The discriminatory power of the T cell receptor.
Ontology highlight
ABSTRACT:
SUBMITTER: Pettmann J
PROVIDER: S-EPMC8219380 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MOTIVATION: RNA family models group nucleotide sequences that share a common biological function. These models can be used to find new sequences belonging to the same family. To succeed in this task, a model needs to exhibit high sensitivity as well as high specificity. As model construction is guided by a manual process, a number of problems can occur, such as the introduction of more than one model for the same family or poorly constructed models. We explore the Rfam database to discover such problems. RESULTS: Our main contribution is in the definition of the discriminatory power of RNA family models, together with a first algorithm for its computation. In addition, we present calculations across the whole Rfam database that show several families lacking high specificity when compared to other families. We give a list of these clusters of families and provide a tentative explanation. Our program can be used to: (i) make sure that new models are not equivalent to any model already present in the database; and (ii) new models are not simply submodels of existing families. AVAILABILITY: www.tbi.univie.ac.at/software/cmcompare/. The code is licensed under the GPLv3. Results for the whole Rfam database and supporting scripts are available together with the software.
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Project description:Precise discrimination of tumor from normal tissues remains a major roadblock for therapeutic efficacy of chimeric antigen receptor (CAR) T cells. Here, we perform a comprehensive in silico screen to identify multi-antigen signatures that improve tumor discrimination by CAR T cells engineered to integrate multiple antigen inputs via Boolean logic, e.g., AND and NOT. We screen >2.5 million dual antigens and ∼60 million triple antigens across 33 tumor types and 34 normal tissues. We find that dual antigens significantly outperform the best single clinically investigated CAR targets and confirm key predictions experimentally. Further, we identify antigen triplets that are predicted to show close to ideal tumor-versus-normal tissue discrimination for several tumor types. This work demonstrates the potential of 2- to 3-antigen Boolean logic gates for improving tumor discrimination by CAR T cell therapies. Our predictions are available on an interactive web server resource (antigen.princeton.edu).
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Project description:A panel of 18 strains of Neisseria gonorrhoeae, known to be temporally and geographically diverse, was used to evaluate a number of typing systems, including conventional auxotyping and serotyping and the molecular methods of arbitrarily primed PCR (AP-PCR), amplified ribosomal-DNA restriction analysis (ARDRA), opa typing, and pulsed-field gel electrophoresis (PFGE). The discriminatory power of the different typing methods were determined with a collection of 87 clinical isolates from commercial sex workers in Indonesia, and Simpson's index of diversity was calculated. Of the two traditional techniques, auxotyping and serotyping, the latter gives the highest discriminatory index (DI) (DI, 0.846). The combination of auxotyping and serotyping yields a high DI (DI, 0. 928). D11344- and D8635-primed PCR showed low DIs of 0.608 and 0.622, respectively, but a combination of the two primers had a DI of 0. 849. The combination of serotyping with D11344-primed or D8635-primed PCR resulted in DIs of 0.936 and 0.937, respectively. ARDRA revealed a low DI of 0.743 alone but a DI of 0.955 in combination with serotyping. PFGE using the restriction enzyme BglII and opa typing produced the highest discrimination (DIs, 0.997 and 0. 996, respectively) for isolates of N. gonorrhoeae.
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Project description:Technology advances in genome-wide association studies (GWAS) has engendered optimism that we have entered a new age of precision medicine, in which the risk of breast cancer can be predicted on the basis of a person's genetic variants. The goal of this study is to evaluate the discriminatory power of common genetic variants in breast cancer risk estimation. We conducted a retrospective case-control study drawing from an existing personalized medicine data repository. We collected variables that predict breast cancer risk: 153 high-frequency/low-penetrance genetic variants, reflecting the state-of-the-art GWAS on breast cancer, mammography descriptors and BI-RADS assessment categories in the Breast Imaging Reporting and Data System (BI-RADS) lexicon. We trained and tested naïve Bayes models by using these predictive variables. We generated ROC curves and used the area under the ROC curve (AUC) to quantify predictive performance. We found that genetic variants achieved comparable predictive performance to BI-RADS assessment categories in terms of AUC (0.650 vs. 0.659, p-value = 0.742), but significantly lower predictive performance than the combination of BI-RADS assessment categories and mammography descriptors (0.650 vs. 0.751, p-value < 0.001). A better understanding of relative predictive capability of genetic variants and mammography data may benefit clinicians and patients to make appropriate decisions about breast cancer screening, prevention, and treatment in the era of precision medicine.
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Project description:Three lung cancer (LC) models have recently been constructed to predict an individual's absolute risk of LC within a defined period. Given their potential application in prevention strategies, a comparison of their accuracy in an independent population is important.We used data for 3197 patients with LC and 1703 cancer-free controls recruited to an ongoing case-control study at the Harvard School of Public Health and Massachusetts General Hospital. We estimated the 5-year LC risk for each risk model and compared the discriminatory power, accuracy, and clinical utility of these models.Overall, the Liverpool Lung Project (LLP) and Spitz models had comparable discriminatory power (0.69), whereas the Bach model had significantly lower power (0.66; P=0.02). Positive predictive values were highest with the Spitz models, whereas negative predictive values were highest with the LLP model. The Spitz and Bach models had lower sensitivity but better specificity than did the LLP model.We observed modest differences in discriminatory power among the three LC risk models, but discriminatory powers were moderate at best, highlighting the difficulty in developing effective risk models.
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Project description:BackgroundSeveral technical hurdles and limitations have restricted the use of IS6110 restriction fragment length polymorphism (IS6110 RFLP), the most effective typing method for detecting recent tuberculosis (TB) transmission events. This has prompted us to conceive an alternative modality, IS6110-5'3'FP, a plasmid-based cloning approach coupled to a single PCR amplification of differentially labeled 5' and 3' IS6110 polymorphic ends and their automated fractionation on a capillary sequencer. The potential of IS6110-5'3'FP to be used as an alternative to IS6110 RFLP has been previously demonstrated, yet further technical improvements are still required for optimal discriminatory power and versatility.ObjectivesHere we introduced critical amendments to the original IS6110-5'3'FP protocol and compared its performance to that of 24-loci multiple interspersed repetitive unit-variable number tandem repeats (MIRU-VNTR), the current standard method for TB transmission analyses.MethodsIS6110-5'3'FP protocol modifications involved: (i) the generation of smaller-sized polymorphic fragments for efficient cloning and PCR amplification, (ii) omission of the plasmid amplification step in E. coli for shorter turnaround times, (iii) the use of more stable fluorophores for increased sensitivity, (iv) automated subtraction of background fluorescent signals, and (v) the automated conversion of fluorescent peaks into binary data.ResultsIn doing so, the overall turnaround time of IS6110-5'3'FP was reduced to 4 hours. The new protocol allowed detecting almost all 5' and 3' IS6110 polymorphic fragments of any given strain, including IS6110 high-copy number Beijing strains. IS6110-5'3'FP proved much more discriminative than 24-loci MIRU-VNTR, particularly with strains of the M. tuberculosis lineage 4.ConclusionsThe IS6110-5'3'FP protocol described herein reached the optimal discriminatory potential of IS6110 fingerprinting and proved more accurate than 24-loci MIRU-VNTR in estimating recent TB transmission. The method, which is highly cost-effective, was rendered versatile enough to prompt its evaluation as an automatized solution for a TB integrated molecular surveillance.
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Project description:We explored the relevance of a Clustered regularly interspaced short palindromic repeats (CRISPR)-based genotyping tool for Streptococcus agalactiae typing and we compared this method to current molecular methods [multi locus sequence typing (MLST) and capsular typing]. To this effect, we developed two CRISPR marker schemes (using 94 or 25 markers, respectively). Among the 255 S. agalactiae isolates tested, 229 CRISPR profiles were obtained. The 94 and 25 markers made it possible to efficiently separate isolates with a high diversity index (0.9947 and 0.9267, respectively), highlighting a high discriminatory power, superior to that of both capsular typing and MLST (diversity index of 0.9017 for MLST). This method has the advantage of being correlated with MLST [through analysis of the terminal direct repeat (TDR) and ancestral spacers] and to possess a high discriminatory power (through analysis of the leader-end spacers recently acquired, which are the witnesses of genetic mobile elements encountered by the bacteria). Furthermore, this "one-shot" approach presents the benefit of much-reduced time and cost in comparison with MLST. On the basis of these data, we propose that this method could become a reference method for group B Streptococcus (GBS) typing.
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