We analyzed the expression profiles of certain MDR-linked genes by PCR array to understand the sensitivity of cancer types to anticancer treatments. In 2009 Yukio et al., identified five novel genes which possess great potential for predicting the efficacy of cisplatin-based chemotherapy against OSCC using Affymetrix U133 Plus 2.0 microarray22. The analysis of the mRNA expression pattern of tumor-specific genes has been employed to understand the association with risk habits and the clinicopathological profile of OSCC cases in Indian population23. In this study, we observed the list of drug resistance linked genes was highly expressed in well-differentiated tumor samples. Moreover, the analysis showed that there was a clear overexpression of resistance genes in tumors as compared to normal tissue (Fig. 1A). Prior to gene expression analysis, patients’ samples were analyzed and categorized based on their clinical stage. It was observed that samples 1, 2, 7, 9, 10, 11, 14, 17, 19, 24 and 27 were found to be well differentiated which mostly resembles normal tissue architecture and usually has a good prognosis; samples 5, 6, 16, 18, 20, 23, 26, 28 and 31 were moderately differentiated i.e. an intermediate forms of tumor with either good or bad prognosis. Samples 3, 4, 8, 12, 13, 15, 21, 22, 25, 29 and 30 were found to be poorly differentiated which metastasis easier and their prognosis will generally be worser24 (Supplementary Fig. 4).

Figure 1 Expression levels of drug response genes in 31 OSCC samples. (A) The mRNA expression pattern of 11 drug response-linked genes. The total mRNA was isolated from fresh tumor tissues and were detected using custom PCR array following the manufacturer’s instructions. The clustergram results of three independent experiments were analyzed using the SA Biosciences online tool. (B) Venn diagram showed the gene expression pattern of drug resistance genes in tumor samples. Samples 9 and 10 overexpress most of the drug response-linked genes. Full size image

Interestingly, it has been noticed that the expression pattern of genes linked to drug response is well correlated with the clinical and pathological characteristics of the patients (Supplementary Table 1 and Fig. 1B). We observed that the average drug response-linked genes expression was higher in the studied male (average RQ = 2.24) population than the female population (average RQ = 1.50). This has been reflected in the drug response that the average apoptotic priming of female (57.15%) was higher than the average apoptotic priming of male (52.22%). The correlation between the drug response linked gene expression with the % apoptotic priming was higher in female tumor samples (−0.532) when compared to male tumor samples (−0.7222) and the differences in drug response between the gender were found to be 19% (Supplementary Table 2).

Similarly, Gillet et al.25 earlier employed the gene expression pattern to reveal clinical anticancer drug resistance25. Membrane drug efflux transporters such as ABCB1, ABCG2 and ABCC1 were generally overexpressed in the patient’s tumor samples according to all clinical and pathological criteria26. Many of these ABC transporters have been shown to efflux out most of the anticancer drugs that were employed in this study27,28,29. Recently, a genome-wide analysis study illustrated the association between variation in the ABCB1 and ABCB4 gene regions and the risk of gallbladder cancer in Indian population30. The expression of transporter genes such as ABCB1, ABCG2 and ABCC1 was found to be increased in the studied Indian OSCC tissue samples (3, 4, 5, 7, 9, 10, 14, 16, 17, 18, 19, 20, 23 and 31), which were found to be highly progressive histologically (Fig. 1A and Supplementary Table 1). Overexpression of ABC transporters such as P-gp, MRP and BCRP has been shown to be responsible for the major portion of MDR31,32,33 and therefore using the expression pattern of ABC transporters for predicting tumor response will be useful information before therapy.

The cyclin-dependent kinases promote cell cycle arrest in response to many stimuli. Alteration in cell cycle regulators significantly renders chemotherapeutic drugs ineffective34. In this study, we found that the cell cycle regulators such as CDKN1A and CDK2 were found to be overexpressed in tumor samples 1, 2, 3, 4, 5, 9, 10, 11, 14, 18, 20, 21, 23, 27 and 31 (Supplementary Table 1). High STAT5 levels mediate imatinib resistance and indicate disease progression in chronic myeloid leukemia35. Multidrug-resistant cancer cells frequently overexpress the 110-kD LRP protein. LRP overexpression has been found to predict a poor response to chemotherapy in acute myeloid leukemia and ovarian carcinoma36. In this analysis, the tumor progression protein LRP1 and cytokine-inducible transcription factor STAT5B were found to be overexpressed in samples 1, 2, 3, 5, 9, 10, 11, 14, 18, 20 and 24 (Supplementary Table 1). Mutations in p53 protein and loss of TP53 function confer MDR in several breast cancer tumor sub-types31,37,38. We observed that TP53 was downregulated in tumor samples 1, 2, 4–8, 10–12, 14–21, and 23–31 (Supplementary Table 1).

The range of gene expression could determine the sensitivity of drugs to the tumor samples. The range of Ct values of ABCB1 for all 31 tumors was between 23 and 31. Those tumors exhibiting Ct values from 19 to 23, the first quartile, may show resistance to anticancer therapy; tumors which showed Ct values from 23 to 26, the second quartile, may be moderately sensitive to therapy; whereas, the third quartile (Ct values 26 to 31) and fourth quartile (Ct values 31 to 32) were tumors with low expression levels of ABCB1 that could be sensitive to anticancer treatment. Similarly, drug response could be easily predicted based on the pattern of expression of other drug response genes (Supplementary Fig. 5). The gene expression pattern alone was not found to be adequate for clinical correlation of drugs due to post-translational modifications of expressed genes and poor dynamic range39. Hence, to further confirm tumor sensitivity to chemotherapeutic drugs, we carried out BH3 profiling in primary cells isolated from the tumor samples. As a preliminary study, before doing BH3 profiling in tumor samples, we determined the expression pattern for genes related to drug response in two different cancer cell lines, including oral tumor-derived KB cell lines and its drug-resistant sub-type KB CHR 8–5. We observed that paclitaxel showed higher apoptotic priming in drug-sensitive parental KB cells when compared to other chemotherapeutic drugs (Fig. 2A). Similarly, significant % apoptotic priming was induced in the case of parental KB oral cancer cell lines when compared to their drug-resistant sub-type KB CHR 8–5. It has been found that paclitaxel induces significant apoptotic priming in these drug-resistant KB CHR 8–5 cells, whereas vincristine showed very poor apoptotic priming (Fig. 2B) in the KB CHR 8–5 cells. Thus, paclitaxel may be considered the most effective cytostatic drug against the drug-resistant KB CHR 8–5 cells than all other drugs studied in this investigation. This pattern of drug sensitivity was also reflected in an MTT-based cytotoxicity assay (Fig. 2C,D). This further validates the importance of understanding apoptotic priming by BH3 profiling when predicting the drug response of tumor cells.

Figure 2 (A,B) The % apoptotic priming in parental KB and drug-resistant KB CHR 8 5 cells. Cells were exposed to different anticancer drugs at equal concentrations (1 μM) for the different time period. The % apoptotic priming was confirmed by treating the cells with DMSO (positive control) and FCCP (negative control), respectively. It was observed that paclitaxel showed maximum apoptotic priming when compared to the other anticancer drugs (Fig. S6). (C,D) Cytotoxicity of chemotherapeutic drugs (1 µM) on parental KB and drug-resistant KB CHR 8–5 cell lines. Time-dependent curves of chemotherapeutic drugs at fixed concentrations in parental KB and resistant KBCHR 8–5 cell lines. The values shown were the average of experiments each done in triplicate. Full size image

We observed significant apoptotic priming in the patient’s tumor samples to chemotherapeutic drugs depending upon the type of gene expression pattern concerning genes related to drug response. As expected, we found that up-regulation of MDR-linked genes facilitated the survival of all tumor samples. Tumor samples 9 and 10, which had the highest level of expression of drug resistance genes, were found to be resistant to all the chemotherapeutic drugs studied when compared to other tumor samples. Furthermore, tumor samples which expressed at least one of the drug resistance markers (viz. 1, 2, 5–7, 11, 14, 16–20, 23, 24, 27 and 31) were also found to be resistant to anticancer therapy. The tumor samples which were generally not showing any drug resistance gene expression (viz. 4, 8, 15, 21, 25, 26, 28, 29 and 30) were found to be sensitive to chemotherapeutic drugs. The mitochondrial apoptotic priming during drug treatment in tumor samples 3, 6, 12, 13 and 22 were found to be only partial (Fig. 3A). It should be noted that the MDR gene expression pattern probably reflects the biologic state of the OSCC since the patients who were the source of the analyzed tumor samples were not treated with any chemotherapeutic agents. Among all the drugs studied, paclitaxel-induced 40–45% apoptotic priming; vinblastine and daunorubicin induced 35–40%; and doxorubicin and vincristine-induced 30–35% apoptotic priming in the tumor cells. The maximum apoptotic priming was observed by paclitaxel (90%) in tumor sample 25 (Supplementary Fig. 6). Equation (1) was used to check the apoptotic priming potential of drugs j (1 to 5) in the tumor samples i (1 to 31). Figure 3B shows the drug of choice for each tumor sample on the basis of apoptotic priming. We noticed that paclitaxel was the best priming drug (BPD), effective in 25 tumors out of 31 samples, followed by vinblastine, effective in 6 tumors out of 31 samples. RD values for 31 tumors were calculated using Equation (2) as per the Supplementary Scheme 1. We found that paclitaxel showed highest apoptotic priming %, the average RD was found to be very less (0.177) i.e. negligible and hence through BPD and RD, it was proved that paclitaxel was the best priming drug. As per Equations (1 and 2), the average RD of the second-ranking drug (vinblastine) with paclitaxel was 2.3% in these 25 tumor samples. The RD of paclitaxel with vinblastine in these 6 tumors was only 0.77%, which could not be considered significant. Daunorubicin was ranked next in efficacy with RD of 5.54% from paclitaxel. We observed that there was a larger RD between paclitaxel and doxorubicin (7.61%) and vincristine (10.9%) in the tumor samples (Fig. 4). The average RD computation of paclitaxel was found to be less than 1. Furthermore, we noticed that about 80% of tumors showed a good response to the drug paclitaxel (Fig. 3B). Hence, it is evident that paclitaxel should be considered as the most potentially effective drug for the studied cohort population from among the other drugs studied. However, it is not rational to conclude that the other three drugs studied (i.e. daunarubicin, doxorubicin and vincristine) were ineffective treatments for the OSCC samples; rather their efficacy was less when compared to the performance of paclitaxel. To further validate these findings, we carried out a correlation analysis between MDR-linked gene expression and percentage of apoptotic priming (average priming values of all five drugs) in order to help design better chemotherapy options for oral carcinoma. On the basis of correlations between expression patterns of genes related to drug response and apoptotic priming, it can be possible to classify the tumors as sensitive (3 < 13 < 12 < 28 < 6 < 22 < 4 < 30 < 15 < 8 < 21 < 29 < 25), moderately responsive (19 < 24 ≤ 20,16,14,7,11,18,27,31 ≤ 23 ≤ 5,1,17,2 < 26) and resistant (9 and 10) to anticancer therapy (Fig. 5). To further validate this classification we employed Partitioning Around Medoids (PAM) and the results were cluster plotted. The PAM is a machine learning algorithm which partitions the dataset into clusters40. The Ct-values of drug response linked gene expression and % apoptotic priming were analyzed by the PAM algorithm and the tumors were clustered as sensitive, moderately responsive and resistant (Supplementary Fig. 7). Therefore, the present results could be used to assign the drug responses for any in vitro cell lines and even could be translated to the cancer clinics to predict the drug response before therapy.

Figure 3 Analysis of apoptotic priming in OSCC samples by BH3 (Bcl2 Homology-3) profiling. (A) Primary cells isolated from tumor biopsies were treated with different chemotherapeutic drugs and BH3 profiling was performed. Individual BH3 profiling analysis was performed using triplicates for controls and BIM (BCL-2-interacting mediator of cell death) BH3 peptide, and the expressed values were the average of three different readings. (B) Drug of choice for the tumor samples based on % apoptotic priming. The maximum priming of a drug against each tumor was identified and differences with the priming efficacy of other drugs were plotted. Full size image

Figure 4 Best priming drug and response differences between drugs. Response Difference (RD) between the best performing drug (BPD) and the other drugs were observed as per Equations (1 and 2). Full size image

Figure 5 Pearson correlation of expression pattern of 11 MDR-linked genes with the % apoptotic priming induced by chemotherapeutic drugs. The Statistical Package, IBM SPSS (Version 21), and Microsoft Excel 2007 (Roselle, IL) were used for the statistical and graphical evaluations. Tumors were classified as resistant, moderately responsive and sensitive based on their response to the chemotherapeutic drugs. Full size image

To the best of our knowledge, this is the first time OSCC tumors have been classified based on their gene expression pattern and apoptotic priming status. When TP53 was overexpressed, all the drugs tested were found to be effective (green box in Fig. 6). The negative correlation of TP53 with other genes (orange box in Fig. 6) inferred that TP53 suppressed the overexpression of other MDR-linked genes in the tumor samples. Furthermore, the expression of all the MDR-linked genes was negatively correlated with anticancer drug performance (red box in Fig. 6). The highest negative correlation (between paclitaxel performance and MDR-linked gene expression) further confirmed paclitaxel as the best choice of treatment for the studied OSCC patients. Hence, we plotted the percentage of apoptotic priming of paclitaxel alone with the expression of each drug response linked genes to show linear regression41,42 (brown lines; Supplementary Fig. 8) and to reveal locally weighted polynomial regression (blue dashed lines in Supplementary Fig. 8). The lines indicate that there was no perfect linear relationship observed. The uncertainty in this relationship suggests that the correlation between MDR gene expression and apoptotic priming might also depend on other factors such as age, sex, tumor grade and tumor stage.

Figure 6 Correlation between MDR-linked gene expression and the efficacy of each anticancer drug. All the MDR-linked genes employed in this study were negatively correlated with chemotherapeutic drug performance, whereas TP53 expression was positively correlated with the anticancer drug performance. Full size image

The linear prediction has previously been employed to identify the most effective chemotherapy drug based on MDR-linked gene expression data43,44. Recently Baidehi et al., (2017) have revealed a set of differentially methylated DNA regions which were replicated in 60–90% cohort of Indian OSCC patients45. In this study, we observed low R-Square value (0.3331) and high p-value (0.5875), which questions the reliability of this linear prediction model. The confidence level of the drug of choice prediction based on 31 tumor samples was only 33%. Therefore, to confirm the drug of choice for this OSCC population the number of tumor samples may be increased so that the personalized choice of drug for oral cancer patients could be more accurately predicted. Previously, there were number of studies illustrating drug prediction based on the small sample number. Montero et al.14 employed 24 bone marrow primary chronic myeloid leukemia samples to predict imatinib response and 16 ovarian adenocarcinoma samples to predict carboplatin response using BH3 profiling14. Burger et al.46 analyzed the mRNA expression levels of BCRP, LRP, MRP1, MRP2 and MDR1 based on 59 breast tumor samples46. Li Su et al.47 examined the association between the MDR1 gene of gastrointestinal tumors and resistance to chemotherapeutic drugs using 38 colon cancer samples, 46 esophageal cancer samples and 42 gastric cancer samples47. Though, the number of samples in this study was only 31, the present findings can be applicable to OSCC when we further account other clinical features like age, sex, tumor grade, tumor stage and clinical staging of the patients. Analyzing these multiple clinical and experimental features using statistical methods like multilinear regression (MLR), MLR-Log and Least Square Method (LSM) will precisely predict the drug response before therapy.