scholarly journals Unearthing New Genomic Markers of Drug Response by Improved Measurement of Discriminative Power

2015 ◽  
Author(s):  
Cuong C. Dang ◽  
Antonio Peón ◽  
Pedro J. Ballester

AbstractBackgroundOncology drugs are only effective in a small proportion of cancer patients. Our current ability to identify these responsive patients before treatment is still poor in most cases. Thus, there is a pressing need to discover response markers for marketed and research oncology drugs in order to improve patient survival, reduce healthcare costs and enhance success rates in clinical trials. Screening these drugs against a large panel of cancer cell lines has been employed to discover new genomic markers of in vitro drug response, which can now be further evaluated on more accurate tumour models. However, while the identification of discriminative markers among thousands of candidate drug-gene associations in the data is error-prone, an appraisal of the effectiveness of such detection task is currently lacking.ResultsHere we present a new non-parametric method to measuring the discriminative power of a drug-gene association. This is enabled by the identification of an auxiliary threshold posing this task as a binary classification problem. Unlike parametric statistical tests, the adopted non-parametric test has the advantage of not making strong assumptions about the data distorting the identification of genomic markers. Furthermore, we introduce a new benchmark to further validate these markers in vitro using more recent data not used to identify the markers. The application of this new methodology has led to the identification of 128 new genomic markers distributed across 61% of the analysed drugs, including 5 drugs without previously known markers, which were missed by the MANOVA test initially applied to analyse data from the Genomics of Drug Sensitivity in Cancer consortium.Abbreviation(WT)wild-type(GDSC)Genomics of Drug Sensitivity in Cancer(TP)true positive(TN)true negative(FP)false positive(FN)false negative(MCC)Matthews Correlation Co-efficient.

Forecasting ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. 1-16
Author(s):  
Hassan Hamie ◽  
Anis Hoayek ◽  
Hans Auer

The question of whether the liberalization of the gas industry has led to less concentrated markets has attracted much interest among the scientific community. Classical mathematical regression tools, statistical tests, and optimization equilibrium problems, more precisely non-linear complementarity problems, were used to model European gas markets and their effect on prices. In this research, the parametric and nonparametric game theory methods are employed to study the effect of the market concentration on gas prices. The parametric method takes into account the classical Cournot equilibrium test, with assumptions on cost and demand functions. However, the non-parametric method does not make any prior assumptions, a factor that allows greater freedom in modeling. The results of the parametric method demonstrate that the gas suppliers’ behavior in Austria and The Netherlands gas markets follows the Nash–Cournot equilibrium, where companies act rationally to maximize their payoffs. The non-parametric approach validates the fact that suppliers in both markets follow the same behavior even though one market is more liquid than the other. Interestingly, our findings also suggest that some of the gas suppliers maximize their ‘utility function’ not by only relying on profit, but also on some type of non-profit objective, and possibly collusive behavior.


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.


2017 ◽  
Author(s):  
Alyssa D. Schwartz ◽  
Lauren E. Barney ◽  
Lauren E. Jansen ◽  
Thuy V. Nguyen ◽  
Christopher L. Hall ◽  
...  

TOC FigureDrug response screening, gene expression, and kinome signaling were combined across biomaterial platforms to combat adaptive resistance to sorafenib.Insight BoxWe combined biomaterial platforms, drug screening, and systems biology to identify mechanisms of extracellular matrix-mediated adaptive resistance to RTK-targeted cancer therapies. Drug response was significantly varied across biomaterials with altered stiffness, dimensionality, and cell-cell contacts, and kinome reprogramming was responsible for these differences in drug sensitivity. Screening across many platforms and applying a systems biology analysis were necessary to identify MEK phosphorylation as the key factor associated with variation in drug response. This method uncovered the combination therapy of sorafenib with a MEK inhibitor, which decreased viability on and within biomaterials in vitro, but was not captured by screening on tissue culture plastic alone. This combination therapy also reduced tumor burden in vivo, and revealed a promising approach for combating adaptive drug resistance.AbstractTraditional drug screening methods lack features of the tumor microenvironment that contribute to resistance. Most studies examine cell response in a single biomaterial platform in depth, leaving a gap in understanding how extracellular signals such as stiffness, dimensionality, and cell-cell contacts act independently or are integrated within a cell to affect either drug sensitivity or resistance. This is critically important, as adaptive resistance is mediated, at least in part, by the extracellular matrix (ECM) of the tumor microenvironment. We developed an approach to screen drug responses in cells cultured on 2D and in 3D biomaterial environments to explore how key features of ECM mediate drug response. This approach uncovered that cells on 2D hydrogels and spheroids encapsulated in 3D hydrogels were less responsive to receptor tyrosine kinase (RTK)-targeting drugs sorafenib and lapatinib, but not cytotoxic drugs, compared to single cells in hydrogels and cells on plastic. We found that transcriptomic differences between these in vitro models and tumor xenografts did not reveal mechanisms of ECM-mediated resistance to sorafenib. However, a systems biology analysis of phospho-kinome data uncovered that variation in MEK phosphorylation was associated with RTK-targeted drug resistance. Using sorafenib as a model drug, we found that co-administration with a MEK inhibitor decreased ECM-mediated resistance in vitro and reduced in vivo tumor burden compared to sorafenib alone. In sum, we provide a novel strategy for identifying and overcoming ECM-mediated resistance mechanisms by performing drug screening, phospho-kinome analysis, and systems biology across multiple biomaterial environments.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Joshua D. Mannheimer ◽  
Ashok Prasad ◽  
Daniel L. Gustafson

Abstract Background One of the current directions of precision medicine is the use of computational methods to aid in the diagnosis, prognosis, and treatment of disease based on data driven approaches. For instance, in oncology, there has been a particular focus on development of algorithms and biomarkers that can be used for pre-clinical and clinical applications. In particular large-scale omics-based models to predict drug sensitivity in in vitro cancer cell line panels have been used to explore the utility and aid in the development of these models as clinical tools. Additionally, a number of web-based interfaces have been constructed for researchers to explore the potential of drug perturbed gene expression as biomarkers including the NCI Transcriptional Pharmacodynamic Workbench. In this paper we explore the influence of drug perturbed gene dynamics of the NCI Transcriptional Pharmacodynamics Workbench in computational models to predict in vitro drug sensitivity for 15 drugs on the NCI60 cell line panel. Results This work presents three main findings. First, our models show that gene expression profiles that capture changes in gene expression after 24 h of exposure to a high concentration of drug generates the most accurate predictive models compared to the expression profiles under different dosing conditions. Second, signatures of 100 genes are developed for different gene expression profiles; furthermore, when the gene signatures are applied across gene expression profiles model performance is substantially decreased when gene signatures developed using changes in gene expression are applied to non-drugged gene expression. Lastly, we show that the gene interaction networks developed on these signatures show different network topologies and can be used to inform selection of cancer relevant genes. Conclusion Our models suggest that perturbed gene signatures are predictive of drug response, but cannot be applied to predict drug response using unperturbed gene expression. Furthermore, additional drug perturbed gene expression measurements in in vitro cell lines could generate more predictive models; but, more importantly be used in conjunction with computational methods to discover important drug disease relationships.


Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 2160-2160
Author(s):  
Jarno Kivioja ◽  
Mika Kontro ◽  
Angeliki Thanasopoulou ◽  
Muntasir Mamun Majumder ◽  
Bhagwan Yadav ◽  
...  

Abstract Background The t(5;11)(q35;p15.5) translocation resulting in fusion of the nucleoporin NUP98 and methyltransferase NSD1 (NUP98-NSD1) genes is a recurrent aberration observed in pediatric and adult AML. The NUP98-NSD1 fusion often co-occurs with the FLT3-ITD mutation and characterizes a group of cytogenetically normal AML patients with very poor prognosis. Despite advances in the understanding of the biology of NUP98-NSD1-positive AML, its therapeutic success rate has remained low. We aimed to identify novel candidate drugs for NUP98-NSD1-positive AML by testing primary patient cells and in vitro cell models with a high-throughput drug sensitivity platform. Methods Leukemic blasts were Ficoll separated from bone marrow (BM) aspirates of an AML patient positive for t(5;11)(q35;p15.5) and FLT3-ITD. RNA extracted from primary cells was used for RNA sequencing and gene expression analysis. NUP98-NSD1 cDNA was amplified from primary cell RNA and expressed from a lentiviral vector (LeGO-iCer2) also encoding the cerulean fluorescent marker. The NUP98-NSD1/LeGo-iCer2 and empty LeGo-iCer2 viruses were used to establish stably expressing Ba/F3 cell lines. Primary murine (BALB/c) BM cells were transduced with NUP98-NSD1 and FLT3-ITD retroviruses alone or in combination (NNF) in vitro (“preleukemic”) or passaged in vivo (“leukemic”) as previously described (Thanasopoulou et al, 2014). For screening, 309 small molecule inhibitors including FDA/EMA-approved and investigational oncology drugs were plated on 384-well plates in a 10,000-fold concentration range. Cells were dispensed on the pre-drugged plates and incubated at 37°C for 72h, and then cell viability measured using the CellTiter-Glo® luminescent assay. Drug response curves were generated and a drug sensitivity score determined (Yadav et al, 2014). Select drug sensitivity was calculated for each drug by comparing results between primary leukemic and healthy donor BM cells or between the cell constructs and empty vector transduced controls cells. Results Primary patient cells and murine BM cells expressing FLT3-ITD alone or in combination with NUP98-NSD1 were selectively sensitive to specific FLT3 inhibitors (e.g. quizartinib, sorafenib and lestaurtinib), and broad-spectrum receptor tyrosine kinase inhibitors targeting FLT3-ITD (e.g. cabozantinib, crenolanib, foretinib, midostaurin, MGCD-265 and ponatinib). Furthermore, these cells were highly sensitive to checkpoint kinase 1/2- inhibitor AZD7762. The primary murine cells expressing both NUP98-NSD1 and FLT3-ITD showed higher sensitivity to all of the above-mentioned drugs compared to cells expressing either of the events alone indicating functional synergy. A very distinct drug response pattern was observed in the leukemic NNF cells cultured in vivo compared to the same cells cultured in vitro suggesting that microenvironment may also affect the observed drug responses. Interestingly, the preleukemic murine cells expressing NUP98-NSD1 with or without FLT3-ITD as well as the primary patient cells showed extreme vulnerability to BCL2/BCL-xL inhibitor navitoclax. Furthermore, primary murine cells expressing NUP98-NSD1 alone showed high select sensitivity to JAK-inhibitors ruxolitinib, BMS-911543, AZD1480 and tofacitinib indicating the fusion may stimulate JAK/STAT-signaling. Similar sensitivity was also observed in the Ba/F3-cells expressing NUP98-NSD1. In support of these findings, gene expression analyses showed high expression of anti-apoptotic factors BCL2, BCL-xL and MCL1 in the patient cells. MCL1 is regulated by STAT3 while BCL-xL is regulated by STAT5, which were also highly expressed. Conclusions In summary, we have observed an enhanced response to specific and non-specific FLT3 inhibitors in cells expressing NUP98-NSD1 and FLT3-ITD together compared to cells expressing either of the two alone. This coincides with previous findings that functional co-operation between NUP98-NSD1 and FLT3-ITD is important in AML (Thanasopoulou et al, 2014). We have seen high in-vitro-in-vivo correlation between primary patient cells and murine cells expressing NUP98-NSD1 and FLT3-ITD. Moreover, we have identified potential candidate compounds targeting oncogenic signaling activated by these two events. These data form a basis for clinical evaluation of candidate compounds for NUP98-NSD1-positive AML. Disclosures Porkka: Bristol-Myers Squibb: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Heckman:Celgene: Research Funding.


2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S765-S765
Author(s):  
Shafiu O Ololade ◽  
Kavya Patel ◽  
Derek G Lafarga ◽  
Senu K Apewokin

Abstract Background Although procalcitonin-guided antimicrobial stewardship has had proven utility in emergency and ICU settings, it is still not widely adopted outside these areas. One limitation to a more universal uptake has been the unreliable performance in discriminating bacterial infected from uninfected individuals. Viral infections have been noted to suppress procalcitonin (PCT) levels through Interferon-gamma (IFN-G)-mediated inhibition of procalcitonin release from parenchymal cells. Unfortunately, clinical application algorithms do not assess INF-G levels at the time evaluation thus treating providers are unable to distinguish a true-negative test from a false-negative test resulting from INF-G-mediated procalcitonin suppression This undermines the performance of PCT, particularly in patients with bacterial and viral co-infections. We hypothesized that adjuvant interferon gamma testing could improve the performance of PCT. To test this hypothesis we prospectively enrolled bacteremic hospitalized patients along with culture-negative controls and then assessed the performance of PCT with adjuvant IFN-G testing. Methods 69 hospitalized patients with bacteremia and 32 culture-negative controls were enrolled. Demographic and clinical parameters were compared between groups alongside INFG and PCT levels Parametric and non-parametric statistical tests were performed where appropriate. Test performance was evaluated by constructing receiver operator curves (ROCs) for PCT, INF-G, and a combination of PCT+INF-G. Results Of 101 patients enrolled, the mean age was 49.46 ± 13.6 years with 47% being female. The following were comparative statistics between the culture-positive vs. culture-negative group: mean age 52.1 ± 15.7 vs. 46.4 ± 14.2 years, P = 0.56; WBC 11.9 ± 9.5 vs. 9.5±5.1, P = 0.170; ANC 8,466 ± 5,686 vs. 8,189 ± 4,769, P = 0.907; eGFR 73.2 ± 23 vs. 74.5 ± 26.1, P = 0.644; PCT 2.79 ± 5.87 vs. 0.71 ± 1.79, P = 0.03. Of these 57 patients had INF-G and PCT values available and their corresponding ROCs are shown in figure. Conclusion Our interim results indicate adjuvant INF-G testing may not improve the performance of procalcitonin in hospitalized bacteremic patients. The additional samples are being analyzed to confirm these findings. Disclosures All authors: No reported disclosures.


Cancers ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 811 ◽  
Author(s):  
Miranda Lin ◽  
Mei Gao ◽  
Prakash K. Pandalai ◽  
Michael J. Cavnar ◽  
Joseph Kim

Pancreatic duct adenocarcinoma (PDAC) is projected to become the second leading cause of cancer-related deaths in the next few years. Unfortunately, the development of novel therapies for PDAC has been challenged by a uniquely complex tumor microenvironment. The development of in vitro cancer organoids in recent years has demonstrated potential to increase therapies for patients with PDAC. Organoids have been established from PDAC murine and human tissues and they are representative of the primary tumor. Further, organoids have been shown beneficial in studies of molecular mechanisms and drug sensitivity testing. This review will cover the use of organoids to study PDAC development, invasiveness, and therapeutic resistance in the context of the tumor microenvironment, which is characterized by a dense desmoplastic reaction, hindered immune activity, and pro-tumor metabolic signaling. We describe investigations utilizing organoids to characterize the tumor microenvironment and also describe their limitations. Overall, organoids have great potential to serve as a versatile model of drug response and may be used to increase available therapies and improve survival for patients with PDAC.


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