Homology modeling predicts the 3D structure of a query protein based on the sequence alignment with one or more template proteins of known structure. Its great importance for biological research is owed to its speed, simplicity, reliability and wide applicability, covering more than half of the residues in protein sequence space. Although multiple templates have been shown to generally increase model quality over single templates, the information from multiple templates has so far been combined using empirically motivated, heuristic approaches.We present here a rigorous statistical framework for multi-template homology modeling. First, we find that the query proteins’ atomic distance restraints can be accurately described by two-component Gaussian mixtures. This insight allowed us to apply the standard laws of probability theory to combine restraints from multiple templates. Second, we derive theoretically optimal weights to correct for the redundancy among related templates. Third, a heuristic template selection strategy is proposed.We improve the average GDT-ha model quality score by 11% over single template modeling and by 6.5% over a conventional multi-template approach on a set of 1000 query proteins. Robustness with respect to wrong constraints is likewise improved. We have integrated our multi-template modeling approach with the popular MODELLER homology modeling software in our free HHpred server http://toolkit.tuebingen.mpg.de/hhpred and also offer open source software for running MODELLER with the new restraints at https://bitbucket.org/soedinglab/hh-suite.

The level of PD-L1 expression in immunohistochemistry (IHC) assays is a key biomarker for the identification of Non-Small-Cell-Lung-Cancer (NSCLC) patients that may respond to anti PD-1/PD-L1 treatments. The quantification of PD-L1 expression currently includes the visual estimation by a pathologist of the percentage (tumor proportional scoring or TPS) of tumor cells showing PD-L1 staining. Known challenges like differences in positivity estimation around clinically relevant cut-offs and sub-optimal quality of samples makes visual scoring tedious and subjective, yielding a scoring variability between pathologists. In this work, we propose a novel deep learning solution that enables the first automated and objective scoring of PD-L1 expression in late stage NSCLC needle biopsies. To account for the low amount of tissue available in biopsy images and to restrict the amount of manual annotations necessary for training, we explore the use of semi-supervised approaches against standard fully supervised methods. We consolidate the manual annotations used for training as well the visual TPS scores used for quantitative evaluation with multiple pathologists. Concordance measures computed on a set of slides unseen during training provide evidence that our automatic scoring method matches visual scoring on the considered dataset while ensuring repeatability and objectivity.

In the context of precision medicine with immunotherapies there is an increasing need for companion diagnostic tests to identify potential therapy responders and avoid treatment coming along with severe adverse events for non-responders. Here, we present a retrospective case study to discover image-based signatures for developing a potential companion diagnostic test for ipilimumab (IPI) in malignant melanoma. Signature discovery is based on digital pathology and fully automatic quantitative image analysis using virtual multiplexing as well as machine learning and deep learning on whole-slide images. We systematically correlated the patient outcome data with potentially relevant local image features using a Tissue Phenomics approach with a sound cross validation procedure for reliable performance evaluation. Besides uni-variate models we also studied combinations of signatures in several multi-variate models. The most robust and best performing model was a decision tree model based on relative densities of CD8+ tumor infiltrating lymphocytes in the intra-tumoral infiltration region. Our results are well in agreement with observations described in previously published studies regarding the predictive value of the immune contexture, and thus, provide predictive potential for future development of a companion diagnostic test.

BackgroundProstate cancer (PCa) has been under investigation as a target for antigen-specific immunotherapies in metastatic disease settings for the last two decades leading to a licensure of the first therapeutic cancer vaccine, Sipuleucel-T, in 2010. However, neither Sipuleucel-T nor other experimental PCa vaccines that emerged later induce strong T-cell immunity.MethodsIn this first-in-man study, VANCE, we evaluated a novel vaccination platform based on two replication-deficient viruses, chimpanzee adenovirus (ChAd) and MVA (Modified Vaccinia Ankara), targeting the oncofetal self-antigen 5T4 in early stage PCa. Forty patients, either newly diagnosed with early-stage PCa and scheduled for radical prostatectomy or patients with stable disease on an active surveillance protocol, were recruited to the study to assess the vaccine safety and T-cell immunogenicity. Secondary and exploratory endpoints included immune infiltration into the prostate, prostate-specific antigen (PSA) change, and assessment of phenotype and functionality of antigen-specific T cells.ResultsThe vaccine had an excellent safety profile. Vaccination-induced 5T4-specific T-cell responses were measured in blood by ex vivo IFN-γ ELISpot and were detected in the majority of patients with a mean level in responders of 198 spot-forming cells per million peripheral blood mononuclear cells. Flow cytometry analysis demonstrated the presence of both CD8+ and CD4+ polyfunctional 5T4-specific T cells in the circulation. 5T4-reactive tumor-infiltrating lymphocytes were isolated from post-treatment prostate tissue. Some of the patients had a transient PSA rise 2–8 weeks following vaccination, possibly indicating an inflammatory response in the target organ.ConclusionsAn excellent safety profile and T-cell responses elicited in the circulation and also detected in the prostate gland support the evaluation of the ChAdOx1-MVA 5T4 vaccine in efficacy trials. It remains to be seen if this vaccination strategy generates immune responses of sufficient magnitude to mediate clinical efficacy and whether it can be effective in late-stage PCa settings, as a monotherapy in advanced disease or as part of multi-modality PCa therapy. To address these questions, the phase I/II trial, ADVANCE, is currently recruiting patients with intermediate-risk PCa, and patients with advanced metastatic castration-resistant PCa, to receive this vaccine in combination with nivolumab.Trial registrationThe trial was registered with the U.S. National Institutes of Health (NIH) Clinical Trials Registry (ClinicalTrials.gov identifier NCT02390063).

The biological complexity reflected in histology images requires advanced approaches for unbiased prognostication. Machine learning and particularly deep learning methods are increasingly applied in the field of digital pathology. In this study, we propose new ways to predict risk for cancer-specific death from digital images of immunohistochemically (IHC) stained tissue microarrays (TMAs). Specifically, we evaluated a cohort of 248 gastric cancer patients using convolutional neural networks (CNNs) in an end-to-end weakly supervised scheme independent of subjective pathologist input. To account for the time-to-event characteristic of the outcome data, we developed new survival models to guide the network training. In addition to the standard H&E staining, we investigated the prognostic value of a panel of immune cell markers (CD8, CD20, CD68) and a proliferation marker (Ki67). Our CNN-derived risk scores provided additional prognostic value when compared to the gold standard prognostic tool TNM stage. The CNN-derived risk scores were also shown to be superior when systematically compared to cell density measurements or a CNN score derived from binary 5-year survival classification, which ignores time-to-event. To better understand the underlying biological mechanisms, we qualitatively investigated risk heat maps for each marker which visualised the network output. We identified patterns of biological interest that were related to low risk of cancer-specific death such as the presence of B-cell predominated clusters and Ki67 positive sub-regions and showed that the corresponding risk scores had prognostic value in multivariate Cox regression analyses (Ki67&CD20 risks: hazard ratio (HR) = 1.47, 95% confidence interval (CI) = 1.15-1.89, p = 0.002; CD20&CD68 risks: HR = 1.33, 95% CI = 1.07-1.67, p = 0.009). Our study demonstrates the potential additional value that deep learning in combination with a panel of IHC markers can bring to the field of precision oncology.

We report the ability of two deep learningbased decision systems to stratify non-small cell lung cancer (NSCLC) patients treated with checkpoint inhibitor therapy into two distinct survival groups. Both systems analyze functional and morphological properties of epithelial regions in digital histopathology whole slide images stained with the SP263 PD-L1 antibody. The first system learns to replicate the pathologist assessment of the Tumor Cell (TC) score with a cut-point for positivity at 25% for patient stratification. The second system is free from assumptions related to TC scoring and directly learns patient stratification from the overall survival time and event information. Both systems are built on a novel unpaired domain adaptation deep learning solution for epithelial region segmentation. This approach significantly reduces the need for large pixel-precise manually annotated datasets while superseding serial sectioning or re-staining of slides to obtain ground truth by cytokeratin staining. The capacity of the first system to replicate the TC scoring by pathologists is evaluated on 703 unseen cases, with an addition of 97 cases from an independent cohort. Our results show Lin's concordance values of 0.93 and 0.96 against pathologist scoring, respectively. The ability of the first and second system to stratify anti-PD-L1 treated patients is evaluated on 151 clinical samples. Both systems show similar stratification powers (first system: HR =0.539, p=0.004 and second system: HR=0.525, p=0.003) compared to TC scoring by pathologists (HR=0.574, p=0.01).

In this report, the effects of a combined treatment with the proteasome inhibitor bortezomib and either a recombinant adenoassociated virus type 2 (rAAV-2)-mediated p53 gene transfer or chemotherapeutic agents, docetaxel and pemetrexed, were tested on p53 positive and p53negative non-small cell lung cancer (NSCLC) cell lines. The combination of bortezomib and rAAV-p53 led to a significant synergistic inhibition of cell growth between 62-82% depending on the p53 status of the cell line and drug concentration. Surviving cells of the combined treatment showed a significant reduced ability to form colonies. Enhanced cell toxicity was associated with a 5.3-14.4-fold increase of the apoptotic rate and intracellular p53 level up to 50.4% following vectormediated p53 restoration and bortezomib treatment. In contrast, an antagonistic effect on tumor cell growth and colony formation was observed for the combination of bortezomib and docetaxel or pemetrexed as a reduction of cell growth between 31 and 48% was found in comparison to 50% using the single agents. Lower cytotoxic effects were associated with significantly reduced apoptosis and an increase of clonogenic growth. The observed antagonistic effects between bortezomib and docetaxel or pemetrexed might influence clinical trials using these compounds. Conversely, p53 restoration and bortezomib treatment led to enhanced, synergistic tumor cell toxicity.

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