Lisa J. Croner

Stability engineering of scFvs for the development of bispecific and multivalent antibodies

Single-chain Fvs (scFvs) are commonly used building blocks for creating engineered diagnostic and therapeutic antibody molecules. Bispecific antibodies (BsAbs) hold particular interest due to their ability to simultaneously bind and engage two distinct targets. We describe a technology for producing stable, scalable IgG-like bispecific and multivalent antibodies based on methods for rapidly engineering thermally stable scFvs. Focused libraries of mutant scFvs were designed using a combination of sequence-based statistical analyses and structure-, and knowledge-based methods. Libraries encoding these designs were expressed in E. coli and culture supernatants-containing soluble scFvs screened in a high-throughput assay incorporating a thermal challenge prior to an antigen-binding assay. Thermally stable scFvs were identified that retain full antigen-binding affinity. Single mutations were found that increased the measured T(m) of either the V(H) or V(L) domain by as much as 14 degrees C relative to the wild-type scFv. Combinations of mutations further increased the T(m) by as much as an additional 12 degrees C. Introduction of a stability-engineered scFv as part of an IgG-like BsAb enabled scalable production and purification of BsAb with favorable biophysical properties.

Conserved amino acid networks involved in antibody variable domain interactions

Engineered antibodies are a large and growing class of protein therapeutics comprising both marketed products and many molecules in clinical trials in various disease indications. We investigated naturally conserved networks of amino acids that support antibody VH and VL function, with the goal of generating information to assist in the engineering of robust antibody or antibody‐like therapeutics. We generated a large and diverse sequence alignment of V‐class Ig‐folds, of which VH and VL domains are family members. To identify conserved amino acid networks, covariations between residues at all possible position pairs were quantified as correlation coefficients (ϕ‐values). We provide rosters of the key conserved amino acid pairs in antibody VH and VL domains, for reference and use by the antibody research community. The majority of the most strongly conserved amino acid pairs in VH and VL are at or adjacent to the VH–VL interface suggesting that the ability to heterodimerize is a constraining feature of antibody evolution. For the VH domain, but not the VL domain, residue pairs at the variable‐constant domain interface (VH–CH1 interface) are also strongly conserved. The same network of conserved VH positions involved in interactions with both the VL and CH1 domains is found in camelid VHH domains, which have evolved to lack interactions with VL and CH1 domains in their mature structures; however, the amino acids at these positions are different, reflecting their different function. Overall, the data describe naturally occurring amino acid networks in antibody Fv regions that can be referenced when designing antibodies or antibody‐like fragments with the goal of improving their biophysical properties. Proteins 2009.

A Plasma-Based Protein Marker Panel for Colorectal Cancer Detection Identified by Multiplex Targeted Mass Spectrometry.

INTRODUCTION Colorectal cancer (CRC) testing programs reduce mortality; however, approximately 40% of the recommended population who should undergo CRC testing does not. Early colon cancer detection in patient populations ineligible for testing, such as the elderly or those with significant comorbidities, could have clinical benefit. Despite many attempts to identify individual protein markers of this disease, little progress has been made. Targeted mass spectrometry, using multiple reaction monitoring (MRM) technology, enables the simultaneous assessment of groups of candidates for improved detection performance. MATERIALS AND METHODS A multiplex assay was developed for 187 candidate marker proteins, using 337 peptides monitored through 674 simultaneously measured MRM transitions in a 30-minute liquid chromatography-mass spectrometry analysis of immunodepleted blood plasma. To evaluate the combined candidate marker performance, the present study used 274 individual patient blood plasma samples, 137 with biopsy-confirmed colorectal cancer and 137 age- and gender-matched controls. Using 2 well-matched platforms running 5 days each week, all 274 samples were analyzed in 52 days. RESULTS Using one half of the data as a discovery set (69 disease cases and 69 control cases), the elastic net feature selection and random forest classifier assembly were used in cross-validation to identify a 15-transition classifier. The mean training receiver operating characteristic area under the curve was 0.82. After final classifier assembly using the entire discovery set, the 136-sample (68 disease cases and 68 control cases) validation set was evaluated. The validation area under the curve was 0.91. At the point of maximum accuracy (84%), the sensitivity was 87% and the specificity was 81%. CONCLUSION These results have demonstrated the ability of simultaneous assessment of candidate marker proteins using high-multiplex, targeted-mass spectrometry to identify a subset group of CRC markers with significant and meaningful performance.

Discovery and validation of a colorectal cancer classifier in a new blood test with improved performance for high-risk subjects

BackgroundThe aim was to improve upon an existing blood-based colorectal cancer (CRC) test directed to high-risk symptomatic patients, by developing a new CRC classifier to be used with a new test embodiment. The new test uses a robust assay format—electrochemiluminescence immunoassays—to quantify protein concentrations. The aim was achieved by building and validating a CRC classifier using concentration measures from a large sample set representing a true intent-to-test (ITT) symptomatic population.Methods4435 patient samples were drawn from the Endoscopy II sample set. Samples were collected at seven hospitals across Denmark between 2010 and 2012 from subjects with symptoms of colorectal neoplasia. Colonoscopies revealed the presence or absence of CRC. 27 blood plasma proteins were selected as candidate biomarkers based on previous studies. Multiplexed electrochemiluminescence assays were used to measure the concentrations of these 27 proteins in all 4435 samples. 3066 patients were randomly assigned to the Discovery set, in which machine learning was used to build candidate classifiers. Some classifiers were refined by allowing up to a 25% indeterminate score range. The classifier with the best Discovery set performance was successfully validated in the separate Validation set, consisting of 1336 samples.ResultsThe final classifier was a logistic regression using ten predictors: eight proteins (A1AG, CEA, CO9, DPPIV, MIF, PKM2, SAA, TFRC), age, and gender. In validation, the indeterminate rate of the new panel was 23.2%, sensitivity/specificity was 0.80/0.83, PPV was 36.5%, and NPV was 97.1%.ConclusionsThe validated classifier serves as the basis of a new blood-based CRC test for symptomatic patients. The improved performance, resulting from robust concentration measures across a large sample set mirroring the ITT population, renders the new test the best available for this population. Results from a test using this classifier can help assess symptomatic patients’ CRC risk, increase their colonoscopy compliance, and manage next steps in their care.