In the vertebrate retina, amacrine and ganglion cells represent the most diverse cell classes. They can be classified into different cell types by several features, such as morphology, light responses, and gene expression profile. Although birds possess high visual acuity (similar to primates that we used here for comparison) and tetrachromatic color vision, data on the expression of transcription factors in retinal ganglion cells of birds are largely missing. In this study, we tested various transcription factors, known to label subpopulations of cells in mammalian retinae, in two avian species: the common buzzard (Buteo buteo), a raptor with exceptional acuity, and the domestic pigeon (Columba livia domestica), a good navigator and widely used model for visual cognition. Staining for the transcription factors Foxp2, Satb1 and Satb2 labeled most ganglion cells in the avian ganglion cell layer. CtBP2 was established as marker for displaced amacrine cells, which allowed us to reliably distinguish ganglion cells from displaced amacrine cells and assess their densities in buzzard and pigeon. When we additionally compared the temporal and central fovea of the buzzard with the fovea of primates, we found that the cellular organization in the pits was different in primates and raptors. In summary, we demonstrate that the expression of transcription factors is a defining feature of cell types not only in the retina of mammals but also in the retina of birds. The markers, which we have established, may provide useful tools for more detailed studies on the retinal circuitry of these highly visual animals.

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Visual (and probably also magnetic) signal processing starts at the first synapse, at which photoreceptors contact different types of bipolar cells, thereby feeding information into different processing channels. In the chicken retina, 15 and 22 different bipolar cell types have been identified based on serial electron microscopy and single‐cell transcriptomics, respectively. However, immunohistochemical markers for avian bipolar cells were only anecdotally described so far. Here, we systematically tested 12 antibodies for their ability to label individual bipolar cells in the bird retina and compared the eight most suitable antibodies across distantly related species, namely domestic chicken, domestic pigeon, common buzzard, and European robin, and across retinal regions. While two markers (GNB3 and EGFR) labeled specifically ON bipolar cells, most markers labeled in addition to bipolar cells also other cell types in the avian retina. Staining pattern of four markers (CD15, PKCα, PKCβ, secretagogin) was species‐specific. Two markers (calbindin and secretagogin) showed a different expression pattern in central and peripheral retina. For the chicken and European robin, we found slightly more ON bipolar cell somata in the inner nuclear layer than OFF bipolar cell somata. In contrast, OFF bipolar cells made more ribbon synapses than ON bipolar cells in the inner plexiform layer of these species. Finally, we also analyzed the photoreceptor connectivity of selected bipolar cell types in the European robin retina. In summary, we provide a catalog of bipolar cell markers for different bird species, which will greatly facilitate analyzing the retinal circuitry of birds on a larger scale.

e13556 Background: Immune checkpoint inhibitors (ICI) are used to manage patients with both small cell (SCLC) and non-small cell (NSCLC) lung cancer. However, ICI response rates are often low, and identifying patients that will benefit from ICIs can be challenging. The value of biomarkers used to predict ICI response, such as PD-L1, Combined Positive Score (CPS) or tumor mutational burden (TMB) have been debated. Furthermore, the resources needed to assess these biomarkers may not be available in many centres. Developing more accurate and accessible tools that predict ICI responses could enable a precision medicine approach that improves patient outcomes. This study aimed to use a novel machine-learning (ML) algorithm to predict response to different ICI therapies in patients with lung cancer based on clinically available data. Methods: 334 eligible records were cleaned and reprocessed from textual to categorical data using one hot encoding. Complete datasets were available for 161 patients. Differences in the data distribution were handled using the Synthetic Minority Oversampling Technique. Six ML algorithms were trained, including Linear regression, Support Vector Classifier, XGBoost Classifier, Random Forest, Decision Tree, and Gaussian Naive Bayes Classifier. These algorithms used 80% of the training data, were tested on 20% of validation data and used the Grid Search Cross-Validation technique for hyperparameter optimization. Results: For the 161 patients included in the final analysis, the mean age was 68 years and 48% were female. 9% of patients had SCLC and 80% had NSCLC. Patients receiving Pembrolizumab, Nivolumab and Atezolizumab comprised 62%, 11% and 25%, respectively. The artificial intelligence (AI) algorithm predicted and stratified ICI response better than PD-L1 levels. Of the ML algorithms, XGBoost Classifier predicted response with the most accuracy, 64% (0.61 F1 score). This model found that good performance status (0-1), female gender and adenocarcinoma sub-type predicted response to ICI. On the other hand, M1, N2 staging, male gender, squamous cell carcinoma sub-type and receiving Atezolizumab were predictive of disease progression. Conclusions: This study developed multiple novel ML models to predict responses to ICIs in lung cancer. XGBoost Classifier used clinically available data to show that the type of ICI a patient receives, their histopathology sub-type and their TMN staging impact ICI response. Future work will aim to improve accuracy and predict ICI toxicity by including data from multiple centres, different cancer types and additional clinical variables. [Table: see text]

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