Synthesis and Activity of T-Cell Tumor-Directing MegaMolecules.

This paper describes the synthesis, characterization, and functional activity of 26 MegaMolecule-based bispecific antibody mimics for T-cell redirection toward HER2+ cancer cells. The work reports functional bispecific MegaMolecules that bind both receptor targets, and recruit and activate T-cells resulting in lysis of the target tumor cells. Changing the orientation of linkage between Fabs against either HER2 or CD3ε results in an approximately 150-fold range in potency. Increasing scaffold valency from Fab dimers up to tetramers improves the potency of the antibody mimics up to 5-fold, but with diminishing returns in effective dose beyond trimeric formats. Antibody mimics that present either one or two Fabs against either receptor target allows for initial engagement of one cell type over the other. Finally, the antibody mimics significantly reduce HER2+ tumor volumes in a humanized xenograft model of breast cancer.

Synthetic Tuning of Domain Stoichiometry in Nanobody-Enzyme Megamolecules.

This paper presents a method to synthetically tune atomically precise megamolecule nanobody-enzyme conjugates for prodrug cancer therapy. Previous efforts to create heterobifunctional protein conjugates suffered from heterogeneity in domain stoichiometry, which in part led to the failure of antibody-enzyme conjugates in clinical trials. We used the megamolecule approach to synthesize anti-HER2 nanobody-cytosine deaminase conjugates with tunable numbers of nanobody and enzyme domains in a single, covalent molecule. Linking two nanobody domains to one enzyme domain improved avidity to a human cancer cell line by 4-fold but did not increase cytotoxicity significantly due to lowered enzyme activity. In contrast, a megamolecule composed of one nanobody and two enzyme domains resulted in an 8-fold improvement in the catalytic efficiency and increased the cytotoxic effect by over 5-fold in spheroid culture, indicating that the multimeric structure allowed for an increase in local drug activation. Our work demonstrates that the megamolecule strategy can be used to study structure-function relationships of protein conjugate therapeutics with synthetic control of protein domain stoichiometry.

Using Peptide Arrays to Profile Phosphatase Activity in Cell Lysates.

Phosphorylation is an important post-translational modification on proteins involved in many cellular processes; however, understanding of the regulation and mechanisms of global phosphorylation remains limited. Herein, we utilize self-assembled monolayers on gold for matrix-assisted laser desorption/ionization mass spectrometry (SAMDI-MS) with three phosphorylated peptide arrays to profile global phosphatase activity in cell lysates derived from five mammalian cell lines. Our results reveal significant differences in the activities of protein phosphatases on phospho- serine, threonine, and tyrosine substrates and suggest that phosphatases play a much larger role in the regulation of global phosphorylation on proteins than previously understood.

Morphological features of single cells enable accurate automated classification of cancer from non-cancer cell lines.

Accurate cancer detection and diagnosis is of utmost importance for reliable drug-response prediction. Successful cancer characterization relies on both genetic analysis and histological scans from tumor biopsies. It is known that the cytoskeleton is significantly altered in cancer, as cellular structure dynamically remodels to promote proliferation, migration, and metastasis. We exploited these structural differences with supervised feature extraction methods to introduce an algorithm that could distinguish cancer from non-cancer cells presented in high-resolution, single cell images. In this paper, we successfully identified the features with the most discriminatory power to successfully predict cell type with as few as 100 cells per cell line. This trait overcomes a key barrier of machine learning methodologies: insufficient data. Furthermore, normalizing cell shape via microcontact printing on self-assembled monolayers enabled better discrimination of cell lines with difficult-to-distinguish phenotypes. Classification accuracy remained robust as we tested dissimilar cell lines across various tissue origins, which supports the generalizability of our algorithm.

3D mesenchymal cell migration is driven by anterior cellular contraction that generates an extracellular matrix prestrain.

We describe a cellular contractile mechanism employed by fibroblasts and mesenchymal cancer cells to migrate in 3D collagen gels. During 3D spreading, fibroblasts strongly deform the matrix. They protrude, polarize, and initiate migration in the direction of highest extracellular matrix (ECM) deformation (prestrain). This prestrain is maintained through anterior cellular contractions behind the leading edge prior to protrusion, coordinating a distinct 3D migration cycle that varies between cell types. Myosin IIA is required for strain polarization, generating anterior contractions, and maintaining prestrain for efficient directional cell migration. Local matrix severing disrupts the matrix prestrain, suppressing directional protrusion. We show that epithelial cancer and endothelial cells rarely demonstrate the sustained prestrain or anterior contractions. We propose that mesenchymal cells sense ECM stiffness in 3D and generate their own matrix prestrain. This requires myosin IIA to generate polarized periodic anterior contractions for maintaining a 3D migration cycle.