PMTDS: a computational method based on genetic interaction networks for Precision Medicine Target‐Drug Selection in cancer

Vasudevaraja, Varshini; Renbarger, Jamie L.; Shah, Ridhhi Girish; Kinnebrew, Garrett; Korc, Murray; Wang, Limei; Huo, Yang; Liu, Enze; Li, Lang; Cheng, Lijun

doi:10.1007/s40484-017-0126-1

Cited by 5 publications

(4 citation statements)

References 49 publications

(57 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study provides novel insights into how genetic alterations such as CNVs can potentially serve as both prognostic biomarkers and predictive biomarkers of therapeutic response in pediatric sarcomas. The systems pharmacology approach described here provides a platform to personalize therapies that have could improve clinical outcomes in aggressive pediatric malignancies [43, 44].…”

Section: Discussionmentioning

confidence: 99%

Integration of genomic copy number variations and chemotherapy-response biomarkers in pediatric sarcoma

et al. 2019

Self Cite

View full text Add to dashboard Cite

BackgroundWhile most pediatric sarcomas respond to front-line therapy, some bone sarcomas do not show radiographic response like soft-tissue sarcomas (rhabdomyosarccomas) but do show 90% necrosis. Though, new therapies are urgently needed to improve survival and quality of life in pediatric patients with sarcomas. Complex chromosomal aberrations such as amplifications and deletions of DNA sequences are frequently observed in pediatric sarcomas. Evaluation of copy number variations (CNVs) associated with pediatric sarcoma patients at the time of diagnosis or following therapy offers an opportunity to assess dysregulated molecular targets and signaling pathways that may drive sarcoma development, progression, or relapse. The objective of this study was to utilize publicly available data sets to identify potential predictive biomarkers of chemotherapeutic response in pediatric Osteosarcoma (OS), Rhabdomyosarcoma (RMS) and Ewing’s Sarcoma Family of Tumors (ESFTs) based on CNVs following chemotherapy (OS n = 117, RMS n = 64, ESFTs n = 25 tumor biopsies).MethodsThere were 206 CNV profiles derived from pediatric sarcoma biopsies collected from the public databases TARGET and NCBI-Gene Expression Omnibus (GEO). Through our comparative genomic analyses of OS, RMS, and ESFTs and 22,255 healthy individuals called from the Database of Genomic Variants (DGV), we identified CNVs (amplifications and deletions) pattern of genomic instability in these pediatric sarcomas. By integrating CNVs of Cancer Cell Line Encyclopedia (CCLE) identified in the pool of genes with drug-response data from sarcoma cell lines (n = 27) from Cancer Therapeutics Response Portal (CTRP) Version 2, potential predictive biomarkers of therapeutic response were identified.ResultsGenes associated with survival and/recurrence of these sarcomas with statistical significance were found on long arm of chromosome 8 and smaller aberrations were also identified at chromosomes 1q, 12q and x in OS, RMS, and ESFTs. A pool of 63 genes that harbored amplifications and/or deletions were frequently associated with recurrence across OS, RMS, and ESFTs. Correlation analysis of CNVs from CCLE with drug-response data of CTRP in 27 sarcoma cell lines, 33 CNVs out of 63 genes correlated with either sensitivity or resistance to 17 chemotherapies from which actionable CNV signatures such as IGF1R, MYC, MAPK1, ATF1, and MDM2 were identified. These CNV signatures could potentially be used to delineate patient populations that will respond versus those that will not respond to a particular chemotherapy.ConclusionsThe large-scale analyses of CNV-drug screening provides a platform to evaluate genetic alterations across aggressive pediatric sarcomas. Additionally, this study provides novel insights into the potential utilization of CNVs as not only prognostic but also as predictive biomarkers of therapeutic response. Information obtained in this study may help guide and prioritize patient-specific therapeutic options in pediatric bone and soft-tissue sarcomas.Electronic supplementary m...

show abstract

Section: Discussionmentioning

confidence: 99%

Integration of genomic copy number variations and chemotherapy-response biomarkers in pediatric sarcoma

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Based on recent new CN gene accumulations, systems biology, bioinformatics tools, robust molecular interactions, and advanced computational methodologies are available to predict CN-speci c candidate genes. [18][19][20][21]. Using these bioinformatics tools, there is a great need to enable accurate discovery of novel CN-related genes.…”

Section: Introductionmentioning

confidence: 99%

Candidate disease genes identification in patients with congenital neutropenia and a new PID condition

Shinwari

Khan

Ivanovna

et al. 2022

Preprint

View full text Add to dashboard Cite

Background: Congenital neutropenia (CN) is a primary immunodeficiency disease (PID), which is associated with recurrent bacterial infections, autoimmunity, hematological malignancies, and neuropsychiatric symptoms. Understanding the protein-protein interaction (PPI) network of CN genes and finding candidate CN genes is crucial for early CN diagnosis. The purpose of current study was to investigate the PPI network of CN genes and use computational techniques to identify candidate CN genes.Methods: PPI data for CN and PID genes provided from the STRING database were examined using network density and biological distance. Gene expression data of patients with CN were obtained from the Gene Expression Omnibus, and then Pearson’s correlation coefficient, a PPI database, and Kyoto Encyclopedia of Genes and Genomes were used to identify candidate CN genes. We then assessed our hypotheses and discovered CN genes that were differently expressed. Results: Characterization of the majority of CN genes were done by high network density and low biological distance whereas low network density and large biological distance were used to characterize most PID genes. The CN genes are functionally relative to each other compared to PID genes. It showed that they are similar and closely related. Next, we found 15 candidate CN genes with biological functions comparable to known CN genes, and other genes have subsequently been identified as CN-related genes. The SRC gene reported in our study may be a good candidate gene for Heneekem syndrome. The candidate gene “STAT3” was downregulated in peripheral blood neutrophils and B cells in CN patients compared to healthy controls (HC), whereas in blood sample the level was up regulated in CN patients relative to HC. Conclusion: Our findings will contribute in the early diagnosis of CN by providing a better understanding of the CN gene complexity.

show abstract

“…An integrated optimization method is used for identifying the deregulated subnetwork for precision medicine in cancer [19]. PMTDS is based on genetic interaction networks for target-drug selection in precision cancer medicine [20]. These strategies build up their own models by patients' genome variation.…”

Section: Introductionmentioning

confidence: 99%

Computational Cancer Cell Models to Guide Precision Breast Cancer Medicine

Cheng

Majumdar

Stover

et al. 2020

Genes

Self Cite

View full text Add to dashboard Cite

Background: Large-scale screening of drug sensitivity on cancer cell models can mimic in vivo cellular behavior providing wider scope for biological research on cancer. Since the therapeutic effect of a single drug or drug combination depends on the individual patient’s genome characteristics and cancer cells integration reaction, the identification of an effective agent in an in vitro model by using large number of cancer cell models is a promising approach for the development of targeted treatments. Precision cancer medicine is to select the most appropriate treatment or treatments for an individual patient. However, it still lacks the tools to bridge the gap between conventional in vitro cancer cell models and clinical patient response to inhibitors. Methods: An optimal two-layer decision system model is developed to identify the cancer cells that most closely resemble an individual tumor for optimum therapeutic interventions in precision cancer medicine. Accordingly, an optimal grid parameters selection is designed to seek the highest accordance for treatment selection to the patient’s preference for drug response and in vitro cancer cell drug screening. The optimal two-layer decision system model overcomes the challenge of heterology data comparison between the tumor and the cancer cells, as well as between the continual variation of drug responses in vitro and the discrete ones in clinical practice. We simulated the model accuracy using 681 cancer cells’ mRNA and associated 481 drug screenings and validated our results on 315 breast cancer patients drug selection across seven drugs (docetaxel, doxorubicin, fluorouracil, paclitaxel, tamoxifen, cyclophosphamide, lapitinib). Results: Comparing with the real response of a drug in clinical patients, the novel model obtained an overall average accordance over 90.8% across the seven drugs. At the same time, the optimal cancer cells and the associated optimal therapeutic efficacy of cancer drugs are recommended. The novel optimal two-layer decision system model was used on 1097 patients with breast cancer in guiding precision medicine for a recommendation of their optimal cancer cells (30 cancer cells) and associated efficacy of certain cancer drugs. Our model can detect the most similar cancer cells for each individual patient. Conclusion: A successful clinical translation model (optimal two-layer decision system model) was developed to bridge in-vitro basic science to clinical practice in a therapeutic intervention application for the first time. The novel tool kills two birds with one stone. It can help basic science to seek optimal cancer cell models for an individual tumor, while prioritizing clinical drugs’ recommendations in practice. Tool associated platform website: We extended the breast cancer research to 32 more types of cancers across 45 therapy predictions.

show abstract

PMTDS: a computational method based on genetic interaction networks for Precision Medicine Target‐Drug Selection in cancer

Cited by 5 publications

References 49 publications

Integration of genomic copy number variations and chemotherapy-response biomarkers in pediatric sarcoma

Integration of genomic copy number variations and chemotherapy-response biomarkers in pediatric sarcoma

Candidate disease genes identification in patients with congenital neutropenia and a new PID condition

Computational Cancer Cell Models to Guide Precision Breast Cancer Medicine

Contact Info

Product

Resources

About