Our study involved a retrospective component where we predicted gene mutations – drug sensitivity associations defined in a recent hypothesis-independent study [23]. In addition, we predicted sensitivity of our profiled patient-derived GBM cell lines to targeted agents and compared these in silico predictions to in vitro experimental data.

Retrospective validation of in Silico tumor model

In the first part of the study, we evaluated the ability of the in silico tumor model to predict drug responses that were reported in the study by Garnett and colleagues [23]. A comparison of our predictions with the associations reported in the Garnett study indicated the predictive capability of our in silico tumor model.

Our modeling library has definitions for 45 of the 639 cell lines used in this study (Additional file 1: Table S2) and supports 70 of the 130 drugs studied (Additional file 1: Table S3). Further, we can represent 51 of the 84 genes screened for mutations (Additional file 1: Table S4). Of the 448 significant gene mutation-drug response associations reported, our in silico model was able to accurately predict 22 of the 25 testable associations from the Garnett study (>85% agreement; Additional file 1: Table S5). The gene mutation–drug response correlations from the Garnett study that are currently not supported by the system are listed in Additional file 1: Table S6. From the 25 gene mutation–drug response associations tested from the Garnett study (Additional file 1: Table S5), a few examples of the correlations are explained below. Figure 1A depicts a representative schematic of this retrospective analysis using the simulation (in silico tumor model).

BRAF Mutations and Drug Sensitivity

The Garnett study showed that cells with BRAF mutation were sensitive to the MEK1/2 inhibitor AZD2644 [23]. To examine this association, we modeled cancer cell variants with wild-type BRAF in silico. Modeling data showed that cells with wild-type BRAF were resistant to AZD6244, when compared to the parent tumor cells with mutant BRAF. Thus, BRAF mutation conferred sensitivity to the MEK1/2 inhibitor in silico; this prediction validates the finding reported in the Garnett study (Figure 1A). 40-60% melanoma patients carry BRAF mutations that activate MAPK signaling [24, 25] and this association could have therapeutic implications for the treatment of patients with BRAF mutant melanoma.

Effect of different mutations on sensitivity to tyrosine Kinase inhibitors

The Garnett study showed that cells with BRAF mutation were sensitive to the MEK1/2 inhibitor AZD2644 [23]. To examine this association, we created cancer cell variants with wild-type BRAF in the in silico model. Simulation data showed that cells with wild-type BRAF were resistant to AZD6244, when compared to cells with mutant BRAF. Thus, BRAF mutation conferred sensitivity to the MEK1/2 inhibitor; this validates the finding reported in the Garnett study (Figure 2A). 40-60% melanoma patients carry BRAF mutations that activate MAPK signaling [24, 25]. This association tested in Figure 2A may have therapeutic implications for the treatment of patients with BRAF mutant melanoma.

Figure 2 Retrospective analysis tests in silico predictions of gene mutations and sensitivity to EGFR family inhibitors. Associations reported in the Garnett study were tested in a blinded manner using our in silico model and predictions obtained were compared to results reported in the Garnett study. A, We created wild-type BRAF variants of four cancer cell lines – COLO205, HT29, MDAMB231 and U266 in silico and compared the effect of MEK1/2 inhibitor AZD2644 on these cell lines and on corresponding parent lines expressing mutant BRAF. Our data demonstrated that BRAF mutation increases sensitivity to AZD6244. B, We simulated three cell lines – H1650, H1975 and SW48 with wild-type or mutant BRAF and tested for sensitivity to the EGFR2 family kinase inhibitor, lapatinib. BRAF mutation decreases sensitivity of cells to lapatinib. C, Similarly, when four cell lines (AGS, H1437, MKN1 and MKN45) were tested for sensitivity to lapatinib, we observed that CDH1 mutation increases sensitivity to lapatinib. D, We generated cell lines with wild-type or MET over-expression and tested the effect of lapatinib (A549, AGS, H358 and HT29 cell lines). MET over-expression increases sensitivity to lapatinib. Full size image

ERBB2 (HER2) amplification is a biomarker for sensitivity to EGFR-family inhibitors [26]. In the in silico model, we tested for sensitivity to EGFR2 family inhibitors, lapatinib and BIBW2992. Specifically, we examined sensitivity of cancer cells in the presence of mutations and/or over-expression of BRAF, CDH1, ERBB2, CCND1 and MET. These predictions from simulations were compared with results obtained in the Garnett study and the predictive capability of our model was determined.

In silico predictions indicate that BRAF mutation decreases sensitivity of cells to lapatinib (Figure 2B), whereas CDH1 mutant lines demonstrated higher sensitivity to lapatinib when compared to variants with wild-type CDH1 (Figure 2C). Further, cMET over-expression showed increased sensitivity to lapatinib, as indicated by decrease in viability in cells with cMET over-expression (Figure 2D). Additionally, ERBB2 and CCND1 over-expression correlated positively with lapatinib sensitivity (Additional file 1: Table S5). In all these simulation experiments testing sensitivity to lapatinib, our in silico predictions corroborated with associations reported in the Garnett study.

CDKN2A mutation and drug sensitivity

The Garnett study reported associations between tumor suppressor gene mutations and several anti-cancer drugs. We tested these associations in our in silico tumor model. In the in silico analysis, cells harboring wild-type CDKN2A were resistant to erlotinib whereas CDKN2A mutation was associated with erlotinib sensitivity (Figure 3A). Similarly, cell lines with mutant CDKN2A showed increased sensitivity to dasatinib (Figure 3B), bortezomib (Figure 3C), and to the CDK4/6 inhibitor PD0332991 (Figure 3D). These predictions/analyses from our simulation corroborated accurately with data from the Garnett study.

Figure 3 Retrospective analysis evaluates CDKN2A mutation – drug response association by in silico modeling. Using simulation modeling, we tested the role of the tumor-suppressor protein CDKN2A on sensitivity to different inhibitors and compared these predictions to those reported in the Garnett study. A, Cells expressing mutant CDKN2A and their wild-type variants were simulated in the in silico tumor model for four lines – BxPC3, H1437, H1650 and SW48. CDKN2A mutation increased sensitivity of cells to erlotinib when compared to wild-type CDKN2A. B, Cells with mutant CDKN2A were more sensitive to dasatinib than cells with wild-type CDKN2A (A549, BxPC3, HCT116 and H460). C, COLO205, HT29, H1437 and SW48 cell lines with mutant CDKN2A were sensitive to bortezomib more than cells expressing wild-type variants. D, CDKN2A mutant cells BxPC3, H1437, H1975 and HT29 also showed higher sensitivity to CDK4-Cyclin D1 inhibitor PD0332991 over the CDKN2A WT variants. Full size image

Other gene mutation-drug response associations examined in our simulation models are illustrated in Additional file 1: Table S5. In addition, Additional file 1: Table S6 lists correlations between gene mutations and drug responses reported in the Garnett study, which are currently not supported by our modeling technology. In spite of these limitations, we obtained ~85% agreement of our simulation data with findings reported by Garnett [23].

Prospective evaluation of tumor model – patient-derived GBM cell lines

Identifying drug sensitivities in tumors/cancers with different mutations is important for designing individualized therapies for cancer. To this end, we created in silico avatars of 8 patient-derived GBM cell lines using genomic data (Methods and Additional file 1: Table S1) and predicted their sensitivity to various targeted therapeutic agents. We then tested these in silico predictions prospectively by comparing then with experimental data obtained by in vitro testing on the same patient-derived GBM cell lines (Figure 1B).

The patient-derived GBM cell lines were obtained from patient tumors resected surgically and cultured in vitro (details in Methods). We have profiled these lines using Affymetrix Gene Chip Human Gene 1.0 ST Array. Using whole-exome sequencing, we recently tested the validity of these cells (maintained in cultures) for development and testing of personalized targeted therapies, based on their accurate representation of the original tumor profiles [27]. We have designated the different patient-derived GBM cell lines as: GBM4, GBM8, SK102, SK262, SK429, SK748, SK987 and SK1035.

After generating in silico profiles of these cells, we optimized these simulation avatars in terms of strength of functional effect of the mutation on key pathways such as EGFR, RAS and Src/PI3K. The rationale for this optimization is that expression data on these cells does not provide an accurate measure of the dominance of different intracellular pathways. In order to interrogate this information on the pathways that play a dominant role in each tumor line (such as EGFR, RAS, PI3K, etc.), we used 3 anti-cancer agents (erlotinib, sorafenib and dasatinib) targeting these pathways. This will achieve “alignment” and train the simulation avatars for further analyses (details in Additional file 1). The alignment for these 3 drugs could be best achieved in the following cell lines: GBM8, SK262, SK429, SK748, and SK1035. In cell lines GBM4 and SK987, there was a mismatch for sorafenib where the predictive trends were reversed. GBM4 was sensitive to sorafenib experimentally but our in silico predictions showed it to be resistant; SK987 was resistant experimentally but sensitive in predictive results. Similarly, the experimental trend for SK102 resistance to dasatinib could not be met predictively. Correlation of predictive trends with alignment drugs is shown in Figure 4 A-F.

Figure 4 In silico modeling analysis and experimental in vitro data for drug responsiveness to 3 alignment drugs. A, Predictive dose response data for erlotinib with percent change in viability. Cells showing decrease in viability of 20% or greater are considered sensitive to the drug. B, In vitro experimental results for effect of 1 μM erlotinib on viability in patient-derived GBM cell lines; viability was determined at 72 h using Alamar Blue assay. C, D, Predictive and experimental data for sorafenib. E, F, Predictive and experimental data for dasatinib. All drugs were tested in vitro at 1 μM. Dose-response curves for in silico data demonstrate the effects of increasing concentrations of the drugs – erlotinib, sorafenib and sunitinib on the viability of profiled patient-derived GBM cell lines in the simulation model. Full size image

Predictions obtained by simulation modeling are presented as dose-response plots for viability; decrease in viability of >20% was considered as sensitive. Experimentally, viability was determined by Alamar blue assay, in response to 1 μM concentration of respective inhibitors at 72 h. These data represent viability as mean values from triplicate samples.

We tested ten anti-cancer drugs in silico on the simulation avatars of the 8 patient-derived GBM cell lines in a blinded prospective study. These simulations generated predictions that we compared with in vitro experimental data (Additional file 1: Table S7A-D). Of the 80 in silico predictions, 61 (76.25%) predictions showed agreement with in vitro experimental results. Analysis of drug sensitivity correlation for all 8 GBM patient-derived cell lines, for all the 13 drugs is summarized in Additional file 1: Table S7. Figures 5A-H and 6A-H show a drug-wise comparison of in silico predictions (dose-response curves) and in vitro experimental results generated with testing 1 μM concentration of each drug on these cell lines.

Figure 5 In silico modeling and experimental in vitro data for drug responsiveness to tyrosine kinase inhibitors. This figure demonstrates in silico predictions of sensitivity and in vitro viability (respectively) in response to treatment with tyrosine kinase inhibitors: A, B, lapatinib, C, D, nilotinib, E, F, Imatinib and G, H, Sunitinib. Cells were exposed in vitro to 1 μM tyrosine kinase inhibitors for 72 h and viability determined using Alamar Blue assay. The dose-response for in silico predictions is generated by iterative simulations with increasing concentrations of the drug in the model and the viability index is calculated. Cells showing decrease in viability of 20% or greater are considered sensitive to the drug. Full size image

Figure 6 In silico modeling and experimental in vitro data for drug responsiveness to different drugs. This figure demonstrates in silico predictions of sensitivity and in vitro viability in response to treatment of patient-derived GBM cell lines with A, B, bortezomib, C, D, everolimus, E, F, celecoxib, and G, H, pitavastatin. All drugs were tested in vitro at 1 μM for 72 h and viability was assayed using Alamar Blue assay. Cells showing decrease in viability of 20% or greater are considered sensitive to the drug. Full size image

Effect of tyrosine kinase inhibitors on patient-derived GBM cells

For the EGFR family inhibitor lapatinib, simulation studies predicted SK429, SK748 and SK1035 to be resistant, which were confirmed by in vitro data. Similarly, modeling predicted GBM8, SK102, SK262 and SK987 to be sensitive and these predictions were in agreement with experimental data (Figure 5A and B). However, modeling predicted GBM4 to be resistant to lapatinib while in vitro data showed GBM4 to be highly sensitive to lapatinib (Figure 5B). For the tyrosine kinase inhibitor nilotinib, the model predicted GBM8 to be sensitive while all the other profiles to be resistant (Figure 5C). In vitro studies demonstrated that GBM8 was indeed sensitive to nilotinib as predicted, but there was a mismatch with the experimental results for two lines – SK262 and SK1035. Experimentally, SK262 was found to be sensitive, whereas SK1035 was on the borderline of sensitivity and resistance (Figure 5D). For imatinib, simulation predicted that all GBM lines except GBM8 were resistant (Figure 5E). The experimental results corroborated with this in silico prediction (Figure 5F). Sunitinib was the other multi-tyrosine kinase inhibitor tested. Our simulation predicted GBM8, SK102 and SK987 to be sensitive to sunitinib; however, only GBM8 was found to be sensitive in vitro. SK262 was predicted to be resistant to sunitinib but in vitro data found it to be moderately sensitive. On the other hand, GBM4, SK429, SK748 and SK1035 were found to be resistant in both simulation and experimental data (Figure 5G-H).

Effect of other drugs on patient-derived GBM cells

Besides the tyrosine kinase inhibitors, correlation between in silico predictions and experimental results for the 8 patient-derived GBM cell lines was also tested for drugs such as pitavastatin (HMG CoA reductase inhibitor), everolimus (mTOR inhibitor), celecoxib (COX2 inhibitor) and bortezomib (proteasome inhibitor) (Figure 6 A-H). For bortezomib, all profiles were predicted to be sensitive and these predictions matched with in vitro experimental results (Figure 6A and B). For everolimus, in vitro results were in agreement with simulation predictions for all lines except SK429 (Figure 6C and D). Our in silico model predicted GBM4, SK262, SK429, SK748 and SK1035 to be resistant to celecoxib; these predictions matched with in vitro results. However, GBM8, SK102 and SK987 were predicted to show moderate sensitivity to celecoxib, but were found to be resistant in vitro (Figure 6E and F). For pitavastatin, the simulation predicted 5 patient-derived GBM cell lines to be sensitive (GBM8, GBM4, SK102, SK262 and SK987), of which SK987 was found to be resistant in vitro. On the other hand, of the cell lines predicted to be resistant (SK429, SK748 and SK1035), SK1035 was sensitive in vitro and did not match with the prediction (Figure 6G and H).

These data demonstrate a 76.25% agreement between in silico predictions of drug response and in vitro experimental data in patient-derived GBM cell lines.