Clustering molecular dynamics conformations of the CC'-loop of the PD-1 immuno-checkpoint receptor.

Molecular mechanisms within the checkpoint receptor PD-1 are essential for its activation by PD-L1 as well as for blocking such an activation via checkpoint inhibitors. We use molecular dynamics to scrutinize patterns of atomic motion in PD-1 without a ligand. Molecular dynamics is performed for the whole extracellular domain of PD-1, and the analysis focuses on its CC'-loop and some adjacent Cα-atoms. We extend previous work by applying common nearest neighbor clustering (Cnn) and compare the performance of this method with Daura clustering as well as UMAP dimension reduction and subsequent agglomerative linkage clustering. As compared to Daura clustering, we found Cnn less sensitive to cutoff selection and better able to return representative clusters for sets of different 3D atomic conformations. Interestingly, Cnn yields results quite similar to UMAP plus linkage clustering.

Flexible Risk Evidence Combination Rules in Breast Cancer Precision Therapy.

Evidence theory by Dempster-Shafer for determination of hormone receptor status in breast cancer samples was introduced in our previous paper. One major topic pointed out here is the link between pieces of evidence found from different origins. In this paper the challenge of selecting appropriate ways of fusing evidence, depending on the type and quality of data involved is addressed. A parameterized family of evidence combination rules, covering the full range of potential needs, from emphasizing discrepancies in the measurements to aspiring accordance, is covered. The consequences for real patient samples are shown by modeling different decision strategies.

Molecular dynamics identifies semi-rigid domains in the PD-1 checkpoint receptor bound to its natural ligand PD-L1.

Cells in danger of being erroneously attacked by leucocytes express PD-L1 on their surface. These cells activate PD-1 on attacking leucocytes and send them to death, thus curbing erroneous, autoimmune attack. Unfortunately, cancer cells exploit this mechanism: By expressing PD-L1, they guard themselves against leucocyte attack and thereby evade immune clearance. Checkpoint inhibitors are drugs which re-enable immune clearance of cancer cells by blocking the binding of PD-L1 to PD-1 receptors. It is therefore of utmost interest to investigate these binding mechanisms. We use three 600 ns all-atom molecular dynamics simulations to scrutinize molecular motions of PD-1 with its binding partner, the natural ligand PD-L1. Usually, atomic motion patterns are evaluated against whole molecules as a reference, disregarding that such a reference is a dynamic entity by itself, thus degrading stability of the reference. As a remedy, we identify semi-rigid domains, lending themselves as more stable and reliable reference frames against which even minute differences in molecular motion can be quantified precisely. We propose an unsupervised three-step procedure. In previous work of our group and others, minute differences in motion patterns proved decisive for differences in function. Here, several highly reliable frames of reference are established for future investigations based on molecular motion.

Decision Theory versus Conventional Statistics for Personalized Therapy of Breast Cancer.

Estrogen and progesterone receptors being present or not represents one of the most important biomarkers for therapy selection in breast cancer patients. Conventional measurement by immunohistochemistry (IHC) involves errors, and numerous attempts have been made to increase precision by additional information from gene expression. This raises the question of how to fuse information, in particular, if there is disagreement. It is the primary domain of Dempster-Shafer decision theory (DST) to deal with contradicting evidence on the same item (here: receptor status), obtained through different techniques. DST is widely used in technical settings, such as self-driving cars and aviation, and is also promising to deliver significant advantages in medicine. Using data from breast cancer patients already presented in previous work, we focus on comparing DST with classical statistics in this work, to pave the way for its application in medicine. First, we explain how DST not only considers probabilities (a single number per sample), but also incorporates uncertainty in a concept of 'evidence' (two numbers per sample). This allows for very powerful displays of patient data in so-called ternary plots, a novel and crucial advantage for medical interpretation. Results are obtained according to conventional statistics (ODDS) and, in parallel, according to DST. Agreement and differences are evaluated, and the particular merits of DST discussed. The presented application demonstrates how decision theory introduces new levels of confidence in diagnoses derived from medical data.

Galectin network in osteoarthritis: galectin-4 programs a pathogenic signature of gene and effector expression in human chondrocytes in vitro.

Galectin-4 (Gal-4) is a member of the galectin family, which have been identified as galactose-binding proteins. Gal-4 possesses two tandem repeat carbohydrate recognition domains and acts as a cross-linking bridge in sulfatide-dependent glycoprotein routing. We herein document its upregulation in osteoarthritis (OA) in correlation with the extent of cartilage degradation in vivo. Primary human OA chondrocytes in vitro respond to carbohydrate-inhibitable Gal-4 binding with the upregulation of pro-degradative/-inflammatory proteins such as interleukin-1ß (IL-1ß) and matrix metalloproteinase-13 (MMP-13), as documented by RT-qPCR-based mRNA profiling and transcriptome data processing. Activation of p65 by phosphorylation of Ser536 within the NF-κB pathway and the effect of three p65 inhibitors on Gal-4 activity support downstream involvement of such signaling. In 3D (pellet) cultures, Gal-4 presence causes morphological and biochemical signs of degradation. Taken together, our findings strongly support the concept of galectins acting as a network in OA pathogenesis and suggest that blocking their activity in disease progression may become clinically relevant in the future.

Decision theory for precision therapy of breast cancer.

Correctly estimating the hormone receptor status for estrogen (ER) and progesterone (PGR) is crucial for precision therapy of breast cancer. It is known that conventional diagnostics (immunohistochemistry, IHC) yields a significant rate of wrongly diagnosed receptor status. Here we demonstrate how Dempster Shafer decision Theory (DST) enhances diagnostic precision by adding information from gene expression. We downloaded data of 3753 breast cancer patients from Gene Expression Omnibus. Information from IHC and gene expression was fused according to DST, and the clinical criterion for receptor positivity was re-modelled along DST. Receptor status predicted according to DST was compared with conventional assessment via IHC and gene-expression, and deviations were flagged as questionable. The survival of questionable cases turned out significantly worse (Kaplan Meier p < 1%) than for patients with receptor status confirmed by DST, indicating a substantial enhancement of diagnostic precision via DST. This study is not only relevant for precision medicine but also paves the way for introducing decision theory into OMICS data science.

Erratum: Gruber, E.S.; et al. The Oncogene AF1Q is Associated with WNT and STAT Signaling and Offers a Novel Independent Prognostic Marker in Patients with Resectable Esophageal Cancer. Cells 2019, 8, 1357.

The authors wish to make the following change to their paper [...].

Microarray Normalization Revisited for Reproducible Breast Cancer Biomarkers.

Precision medicine for breast cancer relies on biomarkers to select therapies. However, the reliability of biomarkers drawn from gene expression arrays has been questioned and calls for reassessment, in particular for large datasets. We revisit widely used data-normalization procedures and evaluate differences in outcome in order to pinpoint the most reliable reprocessing methods biomarkers can be based upon. We generated a database of 3753 breast cancer patients out of 38 studies by downloading and curating patient samples from NCBI-GEO. As gene-expression biomarkers, we select the assessment of receptor status and breast cancer subtype classification. Each normalization procedure is applied separately, and biomarkers are then evaluated for each patient. Differences between normalization pipelines are quantified as percentages of patients having outcomes different for each pipeline. Some normalization procedures lead to quite consistent biomarkers, differing only in 1-2% of patients. Other normalization procedures-some of them have been used in many clinical studies-end up with distrusting discrepancies (10% and more). A good deal of doubt regarding the reliability of microarrays may root in the haphazard application of inadequate preprocessing pipelines. Several modes of batch corrections are evaluated regarding a possible improvement of receptor prediction from gene expression versus the golden standard of immunohistochemistry. Finally, we nominate those normalization methods yielding consistent and trustable results. Adequate bioinformatics data preprocessing is key and crucial for any subsequent statistics to arrive at trustable results. We conclude with a suggestion for future bioinformatics development to further increase the reliability of cancer biomarkers.

The Oncogene AF1Q is Associated with WNT and STAT Signaling and Offers a Novel Independent Prognostic Marker in Patients with Resectable Esophageal Cancer.

AF1q impairs survival in hematologic and solid malignancies. AF1q expression is associated with tumor progression, migration, and chemoresistance, and acts as a transcriptional co-activator in WNT and STAT signaling. This study evaluates the role of AF1q in patients with resectable esophageal cancer (EC). A total of 278 patients operated on for esophageal cancer were retrospectively included, and the expression of AF1q, CD44, and pYSTAT3 was analyzed following immunostaining. Quantified data were processed to correlational and survival analysis. In EC patients, an elevated expression of AF1q was associated with CD44 (p = 0.004), and pYSTAT3 (p = 0.0002). High AF1q expression in primary tumors showed high AF1q expression in the corresponding lymph nodes (p= 0.016). AF1q expression was higher after neoadjuvant therapy (p= 0.0002). Patients with AF1q-positive EC relapsed and died earlier compared to patients with AF1q-negative EC (disease-free survival (DFS), p= 0.0005; disease-specific survival (DSS), p= 0.003); in the multivariable Cox regression model, AF1q proved to be an independent prognostic marker (DFS, p= 0.01; DSS, p= 0.03). AF1q is associated with WNT and STAT signaling; it impairs and independently predicts DFS and DSS in patients with resectable EC. The testing of AF1q could facilitate prognosis estimation and provide a possibility of identifying the patients responsive to the therapeutic blockade of its oncogenic downstream targets.

Co-expressed genes enhance precision of receptor status identification in breast cancer patients.

PURPOSE: Therapeutic decisions in breast cancer patients crucially depend on the status of estrogen receptor, progesterone receptor and HER2, obtained by immunohistochemistry (IHC). These are known to be inaccurate sometimes, and we demonstrate how to use gene-expression to increase precision of receptor status. METHODS: We downloaded data from 3241 breast cancer patients out of 36 clinical studies. For each receptor, we modelled the mRNA expression of the receptor gene and a co-gene by logistic regression. For each patient, predictions from logistic regression were merged with information from IHC on a probabilistic basis to arrive at a fused prediction result. RESULTS: We introduce Sankey diagrams to visualize the step by step increase of precision as information is added from gene expression: IHC-estimates are qualified as 'confirmed', 'rejected' or 'corrected'. Additionally, we introduce the category 'inconclusive' to spot those patients in need for additional assessments so as to increase diagnostic precision and safety. CONCLUSIONS: We demonstrate a sound mathematical basis for the fusion of information, even if partly contradictive. The concept is extendable to more than three sources of information, as particularly important for OMICS data. The overall number of undecidable cases is reduced as well as those assessed falsely. We outline how decision rules may be extended to also weigh consequences, being different in severity for false-positive and false-negative assessments, respectively. The possible benefit is demonstrated by comparing the disease free survival between patients whose IHC could be confirmed versus those for which it was corrected.

Galectin-8 induces functional disease markers in human osteoarthritis and cooperates with galectins-1 and -3.

The reading of glycan-encoded signals by tissue lectins is considered a major route of the flow of biological information in many (patho)physiological processes. The arising challenge for current research is to proceed from work on a distinct protein to family-wide testing of lectin function. Having previously identified homodimeric galectin-1 and chimera-type galectin-3 as molecular switches in osteoarthritis progression, we here provide proof-of-principle evidence for an intra-network cooperation of galectins with three types of modular architecture. We show that the presence of tandem-repeat-type galectin-8 significantly correlated with cartilage degeneration and that it is secreted by osteoarthritic chondrocytes. Glycan-inhibitable surface binding of galectin-8 to these cells increased gene transcription and the secretion of functional disease markers. The natural variant galectin-8 (F19Y) was less active than the prevalent form. Genome-wide array analysis revealed induction of a pro-degradative/inflammatory gene signature, largely under control of NF-κB signaling. This signature overlapped with respective gene-expression patterns elicited by galectins-1 and -3, but also presented supplementary features. Functional assays with mixtures of galectins that mimic the pathophysiological status unveiled cooperation between the three galectins. Our findings shape the novel concept to consider individual galectins as part of a so far not realized teamwork in osteoarthritis pathogenesis, with relevance beyond this disease.

Gene expression information improves reliability of receptor status in breast cancer patients.

Immunohistochemical (IHC) determination of receptor status in breast cancer patients is frequently inaccurate. Since it directs the choice of systemic therapy, it is essential to increase its reliability. We increase the validity of IHC receptor expression by additionally considering gene expression (GE) measurements. Crisp therapeutic decisions are based on IHC estimates, even if they are borderline reliable. We further improve decision quality by a responsibility function, defining a critical domain for gene expression. Refined normalization is devised to file any newly diagnosed patient into existing data bases. Our approach renders receptor estimates more reliable by identifying patients with questionable receptor status. The approach is also more efficient since the rate of conclusive samples is increased. We have curated and evaluated gene expression data, together with clinical information, from 2880 breast cancer patients. Combining IHC with gene expression information yields a method more reliable and also more efficient as compared to common practice up to now. Several types of possibly suboptimal treatment allocations, based on IHC receptor status alone, are enumerated. A 'therapy allocation check' identifies patients possibly miss-classified. Estrogen: false negative 8%, false positive 6%. Progesterone: false negative 14%, false positive 11%. HER2: false negative 2%, false positive 50%. Possible implications are discussed. We propose an 'expression look-up-plot', allowing for a significant potential to improve the quality of precision medicine. Methods are developed and exemplified here for breast cancer patients, but they may readily be transferred to diagnostic data relevant for therapeutic decisions in other fields of oncology.

Spatiotemporal multistage consensus clustering in molecular dynamics studies of large proteins.

The aim of this work is to find semi-rigid domains within large proteins as reference structures for fitting molecular dynamics trajectories. We propose an algorithm, multistage consensus clustering, MCC, based on minimum variation of distances between pairs of Cα-atoms as target function. The whole dataset (trajectory) is split into sub-segments. For a given sub-segment, spatial clustering is repeatedly started from different random seeds, and we adopt the specific spatial clustering with minimum target function: the process described so far is stage 1 of MCC. Then, in stage 2, the results of spatial clustering are consolidated, to arrive at domains stable over the whole dataset. We found that MCC is robust regarding the choice of parameters and yields relevant information on functional domains of the major histocompatibility complex (MHC) studied in this paper: the α-helices and ß-floor of the protein (MHC) proved to be most flexible and did not contribute to clusters of significant size. Three alleles of the MHC, each in complex with ABCD3 peptide and LC13 T-cell receptor (TCR), yielded different patterns of motion. Those alleles causing immunological allo-reactions showed distinct correlations of motion between parts of the peptide, the binding cleft and the complementary determining regions (CDR)-loops of the TCR. Multistage consensus clustering reflected functional differences between MHC alleles and yields a methodological basis to increase sensitivity of functional analyses of bio-molecules. Due to the generality of approach, MCC is prone to lend itself as a potent tool also for the analysis of other kinds of big data.

Geometry Dynamics of α -Helices in Different Class I Major Histocompatibility Complexes.

MHC α-helices form the antigen-binding cleft and are of particular interest for immunological reactions. To monitor these helices in molecular dynamics simulations, we applied a parsimonious fragment-fitting method to trace the axes of the α-helices. Each resulting axis was fitted by polynomials in a least-squares sense and the curvature integral was computed. To find the appropriate polynomial degree, the method was tested on two artificially modelled helices, one performing a bending movement and another a hinge movement. We found that second-order polynomials retrieve predefined parameters of helical motion with minimal relative error. From MD simulations we selected those parts of α-helices that were stable and also close to the TCR/MHC interface. We monitored the curvature integral, generated a ruled surface between the two MHC α-helices, and computed interhelical area and surface torsion, as they changed over time. We found that MHC α-helices undergo rapid but small changes in conformation. The curvature integral of helices proved to be a sensitive measure, which was closely related to changes in shape over time as confirmed by RMSD analysis. We speculate that small changes in the conformation of individual MHC α-helices are part of the intrinsic dynamics induced by engagement with the TCR.

Finding semirigid domains in biomolecules by clustering pair-distance variations.

Dynamic variations in the distances between pairs of atoms are used for clustering subdomains of biomolecules. We draw on a well-known target function for clustering and first show mathematically that the assignment of atoms to clusters has to be crisp, not fuzzy, as hitherto assumed. This reduces the computational load of clustering drastically, and we demonstrate results for several biomolecules relevant in immunoinformatics. Results are evaluated regarding the number of clusters, cluster size, cluster stability, and the evolution of clusters over time. Crisp clustering lends itself as an efficient tool to locate semirigid domains in the simulation of biomolecules. Such domains seem crucial for an optimum performance of subsequent statistical analyses, aiming at detecting minute motional patterns related to antigen recognition and signal transduction.