scholarly journals A Non-Negative Matrix Tri-Factorization based Method for Predicting Antitumor Drug Sensitivity

2021 ◽  
Author(s):  
Sara Pidò ◽  
Carolina Testa ◽  
Pietro Pinoli

AbstractLarge annotated cell line collections have been proven to enable the prediction of drug response in the preclinical setting. We present an enhancement of Non-Negative Matrix Tri-Factorization method, which allows the integration of different data types for the prediction of missing associations. To test our method we retrieved a dataset from CCLE, containing the connections among cell lines and drugs by means of their IC50 values. We performed two different kind of experiments: a) prediction of missing values in the matrix, b) prediction of the complete drug profile of a new cell line, demonstrating the validity of the method in both scenarios.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e14544-e14544
Author(s):  
Eva Budinska ◽  
Jenny Wilding ◽  
Vlad Calin Popovici ◽  
Edoardo Missiaglia ◽  
Arnaud Roth ◽  
...  

e14544 Background: We identified CRC gene expression subtypes (ASCO 2012, #3511), which associate with established parameters of outcome as well as relevant biological motifs. We now substantiate their biological and potentially clinical significance by linking them with cell line data and drug sensitivity, primarily attempting to identify models for the poor prognosis subtypes Mesenchymal and CIMP-H like (characterized by EMT/stroma and immune-associated gene modules, respectively). Methods: We analyzed gene expression profiles of 35 publicly available cell lines with sensitivity data for 82 drug compounds, and our 94 cell lines with data on sensitivity for 7 compounds and colony morphology. As in vitro, stromal and immune-associated genes loose their relevance, we trained a new classifier based on genes expressed in both systems, which identifies the subtypes in both tissue and cell cultures. Cell line subtypes were validated by comparing their enrichment for molecular markers with that of our CRC subtypes. Drug sensitivity was assessed by linking original subtypes with 92 drug response signatures (MsigDB) via gene set enrichment analysis, and by screening drug sensitivity of cell line panels against our subtypes (Kruskal-Wallis test). Results: Of the cell lines 70% could be assigned to a subtype with a probability as high as 0.95. The cell line subtypes were significantly associated with their KRAS, BRAF and MSI status and corresponded to our CRC subtypes. Interestingly, the cell lines which in matrigel created a network of undifferentiated cells were assigned to the Mesenchymal subtype. Drug response studies revealed potential sensitivity of subtypes to multiple compounds, in addition to what could be predicted based on their mutational profile (e.g. sensitivity of the CIMP-H subtype to Dasatinib, p<0.01). Conclusions: Our data support the biological and potentially clinical significance of the CRC subtypes in their association with cell line models, including results of drug sensitivity analysis. Our subtypes might not only have prognostic value but might also be predictive for response to drugs. Subtyping cell lines further substantiates their significance as relevant model for functional studies.


Author(s):  
Akram Emdadi ◽  
Changiz Eslahchi

Predicting tumor drug response using cancer cell line drug response values for a large number of anti-cancer drugs is a significant challenge in personalized medicine. Predicting patient response to drugs from data obtained from preclinical models is made easier by the availability of different knowledge on cell lines and drugs. This paper proposes the TCLMF method, a predictive model for predicting drug response in tumor samples that was trained on preclinical samples and is based on the logistic matrix factorization approach. The TCLMF model is designed based on gene expression profiles, tissue type information, the chemical structure of drugs and drug sensitivity (IC 50) data from cancer cell lines. We use preclinical data from the Genomics of Drug Sensitivity in Cancer dataset (GDSC) to train the proposed drug response model, which we then use to predict drug sensitivity of samples from the Cancer Genome Atlas (TCGA) dataset. The TCLMF approach focuses on identifying successful features of cell lines and drugs in order to calculate the probability of the tumor samples being sensitive to drugs. The closest cell line neighbours for each tumor sample are calculated using a description of similarity between tumor samples and cell lines in this study. The drug response for a new tumor is then calculated by averaging the low-rank features obtained from its neighboring cell lines. We compare the results of the TCLMF model with the results of the previously proposed methods using two databases and two approaches to test the model’s performance. In the first approach, 12 drugs with enough known clinical drug response, considered in previous methods, are studied. For 7 drugs out of 12, the TCLMF can significantly distinguish between patients that are resistance to these drugs and the patients that are sensitive to them. These approaches are converted to classification models using a threshold in the second approach, and the results are compared. The results demonstrate that the TCLMF method provides accurate predictions across the results of the other algorithms. Finally, we accurately classify tumor tissue type using the latent vectors obtained from TCLMF’s logistic matrix factorization process. These findings demonstrate that the TCLMF approach produces effective latent vectors for tumor samples. The source code of the TCLMF method is available in https://github.com/emdadi/TCLMF.


2020 ◽  
Author(s):  
Evanthia Koukouli ◽  
Dennis Wang ◽  
Frank Dondelinger ◽  
Juhyun Park

AbstractCancer treatments can be highly toxic and frequently only a subset of the patient population will benefit from a given treatment. Tumour genetic makeup plays an important role in cancer drug sensitivity. We suspect that gene expression markers could be used as a decision aid for treatment selection or dosage tuning. Using in vitro cancer cell line dose-response and gene expression data from the Genomics of Drug Sensitivity in Cancer (GDSC) project, we build a dose-varying regression model. Unlike existing approaches, this allows us to estimate dosage-dependent associations with gene expression. We include the transcriptomic profiles as dose-invariant covariates into the regression model and assume that their effect varies smoothly over the dosage levels. A two-stage variable selection algorithm (variable screening followed by penalised regression) is used to identify genetic factors that are associated with drug response over the varying dosages. We evaluate the effectiveness of our method using simulation studies focusing on the choice of tuning parameters and cross-validation for predictive accuracy assessment. We further apply the model to data from five BRAF targeted compounds applied to different cancer cell lines under different dosage levels. We highlight the dosage-dependent dynamics of the associations between the selected genes and drug response, and we perform pathway enrichment analysis to show that the selected genes play an important role in pathways related to tumourgenesis and DNA damage response.Author SummaryTumour cell lines allow scientists to test anticancer drugs in a laboratory environment. Cells are exposed to the drug in increasing concentrations, and the drug response, or amount of surviving cells, is measured. Generally, drug response is summarized via a single number such as the concentration at which 50% of the cells have died (IC50). To avoid relying on such summary measures, we adopted a functional regression approach that takes the dose-response curves as inputs, and uses them to find biomarkers of drug response. One major advantage of our approach is that it describes how the effect of a biomarker on the drug response changes with the drug dosage. This is useful for determining optimal treatment dosages and predicting drug response curves for unseen drug-cell line combinations. Our method scales to large numbers of biomarkers by using regularisation and, in contrast with existing literature, selects the most informative genes by accounting for drug response at untested dosages. We demonstrate its value using data from the Genomics of Drug Sensitivity in Cancer project to identify genes whose expression is associated with drug response. We show that the selected genes recapitulate prior biological knowledge, and belong to known cancer pathways.


2019 ◽  
Author(s):  
Rene Quevedo ◽  
Nehme El-Hachem ◽  
Petr Smirnov ◽  
Zhaleh Safikhani ◽  
Trevor J. Pugh ◽  
...  

ABSTRACTBackgroundSomatic copy-number alterations that affect large genomic regions are a major source of genomic diversity in cancer and can impact cellular phenotypes. Clonal heterogeneity within cancer cell lines can affect phenotypic presentation, including drug response.MethodsWe aggregated and analyzed SNP and copy number profiles from six pharmacogenomic datasets encompassing 1,691 cell lines screened for 13 molecules. To look for sources of genotype and karyotype discordances, we compared SNP genotypes and segmental copy-ratios across 5 kb genomic bins. To assess the impact of genomic discordances on pharmacogenomic studies, we assessed gene expression and drug sensitivity data for compared discordant and concordant lines.ResultsWe found 6/1,378 (0.4%) cell lines profiled in two studies to be discordant in both genotypic and karyotypic identity, 51 (3.7%) discordant in genotype, 97 (7.0%) discordant in karyotype, and 125 (9.1%) potential misidentifications. We highlight cell lines REH, NCI-H23 and PSN1 as having drug response discordances that may hinge on divergent copy-number qConclusionsOur study highlights the low level of misidentification as evidence of effective cell line authentication standards in recent pharmacogenomic studies. However, the proclivity of cell lines to acquire somatic copy-number variants can alter the cellular phenotype, resulting in a biological and predictable effects on drug sensitivity. These findings highlight the need for verification of cell line copy number profiles to inform interpretation of drug sensitivity data in biomedical studies.


PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0238757
Author(s):  
Sofia P. Miranda ◽  
Fernanda A. Baião ◽  
Julia L. Fleck ◽  
Stephen R. Piccolo

Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug responses for a given cell line based on molecular features may help to optimize drug-development pipelines and explain mechanisms behind treatment responses. In this study, we focus on DNA methylation profiles as one type of molecular feature that is known to drive tumorigenesis and modulate treatment responses. Using genome-wide, DNA methylation profiles from 987 cell lines in the Genomics of Drug Sensitivity in Cancer database, we used machine-learning algorithms to evaluate the potential to predict cytotoxic responses for eight anti-cancer drugs. We compared the performance of five classification algorithms and four regression algorithms representing diverse methodologies, including tree-, probability-, kernel-, ensemble-, and distance-based approaches. We artificially subsampled the data to varying degrees, aiming to understand whether training based on relatively extreme outcomes would yield improved performance. When using classification or regression algorithms to predict discrete or continuous responses, respectively, we consistently observed excellent predictive performance when the training and test sets consisted of cell-line data. Classification algorithms performed best when we trained the models using cell lines with relatively extreme drug-response values, attaining area-under-the-receiver-operating-characteristic-curve values as high as 0.97. The regression algorithms performed best when we trained the models using the full range of drug-response values, although this depended on the performance metrics we used. Finally, we used patient data from The Cancer Genome Atlas to evaluate the feasibility of classifying clinical responses for human tumors based on models derived from cell lines. Generally, the algorithms were unable to identify patterns that predicted patient responses reliably; however, predictions by the Random Forests algorithm were significantly correlated with Temozolomide responses for low-grade gliomas.


Molecules ◽  
2018 ◽  
Vol 23 (12) ◽  
pp. 3172 ◽  
Author(s):  
Angelika A. Adamus-Grabicka ◽  
Magdalena Markowicz-Piasecka ◽  
Michał B. Ponczek ◽  
Joachim Kusz ◽  
Magdalena Małecka ◽  
...  

The aim of this study was to determine the cytotoxic effect of 3-arylidenechromanone (1) and 3arylideneflavanone (2) on HL-60 and NALM-6 cell lines (two human leukemia cell lines) and a WM-115 melanoma cell line. Both compounds exhibited high cytotoxic activity with higher cytotoxicity exerted by compound 2, for which IC50 values below 10 µM were found for each cell line. For compound 1, the IC50 values were higher than 10 µM for HL-60 and WM-115 cell lines, but IC50 < 10 µM was found for the NALM-6 cell line. Both compounds, at the concentrations close to IC50 (concentration range: 5–24 µM/L for compound 1 and 6–10 µM/L for compound 2), are not toxic towards red blood cells. The synthesized compounds were characterized using spectroscopic methods 1H- and 13C-NMR, IR, MS, elemental analysis, and X-ray diffraction. The lipophilicity of both synthesized compounds was determined using an RP-TLC method and the logP values found were compared with the theoretical ones taken from the Molinspiration Cheminformatics (miLogP) software package. The mode of binding of both compounds to human serum albumin was assessed using molecular docking methods.


Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1623-1623 ◽  
Author(s):  
Karen Dybkær ◽  
Hanne Due ◽  
Rasmus Froberg Brøndum ◽  
Ken H. Young ◽  
Martin Bøgsted

Background: Patients with Diffuse large B-cell lymphoma (DLBCL) in approximately 40% of cases suffer from primary refractory disease and treatment induced immuno-chemotherapy resistance demonstrating that standard provided treatment regimens are not sufficient to cure all patients. Early detection of resistance is of great importance and defining microRNA (miRNA) involvement in resistance could be useful to guide treatment selection and help monitor treatment administration while sparing patients for inefficient, but still toxic therapy. Concept and Aims: With information on drug-response specific miRNAs, we hypothesized that multi-miRNA panels can improve robustness of individual clinical markers and serve as a prognostic classifier predicting disease progression in DLBCL patients. Methods: Fifteen DLBCL cell lines were tested for sensitivity towards rituximab (R), cyclophosphamide (C), doxorubicin (H), and vincristine (O). Cell line specific seeding concentrations was used to ensure exponential growth and each cell line was subjected to 16 concentrations in serial 2-fold dilutions and number of metabolic active cells was evaluated after 48 hours of drug exposure using MTS assay. For each drug, we ranked the cell lines according to their sensitivity and categorized them as sensitive, intermediate responsive, or resistant. Differential miRNA expression analysis between sensitive and resistant cell lines identified 43 miRNAs to be associated with response to compounds of the R-CHOP regimen, by selecting probes with a log fold change larger than 2. Baseline miRNA expression data were obtained for each cell line in untreated condition, and differential miRNA expression analysis identified 43 miRNAs associated to response to R-CHOP. Using the Affymetrix HG-U133+2 platform, expression levels of the miRNA precursors were assessed in 701 diagnostic DLBCL biopsies, and miRNA-panel classifiers were build using multiple Cox regression or random survival forest. Results: Generated prognostic miRNA-panel classifiers were tested for predictive accuracies and were subsequently evaluated by Brier scores and time varying area under the ROC curves (tAUC). Progression-free survival (PFS) was chosen as the outcome, since it is a treatment evaluation parameter as closely as possible to the time of drug exposure and the tested miRNAs were all associated directly to drug specific response. Furthermore, overall survival (OS) was used for verification of findings. Comparison of analyses conducted for the respective cohorts (All DLBCL, ABC, and GCB patients) showed the lowest prediction errors for all models within the GCB subclass with a multivariate Cox miRNA-panel model including miR-146a, miR-155, miR-21, miR-34a, and miR-23a~miR-27a~miR-24-2 cluster performed the best and successfully stratified GCB-DLBCL patients into high- and low-risk of disease progression. In addition, combination of the miRNA-panel and international prognostic index (IPI) substantially increased prognostic performance in GCB classified patients, indicating a prognostic signal from the response-specific miRNAs independent of IPI. In conclusion: We found as proof of concept that adding gene expression data detecting drug-response specific miRNAs to the clinically established IPI improved the prognostic stratification of GCB-DLBCL patients treated with R-CHOP. Disclosures No relevant conflicts of interest to declare.


Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 1394-1394
Author(s):  
Mitsuteru Hiwatari ◽  
Jingqiu Dai ◽  
Wei Liu ◽  
Yu-Dong Zhou ◽  
Dale G. Nagle ◽  
...  

Abstract Quassinoids are natural product compounds known to possess tumor cytotoxicity and antimalarial activity. Neosergiolide and isobrucein B are two quassinoids previously isolated from roots and stems of Picrolemma sprucei. In screening studies to identify inhibitors that target STAT3, we discovered neosergeolide and isobrucein B as active compounds. Approximately 5000 plant-derived extracts were screened using a cell line that stably expresses a STAT3-dependent luciferase reporter and NPM-ALK, which constitutively induces STAT3 transcriptional activity. Of 25 total hits, a P. sprucei extract was potent and selective for STAT3 inhibition, and bioassay-guided isolation identified neosergeolide and isobrucein B as the inhibitory compounds. Western blot analysis confirmed that neosergeolide and isobrucein B not only inhibit the tyrosine phosphorylation and activation of STAT3 but also decrease total STAT3 protein levels via a mechanism due in part to enhanced proteasome-mediated degradation. Small-molecule proteasome inhibitors such as MG132 and ALLN reversed the ability of the two quassinoids to decrease STAT3 protein levels; furthermore, simultaneous incubation of various hematopoietic malignancy cell lines with either neosergeolide or isobrucein B and MG132 or ALLN antagonized the cytotoxic activity of the quassinoids. Assessment of neosergiolide and isobrucein B antitumor effects using an XTT assay revealed both compounds to possess potent cytotoxic activity across a broad spectrum of hematopoietic malignancies, with T-leukemias/lymphomas being especially responsive. For example, mycosis fungoides (MF)- and Sezary syndrome (SS)-derived cell lines, as well as non-MF/SS cutaneous T-cell lymphoma (CTCL) lines, were potently inhibited by both quassinoids (neosergiolide IC50 values: MAC-1, 11.6 nM; MAC-2A, 6.9 nM; Hut-78, 6.6 nM; HH, 4.3 nM; MJ, 7.0 nM; isobrucein B IC50 values: MAC-1, 31.9 nM; MAC-2A, 72.3 nM; Hut-78, 23.5 nM; HH; 20.3 nM; MJ, 13.5 nM). Non-hematopoietic cell lines representing various solid tumors also exhibited potent cytotoxic responses to the quassinoids (e.g., gastric carcinoma line AGS [neosergiolide IC50: 16.9 nM; isobrucein B IC50: 114.9 nM]). With rare exceptions, the cytotoxicity of the quassinoids against a specific tumor cell line correlated with STAT3 activation status; for example, breast cancer line MCF7 with inactive STAT3 was resistant to both quassinoids even at the maximum concentration tested (6.25 μM), whereas breast cancer lines MDA-MB-468 and MDA-MB-435s with activated STAT3 were inhibited by both compounds at low concentrations (neosergiolide IC50: MDA-MB-435s, 31.3 nM; MDA-MB-468, 29.9 nM; isobrucein B IC50: MDA-MB-435s, 209.3 nM; MDA-MB-468, 356.8 nM). The in vitro antitumor activity of the two quassinoids could also be demonstrated in vivo. For example, isobrucein B (1.0 mg/kg IP once q 3d x 5 doses) could be safely administered and potently inhibited the growth in SCID mice of the CD30+ primary CTCL MAC-1 cell line; mice at treatment day 16 showed average subcutaneous tumor volumes of 3839 ± 863 (s.e.) mm3 in the vehicle-control group and 913 ± 349 (s.e.) mm3 in the isobrucein B group (P=0.008, t-test). These results provide strong support for STAT3 targeting in antitumor drug discovery and suggest that quassinoids may have utility in such an approach.


Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 4726-4726
Author(s):  
David W. Rusnak ◽  
Sharon K. Rudolph ◽  
Afshin Safavi ◽  
Connie L. Erickson-Miller

Abstract Abstract 4726 The thrombopoietin receptor agonists (TPO-RA), romiplostim and eltrombopag, are presently indicated for the treatment of certain patient groups with immune thrombocytopenia purpura. In a clinical study with romiplostim in patients with low-/intermediate-1 risk myelodysplastic syndromes (MDS), cases of transient increases in blast cell counts were observed and cases of MDS disease progression to acute myeloid leukemia (AML) were reported. In the present study, we evaluated the impact of romiplostim, eltrombopag, and recombinant human thrombopoietin (TPO) on the proliferation of 5 human AML and 1 TPO-dependent megakaryoblastic cell line. The cell lines evaluated include the TPO-dependent cell line, N2C TPO; the TPO-R positive AML lines, HEL92.1.7 and OCI-AML-3; and the TPO-R negative AML cell lines, HL60, THP-1, and NOMO-1. All cells were exposed to 11-point dose response curves of the 3 agents at concentrations sufficient to generate a full stimulatory response in the N2C TPO cell line. Cells were exposed to concentrations of romiplostim and eltrombopag that met or exceeded the reported Cmax achieved for each agent in high-dose clinical trials and were 3- (eltrombopag) to 30-fold (romiplostim) above trough levels from the same clinical trials. Neither romiplostim nor TPO treatment resulted in detectable stimulation or inhibition of leukemia cell growth at concentrations up to 10 μg/mL. Treatment with eltrombopag up to 40 μg/mL caused inhibition of all AML cell lines with mean IC50 values ranging from 6.4 to 13.5 μg/mL. These IC50 values reflect concentrations that are 3- to 6-fold below the Cmax of a 300 qd dose of eltrombopag (40.5 μg/mL) and at concentrations as low as 2-fold below Ctau levels (12.4 μg/mL). Cmax exceeded the IC90 for these AML cell lines, which ranged from 18.5 to 27.9 μg/mL. No stimulation of AML growth was evident through the range of the eltrombopag dose response curve on any of the cell lines evaluated. The results of this study confirm earlier in vitro studies (Will 2009, Erickson-Miller 2010) showing inhibitory effects of eltrombopag on leukemic cell lines and support clinical studies to evaluate a potential anti-leukemic effect of higher doses of eltrombopag in patients with AML. Disclosures: Rusnak: GlaxoSmithKline: Consultancy, Equity Ownership, Patents & Royalties. Off Label Use: Eltrombopag is an oral TPO agonist indicated for chronic ITP being studied in MDS/AML. Rudolph:GlaxoSmithKline: Consultancy, Equity Ownership. Erickson-Miller:GlaxoSmithKline: Employment, Equity Ownership, Patents & Royalties, Research Funding.


2021 ◽  
Author(s):  
Ali Reza Ebadi ◽  
Ali Soleimani ◽  
Abdulbaghi Ghaderzadeh

Abstract Anti-cancer medicine for a particular patient has been a personal medical goal. Many computational models have been proposed by researchers to predict drug response. But predictive accuracy still remains a challenge. Base on this concept which “Similar cells have similar responses to drugs”, we developed the basic method of matrix factorization method by adding fines to similarity. So that the distance of latent factors to two cell lines or (drug) should be inversely related to similarity. This means that two similar drugs or similar cell lines should have a short distance, whereas two similar cell lines or non-similar drugs should have a large gap with their latent factors. We proposed a Dual similarity-regularized matrix factorization (DSRMF) model, then generated new data for drug similarity from the two-dimensional three-dimensional chemical structure, which were obtained from the CCLE and GDSC databases. In this research, by using the proposed model, and generating new drug similarity data we achieved the average Pearson correlation coefficient (PCC) about 0.96, and average mean square error (RMSE) Root about 0.30, between the observed value and the predicted value for the cell line response to the drug. Our analysis in this research showed, using heterogeneous data, has better results, and can be obtained with the proposed model, using other panels’ cancer cell lines, to calculate similarity between cells. Also, by imposing more restrictions on the similarity between cells, we were able to achieve more accurate prediction for the response of the cell line to the anticancer drug.


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