Biology subjects

Davis, P.

Publications and source records attributed to Davis, P..

4 recordsLinked to original sources

bioRxiv2021

Fast off-rate CD229 chimeric antigen receptor T cells efficiently target multiple myeloma, spare T cells, and exhibit reduced trogocytosis

T cells expressing chimeric antigen receptors have shown remarkable therapeutic activity against different types of cancer. However, their wider use has been hampered by the potential for life-threatening toxicities due to the unintended targeting of healthy cells expressing low levels of the targeted antigen. We have now developed an affinity-tuning approach for the generation of minimally modified, low-affinity antibody variants derived from existing high-affinity antibodies. Using this approach, we engineered low affinity variants of the fully human CD229-specific antibody 2D3. Parental 2D3 originally efficiently targeted multiple myeloma cells but also healthy T cells expressing low levels of CD229. We demonstrate that CAR T cells based on a low affinity variant of 2D3, engineered to also express CJUN to increase CAR T cell expansion, maintain the parental antibodys anti-tumor activity but lack its targeting of healthy T cells in vitro and in vivo. In addition, we found that low affinity CD229 CAR T cells show reduced trogocytosis potentially augmenting CAR T cell persistence. The fast off-rate CAR produced using our affinity tuning approach eliminates a key liability of CD229 CAR T cells and paves the way for the effective and safe treatment of patients with multiple myeloma and other lymphoid malignancies. One sentence summaryRational T cell engineering yields low affinity CD229 CAR T cells overexpressing CJUN, which maintain the parental cells anti-tumor activity but eliminate killing of healthy T cells, increasing CAR T cell expansion, and decreasing trogocytosis.

bioengineering↗

bioRxiv2021

A Genotype-to-Phenotype Modeling Framework to Predict Human Pathogenicity of Novel Coronaviruses

Leveraging prior viral genome sequencing data to make predictions on whether an unknown, emergent virus harbors a phenotype-of-concern has been a long-sought goal of genomic epidemiology. A predictive phenotype model built from nucleotide-level information alone has previously been considered un-tenable with respect to RNA viruses due to the ultra-high intra-sequence variance of their genomes, even within closely related clades. Building from our prior work developing a degenerate k-mer method to accommodate this high intra-sequence variation of RNA virus genomes for modeling frameworks, and leveraging a taxonomic group-shuffle-split paradigm on complete coronavirus assemblies from prior to October 2018, we trained multiple regularized logistic regression classifiers at the nucleotide k-mer level capable of accurately predicting withheld SARS-CoV-2 genome sequences as human pathogens and accurately predicting withheld Swine Acute Diarrhea Syndrome coronavirus (SADS-CoV) genome sequences as non-human pathogens. LASSO feature selection identified several degenerate nucleotide predictor motifs with high model coefficients for the human pathogen class that were present across widely disparate classes of coronaviruses. However, these motifs differed in which genes they were present in, what specific codons were used to encode them, and what the translated amino acid motif was. This emphasizes the importance of a phenetic view of emerging pathogenic RNA viruses, as opposed to the canonical phylogenetic interpretations most-commonly used to track and manage viral zoonoses. Applying our model to more recent Orthocoronavirinae genomes deposited since October 2018 yields a novel contextual view of pathogen-potential across bat-related, canine-related, porcine-related, and rodent-related coronaviruses and critical adaptations which may have contributed to the emergence of the pandemic SARS-CoV-2 virus. Finally, we discuss the utility of these predictive models (and their associated predictor motifs) to novel biosurveillance protocols that substantially increase the pound-for-pound information content of field-collected sequencing data and make a strong argument for the necessity of routine collection and sequencing of zoonotic viruses.

bioinformatics↗

bioRxiv2021

Comparative assessment of genes driving cancer and somatic evolution.

Genetic alterations of somatic cells can drive nonmalignant clone formation and promote cancer initiation. However, the link between these processes remains unclear hampering our understanding of tissue homeostasis and cancer development. Here we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and noncancer somatic evolution in 122 cancer types and 12 noncancer tissues. Mapping the alterations of these genes in 7953 pancancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and noncancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation. Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and noncancer somatic drivers, their literature support and properties are accessible at http://www.network-cancer-genes.org/.

cancer biology↗

bioRxiv2020

Vorpal: A Novel RNA Virus Feature-Extraction Algorithm Demonstrated Through Interpretable Genotype-to-Phenotype Linear Models

In the analysis of genomic sequence data, so-called "alignment free" approaches are often selected for their relative speed compared to alignment-based approaches, especially in the application of distance comparisons and taxonomic classification1,2,3,4. These methods are typically reliant on excising K-length substrings of the input sequence, called K-mers5. In the context of machine learning, K-mer based feature vectors have been used in applications ranging from amplicon sequencing classification to predictive modeling for antimicrobial resistance genes6,7,8. This can be seen as an analogy of the "bag-of-words" model successfully employed in natural language processing and computer vision for document and image classification9,10. Feature extraction techniques from natural language processing have previously been analogized to genomics data11; however, the "bag-of-words" approach is brittle in the RNA virus space due to the high intersequence variance and the exact matching requirement of K-mers. To reconcile the simplicity of "bag-of-words" methods with the complications presented by the intrinsic variance of RNA virus space, a method to resolve the fragility of extracted K-mers in a way that faithfully reflects an underlying biological phenomenon was devised. Our algorithm, Vorpal, allows the construction of interpretable linear models with clustered, representative degenerate K-mers as the input vector and, through regularization, sparse predictors of binary phenotypes as the output. Here, we demonstrate the utility of Vorpal by identifying nucleotide-level genomic motif predictors for binary phenotypes in three separate RNA virus clades; human pathogen vs. non-human pathogen in Orthocoronavirinae, hemorrhagic fever causing vs. non-hemorrhagic fever causing in Ebolavirus, and human-host vs. non-human host in Influenza A. The capacity of this approach for in silico identification of hypotheses which can be validated by direct experimentation, as well as identification of genomic targets for preemptive biosurveillance of emerging viruses, is discussed. The code is available for download at https://github.com/mriglobal/vorpal.

bioinformatics↗