To address this challenge, we introduce a novel algorithm for the systematic inference of protein kinase pathways, and applied it to published mass spectrometry-based phosphotyrosine profile data from 250 lung adenocarcinoma (LUAD) samples. The resulting network includes 43 TKs and 415 inferred, LUAD-specific substrates, which were validated at >60% accuracy by SILAC assays, including “novel’ substrates of the EGFR and c-MET TKs, which play a critical oncogenic role in lung cancer. This systematic, data-driven model supported drug response prediction on an individual sample basis, including accurate prediction and validation of synergistic EGFR and c-MET inhibitor activity in cells lacking mutations in either gene, thus contributing to current precision oncology efforts.To understand drug combination effect, it is necessary to decipher the interactions between drug targets—many of which are signaling molecules. Previously, such signaling pathway models are largely based on the compilation of literature data from heterogeneous cellular contexts. Indeed,  Lung adenocarcinoma (LUAD) is a leading cause of cancer related deaths in United States, representing 40% of 225,500 new lung cancer cases every year, and has a 5-year survival rate of only 16% –7. AmongWT patients have been shown to respond to Afatinib -2,5 diphenyl tetrazolium bromide (MTT) assay for short term sensitivity analysis . For colony formation assays, cells were treated with either an EGFR inhibitor  or a c-MET/ALK inhibitor , either individually or in combination (see IC50 (concentration resulting in 50% cell death). Next, cells were treated with 1 uM of Erlotinib and varying concentrations of Crizotinib to identify combinations resulting in IC50 and used the combination index (CI) statistic [To validate pVIPER-predicted, cell line specific EGFR/c-MET synthetic lethality, we selected a panel of 14 cell lines, 11 of which were predicted to be synergistically dependent on EGFR/c-MET , as well as 3 negative controls with no predicted synergistic or individual dependencies on the two TKs . To measure sensitivity to these agents, we used two different and complementary assays, including: (a) colony formation assay to assess long term sensitivity  to either individual inhibitors  or synergistic sensitivity to the combination (HCC78E+C and H2023 E+C)  or both inhibitors  in isolation, thus making the assessment of synergistic drug sensitivity difficult. In addition, three EGFRWT cell lines harboring BRAF  or KRAS (H2122) mutations were also highly sensitive to Erlotinib as a single agent, as predicted by pVIPER, despite the fact that KRAS pathway mutations are mutually exclusive with EGFR mutations and predictive of Erlotinib resistance . Finally, none of these cell lines was predicted to be sensitive to ALK inhibitors, suggesting that Crizotinib sensitivity is mediated by c-MET specific dependencies. Of the negative controls, only one (H2170) showed high sensitivity to Erlotinib. Taken together, 8/11 cell lines (73%) predicted as sensitive to the inhibitors were validated long term colony formation assays, while only 1/3 negative controls showed sensitivity to these agents (33%).Across all 11 cell lines tested in colony formation assay, 8 showed significant sensitivity  and 1 borderline synergy (CI = 0.82), showing the consistency between two assays. However, for two cell lines, H1666 and H2170 (a negative control), results were inconsistent between long term colony formation and MTT assays. For both H1666 and H2170 cell lines, colony formation and MTT assay to showed sensitivity to Erlotinib alone, where colony formation assay has complete abrogation of colonies at 1 μM of Erlotinib, and later had IC50 = 1.25 μM and 3.7 μM for H1666 and H2170 respectively. However, in combination therapy, MTT assay showed antagonism (CI >1), despite the fact that colony formation assay still showed complete abrogation which could be either the difference in the nature of the two assays or associated to the accumulation of new mutations in these cell lines. However, this is just hypotheses and needs to be verified by further experiments such as sequencing of these cell lines pre-and post-treatment.To evaluate the short-term interaction between EGFR and c-MET, we performed MTT assay across 11 cell lines  including 2 negative control cell lines (H520 and H2170). Similar to colony formation assay, we found synergistic sensitivity to EGFR and c-MET inhibitors in 6/9 cell lines (67%), with 5 cell lines showing strong synergy (CI ≤ 0.8) , cell lines responded to Erlotinib in isolation, while when predictions were based on phosphosites EGFR1197 and c-MET1003 , cells exhibited bona fide synergistic sensitivity to the two inhibitors, with the only possible exception of Calu-3, which showed synergistic sensitivity in MTT assays and additive sensitivity to both inhibitors in colony formation assays. Conversely, when predictions were not based on either phosphotyrosine, cells exhibited no sensitivity to the individual inhibitors or the combination . Thus, predictions based on these two phosphosites produced no false positives  and only 2 false negatives (H2170 and H2122), resulting in an error rate of only 2 out of 14 cell lines .In previous section, we assessed the pVIPER predictions after consolidating the result at protein level. Following the results from MTT and colony formation assays, we reanalyzed the pVIPER predictions at the phosphopeptide level. Interestingly, this revealed that whenever synergistic EGFR/c-MET dependencies were predicted from phosphosite EGFR1197 and c-MET1003, cell lines did not respond to the inhibitors, either individually or in combination. For these two peptides, we found their common substrates to be hyper phosphorylated in the sensitive cell lines  compared to the specific substrates of each of them, whereas cell line responding only to EGFR inhibitors showed more hyper phosphorylation of EGFR only substrates . Cell lines resistant to both EGFR and c-MET inhibitors either showed no change in the phosphorylation status or hypo-phosphorylation compared to the normal samples . Hence, either the common substrates of EGFR and c-MET, or the phosphorylation status of EGFR1197 and c-MET1003 could potentially be used as biomarkers for predicting therapy with the dual inhibitors. However, this conclusion is based on a very limited number of observations and lacks the statistical power. This finding needs a re-evaluation/validation using larger cohort of samples to establish an appropriate biomarker for combination therapy.This finding is in agreement with the previously reported finding that the level of tyrosine-1197 phosphorylation on EGFR is predictive of sensitivity . IntriguFig 6). We inferred EGFR dependency in 12 patients. Of these, 5 harbored EGFR mutations, while the remaining 7 patients had not been tested for this mutation, showing a high consistency between our predictions and the genetic predisposition for sensitivity to EGFR inhibitors. In the entire cohort, there were only 3 patients with EGFR mutations that were not identified as EGFR dependent by pVIPER, resulting in an overall sensitivity of 62.5% (5/8). However, it is well known from literature that >50% of patients harboring EGFR mutations do not respond to Erlotinib [Mut state. Similarly, our analysis identified candidate ALK dependencies in 4 patients. Of these one had an established TFG-ALK fusion, whereas the others had not been tested for ALK fusion events.Similar to cell lines, when applied on patient data , pVIPER rlotinib  suggestiAcross all patient samples, we observed Discoidin Domain Receptor-1 (DDR1) to be the most frequent addiction point, which was not predicted for any of the 46 cell lines. One reason for the difference is that DDR1 is collagen dependent and there may be differences in the 3D structure of the tumor and the cell lines growing on the plate. An independent study  in a cohIn this paper, we developed pARACNe to infer Tyrosine Kinase (TK) signaling network using published global phosphoproteomic data from lung cancer. The network prediction was validated using SILAC experiments, with high accuracy. Interrogation of the predicted TK-substrate network generated biologically meaningful hypotheses, followed by experimental validations illustrating the effectiveness of predicted kinase inhibitor combination, EGFR and c-MET combination inhibitors, in treating lung cancer cell lines. Furthermore, Master Regulator Analysis using patient proteomics data provides implications for using targeted agent combinations to treat patients based on their proteomic profile data.pARACNe, addresses critical issues that prevented the direct application of the original ARACNe algorithm on phosphoproteomic profile data. Briefly, the new algorithm addresses critical computational challenges presented by LC-MS/MS and spectral counting data, while incorporating enzymatic signaling characteristics into the algorithm design. In particular, pARACNe is designed to handle three critical issues resulting from the use of LC-MS/MS assays, including the highly sparse nature of phosphopeptide abundance data, the large amount of noise and missing data, and the degenerate peptides-to-protein mapping. The algorithm could be applied to proteomics measurements after preprocessing of the original data. For example, where there are datasets from different programs, they could be first transformed into rank based on total peptides identified. When mapping across datasets, one can fill in missing data using 0 –this type of data could be properly handled by the Iterative Quantile Discretization process in pARACNe. Future improvement of pARACNe includes design of a new schema to directly compute Mutual Information from continuous MS/MS data with many missing value produced by multiplexing platforms.The new method, Notably, pARACNe is significant and powerful as of its global scale and context-specificity in discovering global signaling cascading relationships, which were missing by previous methods. For example, methods proposed by Linding et al.  combine To be noted, as the LC-MS/MS experiments used here was generated based on Tyrosine-kinase enrichment, which is only about ~2% of whole phosphoproteome. pARACNe is shown only on TK-substrates network. The current methodology could be extended to whole phosphoproteomic data based signaling network reconstruction where the data is available. In addition to label free based LC-MS/MS proteomics data used in this work, label-based approaches, such as ITRAQ or TMT, could generate higher throughput whole proteomic profiles which might require future redesign of ARACNe to incorporate both kinases and phosphatases in regulating their downstream substrates. It is reasonable to expect that a version of ARACNe developed specifically to dissect signaling networks should work at least as well as its transcriptional counterpart. Since the relationship between the mRNA abundance of a gene encoding a transcription factor (TF) and the activity of the corresponding protein is much looser than that between the abundance of a phospho-isoform of a kinase and its enzymatic activity.Even though research has attempted to identify addiction points based on gene expression data , predictThe previously published phosphoproteomic data used to reconstruct signaling network was download from . This daSince phospho-tyrosine represents less than 1% of the cellular phosphoproteome, as determined by tandem mass spectrometry (MS/MS), and is difficult to analyze by conventional methods, immuno-affinity purification was performed with a phospho-tyrosine antibody to enrich for phospho-tyrosine containing peptides prior to tandem mass spectrometry. All tumors were identified as NSCLC based on standard pathology. Only those tumors with greater than 50% of cancer cells were considered for further analysis. NSCLC cell lines were grown overnight in low serum to reduce background phosphorylation from culture conditions.http://www.phosphosite.org), a comprehensive resource of known phosphorylation sites.Tandem MS profiling identified 3920 tyrosine phosphorylation sites on approximately 2600 different proteins. 85% of these sites appeared to be novel when compared against PhosphoSite  are then approximated by the corresponding relative frequencies of occurrence I between datasets X and Y is expressed asARACNe is originally designed for gene expression data, where expression of genes is usually continuous and non-sparse. Quantitative data obtained from label-free LC-MS/MS by data-dependent acquisition via spectral counting is discrete and very sparse, with many phosphopeptides counts not observed for multiple peptides in each sample causing the current version of ARACNe to be not suitable for this data, which thus required major modifications to handle discrete data. To handle discrete abundances, we modified the mutual information computation approach from a kernel density estimation based method to a Naïve based estimation of mutual information, which is a histogram based technique. Brieflykij denotes the number of measurements where X lies in ai and Y in aj and N total number of samples.Here Accuracy of mutual information is dependent on correct numbers of bins, M. To find the optimal number of bins we applied ARACNe on the whole dataset by varying M from 1 to 20 and testing the connections in predicted sub-network against the set of known connections (gold standard) from databases (phosphoDB) .. The basic idea is to divide the number of N data points into M with each bin containing equal number of data point. If the data point(s) with the same value falls into consecutive bin(s), we put those data point(s) into current bin and repartition the remaining points into remaining number of bins. We keep iterating this till we finish either the bins or there are no more data points to bin. For example, in Fig 1C step (3), we initially partition N points into 4 bins. The data points with 0 value does not fit into first bin and falls into subsequent bins, so we assign all data points with 0 value into first bin and repartition the remaining points into 3 bins. We keep on doing this process till we achieve 4 bins.In case of continuous data, partitioning can be achieved by dividing the range of data into M equally spaced distance bins. Our data being discrete, equally spaced partitioning was not possible. So, to overcome this problem, we used an iterative approach of partitioning  which gave us precision of 14% and sensitivity of 24%. This precision is an underestimate of real precision as in the gold standard many interactions are not present.To evaluate initial performance and decide number of bins, we computed the network among all tyrosine kinases and substrates, parsed the sub-network between 49 tyrosine kinases and 114 substrates which were present in PhosphoSite database and compared the results with the connections present in database. From our analysis, we found that M = 10 to be an optimal number To discover the master regulator in various cell lines, we interrogated the network obtained from pARACNe using a novel algorithm, VIPER  , designeSkin, was obtained by t-test analysis by comparing each cell line against all normal samples. First, we associate each kinase with positive and negative activity targets, by computing the correlation between each kinase and its predicted substrates and selecting only those substrates which had a significant correlation . Second, for each kinase we computed an activity by measuring the enrichment of the Skin signature in predicted substrates list, separately for both positive and negative correlated, (Skin-enrichment). Enrichment was computed by Gene Set Enrichment Analysis (GSEA). Since very small percentage of kinases are found to have negative correlation, we did not use those interactions to evaluate enrichment.VIPER requires a network model and signature of the phenotype transition . Here, the signature, 2. Cell lines were fingerprinted using the Perplex 1.2 system . Fingerprints were compared to those generated at ATCC and/or our internal database.All cell lines were grown in RPMI-1640 with 5% fetal bovine serum and incubated at 37°C in a humidified atmosphere containing 5% COueous One Solution Cell Proliferation Assay, Promega, Madison WI) were performed as previously described in [IC50 (Inhibitory Concentration of 50%) value. The assays were repeated at least 3 times and the IC50s are the average of all replicates.Short term MTT assays (CellTiter 96 AQribed in . SpecifiLong term colony formation assays were performed in triplicate in 6-well plates. Cells were added to media containing drug and incubated for 1–2 weeks such that control wells (no drug) contained colonies of 50–70 cells each. At such time media was removed and all wells stained with a solution containing 0.5% crystal violet and 6% glutaraldehyde for 1 hour. The plates were then rinsed, dried, and colonies were manually counted.EGFR SILAC experiment was performed in H3255 cell line by treating samples with Gefitinib. c-MET SILAC experiment was performed in c-MET-driven gastric cancer cell line, MKN45, by using c-MET inhibitor Su11274. For both genes, cells were treated with inhibitors for 3 and 24hr. For control, cells were grown in same conditions but were not treated with the drug. For our comparison we combined the peptides, which were differentially obtained between treated and untreated samples, for 3 and 24 hr. More details about the experiment can be obtained from Guo et al .In PhosphoSite database, there were 282 connections between 49 tyrosine kinases and 114 substrates.S1 FigTo select optimal bin number in pARACNe algorithm, precision and recall curves for various number of bins were computed. Black curve is when no binning of data is done. When using 10 bins, the algorithm achieved the best performance.(PDF)Click here for additional data file.S1 TableA three-column table. Column one and two are the two interacting peptides. Column one represents the gene symbol with dollar sign ($) and numbers specify the sites of phosphorylation in the protein. Column two is the substrate, representing a gene symbol followed by underscore and a number of phosphorylation sites. Column three is the mutual information between the two peptides in column one and column two.(XLSX)Click here for additional data file.S2 TableA three column table includes kinase, mutual information and substrate. Column two is the maximum mutual information when aggregating the peptide network into protein level network.(XLSX)Click here for additional data file.S3 TableThe results for colony formation assay. First column is the cell line, second column is the treatment used, and third column is the mean number of colonies (from triplicates) after normalizing to the control group for each cell line.(XLSX)Click here for additional data file.S4 Table50 for Erlotinib, third Column is the IC50 for Crizotinib, fourth column is the IC50 of Crizotinib when cell cells were treated with 1 uM of Erlotinib and varying concentrations of Crizotinib and fifth column is the combination index (CI).The results for MTT assay. First column is the cell line, second column is the IC(XLSX)Click here for additional data file.


BRAF-V600E melanomas, the non-melanoma basket trial was unsuccessful, suggesting mutation status is insufficient to predict response. We hypothesized that proteomic data would complement mutation status to identify vemurafenib-sensitive tumors and effective co-treatments for BRAF-V600E tumors with inherent resistance.Genetics-based basket trials have emerged to test targeted therapeutics across multiple cancer types. However, while vemurafenib is FDA-approved for BRAF-V600E cancer cell lines were curated. Linear and nonlinear regression models using RPPA protein or RNAseq were evaluated and compared based on their ability to predict BRAF-V600E cell line sensitivity (area under the dose response curve). Accuracies of all models were evaluated using hold-out testing. CausalPath software was used to identify protein-protein interaction networks that could explain differential protein expression in resistant cells. Human examination of features employed by the model, the identified protein interaction networks, and model simulation suggested anti-ErbB co-therapy would counter intrinsic resistance to vemurafenib. To validate this potential co-therapy, cell lines were treated with vemurafenib and dacomitinib (a pan-ErbB inhibitor) and the number of viable cells was measured.Reverse Phase Proteomic Array , RNAseq (Cancer Cell Line Encyclopedia) and vemurafenib sensitivity  data for BRAF-V600E cell lines than other leading machine learning methods, specifically Random Forests, Support Vector Regression (linear and quadratic kernels) and LASSO-penalized regression. Additionally, use of transcriptomic in place of proteomic data weakened model performance. Model analysis revealed that resistant lines had elevated expression and activation of ErbB receptors, suggesting ErbB inhibition could improve vemurafenib response. As predicted, experimental evaluation of vemurafenib plus dacomitinb demonstrated improved efficacy relative to monotherapies.Orthogonal partial least squares (O-PLS) predicted vemurafenib sensitivity with greater accuracy in both melanoma and non-melanoma Conclusions: Combined, our results support that inclusion of proteomics can predict drug response and identify co-therapies in a basket setting. This is due, in part, to the emergence of large-scale DNA sequence analysis that has identified actionable genetic mutations across multiple tumor types [BRAF are present in up to 15% of all cancers [BRAF-V600E melanoma patients with metastatic disease [In recent decades, there has been a shift to add targeted therapeutics .Unless otherwise stated, all reagents were purchased from ThermoFisher . Cancer Cell Line Encyclopedia lines A375, LS411N, and MDAMB361 were purchased from American Type Culture Collection . Cells were maintained at 37 °C in a humidified 5% COBRAF-V600E mutational status of cancer cell lines was obtained through the CCLE portal . The RPPA data for the 26 BRAF mutated cancer cell lines  [50AUC) was procured from the Quantitative Analysis of Pharmacogenomics in Cancer  [rg/mclp) . Of the u/QAPC/) .50AUC with RPPA protein expression was analyzed by Support Vector Regression with linear and quadratic polynomial kernels , cross-2Y, the coefficient of determination for predicted behavior Y, describes how well the model fits the predicted behavior, while Q2Y measures the predictive value of the model based upon 7-fold cross validation. Predictive and orthogonal components were defined sequentially, and if Q2Y increased significantly (> 0.05) with the addition of the new component, that component was retained, and the algorithm continued until Q2Y no longer significantly increased. The variable importance of projection (VIP) score summarizes the overall contribution of each protein’s measurement to the O-PLS model, and the VIP score for variable j is defined via the following equation:p is the total number of variables, M is the number of principal components, wmj is the weight for the j-th variable in the m-the principal component and SS (bm∙tm) is the percent variance in y explained by the m-th principal component. Proteins whose VIP score is greater than 1 are considered important towards the predictive power of the model.In O-PLS model, R50 AUC, using all 26 cell lines for training. To simulate pan-ErbB inhibition for MDA-MB-361, LS411N, and A375, the RPPA values for EGFR, HER2, and HER3 phosphorylated receptors were set to each protein’s minimum value in the original data set.For a receptor-only built O-PLS model, expression of AR, CMET, CMET-Y1235, EGFR, EGFR-Y1068, EGFR-Y1173, ERα, ERα-S118, HER2, HER2-Y1248, HER3, HER3-Y1289, IGFRB, PDGFRB, PR, and VEGFR2 were used to predict vemurafenib IChttps://software.broadinstitute.org/morpheus, Broad Institute). Resulting heatmap plots were created in GraphPad Prism software .Mean-centered and variance scaled RPPA data for training and testing set cell lines were hierarchically clustered (1-Pearson) with publicly available Morpheus software (50 AUC < 0.2) compared to the sensitive cell lines. For analysis of predictive protein interactions, proteins with a VIP > 1 were examined , and significant change in the mean expression of each protein/phosphorylated protein between the two groups was determined with 10,000 permutations and a FDR of 0.2 for total and phosphorylated proteins. This relaxed discovery rate is consistent with prior use of this algorithm with a constrained subset of proteins [CausalPath software  was usedproteins .2, 5000 cells/cm2, and 10,000 cells/cm2 respectively in duplicate in 96 well opaque, white assay plates for 24 h. Vemurafenib , dacomitinib, or a 1:2 dual treatment of vemurafenib:dacomitinib were tested using 2-fold concentration ranges (highest concentration of 33 μM and 66 μM respectively) for 72 h. ATP levels were measured using CellTiter-Glo  to assess cell viability. ATP levels were simultaneously measured in cells treated with vehicle (0.2% DMSO) cells, and all values were corrected by subtraction of measurements from blank wells. The ATP level of vehicle-treated cells was set as Amin and percent inhibition was calculated using the following formula:A375, LS411N, and MDAMB361 were seeded at 3000 cells/cmGraphPad was used to calculate nonlinear log (inhibitor) fit of each dose response curve using the following formula:1, x2 represent the dual therapy IC50 concentrations for each drug, and X1LOEWE, X2LOEWE represent the monotherapy IC50 for each drug. Model values less than 1 indicate synergy.Loewes additive model  was usedTo compare the different machine learning models, each model was evaluated on all 26 cell lines using leave one out cross validation. Errors for each cell line prediction were calculated, and models were evaluated on the number of cell lines for which they had the smallest error in comparison with O-PLS. A binomial t-test was performed in Prism for each model against O-PLS.BRAF-V600E tumor cells to the BRAF inhibitor vemurafenib, appropriate cell line models were explored. Of the cell lines characterized by the Cancer Cell Line Encyclopedia (CCLE) that possess a BRAF-V600E mutation (n = 94), and the Reverse Phase Protein Array (RPPA) data available from the MD Anderson Cell Line Project , 26 overlapped and had data pertaining to vemurafenib sensitivity in the Cancer Therapeutic Response Portal (CTRP) , analysis of IC50 doses of vemurafenib in these 26 cell lines showed that many exceeded the maximal dose tested in the CTRP database [50 AUC) was used as a measure of vemurafenib sensitivity. This response metric has been used in other pharmacogenomic studies to better capture sensitivity of cells to a drug, either using AUC < 0.2 as a classifier of resistant cell lines, or predicting sensitivity as a continuous response (0 < AUC < 1) [n = 7/11), while most melanoma cell lines were sensitive to vemurafenib , we aimed to predict the continuous response to vemurafenib, rather than classify resistant and sensitive cells alone.To examine the ability of protein expression and activity to predict response in database , 17. TheAUC < 1) . Analysi50 AUC in BRAF mutated cell lines based on their RPPA protein expression data, we compared various types of regression models to determine the model that performed with the highest accuracy. Regression models, such as support vector regression (SVR) with linear kernels, orthogonal partial least squares regression (O-PLS), and LASSO-penalized linear regression, utilize linear relationships between the protein expression and vemurafenib sensitivity for prediction. One limitation of our data set is the relatively low number of cell lines  relative to RPPA proteins ; given a data set with more variables than observations, over-fitting of the training data is always a concern. O-PLS addresses this issue by reducing the dimension to predictive and orthogonal principal components that represent linear combinations of the original protein expression cohort [L1 regularization term that penalizes non-zero weights given to proteins in the model [, not penalizing predictions that are within an “ε-tube” around the correct value [Since the goal was to predict the continuous ICn cohort , while Lhe model . While tct value , 22.50 AUC > 0.2, a melanoma cell line with IC50 AUC < 0.2, a non-melanoma cell line with IC50 AUC > 0.2, and a non-melanoma cell line with IC50 AUC < 0.2. Figure 50 AUC metric across the 6 validation set cell lines , LASSO, Random Forest, and O-PLS algorithms, the original set of 26 cell lines was split into a training set of 20 and testing set of 6 cell lines , had the most accurate prediction in the single train-test split previously described, and maintained reasonable prediction accuracy during cross validation . Additionally, removal of the orthogonal component to produce an O-PLS model using only the first component reduced the predictive power of the model (Q2Y = 0.0842). These results suggest that the improved prediction success of O-PLS may result from its use of orthogonal components, which here identify and distinguish protein expression patterns that correlate to tumor type independent of protein patterns that correlate to vemurafenib-sensitivity.The O-PLS model accurately captured the high variance in vemurafenib sensitivity . In comparison to the O-PLS model built on RPPA protein expression  and was less predictive (Q2Y = 0.34 vs. 0.40). Additionally, this change resulted in an increased RMSE during model evaluation on the training set using 7-fold cross validation, as well as an overestimation of melanoma cell lines in the testing set . Additionally, this model iteration showed the lowest predictive ability between the three O-PLS models tested (Q2Y = 0.31) and the highest error in the training set  and the test set of cell lines, particularly in non-melanoma cell lines  than their total protein expression, and 14/18 had higher correlation than the gene expression, including p-MEK1 (R2 = 0.4006) and p-HER3 (R2 = 0.2215). Notedly, some gene/protein pairs such as MAP2K1/MEK1 had discordant trends in the correlation with sensitivity  or phosphorylated proteins (n = 59 variables) represented in the RPPA dataset. The O-PLS model built from total protein expression maintained the high variance in IC50 AUC captured from the original full RPPA (n = 232 variables) O-PLS model (R2Y = 0.99 for both) but had lower predictive ability (Q2Y = 0.37 vs. Q2Y = 0.40). Additionally, the total protein O-PLS model had higher error in prediction for the held aside test set . However, this phosphoprotein-built O-PLS favored more accurate prediction of non-melanoma cell lines  that explained significant changes in mean expression of the predictive total and phosphoproteins (VIP score > 1) in the resistant cohort of cell lines. This computational method identified that the resistant subset had increased expression of EGFR and HER3-Y1289, which could be explained by the biological prior knowledge that EGFR transphosphorylates HER3 in EGFR-HER3 heterodimers . Because the important proteins in the O-PLS model , we tested whether pan-ErbB inhibition would increase vemurafenib sensitivity in the more resistant cell lines. To explore this scenario, an O-PLS model was built using the expression and activation of receptors from the RPPA dataset (16 proteins) in order to more easily simulate the impact of receptor inhibition without the confounding element of having to simulate the impact of receptor inhibition on downstream proteins. While model performance suffered , receptors with the highest VIP scores were EGFR, HER3, and HER3 Y1289 , as well as upstream ErbB signaling that could activate pathways parallel to BRAF, including PI3K. The computational results shown here illustrate the utility of O-PLS modeling to predict vemurafenib sensitivity in an in vitro setting mimicking a basket trial. Additionally, the ease of interpreting the O-PLS model allowed for identification and in vitro validation of vulnerabilities in vemurafenib-resistant cell lines in order to increase the efficacy of treatment.From the pathway analysis, we hypothesized that increased ErbB family signaling led to intrinsic vemurafenib resistance. As receptor-level inhibition of cellular signaling is a common therapeutic approach .One of the key findings of our work is that proteomic data outperforms transcriptomic data to predict response in the basket setting. This is consistent with results obtained since the release of the RPPA expression dataset from CCLE and TCGA cohort analyses , 30, 31.Our results also demonstrated that broad inclusion of protein expression and activity measurements can identify altered signaling pathways that influence drug response. For example, vemurafenib targets the BRAF signaling cascade and model analysis of the data supported that lines with elevated sensitivity to vemurafenib had increased phosphorylation of BRAF, MEK, and MAPK proteins Fig. d bolded.BRAF-V600E colorectal patient cohort did not respond as well to cetuximab monotherapy in comparison to the wild-type BRAF cohort. Interestingly, in the vemurafenib basket clinical trial, colorectal patients were split into vemurafenib or vemurafenib/cetuximab treatment arm. The outcomes showed that the dual treatment arm had an increase in partial and stable responders, suggesting a potential synergy between these two inhibitors, similar to the synergy we observed in multiple tumor cell types [While our prediction of anti-ErbB therapies was based on model analysis rather than prior knowledge, there is evidence that this synergy is clinically relevant. Our model indicated that tumor cells, including colorectal cancer cells, with increased HER3 phosphorylation exhibited increased resistance to vemurafenib. In vitro, colorectal tumor stem cells with increased HER3 expression exhibited resistance to vemurafenib in the presence of the HER3 ligand, NRG-1 . Additioll types .BRAF-V600E cell lines from RPPA data. We determined that O-PLS predicted vemurafenib response more accurately than SVR, LASSO, and Random Forests, and the O-PLS model performed superiorly with proteomic data compared to transcriptomic data. Additionally, causal analysis identified that ErbB and PI3K signaling were upregulated in resistant cells, and that dual inhibition of ErbB receptors and BRAF increased vemurafenib sensitivity in resistant cells. Collectively, this study illustrates how an unbiased approach like O-PLS can be used to develop a model from proteomic data in a basket clinical trial setting in order to predict drug sensitivity and identify mechanisms of resistance.Here, we compared the predictive ability of leading machine learning algorithms for regression to predict vemurafenib sensitivity in Additional file 1: Table S1. Characterization of cell lines in training and testing sets.Additional file 2: Table S2. Summary of prediction performance from regression models.Additional file 3: Table S3. Binomial test results for comparison of predictive models versus O-PLS.Additional file 4: Table S4. Prediction results from RNA-seq- built O-PLS models.Additional file 5: Table S5. Comparison of correlation coefficients of protein/phosphoprotein/gene groups with vemurafenib IC50 AUC.Additional file 6: Table S6. Prediction results from total protein-only and phosphoprotein-only built O-PLS models.Additional file 7: Table S7. Loewes additivity model values.Additional file 8: Figure S1. Prediction comparisons between O-PLS models built from total or phosphoprotein- only expression.Additional file 9: Figure S2. Resistant cell lines correlate with activation of ErbB/PI3K pathway.


Cancer is one of the most serious medical problem and second leading cause of death in the world, characterized by a deregulation of the cell cycle which mainly results in a progressive loss of cellular differentiation and uncontrolled cellular growth. The benzimidazole is a heterocyclic moiety found in extensive number of natural and biological active molecules. Benzimidazole derivatives might be considered as auxiliary isosters of nucleotides having attached heterocyclic cores in their structures, cooperate effortlessly with biopolymers and have potential action for chemotherapeutic applications. Benzimidazole and its derivatives displayed a wide range of biological activity because of its structural similarity with the naturally occurring nucleotides. Benzimidazole has established huge alertness in current time and is extremely significant heterocyclic pharmacophore in recent drug innovation and medicinal chemistry. The present review summarizes the chemistry of various substituted benzimidazole derivatives with their antiproliferative significance towards the various cancer cell lines such as HCT116, MCF7, HeLa, HepG2, A549 and A431. Cancer is one of the most serious medical problem and second leading cause of death in the world, characterized by a deregulation of the cell cycle which mainly results in a progressive loss of cellular differentiation and uncontrolled cellular growth. Hence there is a need to develop those agents whose chemical characteristics clearly differ from those existing agents and can overcome the problem of resistance. In present situation, the most engaged and demanding undertaking is the design, synthesis and development of new biologically active heterocycle compounds. Heterocyclic entities act as medications since they have precise synthetic reactivity and they give advantageous site to which bioactive substituents can be bind. Subsequently, there is need for the improvement of pharmacologically active heterocycles in synthetic and therapeutic science with certain focal points including its effortlessness of activity, greener methodology, simple workup strategy, selectivity, higher yields and high-particle monetary , 2.In the medicinal field, the utility of heterocyclic entities has been raising each day because of structural similarities with biological molecules like nutrients, antibiotics. In spite of the fact that it including almost one-fourth of best hundred offering drugs yet because of issues like obstruction, poisonous quality, there is a requirement for minor change in existing drug molecules and to structure novel molecules which fuse benzimidazole as pharmacophore which are active against new targets . SubstitBenzimidazole is also named as 3-azaindole, azindole, benziminazole, benzoglyoxaline, 3-benzodiazole, 1,3-diazaindene having melting point of 170–172 °C and occurs as white crystals . Benzimi1a and 1b , respectively and LC50 values more than 100 µM -16a and 6b showed 10 times superior inhibitory result than etoposide as reference  against 21 human tumor cell lines  by SRB assay. Compounds 7a exhibited maximum cytotoxicity against both K562 (IC50 = 2.68 µM) and HepG-2 (IC50 = 8.11 µM) cells as compared to standard drugs colchicin (IC50 = 1.80 µM for HepG-2) and imatinib (IC50 = 0.47 µM for K562)  and comparable to mitomycin C with IC50 ± 0.9 µM  using MTT assay. In this series, compound es Table  26].Tab.Tab10a d11a and 11b displayed highest antiproliferative activity , SW620 , MiaPaCa-2 (pancreatic carcinoma), MCF-7  and WI38  by MTT assay. From the synthesized compounds, compounds 12a and 12b displayed the highest activity on tested cell lines and demonstrated an exceptional selectivity for HeLa cells , colon (SW 620), MCF-7 and lung (H 460) cell lines by MTT assay. Among them, compounds ls Table , Fig. 2 , colon   against NCI 60 cell lines by five dose assay. Compound −5 M) toward NCI 60 cell lines by five dose assay. Among the synthesized compounds, compound 14a  ..17a and 18a (IC50 = 0.0390 µM) exhibited promising antitumor activity  and melanoma (LOX IMVI) cancer cell lines, respectively and comparable to the standard drug (5-FU)  Table , Fig. 3 20a, 20b and 20c were found to be most active against selected cancer cell lines  among the synthesized derivatives  using DMSO as a vehicle control. Compounds, ves Fig.  38]..22a and 23a with GI50 values between 0.79 and 17.8 µM showed significant anticancer activity against tested cell lines  in NCI 60 cell line panel using SRB assay. In this series, compound 24a, 24b and 24c exhibited effective anti-proliferative activity toward cancer tested cell lines  against lung-A549, MCF-7, HeLa and human keratinocyte cells-HaCaT using MTT assay. Among the synthesized derivatives, compounds es Table , Fig. 4 25a and 25b showed the highest potency against HEPG2 while compounds, 25c, 25d and 25e showed promising activity against MCF7. Compounds, 25d and 25e showed moderate activity against HCT116  and NZB5 (wild-type p110α enzyme) using cell proliferation assay. From this series, compound 6% Table , Fig. 4 27a showed 87% tumor growth inhibition on carcinoma 755 at the dose of 250.0 mg/kg day as compared to control cisplatin , transplanted in mice. Among the synthesized compounds, compound tin Fig.  42]..27a show28a displayed promising activity against MDA-MB-468 (breast cancer) and SK-MEL-28 (melanoma)   than the reference 5-FU, GI50 = 22.60 µM -benzimidazole derivatives and screened for its tubulin polymerization inhibitory activity and cytotoxicity against anthropic A549, HepG2 and MCF-7 tumor cell lines by MTT assay. Among the synthesized derivatives, compound ds Table , Fig. 5 34a exhibited the potent antiproliferative activity and compared to standard drugs  by MTT assay. From the synthesized compounds, compound es Table , Fig. 5 36a displayed the most potent anticancer activity  cell line by SRB technique and compared to 5-FU and carboplatin standard drugs. In this series, compound ty Table , Fig. 5 H-benzo[d]imidazol-2-ylthio)-N-substituted acetamide derivatives and evaluated for their anticancer activity against MCF7 and HCT116 cancer cell lines by SRB assay using tamoxifen and 5-FU as references. Among the synthesized compounds, compounds 37a and 37b emerged out as excellent anticancer agents   Table , Fig. 6 42a–42c showed promising anticancer activity, particularly in the melanoma A375 cancer cell line  pyridine-benzimidazole complexes and evaluated for their antitumor activity against human SKOV-3, A375, MCF-7 and A549 cancer cell lines by MTT assay using Auphen (stock solution 10 mM in DMSO) as reference. Compounds ne Table , Fig. 6 43a and 43b inhibited the growth of all tested cell lines , T-lymphoblastic leukemia (CEM), cervix carcinoma (HeLa) and pancreas carcinoma (Mia Paca-2) cell lines. In this series, compounds es Table , Fig. 6 44a and 44b were found to be most potent compounds against tested cell line  cell lines employing MTT assay. Among the synthesized compounds, compound FU Table , Fig. 6 Benzimidazole is a promising category of bioactive heterocyclic compound that exhibit wide variety of biological activities because of its structural similarity with the naturally occurring nucleotides and also a focusable moiety in discovery of novel drug design in medicinal field. The present review summarizes the chemistry of various substituted benzimidazole derivatives with their antiproliferative significance towards the various cancer cell lines such as HCT116, MCF7, HepG2, HeLa, A549 and A431. Benzimidazole has established huge alertness in current time and is extremely significant heterocyclic pharmacophore in recent drug innovation and medicinal chemistry.


Addition of CDK4/6 inhibitors (CDK4/6i) to endocrine therapy significantly increased progression-free survival, leading to their approval and incorporation into the metastatic breast cancer treatment paradigm. With these inhibitors being routinely used for patients with advanced estrogen receptor-positive (ER+) breast cancer, resistance to these agents and its impact on subsequent therapy needs to be understood. Considering the central role of ER in driving the growth of ER+ breast cancers, and thus endocrine agents being a mainstay in the treatment paradigm, the effects of prior CDK4/6i exposure on ER signaling and the relevance of ER-targeted therapy are important to investigate. The objective of this study was to evaluate the anti-tumor activity of elacestrant, a novel oral selective estrogen receptor degrader (SERD), in preclinical models of CDK4/6i resistance.Elacestrant was evaluated as a single agent, and in combination with alpelisib or everolimus, in multiple in vitro models and patient-derived xenografts that represent acquired and “de novo” CDK4/6i resistance.Elacestrant demonstrated growth inhibition in cells resistant to all three approved CDK4/6i  in both ESR1 wild-type and mutant backgrounds. Furthermore, we demonstrated that elacestrant, as a single agent and in combination, inhibited growth of patient-derived xenografts that have been derived from a patient previously treated with a CDK4/6i or exhibit de novo resistance to CDK4/6i. While the resistant lines demonstrate distinct alterations in cell cycle modulators, this did not affect elacestrant’s anti-tumor activity. In fact, we observe that elacestrant downregulates several key cell cycle players and halts cell cycle progression in vitro and in vivo.We demonstrate that breast cancer tumor cells continue to rely on ER signaling to drive tumor growth despite exposure to CDK4/6i inhibitors. Importantly, elacestrant can inhibit this ER-dependent growth despite previously reported mechanisms of CDK4/6i resistance observed such as Rb loss, CDK6 overexpression, upregulated cyclinE1 and E2F1, among others. These data provide a scientific rationale for the evaluation of elacestrant in a post-CDK4/6i patient population. Additionally, elacestrant may also serve as an endocrine backbone for rational combinations to combat resistance. Breast cancer remains one of the most commonly diagnosed cancers in women and has affected over 200,000 women in the USA in 2018 alone . CurrentWhile endocrine monotherapy has been a mainstay in treating metastatic ER+ breast cancer for decades, multiple pivotal clinical trials in recent years have demonstrated that the addition of cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) to an endocrine agent, such as an aromatase inhibitor or a SERD such as fulvestrant, significantly increases progression-free survival (PFS) when compared to the endocrine agent alone , 12–17. Recent overall survival (OS) data demonstrate that patients derive benefit from the combination of CDK4/6i with endocrine therapy. In patients with prior endocrine sensitivity in the PALOMA-3 trial, treatment with palbociclib and fulvestrant led to longer OS (10 months) than treatment with placebo and fulvestrant . The MONWhile the addition of a CDK4/6i to endocrine therapy approximately doubles PFS and improves OS, a portion of these patients will eventually relapse and will require additional treatment. It is important, therefore, to understand the molecular makeup of a tumor that has progressed on CDK4/6i treatment, in order to help inform subsequent treatment options. Several CDK4/6i resistance mechanisms have been described preclinically, including multiple alterations of the cell cycle pathway such as cyclin E1/E2 amplification, E2F1 overexpression, retinoblastoma protein (Rb) loss, and CDK6 overexpression driven by the loss of the tumor suppressor FAT1 among others –28. In tER signaling is known to regulate several proteins that are important for cell cycle progression , 34, 35.Given the impact of ER on cell cycle progression, it is important to evaluate antiestrogen therapy in a post-CDK4/6i setting. Fulvestrant, the only approved SERD, is effective in patients; however, its low oral bioavailability and intramuscular route of administration pose some limitations , 40. Thi2. MCF7 cells harboring wild-type ER were genetically modified using CRISPR-Cas9 to express either the ESR1:Y537S or the ESR1:D538G mutated proteins. Briefly, single-guide RNAs were utilized to create the KI/KI cell line containing ESR1:Y537S and the KI/KO cell line containing ESR1:D538G, and single-cell clones were isolated and grown to establish these cells. The MCF7-Y537S/D538G cell lines were maintained in RPMI phenol red-free medium supplemented with 10% charcoal-stripped FBS  and 1% pen-strep (Thermo Fisher Scientific) at 5% CO2.Elacestrant (RAD1901) ((6R)-6-(2-(N-(4-(2-(ethylamino)ethyl)benzyl)-N-ethylamino)-4-methoxyphenyl)5,6,7,8-tetrahydronaphthalen-2-ol dihydrochloride) was manufactured by Patheon. Elacestrant lots used in this study were periodically checked to ensure purity, stability, and chirality. HCC1428 cells were purchased from ATCC, and HCC1428-LTED (long-term estrogen deprived) were developed by maintaining the cells in RPMI phenol red-free medium supplemented with 10% charcoal-stripped FBS  and 1% pen-strep (Thermo Fisher Scientific) at 5% COR, HCC1428-LTED-RiboR, and HCC1428-LTED-AbemaR cells were developed by exposing the HCC1428-LTED cells to increasing concentrations of the respective CDK4/6i to a final concentration of 500 nM for palbociclib, 1 μM for ribociclib, and 250 nM for abemaciclib. MCF7-LTED-Y537S-CDK4/6iR and MCF7-LTED-D538G-CDK4/6iR cell lines were developed by exposing MCF7-LTED-Y537S and MCF-LTED-D538G cells, respectively, to increasing concentrations of the respective CDK4/6i to a final concentration of 500 nM for palbociclib, 1 μM for ribociclib, and 250 nM for abemaciclib. Doses for generating resistant cell lines were chosen based upon the previously reported literature . Colonies were allowed to grow for 2–5 weeks depending on the growth rate of each cell line. The treatments were performed in triplicate, and media and drug were replaced weekly. At the end of the treatment, cells were fixed in paraformaldehyde and stained with 0.05% crystal violet for visualization. A representative well is pictured  after either 24 or 48 h of treatment, and total protein was separated by SDS-PAGE and transferred to the membranes and immunostained using antibodies specific to the indicated proteins. For in vivo pharmacodynamic studies, end of study flash-frozen tumors (4 h post-last dose) were fractured using a cryoPREP instrument (Covaris), and pulverized tissue was lysed in CelLyticMT lysis buffer (Sigma-Aldrich). Total protein was analyzed by Western blot analysis as described above. Protein expression was analyzed using standard practice and antibodies as follows: ERα, PR, E2F1, CCNE1, CCNE2, CCND1, total Rb, phospho-Rb S807/811, CDK2, CDK4, CDK6, Actin , phospho-p107, p107, phospho-p130, p130 , GREB1 , and Vinculin . Protein expression was quantified using the AzureSpot software and normalized to the expression of the vinculin protein.Cells were harvested and lysed in CelLyticT kit (Life Technologies), and the lysates were processed according to the manufacturer’s instructions. qRT-PCR was performed using the one-step master mix and TaqMan™ probes (Applied Biosystems). For in vivo pharmacodynamic studies, end of study flash-frozen tumors were pulverized using the cryoPREP instrument (Covaris). From the pulverized tissue, total RNA was extracted using the RNeasy Mini Kit (Qiagen); qRT-PCR was performed using the TaqMan Fast Virus One-Step Master Mix and TaqMan™ probes (Applied Biosystems). The CT values were analyzed to assess the relative changes in the expression of the TFF1 , GREB1 , and PGR  genes, with GAPDH (4310884E) as an internal control, using the 2^−ΔΔCT method  × 100.Statistical and graphical presentations were performed using GraphPad Prism 7. For cell proliferation assays, the ICwt) or mutant  ER. These cells are herein referred to as “CDK4/6i-sensitive” cell lines. All three CDK4/6i significantly inhibited the proliferation of these cells with varied potency and extent of inhibition  setting and harbor either wild-type , RiboS (ribociclib-sensitive), AbemaS (abemaciclib-sensitive)] and the cells that were exposed to CDK4/6i  were assessed in short- and long-term proliferation assays. The resistant cell lines exhibited decreased sensitivity to the respective CDK4/6i in both assays  with the corresponding CDK4/6i and compared to their respective controls (no treatment for the duration of assay). The observed molecular changes are summarized in Additional file The most frequently observed finding was upregulation in the expression of E2F1 and its downstream target cyclin E1 (CCNE1) , upregulation of CDK6 (in ESR1mut: D538G (except RiboR) and ESR1mut: Y537S lines), and upregulation of cyclinD1 (in ESR1mut: Y537S lines). Interestingly, a trend towards increased expression of Rb (pRb), phosphorylated Rb (p-pRb) and its family members, and p107 and p130 in the ESR1wt-RiboR and ESR1wt-AbemaR cell lines was observed. CDK2 remained mostly unchanged while cyclin D1 and CDK4 showed trends of upregulation in the ESR1mut: Y537S cell lines. Cyclin E2 did not show a consistent trend of up- or downregulation in the resistant lines.Modulation of Rb/Rb family members, cyclinsD1/E2, and cyclin-dependent kinases varied in the resistant cell lines, implying that these cells adapt differently to CDK4/6i exposure  and a long-term 3–5 weeks) cell assay. Elacestrant inhibited cell growth of the ESR1–5 weeks wt-CDK4/6i sensitive and resistant lines and downregulated GREB1 expression in all these cell lines. In the ESR1mut: D538G-CDK4/6i sensitive and resistant lines, a similar downregulation of ER expression was seen with a concurrent downregulation in PR expression. Elacestrant did not degrade ER in our genetically modified ESR1mut: Y537S cell lines; however, the downregulation of downstream ER signaling was still observed. Despite the varying effects on ER, a consistent downregulation of GREB1 or PR was observed across all the cell lines, indicating abrogation of ER signaling upon elacestrant treatment  and in PDX models that are innately resistant to CDK4/6i.Investigation of combination therapies to combat resistance and target truncal drivers has been on the rise in the metastatic breast cancer setting. One such therapy that has been studied is the combination of alpelisib, a PI3Kα inhibitor, with endocrine agents such as letrozole or fulvestrant , 52. ThePIK3CA while the CTG-2308 models harbored an E545K mutation. The CTG-2432 model harbored an E545K and an E722K mutation in the PIK3CA gene and also harbors an E380Q mutation in ESR1. In the ST3932 model, palbociclib did not exhibit significant TGI  or fulvestrant and palbociclib (CTG-2432). The ST3932 model harbored an R88Q mutation in In the CTG-2308 model, neither elacestrant nor fulvestrant resulted in significant TGI compared to the vehicle control  resistance models developed in ESR1wt and ESR1mut: D538G/Y537S backgrounds. CellTiter-Glo assay, colony formation assay and western-blot analysis of (A) ESR1wt/ESR1mut: D538G/ESR1mut: Y537S-RiboS and ESR1wt/ESR1mut: D538G/ESR1mut: Y537S-RiboR cells and (B) ESR1wt/ESR1mut: D538G/ESR1mut: Y537S-AbemaS and ESR1wt/ESR1mut: D538G/ESR1mut: Y537S-AbemaR cells; treated with controls and the pertinent CDK4/6i at the indicated doses. Figure S2. Elacestrant downregulates upregulated ER pathway genes in palbociclib-resistant cells. A. Log2FC for genes modulated in the ER signaling pathway in ESR1wt-PalboR cell line vs ESR1wt-PalboS cell line. B. Log2FC for genes modulated by elacestrant (300 nM) treatment of ESR1wt-PalboR cells. Figure S3. Comparative expression of cell cycle proteins in palbociclib-sensitive and palbociclib-resistant in vitro and in vivo models. A. Western blot analysis of indicated proteins from baseline/vehicle-treated samples of the denoted palbociclib-sensitive and palbociclib-resistant models. B. Summarized treatment histories and palbociclib responses for the PDX models shown in this paper. Figure S4. Single-dose pharmacokinetic profile for palbociclib in non-tumor bearing mice. Mice were treated with the indicated doses of palbociclib and plasma collected at the indicated timepoints after single dose. Mean concentration ± SD of palbociclib is depicted (n=4/timepoint/dose). Area under the curve (AUC0-inf) was calculated and divided by the AUC0-inf for the clinical regimen of palbociclib (Ibrance-125 mg). Figure S5. ER-independent growth of PDX model previously treated with AI + palbociclib. A. Mean tumor volume of CTG-2308 PDX model, asterisks represent differences between the indicated groups at the end-of-study; p-values *, p<0.05, **, p<0.01, ***, p<0.001, ****, p<0.0001. B. Western blot analysis of phospho-RB and RB from tumors harvested 4h post-last dose. C. Western-blot analysis of indicated proteins from tumors harvested 4h post-last dose for the indicated treatment arms. D. qRT-PCR analysis of PGR, TFF1, and GREB1 in end-of-study tumors treated with the elacestrant and fulvestrant. Table S1. Commonly observed mechanisms upregulated in CDK4/6i-resistance models include CCNE1 and E2F1 overexpression. Summarized results from Fig.


However, without due consideration, the thousands of new human iPSC lines generated in the past decade will inevitably affect the reproducibility of iPSC-based experiments. Differences between donor individuals, genetic stability and experimental variability contribute to iPSC model variation by impacting differentiation potency, cellular heterogeneity, morphology, and transcript and protein abundance. Such effects will confound reproducible disease modelling in the absence of appropriate strategies. In this Review, we explore the causes and effects of iPSC heterogeneity, and propose approaches to detect and account for experimental variation between studies, or even exploit it for deeper biological insight.Induced pluripotent stem cell (iPSC) technologies have provided  Summary: This Review discusses approaches to deal with and reduce cellular heterogeneity in induced pluripotent stem cell-based studies, enabling experiments to be reproducible and meaningful. Cellular heterogeneity: cell type diversity within the experimental cellular population, i.e. due to the presence of multiple cell types and to diversity in morphology, maturation and functionality within each cell type present.Expression quantitative trait-associated loci (eQTLs): genetic variants that are associated with changes in the expression of a gene.Genetic background: the entire set of genes in a genome.Genome-wide association studies (GWAS): hypothesis-free methods that identify associations between genetic loci and phenotypic traits.Induced pluripotent stem cells (iPSCs): stem cells that are generated by induced reprogramming of somatic cells through the forced expression of transcription factors.Induced reprogramming: briefly, it consists of induction of proliferation and downregulation of cell type-specific transcription in a first step, followed by continuous expression of key transcription factors until the iPSC state is established.Isogenic iPSC lines: lines derived from the same individual that are engineered to differ at only one specific locus and are otherwise genetically identical.Penetrant genetic variant: a disease-causing mutation that will cause disease symptoms across most of the individuals carrying it.Polygenic risk: disease risk given by the combined contribution of multiple genetic variants, each variant often of small individual effect.Principal component analysis: a statistical approach that uses orthogonal transformation to identify a set of principal components, each successive linear component capturing as much of the variation in the data as possible.Probabilistic estimation of expression residuals (PEER): based on factor analysis, PEER takes as input transcript profiles and covariates from a set of individuals and outputs hidden factors that explain much of the expression variability.Removal of unwanted variation (RUV): a normalisation method that identifies and removes unwanted sources of variation within omics readouts.Rosetta line: an iPSC line that is commonly used within all experiments by multiple laboratories, and that enables researchers to address experimental variation between those laboratories' results.Somatic mutations: acquired (not inherited) genetic alterations that either pre-exist in the somatic cells or can be acquired in the handling of the cells during reprogramming over culture.Subclonal: a mutation that is present in only a fraction of the cells within a population.Since their invention just over a decade ago, induced pluripotent stem cell  measures, ‘gold standard’ iPSC lines and standardised protocols, as well as robust statistical analysis to ensure that the information obtained from these efforts are reproducible and meaningful. Reproducibility is a cornerstone of scientific knowledge and, without greater efforts to make iPSC-derived model experiments comparable, this community will be highly vulnerable to error. The aim of this Review is, therefore, to summarise the variables affecting iPSC reproducibility and to propose strategies for each stage of the multistep process to overcome these challenges, thereby enabling experiments to be more readily compared.in vivo counterparts and also mirror tissue-specific functional genetic variation -associated mutations are a major contributor to the heterogeneity in mutation rates across iPSC lines. Thus, source tissue UV exposure will influence the somatic mutation  rate D'. ReprogrLastly, several groups reported that variation in routine cell culturing and maintenance such as variation in passage number, growth rate and culture medium contribute to iPSC variability , and thaAcknowledging, measuring and reducing experimental variability must be part of the experimental design. We agree with others that the use of standardised methodologies, along with the documentation of fate, yield and purity of the derived cell types, would increase the reliability of phenotype comparisons and thus improve reproducibility across different laboratories . Fig. 2 Several large-scale consortia have generated large banks totalling over a thousand iPSC lines and have made these lines available to the research community, including Stem Cells for Biological Assays of Novel Drugs and Predictive Toxicology (StemBANCC) , HipSci Despite these QC advantages, we argue that it is the re-use of lines between studies that will prove key to accounting for variation within these models . The genThe ability of a human iPSC line to model polygenic influence is a unique strength, and the genetic capacity of these models to capture polygenic risk  is excitTo ensure valid disease modelling experiments, the choice of donors and lines, differentiation protocol, number of samples (both cases and controls) and number of lines from each patient have to be agreed primarily based on the disease to be modelled, the likely effect size of disease-relevant phenotypes and the readouts of interest. The highly detailed and curated iPSC banks and reference panels described above provide excellent starting points to help define a controlled and robust experimental design.Assuming a straightforward study, such as the comparison of two populations – cases versus controls, we recommend increasing the number of cases and controls rather than increasing the number of lines derived from each case or control individual, as this powers the more interesting population comparison. An analysis of transcriptomic profiles of undifferentiated iPSCs found that four to six individuals per group provided a reasonable balance of sensitivity and specificity , althougControl lines should be matched for age, sex and ethnicity. Whenever possible, these should also match the time in culture. If female iPSC lines are used, they should be characterized for X-chromosome inactivation status. Considerable variation in the amount of X-chromosome reactivation in early-passage lines and random re-inactivation of X chromosome in later passages affect differentiation potential .Alternatively, a widely used strategy to deal with genetic background influence on the expression of a disease phenotype in the case of a known genetic variant is to use clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9-mediated genomic editing technologies to generate isogenic cell line  pairs C. AlthougWhen differentiating iPSCs into the cell type of interest, the cellular composition of the resulting culture is a primary source of inter- and intra-experimental variation . Althougin vivo gene expression and/or in vitro cellular physiological data, can be used to identify the molecular signatures of different functional states of differentiated cells .When the causes of variation are unknown and cannot be easily regressed out from the data, factor analysis-based approaches can capture variance between samples that, upon correlation to recorded sources of variation such as experimental confounding factors, can be safely removed from the data. Clearly associating trends in the data with unwanted experimental variation is important in order to justify their removal. Researchers cannot simply ignore trends because their biological meaning is unclear or uninteresting. Methods range from the simple principal component analysis  to the mIn culture, cells that are used to model a disease-relevant process are unlikely to be undergoing that process in a highly synchronised manner. Taking a single measurement of a property of the culture cannot capture this heterogeneity. Instead, it captures cells at various points in the process, summed into a single value that usually does not recapitulate the modelled process in a helpful way. However, single-cell transcriptomics allows researchers to distinguish cells that are undergoing distinct processes or are at different stages within the same process. The presence of distinct cellular processes between patient-derived cell models can identify distinct aetiologies between patients, which can prompt clinical re-evaluation, even leading to a different diagnosis .For cells undergoing the same biological process, pseudotemporal ordering approaches attempt to arrange the cells based on their progression through that process B. ReasoniPSC models offer tremendous opportunities to advance our understanding across a wide range of biology. Each model is as unique as the individual from whom it was derived, along with a large amount of known and unknown experimental variation. Although every effort should be made to understand and reduce experimental variation, a more immediate strategy would be for each iPSC modelling community to adopt a set of appropriate common case and control lines that would enable them to identify experimental variation across studies. Through this simple step, bioinformatics approaches can help to identify and remove bias from omics measurements, aiding inter-study comparisons and thus scientific reproducibility. Finally, single-cell assay technologies may provide opportunities not only to reduce study heterogeneity but also to convert process and progressional heterogeneity into significantly insightful biology.


The therapeutic potential of allogeneic hematopoietic cell transplantation  for the treatment of malignant diseases relies on the graft-vs.-leukemia (GVL) or graft- vs.-tumor (GVT) responses to eradicate residual tumor cells through immunologic mechanisms. Graft- vs.-host disease (GVHD) is a major cause of transplant-related morbidity and mortality following allo-HCT . GVHD is clinically described in two forms: acute (aGVHD) and chronic (cGVHD). aGVHD is primarily induced by T cells commonly characterized by a type I T-cell response; whereas cGVHD is induced by both T and B cells, similar in nature to that of autoimmune disorders. Additionally, late acute GVHD, defined as occurring beyond 3 months post-transplant, is associated with high lethality. Despite advances in patient care and pharmacologic prophylaxis strategies, the incidence of GVHD, particularly cGVHD, has not greatly declined over time. In fact, effective treatment options are very limited beyond steroids. Currently, the only FDA-approved agents for steroid-refractory acute and chronic GVHD are ruxolitinib and ibrutinib, respectively. Even with these new agents, GVHD mortality and its impact on quality of life remains a major clinical challenge. Therefore, it is urgently required to further understand GVHD pathogenesis and identify novel therapeutic targets for the prevention and treatment of this devastating disease as a major complication of allo-HCT.Since allo-reactive donor T cells are central to aGVHD pathophysiology, research has focused on donor T-cell activation, metabolism, co-stimulatory/co-inhibitory signals, differentiation, memory, and migration. Regulation of T-cell allo-responses via protein kinases, metabolites, non-coding RNAs, and other post-transcriptional pathways has also gained substantial attention in recent years. Beyond donor T cells, antigen-presenting cells (APCs) especially dendritic cells (DCs) and other lymphoid cells including natural killer (NK), NKT and innate cells (ILCs) also contribute to aGVHD pathogenesis. In addition, microbiota, tissue injury/repair, and thymopoiesis are also critically involved in aGVHD pathogenesis. The pathophysiology of cGVHD is characterized by fibrosis with inflammation resulting in organ dysfunction. Immunological mechanisms of cGVHD involve (i) aberrant conventional T and B cell activation, differentiation and interactions; and (ii) decreased production and development of regulatory T cells (Tregs). Therefore, cGVHD research is now moving toward a better understanding of the roles of B-cell signaling, activation, germinal center formation, and plasma cell differentiation; as well as the roles of T-cell signaling, activation, and differentiation into T helper and regulatory subsets. In this Research Topic, we brought together six outstanding original research and 10 state-of-the-art Review articles on the pathogenesis and therapy of GVHD that cover the following sub-topics:Yu et al. focused on the opposing side of the immunologic synapse, and describe how DCs mediate T cell allo-sensitization or immune tolerance after allo-HCT. In an original research paper, the Betts Lab identified a new approach to prevent GVHD that impairs monocyte-derived DC alloactivation of T cells, yet preserves GVL effect Betts et al. They demonstrated that Inhibition of XBP-1 splicing reduces migration of human monocyte-derived DCs, allo-stimulatory potency, and curtails their ability to produce IL-1β, TGFβ, and p40 cytokines, suppressing Th1 and Th17 cell priming without interfering with Treg function or GVL effects by CTL and NK cells.Significant progress has been made in defining the dichotomous role of DCs in the development of GVHD. Host-derived DCs are important to elicit allogeneic T cell responses, whereas certain donor-types of DCs derived from newly engrafted hematopoietic stem/progenitor cells (HSPCs) can amplify this GVH reaction. In contrast, some DCs also play non-redundant roles in mediating immune tolerance. They induce apoptotic deletion of host-reactive donor T cells while promoting expansion and function of Tregs. Copsel et al. Lab demonstrates that BET inhibition prevents GVHD, particularly by supporting the expansion of highly potent Tregs. Chen and Mayne describe the limited ability of allospecific CD44high central memory T cells (Tmem) to exert clinical GVHD, owing to functional exhaustion Huang et al. In addition, Shao et al. characterize how ILCs influence GVHD after allo-HCT. Altogether, these primary and review papers delineate the contributions of DCs, Tmems, Tregs, and ILCs in GVHD pathogenesis.Original research from the Piper and Drobyski explore the role of STAT3-dependent inflammatory cytokines in acute GVHD of the gut. They also highlight novel translational approaches to target such cytokines to improve outcomes after allo-HCT. The report from Bastian et al. focuses on the unique anti- and pro-inflammatory properties of the IL-12 cytokine family, and how they impact GVHD onset and severity. Their article features data from pre-clinical studies and early phase clinical trials that identify p40 cytokines and receptor signaling elements, as pathogenic, and thus, candidates for therapeutic intervention. Further, primary research from Yoshihara et al. identifies how TNFα opposes stem cell engraftment. Their work suggests cytokine blockade with etanercept could enhance engraftment and possibly prevent GVHD after allo-HCT. Though a separate clinical entity from GVHD, Senyuk et al. demonstrate that post-transplant hemophagocytic lymphohistiocytosis is similarly driven by alloreactive T cells and inflammatory macrophages.With regard to inflammatory cytokine networks in GVHD pathophysiology, Kumar et al. characterize how co-stimulation or co-inhibitory molecules influence donor T cell allo-activation, including common co-stimulatory molecules such as CD28, ICOS, CD40, CD30, CD27, OX40, and 4-1BB and common negative regulators such as CTLA-4, PD-1, TIM-3, and LAG-3. They discuss how these co-stimulatory and co-inhibitory pathways are involved in T-cell function and contribute substantially in GVHD pathogenesis. They urge that further intensive exploration of these pathways is needed before these potential therapeutic targets could become new clinical options to control GVHD without causing severe side effects. Cassady et al. focus on how PD-L1 interactions with PD-1 and CD80 that differentially regulate auto- and allo-immunity. They discuss how these interactions can separate GVL activity from GVHD in preclinical animal models and highlight the recent clinical application and challenges of PD-L1/PD-1 blockade after HCT for augmenting GVL activity. Original research from the Zhang et al. Lab characterizes the role of ICOS in chronic GVHD pathogenesis as well as its critical influence on Treg biology. They demonstrate that ICOS promotes T- and B-cell activation and differentiation, which can promote cGVHD development; whereas ICOS is also critical for the survival and homeostasis of iTregs, which can suppress cGVHD. Hence, ICOS balances the development of cGVHD and could offer a druggable target to improve clinical outcomes after allo-HCT.Tijaro-Ovalle et al. highlight the metabolic features of malignant hematopoietic cells and discuss the metabolic features that guide the function of T cells and APCs during processes involved in GVH and GVL responses. They also provide rationale for potential therapeutic interventions by targeting metabolic pathways that guide the differentiation and function of these immune cells in the context of allo-HCT. Emerging clinical and pre-clinical evidence indicates that certain micronutrients may participate in regulating GVHD risk after allo-HCT. Dietary micronutrients contribute significantly to modulating various immune responses including cell metabolisms and may influence the susceptibility to autoimmune and inflammatory diseases. Chen and Mayne summarize recent advances in our understanding with respect to the potential role of micronutrients in the pathogenesis of acute and chronic GVHD, focusing on vitamins A and D. They reveal the therapeutic benefits of vitamins A and D in controlling alloreactive T cells.Metabolism is an attractive therapeutic target to optimize cancer immunotherapy and GVHD prevention. T-cell subsets are poised to distinct metabolic pathways that can determine their function and differentiation. Because distinct T-cell subsets mediate GVH vs. GVL response, the dominant metabolic properties of these distinct subsets might serve as new therapeutic targets that can be exploited to prevent GVHD without compromising GVL activity. Chang et al. summarize the biology of GVH and GVL responses in pre-clinical models and discuss potential novel therapeutic strategies to reduce the relapse rate after allo-HCT. They also review the approaches, including optimal donor selection, conditioning regimens, donor lymphocyte infusion, BCR/ABL-specific CTL, and chimeric antigen receptor-modified T cells, which have been successfully used in the clinic to enhance and preserve the GVL without aggravating GVHD. CTL plays a critical role in mediating the GVL effect. Du and Cao detail how cytotoxic pathways, particularly Fas/Fas ligand, perforin/granzyme, and cytokines, in T cells differentially contribute to GVHD vs. GVL effect.Separating pathogenic GVHD from beneficial GVL is an area of substantial interest and research among the field. In a comprehensive review, The collection of articles in “Pathogenesis and Therapy of Graft-vs.-Host Disease” clearly provides an in depth review of our current understanding of GVHD pathophysiology. Moreover, contributions to this collection also present innovative strategies to prevent acute and chronic GVHD, preserve GVL, and support tolerizing Tregs or DCs after allo-HCT.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In vitro cancer cell cultures are facile experimental models used widely for research and drug development. Many cancer cell lines are available and efforts are ongoing to derive new models representing the histopathological and molecular diversity of tumours. Cell models have been generated by multiple laboratories over decades and consequently their annotation is incomplete and inconsistent. Furthermore, the relationships between many patient-matched and derivative cell lines have been lost, and accessing information and datasets is time-consuming and difficult. Here, we describe the Cell Model Passports database; cellmodelpassports.sanger.ac.uk, which provides details of cell model relationships, patient and clinical information, as well as access to associated genetic and functional datasets. The Passports database currently contains curated details and standardized annotation for >1200 cell models, including cancer organoid cultures. The Passports will be updated with newly derived cell models and datasets as they are generated. Users can navigate the database via tissue, cancer-type, genetic feature and data availability to select a model most suitable for specific applications. A flexible REST-API provides programmatic data access and exploration. The Cell Model Passports are a valuable tool enabling access to high-dimensional genomic and phenotypic cancer cell model datasets empowering diverse research applications. The currently available set of ∼1000 human cancer cell lines are widely used in research and drug development due to their ease of use, broad utility and low reagent costs. Cancer cell lines have made important contributions to the development of anti-cancer drugs, such as the epidermal growth factor receptor (EGFR) inhibitor gefitinib for the treatment of non-small-cell lung cancer  and anapDue to their extensive use over decades, there are several challenges when working with and selecting cancer cell models. Many models have inadvertently been cross-contaminated  or are acellmodelpassports.sanger.ac.uk. The Cell Model Passports is a central portal providing manual or programmatic access to a cancer cell model database containing curated patient, sample and model relationship information as well as genomic and functional datasets. Here, we describe the Passports and available datasets, and provide information on how they can be accessed through a user-friendly web application and programmatically through a REST Application Programming Interface (API).To address these problems, we have created Cell Model Passports; More than 1200 established cancer cell lines and newly derived organoid models comprise the foundational model set of the Passports, including all those utilized in the Sanger’s GDSC project , 18 as wCell model names are often the only means to integrate and compare datasets but they are also one of the most variable properties. The Passport model name is accompanied by synonyms and most importantly unique identifiers utilized by established resources such as Research Resource Identifiers (RRID) , the Catn = 29 categories), ‘Cancer Type’ (n = 44) and ‘Cancer Type Detail’ (n = 194). The cancer type descriptors use the NCI-Thesaurus terms, benefitting from established definitions that assist integration with other ontologies. Models in the Passports have been derived from primary, metastatic, pre-cancerous and normal samples as indicated by ‘Tissue Status’, the anatomical site from which the sample was obtained ‘Sample Site’ is also provided.Model lineage, a requirement for tissue-specific analyses, is provided through three progressively granular fields: ‘Tissue’  have associated somatic nucleotide variant, gene expression, copy number variation or methylation datasets Figure . CurrentEach processed genomic dataset is stored in a dedicated database table. Tables hold the data in long form, with each row a gene along with its associated values. In the case of mutations, there can be multiple rows for any given gene due to the presence of multiple mutations in a gene of a given model. The processed datasets provide gene-level information, rather than segment or transcript, allowing direct integration and cross-comparisons with other datasets within the Passports. All genes have been assigned an internal ID, allowing mapping to current and previous HGNC gene symbols, Ensembl Gene IDs (v91) and other external gene identifiers. All genes with an HGNC-approved symbol as of April 2018 are currently included in the Passports, including those without a protein product. Any dataset values that were mapped to genes without an official gene symbol have been discarded from processing, but continue to be available in raw data downloads.Model annotation was compiled from information provided by commercial cell banks  academic collaborators, databases (Cellosaurus  and CCLEThe Cell Model Passports resource was constructed using a PostgreSQL (v9.6) database and is hosted at the Wellcome Sanger Institute. The REST API is developed in Python 3.6 using the Flask framework (v0.12) and SQLAlchemy (v1.2). The web interface utilizes the React framework (v16.4), with Bootstrap (v4.0) providing user interface elements and responsive adaptation to screen size.KRAS and subsequently access the expression and drug sensitivity data sets for this cohort of models. It is also possible to filter models based on the availability of specific datasets, for example, the subset of breast cancer models with both mutation and gene expression data.The website enables users to search and/or filter for cell models via a variety of features as well as providing access to datasets. The search and filter functions allow users to not only conduct direct queries for an individual model or cancer, but also combine filters to identify a cohort of cell models with a particular set of characteristics. For example, one can filter for non-small-cell lung cancer cell models derived from a metastatic lesion with a mutation in Multiple information panels are displayed on the Model Page Figure . ‘Model https://api.cellmodelpassports.sanger.ac.uk on specific endpoints. Requests should follow the JSONAPI v1.0 standard (jsonapi.org), though a few additions are available to accommodate search and aggregations. Responses strictly follow the JSONAPI v1.0 standards. Endpoints are available for lists of patients, samples, models, identifiers, files, all processed datasets and cancer driver mutations. Requests can have modifiers that allow users to select, filter and sort the results and control pagination. To facilitate access to available resources, all relationships between resources are fully annotated within the API, including API URLs to related resources. Full documentation and examples can be found in the API user guide available on the Passports website.Programmatic access to the Cell Model Passports database is available through a flexible API. The API can be accessed through HTTPS GET requests to Data files are hosted on a robust S3 object store platform or in third party repositories; the Cell Model Passport database contains links to these files together with associated metadata. There are two categories of files, each with their own database table: generic files and model files. Model files are tied to a specific model and are currently used for model-specific datasets, images, documentation or authentication details. Generic files hold data for multiple models or are files not otherwise associated with an individual model.The Cell Model Passport is a centralized cell model hub, providing a platform for researchers to investigate key patient and sample information, manually and programmatically access multiple genetic and functional datasets, and facilitate integration of data from different sources through mapping of unique identifiers.Given the widespread usage of cancer cell models several resources exist that hold cell model information such as Cellosaurus , Pharmacdepmap.sanger.ac.uk), generating additional datasets, such as whole-genome CRISPR-knockout screens, calling of fusion transcripts and additional drug response data. The Cell Model Passports will incorporate these newly generated datasets to facilitate their utilization for scientific research. The Passports currently focus on high quality and consistent datasets generated by the Wellcome Sanger Institute, but in the future will expand to include cell models and datasets generated by external resources while maintaining an emphasis on quality and curation. Furthermore, with recent technological advancements , a new generation of cancer cell models is being derived to allow full representation of the histopathological and molecular diversity of tumours (In addition to the existing deep characterization, cell lines are continually being used in new initiatives such as the Dependency Map at Sanger ( tumours . The SanIn summary, the Cell Model Passports provide a resource to reduce the mis-identification of cell models, facilitate data integration and subsequently improve experimental reproducibility in cancer research and related fields. We anticipate that the Passports will be widely used to inform the selection of cancer models and to provide user-friendly access to cell model relationship information as well as clinical, genomic and functional datasets.Supplementary DataClick here for additional data file.


That idea that problem-based learning (PBL) is more motivating that traditional education has been prevalent since the inception of PBL at McMaster University in the late 1960s. Evidencing this through empirical research, however, has proven to be a lot more problematic. This paper retraces how the discourse on motivation started from a laymen’s conception in the early days of PBL, and slowly evolved into a field of scientific inquiry in the 1980s and 1990s. However, looking at the evolution of motivation theory over the same period, we show that motivation discourse in the burgeoning literature on motivation and PBL remained largely wedded to the laymen’s approach, and failed to catch up with the new achievement-goal theory and self-determination theory approaches. This paper proceeds to analyse the explosion of studies on PBL and motivation after 2000, acknowledging efforts to move away from anecdotal accounts and provide theoretical grounding to the research. However, once again, we show that the majority of the research employed outdated motivational measures that do not fully grasp the complexity of contemporary motivation theory. The paper concludes on the observation that single-course and curriculum-wide research interventions have yielded no conclusive results on the effect of PBL on intrinsic motivation, and that future research should therefore seek to use up-to-date motivational constructs in more targeted interventions. Motivation is one of the psychological aspects of problem-based learning (PBL) that has received the most research attention. In the 50 years since PBL’s inception at McMaster University, there have been over 1000 scientific papers published on the link between PBL and motivation. This makes sense to educators familiar with PBL. The learning format in which a small group of students tackles a realistic problem that acts as the starting point for learning under the guidance of a tutor  responsible for developing the curriculum grew and preparations for the opening of the Faculty of Medicine in September After the initial flurry of enthusiasm, no doubt buoyed by the Founding Dean’s rejection of his own dull medical school experience McAuley , talk ofHet Tutorensysteem  nominally headed by Wijnand Wijnen, but de facto run by Henk Schmidt and Peter Bouhuijs Servant . Mention Schmidt . Schmidt Schmidt , 1978 to Schmidt . The firFrom the late 1980s, the volume of publications on the subject of motivation in PBL expanded significantly. In order to examine the developments of motivation discourse within that explosion of literature, we switched from the historical approach used in the previous section to a database search using the search terms “problem based learning” AND  in Web of Science Core Collection and Medline, PsycINFO (Ovid interface), ProQuest Dissertations and Theses, ERIC, and Social Science Database (ProQuest platform). Our search produced 1077 documents after the removal of duplicates . What our search shows is that, as evidenced in Fig. Further complicating the research and discussion of the topic of motivation and PBL, is the increasing complexity of motivation as a complex subject of inquiry, which has spawned many different conceptualizations, theories, and measures over the years . Unlike SDT, it does not have clear founding fathers, but is related to prior work on mastery goals (developing competence) and performance goals (outperforming others) by scholars such as Ames and Archer , Dweck  and NichBecause AGT was developed by several theorists coming from different theoretical frameworks, AGT is marred by different labels, measures, and definitions that yield unresolved controversy amongst proponents of the theory , and perceptions of task value (including interest), investigated in 31 studies (d = 0.587). Furthermore, an effect was found on students’ attitude toward learning, investigated in 23 studies (d = 0.240). Attitude is a popular construct that is often measured in STEM disciplines to indicate a students’ global positive or negative feeling toward learning or engaging in an activity (Germann d = 0.022), indicating that PBL does not seem to affect students’ intrinsic or extrinsic motives for studying . As is the experience that, for instance, rigidly following the seven-step approach to PBL (Wijnia et al. Although the founders of PBL believed intuitively that working with problems would be more motivating for students than cramming in basic sciences knowledge delivered through lectures for years before seeing a patient, they had no scientific basis for making this assumption. The promise that PBL would be more intrinsically motivating than traditional educational approaches failed to stand up to scrutiny when looked at globally. However, beyond the prevalence of a laymen’s conception of motivation in the early literature, we have shown that the outdated measures employed in the majority of studies and the lack of nuances in studies that make use of contemporary theories could explain this result. PBL is a broad learning approach that combines many interacting elements, namely problems, tutors, group dynamics etc. Each of these individual elements can be of low or high quality, as can the manner in which they are arranged together. And all of these complex factors together can have an effect on students’ motivation. While motivation theory has increasingly endeavoured to reflect these complexities, this has not translated into the research on PBL which has tried and failed to capture motivation as a product of a sum-total PBL environment, rather than a combination of factors within PBL. To design motivating PBL environments will therefore require more focused research on the specific elements within PBL, with the understanding that whereas one specific combination of elements might yield overall greater motivation, another might not, even though both claim to take place in a PBL environment. Such research will not only benefit students, but it will also help the community of PBL scholars and practitioners focus somewhat more the definition of PBL and the elements that make it effective.