scholarly journals pVACtools: a computational toolkit to identify and visualize cancer neoantigens

2018 ◽  
Author(s):  
Jasreet Hundal ◽  
Susanna Kiwala ◽  
Joshua McMichael ◽  
Christopher A. Miller ◽  
Alexander T. Wollam ◽  
...  
Keyword(s):  
Sequence Analysis ◽  
In Silico ◽  
Cancer Vaccines ◽  
Checkpoint Blockade ◽  
Peptide Vaccines ◽  
Web Based ◽  
Critical Step ◽  
Somatic Alterations ◽  
Dna Vector ◽  
Personalized Cancer

AbstractIdentification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. We have developed an in silico sequence analysis toolkit - pVACtools, to facilitate comprehensive neoantigen characterization. pVACtools supports a modular workflow consisting of tools for neoantigen prediction from somatic alterations (pVACseq and pVACfuse), prioritization and selection using a graphical web-based interface (pVACviz) and design of DNA vector-based vaccines (pVACvector) and synthetic long peptide vaccines. pVACtools is available at pvactools.org.

Validation of an expanded neoantigen identification platform for therapeutic and diagnostic use in immuno-oncology.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 11589-11589 ◽  
Author(s):  
Sean Michael Boyle ◽  
Jason Harris ◽  
Gabor Bartha ◽  
Ravi Alla ◽  
Patrick Jongeneel ◽  
...  
Keyword(s):  
In Silico ◽  
Cancer Vaccines ◽  
Clinical Applications ◽  
Ffpe Tissue ◽  
Hla Typing ◽  
Clinical Settings ◽  
Analytical Validation ◽  
Diagnostic Use ◽  
Complex Processes ◽  
Personalized Cancer

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.

In silico prediction of influenza vaccine effectiveness by sequence analysis

Vaccine ◽  
2021 ◽  
Vol 39 (7) ◽  
pp. 1030-1034
Author(s):  
Lirong Cao ◽  
Jingzhi Lou ◽  
Shi Zhao ◽  
Renee W.Y. Chan ◽  
Martin Chan ◽  
...  
Keyword(s):  
Sequence Analysis ◽  
Influenza Vaccine ◽  
In Silico ◽  
Vaccine Effectiveness ◽  
In Silico Prediction

The Potential of Personalized Cancer Vaccines

2017 ◽  
Vol 39 (18) ◽  
pp. 1
Author(s):  
Danielle Bullen Love
Keyword(s):  
Cancer Vaccines ◽  
Personalized Cancer

scholarly journals A Web-based Tool for in Silico Biomarker Discovery Based on Tissue-specific Protein Profiles in Normal and Cancer Tissues

2007 ◽  
Vol 7 (5) ◽  
pp. 825-844 ◽  
Author(s):  
Erik Björling ◽  
Cecilia Lindskog ◽  
Per Oksvold ◽  
Jerker Linné ◽  
Caroline Kampf ◽  
...  
Keyword(s):  
In Silico ◽  
Biomarker Discovery ◽  
Specific Protein ◽  
Protein Profiles ◽  
Web Based ◽  
Tissue Specific ◽  
Cancer Tissues

scholarly journals mGenomeSubtractor: a web-based tool for parallel in silico subtractive hybridization analysis of multiple bacterial genomes

2010 ◽  
Vol 38 (suppl_2) ◽  
pp. W194-W200 ◽  
Author(s):  
Yucheng Shao ◽  
Xinyi He ◽  
Ewan M. Harrison ◽  
Cui Tai ◽  
Hong-Yu Ou ◽  
...  
Keyword(s):  
In Silico ◽  
Subtractive Hybridization ◽  
Hybridization Analysis ◽  
Bacterial Genomes ◽  
Web Based

scholarly journals A Free Web-Based Protocol to Assist Structure-Based Virtual Screening Experiments

2019 ◽  
Vol 20 (18) ◽  
pp. 4648 ◽  
Author(s):  
Nathalie Lagarde ◽  
Elodie Goldwaser ◽  
Tania Pencheva ◽  
Dessislava Jereva ◽  
Ilza Pajeva ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Web Service ◽  
Molecular Mechanics ◽  
In Silico ◽  
Chemical Biology ◽  
In Silico Screening ◽  
Web Based ◽  
Screening Experiments ◽  
The Web

Chemical biology and drug discovery are complex and costly processes. In silico screening approaches play a key role in the identification and optimization of original bioactive molecules and increase the performance of modern chemical biology and drug discovery endeavors. Here, we describe a free web-based protocol dedicated to small-molecule virtual screening that includes three major steps: ADME-Tox filtering (via the web service FAF-Drugs4), docking-based virtual screening (via the web service MTiOpenScreen), and molecular mechanics optimization (via the web service AMMOS2 [Automatic Molecular Mechanics Optimization for in silico Screening]). The online tools FAF-Drugs4, MTiOpenScreen, and AMMOS2 are implemented in the freely accessible RPBS (Ressource Parisienne en Bioinformatique Structurale) platform. The proposed protocol allows users to screen thousands of small molecules and to download the top 1500 docked molecules that can be further processed online. Users can then decide to purchase a small list of compounds for in vitro validation. To demonstrate the potential of this online-based protocol, we performed virtual screening experiments of 4574 approved drugs against three cancer targets. The results were analyzed in the light of published drugs that have already been repositioned on these targets. We show that our protocol is able to identify active drugs within the top-ranked compounds. The web-based protocol is user-friendly and can successfully guide the identification of new promising molecules for chemical biology and drug discovery purposes.

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