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the official website of the antibody society

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FDA approves anti-LAG-3 relatlimab-rmbw as part of a combination therapy for melanoma

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On March 18, 2022, Bristol Myers Squibb announced that Opdualag a fixed-dose combination of anti-PD-1 nivolumab and relatlimab-rmbw, administered as a single intravenous infusion, was approved by the U.S. Food and Drug Administration (FDA) for the treatment of adult and pediatric patients 12 years of age or older with unresectable or metastatic melanoma.

Relatlimab (BMS-986016, ONO4482) is a human IgG4k antibody that targets LAG-3, which, like PD-1, is an immune checkpoint. Bristol Myers Squibb and Ono have a strategic collaboration agreement to jointly develop and commercialize multiple immunotherapies – as single agents and combination regimens – for patients with cancer in Japan, South Korea, and Taiwan. An marketing authorization application is undergoing evaluation by the European Medicines Agency.

FDA’s approval was based on data from the Phase 2/3 RELATIVITY-047 trial (NCT03470922), which evaluated the effects of relatlimab combined with nivolumab versus nivolumab in a total of 714 patients with previously untreated metastatic or unresectable melanoma. Patients were randomized 1:1 and administered a fixed-dose combination of 160 mg relatlimab and 480 mg nivolumab or 480 mg nivolumab by intravenous infusion every 4 weeks until disease recurrence, unacceptable toxicity or withdrawal of consent. The study’s primary endpoint, progression-free survival (PFS) by blinded independent central review, was met. The median PFS in the group that received both relatlimab and nivolumab (n=355) was significantly longer (10.1 months [95% CI, 6.4–15.7]) than in the group that received nivolumab only (4.6 months [95% CI, 3.4–5.6]; hazard ratio: 0.75 [95% CI, 0.6–0.9]; P = 0.0055). [1]

1. Tawbi HA, Schadendorf D, Lipson EJ, et al. Relatlimab and nivolumab versus nivolumab in untreated advanced melanoma. N Engl J Med. 2022;386:24-34.

Opdualag is the 4th antibody-based therapeutic granted a first approval for marketing in the EU or US in 2022. Explore our searchable table of antibody therapeutics approved in the EU or US for details.

Discovering and Targeting Neo-epitopes in Cancer

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Antibody Engineering & Therapeutics, held in December 2021, offered many opportunities to hear exciting and informative presentations by experts in the field. We are pleased to present here a summary of a plenary lecture by Prof. James Wells (USCF), kindly written by Dr. Czeslaw Radziejewski.

 


Discovering and Targeting Neo-epitopes in Cancer.
James Wells, Professor and Chair, Department of Pharmaceutical Chemistry, UCSF

Professor Wells presented the plenary lecture on the identification of cancer-associated proteolytic neo-epitopes in cell membrane proteins and the identification of novel cancer-specific MHC-1 peptide complexes. Cell surface proteins are the targets of most biologic and small molecule drugs. Professor Wells and colleagues use cell surface proteomics to examine changes in the cell surface proteins upon transformation with oncogenes such as KRAS, HER2, EGFR, BRAF, MEK, and Myc. Ecto-domains of identified proteins, which generally belong to the single pass trans-membrane class, are expressed as Fc fusion proteins and antibodies are generated against these proteins via screening phage libraries. Specificities of the antibodies are verified by testing against full-length trans-membrane proteins expressed by cells transfected with appropriate vectors.

Proteolysis is a primary post-translational modification of cell surface proteins. There are approximately 500 human proteases, and proteolysis plays an important role in disease progression, such as angiogenesis, invasion and metastasis, inflammation, and immune evasion. Well’s lab is exploring methods to identify proteolytic cleavage sites on the surfaceome of cancer cells.[1] To accomplish this, they devised a technology called N-terminomics, which uses the peptide ligase called subtiligase. Subtiligase ligates peptide esters to the N-terminus of a protein or a peptide. This enzyme can be used for other purposes, such as peptide cyclization and protein bioconjugation. The lab used peptides tagged with biotin or fluorescently labelled in conjunction with mass spectrometry to identify sites of proteolytic cleavage.[2,3] Prof. Wells showed an example of this strategy used to identify sites of cleavage by caspase in the proteome of a human cell line in which apoptosis was induced. This approach, however, identified only a limited number of cleaved proteins. In the next implementation of the strategy, cells were directly transfected with subtiligase. This strategy allowed the identification of hundreds of extracellular proteins that were proteolytically modified.

The newest strategy invented in Prof. Wells’ lab (unpublished) involves tethering subtiligase to glycans of cell surface proteins instead of transacting cells. Using this latest strategy in Kras-transformed cells, 611 cell surface cleavage events were observed. In HER2-transfected cells, 267 cleavage events were observed and the majority of events were not related to cleavage of signal peptide from extracellular proteins. Interestingly, the extent of proteolytic modification of some proteins in oncogene-transformed cells can either increase or decrease. Similarly, expression levels of the same proteins also change in both directions. N-terminomics of Kras- and HER2-transformed cells was thus different.

This study also identified an interesting protein called CDCP1, which has cleavage and expression that is upregulated in pancreatic cancer. The cleavage is indeed specific to cancer cells. Three closely nested cleavage sites were found in CDCP1. Antibodies (CL03.2) were developed in the lab against the cleaved form  of CDCP1. Cells containing the cleaved form were efficiently killed by the anti-CDCP1 antibody formatted as an antibody-drug conjugate (ADC). In Jurkat cells, an anti-CD3/anti-CDCP1 bispecific single-chain variable fragment showed killing activity. For in vivo studies, mouse-specific antibodies toward the truncated form of CDCP1 were generated and used to produce an auristatin (MMAF)-based ADC. An ADC against the truncated form of CDCp1 was well tolerated in non-tumor-bearing mouse, but the animals lost weight when treated with an ADC targeting the full-length protein. In a study of mice bearing xenograph tumors, the animals were administered antibody against the truncated form that was radiolabeled with isotope Lu 177 and a dramatic decrease of tumor growth was observed.

[Read more…]

Announcing AIRR Community Service Prize 2022 Nominations!

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The lifeblood of the AIRR Community is its members and others who join us, typically on a volunteer and uncompensated basis, to advance the AIRR Community mission to standardize, analyze and share AIRR-seq data. The AIRR Community Service Prize recognizes individuals who have contributed to the AIRR Community in significant and often under-appreciated ways.

 

 

Activities exemplifying the award criteria include:

  • Participating in one or more Working Groups or Sub-committees
  • Mentoring junior or trainee members
  • Assisting with organizing meetings, publications, or other communications 
  • Contributing to the AIRR Data Commons, e.g. substantial reviewing or data deposits
  • Other tasks that, in the opinion of AIRR Community leaders, constitute selfless acts of citizenship that reflect our core mission and values

The AIRR Community Service Prize(s) recipient(s) will be announced and awarded at the Gala Dinner during the AIRR Community Meeting VI in May 2022.

Nomination Criteria:

  • Must be a Member to nominate
  • Nominate more than one nominee
  • Don’t need to be a Member to receive the award
  • Nominations close April 10th, 2022

 Click here to log in to vote (AIRR Community Members only).

Application of Machine Learning and Informatics in Antibody and Protein Research

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Antibody Engineering & Therapeutics, held in December 2021, offered many opportunities to hear exciting and informative presentations by experts in the field. We are pleased to present here a summary of a plenary lecture by Prof. Charlotte Deane (University of Oxford), kindly written by Dr. Czeslaw Radziejewski.

Application of Machine Learning and Informatics in Antibody and Protein Research
Charlotte Deane, Professor of Structural Bioinformatics, Department of Statistics, University of Oxford

Machine learning relies heavily on the availability of large databases. Three databases for antibody research were developed in Prof. Dean’s lab: OAS (Observed Antibody Space),[1] SAbDab (Structural Antibody Database),[2] and Thera-SAbDab (database of immunotherapeutic variable domain sequences). OAS contains about 2 billion redundant antibody sequences across diverse immune states, organisms, and individuals. SAbDab is a fully automated self-updating collection of publicly available antibody structure data. It contains 5650 structures, but about 1000 truly non-redundant structures, 4213 antigen-antibody complexes and 890 structures of nanobodies. Thera-SabDob contains 696 structures as of October 2021. In addition, the lab has a CoV-AbDab database that contains sequences and structures for coronavirus antibodies for SARS-CoV-2, SARS-CoV-1 and MERS-CoV. This database contains about 5000 data points. The lab developed the SAbPred suite of tools for antibody prediction, comprising AntibodyBuilder, SPHINX, SCALOP, PEARS, ANARCI, ABangle, Hu-mAb SAAB+,TAP, Epitope Profiling SPACE and Ab-Ligaty. SCALOP, ABodybuilder, SPHINX are designed for building antibody models. ABlooper tool builds complementary-determining region (CDR) structures. ABangle is a tool for calculating and analyzing the VH-VL orientation in antibodies. TAP (Therapeutic Antibody Profiler) considers the drug-like properties of therapeutic antibodies.[3] It evaluates variable domains in antibody of interest using five developability criteria derived from post clinical Phase 1 antibody therapeutics. Epitope Profiling-SPACE and Paratyping Ab-Ligity can used to determine if two antibodies with divergent sequences can bind to the same epitope.[4] ANARCI is a tool for annotating antibody sequences and Hu-Mab is a computational tool for antibody humanization. Dlab is a deep learning method for virtual screening of antibody sequences that can bind specific antigens.

Professor Deane provided examples of using some of her computational tools. Antibody humanization is currently inefficient, as it is carried out experimentally in a largely trial and error process. Applying machine learning to an edited OAS database (with redundancies removed) led to classifiers that could distinguish between human and non-human antibody variable domain sequences. These classifiers were used to create the computational humanization tool Hu-mAb. Available sequences of therapeutic antibodies from different stages of development were subjected to Hu-mAb analysis. The high Hu-mAb scores correlated with low observed immunogenicity of an antibody and low scores correlated with higher observed immunogenicity. Twenty-five experimentally humanized antibody sequences for which rodent or rabbit precursor sequences were available were assessed by Hu-mAb. Most of the mutations that Hu-mAb generated were either the same or chemically similar for VH (77% and 85%, respectively) and for VL (59% and 58%, respectively). Hu-mAb suggested overall fewer mutations and fewer mutations to VH-VL interface than the experimental approach, therefore such humanized antibodies would more likely have preserved structure and function.

The Therapeutic Antibody Profiler evaluates properties thought to determine antibody developability, including CDRH3 or total CDR length; patches of surface hydrophobicity across CDR vicinity; patches of positive charges and negative charges across CDR vicinity; and structural Fv charge symmetry. These properties are related to aggregation, viscosity, poor expression and polyspecificity of antibody molecules.[5] TAP was applied in a study that used 137 post Phase 1 therapeutic models,14000 representative Human Antibody Models and 2 datasets of MedImmune Developability Failures. The study revealed that therapeutic antibodies tend to have shorter CDRH3 and smaller hydrophobic patches than natural ones. However, positive and negative patches of natural and therapeutic antibodies have similar profiles and Fv charge symmetry is also very similar. Both therapeutic and natural antibodies have an aversion to strongly oppositely charged VH and VL chains.

ABlooper [6] uses similar architecture as AlphaFold. It predicts structures of all six CDR loops and estimates the accuracy of prediction. The root-mean-square deviation from AlphaFold2 for CDRH3 prediction (2.87A) were comparable with ABlooper (2.49 A). Unlike AlphaFold2, ABlooper generates a series of predicted structures from which a prediction of accuracy can be estimated. If the predicted structures are widely divergent, then the quality of prediction is low. ABlooper is also much faster than other deep learning methods such as AlphaFold (100 structures predicted in 5 second vs one structure in 20 min). All tools are available freely for academic institutions.

  1. Olsen TH, Boyles F, Deane CM. Observed Antibody Space: A diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences. Protein Sci. 2022 Jan;31(1):141-146. doi: 10.1002/pro.4205.
  2. Schneider C, Raybould MIJ, Deane CM. SAbDab in the age of biotherapeutics: updates including SAbDab-nano, the nanobody structure tracker. Nucleic Acids Res. 2022 Jan 7;50(D1):D1368-D1372. doi: 10.1093/nar/gkab1050.
  3. Raybould MIJ, Deane CM. The Therapeutic Antibody Profiler for Computational Developability Assessment. Methods Mol Biol. 2022;2313:115-125. doi: 10.1007/978-1-0716-1450-1_5.
  4. Wong et al. Ab-Ligity: identifying sequence-dissimilar antibodies that bind to the same epitope. MAbs 2021. DOI: 10.1080/19420862.2021.1873478.
  5. Khetan et al. Current advances in biopharmaceutical informatics: guidelines, impact and challenges in the computational developability assessment of antibody therapeutics. MAbs 2022. DOI: 10.1080/19420862.2021.2020082.
  6. Abanades B, Georges G, Bujotzek A, Deane CM. ABlooper: Fast accurate antibody CDR loop structure prediction with accuracy estimation. Bioinformatics. 2022 Jan 31:btac016. doi: 10.1093/bioinformatics/btac016.

AIRR-C Meeting VI – Registration Is Live!

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The AIRR-C Meetings Sub-committee is excited to announce that registration is live!  Be sure to sign up soon to take advantage of the early bird discount!

AIRR Community Meeting VI “Exploring New Frontiers” will be held in La Jolla, CA from May 17-19, 2022.

REGISTER NOW

Exploring New Frontiers:  This meeting has two themed “Challenge Sessions” meant to:
(i) Initiate and implement a strategic plan for the AIRR Community that integrates the Working Groups’ activities toward the central goal of universally accepted AIRR-seq data standards;
(ii) Introduce the Community to multidimensional systems approaches for characterizing immune responses, and how AIRR-seq data can benefit such approaches.

 

Meetings and presentations will take place at the Hilton La Jolla Torrey Pines and a hotel room block has been arranged at a discounted rate. The meeting will provide opportunities for investigators and trainees to network, participate in AIRR Community Working Groups and Sub-committee meetings, enjoy world class presentations, poster sessions, and deep dive tutorials.

Sign up today!