Journal of bioinformatics and systems biology : Open access最新文献

{"title":"Abstract 180: Identifying signatures of vulnerability through machine learning in an umbrella trial for glioblastoma","authors":"Matthew Lee, N. Tang, M. Ahluwalia, E. Fonkem, K. Fink, Harshil Dhurv, M. Berens, S. Peng","doi":"10.1158/1538-7445.AM2021-180","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-180","url":null,"abstract":"Glioblastoma is characterized by intra- and inter-tumoral heterogeneity. An umbrella trial tests multiple investigational treatment arms depending on corresponding biomarker signatures. A contingency of an efficient umbrella trial is a suite of preferably orthogonal molecular biomarkers to classify patients into the likely-most-beneficial arm. Assigning thresholds of molecular signatures to classify a patient as a “most-likely responder” for one specific treatment arm is a crucial task. Gene Set Variation Analysis (GSVA) of specimens from a GBM clinical trial of methoxyamine associated differential enrichment in DNA repair pathways activities with patient response. However, the large number of DNA-repair related pathways confound confident “high” enrichment of responder, as well as obscuring to what degree each feature contributes to the likelihood of a patient9s response. Here, we utilized semi-supervised machine learning, Entropy-Regularized Logistic Regression (ERLR) to predict vulnerability classification. By first training all available data using semi-supervised algorithms we transformed unclassified TCGA GBM samples with highest certainty of predicted response into a self-labeled dataset. In this case, we developed a predictive model which has a larger sample size and potential better performance. Our umbrella trial design currently includes three treatment arms for GBM patients: arsenic trioxide, methoxyamine, and pevonedistat. Each treatment arm manifests its own signature developed by the above (or similar) machine learning pipeline based on selected gene mutation status and whole transcriptome data. In order to increase the robustness and scalability (with future more treatment arms), we also developed a multi-label classification ensemble model that9s capable of predicting a probability of “fitness” of each novel therapeutic agent for each patient. By expansion to three, independent treatment arms within a single umbrella trial, a “mock” stratification of TCGA GBM patients labeled 56% of all cases into at least one “high likelihood of response” arm. Predicted vulnerability using genomic data from preclinical PDX models placed 4 out of 6 models into a “high likelihood of response” regimen. Our utilization of multiple vulnerability signatures in an umbrella trial demonstrates how a precision medicine model can support an efficient clinical trial for heterogeneous diseases such as GBM. Citation Format: Matthew Eric Lee, Nanyun Tang, Manmeet Ahluwalia, Ekokobe Fonkem, Karen Fink, Harshil Dhurv, Harshil Dhurv, Michael E. Berens, Sen Peng. Identifying signatures of vulnerability through machine learning in an umbrella trial for glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 180.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79612666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 156: Mutational signature 3 predicts responses to olaparib plus buparlisib in triple-negative breast cancer and high-grade serous ovarian cancer","authors":"Felipe Batalini, D. Gulhan, V. Mao, Madeline Polak, E. Winer, E. Mayer, U. Matulonis, P. Konstantinopoulos, P. J. Park, G. Wulf","doi":"10.1158/1538-7445.AM2021-156","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-156","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72435738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 159: Pancreatic ductal adenocarcinoma developmental cell state signatures identified by single cell RNA sequencing are prognostic when applied to bulk RNA-seq data","authors":"P. Chati, E. Storrs, A. Usmani, B. Krasnick, C. Wetzel, T. Hollander, Faridi Quium, I. Sloan, H. Anthony, Badiyan Shahed, G. Lang, N. Cosgrove, V. Kushnir, D. Early, W. Hawkins, L. Ding, R. Fields, K. Das, A. Chaudhuri","doi":"10.1158/1538-7445.AM2021-159","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-159","url":null,"abstract":"INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer type with a poor prognosis. Patients with the classical histologic subtype typically have a better prognosis than those with a squamous-like histology. Still, survival outcomes vary significantly, even in early-stage patients, making it challenging to personalize treatment via subtyping. Here, we utilize CytoTRACE to better classify PDAC based on tumor cell-intrinsic developmental states, to more accurately prognosticate patients from the time of initial surgical resection. METHODS: We performed core needle pancreatic biopsies in 13 patients and surgical PDAC resections in five patients, and analyzed the resulting single-cell RNA sequencing (scRNA-seq) data to identify tumor cell clusters. We then applied CytoTRACE for developmental state analysis. Following developmental state quantification, we classified PDAC tumor cells into 3 distinct subtypes: squamous-like, classical early developmental (ED), and classical late developmental (LD). We developed a gene signature for each subtype, which we then applied to two external bulk RNA-seq datasets - 1) The Cancer Genome Atlas (TCGA): 125 early-stage PDAC tumors, and 2) Bailey et al (Nature 2016): 86 predominantly early-stage PDAC tumors. RESULTS: scRNA-seq data was partitioned into two subtypes, classical and squamous-like, based on marker gene expression. The classical subtype was further partitioned into ED versus LD cell states using the developmental index from CytoTRACE. For the squamous-like group, we identified the top 20 differentially expressed genes (squamous-like gene signature). For the ED and LD subtypes, we identified the top 20 genes correlating with the CytoTRACE developmental index (ED gene signature). Using a multivariate cox proportional hazards regression, we showed that the squamous-like signature was associated with significantly worse overall survival in TCGA (HR = 6.8, P = .01). Strikingly, our newly derived ED cell state signature was also associated with inferior overall survival in TCGA (HR = 5.9, P = .02). Kaplan-Meier analysis using optimized cutpoints between squamous-like and classical subtype scores, and between ED and LD cell state scores, again showed that patients with predominantly squamous-like tumors had significantly worse survival (HR = 4.4, P = .04); and that predominantly classical tumors enriched for the ED cell state had significantly inferior overall survival compared to LD (median 15.0 vs. 22.0 months, HR = 4.6, P = .03). The same trends were observed in the less-powered Bailey et al cohort. CONCLUSION: We showed that three developmental cell states, learned through the analysis of PDAC scRNA-seq data, can prognosticate patients with bulk RNA-seq expression data. This could help facilitate more personalized risk-adapted approaches for PDAC in the future. Citation Format: Prathamesh Mandar Chati, Erik Storrs, Abul Usmani, Bradley Krasnick, Chris Wetzel, Thomas Hollander, Faridi Q","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83191891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 255: TransVAF: A transfer learning approach for recognize genomic mutations with various tumor purity and clonal proportions","authors":"Tian Zheng, Jiayin Wang, Xiao-e Xiao, Xiaoyan Zhu, Xuanping Zhang, Xin Lai, Yanfang Guan, X. Yi","doi":"10.1158/1538-7445.AM2021-255","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-255","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73277649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 176: Detecting neoepitopes from tumor RNA sequencing datasets","authors":"D. Thompson, O. Vaske, A. Rao, Holly C. Beale","doi":"10.1158/1538-7445.AM2021-176","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-176","url":null,"abstract":"Epitopes are peptides that present on the surface of the cell and can be recognized by immune cells to initiate the immune response. Identification of neoepitopes – tumor-specific, MHC-bound epitopes recognized specifically by T-cells – is valuable for predicting response to immunotherapies, including checkpoint blockade therapies. Tumors with more neoepitopes tend to be more responsive to immune checkpoint therapies compared to tumors with fewer neoepitopes. ProTECT is a previously published computational method that uses Illumina whole genome and transcriptome sequencing data from tumor and matched normal tissues to identify neoepitopes. Tumor and normal whole genome sequencing data are used to infer a patient9s HLA haplotypes, as well as annotate variants as either somatic or germline. While whole genome sequencing is comprehensive, it is quite costly and not available for many samples. Here we adapt ProTECT to use only tumor RNA sequencing data and HLA haplotype information available to the clinician to identify neoepitopes in a tumor sample. Prior to running ProTECT, we use the computational tools Opossum and Platypus for variant calling instead of Radia (which is designed for variant calling using both RNA and DNA sequencing data as input). To determine which variants are somatic and therefore could represent tumor neoepitopes, variants found in RNA are compared to a panel of normals, for example the Genome Aggregation Database (gnomAD; containing variants from 125,748 exome sequences and 15,708 whole-genome sequences). With the resulting somatic variants and the HLA type, ProTECT proceeds as usual, with translation of variants into proteins, MHC:Peptide binding predictions and neoepitope ranking. We find that high quality neoepitopes are identifiable using an RNA-only approach, when genomic data is absent. Future work will validate the sensitivity of our method by benchmarking it against the original ProTECT predictions in the TCGA Prostate Adenocarcinoma cohort. Citation Format: Drew Thompson, Olena M. Vaske, Arjun Rao, Holly C. Beale. Detecting neoepitopes from tumor RNA sequencing datasets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 176.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88737231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 231: Evaluation of pharmacologic mechanisms to overcome IgG1 antibody (Ab) resistance via quantitative systems pharmacology (QSP) modeling of antibody-dependent cell mediated cytotoxicity (ADCC)","authors":"Kaitlyn E. Johnson, M. Ciocanel, Josua Aponte, N. Bajeux, Fanwang Meng, D. Bottino","doi":"10.1158/1538-7445.AM2021-231","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-231","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75332612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 247: Identification of an ovarian cancer selective splice variant of mesothelin utilizing the Kiromic proprietary search engine CancerDiff","authors":"L. Piccotti, L. Mirandola, M. Chiriva-Internati","doi":"10.1158/1538-7445.AM2021-247","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-247","url":null,"abstract":"The advancement of cures for cancer needs the development of novel, more efficacious, and more specific immunotherapeutic approaches through the discovery of novel target candidates displaying differential expression between healthy and malignant tissues. CancerDiff is a proprietary software module for the identification of potential new immunotherapeutic cancer targets that originate from differentially expressed, alternatively spliced transcripts. When utilized to analyze Ovarian Cancer (OV) datasets, CancerDiff identified a selectively upregulated mesothelin (MSLN) splice variant translated into a protein isoform (IsoMSLN) bearing a distinct unique peptide absent in the canonical protein sequence. To validate this prediction and to confirm the upregulation of IsoMSLN in OV, datasets from publicly available proteomic repositories were searched for its unique signature peptide. In agreement with CancerDiff prediction, IsoMSLN peptide was detected in 71% of OV samples and 61% of adjacent normal tissues. Molecular modeling tools predicted this peptide to be part of the extracellular portion of the protein in an antibody accessible region. These results indicate IsoMSLN unique peptide as a suitable target for immunotherapy for OV cancer. Citation Format: Lucia Piccotti, Leonardo Mirandola, Maurizio Chiriva-Internati. Identification of an ovarian cancer selective splice variant of mesothelin utilizing the Kiromic proprietary search engine CancerDiff [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 247.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74091036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 244: A computation method for noise reduction based on ultra-deep targeted sequencing data","authors":"Weiwei Bian, F. Kebede, Zongli Zheng","doi":"10.1158/1538-7445.AM2021-244","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-244","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74510641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 251: Data distribution for easy pancancer analysis","authors":"Caleb M Lindgren, Chelsie Minor, Lindsey K. Olsen, B. Henderson, Cptac Investigators, S. Payne","doi":"10.1158/1538-7445.AM2021-251","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-251","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74790788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract 257: Tumor mutation burden of Egyptian breast cancer patients based on next generation sequencing","authors":"Auhood Nassar, Ahmed M. Lymona, Mai M. Lotfy, A. Youssef, A. N. Zekri","doi":"10.1158/1538-7445.AM2021-257","DOIUrl":"https://doi.org/10.1158/1538-7445.AM2021-257","url":null,"abstract":"","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74792845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}