Feasibility and Safety of Personalized, Multi-Target, Adoptive Cell Therapy (IMA101): First-in-Human Clinical Trial in Patients with Advanced Metastatic Cancer.

IMA101 is an actively personalized, multi-targeted adoptive cell therapy (ACT), whereby autologous T cells are directed against multiple novel defined peptide-HLA (pHLA) cancer targets. HLA-A*02:01-positive patients with relapsed/refractory solid tumors expressing ≥1 of 8 predefined targets underwent leukapheresis. Endogenous T cells specific for up to 4 targets were primed and expanded in vitro.

Quantitative immunopeptidomics reveals a tumor stroma-specific target for T cell therapy.

T cell receptor (TCR)-based immunotherapy has emerged as a promising therapeutic approach for the treatment of patients with solid cancers. Identifying peptide-human leukocyte antigen (pHLA) complexes highly presented on tumors and rarely expressed on healthy tissue in combination with high-affinity TCRs that when introduced into T cells can redirect T cells to eliminate tumor but not healthy tissue is a key requirement for safe and efficacious TCR-based therapies. To discover promising shared tumor antigens that could be targeted via TCR-based adoptive T cell therapy, we employed population-scale immunopeptidomics using quantitative mass spectrometry across ~1500 tumor and normal tissue samples.

Pitfalls in HLA Ligandomics-How to Catch a Li(e)gand.

Knowledge about the peptide repertoire presented by human leukocyte antigens (HLA) holds the key to unlock target-specific cancer immunotherapies such as adoptive cell therapies or bispecific T cell engaging receptors. Therefore, comprehensive and accurate characterization of HLA peptidomes by mass spectrometry (immunopeptidomics) across tissues and disease states is essential. With growing numbers of immunopeptidomics datasets and the scope of peptide identification strategies reaching beyond the canonical proteome, the likelihood for erroneous peptide identification as well as false annotation of HLA-independent peptides as HLA ligands is increasing.

Actively personalized vaccination trial for newly diagnosed glioblastoma.

Patients with glioblastoma currently do not sufficiently benefit from recent breakthroughs in cancer treatment that use checkpoint inhibitors. For treatments using checkpoint inhibitors to be successful, a high mutational load and responses to neoepitopes are thought to be essential. There is limited intratumoural infiltration of immune cells in glioblastoma and these tumours contain only 30-50 non-synonymous mutations.

RNA editing derived epitopes function as cancer antigens to elicit immune responses.

In addition to genomic mutations, RNA editing is another major mechanism creating sequence variations in proteins by introducing nucleotide changes in mRNA sequences. Deregulated RNA editing contributes to different types of human diseases, including cancers. Here we report that peptides generated as a consequence of RNA editing are indeed naturally presented by human leukocyte antigen (HLA) molecules.

Translating Immunopeptidomics to Immunotherapy-Decision-Making for Patient and Personalized Target Selection.

Immunotherapy is revolutionizing cancer treatment and has shown success in particular for tumors with a high mutational load. These effects have been linked to neoantigens derived from patient-specific mutations. To expand efficacious immunotherapy approaches to the vast majority of tumor types and patient populations carrying only a few mutations and maybe not a single presented neoepitope, it is necessary to expand the target space to non-mutated cancer-associated antigens.

The hpCADD NDDO Hamiltonian: Parametrization.

A neglect of diatomic differential overlap (NDDO) Hamiltonian has been parametrized as an electronic component of a polarizable force field. Coulomb and exchange potentials derived directly from the NDDO Hamiltonian in principle can be used with classical potentials, thus forming the basis for a new generation of efficiently applicable multipolar polarizable force fields. The new hpCADD Hamiltonian uses force-field-like atom types and reproduces the electrostatic properties (dipole moment, molecular electrostatic potential) and Koopmans' theorem ionization potentials closely, as demonstrated for a large training set and an independent test set of small molecules.

Targeting Drug Resistance in EGFR with Covalent Inhibitors: A Structure-Based Design Approach.

Receptor tyrosine kinases represent one of the prime targets in cancer therapy, as the dysregulation of these elementary transducers of extracellular signals, like the epidermal growth factor receptor (EGFR), contributes to the onset of cancer, such as non-small cell lung cancer (NSCLC). Strong efforts were directed to the development of irreversible inhibitors and led to compound CO-1686, which takes advantage of increased residence time at EGFR by alkylating Cys797 and thereby preventing toxic effects. Here, we present a structure-based approach, rationalized by subsequent computational analysis of conformational ligand ensembles in solution, to design novel and irreversible EGFR inhibitors based on a screening hit that was identified in a phenotype screen of 80 NSCLC cell lines against approximately 1500 compounds.

Three-Dimensional RISM Integral Equation Theory for Polarizable Solute Models.

Modeling solute polarizability is a key ingredient for improving the description of solvation phenomena. In recent years, polarizable molecular mechanics force fields have emerged that circumvent the limitations of classical fixed charge force fields by the ability to adapt their electrostatic potential distribution to a polarizing environment. Solvation phenomena are characterized by the solute's excess chemical potential, which can be computed by expensive fully atomistic free energy simulations.

Distance dependency and minimum amino acid alphabets for decoy scoring potentials.

The validity and accuracy of a proposed tertiary structure of a protein can be assessed in several ways. Scoring such a structure by a knowledge-based potential is a well-known approach in molecular biophysics, an important task in structure prediction and refinement, and a key step in several experiments on protein structures. Although several parameterizations for such models have been derived over the course of time, improvements in accuracy by explicitly using continuous distance information have not been suggested yet.

Structure-based, biophysical annotation of molecular coevolution of acetylcholinesterase.

Acetylcholinesterase (AChE) is an important enzyme in the nervous system. It terminates signal transmission at chemical synapses by degrading the neurotransmitter acetylcholine and was found to play a role in plaque formation in Alzheimer's disease. Several functional parts of its structure have been identified in the past.

Structural model of the gas vesicle protein GvpA and analysis of GvpA mutants in vivo.

Gas vesicles are gas-filled protein structures increasing the buoyancy of cells. The gas vesicle envelope is mainly constituted by the 8 kDa protein GvpA forming a wall with a water excluding inner surface. A structure of GvpA is not available; recent solid-state NMR results suggest a coil-α-β-β-α-coil fold.

Membrane anchoring and interaction between transmembrane domains are crucial for K+ channel function.

The small viral channel Kcv is a Kir-like K(+) channel of only 94 amino acids. With this simple structure, the tetramer of Kcv represents the pore module of all complex K(+) channels. To examine the structural contribution of the transmembrane domains (TMDs) to channel function, we performed Ala scanning mutagenesis of the two domains and tested the functionality of the mutants in a yeast complementation assay.

Distance-dependent classification of amino acids by information theory.

Reduced amino acid alphabets are useful to understand molecular evolution as they reveal basal, shared properties of amino acids, which the structures and functions of proteins rely on. Several previous studies derived such reduced alphabets and linked them to the origin of life and biotechnological applications. However, all this previous work presupposes that only direct contacts of amino acids in native protein structures are relevant.

BioPhysConnectoR: Connecting sequence information and biophysical models.

Background: One of the most challenging aspects of biomolecular systems is the understanding of the coevolution in and among the molecule(s).A complete, theoretical picture of the selective advantage, and thus a functional annotation, of (co-)mutations is still lacking. Using sequence-based and information theoretical inspired methods we can identify coevolving residues in proteins without understanding the underlying biophysical properties giving rise to such coevolutionary dynamics.

Estimating sufficient statistics in co-evolutionary analysis by mutual information.

Mutual information (MI) is a standard measure in information theory to observe and quantify correlated signals and events in both, empirical data sets and theoretical models. In the field of computational biology the MI turned out to be particularly useful in studies on co-evolutionary signals of sites within biomolecules. A key issue in the applicability of the MI is, however, a correct reference system or null model to understand finite-size effects in the underlying, finite data set.