinib was shown to be more sensitive in the doxorubicin-resistant human breast cancer cell lines and paclitaxel-resistant human ovarian cancer cell lines  and the sensitivity was positively correlated with EGFR expression. More references were provided in Additional file 1, Supplementary information Text II.
There is room to improve our eight-gene signature for drug-sensitivity prediction. The overlap in distribution between the drug-sensitivity group and the drug-resistant group (Figure 6) suggests that drug response in cell lines is a very complex phenotype which is not fully characterized by our gene signature. Other genomic components, such as DNA copy number, single-nucleotide polymorphisms, methylation and microRNA, have not been considered in our study. In addition, differences in lab environment may also contribute to the variations observed in the data.