HER2 quantitative continuous scoring for accurate patient selection in HER2 negative trastuzumab deruxtecan treated breast cancer.

Many targeted cancer therapies rely on biomarkers assessed by scoring of immunohistochemically (IHC)-stained tissue, which is subjective, semiquantitative, and does not account for expression heterogeneity. We describe an image analysis-based method for quantitative continuous scoring (QCS) of digital whole-slide images acquired from baseline human epidermal growth factor receptor 2 (HER2) IHC-stained breast cancer tissue. Candidate signatures for patient stratification using QCS of HER2 expression on subcellular compartments were identified, addressing the spatial distribution of tumor cells and tumor-infiltrating lymphocytes. Using data from trastuzumab deruxtecan-treated patients with HER2-positive and HER2-negative breast cancer from a phase 1 study (NCT02564900; DS8201-A-J101; N = 151), QCS-based patient stratification showed longer progression-free survival (14.8 vs 8.6 months) with higher prevalence of patient selection (76.4 vs 56.9%) and a better cross-validated log-rank p value (0.026 vs 0.26) than manual scoring based on the American Society of Clinical Oncology / College of American Pathologists guidelines. QCS-based features enriched the HER2-negative subgroup by correctly predicting 20 of 26 responders.

Antitumor activity of AZD0754, a dnTGFßRII-armored, STEAP2-targeted CAR-T cell therapy, in prostate cancer.

Prostate cancer is generally considered an immunologically "cold" tumor type that is insensitive to immunotherapy. Targeting surface antigens on tumors through cellular therapy can induce a potent antitumor immune response to "heat up" the tumor microenvironment. However, many antigens expressed on prostate tumor cells are also found on normal tissues, potentially causing on-target, off-tumor toxicities and a suboptimal therapeutic index. Our studies revealed that six-transmembrane epithelial antigen of prostate-2 (STEAP2) was a prevalent prostate cancer antigen that displayed high, homogeneous cell surface expression across all stages of disease with limited distal normal tissue expression, making it ideal for therapeutic targeting. A multifaceted lead generation approach enabled development of an armored STEAP2 chimeric antigen receptor T cell (CAR-T) therapeutic candidate, AZD0754. This CAR-T product was armored with a dominant-negative TGF-ß type II receptor, bolstering its activity in the TGF-ß-rich immunosuppressive environment of prostate cancer. AZD0754 demonstrated potent and specific cytotoxicity against antigen-expressing cells in vitro despite TGF-ß-rich conditions. Further, AZD0754 enforced robust, dose-dependent in vivo efficacy in STEAP2-expressing cancer cell line-derived and patient-derived xenograft mouse models, and exhibited encouraging preclinical safety. Together, these data underscore the therapeutic tractability of STEAP2 in prostate cancer as well as build confidence in the specificity, potency, and tolerability of this potentially first-in-class CAR-T therapy.

Spatial heterogeneity of cancer associated protein expression in immunohistochemically stained images as an improved prognostic biomarker.

The identification of new tumor biomarkers for patient stratification before therapy, for monitoring of disease progression, and for characterization of tumor biology plays a crucial role in cancer research. The status of these biomarkers is mostly scored manually by a pathologist and such scores typically, do not consider the spatial heterogeneity of the protein's expression in the tissue. Using advanced image analysis methods, marker expression can be determined quantitatively with high accuracy and reproducibility on a per-cell level. To aggregate such per-cell marker expressions on a patient level, the expression values for single cells are usually averaged for the whole tissue. However, averaging neglects the spatial heterogeneity of the marker expression in the tissue. We present two novel approaches for quantitative scoring of spatial marker expression heterogeneity. The first approach is based on a co-occurrence analysis of the marker expression in neighboring cells. The second approach accounts for the local variability of the protein's expression by tiling the tissue with a regular grid and assigning local spatial heterogeneity phenotypes per tile. We apply our novel scores to quantify the spatial expression of four different membrane markers, i.e., HER2, CMET, CD44, and EGFR in immunohistochemically (IHC) stained tissue sections of colorectal cancer patients. We evaluate the prognostic relevance of our spatial scores in this cohort and show that the spatial heterogeneity scores clearly outperform the marker expression average as a prognostic factor (CMET: p-value=0.01 vs. p-value=0.3).

Automatic discovery of image-based signatures for ipilimumab response prediction in malignant melanoma.

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.

Tissue Phenomics for prognostic biomarker discovery in low- and intermediate-risk prostate cancer.

Tissue Phenomics is the discipline of mining tissue images to identify patterns that are related to clinical outcome providing potential prognostic and predictive value. This involves the discovery process from assay development, image analysis, and data mining to the final interpretation and validation of the findings. Importantly, this process is not linear but allows backward steps and optimization loops over multiple sub-processes. We provide a detailed description of the Tissue Phenomics methodology while exemplifying each step on the application of prostate cancer recurrence prediction. In particular, we automatically identified tissue-based biomarkers having significant prognostic value for low- and intermediate-risk prostate cancer patients (Gleason scores 6-7b) after radical prostatectomy. We found that promising phenes were related to CD8(+) and CD68(+) cells in the microenvironment of cancerous glands in combination with the local micro-vascularization. Recurrence prediction based on the selected phenes yielded accuracies up to 83% thereby clearly outperforming prediction based on the Gleason score. Moreover, we compared different machine learning algorithms to combine the most relevant phenes resulting in increased accuracies of 88% for tumor progression prediction. These findings will be of potential use for future prognostic tests for prostate cancer patients and provide a proof-of-principle of the Tissue Phenomics approach.

An objective comparison of cell-tracking algorithms.

We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.

The long non-coding RNA LINC00152 is essential for cell cycle progression through mitosis in HeLa cells.

In recent years, long non-coding RNA (lncRNA) research has identified essential roles of these transcripts in virtually all physiological cellular processes including tumorigenesis, but their functions and molecular mechanisms are poorly understood. In this study, we performed a high-throughput siRNA screen targeting 638 lncRNAs deregulated in cancer entities to analyse their impact on cell division by using time-lapse microscopy. We identified 26 lncRNAs affecting cell morphology and cell cycle including LINC00152. This transcript was ubiquitously expressed in many human cell lines and its RNA levels were significantly upregulated in lung, liver and breast cancer tissues. A comprehensive sequence analysis of LINC00152 revealed a highly similar paralog annotated as MIR4435-2HG and several splice variants of both transcripts. The shortest and most abundant isoform preferentially localized to the cytoplasm. Cells depleted of LINC00152 arrested in prometaphase of mitosis and showed reduced cell viability. In RNA affinity purification (RAP) studies, LINC00152 interacted with a network of proteins that were associated with M phase of the cell cycle. In summary, we provide new insights into the properties and biological function of LINC00152 suggesting that this transcript is crucial for cell cycle progression through mitosis and thus, could act as a non-coding oncogene.

High-Throughput RNAi Screening Identifies a Role for the Osteopontin Pathway in Proliferation and Migration of Human Aortic Smooth Muscle Cells.

PURPOSE: Understanding of the mechanisms of vascular smooth muscle cells (VSMCs) phenotypic regulation is critically important to identify novel candidates for future therapeutic intervention. While HTS approaches have recently been used to identify novel regulators in many cell lines, such as cancer cells and hematopoietic stem cells, no studies have so far systematically investigated the effect of gene inactivation on VSMCs with respect to cell survival and growth response. METHODS AND RESULTS: 257 out of 2000 genes tested resulted in an inhibition of cell proliferation in HaoSMCs. After pathway analysis, 38 significant genes were selected for further study. 23 genes were confirmed to inhibit proliferation, and 13 genes found to induce apoptosis in the synthetic phenotype. 11 genes led to an aberrant nuclear phenotype indicating a central role in cell mitosis. 4 genes affected the cell migration in synthetic HaoSMCs. Using computational biological network analysis, 11 genes were identified to have an indirect or direct interaction with the Osteopontin pathway. For 10 of those genes, levels of proteins downstream of the Osteopontin pathway were found to be down-regulated, using RNAi methodology. CONCLUSIONS: A phenotypic high-throughput siRNA screen could be applied to identify genes relevant for the cell biology of HaoSMCs. Novel genes were identified which play a role in proliferation, apoptosis, mitosis and migration of HaoSMCs. These may represent potential drug candidates in the future.

Quantitative analysis of chromatin interaction changes upon a 4.3 Mb deletion at mouse 4E2.

BACKGROUND: Circular chromosome conformation capture (4C) has provided important insights into three dimensional (3D) genome organization and its critical impact on the regulation of gene expression. We developed a new quantitative framework based on polymer physics for the analysis of paired-end sequencing 4C (PE-4Cseq) data. We applied this strategy to the study of chromatin interaction changes upon a 4.3 Mb DNA deletion in mouse region 4E2. RESULTS: A significant number of differentially interacting regions (DIRs) and chromatin compaction changes were detected in the deletion chromosome compared to a wild-type (WT) control. Selected DIRs were validated by 3D DNA FISH experiments, demonstrating the robustness of our pipeline. Interestingly, significant overlaps of DIRs with CTCF/Smc1 binding sites and differentially expressed genes were observed. CONCLUSIONS: Altogether, our PE-4Cseq analysis pipeline provides a comprehensive characterization of DNA deletion effects on chromatin structure and function.

Large-scale tracking and classification for automatic analysis of cell migration and proliferation, and experimental optimization of high-throughput screens of neuroblastoma cells.

Computational approaches for automatic analysis of image-based high-throughput and high-content screens are gaining increased importance to cope with the large amounts of data generated by automated microscopy systems. Typically, automatic image analysis is used to extract phenotypic information once all images of a screen have been acquired. However, also in earlier stages of large-scale experiments image analysis is important, in particular, to support and accelerate the tedious and time-consuming optimization of the experimental conditions and technical settings. We here present a novel approach for automatic, large-scale analysis and experimental optimization with application to a screen on neuroblastoma cell lines. Our approach consists of cell segmentation, tracking, feature extraction, classification, and model-based error correction. The approach can be used for experimental optimization by extracting quantitative information which allows experimentalists to optimally choose and to verify the experimental parameters. This involves systematically studying the global cell movement and proliferation behavior. Moreover, we performed a comprehensive phenotypic analysis of a large-scale neuroblastoma screen including the detection of rare division events such as multi-polar divisions. Major challenges of the analyzed high-throughput data are the relatively low spatio-temporal resolution in conjunction with densely growing cells as well as the high variability of the data. To account for the data variability we optimized feature extraction and classification, and introduced a gray value normalization technique as well as a novel approach for automatic model-based correction of classification errors. In total, we analyzed 4,400 real image sequences, covering observation periods of around 120 h each. We performed an extensive quantitative evaluation, which showed that our approach yields high accuracies of 92.2% for segmentation, 98.2% for tracking, and 86.5% for classification.

Live Cell Analysis and Mathematical Modeling Identify Determinants of Attenuation of Dengue Virus 2'-O-Methylation Mutant.

Dengue virus (DENV) is the most common mosquito-transmitted virus infecting ~390 million people worldwide. In spite of this high medical relevance, neither a vaccine nor antiviral therapy is currently available. DENV elicits a strong interferon (IFN) response in infected cells, but at the same time actively counteracts IFN production and signaling. Although the kinetics of activation of this innate antiviral defense and the timing of viral counteraction critically determine the magnitude of infection and thus disease, quantitative and kinetic analyses are lacking and it remains poorly understood how DENV spreads in IFN-competent cell systems. To dissect the dynamics of replication versus antiviral defense at the single cell level, we generated a fully viable reporter DENV and host cells with authentic reporters for IFN-stimulated antiviral genes. We find that IFN controls DENV infection in a kinetically determined manner that at the single cell level is highly heterogeneous and stochastic. Even at high-dose, IFN does not fully protect all cells in the culture and, therefore, viral spread occurs even in the face of antiviral protection of naïve cells by IFN. By contrast, a vaccine candidate DENV mutant, which lacks 2'-O-methylation of viral RNA is profoundly attenuated in IFN-competent cells. Through mathematical modeling of time-resolved data and validation experiments we show that the primary determinant for attenuation is the accelerated kinetics of IFN production. This rapid induction triggered by mutant DENV precedes establishment of IFN-resistance in infected cells, thus causing a massive reduction of virus production rate. In contrast, accelerated protection of naïve cells by paracrine IFN action has negligible impact. In conclusion, these results show that attenuation of the 2'-O-methylation DENV mutant is primarily determined by kinetics of autocrine IFN action on infected cells.

Characterizing protein interactions employing a genome-wide siRNA cellular phenotyping screen.

Characterizing the activating and inhibiting effect of protein-protein interactions (PPI) is fundamental to gain insight into the complex signaling system of a human cell. A plethora of methods has been suggested to infer PPI from data on a large scale, but none of them is able to characterize the effect of this interaction. Here, we present a novel computational development that employs mitotic phenotypes of a genome-wide RNAi knockdown screen and enables identifying the activating and inhibiting effects of PPIs. Exemplarily, we applied our technique to a knockdown screen of HeLa cells cultivated at standard conditions. Using a machine learning approach, we obtained high accuracy (82% AUC of the receiver operating characteristics) by cross-validation using 6,870 known activating and inhibiting PPIs as gold standard. We predicted de novo unknown activating and inhibiting effects for 1,954 PPIs in HeLa cells covering the ten major signaling pathways of the Kyoto Encyclopedia of Genes and Genomes, and made these predictions publicly available in a database. We finally demonstrate that the predicted effects can be used to cluster knockdown genes of similar biological processes in coherent subgroups. The characterization of the activating or inhibiting effect of individual PPIs opens up new perspectives for the interpretation of large datasets of PPIs and thus considerably increases the value of PPIs as an integrated resource for studying the detailed function of signaling pathways of the cellular system of interest.

A benchmark for comparison of cell tracking algorithms.

MOTIVATION: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. RESULTS: The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately. AVAILABILITY AND IMPLEMENTATION: The challenge Web site (http://www.codesolorzano.com/celltrackingchallenge) provides access to the training and competition datasets, along with the ground truth of the training videos. It also provides access to Windows and Linux executable files of the evaluation software and most of the algorithms that competed in the challenge.

Stathmin regulates keratinocyte proliferation and migration during cutaneous regeneration.

Cutaneous regeneration utilizes paracrine feedback mechanisms to fine-tune the regulation of epidermal keratinocyte proliferation and migration. However, it is unknown how fibroblast-derived hepatocyte growth factor (HGF) affects these mutually exclusive processes in distinct cell populations. We here show that HGF stimulates the expression and phosphorylation of the microtubule-destabilizing factor stathmin in primary human keratinocytes. Quantitative single cell- and cell population-based analyses revealed that basal stathmin levels are important for the migratory ability of keratinocytes in vitro; however, its expression is moderately induced in the migration tongue of mouse skin or organotypic multi-layered keratinocyte 3D cultures after full-thickness wounding. In contrast, clearly elevated stathmin expression is detectable in hyperproliferative epidermal areas. In vitro, stathmin silencing significantly reduced keratinocyte proliferation. Automated quantitative and time-resolved analyses in organotypic cocultures demonstrated a high correlation between Stathmin/phospho-Stathmin and Ki67 positivity in epidermal regions with proliferative activity. Thus, activation of stathmin may stimulate keratinocyte proliferation, while basal stathmin levels are sufficient for keratinocyte migration during cutaneous regeneration.

Overlapping gene coexpression patterns in human medullary thymic epithelial cells generate self-antigen diversity.

Promiscuous expression of numerous tissue-restricted self-antigens (TRAs) in medullary thymic epithelial cells (mTECs) is essential to safeguard self-tolerance. A distinct feature of promiscuous gene expression is its mosaic pattern (i.e., at a given time, each self-antigen is expressed only in 1-3% of mTECs). How this mosaic pattern is generated at the single-cell level is currently not understood. Here, we show that subsets of human mTECs expressing a particular TRA coexpress distinct sets of genes. We identified three coexpression groups comprising overlapping and complementary gene sets, which preferentially mapped to certain chromosomes and intrachromosomal gene clusters. Coexpressed gene loci tended to colocalize to the same nuclear subdomain. The TRA subsets aligned along progressive differentiation stages within the mature mTEC subset and, in vitro, interconverted along this sequence. Our data suggest that single mTECs shift through distinct gene pools, thus scanning a sizeable fraction of the overall repertoire of promiscuously expressed self-antigens. These findings have implications for the temporal and spatial (re)presentation of self-antigens in the medulla in the context of tolerance induction.

MYCN-mediated overexpression of mitotic spindle regulatory genes and loss of p53-p21 function jointly support the survival of tetraploid neuroblastoma cells.

High-risk neuroblastomas often harbor structural chromosomal alterations, including amplified MYCN, and usually have a near-di/tetraploid DNA index, but the mechanisms creating tetraploidy remain unclear. Gene-expression analyses revealed that certain MYCN/MYC and p53/pRB-E2F target genes, especially regulating mitotic processes, are strongly expressed in near-di/tetraploid neuroblastomas. Using a functional RNAi screening approach and live-cell imaging, we identified a group of genes, including MAD2L1, which after knockdown induced mitotic-linked cell death in MYCN-amplified and TP53-mutated neuroblastoma cells. We found that MYCN/MYC-mediated overactivation of the metaphase-anaphase checkpoint synergizes with loss of p53-p21 function to prevent arrest or apoptosis of tetraploid neuroblastoma cells.

Time-lapse imaging of neuroblastoma cells to determine cell fate upon gene knockdown.

Neuroblastoma is the most common extra-cranial solid tumor of early childhood. Standard therapies are not effective in case of poor prognosis and chemotherapy resistance. To improve drug therapy, it is imperative to discover new targets that play a substantial role in tumorigenesis of neuroblastoma. The mitotic machinery is an attractive target for therapeutic interventions and inhibitors can be developed to target mitotic entry, spindle apparatus, spindle activation checkpoint, and mitotic exit. We present an elaborate analysis pipeline to determine cancer specific therapeutic targets by first performing a focused gene expression analysis to select genes followed by a gene knockdown screening assay of live cells. We interrogated gene expression studies of neuroblastoma tumors and selected 240 genes relevant for tumorigenesis and cell cycle. With these genes we performed time-lapse screening of gene knockdowns in neuroblastoma cells. We classified cellular phenotypes and used the temporal context of the perturbation effect to determine the sequence of events, particularly the mitotic entry preceding cell death. Based upon this phenotype kinetics from the gene knockdown screening, we inferred dynamic gene functions in mitosis and cell proliferation. We identified six genes (DLGAP5, DSCC1, SMO, SNRPD1, SSBP1, and UBE2C) with a vital role in mitosis and these are promising therapeutic targets for neuroblastoma. Images and movies of every time point of all screened genes are available at https://ichip.bioquant.uni-heidelberg.de.

miR-17-5p regulates endocytic trafficking through targeting TBC1D2/Armus.

miRNA cluster miR-17-92 is known as oncomir-1 due to its potent oncogenic function. miR-17-92 is a polycistronic cluster that encodes 6 miRNAs, and can both facilitate and inhibit cell proliferation. Known targets of miRNAs encoded by this cluster are largely regulators of cell cycle progression and apoptosis. Here, we show that miRNAs encoded by this cluster and sharing the seed sequence of miR-17 exert their influence on one of the most essential cellular processes - endocytic trafficking. By mRNA expression analysis we identified that regulation of endocytic trafficking by miR-17 can potentially be achieved by targeting of a number of trafficking regulators. We have thoroughly validated TBC1D2/Armus, a GAP of Rab7 GTPase, as a novel target of miR-17. Our study reveals regulation of endocytic trafficking as a novel function of miR-17, which might act cooperatively with other functions of miR-17 and related miRNAs in health and disease.

Tracking and quantitative analysis of dynamic movements of cells and particles.

Understanding complex cellular processes requires investigating the underlying mechanisms within a spatiotemporal context. Although cellular processes are dynamic in nature, most studies in molecular cell biology are based on fixed specimens, for example, using immunocytochemistry or fluorescence in situ hybridization (FISH). However, breakthroughs in fluorescence microscopy imaging techniques, in particular, the discovery of green fluorescent protein (GFP) and its spectral variants, have facilitated the study of a wide range of dynamic processes by allowing nondestructive labeling of target structures in living cells. In addition, the tremendous improvements in spatial and temporal resolution of light microscopes now allow cellular processes to be analyzed in unprecedented detail. These state-of-the-art imaging technologies, however, provide a huge amount of digital image data. To cope with the enormous amount of image data and to extract reproducible as well as quantitative information, computer-based image analysis is required. In this article, we describe methods for computer-based analysis of multidimensional live cell microscopy images and their application to study the dynamics of cells and particles. First, we sketch a general workflow for quantitative analysis of live cell images. Then, we detail computational methods for automatic image analysis comprising image preprocessing, segmentation, registration, tracking, and classification. We conclude with a discussion of quantitative analysis and systems biology.

Automatic analysis of dividing cells in live cell movies to detect mitotic delays and correlate phenotypes in time.

Live-cell imaging allows detailed dynamic cellular phenotyping for cell biology and, in combination with small molecule or drug libraries, for high-content screening. Fully automated analysis of live cell movies has been hampered by the lack of computational approaches that allow tracking and recognition of individual cell fates over time in a precise manner. Here, we present a fully automated approach to analyze time-lapse movies of dividing cells. Our method dynamically categorizes cells into seven phases of the cell cycle and five aberrant morphological phenotypes over time. It reliably tracks cells and their progeny and can thus measure the length of mitotic phases and detect cause and effect if mitosis goes awry. We applied our computational scheme to annotate mitotic phenotypes induced by RNAi gene knockdown of CKAP5 (also known as ch-TOG) or by treatment with the drug nocodazole. Our approach can be readily applied to comparable assays aiming at uncovering the dynamic cause of cell division phenotypes.