Personalized network inference on diverse clinical and in vitro model systems across cancer types can be used to delineate specific regulatory mechanisms, uncover drug targets and pathways, and develop individualized predictive models in cancer.We developed TransPRECISE , a multiscale Bayesian network modeling framework, to analyze the pan-cancer patient and cell line interactome to identify differential and conserved intrapathway activities, to globally assess cell lines as representative models for patients, and to develop drug sensitivity prediction models. We assessed pan-cancer pathway activities for a large cohort of patient samples  from the Cancer Proteome Atlas across ≥ 30 tumor types, a set of 640 cancer cell lines from the MD Anderson Cell Lines Project spanning 16 lineages, and ≥ 250 cell lines’ response to > 400 drugs.v 654 days of median overall survival; P = .02). High predictive accuracy was observed for drug sensitivities in cell lines across multiple drugs (median area under the receiver operating characteristic curve > 0.8) using Bayesian additive regression tree models with TransPRECISE pathway scores.TransPRECISE captured differential and conserved proteomic network topologies and pathway circuitry between multiple patient and cell line lineages: ovarian and kidney cancers shared high levels of connectivity in the hormone receptor and receptor tyrosine kinase pathways, respectively, between the two model systems. Our tumor stratification approach found distinct clinical subtypes of the patients represented by different sets of cell lines: patients with head and neck tumors were classified into two different subtypes that are represented by head and neck and esophagus cell lines and had different prognostic patterns (456 Our study provides a generalizable analytic framework to assess the translational potential of preclinical model systems and to guide pathway-based personalized medical decision making, integrating genomic and molecular data across model systems. The rapid accumulation of large-scale panomic molecular data across multiple cancers on patients , a network-based tool to assess pathway similarities between patients and cell lines at a sample-specific level. Using proteomic data across multiple tumor types, TransPRECISE identified several key pathways linking patient tumors and cell lines . Using predictive models trained on cell lines, TransPRECISE predicted high response rates for several known drug-cancer combinations .RelevanceThe TransPRECISE framework has potential use in identifying appropriate preclinical models for prioritizing specific drug targets across tumor types and in guiding individualized clinical decision making.18 It is well established that complex molecular networks and systems are formed by a large number of interactions of genes and their products operating in response to different cellular conditions and cell environments .19 To date, most, if not all, approaches to mechanism and drug discovery have been constrained by the biologic system21 (patients or cell lines), specific cancer lineage,23 or prior knowledge of specific genomic alterations.25 Hence, there is a critical need for robust analytic methods that integrate molecular profiles across large cohorts of patients and model systems from multiple tumor lineages in a data-driven manner to delineate specific regulatory mechanisms, uncover drug targets and pathways, and develop individualized predictive models in cancer.Complex diseases such as cancer are often characterized by small effects in multiple genes and proteins that are interacting with each other by perturbing downstream cellular signaling pathways.26 Using a large cohort of patients  from TCGA across ≥ 30 tumor types, we have shown that PRECISE identifies pan-cancer commonalities and differences in proteomic network biology within and across tumors, allows robust tumor stratification that is both biologically and clinically informative, and has superior prognostic power compared with multiple existing approaches.26 In this article, we present translational PRECISE , a generalization of the PRECISE framework, to establish the translational relevance of these pathway signatures. Briefly, TransPRECISE uses a multiscale Bayesian modeling strategy that infers de novo differential and conserved networks of intrapathway circuitry between the two biologic systems (patients and cell lines) for multiple cancers. Furthermore, it identifies cell-line “avatars” for patients based on pathway activities and develops machine learning–based predictive models for drug sensitivity in both cell lines and patients to potentially guide pathway-based individualized medical decision making. We have also developed an online, publicly available, comprehensive, interactive database and visualization tool of our findings, together with software code.27We have recently developed a network-based framework called PRECISE  to estimate cancer-specific networks, infer patient-specific networks, and elicit interpretable pathway-level signatures.5 (Data Supplement). TCPA offers reverse-phase protein array (RPPA)–based proteomics data sets, profiled using extensively validated antibodies to nearly 200 proteins and phosphoproteins. The functional space of the antibodies covers major functional and signaling pathways relevant to human cancers. For this work, we used a total of 12 pathways, including DNA damage response, epithelial–mesenchymal transition (EMT), hormone signaling, apoptosis, tuberous sclerosis complex/mammalian target of rapamycin (TSC/mTOR), and RAS/mitogen-activated protein kinase .We used a data set of 7,714 patient samples across 31 different cancer types available from TCPA;8 In a set of 640 cancer cell lines spanning 16 lineages, each cell line has RPPA expression data that are based on the same set of proteins as in the patient tumors (Data Supplement). In addition, we used drug sensitivity data from the GDSC6 database, with the sensitivity of 481 drugs assessed on a subset of 254 cell lines (Data Supplement). In this article, we will denote cell line samples in lowercase and patient samples in uppercase letters.We used RPPA-based protein expression data for cell lines available via the MCLP.The TransPRECISE implementation can be classified broadly into 3 modules . The firUsing the de novo cancer-specific population-level networks (from step 1 of TransPRECISE), we evaluated intrapathway edge rewiring (Data Supplement) across lineages of the two model systems to identify highly conserved and differential edges and to link patient and cell line tumor types by measuring intrapathway circuitry.We determined the extent to which protein-protein edges in each of the pathways were shared across tumor sites in the patients and the cell lines. We found highly conserved edges across lineages for both cell lines and patients  of a specific pathway network, we defined the connectivity score (CS) as the ratio of the observed number of edges in a given network to the total number of possible edges in the pathway, because more edges imply a higher level of cross-signaling within a pathway (Data Supplement). In addition, we quantified the level of significance for the observed CS value by comparing it with CS values obtained from random permutation of the network, called randomCS; lower values of randomCS provide evidence against the observed CS value being obtained under random chance (Data Supplement). On the basis of the randomCS, we evaluated the similarity between cell line and patient tumor types in terms of network cross-signaling. Specifically, we declared two lineages were similar for a pathway if both of them showed high levels of cross-signaling . Some key triplets of cell line/pathways/patient are summarized in We deconvolved the global population-level networks to obtain sample-specific pathway-level functional summaries of the proteomic crosstalk within a pathway; in other words, for a given pathway, each sample has three different scores for activated, neutral, and suppressed statuses of the pathway. For tumor stratification, we used the network aberration score, defined as the sum of the activated and suppressed TransPRECISE scores for each sample.For linking cell lines and patients, we computed the Pearson’s correlation for aberration score vectors (across 12 pathways) from each cell line–patient pair. The majority of the cell line–patient pairs for sarcoma-SARC (green), kidney-KIRC (light green), breast-BRCA (orange), and brain-LGG and -GBM  have patient tumors, whereas cluster C29 includes only cell lines . Cluster C4 showed a high level of fidelity in lineages between cell-line and patient tumor types; it includes 81% of ovary cell lines and 11% of patients with ovarian cancer (OV), 72% of head and neck cell lines and 38% of patients with HNSC, and 20% of pancreas cell lines (another 70% of them being located in C2 with notable aberration of the RAS/MAPK pathway) and 80% of patients with PAAD, exhibiting high aberration in apoptosis and DNA damage response pathways (Data Supplement). Within cluster C4, we observed significant correlations between the patient–cell line samples from ovary-PAAD, OV, BLCA, skin-PAAD, and head and neck–BLCA, HNSC (Data Supplement). More specifically, the HNSC samples were almost exclusively divided into the 2 clusters, C4 (n = 78 [38%]) and C15 (n = 122 [60%]), that include 38 head and neck cell lines (73%) and five esophagus cell lines (100%), respectively (Data Supplement). The co-occurrence of squamous cell carcinoma of the head and neck and esophageal cancer is not uncommon.30To find robust pan-cancer stratification across model systems, we applied hierarchic clustering using the complete linkage methodll lines , most ofP value of .02 (2 test P < .0001).We focused on a case study using only the head and neck cell lines in conjunction with all the patient samples from TCGA. As presented in e of .02 . The pat31 a machine learning method, to build predictive models from the network aberration scores for the 12 pathways. For each cancer, we fit BART, with drug response (sensitive or resistant), as a binary outcome and TransPRECISE scores as predictors, for the drugs having profiles of ≥ 10 cell lines for that cancer type.For the subset of cell lines in which drug sensitivity data are available (Data Supplement), we used Bayesian additive regression trees (BART),32We found that TransPRECISE scores conferred high predictive power, translating to high median test-set areas under the receiver operating characteristic curves (AUCs) across the lineages; all lineages had median AUCs > 0.8, with lung, breast, and colon being the top 3, having median AUCs > 0.9 (Data Supplement). From the radar plot summarizing the top pathway predictors across all drugs for each lineage , we obse34 For all the CORE patients, our model, trained on the colon cell lines for the drug lapatinib, predicts the true exposure correctly . Furthermore, for > 90% of the patients with OV, our model fitted on the ovary cell lines managed to correctly predict the response to the drug paclitaxel, which, by current standards, remains an integral part of the chemotherapeutic treatment of OV.37For each cell-line cancer lineage, for which the training models were fitted with the TransPRECISE pathway scores (as described earlier in the text), we predicted drug sensitivity in patient tumors within matched tissue types . We found drugs that had 100% response rates, especially in BRCA, CORE, LIHC, PAAD, and SKCM, some of which are under clinical investigations in their respective cancers (Data Supplement). For example, all patients with BRCA were predicted to be responsive to ibrutinib, which targets Bruton tyrosine kinase with RAS/MAPK, PI3K/AKT, and EMT as the top predictive pathways (Data Supplement). Using patient drug exposure data from the Gene-Drug Interactions for Survival in Cancer (GDISC) database, we evaluated the models’ predictive performances (Data Supplement).The investigation of patient tumors and cell line interactome offers insights into the translational potential of preclinical model systems. This requires the development of analytic models that capture the molecular heterogeneity of a cancer type in an unbiased manner and accurate calibration of aberrant biologic pathways. We propose TransPRECISE, a multiscale Bayesian network modeling framework, whose overarching goals are 3-fold: to identify differential and conserved intrapathway activities between two different model systems (patient tumors and cell lines) across multiple cancers; to globally assess cell lines as representative in vitro models for patients on the basis of their inferred pathway circuitry; and to build drug sensitivity prediction models for both cell lines and patients to aid pathway-based personalized medical decision making. To the best of our knowledge, TransPRECISE is the first computational approach that provides a conflation of these goals.38 prostate cancer subtype 1,39 hepatocellular carcinoma,40 and osteosarcoma.41 The aberration of the highly shared edge CTNNB1-SERPINE1 in the EMT pathway has been found to affect the growth of malignant cell masses in several cancers, including cancers of the gastric system,43 pancreatic cancer,44 and breast cancer.45 We also found a high degree of fidelity to their histologic sites between model systems based on the level of network cross-signaling .49 As additional validation, TransPRECISE implicated cross-signaling in the EMT pathway in SKCM and UCEC, which is expected because the SKCM cohort contains many metastatic samples50 and UCEC includes epithelial-like endometrioid samples as well as mesenchymal-like serous samples.51 TransPRECISE implicated the hormone receptor pathway in lung cancer, which is another known observation that is being studied for its translational potential.52 Our sample-specific inference of pathway activity provided robust tumor stratification across model systems that include distinct prognostic information –amplified breast cancers.56 Similarly, lapatinib, in combination with trastuzumab, has recently been tested clinically for HER2-amplified metastatic colorectal cancer.57Our Bayesian prediction models using the pathway scores on a cell line’s drug sensitivity provided high prediction accuracies  and selected cancer-specific pathway signatures in predicting drug response, such as hormone receptor–breast,58 open up the opportunity to include more proteins  in the network analyses, leading to a more global coverage of the proteomic crosstalk between model systems. Furthermore, the PRECISE26 pipeline, which lies at the core of TransPRECISE analyses, allows the integration of upstream regulatory information and multiomics layers such as mutations, copy number, methylation, and mRNA expression. These modalities can be leveraged for better and holistic rewiring of pathway circuitry. Finally, our framework can be applied, in principle, to emerging model systems, such as patient-derived xenografts60 and organoids,61 that allow better recapitulation of the human tumor microenvironment. In summary, TransPRECISE offers the potential to bridge the gap between human and preclinical models to delineate actionable cancer-pathway-drug interactions to assist personalized systems biomedicine approaches in the clinic.The TransPRECISE algorithm can be generalized to any disease system that provides matched genomic or molecular data on model and primary patient samples. For example, the transition from RPPA to other advanced high-throughput platforms and the development of databases, such as CPTAC,https://bayesrx.shinyapps.io/TransPRECISE/) that is a comprehensive database and visualization repository of our findings. All codes used in generating our results are available, along with the documentation, on https://github.com/bayesrx/TransPRECISE.We have created an online, publicly available R shiny app (available at


Research on gallbladder cancer (GBC) has been largely neglected and molecular GBC data is underrepresented in public databases. Cancer cell lines constitute a valuable tool to examine the mechanisms of malignant transformation and identify potential therapeutic targets. Here we use RNA sequencing to characterize 23 commercial hepatobiliary cancer cell lines, including ten GBC cell lines, and provide detailed mutation and gene expression data to the research community. We illustrate the practical utility of the released information by (1) assessing the presence of specific mutations in the investigated cancer cell lines, (2) comparing global gene expression patterns in cell lines and primary biliary tumours and (3) examining the expression levels of specific genes. The released data and showcase applications will ease the design of in vitro cell culture assays for future studies.Cancer cell lines allow the identification of clinically relevant alterations and the prediction of drug response. However, sequencing data for hepatobiliary cancer cell lines in general, and particularly gallbladder cancer (GBC), are sparse. Here, we apply RNA sequencing to characterize 10 GBC, eight hepatocellular carcinoma, and five cholangiocarcinoma (CCA) cell lines. RNA extraction, quality control, library preparation, sequencing, and pre-processing of sequencing data were implemented using state-of-the-art techniques. Public data from the MSK-IMPACT database and a large cohort of Japanese biliary tract cancer patients were used to illustrate the usage of the released data. The total number of exonic mutations varied from 7207 for the cell line NOZ to 9760 for HuCCT1. Researchers planning experiments that require TP53 mutations could use the cell lines NOZ, OCUG-1, SNU308, or YoMi. Mz-Cha-1 showed mutations in ATM, SNU308 presented SMAD4 mutations, and the only investigated cell line that showed ARID1A mutations was GB-d1. SNU478 was the cell line with the global gene expression pattern most similar to GBC, intrahepatic CCA, and extrahepatic CCA. EGFR, KMT2D, and KMT2C generally presented a higher expression in the investigated cell lines than in Japanese primary GBC tumors. We provide the scientific community with detailed mutation and gene expression data, together with three showcase applications, with the aim of facilitating the design of future in vitro cell culture assays for research on hepatobiliary cancer. Cancer cell lines constitute a valuable tool to examine the mechanisms involved in malignant transformation and to inspect and predict drug response . ImmenseCancer types that are common in low- and middle-income countries but relatively rare in industrialized regions, such as gallbladder cancer (GBC), are underrepresented in the existing databases. For example, the largest in vitro drug-screening database, which contains drug response data for more than 700 compounds tested on 1691 well-characterized cell lines derived from 41 different tissues, includes data for only three GBC cell lines [Hepatobiliary cancer comprises hepatocellular carcinoma  C22.0), intrahepatic cholangiocarcinoma , extrahepatic cholangiocarcinoma , and GBC (ICD-10 C23). Worldwide, hepatobiliary cancer represents the second leading cause of cancer-related death , with strong regional variation in incidence and mortality around the globe ,10.n = 10, HCC n = 8, CCA n = 5), aiming at providing detailed mutation and gene expression data to the research community. Furthermore, we also illustrate the usefulness of the released information in three typical experiment-planning applications: (1) assessment of the presence of specific mutations in the investigated hepatobiliary cancer cell lines; (2) comparison of the global gene expression patterns in the sequenced cell lines and in primary biliary tumors; (3) examination of the expression levels of particular genes in the cell lines. We anticipate that the released data and showcase applications will facilitate the design of in vitro cell culture assays for future studies and provide a valuable resource to the community.In the work described here, we applied next-generation RNA sequencing to comprehensively characterize 23 commercially available hepatobiliary cancer cell lines  and PolyPhen-2 pathogenicity predictions, is displayed in n = 19), eCCA (n = 40), and iCCA (n = 121) with RNA sequencing data investigated by Nakamura et al. [Researchers could also be interested in cell lines with global gene expression profiles similar to those of a given collection of primary tumors. To illustrate the use of the released data, we considered the sets of primary Japanese GBC  [EGFR, KMT2D, and KMT2C).In our third and last showcase application, we examine the expression of particular genes in the investigated gallbladder cancer cell lines. We exemplarily consider 12 genes previously reported to be altered in at least 10% of primary GBC tumors from Japanese patients  . We quanTP53 overexpression could use OCUG-1 and should not use TGBC1 or TGBC2. Researchers planning in vitro experiments that require TERT expression levels consistent with the average expression observed in Japanese primary GBC tumors could use Mz-Cha-1. Most genes and particularly EGFR, KMT2D, and KMT2C presented a higher expression in the investigated cell lines than in the GBC tumors; ERBB3 was somewhat of an exception. TPM expression values from RNA sequencing for all genes and all investigated cell lines are provided in Researchers planning in vitro experiments that require Cancer cell lines are valuable tools for researchers to investigate and identify mechanisms of cancer development and treatment success. In recent years, the (epi)genetic features of hundreds of cell lines were characterized, and the data were made publicly available, generating resources that greatly facilitate tumor modeling and drug screening. However, the existing resources focus mainly on common cancer types and do not support research into cancers such as GBC, considered an orphan disease.The present study provides mutation and transcriptome data for hepatobiliary cancer cell lines, with a special focus on GBC. We illustrate the scientific potential of the generated data with three showcase applications: (1) assessment of the presence of specific mutations in the investigated hepatobiliary cancer cell lines, (2) comparison of global gene expression patterns in the sequenced cell lines and in primary biliary tract cancers, and (3) examination of the expression levels of particular genes in the cell lines.NF1 and TP53, the mutation frequencies differed appreciably between GBC cell lines and primary GBC tumors. This also applies to three recently characterized GBC cell lines of Chilean origin, in which none of the genes most frequently altered in MSK-IMPACT and the study by Li et al. , GB-d1, Mz-Cha-1, NOZ, OCUG-1, OZ, SNU308, TGBC1 , TGBC2 , YoMi), eight HCC cell lines , and five CCA cell lines   21,22,2,222,24. l-glutamine and non-essential amino acids. All media and supplements were obtained from Sigma-Aldrich . The immortalized hepatocyte cell line HHT4 was provided by Curtis C. Harris and cultured as previously described [2 atmosphere.G-415, SNU308, HepG2, HuCCT1, SNU478, and TFK-1 cells were cultured in RPMI-1640 medium; GB-d1, Mz-Cha-1, OCUG-1, TGBC1, TGBC2, YoMi, HLE, HLF, HuH1, HuH6, HuH7, EGI-1, and KMCH were cultured in DMEM medium; Hep3B was cultured in MEM medium; OZ and NOZ cells were cultured in William’s E medium. All media were supplemented with 10% fetal bovine serum  and 1% penicillin–streptomycin (100 IU/mL and 100 g/mL). EGI-1 was additionally supplemented with escribed ,25. CellTotal RNA was extracted from cells with NucleoSpin RNA Kit  according to the manufacturer’s protocol. RNA integrity was determined using the Agilent RNA Nano 6000 chips on the Agilent Bioanalyzer 2100 system . In total, 1.5 µg of RNA was used to prepare RNA sequencing libraries. The libraries were generated using the NEBNext Ultra II Directional RNA Preparation Kit  in conjunction with the NEBNext Poly A Selection Module, and the NEBNext Multiplex Oligo’s for Illumina. Libraries were quantified on the Qubit  with the High Sensitivity DNA Assay and quality-checked with the DNA 1000 Chip on the Agilent Bioanalyzer. Using these values, samples were equimolar pooled and sequenced on the Illumina HiSeq 2500  in 125-bp paired-end mode.FastQC version 0.11.2  was usedRNA sequencing data of all investigated cell lines are deposited .n = 19) and excluded the second sample from one patient, who contributed with two samples, resulting in n = 18 MSK-IMPACT samples in total. The study by Li et al. comprised n = 32 samples. We also compared the global gene expression profiles of the investigated cell lines with the expression patterns of the collection of primary Japanese GBC, eCCA, and iCCA provided by Nakamura et al. [To illustrate the utility of the released mutation data, we considered the list of genes most frequently mutated in primary GBC tumors according to the MSK-IMPACT database and the study by Li et al. ,12. We ia et al. ,37. Finaa et al. ) in the ® Small RNA Library Prep Set for Illumina  with a cut size on the pippin prep  covering RNA molecules from 17 to 47 nucleotides, which enables capture of messenger RNA (mRNA) fragments as shown previously [TP53, TERT, ARID2, EGFR, CCND1, CCND3, ERBB2, KMT2D, KMT2C, TET1, TET2, TET3) based on RNA vs. small RNA sequencing data for the GBC cell lines.We also analyzed small RNA sequencing data, which were available for nine out of the 10 characterized GBC cell lines, to examine the robustness of results described in our second and third showcase applications. Small RNA sequencing was performed using NEBNexteviously . Librarieviously , and reaBased on the data generated in this study, researchers will be able to select cell lines that best reflect their experimental requirements from a selection of GBC, CCA, and HCC cell lines, all of which are available from commercial distributors. The RNA sequencing data described here will facilitate further studies into GBC, an orphan disease not only in research but also in public databases.


Drug Sensitivity Prediction using Logistic Matrix Factorization) approach based on Recommender Systems. DSPLMF focuses on discovering effective features of cell lines and drugs for computing the probability of the cell lines are sensitive to drugs by logistic matrix factorization approach. Since similar cell lines and similar drugs may have similar drug responses and incorporating similarities between cell lines and drugs can potentially improve the drug response prediction, gene expression profile, copy number alteration, and single-nucleotide mutation information are used for cell line similarity and chemical structures of drugs are used for drug similarity. Evaluation of the proposed method on CCLE and GDSC datasets and comparison with some of the state-of-the-art methods indicates that the result of DSPLMF is significantly more accurate and more efficient than these methods. To demonstrate the ability of the proposed method, the obtained latent vectors are used to identify subtypes of cancer of the cell line and the predicted IC50 values are used to depict drug-pathway associations. The source code of DSPLMF method is available in https://github.com/emdadi/DSPLMF.The ability to predict the drug response for cancer disease based on genomics information is an essential problem in modern oncology, leading to personalized treatment. By predicting accurate anticancer responses, oncologists achieve a complete understanding of the effective treatment for each patient. In this paper, we present DSPLMF ( Cancer is a genetic disease that results when cellular changes and accumulation of different types of mutations cause the uncontrolled growth and division of cells. There are more than 200 different types of cancer, having a significant global impact on public health. Since cancer is a disease of genetic complexity and diversity, the drug response for different patients can be different. The main reason for this occurrence is the difference in the molecular and genetic information of individuals, such as gene expression data, the type of mutation in the genome and copy number alteration information. These findings and achievements have recently made a significant challenge in the prediction of drug response for an individual patient in the research of precision medicine.High-throughput drug screening technologies on several panels of cancer cell lines have been provided. For instance, two recent consortiums Genomics of Drug Sensitivity in Cancer (GDSC) k-nearest neighbor (KNN) algorithm based on selected features that are achieved by information entropy In the challenge of the DREAM project, the performance of 44 drug response prediction algorithms was considered for breast cancer cell lines. The introduced algorithms were evaluated using the weighted probabilistic c-index (WPC-index) and resampled Spearman correlation In this paper, we modeled the cancer drug sensitivity problem based on “Recommender Systems” approach. A logistic matrix factorization algorithm was used for predicting drug cancer response. By applying the proposed model to GDSC and CCLE datasets, we proved that DSPLMF is of excellent prediction accuracy.k-nearest cell lines. At the end, 555 cell lines and 98 drugs remain without any missing value for GDSC and 363 cell lines and 24 drugs for CCLE datasets.The performance of drug response prediction algorithms was evaluated on two benchmark datasets, including GDSC and CCLE. The datasets were downloaded by using R package PharmacoGx The main idea of the model DSPLMF is to construct a classification model for predicting how a cell line responds to a drug. Since drug response can be divided into two classes “sensitivity” and “resistance,” there are many ways for the purpose of classification based on IC50 values. By considering the histograms of IC50, we observed some histograms are normal-like, and others have skewness. Also, it can be supposed that the labels of classes should be determined by the data of individual drugs. For normal-like histograms, median, and mean are the same. If the histogram is skewed right, the mean is greater than the median, and if the histogram is skewed left, the mean is smaller than the median. We chose medium because we wanted to set a single, universal standard threshold for all drugs. So, the strategy introduced by In the first step, by converting the model to a classification problem, a 0,1 observation matrix was achieved, as cell lines and drugs are rows and columns of the matrix, respectively. Then, a logistic matrix factorization method for constructing the latent vectors for each cell line and drug is applied. In the second step, for improving the prediction accuracy of the model, the similarity information for cell lines and drugs are used. In the third step, a model is applied to learn to predict the probability that a new cell line would sensitive to a drug. Subsequently, with applying the threshold to predicted probabilities of the cell line-drug pairs, we classified each pair to sensitive or resistance class. In the next section, first the similarity matrices used in the model, were introduced and then the details of each step are explained in the following steps. The main scheme of DSPLMF algorithm is represented in In this part, the four similarities between each pair of cell lines based on the information of gene expression, single-nucleotide mutation, copy number alteration, and IC50 values were defined.Gene expression Similarity, Simexp Gene expression information is an auxiliary feature for similarity between cell lines. Let ei denoted the gene expression vector of cell line ic in cancerous conditions. For pair of cell lines ic and jc, expSim is defined as the Pearson correlation between the vectors ie and je and the gene expression similarity matrix between cell lines considered as expSim = [expSim]n×n, where n is the numbers of cell lines. Each entry of these metrics is in . The numbers of considered genes for two datasets GDSC and CCLE for similarity measure are 11,712 and 19,389, respectively. So the length of vector ie is 11,712 and 19,389 for GDSC and CCLE dataset, respectively.Q[SpecialChar] Verify that all the equations and special characters are displayed correctly.Single-nucleotide mutation Similarity, Simmut Let zero-one vectors im indicate that whether a mutation occurred in the set of genes for cell line ic or not. mutSim is defined as the Jaccard similarity between the vectors jm and jm and the single-nucleotide mutation similarity matrix between cell lines considered as mutSim = [mutSim]n×n. Each entry of these metrics is in . The mutation information of 54 genes are accessible for cell lines in GDSC dataset and 1667 genes for cell lines in CCLE dataset, respectively.Copy number alteration Similarity, Simcnv Let iv denoted the copy number alteration vector for cell line ic. cnvSim is defined as the Pearson correlation between the vectors iv and jv and the copy number alteration similarity matrix between cell lines considered as cnvSim = [cnvSim]n×n. Each entry of these metrics is in . The information of copy number alteration of 24,959 and 24,960 genes for two GDSC and CCLE datasets are accessible, respectively.IC50 value Similarity, SimIC50 Moreover, the similarity between cell lines proposed by iIC denoted the vector of IC50 values of drugs in cell line ic. ICSim50 is defined as the Pearson correlation between the vectors iIC and jIC and the similarity based on IC50 matrix between cell lines considered as ICSim50 = [ICSim50]n×n and each element of these metrics in .totalSim = [ijSC]n×n, the following formula is used:To aggregate these similarities to a single matrix, λ, ϕ and ψ are parameters that represent the importance of each of the matrix and tuned in the model. The numbers of considered genes for two datasets GDSC and CCLE for expSim are 11,712 and 19,389, respectively. The mutation information of 54 genes is accessible for cell lines in GDSC dataset and 1,667 genes for cell lines in CCLE dataset. The information of copy number alteration of 24,959 and 24,960 genes for two GDSC and CCLE datasets are accessible, respectively. Since three matrices expSim, cnvSim, and mutSim have been constructed by different sets of genes (the number of common genes between them is about 50%), there is not an additive relation between them. In general, an absolute correlation coefficient of >0.7 among two or more predictors indicates the presence of collinearity. But as where γ, id and jd. Similarity  is considered as Jaccard similarity between these two vectors. We construct the matrix drugSim = [ijSD]m×m as similarity matrix between each pair of drugs.Since it is expected that similar drugs have the same effect on cell lines, drug similarity information for predicting drug response was used in the proposed method. A drug can be represented as a binary feature vector, by using drug substructures, drug transporters, drug targets, drug enzymes, drug pathways, drug indications, or drug side effects information. Since there is only information about chemical substructures, for each drug we have a zero-one vector of size 881, where 881 is the number of known chemical substructures of a drug. In this vector one indicates the presence of a substructure of drug and zero otherwise. We downloaded the substructure for each drug from PubChem. The PubChem system generates a binary substructure fingerprint for chemical structures. These fingerprints are used by PubChem for similarity neighboring and similarity searching. Let c1, c2, …, nc } and the set of drugs is denoted by D = {d1, d2, …, md }, where n and m are the numbers of cell lines and the numbers of drugs, respectively. The relationship between cell lines and drugs are represented by a binary matrix Q = [ijq]n×m, where each element ijq ∈ {0, 1}. If a cell line is ic sensitive to a drug jd, ijq = 1 and otherwise ijq = 0. The probability of sensitivity of a cell line to a drug is defined by a logistic function as follows:Assume the set of cell lines is denoted by C = {iu nd jv are the latent vectors of size L corresponding to i-th cell line and j-th drug, respectively and the latent vectors of all cell lines and all drugs are denoted by U and V, respectively. On the other hands, the non-negative values cβ ∈ ℝn × 1 and dβ ∈ ℝm × 1 as bias vectors for cell lines and drugs, respectively. Bias parameters are considered because some cell lines respond significantly to many drugs and there are cell lines that respond to few drugs. Similarly for some drugs, there are many cell lines that respond to them, and there are drugs that most cell lines do not respond to significantly. Thus, by applying these parameters, we try to reduce bias. The vectors where Q occurred, considering the latent and bias vectors, can be computed as:In this model, all the data in the training set are assumed to be independent. So the probability that matrix ijq = 1 then both r (1 – ijq) and 1 – ijq are zero. Similarly, when ijq = 0, ijrq = ijq = 0. So, formula 3 is rewritten as follows:When Finally, the above probability is shown as follows:r ≧̸ 1) is used to control the importance levels of observed interactions. In some classification problems with two classes (0 and 1), lack of information make us to assign label zero to some objects. But, it may be that the real label of these objects are one. So, the members of class one are highly trusted, while some members assign to class zero because of lack of information. As an example, in drug-target prediction or drug-drug interaction prediction models, the observed interacting drug-target pairs or drug-drug pairs have been experimentally verified; thus, they are more trustworthy and important than the unknown pairs. Toward more accurate modeling for these prediction models, the authors can assign higher importance levels to the interaction pairs than unknown pairs. This importance weighting strategy (considering r > 1) has been demonstrated to be effective for personalized recommendations. On the other hand, in DSPLMF model, both classes (sensitivity and resistance) have the same importance and validity. So, we set r to be one.Where  that denotes the k-most similar cell lines to c (except ic) using totalSim matrix. We constructed adjacency matrix ij]nxnA= m×m, where;Similarly, for a drug B is an m × m matrix, which for the row corresponding to drug id, the entries of columns corresponding to the k-most similar drugs of id are obtained from their similarities, drugSim matrix, and the other elements of this row are zero.k = 5 and 24 drugs in CCLE dataset, the similarity matrix B is denoted in i of the matrix has five nonzero elements corresponding to the five-most similar drugs of id in drugSim matrix, and the other elements are zero. In To illustrate the data structure of these similarity matrices, as an example, for i and vectors of its nearest neighbors in latent space, we minimize two objective functions in formulas 15, 16 as follows:To minimize the distance between feature vector corresponding to cell line cE and dE and α and β are two parameters for weighting the similarity between cell lines and drugs, respectively.Where tr(.) is the trace of a matrix, A and B show the similarity of the cell lines to each other and the similarity of the drugs to each other, respectively. Using the calculation of Frobenius norm multiplied by the elements in A and B is because of we would like more similar cell lines (drugs), have closer latent vectors in the latent space. But, the parameters α and β determine the effectiveness of these two matrices A and B in the objective function. By these strategies and tuning the parameters α and β, we determine the impact of cell line similarity and drug similarity in DSPLMF method.The values of two matrices By plugging two equations 15, 16 into formula 12, we will have the following:Finally, we upgrade the formula 17 as follows:By this function, we try to predict the latent vectors of cell lines and drugs, where the similar cell lines or drugs have closer latent vectors to their KNNs.U and V and V and U and Y and the partial gradients of biases and latent vectors are calculated as follow:For optimization the above function, the alternating gradient descent method was used. In each iteration of this algorithm, first U and V and the biases i to drug j can be estimated by logistic function in formula 2. Since in our model, the importance of the positive observations and negative observations are the same, we set r = 1 in this logistic function.Once the latent matrices IC50Sim matrix values cannot be calculated, while it must be calculated to predict the latent vectors of this new cell line. In this section, we introduced a classification model for predicting t-most nearest neighbors by using the similarity values between cell lines which are obtained from gene expression profile, copy number alteration and single-nucleotide mutation information. The purpose of this model is to find t-most nearest neighbors for the new cell line and then to estimate the latent vector for this new cell line based on average of latent vectors of its neighbors. After obtaining the latent vector, we can predict the IC50 values across all drugs for the new cell line. For training the model, 10-fold cross validation technique is used on cell line dataset, so the dataset was partitioned into 10 equal-sized subsets, nine subsets were used as the train set for learning this classification model. A single subset was used as the test set to predict the t-most nearest neighbors for each cell line of this set.When a new cell line is given, its information of IC50 of the drugs is unknown and IC50Sim matrix of train set were converted to 0 or 1. To do this, the values of each row of the matrix are sorted in descending order and then t-largest values are set to 1 and remaining values are set to 0. Among the methods available for classification, we chose “Decision Tree Classifier” method. It is one of the predictive modeling approaches that used tree models to predict the value of a target variable based on several input features. Where leaves represent class labels and branches denote conjunctions of features that lead to those class labels. Learned trees can be represented as sets of if-then rules. Decision tree classifier is a heuristic and nonbacktracking search through the space of all possible decision trees. The main idea of decision tree classification is recursively partition data into subgroups. The functionality of decision tree classification is as follows: Polat and Güneş (2007)In this classification model, the amounts of Choosing an attribute and formulating a logical attribute test.Branching on each test result, transferring subset of examples (training information) to the appropriate child node to satisfy that result.Running each child’s node recursively.The end rule indicates when a leaf node is to be declared.expSim, cnvSim, and mutSim, are considered as input and 0 or 1 value of each pair  are considered as output and then as the classifier train. If the number of predicted nearest neighbors for a cell line was less than t, we considered them as nearest neighbors for this cell line. If this number was greater than t, t neighbors were selected randomly. Finally, iu was estimated as the average of latent vectors of neighbors of the new cell line ic.For decision tree classifier, the three features of train set, When the latent vector of the new cell line is predicted, the probabilities that this cell line is sensitive to drugs are estimated. Eventually, a threshold on probabilities to assign sensitive or resistance class to each cell line-drug pair is applied. So if the predicted value is lower than this threshold for a cell line-drug pair, the resistance class is assigned to it; otherwise, it is labeled as a sensitive class.We empirically evaluate our proposed approach and compare it against some of the state-of-the-art methods. This section first describe evaluation criteria and then demonstrate the performance of DSPLMF method.To evaluation the performance of DSPLMF method, the 10-fold cross-validation Was performed and this process was repeated 30 times. The mean of following criteria was obtained in the 30 times and it was used as the final criteria to evaluate the predictive performance of the methods.TP or true positive prediction is the number of cell lines labeled with sensitivity and predicted as sensitivity. TN or true negative is the number of cell lines labeled with resistance and predicted as resistance. FP or false positive is the number of cell lines labeled with resistance and predicted as sensitivity. FN or false negative is the number of cell lines labeled with sensitivity and predicted as resistance.where AUC), which is one of the most important evaluation metrics for checking the performance of any classification model. This metric was calculated for the methods.In addition to the above metrics, we used area under the receiver operating characteristic curve  in equation 13 and the number of t-nearest neighbors in prediction section, were selected from 1 to 50 by step 2. The impact factors of nearest neighbors α and β in equations 15 and 16 were picked from {2–5, 2–4,…, 22} and the variance controlling parameters, λc and λd, were chosen from{2–5, 2–4,…, 21}. The γ, λ, ϕ and ψ parameters represent the importance of each similarity measure between cell lines in formula 1 and were selected from 1 to 10. Threshold parameter applied on equation 2 for determining the label of the class for each new cell line ic, and was picked from 0.1 to 1 by step 0.1, and the best accuracy of the result is obtained by threshold=0.6.The dimension of latent space, L, that is calculated for CCLE dataset separately and for this dataset it is set as 23.In 1v, 2v and 3v are three latent vectors obtained DSPLMF method of length 95 corresponding to Breast cancer cell line BT − 20, Breast cancer cell line BT − 549 and hematopoietic cancer cell line CA46, respectively. 1v = 1x95, 2v = 1x95 and 3v = 1x95. Similarity  = 0.78, Similarity  = 0.13 and Similarity = 0.04. As the results show, two vectors belonging to the same tissue types are more similar than two vectors that belong to two different tissue types. Also, in the t-SNE plot, these two vectors belonging to the same tissue types are closer than two vectors that belong to two different tissue types.We used 555 cell lines from different cancerous tissue types in GDSC dataset. For representing the higher similarity between latent vectors In For inferring drug-pathway associations, the heatmap of Pearson correlation between predicted drug responses and pathway activity scores similar to PW is a pathway and G(PW) = {g1,g2,…, rg} is the set of genes corresponding to pathway PW. Let fold-change value of ig in cell line jc is ijx, which is obtained by:Let PW for cell line jc, j(PW)PAS was calculated by formula 22.Pathway activity score of pathway PW for all cell line, PAS(PW), are considered as the vector PAS(PW) = , where n is the numbers of cell lines. Also, the predicted drug responses by DSPLMF for each drug were considered as the vector IC50predicted = .Pathway activity score of jd and pathway PW is computed by the Pearson correlation between predictedIC50 for drug jd and PAS(PW). A positive correlation indicates that a pathway plays a role in drug resistance and negative correlation demonstrated that a pathway is important in drug sensitivity. The result of the Pearson correlation of 30 pathway gene sets and 24 drugs of CCLE dataset is shown in Then, the association between drug The activation score of the HDAC (Histone deacetylases) pathway is negatively correlated (assistant association) with predicted IC50 value of some drugs such as Panobinostat. These observations were consistent with two studies, showing that the Panobinostat can inactive HDAC pathway We observed the RELA (Acetylation and Deacetylation of RelA in The Nucleus) pathway had an assistant association with the 17-AAG (HSP90 inhibitor) drug. The RELA gene is one member of the NF-kB family and two important roles of the RELA are the transcriptional regulation and NF-kB signed transduction. Since the 17-AAG drug affects the NF-kB activity, it also affects the RELA gene and RELA pathway EGFR − SMRTE pathway was negatively correlated with predicted IC50 value of four EGFR inhibitors drugs, namely, Lapatinib, Erlotinib, Vandetanib, and AZD0530. These observations matched the previous study that denoted the amplification of the EGFR gene is correlated with a high response to EGFR inhibitors The activation score of the The MTA3 (Downregulated of MTA-3 in ER-negative Breast Tumors) pathway was associated (positively correlated) with two predicted IC50 vectors belong to L-685458(gamma-secretase) and PD-0332991(CDK4/6) drugs. Therefore, the cell lines with inactivated MTA3 pathway tend to sensitive to these two drugs RAF/MEK/ERK pathway by RAF inhibitors The VEGF-Hypoxia-Angiogenesis (VEGF) pathway was assistance associated with two RAF inhibitors drugs, namely, PLX4720 and RAF265 drugs that were verified in the previous researches. One of these studies considered inducing the VEGF expression by Raf promotes angiogenesis and blocking The activity score of the mTOR Signaling Pathway that is a central regulator of metabolism and physiology was negatively correlated with predicted Ic50 vector of some drugs such as Panobinostat. Various preclinical studies have been performed to combine panobinostat with several drugs as mTOR inhibitor It has been shown that c-met inhibitor drugs such as PHA-665752 and Crizotinib can inhibit WNT pathway activity in tumour cells. We observed the activity score of this pathway was negatively correlated with predicted IC50 vectors of these drugs L − 685458 drug and IGF-1 MTOR pathways. These observations were also reported by Shih et al The assistant association was observed between AZD6244 and PD − 0325901 were positively correlated with activity scores for the EIF2 pathway. Therefore, as mentioned in the previous researches, the cell lines with inactivated EIF2 p athway were sensitive to these drugs We observed that the MEK inhibitors such as In this work, we introduce a novel method for cancer drug sensitivity prediction based on a recommender system approach. A logistic matrix factorization is applied to predict the extent to which a cell line is sensitive to a drug. The advantage of this method is to obtain latent features of cell lines and drugs for better prediction performance. Since the similarity information of cell lines and drugs can improve higher predictive power, some information such as gene expression profile, copy number alteration and single-nucleotide mutation data for cell lines and Chemical structures of drugs are used.To demonstrate the validity of DSPLMF method for identifying drug response 10-fold cross validation on CCLE and GDSC datasets are performed. The comparison of DSPLMF with six other the state-of-the-art prediction methods showed that DSPLMF outperformed other methods. The results indicated that the proposed method was able to uncover much more effective features than the other methods for drug response prediction.Datasets folder for GDSC and CCLE datasets as input data are available in https://github.com/emdadi/DSPLMF and The source code of proposed method and AE designed the algorithm, performed the experiments, and wrote the main manuscript text and the programming codes. CE conducted the experiments and analyzed the results. All authors reviewed the manuscript.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.


K-ras are frequently found in pancreatic cancers, but these have not yet been targeted by cancer therapies. The Keap1-Nrf2 system plays a crucial role in mediating the oxidative stress response, which also contributes to cancer progression. Nrf2 activation reprograms the metabolic profile to promote the proliferation of cancer cells. A recent report suggested that K-ras- and Nrf2-active lung cancer cells are sensitive to glutamine depletion. This finding led to the recognition of glutaminase inhibitors as novel anticancer agents. In the current study, we used murine pancreatic cancer tissues driven by mutant K-ras and p53 to establish cell lines expressing constitutively activated Nrf2. Genetic or pharmacological Nrf2 activation in cells via Keap1 deletion or Nrf2 activation sensitized cells to glutaminase inhibition. This phenomenon was confirmed to be dependent on K-ras activation in human pancreatic cancer cell lines harboring mutant K-ras, i.e., Panc-1 and MiaPaCa-2 in response to DEM pretreatment. This phenomenon was not observed in BxPC3 cells harboring wildtype K-ras. These results indicate the possibility of employing Nrf2 activation and glutaminase inhibition as novel therapeutic interventions for K-ras mutant pancreatic cancers.Pancreatic cancer remains intractable owing to the lack of effective therapy for unresectable cases. Activating mutations of  KIT mutation and ALK translocation, respectively, have led to better prognosis [K-ras mutations [The prognosis of pancreatic cancer remains dismal despite improved diagnostic methods. The main reason for this is the lack of effective therapies for advanced pancreatic cancer. Targeted therapies for driver mutations in several types of malignancies such as gastrointestinal stromal tumor and non-small-cell lung cancer with rognosis ,2. Howevutations , but theutations  and protutations .Nrf2 in mouse models with mutant K-ras‒driven pancreatic cancer resulted in reduced formation of pancreatic lesions [Keap1—or degradation-resistant mutations—in Nrf2—resulting in the constitutive stabilization of Nrf2 [The Keap1-Nrf2 system is pivotal in the maintenance of normal tissue structure and organ protection from oxidative stress. Conformational changes in Keap1 induced by cellular reactive oxygen species and electrophiles result in the nuclear accumulation of Nrf2, a transcription factor that induces the expression of cytoprotective genes . The del lesions ,8. A few of Nrf2 ,10. Dire of Nrf2 .Keap1 promotes activating mutant K-ras-driven, glutaminolysis-dependent lung cancer development [Activation of Nrf2 affects cellular metabolism. The aberrant accumulation of p62/Sequestosome 1 (Sqstm1) stabilizes Nrf2 in hepatocellular carcinoma. This stabilization reprograms glucose and glutamine metabolism, and results in the development of resistance against chemotherapeutic agents . Similarelopment . Severalelopment  and bis-elopment . The antelopment  and viceelopment , suggestKeap1 deletion, which resulted in the constitutive activation of Nrf2. These cell lines were more susceptible to glutaminase inhibitors than cell lines lacking Keap1 and Nrf2. Furthermore, pretreatment of mutant K-ras expressing mouse and human pancreatic cancer cell lines with diethyl malate (DEM), an electrophilic stress inducer, sensitized the cells to a glutaminase inhibitor. These data suggest that the combination of an Nrf2 activator and a glutaminase inhibitor might serve as an effective therapeutic approach for pancreatic cancer.To clarify whether Nrf2 activation has similar effects in pancreatic cancer, we established mouse pancreatic cancer cell lines harboring LSL-G12D/+::p53LSL-R172H/+::Keap1fl/fl::Nrf2+/−Pdx-1-Cre::K-ras (KPC::K0N1) and LSL-G12D/+::p53LSL-R172H/+::Keap1fl/fl::Nrf2−/−-Pdx-1-Cre::K-ras (KPC::K0N0) mice developed invasive pancreatic cancers  within 90 days of birth. We established cell lines expressing constitutively activated Nrf2 or with Keap1/Nrf2 deletion from these pancreatic cancer tissues using a pre-established protocol [−/−-mice‒derived pancreatic cancer cell lines (KPCN lines 1 and 2) lack Nrf2, and KPC::K0N0 mice-derived cell lines (K0N0 line 1 and 2) lacked both Nrf2 and Keap1 expression.protocol . As showglutathione S-transferase M1 (Gstm1) and a cytoskeletal gene cytokeratin 19 in the K0N1 and K0N0 lines. The K0N1 cell lines exhibited higher expression of Gstm1 compared with K0N0 lines  also exhibited pancreatic atrophy . These results with the current observations suggest that excess Nrf2 activation might be associated with certain disadvantages in pancreatic lineage cells. In contrast to that in KPC::Keap1 CKO mice, Keap1 deletion in mice expressing liver-specific mutant K-ras and p53 resulted in accelerated cholangiocarcinoma development [K-ras and activation of Nrf2 might suppress the unknown machinery that is indispensable for cell survival in pancreatic cancer. The organ-specific role of Nrf2 activation might be a specific target in pancreatic cancer.Interestingly, constitutive activation of Nrf2 in the K0N1 cell lines did not induce tumorigenicity in vivo. We had previously reported that pancreas-specific mutant  atrophy . This phelopment . TherefoK-ras mutant lung cancer, Nrf2 also contributes to glutamine dependence, which could be a therapeutic target [Glutamine deprivation in pancreatic cancer cells has recently attracted attention as a therapeutic strategy . The curc target . Glutamic target . The curc target . Togethec target  and are c target , respectAnti-histone H3 antibody (4499) and peroxidase-conjugated anti-rabbit antibody (7074) were purchased from Cell Signaling Technology . Anti-Nrf2 and anti-Keap1 antibodies have been described previously ,25. Pero−/−Nrf2 and fl/flKeap1 mice have been described previously [Pdx-1-Cre, LSL-G12D/+K-ras, and LSL-R172H/+p53 mice were obtained from the NCI Mouse Repository  [LSL-G12D/+::p53LSL-R172H/+::Keap1fl/fl::Nrf2+/−Pdx-1-Cre::K-ras (KPC::K0N1) and LSL-G12D/+::p53LSL-R172H/+::Keap1fl/fl::Nrf2−/−Pdx-1-Cre::K-ras (KPC::K0N0). The mice were fed standard chow and water ad libitum. They were treated according to the Guidelines for Proper Conduct of Animal Experiments proposed by the Ministry of Education, Culture, Sports, Science, and Technology of Japan. This study was approved by the Institutional Animal Care and Use Committee of the Tohoku University Environmental & Safety Committee (Article no. 2019MdA-154).eviously ,27. Pdx-MD, USA) . These m−/−-mouse‒derived pancreatic cancer cell lines (KPCN lines 1 and 2) have been described previously [2.Panc-1, MIAPaCa-2, and BxPC3 cells (human pancreatic cancer cell lines) were purchased from ATCC . KPC-mouse‒derived pancreatic cancer cell lines (KPC lines 1 and 2) and KPC::Nrf2eviously . Pancreaβ-actin  [cytokeratin 19  [Gstm1  [Total RNA was extracted from each cell line using the RNeasy kit . One microgram of RNA was subjected to cDNA synthesis using SuperScript VIRO Master Mix . The expression of genes of interest was quantified using the StepOnePlus real-time PCR system (Thermo Fisher Scientific) and Fast SYBR Green Master Mix (Thermo Fisher Scientific) with the following primers: ATGT-3′) , cytokerAAGC-3′) , and GstTCTC-3′) .For total cell lysates, cells were lysed in RIPA buffer. The nuclear fraction was prepared using NE-PER Nuclear and Cytoplasmic Extraction Reagents (Thermo Fisher Scientific). Lysates were electrophoresed using NuPAGE 8% Bis-Tris Gel (Thermo Fisher Scientific) and transfered onto Immobilon-P Membrane . Membranes were incubated overnight at 4ºC with primary antibody (1:1000 dilution). After incubation with peroxidase-conjugated antibody (1:3000 dilution), reactive bands were detected using ECL Western blotting detection reagents .Cells were seeded in 96-well plate at 5000 cells/well density. After 24 h of incubation, cellular growth was assessed by bromodeoxyuridine (BrdU) incorporation assay by using cell proliferation ELISA (Sigma-Aldrich) according to the manufacture’s protocol.Cells were seeded in 96-well plates (5000 cells/well). Cells were treated with 100 μM DEM for 24 h, followed by treatment with 0°−10 μM of CB-839 or BPTES for 48 h. Cell viability was measured using the 3--2,5-diphenyltetrazolium bromide (MTT) assay. After incubating cells with 5 μg/mL MTT for 2 h, dimethyl sulfoxide was added (50 μL/well). The optical density was measured using a spectrophotometer  at a wavelength of 570 nm, with a reference wavelength of 690 nm.p < 0.05 was considered significant. The error bars show standard deviations.Statistical analysis was performed using the JMP Pro 15 software . The differences between more than two groups were analyzed using the Tukey–Kramer method.


The intramural the National Cancer Institute (NCI) and more recently the University of Texas Southwestern Medical Center with many different collaborators comprised a complex, multi‐disciplinary team that collaborated to generated large, comprehensively annotated, cell‐line related research resources which includes associated clinical, and molecular characterization data. This material has been shared in an anonymized fashion to accelerate progress in overcoming lung cancer, the leading cause of cancer death across the world. However, this cell line collection also includes a range of other cancers derived from patient‐donated specimens that have been remarkably valuable for other types of cancer and disease research. A comprehensive analysis conducted by the NCI Center for Research Strategy of the 278 cell lines reported in the original Journal of Cellular Biochemistry Supplement, documents that these cell lines and related products have since been used in more than 14 000 grants, and 33 207 published scientific reports. This has resulted in over 1.2 million citations using at least one cell line. Many publications involve the use of more than one cell line, to understand the value of the resource collectively rather than individually; this method has resulted in 2.9 million citations. In addition, these cell lines have been linked to 422 clinical trials and cited by 4700 patents through publications. For lung cancer alone, the cell lines have been used in the research cited in the development of over 70 National Comprehensive Cancer Network clinical guidelines. Finally, it must be underscored again, that patient altruism enabled the availability of this invaluable research resource. Infographic showing the results of a comprehensive analysis conducted by the NCI Center for Research Strategy of the downstream use of the 278 human cancer cell lines reported in the original cell line supplement issue (1). These cell lines and related meta data were available to the research community across the world at the cost of only shipping and cell line curation. The research and care impact of this access over the ensuing twenty five years was determined for publications, citations, supporting grants, patents and linked clinical trials. Lung cancer deaths were peaking in the 1970s and 1980s based on cigarette smoking patterns. This prompted the National Cancer Institute (NCI) to develop an intramural program to study and treat patients with this deadly disease. The work was part of intramural NCI research conducted first at the NCI‐Veteran's Administration Medical Oncology Branch (NCI‐VA Branch) at the Washington, DC VA Medical Center (1975) and then at the NCI‐Navy Medical Oncology Branch (NCI‐Navy Branch) at the National Naval Medical Center  in Bethesda, MD. Both branches were established under NCI leadership efforts of the former NCI Director, Vincent DeVita. The Branches were part of the NCI's Division of Cancer Treatment's Clinical Oncology Program (Directed at that time by Samuel Broder and Gregory A. Curt). Because military veterans had a high rate of lung cancer related to a high rate of cigarette smoking and lung cancer has a high mortality rate, lung cancer was the dominant focus of the Branch's research.A major product of these studies was the generation of hundreds of lung cancer cell lines. In addition, the investigators at this government‐sponsored program were interested in the open sharing of research tools arising from this translational effort. In 1996, a supplemental issue of the Journal of Cellular Biochemistry described the cell line collection including relevant clinical, molecular, and biological data on over 300 human cancer cell lines (with a focus on lung cancer), along with 40 autologous B‐cell lymphoblastoid lines arising from some of the same patients who donated the lung cancer specimen .Now, more than two decades after that supplement was published, there is still an avid interest in the articles contained in that publication. In light of this persistent interest, it was decided to look back over the intervening time since the Supplement to assess what was the extent of utilization of the cell line resource and further to evaluate what was the impact of those activities.1.2The lung tumor cell line generation and characterization effort were combined team efforts that involved laboratory and clinical scientists and focused on comprehensive studies of patients entered onto IRB‐approved NCI clinical trials. Thus, after informed patient consent, detailed clinical data were collected; tumor samples and other patient biosamples from multiple protocol approved staging procedures in use at the time  were obtained as permitted. The types of staging, imaging, biospecimen procurement procedures, and patient confidentiality issues have evolved from 1975 until the present, but the investigators used state of the art approaches for the time. The effort over a 16‐ year period (1975‐1991) involved Adi Gazdar, John Minna, Paul Bunn, Daniel Ihde, Martin Cohen, Desmond Carney, Mary Matthews, Byron Fossieck, George Higgins, Geraldine Schecter, Betty Fishman, James Mulshine, James Batty, Frank Cuttitta, Bruce Johnson, Edward Sausville, Eli Glatstein, Harvey Pass, Jack Roth, Barry Kramer, Ilona Linnoila, Marion Nau, and Stephen Veach as well as many other Navy Hospital Bethesda and NCI faculty, NCI Clinical Oncology Program fellows in training , and Navy Hospital Bethesda house officers. This multi‐disciplinary “team” included clinical and laboratory investigators, had pathologists, medical, surgical, and radiation oncologists, hematologists, dermatologists, cellular and molecular biologists, nurses, statisticians as well as cell biologists skilled in mammalian cell culture.An innovative strategy of the NCI‐VA and NCI‐Navy Branches was to encourage tight interprofessional interaction among all experts. This included the embedded presence of lung pathologists in all aspects of research which fostered a deep understanding of lung pathological classification within the framework of tumor biology. A more systematic approach to lung cancer research resulted in a focused exploration of lung preneoplasia and cancer histologic types from the perspective of neuroendocrine, adenomatous, and/or squamous differentiation across the spectrum from normal epithelium to metastatic disease. This disease orientation seeking to elucidate mechanistic scaffolding for cancer progression has now been clearly pursued across many international research teams but the integrated approach was novel in the 1970s and 1980s.1.3A key aspect of this study dynamic was the linkage of the Branch to the NCI intramural oncology training program which was a common initial starting point for many of the Branch faculty. An integral part of the fellowship experience was that all trainees participated in a laboratory project over the final 2 years of their fellowship. The Branch's research productivity was enhanced by a steady flow of highly motivated oncology fellows populating the research environment along with numerous visiting international clinicians and scientists who came to spend 1 to 3 years working on doctoral or post‐doctoral research projects.In the NCI research setting, systematic experimental research was enabled by the availability of tumor‐derived cell lines. The generation of tumor cell lines permitted more extensive molecular and genomic characterization and the ability to test patient materials for sensitivity to potentially active chemotherapeutic agents and then relating those results back to the actual response of the patients to their various treatments. Many of the Branch research protocols conducted during that time included this correlative activity. While lung cancer research was the principal focus at the Branch, the approach to other diseases including cutaneous T‐cell lymphoma involved a similar strategy in the handling of the biopsied specimens. As a result, hundreds of human cell lines and related products, mostly of lung cancer origin, were derived from human clinical specimens to enable further research to improve patient outcomes for their cancers. Using the best available cell culture methods available at the time, efforts were also put into place to protect patient confidentiality for patient research volunteers.2Research activities of the Branch performed after Institutional Review Board approval involved collecting clinical information that was attached to the patient biospecimens obtained and then for the related cell lines generated. Typically, this included age, gender, associated medical conditions, smoking history, performance status, histopathologic diagnosis, tumor stage, and prior cancer treatment. The pathologic diagnoses of tissue specimens and cytology of cellular specimens were defined by a panel of pathologists including Mary Matthews, Adi Gazdar, and Ilona Linnoila through a collegial consensus process. These studies also led them to teach and lead interactions with other pathologists around the world concerning the study of the evolving state of lung cancer histopathologic diagnosis. On a more direct teaching note, these highly regarded pathologists reviewed “at the microscope” individual patient diagnoses with the actual physicians responsible for caring for each of the patients, thus teaching nonpathologists about the methods and importance of histologic diagnoses. A team of highly engaged research nurses obtained and recorded information which was then validated by the Study Principal Investigator. Data team meetings followed presentations of each individual's case at the Branch's' weekly, multi‐disciplinary tumor board attended by all members of the research team. The novel, hierarchical data management system used at the Branch, was developed by Marc Serber, Memorial Sloan Kettering Cancer Center and through a highly productive and sustained interaction with Branch coordinated by Ruby Phelps. The data system could be flexibly deployed with predefined data elements required for a particular protocol while enabling efficient storage and reanalyses the accreted metadata.2.1Adi Gazdar established a working relationship with Robert J. Hay of the American Type Culture Collection (ATCC) in Rockville, MD to coordinate an efficient mechanism of cell line storage and dissemination by the ATCC of the hundreds of derived cell lines.The seminal paper of Hanahan and Weinberg crystalized the shared “hallmark” features of cancers providing a rational framework for new prevention and therapeutic approaches.https://dtp.cancer.gov/discovery_development/nci‐60/cell_list.htm.The work of Shoemakerhttps://portals.broadinstitute.org/ccle) “COSMIC” cell line database  (https://cancer.sanger.ac.uk/cell_lines), and The EMBL Human Expression Atlas (https://www.ebi.ac.uk/gxa/experiments/E‐MTAB‐2770/Results).An important feature of cell lines as research tools is that they can be cryopreserved, stored, and available for continuous propagation at different times when experimentally needed, and then be distributed on request to other laboratories. This allowed access to the same cell line with defined biochemical or molecular features to laboratories across the world to enable reproducible experiments to be performed, reported and then independently validated. While there was undoubtedly some “drift” in characteristics of individual tumor cell lines as they were propagated around the world, an important featured turned out to be the stability of their major oncogenotype (DNA mutations) and messenger RNA (mRNA) expression characteristics. An easy way to compare these aspects is to look at mutation and expression data in multiple large databases such as the Cell Line Encyclopedia‐CCLE, COSMIC, CellExpress, EMBL‐EBI Expression Atlas, The Human Protein Atlas, CellLineNavigator, Gene Expression Database of Normal and Tumor Tissues). For example, hundreds of labs have found the main driver mutations  to remain stable in the tumor cell lines. A study comparing molecular features of 12 non‐small‐cell lung carcinoma (NSCLC) cell lines established at UTSW and corresponding primary tumor tissues demonstrated that NSCLC cell lines in the large majority of instances retain the properties of their parental tumors for lengthy culture periods, and they appear very representative of the lung cancer tumor from which they were derived.2.2https://app.dimensions.ai/discover/publication). Full‐text search was employed as the cell lines used in an experiment may have discussed only in the methods or results section of an article and these publications would be excluded from a search based only on title and abstract. To reduce the number of false positives, the search was limited to publications that included the phrase “lung cancer” in the full text or those that had been classified as Lung Cancer using the Dimensions RCDC category scheme. In addition, the returned results were limited to articles that were classified as Cancer using either the Fields of Research or Dimensions RCDC categories. Publications were included if they were indexed in Dimensions before 30 April 2019. A total of 322 cell lines were searched, and 278 cell lines were associated with one or more publications using this method.To quantitatively assess the impact of the specimens donated by the NCI VA/Navy patients, we searched for publications associated with each of the 322 cell lines from the NCI/UTSW‐Cell Line Database, which were identified using full text search for the name of the cell line (such as NCI‐H460 or H460) in the Dimensions publication database . A full table of impact by cell line is provided in Data S1 and hosted as a Database.As a full text search was utilized, the search was likely to include articles that did not primarily employ the cell line. For example, results might have included articles that cite a publication with the cell line name in the title of the reference as well as articles that mention prior work with the cell line in the introduction. Publications are linked to a clinical trial using two methods. First, the publication can reference the clinical trial. This occurs for example when the publication includes results from the clinical trial. Second, the clinical trial can reference the publication when the trial is registered on A comprehensive analysis conducted by the NCI Center for Research Strategy of the 278 cell lines reported in the original supplement identified more than 33 207 published scientific reports that have used these cell lines along with 14 057 awarded grants. The research impact of these publications is summarized in Figure As an objective measure of the collective influence of the cell lines, the number of scientific outputs, such as publications that resulted from research that utilizes the cell lines is impressive. Table Overall for all cell lines, publications involving one or more of the cell lines have been cited over 1.2 million times. As multiple cell lines may be mentioned in an individual paper, one can also consider the sum of citations to the individual cell lines to understand the value of the resource collectively rather than individually; this method leads to 2.9 million citations. In addition, these publications have been linked to 422 clinical trials and cited by 4700 patents. Collectively these results highlight the impact that these cell lines have had on the scientific community (as indicated by the number of publications and citations) as well as on the translation of research to the clinic .The global impact of the resource can be seen in Figure Finally, it is also important to note that upwards of 10 cell lines were developed from rare tumors including mesothelioma for which there are only 3000 cases per year in the United States. To date, there have been over 200 peer reviewed publications involving the most frequently used mesothelioma cell lines representing various tumor histologies  H28, H2052, H2373, and H2452.2.3In 1991, John Minna and Adi Gazdar moved from the NCI to the University of Texas Southwestern (UTSW) taking the cell line collection with them. There they joined forces with the UT MD Anderson Cancer Center (MDACC) team led by Jack Roth and secured an NCI Specialized Center of Research Excellence (SPORE) grant in lung cancer. This large NCI‐driven translational program gave new opportunities for collaborations with other Lung Cancer SPOREs and lung cancer investigators in the USA and abroad. The tumor cell line repository became a central part of the SPORE Pathology Core which was co‐led by Dr. Gazdar at UTSW and Dr. Ignacio Wistuba at MDACC. The further molecular characterization of the tumor lines as part of the SPORE and their use resulted, for example, in seminal discoveries of the mutational landscape in lung cancer that would drive the development of targeted therapies representing a new era in lung cancer treatment.2.4Elegant examples of the impact of the role of the intramural cell lines are cited with the molecular analysis on epidermal growth factor receptor (EGFR) mutations and anaplastic lymphoma kinase (ALK) rearrangements.The identification of ALK rearrangements was initially done in childhood anaplastic lymphomas.2.5More recently, the lung cancer cell lines were used in exploring the relationship of oncogenotypes (like TSK11/LKB1 mutations to resistance to checkpoint inhibitors) to new immunotherapies for lung cancer.Now, 25 years after the publication of the original cell line supplement, we are seeing an unprecedented acceleration of drugs approved for precision medicine as well as general therapeutic applications in cancer, and especially in lung cancer. The direct impact of NCI cell lines on the current standard of cancer care can be also measured by screening all references in the National Cancer Comprehensive Network (NCCN) Clinical Practice Guidelines in Oncology. These guidelines are the recognized standard for clinical policy in cancer care and are the most detailed and most frequently updated clinical practice guidelines available in any area of medicine. As of March 2019, there were 71 references related to these cell lines in the NCCN Guidelines. The Guidelines are adopted by many countries and thus today hundreds of thousands of patients from all over the world benefit from the legacy of the NCI cell line work, all over the world.2.6The NCI‐VA Medical Oncology Branch developed a number of clinical trials for patients with cutaneous T‐cell lymphomas (CTCLs) which included mycosis fungoides and the Sezary syndrome. Because the branch members were interested in growth factors that stimulated cancer growth and progression and because their cell biology models included the establishment of cultures of human cancer cells, the cells from CTCL patients were cultured. Originally, conventional techniques were used but resulted in no continuous cultures in 32 specimens from 25 patients.2.7The NCI‐VA Branch began in the mid‐seventies, just as the public moved beyond discussing cancer in hushed voices and advocacy organizations started to emerge. More specifically, this was the beginning of cancer lineage (organ)‐specific organizations (such as breast and prostate cancer) that enabled patients to take an active and directive role, not only in their own treatment but in public health policy and research. It was during this time that donating cell lines and tumor tissue became a uniquely personal way of contributing to the presidentially mandated “War on Cancer” ‐ the popular name given to the National Cancer Act of 1971. For lung cancer patients, isolated by the sheer lethality of the disease often coupled with the burden of blame from smoking cigarettes, donating their clinical specimen for research purposes to generate a cell line was considered by many to provide a meaningful and positive legacy. While now the role of tobacco companies in manipulating nicotine content to increase addiction is public knowledge now, 25‐years ago, the majority of the newly diagnosed lung cancer patients at the NCI had already stopped smoking. We are now more aware of the stigma associated with the diagnosis of lung cancer and this great burden is even experienced by individuals with lung cancer who have never smoked. It is painful to remember that at the time of these studies that both the lay and scientific public put the “blame” for lung cancer on the patients themselves. In this environment, many patients spontaneously expressed that it gave them solace to think that in the future, others could thus be spared the devastating consequences of their cancer through their donation of clinical specimens and access to clinical data.2.7.1Now more than two decades after reporting the initial cell line supplement issue, we want to provide stewardship by reporting on the impact of the contribution of patients who did enroll in the relevant intramural clinical trials described in this manuscript and gave informed consent for participation in the clinical protocols and sample collection for research. Since an important motivation for many study participants was their hope that knowledge derived from the research study may help them or others in the future experience better outcomes, we are now reporting objective feedback on the impact of their donation. In addition, the commitment made to patient study subjects in Bethesda was to share all the materials and knowledge generated by the research, as broadly as possible. We are also now reporting data to reflect how widely available these patient donations were shared. From the objective analysis data in this report, it is clear that thousands of other investigators from around the world had access to the cell line and related research material, either directly from the NCI‐VA or NCI‐Navy Branches, from the large collection was deposited at the American Type Culture Collection (ATCC), or from the investigators who had worked in past at the NCI‐ Navy Branch. The initial step to address the issue of wide and equitable dissemination of the cell lines and associated data was the Cell‐line Supplement developed in collaboration with the American Type Tissue Culture Collection and the Journal of Cellular Biochemistry.For the advocacy community frustrated with the cost and slow pace of innovation, patient‐derived cell lines have an important dimension. Many aspects of conducting cancer research are expensive. Through the work of these NCI branches led by Gazdar and colleagues, hundreds of cell lines were made available at the modest cost of shipping and handling charges, which makes internationally impactful biomedical research possible. Through the Gazdar arrangement with the American Type Tissue Culture Collection, all of the carefully curated, NCI cell lines were economically, efficiently distributed to the entire international cancer research community. This aspect of the cell line legacy is indeed respectful of the broadly shared donor‐intent to maximize the public good of this patient donated material. Fortunately, this dimension of cancer research equity persists to this day and with the evolution of sub‐collections of cell lines sharing particular mutational signatures, cell lines have become an aspect of the “lingua franca” of international cancer research, especially in regard to precision medicine for lung cancer.2.7.2Patients today are more educated about their cancers and are aware that extraordinary advances have taken place in recent decades with early detection, diagnosis, and treatment of all types of cancers and diseases. However, most of society is not aware that patent‐donated specimens in general and derived cell lines in particular, along with the shared associated clinical information aggregated in open research databases have been foundational in enabling the rapid pace and the extent of these biomedical transformations, especially in lung cancer. In this regard, the impact of the altruistic gestures of previous clinical research participants has been overlooked. An important motivation for the analysis contained in this manuscript is to celebrate the importance of the many patients who so generously supported the NCI‐VA and NCI‐Navy open research efforts.Tracking the history of these cell lines, from concept discussions at NCI in 1975, to the specific breakthroughs that underpin so many major advances emanating from them since then will establish an accurate record, recognize the foresight of Drs. Gazdar, Minna and all their translational research colleagues, and gives donor patients and the families of donor patients the satisfaction of knowing that they have indeed made an important difference in cancer innovation. This recognition reinforces the motivation for ongoing patient participation in research including the donation of their data and specimens.2.7.3Another important aspect of this analysis is reporting the strategic yield of the sustained NIH investment that enabled this remarkable productive global research resource. As shown in Figure Equally important was the NIH establishing a path for these tumor lines to be used by the pharmaceutical industry. Remember at the time the cell line project was initiated, there was not the current degree of interaction between Academia and PharmaBiotech nor the support by the Federal Government in getting NIH funded discoveries into the PharmaBiotech “pipeline” for generating new medicines. The decision by the US Government in the 1980s for such an effort was timely and expanded the impact of the NCI Branches' efforts to generate and share the tumor lines/data resource. The NIH/NCI coordinated and led the effort to widely license the tumor lines to PharmaBiotech and in so doing provided PharmaBiotech with a clear path to use the tumor lines for their discovery efforts. Without this licensing, discovery efforts would have been held back. The multi‐disciplinary team that achieved these milestones collaborated for close to fifty years. These activities involved vibrant interactions with leading researchers across the world in both industry and academia. The largely federally‐funded research focus evolved in a sustain, strategic, and scientific opportunity‐driven fashion that was then leveraged to assist targeted drug therapy efforts of PharmaBiotech. It is an open question as to whether the reported outcomes represent appropriate dividends for continuous taxpayer investment? We convey the information in this report to inform the discussion regarding the value of long‐term federal support of biomedical research.2.8For the recently deceased, Adi Gazdar, establishing cancer cell lines was a means to an end. Early in his career, Adi Gazdar sought to collect sufficient cancer cell mass to perform a systematic analysis of the cancer cells for specific features. For example, having sufficient tumor cells to evaluate for in vitro drug sensitivity was an important research activity. Successfully generating a cell line to enable extensive and repetitive testing as well as sharing of the cell line so that other collaborators could interrogate the same cancer cells for their purpose. In a longtime collaboration with John Minna, this activity was continued for many decades in the intramural program of the National Cancer Institute (1975‐1991) and then moved to the University of Texas Southwest Medical Center in Dallas (1991‐2018). Over that period of time, Professor Gazdar published over 800 publications that were cited over 110 000 times including many published jointly with John Minna. Professor Gazdar mentored over 100 post‐doctoral fellows and he established an ongoing translational research fellowship award with the International Association for the Study of Lung Cancer, which he personally funded.3The tumor cell line database has expanded over the years to include generation of breast cancer, ovarian cancer, colon cancer, and rare tumor type cell lines, and a large panel of immortalized lung epithelial cell lines.Christine Burgess: I have the following conflicts as related to below with management plan. I am currently employed by Digital Science, the maker of the Dimensions publication database utilized in the analysis; Bruce Johnson: I receive post‐marketing royalties from Dana‐Farber for EGFR mutation testing Paid Consultant for Novartis, Hengrui Therapeutics, and Foundation Medicine Steering Committee for Novartis and Array Biopharma Research Support from Novartis and Cannon Imaging; John D. Minna: Has received cell line licensing royalties from the NCI and cell line licensing royalties from the University of Texas Southwestern Medical Center; Jack A. Roth: Consultant, stock owner (including pending patents) in Genprex, Inc.; Harvey I. Pass: Dr. Pass receives royalties from the NCI for the licensing of cell lines described in this manuscript; Ignacio Wistuba: Advisory Board: Genentech/Roche, Bayer, Bristol‐Myers Squibb, Astra Zeneca/Medimmune, Pfizer, HTG Molecular, Asuragen, Merck, GlaxoSmithKline, Guardant Health and MSD. Speaker: Medscape, MSD, Genentech/Roche, Pfizer. Research support: Genentech, Oncoplex, HTG Molecular, DepArray, Merck, Bristol‐Myers Squibb, Medimmune, Adaptive, Adaptimmune, EMD Serono, Pfizer, Takeda, Amgen, Karus, Johnson & Johnson, Bayer, Iovance, 4D, Novartis, and Akoya.Dr. Mulshine had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All thirteen authors have all been substantially involved in the conceptualization, writing, and review of this manuscript. All have reviewed the final contents of the manuscript and accept the responsibility of authorship.Table of impact by cell line is provided and hosted as a DatabaseClick here for additional data file.