Therapeutic Antibodies against Intracellular Tumor Antigens

Trenevska, Iva; Li, Demin; Banham, Alison H.

doi:10.3389/fimmu.2017.01001

Cited by 99 publications

(77 citation statements)

References 118 publications

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“…CART‐19 therapy has vastly improved overall survival in certain B cell lymphomas relative to past standard of care, 87,88 but both normal and malignant B cells are eliminated. Whereas CAR T cells to date target cell surface proteins, TCR “mimic” (TCRm) antibodies have been described that bind tumor associated antigens in the context of HLA 89 . Thus, NeoAg‐reactive TCRm antibodies could be used with current CAR technology to provide specificity against NeoAgs in the near future.…”

Section: Help In Therapeutic Contextmentioning

confidence: 99%

“…Whereas CAR T cells to date target cell surface proteins, TCR "mimic" (TCRm) antibodies have been described that bind tumor associated antigens in the context of HLA. 89 Thus, NeoAg-reactive TCRm antibodies could be used with current CAR technology to provide specificity against NeoAgs in the near future. Surprisingly, recent mouse studies have highlighted that CD4 + CAR T cells alone mediate superior antitumor activity relative to their CD8 + counterparts.…”

Section: Adoptive Cellular Therapymentioning

confidence: 99%

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Harnessing neoantigen specific CD4 T cells for cancer immunotherapy

Brightman

Naradikian

Miller

et al. 2020

Journal of Leukocyte Biology

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The goal of precision immunotherapy is to direct a patient's T cell response against the immunogenic mutations expressed on their tumors. Most immunotherapy approaches to‐date have focused on MHC class I‐restricted peptide epitopes by which cytotoxic CD8+ T lymphocytes (CTL) can directly recognize tumor cells. This strategy largely overlooks the critical role of MHC class II‐restricted CD4+ T cells as both positive regulators of CTL and other effector cell types, and as direct effectors of antitumor immunity. In this review, we will discuss the role of neoantigen specific CD4+ T cells in cancer immunotherapy and how existing treatment modalities may be leveraged to engage this important T cell subset.

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Section: Help In Therapeutic Contextmentioning

confidence: 99%

Section: Adoptive Cellular Therapymentioning

confidence: 99%

Harnessing neoantigen specific CD4 T cells for cancer immunotherapy

Brightman

Naradikian

Miller

et al. 2020

Journal of Leukocyte Biology

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“…While this feature is critical for the protection of the cell, it also prevents entry of many promising therapeutic macromolecules, stifling the development of biopharmaceuticals for diseases with intracellular targets. (8)(9)(10)(11)(12) Antisense oligonucleotides have emerged as valuable tools for functional genomics, target validation, and more recently as therapeutics. (13) Structural modifications to the DNA backbone imparted high hybridization affinity to mRNA targets as well as greater stability towards nucleases.…”

Section: Main Textmentioning

confidence: 99%

Interpretable Deep Learning for De Novo Design of Cell-Penetrating Abiotic Polymers

Schissel

Mohapatra

Wolfe

et al. 2020

Preprint

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There are more amino acid permutations within a 40-residue sequence than atoms on Earth. This vast chemical search space hinders the use of human learning to design functional polymers. Here we couple supervised and unsupervised deep learning with highthroughput experimentation to drive the design of high-activity, novel sequences reaching 10 kDa that deliver antisense oligonucleotides to the nucleus of cells. The models, in which natural and unnatural residues are represented as topological fingerprints, decipher and visualize sequenceactivity predictions. The new variants boost antisense activity by 50-fold, are effective in animals, are nontoxic, and can also deliver proteins into the cytosol. Machine learning can discover functional polymers that enhance cellular uptake of biotherapeutics, with significant implications toward developing therapies for currently untreatable diseases.One sentence summary: Deep learning generates de novo large functional abiotic polymers that deliver antisense oligonucleotides to the nucleus. typically occurs by energy-dependent uptake, meaning that endosomal escape presents an additional challenge. While some peptides can efficiently escape the endosome, designing a novel CPP sequence for this task is nearly impossible. In addition to the diversity of physicochemical properties of CPPs, variation in experimental design has resulted in inconsistent and sometimes contradictory datasets.(22) These inconsistencies preclude establishing sequence-activity relationships to guide the design of next-generation CPPs and can be remedied by testing PMO-CPP conjugates in a nuclear delivery-based assay that provides quantitative activity data and selects for sequences that can escape the endosome. In order to uncover CPP design principles for PMO delivery, it is necessary to have a standardized, biologically relevant dataset with which to train machine learning models.

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“…Given the similarity in the genetic mechanisms, fold and the limited conformational variability in the canonical CDRs, it might be reasonable to expect structural similarity between TCRs and antibodies. Comparing TCRs and antibodies should identify potential characteristics that may inspire antibody-like TCR design, TCR-mimic antibodies and soluble TCRs (14, 16, 52, 57). Such analyses can also highlight structural signatures that may relate to their different biological functions, such as MHC restriction in TCRs (5) and the virtually unconstrained antigen binding in antibodies (46).…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of the CDR loops of antigen receptors

Wong

Leem

Deane

2019

Preprint

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5The adaptive immune system uses two main types of antigen receptors: T-cell receptors (TCRs) 6 and antibodies. While both proteins share a globally similar β-sandwich architecture, TCRs are 7 specialised to recognise peptide antigens in the binding groove of the major histocompatibility 8 complex, while antibodies can bind an almost infinite range of molecules. For both proteins, 9 the main determinants of target recognition are the complementarity-determining region (CDR) 10 loops. Five of the six CDRs adopt a limited number of backbone conformations, known as the 11 'canonical classes'; the remaining CDR (β3 in TCRs and H3 in antibodies) is more structurally 12 diverse. In this paper, we first update the definition of canonical forms in TCRs, build an auto-13 updating sequence-based prediction tool (available at http://opig.stats.ox.ac.uk/resources) and 14 demonstrate its application on large scale sequencing studies. Given the global similarity of TCRs 15 and antibodies, we then examine the structural similarity of their CDRs. We find that TCR and 16 antibody CDRs tend to have different length distributions, and where they have similar lengths, 17 they mostly occupy distinct structural spaces. In the rare cases where we found structural simi-18 larity, the underlying sequence patterns for the TCR and antibody version are different. Finally, 19 where multiple structures have been solved for the same CDR sequence, the structural variability 20 in TCR loops is higher than that in antibodies, suggesting TCR CDRs are more flexible. These 21 structural differences between TCR and antibody CDRs may be important to their different bio-22 logical functions. 23 1 Introduction 24The adaptive immune system defends the host organism against a wide range of foreign molecules, 25 or antigens, using two types of receptors: T-cell receptors (TCRs) and antibodies (23). TCRs typi-26 cally recognise peptide antigens presented via the major histocompatibility complex (MHC; 44), 27 while antibodies can bind almost any antigen, including proteins, peptides, and haptens (46). 28Despite their different roles in the immune response, these proteins share a β-sandwich fold (Fig-29 ure 1; 16, 51).30 In humans, most TCRs are αβTCRs, consisting of one TCRα chain and one TCRβ chain, while 31 most antibodies are comprised of two heavy(H)-light(L) chain dimers (Figure 1). All four types 32 of chains (α, β, H and L) are formed from the somatic rearrangement of the respective V, D, and 33 J genes of the TCR or antibody loci. The random combination of these genes, alongside further 34 diversification mechanisms (e.g. random nucleotide addition), are estimated to yield trillions of 35 unique TCRs and antibodies (5, 20). TCRα and antibody light chains are made from the V and J 36 genes, while TCRβ and antibody heavy chains are assembled from the V, D, and J genes, making 37 the L-chain equivalent to α-chain and H-chain equivalent to β-chain (5, 45). In both types of 38 antigen receptors, sequence and structural diversity is concentrated i...

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Therapeutic Antibodies against Intracellular Tumor Antigens

Cited by 99 publications

References 118 publications

Harnessing neoantigen specific CD4 T cells for cancer immunotherapy

Harnessing neoantigen specific CD4 T cells for cancer immunotherapy

Interpretable Deep Learning for De Novo Design of Cell-Penetrating Abiotic Polymers

Comparative analysis of the CDR loops of antigen receptors

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