Transcriptomic Maps of Colorectal Liver Metastasis: Machine Learning of Gene Activation Patterns and Epigenetic Trajectories in Support of Precision Medicine.

The molecular mechanisms of the liver metastasis of colorectal cancer (CRLM) remain poorly understood. Here, we applied machine learning and bioinformatics trajectory inference to analyze a gene expression dataset of CRLM. We studied the co-regulation patterns at the gene level, the potential paths of tumor development, their functional context, and their prognostic relevance. Our analysis confirmed the subtyping of five liver metastasis subtypes (LMS). We provide gene-marker signatures for each LMS, and a comprehensive functional characterization that considers both the hallmarks of cancer and the tumor microenvironment. The ordering of CRLMs along a pseudotime-tree revealed a continuous shift in expression programs, suggesting a developmental relationship between the subtypes. Notably, trajectory inference and personalized analysis discovered a range of epigenetic states that shape and guide metastasis progression. By constructing prognostic maps that divided the expression landscape into regions associated with favorable and unfavorable prognoses, we derived a prognostic expression score. This was associated with critical processes such as epithelial-mesenchymal transition, treatment resistance, and immune evasion. These factors were associated with responses to neoadjuvant treatment and the formation of an immuno-suppressive, mesenchymal state. Our machine learning-based molecular profiling provides an in-depth characterization of CRLM heterogeneity with possible implications for treatment and personalized diagnostics.

Molecular mechanism of phosphopeptide neoantigen immunogenicity.

Altered protein phosphorylation in cancer cells often leads to surface presentation of phosphopeptide neoantigens. However, their role in cancer immunogenicity remains unclear. Here we describe a mechanism by which an HLA-B*0702-specific acute myeloid leukemia phosphoneoantigen, pMLL747-755 (EPR(pS)PSHSM), is recognized by a cognate T cell receptor named TCR27, a candidate for cancer immunotherapy. We show that the replacement of phosphoserine P4 with serine or phosphomimetics does not affect pMHC conformation or peptide-MHC affinity but abrogates TCR27-dependent T cell activation and weakens binding between TCR27 and pMHC. Here we describe the crystal structures for TCR27 and cognate pMHC, map of the interface produced by nuclear magnetic resonance, and a ternary complex generated using information-driven protein docking. Our data show that non-covalent interactions between the epitope phosphate group and TCR27 are crucial for TCR specificity. This study supports development of new treatment options for cancer patients through target expansion and TCR optimization.

The manufacturing considerations of bispecific antibodies.

INTRODUCTION: Antibody therapies have made huge strides in providing safe and efficacious drugs for autoimmune, cancer, and infectious diseases. These bispecific antibodies can be assembled from the basic building blocks of IgGs, resulting in dozens of formats. AREAS COVERED: It is important to consider the manufacturability of these formats early in the antibody discovery phases. Broadly categorizing bispecific antibodies into IgG-like, fragment-based, appended, and hybrid formats can help in looking at early manufacturability considerations. EXPERT OPINION: Ideally, bispecific antibody manufacturing should contain a minimal number of steps, with processes that give high yields of protein with no contaminants. Many of these have been determined for the fragment-based bispecific blinatumomab and the IgG-like bispecifics from hybridomas. However, for new formats, these need to be considered early in the research and development pipeline. The hybrid formats offer an unusual alternative in generating high pure yields of bispecific molecules if the engineering challenges can be deciphered.

Characteristics of Immune Memory and Effector Activity to Cancer-Expressed MHC Class I Phosphopeptides Differ in Healthy Donors and Ovarian Cancer Patients.

Elevated immunity to cancer-expressed antigens can be detected in people with no history of cancer and may contribute to cancer prevention. We have previously reported that MHC-restricted phosphopeptides are cancer-expressed antigens and targets of immune recognition. However, the extent to which this immunity reflects prior or ongoing phosphopeptide exposures was not investigated. In this study, we found that preexisting immune memory to cancer-expressed phosphopeptides was evident in most healthy donors, but the breadth among donors was highly variable. Although three phosphopeptides were recognized by most donors, suggesting exposures to common microbial/infectious agents, most of the 205 tested phosphopeptides were not recognized by peripheral blood mononuclear cells (PBMC) from any donor and the remainder were recognized by only 1 to 3 donors. In longitudinal analyses of 2 donors, effector immune response profiles suggested active reexposures to a subset of phosphopeptides. These findings suggest that the immunogens generating most phosphopeptide-specific immune memory are rare infectious agents or incipient cancer cells with distinct phosphoproteome dysregulations, and that repetitive immunogenic exposures occur in individual donors. Phosphopeptide-specific immunity in PBMCs and tumor-infiltrating lymphocytes from ovarian cancer patients was limited, regardless of whether the phosphopeptide was expressed on the tumor. However, 4 of 10 patients responded to 1 to 2 immunodominant phosphopeptides, and 1 showed an elevated effector response to a tumor-expressed phosphopeptide. As the tumors from these patients displayed many phosphopeptides, these data are consistent with lack of prior exposure or impaired ability to respond to some phosphopeptides and suggest that enhancing phosphopeptide-specific T-cell responses could be a useful approach to improve tumor immunotherapy.

TCR Fingerprinting and Off-Target Peptide Identification.

Adoptive T cell therapy using patient T cells redirected to recognize tumor-specific antigens by expressing genetically engineered high-affinity T-cell receptors (TCRs) has therapeutic potential for melanoma and other solid tumors. Clinical trials implementing genetically modified TCRs in melanoma patients have raised concerns regarding off-target toxicities resulting in lethal destruction of healthy tissue, highlighting the urgency of assessing which off-target peptides can be recognized by a TCR. As a model system we used the clinically efficacious NY-ESO-1-specific TCR C259, which recognizes the peptide epitope SLLMWITQC presented by HLA-A*02:01. We investigated which amino acids at each position enable a TCR interaction by sequentially replacing every amino acid position outside of anchor positions 2 and 9 with all 19 possible alternative amino acids, resulting in 134 peptides (133 altered peptides plus epitope peptide). Each peptide was individually evaluated using three different in vitro assays: binding of the NY-ESOc259 TCR to the peptide, peptide-dependent activation of TCR-expressing cells, and killing of peptide-presenting target cells. To represent the TCR recognition kernel, we defined Position Weight Matrices (PWMs) for each assay by assigning normalized measurements to each of the 20 amino acids in each position. To predict potential off-target peptides, we applied a novel algorithm projecting the PWM-defined kernel into the human proteome, scoring NY-ESOc259 TCR recognition of 336,921 predicted human HLA-A*02:01 binding 9-mer peptides. Of the 12 peptides with high predicted score, we confirmed 7 (including NY-ESO-1 antigen SLLMWITQC) strongly activate human primary NY-ESOc259-expressing T cells. These off-target peptides include peptides with up to 7 amino acid changes (of 9 possible), which could not be predicted using the recognition motif as determined by alanine scans. Thus, this replacement scan assay determines the "TCR fingerprint" and, when coupled with the algorithm applied to the database of human 9-mer peptides binding to HLA-A*02:01, enables the identification of potential off-target antigens and the tissues where they are expressed. This platform enables both screening of multiple TCRs to identify the best candidate for clinical development and identification of TCR-specific cross-reactive peptide recognition and constitutes an improved methodology for the identification of potential off-target peptides presented on MHC class I molecules.

Toxicological and pharmacological assessment of AGEN1884, a novel human IgG1 anti-CTLA-4 antibody.

CTLA-4 and CD28 exemplify a co-inhibitory and co-stimulatory signaling axis that dynamically sculpts the interaction of antigen-specific T cells with antigen-presenting cells. Anti-CTLA-4 antibodies enhance tumor-specific immunity through a variety of mechanisms including: blockade of CD80 or CD86 binding to CTLA-4, repressing regulatory T cell function and selective elimination of intratumoral regulatory T cells via an Fcγ receptor-dependent mechanism. AGEN1884 is a novel IgG1 antibody targeting CTLA-4. It potently enhanced antigen-specific T cell responsiveness that could be potentiated in combination with other immunomodulatory antibodies. AGEN1884 was well-tolerated in non-human primates and enhanced vaccine-mediated antigen-specific immunity. AGEN1884 combined effectively with PD-1 blockade to elicit a T cell proliferative response in the periphery. Interestingly, an IgG2 variant of AGEN1884 revealed distinct functional differences that may have implications for optimal dosing regimens in patients. Taken together, the pharmacological properties of AGEN1884 support its clinical investigation as a single therapeutic and combination agent.

Effects of heterocyclic aromatic substituents on binding affinities at two distinct sites of somatostatin receptors. Correlation with the electrostatic potential of the substituents.

In our continuing program exploring glucose-based peptidomimetics of somatostatin (SRIF-14), we sought to improve the water solubility of our glycosides. This led to insights into the nature of the ligand binding sites at the SRIF receptor. Replacement of the C4 benzyl substituent in glucoside (+)-2 with pyridinylmethyl or pyrazin-2-ylmethyl congeners increased water solubility and enhanced affinity for the human SRIF subtype receptor 4 (sst4). We attribute this effect to hydrogen bond formation. The pyridin-3-ylmethyl substituent at C4, when combined with the imidazol-4-ylmethyl group at C2, generated (-)-19, which has the highest affinity of a glucose-based peptidomimetic at a human SRIF receptor to date (K(i) 53 +/- 23 nM, n = 6 at sst4). The C4 heterocyclic congeners of glucosides bearing a 1-methoxy substituent rather than an indole side chain at the anomeric carbon, such as (+)-16, also provided information about the Trp(8) binding pocket. We correlated the SARs at both the C4 and the Trp(8) binding pockets with calculations of the electrostatic potentials of the diverse C4 aromatic substituents using Spartan 3-21G(*) MO analysis. These calculations provide an approximate analysis of a molecule's ability to interact within a receptor binding site. Our binding studies show that benzene and indole rings, but not pyridinylmethyl nor pyrazin-2-ylmethyl rings, can bind the hydrophobic Trp(8) binding pocket of sst4. The Spartan 3-21G(*) MO analysis reveals significant negative electrostatic potential in the region of the pi-clouds for the benzene and indole rings but not for the pyridinylmethyl or pyrazin-2-ylmethyl congeners. Our data further demonstrate that the replacement of benzene or indole side chains by heterocyclic aromatic rings typified by pyridine and pyrazine not only enhances water solubility and hydrogen bonding capacity as expected, but can also profoundly diminish the ability of the pi-cloud of the aromatic substituent to interact with side chains of an aromatic binding pocket such as that for Trp(8) of SRIF-14. Conversely, these calculations accommodate the experimental findings that pyrazin-2-ylmethyl and pyridinylmethyl substituents at C4- of C1-indole-substituted glycosides afford higher affinities at sst4 than the C4-benzyl group of (+)-2. This result is consistent with the high electron density in the plane of the heterocycle depicted in Figure 6 which can accept hydrogen bonds from the C4 binding pocket of the receptor. Unexpectedly, we found that the 2-fluoropyridin-5-ylmethyl analogue (+)-14 more closely resembles the binding affinity of (+)-8 than that of (+)-2, thus suggesting that (+)-14 represents a rare example of a carbon linked fluorine atom acting as a hydrogen bond acceptor. We attribute this result to the ability of the proton to bind the nitrogen and fluorine atoms simultaneously in a bifurcated arrangement. At the NK1 receptor of substance P (SP), the free hydroxyl at C4 optimizes affinity.

Discovery informatics: its evolving role in drug discovery.

Drug discovery and development is a highly complex process requiring the generation of very large amounts of data and information. Currently this is a largely unmet informatics challenge. The current approaches to building information and knowledge from large amounts of data has been addressed in cases where the types of data are largely homogeneous or at the very least well-defined. However, we are on the verge of an exciting new era of drug discovery informatics in which methods and approaches dealing with creating knowledge from information and information from data are undergoing a paradigm shift. The needs of this industry are clear: Large amounts of data are generated using a variety of innovative technologies and the limiting step is accessing, searching and integrating this data. Moreover, the tendency is to move crucial development decisions earlier in the discovery process. It is crucial to address these issues with all of the data at hand, not only from current projects but also from previous attempts at drug development. What is the future of drug discovery informatics? Inevitably, the integration of heterogeneous, distributed data are required. Mining and integration of domain specific information such as chemical and genomic data will continue to develop. Management and searching of textual, graphical and undefined data that are currently difficult, will become an integral part of data searching and an essential component of building information- and knowledge-bases.