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Journal articles on the topic 'In silico oncology'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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1

Abler, D., P. Büchler, and G. S. Stamatakos. "CHIC – A Multi-scale Modelling Platform for in-silico Oncology." Radiotherapy and Oncology 118 (February 2016): S1. http://dx.doi.org/10.1016/s0167-8140(16)30001-9.

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2

Kim, Eugene, Samantha Duarte, Stas Fridland, Myungwoo Nam, Jin Young Hwang, Alice Daeun Lee, Grace Lee, Emma Yu, and Young Kwang Chae. "Evaluation of in silico tools for variant classification in clinically actionable NSCLC variants." Journal of Clinical Oncology 39, no. 15_suppl (May 20, 2021): e13545-e13545. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e13545.

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e13545 Background: Genetic variants beyond FDA-approved drug targets are often identified in NSCLC patients. To address this challenge, in silico variant classification tools are available to determine whether specific variants contribute to disease pathogenicity or remain benign. Although the performance of in silico tools has been analyzed in previous studies, it has not been analyzed for actionable targets of FDA-approved therapies for NSCLC. The aim of this study is to compare the performance of commonly used in silico tools in classifying the pathogenicity of actionable variants in NSCLC. Methods: We evaluated the performance of several in silico tools: PolyPhen-2, Align-GVGD, and MutationTaster2. A curated set of targetable NSCLC missense variants (n = 179) was used. The dataset consisted of variants in the BRAF, EGFR, ERBB2, KRAS, MET, ALK, and ROS1 genes based on their indications as molecular targets in the NCCN Guidelines for NSCLC. Pathogenic variants (n = 80) were curated based on available literature and annotations according to the NCCN Guidelines, OncoKB, My Cancer Genome, and AACR Project GENIE. Benign variants (n = 99) were curated from the dbSNP database with the inclusion criteria of a benign or likely benign ClinVar assertion. The overall accuracy, sensitivity, specificity, and Matthews correlation coefficient (MCC) of each in silico tool were determined. The performance of each in silico tool in predicting pathogenicity for subsets of sensitizing (n = 18) and resistant (n = 57) variants was also evaluated. Results: PolyPhen-2 HumVar demonstrated the highest overall accuracy (0.80), specificity (0.69), and MCC (0.63) of the in silico tools analyzed. PolyPhen-2 HumDiv (0.75) and MutationTaster2 (0.69) had similar overall accuracies while Align-GVGD (0.50) had the lowest overall accuracy. Align-GVGD also had the lowest MCC (0.08), with the other in silico tools ranging from 0.50 to 0.63. All the in silico tools achieved high sensitivities, with MutationTaster2 performing the highest (1.00) and Align-GVGD performing the lowest (0.86). The specificities were remarkably low (0.20-0.69) for all the in silico tools, with the lowest specificity demonstrated by Align-GVGD. The overall accuracies when classifying the subsets of sensitizing and resistant variants were generally high, ranging from 0.84 to 1.00. Conclusions: These results suggest that the performance of the evaluated in silico tools to predict the pathogenicity of clinically actionable NSCLC missense variants is not fully reliable. The tools analyzed in this study could be acceptable to rule out pathogenicity in variants given their higher sensitivities, but are limited when it comes to identifying pathogenicity in variants due to low specificities.

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Johnson, David, Steve McKeever, Georgios Stamatakos, Dimitra Dionysiou, Norbert Graf, Vangelis Sakkalis, Konstantinos Marias, Zhihui Wang, and Thomas S. Deisboeck. "Article Commentary: Dealing with Diversity in Computational Cancer Modeling." Cancer Informatics 12 (January 2013): CIN.S11583. http://dx.doi.org/10.4137/cin.s11583.

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This paper discusses the need for interconnecting computational cancer models from different sources and scales within clinically relevant scenarios to increase the accuracy of the models and speed up their clinical adaptation, validation, and eventual translation. We briefly review current interoperability efforts drawing upon our experiences with the development of in silico models for predictive oncology within a number of European Commission Virtual Physiological Human initiative projects on cancer. A clinically relevant scenario, addressing brain tumor modeling that illustrates the need for coupling models from different sources and levels of complexity, is described. General approaches to enabling interoperability using XML-based markup languages for biological modeling are reviewed, concluding with a discussion on efforts towards developing cancer-specific XML markup to couple multiple component models for predictive in silico oncology.

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Graf, N., A. Hoppe, E. Georgiadi, R. Belleman, C. Desmedt, D. Dionysiou, M. Erdt, et al. "‘In Silico’ Oncology for Clinical Decision Making in the Context of Nephroblastoma." Klinische Pädiatrie 221, no. 03 (March 2009): 141–49. http://dx.doi.org/10.1055/s-0029-1216368.

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Stamatakos, G. S., D. D. Dionysiou, E. I. Zacharaki, N. A. Mouravliansky, K. S. Nikita, and N. K. Uzunoglu. "In silico radiation oncology: combining novel simulation algorithms with current visualization techniques." Proceedings of the IEEE 90, no. 11 (November 2002): 1764–77. http://dx.doi.org/10.1109/jproc.2002.804685.

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6

Filippova, Darya, Matthew H. Larson, M. Cyrus Maher, Robert Calef, Monica Pimentel, Yiqi Zhou, Joshua Newman, et al. "The Circulating Cell-free Genome Atlas (CCGA) Study: Size selection of cell-free DNA (cfDNA) fragments." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): 3103. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.3103.

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3103 Background: Detection of somatic copy number aberrations in individuals with cancer via cfDNA whole-genome sequencing (WGS) is challenging at low tumor fractions. Given that tumor-derived cfDNA fragments are shorter than those from healthy tissues, this exploratory analysis evaluated the potential effect of size selection on the ability to detect cancer. Methods: CCGA WGS libraries were in silico and in vitro size selected to estimate the change in tumor fraction by tumor types (breast, lung, and colorectal [CRC]) and stage (I-III vs IV). In silico analyses used clinically evaluable training set samples with WGS assay results (n = 1422: 560 non-cancer [NC], 862 cancer [C] stages I-IV); classification (cancer/non-cancer) performance was estimated using fragments within the 90-150 bp range. In vitro analyses used a subset of samples (n = 93: 28 NC, 65 C stages I-IV), including C cases sampled within a range of tumor fractions; tumor fraction was also measured at each progressive removal of maximum-length fragments (intervals of 10 bp: 150 bp down to 50 bp). Results: In silico and in vitro analyses, respectively, resulted in median 2.00±0.58-fold (at 6.91±2.64X depth) and 2.00±0.52-fold (at 23±4.45X depth) increases, in overall tumor fraction (compared to non-size-selected 36X depth). This was consistent across tumor types ( in silico: 1.78±0.73 breast, 2.00±0.58 CRC, 2.00±0.41 lung; in vitro: 2.00±0.82 breast, 2.51±0.52 CRC, 2.53±0.94 lung) and stages ( in silico: 2.00±0.74 I-III, 1.78±0.52 IV; in vitro: 2.00±0.55 I-III, 1.68±0.29 IV). Tumor fraction increased with initial fragment length titrations, but not following size selection to shorter lengths ( < 140 bp). Classifier trained on in silico size-selected data had increased sensitivity at 98% specificity compared to those trained on non-size-selected data (p < 1e-5). Conclusions: In silico and in vitro size selection consistently increased tumor fraction across cancer types and stages, and this increase was maximized by tuning the length range of size selection. Relative to full-depth data, classification performance improved significantly. These data suggest that size selection targeting cfDNA under 140 bp may enhance cfDNA-based cancer detection. Clinical trial information: NCT02889978.

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Minussi, Darlan Conterno, Bernardo Henz, Mariana dos Santos Oliveira, Eduardo C. Filippi-Chiela, Manuel M. Oliveira, and Guido Lenz. "esiCancer: Evolutionary In Silico Cancer Simulator." Cancer Research 79, no. 5 (December 18, 2018): 1010–13. http://dx.doi.org/10.1158/0008-5472.can-17-3924.

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8

Jackson, Robert C. "Pharmacodynamic Modelling of Biomarker Data in Oncology." ISRN Pharmacology 2012 (February 16, 2012): 1–12. http://dx.doi.org/10.5402/2012/590626.

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The development of pharmacodynamic (PD) biomarkers in oncology has implications for design of clinical protocols from preclinical data and for predicting clinical outcomes from early clinical data. Two classes of biomarkers have received particular attention. Phosphoproteins in biopsy samples are markers of inhibition of signalling pathways, target sites for many novel agents. Biomarkers of apoptosis in plasma can measure tumour cell killing by drugs in phase I clinical trials. The predictive power of PD biomarkers is enhanced by data modelling. With pharmacokinetic models, PD models form PK/PD models that predict the time course both of drug concentration and drug effects. If biomarkers of drug toxicity are also measured, the models can predict drug selectivity as well as efficacy. PK/PD models, in conjunction with disease models, make possible virtual clinical trials, in which multiple trial designs are assessed in silico, so the optimal trial design can be selected for experimental evaluation.

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Katoh, Masuko, and Masaru Katoh. "Characterization of human ARHGAP10 gene in silico." International Journal of Oncology 25, no. 4 (October 1, 2004): 1201–7. http://dx.doi.org/10.3892/ijo.25.4.1201.

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10

Hede, K. "In Silico Research: Pushing It Into the Mainstream." JNCI Journal of the National Cancer Institute 102, no. 4 (February 9, 2010): 217–19. http://dx.doi.org/10.1093/jnci/djq035.

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11

Pusztai, L., and B. Leyland-Jones. "Promises and caveats of in silico biomarker discovery." British Journal of Cancer 99, no. 3 (July 29, 2008): 385–86. http://dx.doi.org/10.1038/sj.bjc.6604495.

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12

Garritano, Sonia, Alessandro Romanel, Yari Ciribilli, Alessandra Bisio, Antoneta Gavoci, Alberto Inga, and Francesca Demichelis. "In silico identification and functional validation of allele-dependent AR enhancers." Oncotarget 6, no. 7 (February 27, 2015): 4816–28. http://dx.doi.org/10.18632/oncotarget.3019.

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13

Pierotti, Marco A., Elena Tamborini, Tiziana Negri, Sabrina Pricl, and Silvana Pilotti. "Targeted therapy in GIST: in silico modeling for prediction of resistance." Nature Reviews Clinical Oncology 8, no. 3 (March 2011): 161–70. http://dx.doi.org/10.1038/nrclinonc.2011.3.

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14

Christodoulou, Nikolaos A., Nikolaos E. Tousert, Eleni Ch Georgiadi, Katerina D. Argyri, Fay D. Misichroni, and Georgios S. Stamatakos. "A Modular Repository-based Infrastructure for Simulation Model Storage and Execution Support in the Context of In Silico Oncology and In Silico Medicine." Cancer Informatics 15 (January 2016): CIN.S40189. http://dx.doi.org/10.4137/cin.s40189.

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The plethora of available disease prediction models and the ongoing process of their application into clinical practice – following their clinical validation – have created new needs regarding their efficient handling and exploitation. Consolidation of software implementations, descriptive information, and supportive tools in a single place, offering persistent storage as well as proper management of execution results, is a priority, especially with respect to the needs of large healthcare providers. At the same time, modelers should be able to access these storage facilities under special rights, in order to upgrade and maintain their work. In addition, the end users should be provided with all the necessary interfaces for model execution and effortless result retrieval. We therefore propose a software infrastructure, based on a tool, model and data repository that handles the storage of models and pertinent execution-related data, along with functionalities for execution management, communication with third-party applications, user-friendly interfaces to access and use the infrastructure with minimal effort and basic security features.

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15

Lepkes, Louisa, Mohamad Kayali, Britta Blümcke, Jonas Weber, Malwina Suszynska, Sandra Schmidt, Julika Borde, et al. "Performance of In Silico Prediction Tools for the Detection of Germline Copy Number Variations in Cancer Predisposition Genes in 4208 Female Index Patients with Familial Breast and Ovarian Cancer." Cancers 13, no. 1 (January 1, 2021): 118. http://dx.doi.org/10.3390/cancers13010118.

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The identification of germline copy number variants (CNVs) by targeted next-generation sequencing (NGS) frequently relies on in silico CNV prediction tools with unknown sensitivities. We investigated the performances of four in silico CNV prediction tools, including one commercial (Sophia Genetics DDM) and three non-commercial tools (ExomeDepth, GATK gCNV, panelcn.MOPS) in 17 cancer predisposition genes in 4208 female index patients with familial breast and/or ovarian cancer (BC/OC). CNV predictions were verified via multiplex ligation-dependent probe amplification. We identified 77 CNVs in 76 out of 4208 patients (1.81%); 33 CNVs were identified in genes other than BRCA1/2, mostly in ATM, CHEK2, and RAD51C and less frequently in BARD1, MLH1, MSH2, PALB2, PMS2, RAD51D, and TP53. The Sophia Genetics DDM software showed the highest sensitivity; six CNVs were missed by at least one of the non-commercial tools. The positive predictive values ranged from 5.9% (74/1249) for panelcn.MOPS to 79.1% (72/91) for ExomeDepth. Verification of in silico predicted CNVs is required due to high frequencies of false positive predictions, particularly affecting target regions at the extremes of the GC content or target length distributions. CNV detection should not be restricted to BRCA1/2 due to the relevant proportion of CNVs in further BC/OC predisposition genes.

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Baur, Florentin, Sarah L. Nietzer, Meik Kunz, Fabian Saal, Julian Jeromin, Stephanie Matschos, Michael Linnebacher, Heike Walles, Thomas Dandekar, and Gudrun Dandekar. "Connecting Cancer Pathways to Tumor Engines: A Stratification Tool for Colorectal Cancer Combining Human In Vitro Tissue Models with Boolean In Silico Models." Cancers 12, no. 1 (December 20, 2019): 28. http://dx.doi.org/10.3390/cancers12010028.

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To improve and focus preclinical testing, we combine tumor models based on a decellularized tissue matrix with bioinformatics to stratify tumors according to stage-specific mutations that are linked to central cancer pathways. We generated tissue models with BRAF-mutant colorectal cancer (CRC) cells (HROC24 and HROC87) and compared treatment responses to two-dimensional (2D) cultures and xenografts. As the BRAF inhibitor vemurafenib is—in contrast to melanoma—not effective in CRC, we combined it with the EGFR inhibitor gefitinib. In general, our 3D models showed higher chemoresistance and in contrast to 2D a more active HGFR after gefitinib and combination-therapy. In xenograft models murine HGF could not activate the human HGFR, stressing the importance of the human microenvironment. In order to stratify patient groups for targeted treatment options in CRC, an in silico topology with different stages including mutations and changes in common signaling pathways was developed. We applied the established topology for in silico simulations to predict new therapeutic options for BRAF-mutated CRC patients in advanced stages. Our in silico tool connects genome information with a deeper understanding of tumor engines in clinically relevant signaling networks which goes beyond the consideration of single drivers to improve CRC patient stratification.

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Murray, David, Peter Doran, Padraic MacMathuna, and Alan C. Moss. "In silico gene expression analysis – an overview." Molecular Cancer 6, no. 1 (2007): 50. http://dx.doi.org/10.1186/1476-4598-6-50.

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18

Piñeiro-Yáñez, Elena, María José Jiménez-Santos, Gonzalo Gómez-López, and Fátima Al-Shahrour. "In Silico Drug Prescription for Targeting Cancer Patient Heterogeneity and Prediction of Clinical Outcome." Cancers 11, no. 9 (September 13, 2019): 1361. http://dx.doi.org/10.3390/cancers11091361.

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In silico drug prescription tools for precision cancer medicine can match molecular alterations with tailored candidate treatments. These methodologies require large and well-annotated datasets to systematically evaluate their performance, but this is currently constrained by the lack of complete patient clinicopathological data. Moreover, in silico drug prescription performance could be improved by integrating additional tumour information layers like intra-tumour heterogeneity (ITH) which has been related to drug response and tumour progression. PanDrugs is an in silico drug prescription method which prioritizes anticancer drugs combining both biological and clinical evidence. We have systematically evaluated PanDrugs in the Genomic Data Commons repository (GDC). Our results showed that PanDrugs is able to establish an a priori stratification of cancer patients treated with Epidermal Growth Factor Receptor (EGFR) inhibitors. Patients labelled as responders according to PanDrugs predictions showed a significantly increased overall survival (OS) compared to non-responders. PanDrugs was also able to suggest alternative tailored treatments for non-responder patients. Additionally, PanDrugs usefulness was assessed considering spatial and temporal ITH in cancer patients and showed that ITH can be approached therapeutically proposing drugs or combinations potentially capable of targeting the clonal diversity. In summary, this study is a proof of concept where PanDrugs predictions have been correlated to OS and can be useful to manage ITH in patients while increasing therapeutic options and demonstrating its clinical utility.

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Katoh, Masuko, and Masaru Katoh. "Identification and characterization of human CXXC10 gene in silico." International Journal of Oncology 25, no. 4 (October 1, 2004): 1193–202. http://dx.doi.org/10.3892/ijo.25.4.1193.

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20

Chen, Zhigang, Jun Wu, Hailin Xu, Xiuyan Yu, and Ke Wang. "In silico analysis of the prognostic value of FAS mRNA in malignancies." Journal of Cancer 11, no. 3 (2020): 542–50. http://dx.doi.org/10.7150/jca.35614.

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21

Sanga, Sandeep, Hermann B. Frieboes, Xiaoming Zheng, Robert Gatenby, Elaine L. Bearer, and Vittorio Cristini. "Predictive oncology: A review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth." NeuroImage 37 (January 2007): S120—S134. http://dx.doi.org/10.1016/j.neuroimage.2007.05.043.

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22

Noskova, Hana, Michal Kyr, Karol Pal, Tomas Merta, Peter Mudry, Kristyna Polaskova, Tina Catela Ivkovic, et al. "Assessment of Tumor Mutational Burden in Pediatric Tumors by Real-Life Whole-Exome Sequencing and In Silico Simulation of Targeted Gene Panels: How the Choice of Method Could Affect the Clinical Decision?" Cancers 12, no. 1 (January 17, 2020): 230. http://dx.doi.org/10.3390/cancers12010230.

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Background: Tumor mutational burden (TMB) is an emerging genomic biomarker in cancer that has been associated with improved response to immune checkpoint inhibitors (ICIs) in adult cancers. It was described that variability in TMB assessment is introduced by different laboratory techniques and various settings of bioinformatic pipelines. In pediatric oncology, no study has been published describing this variability so far. Methods: In our study, we performed whole exome sequencing (WES, both germline and somatic) and calculated TMB in 106 patients with high-risk/recurrent pediatric solid tumors of 28 distinct cancer types. Subsequently, we used WES data for TMB calculation using an in silico approach simulating two The Food and Drug Administration (FDA)-approved/authorized comprehensive genomic panels for cancer. Results: We describe a strong correlation between WES-based and panel-based TMBs; however, we show that this high correlation is significantly affected by inclusion of only a few hypermutated cases. In the series of nine cases, we determined TMB in two sequentially collected tumor tissue specimens and observed an increase in TMB along with tumor progression. Furthermore, we evaluated the extent to which potential ICI indication could be affected by variability in techniques and bioinformatic pipelines used for TMB assessment. We confirmed that this technological variability could significantly affect ICI indication in pediatric cancer patients; however, this significance decreases with the increasing cut-off values. Conclusions: For the first time in pediatric oncology, we assessed the reliability of TMB estimation across multiple pediatric cancer types using real-life WES and in silico analysis of two major targeted gene panels and confirmed a significant technological variability to be introduced by different laboratory techniques and various settings of bioinformatic pipelines.

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Sareen, Srishti, Matthew Stein, Lindsay Kaye Morris, Saradasri Karri, Kruti Patel, David Shibata, Ari M. Vanderwalde, Lee Steven Schwartzberg, and Michael Gary Martin. "Localization of non-receptor tyrosine kinase (nRTK) variants in solid tumor patients using next-generation sequencing (NGS)." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): 1536. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.1536.

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1536 Background: Non-synonymous SNPs (nsSNPs) in nRTKs may serve as oncologic targets and predictive biomarkers, with significant lesions described in various nRTK regions including the tyrosine kinase domain (TKD). NGS allows the entire coding sequence to be evaluated, facilitating the identification of novel lesions. Methods: We searched all nsSNPs in 14 nRTKs in the tumors of patients (pts) at our institution that received NGS with Caris from 2013-2015 with a diagnosis of advanced breast, colon or lung cancer. Substitutions were classified as either within or extra-TKD; in the case of JAK1-3, pseudokinase domain lesions were also identified. In order to predict the pathogenicity of nsSNPs, in silico analysis with PolyPhen-2 (Harvard) was completed. Results: 356 pts (79 breast, 110 colon and 165 lung (156 NSCLC, 11 small cell)) were identified with a median age of 61 years (range 26-86); 58% female; 62% white, 35% black. 245 variants were found, with 200 nsSNPs and 45 known pathologic mutations (Pmut); Pmut were PIK3CA (21 breast, 13 colon, 5 NSCLC) and AKT1 (6 breast). 169/356 (47%) pts had ≥1 nRTK lesion (0-8). 52/200 (26%) nsSNPs were predicted-damaging (pnsSNPs) with in silico analysis among 49 pts (6 breast, 13 colon and 30 NSCLC). pnsSNPs were found in 14/14 nRTKs with median 3 (1-10). The most frequently mutated nRTKs in breast were SRC (2/2 variants were pnsSNPs) and ABL2 (1/5); in colon ABL1 (5/10), JAK3 (3/27) and CDK12 (2/8); and in NSCLC JAK3 (6/20), BTK (5/8), ABL1 (3/12), JAK2 (3/11), CDK12 (3/9) and JAK1 (3/3). Of 180 nsSNPs with in silico results, 68% were extra-TKD (29/122 variants were pnsSNPs), 23% within the TKD (13/42) and 9% in pseudokinase domains of JAK1-3 (10/16). Notably, 8/10 pseudokinase domain pnsSNPs were in NSCLC pts (3 JAK1, 2 JAK2 and 3 JAK3). Conclusions: > 13% solid tumors held an nRTK nsSNP that was predicted-damaging by in silico analysis, with 69% of these mutations occurring outside of the TKD-proper. Further work is needed to determine how these pnsSNPs affect function and if they are clinically actionable.

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Boyle, Sean Michael, Jason Harris, Gabor Bartha, Ravi Alla, Patrick Jongeneel, Mirian Karbelashvili, Scott Kirk, et al. "Validation of an expanded neoantigen identification platform for therapeutic and diagnostic use in immuno-oncology." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): 11589. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.11589.

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11589 Background: Neoantigen identification is increasingly critical for clinical immuno-oncology applications including predicting immunotherapy response and neoantigen-based personalized cancer vaccines. Although standard research pipelines have been developed to aid neoantigen identification, building a robust, validated neoantigen identification platform suitable for clinical applications has been challenging due to the complex processes involved. Methods: To improve neoantigen identification, we extended standard sequencing and informatics methods. We developed an augmented and content enhanced (ACE) exome sequenced at 200X to increase sensitivity to SNPs and indels used for neoantigen identification as well as HLA performance. To accurately identify fusions and variants from RNA, we optimized our ACE transcriptome for FFPE tissue. To improve neoantigen pipelines based on MHC binding algorithms, we developed peptide phasing, high accuracy HLA typing, TCR interaction predictors, and transcript isoform estimation tools to detect neoantigens from indel and fusion events. We performed comprehensive analytical validation of the platform including the ACE Exome, somatic SNV/indel calls, RNA based variant and fusion calls, and HLA typing. This was followed by an overall in silico validation of neoantigen identification using 23 experimentally validated immunogenic neoepitopes spiked into exome data. Results: Analytical validation of our ACE exome platform showed > 97% sensitivity for small variants with a specificity of > 98% at minor allele frequency > 10%. From the ACE transcriptome we achieved a fusion sensitivity of > 99% and RNA based variant calls sensitivity of > 97%. Our ACE exome based HLA typing was 98% and 95% concordant with Class I and II HLA results (respectively) from clinical testing. Our in silico validation of neoantigen predictions resulted in identification of 22 out of 23 immunogenic neoepitopes. Conclusions: We developed sequencing and informatics improvements to standard approaches that can enhance neoantigen identification and demonstrated a comprehensive validation approach that may support neoantigen use in future clinical settings.

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Muhammad, Ijaz, Noor Rahman, Gul E. Nayab, Sadaf Niaz, Mohibullah Shah, Sahib G. Afridi, Haroon Khan, Maria Daglia, and Esra Capanoglu. "The Molecular Docking of Flavonoids Isolated from Daucus carota as a Dual Inhibitor of MDM2 and MDMX." Recent Patents on Anti-Cancer Drug Discovery 15, no. 2 (October 27, 2020): 154–64. http://dx.doi.org/10.2174/1574892815666200226112506.

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Background: Cancer is characterized by overexpression of p53 associated proteins, which down-regulate P53 signaling pathway. In cancer therapy, p53 activity can be restored by inhibiting the interaction of MDMX (2N0W) and MDM2 (4JGR) proteins with P53 protein. Objective: In the current, study in silico approaches were adapted to use a natural product as a source of cancer therapy. Methods: In the current study in silico approaches were adapted to use a natural product as a source of cancer therapy. For in silico studies, Chemdraw and Molecular Operating Environment were used for structure drawing and molecular docking, respectively. Flavonoids isolated from D. carota were docked with cancerous proteins. Result: Based on the docking score analysis, we found that compound 7 was the potent inhibitor of both cancerous proteins and can be used as a potent molecule for inhibition of 2N0W and 4JGR interaction with p53. Conclusion: Thus the compound 7 can be used for the revival of p53 signaling pathway function however, intensive in vitro and in vivo experiments are required to prove the in silico analysis.

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Falco, Jacopo, Abramo Agosti, Ignazio G. Vetrano, Alberto Bizzi, Francesco Restelli, Morgan Broggi, Marco Schiariti, et al. "In Silico Mathematical Modelling for Glioblastoma: A Critical Review and a Patient-Specific Case." Journal of Clinical Medicine 10, no. 10 (May 17, 2021): 2169. http://dx.doi.org/10.3390/jcm10102169.

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Glioblastoma extensively infiltrates the brain; despite surgery and aggressive therapies, the prognosis is poor. A multidisciplinary approach combining mathematical, clinical and radiological data has the potential to foster our understanding of glioblastoma evolution in every single patient, with the aim of tailoring therapeutic weapons. In particular, the ultimate goal of biomathematics for cancer is the identification of the most suitable theoretical models and simulation tools, both to describe the biological complexity of carcinogenesis and to predict tumor evolution. In this report, we describe the results of a critical review about different mathematical models in neuro-oncology with their clinical implications. A comprehensive literature search and review for English-language articles concerning mathematical modelling in glioblastoma has been conducted. The review explored the different proposed models, classifying them and indicating the significative advances of each one. Furthermore, we present a specific case of a glioblastoma patient in which our recently proposed innovative mechanical model has been applied. The results of the mathematical models have the potential to provide a relevant benefit for clinicians and, more importantly, they might drive progress towards improving tumor control and patient’s prognosis. Further prospective comparative trials, however, are still necessary to prove the impact of mathematical neuro-oncology in clinical practice.

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Vargas-Toscano, Andres, Ann-Christin Nickel, Guanzhang Li, Marcel Alexander Kamp, Sajjad Muhammad, Gabriel Leprivier, Ellen Fritsche, et al. "Rapalink-1 Targets Glioblastoma Stem Cells and Acts Synergistically with Tumor Treating Fields to Reduce Resistance against Temozolomide." Cancers 12, no. 12 (December 21, 2020): 3859. http://dx.doi.org/10.3390/cancers12123859.

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Glioblastoma (GBM) is a lethal disease with limited clinical treatment options available. Recently, a new inhibitor targeting the prominent cancer signaling pathway mTOR was discovered (Rapalink-1), but its therapeutic potential on stem cell populations of GBM is unknown. We applied a collection of physiological relevant organoid-like stem cell models of GBM and studied the effect of RL1 exposure on various cellular features as well as on the expression of mTOR signaling targets and stem cell molecules. We also undertook combination treatments with this agent and clinical GBM treatments tumor treating fields (TTFields) and the standard-of-care drug temozolomide, TMZ. Low nanomolar (nM) RL1 treatment significantly reduced cell growth, proliferation, migration, and clonogenic potential of our stem cell models. It acted synergistically to reduce cell growth when applied in combination with TMZ and TTFields. We performed an in silico analysis from the molecular data of diverse patient samples to probe for a relationship between the expression of mTOR genes, and mesenchymal markers in different GBM cohorts. We supported the in silico results with correlative protein data retrieved from tumor specimens. Our study further validates mTOR signaling as a druggable target in GBM and supports RL1, representing a promising therapeutic target in brain oncology.

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Otto, Raik, Christine Sers, and Ulf Leser. "Robust in-silico identification of cancer cell lines based on next generation sequencing." Oncotarget 8, no. 21 (March 10, 2017): 34310–20. http://dx.doi.org/10.18632/oncotarget.16110.

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van Dam, Peter A., Pieter-Jan H. H. van Dam, Christian Rolfo, Marco Giallombardo, Christophe van Berckelaer, Xuan Bich Trinh, Sevilay Altintas, et al. "In silico pathway analysis in cervical carcinoma reveals potential new targets for treatment." Oncotarget 7, no. 3 (December 19, 2015): 2780–95. http://dx.doi.org/10.18632/oncotarget.6667.

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Skomorovski, K., M. Vardi, H. Harpak, and Z. Agur. "Using ‘in silico mouse’ for predicting therapeutic protocols on thrombopoiesis." European Journal of Cancer 37 (April 2001): S362. http://dx.doi.org/10.1016/s0959-8049(01)81840-2.

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Phillips, R., P. M. Loadman, C. J. Evans, P. F. Jones, S. W. Smye, B. D. Sleeman, and C. J. Twelves. "1202 In silico modelling of Doxorubicin penetration through multicell layers." European Journal of Cancer Supplements 7, no. 2 (September 2009): 121. http://dx.doi.org/10.1016/s1359-6349(09)70414-8.

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Deisboeck, Thomas S., Le Zhang, Jeongah Yoon, and Jose Costa. "In silico cancer modeling: is it ready for prime time?" Nature Clinical Practice Oncology 6, no. 1 (October 14, 2008): 34–42. http://dx.doi.org/10.1038/ncponc1237.

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Athanaileas, Theodoros, Andreas Menychtas, Dimitra Dionysiou, Dimosthenis Kyriazis, Dimitra Kaklamani, Theodora Varvarigou, Nikolaos Uzunoglu, and Georgios Stamatakos. "Exploiting grid technologies for the simulation of clinical trials: the paradigm of in silico radiation oncology." SIMULATION 87, no. 10 (July 9, 2010): 893–910. http://dx.doi.org/10.1177/0037549710375437.

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Cuplov, Vesna, Guillaume Sicard, Dominique Barbolosi, Joseph Ciccolini, and Fabrice Barlesi. "Harnessing tumor immunity with chemotherapy: Mathematical modeling for decision-making in combinatorial regimen with immune-oncology drugs." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e14095-e14095. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e14095.

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e14095 Background: Combining chemotherapy and immune checkpoint inhibitors (ICI) is challenging due to the near-infinite choice of dosing, scheduling and sequencing between drugs. The aim of this work is to develop a phenomenological model that describes the synergistic effect between cytotoxics and immune check point inhibitors in patients with cancer. Methods: Inspired from literature, we have developed an integrative mathematical model that includes tumor cells, cytotoxic T cells (CTLs) and regulatory T cells (TREGs) plus pharmacokinetics (PK) inputs. Loss in tumor mass is due to combined effect of direct chemotherapy-induced cytotoxicity and CTLs immune response, which is in turn inhibited by the tumor and mitigated by TREGs in the tumor micro-environment. The model describes as well the impact of chemotherapy-induced lymphodepletion on immune tolerance, whereas ICIs protect CTLs against tumor inhibition. Identification of model’s parameters and simulations of various scheduling were performed using Mlxplore software and a Python standalone code. In vitro and in vivo experiments using lung cancer models generate experimental data to adjust model parameters. Results: Complex interplays between cytotoxics and immune cells were best described by a 10-parameters model so as to ensure better identifiability. PK/PD relationships were integrated using compartmental modeling. In silico simulations show how changes in dosing and scheduling impact efficacy endpoints, an observation in line with data from the literature. Ongoing in vitro and in vivo experiments with pemetrexed-cisplatin doublet and anti-PD1 pembrolizumab help optimizing the model’s parameters in a self-learning loop. Conclusions: This work is at the frontier between mathematical modeling and experimental therapeutics with ICIs. In silico modeling and simulations could help narrow down the treatment choices and define optimal combinations prior to running clinical trials. Such model will help identify optimal dosing and scheduling, so as to achieve better synergism and efficacy.

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Pessetto, Ziyan Y., Bin Chen, Hani Alturkmani, Stephen Hyter, Colleen A. Flynn, Michael Baltezor, Yan Ma, et al. "In silico and in vitro drug screening identifies new therapeutic approaches for Ewing sarcoma." Oncotarget 8, no. 3 (November 16, 2016): 4079–95. http://dx.doi.org/10.18632/oncotarget.13385.

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Kardani, Kimia, and Azam Bolhassani. "Antimicrobial/anticancer peptides: bioactive molecules and therapeutic agents." Immunotherapy 13, no. 8 (June 2021): 669–84. http://dx.doi.org/10.2217/imt-2020-0312.

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Antimicrobial peptides (AMPs) have been known as host-defense peptides. These cationic and amphipathic peptides are relatively short (∼5–50 L-amino acids) with molecular weight less than 10 kDa. AMPs have various roles including immunomodulatory, angiogenic and antitumor activities. Anticancer peptides (ACPs) are a main subset of AMPs as a novel therapeutic approach against tumor cells. The physicochemical properties of the ACPs influence their cell penetration, stability and efficiency of targeting. Up to now, several databases and web servers for in silico prediction of AMPs/ACPs have been established prior to the lab analysis. The present review focuses on the recent advancement about AMPs/ACPs activities including their in silico prediction by computational tools and their potential applications as therapeutic agents especially in cancer.

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D’Arcangelo, Daniela, Francesca Scatozza, Claudia Giampietri, Paolo Marchetti, Francesco Facchiano, and Antonio Facchiano. "Ion Channel Expression in Human Melanoma Samples: In Silico Identification and Experimental Validation of Molecular Targets." Cancers 11, no. 4 (March 29, 2019): 446. http://dx.doi.org/10.3390/cancers11040446.

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Expression of 328 ion channel genes was investigated, by in silico analysis, in 170 human melanoma samples and controls. Ninety-one members of this gene-family (i.e., about 28%) show a significant (p < 0.05) differential expression in melanoma- vs. nevi-biopsies, taken from the GEO database. ROC (receiver operating characteristic) analysis selected 20 genes as potential markers showing the highest discrimination ability of melanoma vs. nevi (AUC > 0.90 and p < 0.0001). These 20 genes underwent a first in silico-validation round in an independent patients-dataset from GEO. A second-in silico-validation step was then carried out on a third human dataset in Oncomine. Finally, five genes were validated, showing extremely high sensitivity and specificity in melanoma detection (>90% in most cases). Such five genes (namely, SCNN1A, GJB3, KCNK7, GJB1, KCNN2) are novel potential melanoma markers or molecular targets, never previously related to melanoma. The “druggable genome” analysis was then carried out. Miconazole, an antifungal drug commonly used in clinics, is known to target KCNN2, the best candidate among the five identified genes. Miconazole was then tested in vitro in proliferation assays; it dose-dependently inhibited proliferation up to 90% and potently induced cell-death in A-375 and SKMEL-28 melanoma cells, while it showed no effect in control cells. Moreover, specific silencing of KCNN2 ion channel was achieved by siRNA transfection; under such condition miconazole strongly increases its anti-proliferative effect. In conclusion, the present study identified five ion channels that can potentially serve as sensitive and specific markers in human melanoma specimens and demonstrates that the antifungal drug miconazole, known to target one of the five identified ion channels, exerts strong and specific anti-melanoma effects in vitro.

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van de Grift, Yorick Bernardus Cornelis, Nika Heijmans, and Renée van Amerongen. "How to Use Online Tools to Generate New Hypotheses for Mammary Gland Biology Research: A Case Study for Wnt7b." Journal of Mammary Gland Biology and Neoplasia 25, no. 4 (December 2020): 319–35. http://dx.doi.org/10.1007/s10911-020-09474-z.

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AbstractAn increasing number of ‘-omics’ datasets, generated by labs all across the world, are becoming available. They contain a wealth of data that are largely unexplored. Not every scientist, however, will have access to the required resources and expertise to analyze such data from scratch. Fortunately, a growing number of investigators is dedicating their time and effort to the development of user friendly, online applications that allow researchers to use and investigate these datasets. Here, we will illustrate the usefulness of such an approach. Using regulation of Wnt7b expression as an example, we will highlight a selection of accessible tools and resources that are available to researchers in the area of mammary gland biology. We show how they can be used for in silico analyses of gene regulatory mechanisms, resulting in new hypotheses and providing leads for experimental follow up. We also call out to the mammary gland community to join forces in a coordinated effort to generate and share additional tissue-specific ‘-omics’ datasets and thereby expand the in silico toolbox.

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Benzekry, Sebastien, Amanda Tracz, Michalis Mastri, Ryan Corbelli, Dominique Barbolosi, and John M. L. Ebos. "Modeling Spontaneous Metastasis following Surgery: An In Vivo-In Silico Approach." Cancer Research 76, no. 3 (October 28, 2015): 535–47. http://dx.doi.org/10.1158/0008-5472.can-15-1389.

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Haviari, Skerdi, Benoît You, and Michel Tod. "In Silico Evaluation of Pharmacokinetic Optimization for Antimitogram-Based Clinical Trials." Cancer Research 78, no. 7 (January 9, 2018): 1873–82. http://dx.doi.org/10.1158/0008-5472.can-17-1710.

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Phillips, R., P. Loadman, P. Jones, S. Smye, C. Twelves, B. Sleeman, and C. Evans. "122 POSTER In silico modelling of doxorubicin penetration through multicell layers." European Journal of Cancer Supplements 6, no. 12 (October 2008): 40. http://dx.doi.org/10.1016/s1359-6349(08)72054-8.

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42

Goswami, Chirayu Pankaj, Oscar D. Cano, Yesim Gokmen-Polar, and Sunil S. Badve. "In silico identification of an epithelial core signature in human tumors." Journal of Clinical Oncology 30, no. 15_suppl (May 20, 2012): 10628. http://dx.doi.org/10.1200/jco.2012.30.15_suppl.10628.

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10628 Background: Gene expression analysis is performed on grossly selected specimens often without any microscopic analysis of tumor content. In studies where histological analyses have been performed, cases having 80% or more tumor content are used for microarray analysis. The variability in amount of epithelial and stromal cells may generate to misleading differential expression analysis and selection for wrong targets for therapeutics. It is also often unclear, whether the genes identified are stromal or epithelial in origin. The goal of this study was to identify genes that define core epithelial phenotype; these genes could provide means of normalization of expression data. Methods: The CABIG GSK microarray (HG-U133_plus_2) data consisting of 950 cell lines from carcinoma (n=562), non-carcinoma (n=385) and normal tissue (n=3) was analyzed to identify epithelial specific genes. 10 carcinomas each from 11 sites (n=110) and an equal number of non-carcinomas were randomly selected. In silico analyses were performed by 1) identifying genes differentially expressed between carcinoma and non-carcinoma samples using a one way ANOVA; 2) identifying gene signature associated with carcinoma using Predictive Analysis of Microarrays (PAM) and 3) a weighted gene coexpression network analysis (WGCNA) was performed to identify co-expression modules. A similar analysis was also performed on tissue samples (E-GEOD-12360) from carcinomas and non-carcinomas. Venn-diagram was generated to identify intersecting set. Results: Comparison of the carcinoma and non-carcinoma samples using ANOVA identified 1455 differential expressed gene probes in cell lines and 540 gene probes in tissues (FDR=1E-10). The cell lines analysis identified 5 modules and a 65-gene signature (43 core and 22 accessory set) that was specific for epithelial cells. In the tissue analysis a 188-gene signature was similarly identified. Cross-comparison identified a smaller 31 gene intersecting set; this was not associated with loss of discriminatory power. Conclusions: A 31 geneset which can be used to determine the epithelial content of heterogeneous tumors, was identified. This study has the potential to significantly impact the use of microarray based gene expression data.

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Nakajima, H., S. Tanuma, I. Fujiwara, N. Mizuta, and K. Sakaguchi. "In silico design of novel anticancer antibody mimetic molecules targeting HER2." Journal of Clinical Oncology 25, no. 18_suppl (June 20, 2007): 14149. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.14149.

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14149 Background: HER2 is a unique receptor molecule for which no ligand has been found and functions as a coreceptor to form homo-and hetero-dimers with other three HER (1, 3 and 4) family members. The dimerization results in the activation of HER tyrosine kinase. This, in turn, promotes the tyrosine phosphorylation of certain proteins, leading to the stimulation of cell proliferation, invasion, and antiapoptosis. The overexpression of HER2 in breast cancer correlates with increased tumor growth and metastatic potential, and thereby poor long-term survival for the patient. A monoclonal antibody against HER2, named trastuzumab, is approved for breast cancer patients, while pertuzumab is currently in phaseIIclinical trial. The two antibodies bind to different epitopes in the extracellular domains of HER2. Trastuzumab binding mainly mediates the antibody-dependent cytotoxicity (ADCC). On the other hand, pertuzumab binding directly inhibits HER2 dimerization with its partner receptors, blocking the growth signaling and inducing apoptosis. Furthermore, the unique binding pockets on HER2 for trastuzumab and pertuzumab have been resolved and provide the important target domains for creation of new anticancer drugs. Methods: Based on these mechanisms, we are trying to design and create both antibodies-mimetic molecules using our in silico methodologies COSMOS (Conversion to small molecules through optimized-peptide strategy) and SARM (Self-assembling regulatory molecule). We design HRAP (HER2 reactive peptide)- SARM and HRAP- SARM- FAB (Fc?-binding peptide). HRAP-SARM may bind to HER2 pertuzumab binding site and sterically interferes with HER2 dimerization and induces apoptosis. HRAP- SARM- FAB is expected to induce both ADCC and apoptosis. Results and Conclusion: In this presentation, we will show the preliminary results and functions of the Ab-mimetics, HRAP- SARM and HRAP- SARM- FAB on human breast cancer cells overexpressing HER2, as compared to those of trastuzumab and pertuzumab. Since those Ab-mimetics are small molecules and cheap, the successful results are sure to promise the revolutionary therapy for refractory breast cancer. No significant financial relationships to disclose.

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Palladini, Arianna, Giordano Nicoletti, Francesco Pappalardo, Annalisa Murgo, Valentina Grosso, Valeria Stivani, Marianna L. Ianzano, et al. "In silico Modeling and In vivo Efficacy of Cancer-Preventive Vaccinations." Cancer Research 70, no. 20 (October 5, 2010): 7755–63. http://dx.doi.org/10.1158/0008-5472.can-10-0701.

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45

Zhu, Zhipeng, Jiuhua Xu, Xiaofang Wu, Sihao Lin, Lulu Li, Weipeng Ye, and Zhengjie Huang. "In Silico Identification of Contradictory Role of ADAMTS5 in Hepatocellular Carcinoma." Technology in Cancer Research & Treatment 20 (January 1, 2021): 153303382098682. http://dx.doi.org/10.1177/1533033820986826.

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Background: ADAMTS5 has different roles in multiple types of cancers and participates in various molecular mechanisms. However, the prognostic value of ADAMTS5 in patients with hepatocellular carcinoma (HCC) still remains unclear. We carried the study to evaluate the prognostic value and identified underlying molecular mechanisms in HCC. Methods: Firstly, the association of ADAMTS5 expression and clinicopathological parameters was evaluated by in GSE14520. Next, ADAMTS5 expression in HCC was performed using GSE14520, GSE36376, GSE76427 and The Cancer Genome Atlas (TCGA) profile. Furthermore, Kaplan-Meier analysis, Univariate and Multivariate Cox regression analysis, subgroup analysis was performed to evaluate the prognostic value of ADAMTS5 in HCC. Finally, GO enrichment analysis, gene set enrichment analysis (GSEA) and weighted gene co-expression network analysis (WGCNA) were performed to revealed underlying molecular mechanisms. Result: The expression of ADAMTS5 was positively correlated with the development of HCC. Next, high ADAMTS5 expression was significantly associated with poorer survival (all P < 0.05) and the impact of ADAMTS5 on all overall survival (OS), disease-free survival (DFS), relapse-free survival (RFS), disease specific survival (DSS) and progression free interval (PFI) was specific for HCC among other 29 cancer types. Subgroup analysis showed that ADAMTS5 overexpression was significantly associated with poorer OS in patients with HCC. Finally, ADAMTS5 might participate in the status conversion from metabolic-dominant to extracellular matrix-dominant, and the activation of ECM-related biological process might contribute to high higher mortality risk for patients with HCC. Conclusion: ADAMTS5 may play an important role in the progression of HCC, and may be considered as a novel and effective biomarker for predicting prognosis for patients with HCC.

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Towner, Rheal A., Randy L. Jensen, Brian Vaillant, Howard Colman, Debra Saunders, Cory B. Giles, and Jonathan D. Wren. "Experimental validation of 5 in-silico predicted glioma biomarkers." Neuro-Oncology 15, no. 12 (October 24, 2013): 1625–34. http://dx.doi.org/10.1093/neuonc/not124.

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Hamis, Sara, Gibin G. Powathil, and Mark A. J. Chaplain. "Blackboard to Bedside: A Mathematical Modeling Bottom-Up Approach Toward Personalized Cancer Treatments." JCO Clinical Cancer Informatics, no. 3 (December 2019): 1–11. http://dx.doi.org/10.1200/cci.18.00068.

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Cancers present with high variability across patients and tumors; thus, cancer care, in terms of disease prevention, detection, and control, can highly benefit from a personalized approach. For a comprehensive personalized oncology practice, this personalization should ideally consider data gathered from various information levels, which range from the macroscale population level down to the microscale tumor level, without omission of the central patient level. Appropriate data mined from each of these levels can significantly contribute in devising personalized treatment plans tailored to the individual patient and tumor. Mathematical models of solid tumors, combined with patient-specific tumor profiles, present a unique opportunity to personalize cancer treatments after detection using a bottom-up approach. Here, we discuss how information harvested from mathematical models and from corresponding in silico experiments can be implemented in preclinical and clinical applications. To conceptually illustrate the power of these models, one such model is presented, and various pertinent tumor and treatment scenarios are demonstrated in silico. The presented model, specifically a multiscale, hybrid cellular automaton, has been fully validated in vitro using multiple cell-line–specific data. We discuss various insights provided by this model and other models like it and their role in designing predictive tools that are both patient, and tumor specific. After refinement and parametrization with appropriate data, such in silico tools have the potential to be used in a clinical setting to aid in treatment protocols and decision making.

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Ogilvie, Lesley A., Christoph Wierling, Thomas Kessler, Hans Lehrach, and Bodo M. H. Lange. "Article Commentary: Predictive Modeling of Drug Treatment in the Area of Personalized Medicine." Cancer Informatics 14s4 (January 2015): CIN.S19330. http://dx.doi.org/10.4137/cin.s19330.

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Despite a growing body of knowledge on the mechanisms underlying the onset and progression of cancer, treatment success rates in oncology are at best modest. Current approaches use statistical methods that fail to embrace the inherent and expansive complexity of the tumor/patient/drug interaction. Computational modeling, in particular mechanistic modeling, has the power to resolve this complexity. Using fundamental knowledge on the interactions occurring between the components of a complex biological system, large-scale in silico models with predictive capabilities can be generated. Here, we describe how mechanistic virtual patient models, based on systematic molecular characterization of patients and their diseases, have the potential to shift the theranostic paradigm for oncology, both in the fields of personalized medicine and targeted drug development. In particular, we highlight the mechanistic modeling platform ModCell™ for individualized prediction of patient responses to treatment, emphasizing modeling techniques and avenues of application.

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Jungwirth, Gerhard, Junguo Cao, Tao Yu, Rolf Warta, Andreas Unterberg, and Christel Herold-Mende. "EXTH-63. IN VITRO DRUG SCREENING BASED ON IN SILICO DATA IDENTIFIES NEW THERAPEUTIC AGENTS FOR AGGRESSIVE MENINGIOMA." Neuro-Oncology 22, Supplement_2 (November 2020): ii101. http://dx.doi.org/10.1093/neuonc/noaa215.417.

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Abstract The mainstay of treatment for progressive or recurrent meningiomas is surgery and/or radiotherapy. Patients with refractory cancer might benefit from systemic treatment options. However, to date, no effective chemotherapy is available for these patients. For this reason, novel inhibitors for the treatment of aggressive meningiomas are urgently needed. Therefore, we used our previously published Affymetrix microarray dataset (GSE74385) consisting of 62 meningiomas enriched with 28 WHO°III MGMs to screen substantially expressed genes that can be targeted by available inhibitors. For each targeted gene, three compounds were selected based on their clinical development. This filter process resulted in 107 drugs targeting 57 different genes. Most compounds were oncology-related (n = 94, 88%) with the remaining compounds being non-oncology agents (n = 13, 12%). Thereafter, a 2-stage screening strategy was employed. First, drugs were screened at a single dose (2.5 µM) in two malignant meningioma cell lines (NCH93 and IOMM-Lee) with CellTiter-Glo (Promega). Only drugs resulting in a cell viability of 50% or less of either cell line were considered for further validation. Remaining drug candidates (n = 33) exclusively belonged to the oncology-related group, consisting of 4 FDA-approved antineoplastic drugs (12%). The other drugs are currently tested in clinical trials (24% in phase III, 39% in phase II, and 24% in phase I). Drug candidates were further analyzed in a six-point dose-response scheme ranging from 0.1 nM to 10 µM in three meningioma cell lines (Ben-Men-1, NCH93, IOMM-Lee). The top 5 drugs were selected based on the lowest mean of z-transformed area under the curve. Resulting drugs and corresponding targets are: OTSSP167 (MELK), Panobinostat (HDAC), Picropodophyllin (IGF-1R), KPT-9274 (PAK4 and NAMPT), and BI-2536 (PLK1). Taken together, our drug screening approach utilized in silico data to identify potential inhibitors for the treatment of aggressive meningiomas warranting further preclinical investigation.

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Millán-Gómez, Dalia, Salvador Dueñas, Patricia L. A. Muñoz, Tanya Camacho-Villegas, Carolina Elosua, Olivia Cabanillas-Bernal, Teresa Escalante, et al. "In silico-designed mutations increase variable new-antigen receptor single-domain antibodies for VEGF165 neutralization." Oncotarget 9, no. 46 (June 15, 2018): 28016–29. http://dx.doi.org/10.18632/oncotarget.25549.

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