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The drivers of ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) transition are poorly understood. Here, we conducted an integrated genomic, transcriptomic, and whole-slide image analysis to evaluate changes in copy-number profiles, mutational profiles, expression, neoantigen load, and topology in 6 cases of matched pure DCIS and recurrent IDC. We demonstrate through combined copy-number and mutational analysis that recurrent IDC can be genetically related to its pure DCIS despite long latency periods and therapeutic interventions. Immune “hot” and “cold” tumors can arise as early as DCIS and are subtype-specific. Topologic analysis showed a similar degree of pan-leukocyte-tumor mixing in both DCIS and IDC but differ when assessing specific immune subpopulations such as CD4 T cells and CD68 macrophages. Tumor-specific copy-number aberrations in MHC-I presentation machinery and losses in 3p, 4q, and 5p are associated with differences in immune signaling in estrogen receptor (ER)-negative IDC. Common oncogenic hotspot mutations in genes including TP53 and PIK3CA are predicted to be neoantigens yet are paradoxically conserved during the DCIS-to-IDC transition, and are associated with differences in immune signaling. We highlight both tumor and immune-specific changes in the transition of pure DCIS to IDC, including genetic changes in tumor cells that may have a role in modulating immune function and assist in immune escape, driving the transition to IDC. Implications: We demonstrate that the in situ to IDC evolutionary bottleneck is shaped by both tumor and immune cells.

Crucial transitions in cancer-including tumor initiation, local expansion, metastasis, and therapeutic resistance-involve complex interactions between cells within the dynamic tumor ecosystem. Transformative single-cell genomics technologies and spatial multiplex in situ methods now provide an opportunity to interrogate this complexity at unprecedented resolution. The Human Tumor Atlas Network (HTAN), part of the National Cancer Institute (NCI) Cancer Moonshot Initiative, will establish a clinical, experimental, computational, and organizational framework to generate informative and accessible three-dimensional atlases of cancer transitions for a diverse set of tumor types. This effort complements both ongoing efforts to map healthy organs and previous large-scale cancer genomics approaches focused on bulk sequencing at a single point in time. Generating single-cell, multiparametric, longitudinal atlases and integrating them with clinical outcomes should help identify novel predictive biomarkers and features as well as therapeutically relevant cell types, cell states, and cellular interactions across transitions. The resulting tumor atlases should have a profound impact on our understanding of cancer biology and have the potential to improve cancer detection, prevention, and therapeutic discovery for better precision-medicine treatments of cancer patients and those at risk for cancer.

Stress pathways monitor intracellular systems and deploy a range of regulatory mechanisms upon stress. One of the best characterised pathways with wide implications in disease, the unfolded protein response (UPR), is the endoplasmic reticulum (ER) guarding to maintain homeostasis. In eukaryotes, the UPR comprises of three highly conserved transducers leading to the regulation of hundreds of targets by activating UPR-specific transcription factors (Fun and Thibault, 2019). Developed UPR inhibitors to treat diseases have serious potential long term side effects on the functions of the pancreas, the immune system, and the liver as the UPR programme is too broad to be inhibited from the upstream players (Hetz et al., 2013). Additionally, the inhibition or deletion of one of the three ER stress transducers, IRE1, PERK, and ATF6, leads to a compensatory mechanism from the remaining two ER stress transducers. This phenomenon may complicate a search for new ER stress transducer inhibitors. Here, we report a fully functional human PERK (hPERK) chimeric protein expressed in Saccharomyces cerevisiae that could be used for high throughput screen to identify new PERK inhibitory or activating compounds.

Living longer with sustainable quality of life is becoming increasingly important in aging populations. Understanding associative biological mechanisms have proven daunting, because of multigenicity and population heterogeneity. Although Big Data and Artificial Intelligence (AI) could help, naïve adoption is ill advised. We hold the view that model organisms are better suited for big-data analytics but might lack relevance because they do not immediately reflect the human condition. Resolving this hurdle and bridging the human-model organism gap will require some finesse. This includes improving signal:noise ratios by appropriate contextualization of high-throughput data, establishing consistency across multiple high-throughput platforms, and adopting supporting technologies that provide useful in silico and in vivo validation strategies.

Industrial companies often require complete inventories of their infrastructure. In many cases, a better inventory leads to a direct reduction of cost and uncertainty of engineering. While large scale panoramic surveys now allow these inventories to be performed remotely and reduce time on-site, the time and money required to visually segment the many types of components on thousands of high resolution panoramas can make the process infeasible. Recent studies have shown that deep learning techniques, namely deep neural networks, can accurately perform panoptic segmentation of things and stuff and hence be used to inventory the components of a picture. In order to train those deep architectures with specific industrial equipment, not available in public datasets, our approach uses an as-built 3D model of an industrial building to procedurally generate labels. Our results show that, despite the presence of errors during the generation of the dataset, our method is able to accurately perform panoptic segmentation on images of industrial scenes. In our testing, 80% of generated labels were correctly identified (non null intersection over union, i.e. true positive) by the panoptic segmentation, with great performance levels even for difficult classes, such as reflective heat insulators. We then visually investigated the 20% of true negative, and discovered that 80% were correctly segmented, but were counted as true negative because of errors in the dataset generation. Demonstrating this level of accuracy for panoptic segmentation on industrial panoramas for inventories also offers novel perspectives for 3D laser scan processing.

We aimed to determine whether multiresolution fractal analysis of voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps can provide early prediction of breast cancer response to neoadjuvant chemotherapy (NACT). In total, 55 patients underwent 4 DCE-MRI examinations before, during, and after NACT. The shutter-speed model was used to analyze the DCE-MRI data and generate parametric maps within the tumor region of interest. The proposed multiresolution fractal method and the more conventional methods of single-resolution fractal, gray-level co-occurrence matrix, and run-length matrix were used to extract features from the parametric maps. Only the data obtained before and after the first NACT cycle were used to evaluate early prediction of response. With a training (N = 40) and testing (N = 15) data set, support vector machine was used to assess the predictive abilities of the features in classification of pathologic complete response versus non-pathologic complete response. Generally the multiresolution fractal features from individual maps and the concatenated features from all parametric maps showed better predictive performances than conventional features, with receiver operating curve area under the curve (AUC) values of 0.91 (all parameters) and 0.80 (Ktrans), in the training and testing sets, respectively. The differences in AUC were statistically significant (P &lt, 05) for several parametric maps. Thus, multiresolution analysis that decomposes the texture at various spatial-frequency scales may more accurately capture changes in tumor vascular heterogeneity as measured by DCE-MRI, and therefore provide better early prediction of NACT response.

SUMMARY The emergence of megascale single-cell multiplex tissue imaging (MTI) datasets necessitates reproducible, scalable, and robust tools for cell phenotyping and spatial analysis. We developed open-source, graphics processing unit (GPU)-accelerated tools for intensity normalization, phenotyping, and microenvironment characterization. We deploy the toolkit on a human breast cancer (BC) tissue microarray stained by cyclic immunofluorescence and present the first cross-validation of breast cancer cell phenotypes derived by using two different MTI platforms. Finally, we demonstrate an integrative phenotypic and spatial analysis revealing BC subtype-specific features., In brief Burlingame et al. describe a GPU-accelerated workflow for normalization, phenotyping, and spatial analysis of single-cell multiplex tissue imaging (MTI) data. This workflow is deployed on breast cancer (BC) tissues to derive a cell type dictionary, which is validated between MTI platforms. Tissue architecture is used to discriminate between BC subtypes., Graphical Abstract

SummaryRecent advances in Focused Ion Beam-Scanning Electron Microscopy (FIB-SEM) allows the imaging and analysis of cellular ultrastructure at nanoscale resolution, but the collection of labels and/or noise-free data sets has several challenges, often immutable. Reasons range from time consuming manual annotations, requiring highly trained specialists, to introducing imaging artifacts from the prolonged scanning during acquisition. We propose a fully unsupervised Noise Reconstruction and Removal Network for denoising scanning electron microscopy images.The architecture, inspired by gated recurrent units, reconstructs and removes the noise by synthesizing the sequential data. At the same time the fully unsupervised training guides the network in distinguishing true signal from noise and gives comparable results to supervised architectures. We demonstrate that this new network specialized on 3D electron microscopy data sets, achieves comparable and even better results than supervised networks.

SummaryA deeper understanding of the cellular and subcellular organization of tumor cells and their interactions with the tumor microenvironment will shed light on how cancer evolves and guide effective therapy choices. Electron microscopy (EM) images can provide detailed view of the cellular ultrastructure and are being generated at an ever-increasing rate. However, the bottleneck in their analysis is the delineation of the cellular structures to enable interpretable rendering. We have mitigated this limitation by using deep learning, specifically, the ResUNet architecture, to segment cells and subcellular ultrastructure. Our initial prototype focuses on segmenting nuclei and nucleoli in 3D FIB-SEM images of tumor biopsies obtained from patients with metastatic breast and pancreatic cancers. Trained with sparse manual labels, our method results in accurate segmentation of nuclei and nucleoli with best Dice score of 0.99 and 0.98 respectively. This method can be extended to other cellular structures, enabling deeper analysis of inter- and intracellular state and interactions.

Accurate diagnosis of metastatic cancer is essential for prescribing optimal control strategies to halt further spread of metastasizing disease. While pathological inspection aided by immunohistochemistry staining provides a valuable gold standard for clinical diagnostics, deep learning methods have emerged as powerful tools for identifying clinically relevant features of whole slide histology relevant to a tumor’s metastatic origin. Although deep learning models require significant training data to learn effectively, transfer learning paradigms provide mechanisms to circumvent limited training data by first training a model on related data prior to fine-tuning on smaller data sets of interest. In this work we propose a transfer learning approach that trains a convolutional neural network to infer the metastatic origin of tumor tissue from whole slide images of hematoxylin and eosin (H&E) stained tissue sections and illustrate the advantages of pre-training network on whole slide images of primary tumor morphology. We further characterize statistical dissimilarity between primary and metastatic tumors of various indications on patch-level images to highlight limitations of our indication-specific transfer learning approach. Using a primary-to-metastatic transfer learning approach, we achieved mean class-specific areas under receiver operator characteristics curve (AUROC) of 0.779, which outperformed comparable models trained on only images of primary tumor (mean AUROC of 0.691) or trained on only images of metastatic tumor (mean AUROC of 0.675), supporting the use of large scale primary tumor imaging data in developing computer vision models to characterize metastatic origin of tumor lesions.

A functional association is uncovered between the ribosome-associated trigger factor (TF) chaperone and the ClpXP degradation complex. Bioinformatic analyses demonstrate conservation of the close proximity of tig, the gene coding for TF, and genes coding for ClpXP, suggesting a functional interaction. The effect of TF on ClpXP-dependent degradation varies based on the nature of substrate. While degradation of some substrates are slowed down or are unaffected by TF, surprisingly, TF increases the degradation rate of a third class of substrates. These include λ phage replication protein λO, master regulator of stationary phase RpoS, and SsrA-tagged proteins. Globally, TF acts to enhance the degradation of about 2% of newly synthesized proteins. TF is found to interact through multiple sites with ClpX in a highly dynamic fashion to promote protein degradation. This chaperone–protease cooperation constitutes a unique and likely ancestral aspect of cellular protein homeostasis in which TF acts as an adaptor for ClpXP., ClpXP is the main ATP-dependent proteolytic complex in bacteria, is essential for maintaining cellular protein homeostasis and is also critical for bacterial pathogenesis. Here, the authors establish a functional link between ClpXP and trigger actor, a chaperone involved in the early stages of protein folding.

SummaryMechanisms of therapeutic resistance manifest in metastatic cancers as tumor cell intrinsic alterations and extrinsic microenvironmental influences that can change during treatment. To support the development of methods for the identification of these mechanisms in individual patients, we present here an Omic and Multidimensional Spatial (OMS) Atlas generated from four serial biopsies of a metastatic breast cancer patient during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata including treatment times and doses, anatomic imaging, and blood-based response measurements to exploratory analytics including comprehensive DNA, RNA, and protein profiles, images of multiplexed immunostaining, and 2- and 3-dimensional scanning electron micrographs. These data reveal aspects of therapy-associated heterogeneity and evolution of the cancer’s genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples showing how integrative analyses of these data provide insights into potential mechanisms of response and resistance, and suggest novel therapeutic vulnerabilities.

Mechanistic disease progression studies using animal models require objective and quantifiable assessment of tissue pathology. Currently quantification relies heavily on staining methods which can be expensive, labor/time-intensive, inconsistent across laboratories and batch, and produce uneven staining that is prone to misinterpretation and investigator bias. We developed an automated semantic segmentation tool utilizing deep learning for rapid and objective quantification of histologic features relying solely on hematoxylin and eosin stained pancreatic tissue sections. The tool segments normal acinar structures, the ductal phenotype of acinar-to-ductal metaplasia (ADM), and dysplasia with Dice coefficients of 0.79, 0.70, and 0.79, respectively. To deal with inaccurate pixelwise manual annotations, prediction accuracy was also evaluated against biological truth using immunostaining mean structural similarity indexes (SSIM) of 0.925 and 0.920 for amylase and pan-keratin respectively. Our tool’s disease area quantifications were correlated to the quantifications of immunostaining markers (DAPI, amylase, and cytokeratins; Spearman correlation score = 0.86, 0.97, and 0.92) in unseen dataset (n = 25). Moreover, our tool distinguishes ADM from dysplasia, which are not reliably distinguished with immunostaining, and demonstrates generalizability across murine cohorts with pancreatic disease. We quantified the changes in histologic feature abundance for murine cohorts with oncogenic Kras-driven disease, and the predictions fit biological expectations, showing stromal expansion, a reduction of normal acinar tissue, and an increase in both ADM and dysplasia as disease progresses. Our tool promises to accelerate and improve the quantification of pancreatic disease in animal studies and become a unifying quantification tool across laboratories.

The drivers of ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) transition are poorly understood. Here, we conducted an integrated genomic, transcriptomic, and whole-slide image analysis to evaluate changes in copy number profiles, mutational profiles, expression, neoantigen load and topology in 6 cases of matched pure DCIS and recurrent IDC. We demonstrate through combined copy number and mutational analysis that recurrent IDC can be genetically related to its pure DCIS despite long latency periods and therapeutic interventions. Immune “hot” and “cold” tumors can arise as early as DCIS and are subtype-specific. Topologic analysis showed a similar degree of pan-leukocyte-tumor mixing in both DCIS and IDC but differ when assessing specific immune subpopulations such as CD4 T-cells and CD68 macrophages. Tumor-specific copy number aberrations in MHC-I presentation machinery and losses in 3p,4q, and 5p are associated with differences in immune signaling in estrogen receptor (ER) negative IDC. Common oncogenic hotspot mutations in genes including TP53 and PIK3CA are predicted to be neoantigens yet are paradoxically conserved during the DCIS-to-IDC transition, and are associated with differences in immune signaling. We highlight both tumor and immune-specific changes in the transition of pure DCIS to IDC, including genetic changes in tumor cells that may have a role in modulating immune function and assist in immune escape, driving the transition to IDC.

Spatially-resolved molecular profiling by immunostaining tissue sections is a key feature in cancer diagnosis, subtyping, and treatment, where it complements routine histopathological evaluation by clarifying tumor phenotypes. In this work, we present a deep learning-based method called speedy histological-to-immunofluorescent translation (SHIFT) which takes histologic images of hematoxylin and eosin (H&E)-stained tissue as input, then in near-real time returns inferred virtual immunofluorescence (IF) images that estimate the underlying distribution of the tumor cell marker pan-cytokeratin (panCK). To build a dataset suitable for learning this task, we developed a serial staining protocol which allows IF and H&E images from the same tissue to be spatially registered. We show that deep learning-extracted morphological feature representations of histological images can guide representative sample selection, which improved SHIFT generalizability in a small but heterogenous set of human pancreatic cancer samples. With validation in larger cohorts, SHIFT could serve as an efficient preliminary, auxiliary, or substitute for panCK IF by delivering virtual panCK IF images for a fraction of the cost and in a fraction of the time required by traditional IF.

Image cytometry enables quantitative cell characterization with preserved tissue architecture; thus, it has been highlighted in the advancement of multiplex immunohistochemistry (IHC) and digital image analysis in the context of immune-based biomarker monitoring associated with cancer immunotherapy. However, one of the challenges in the current image cytometry methodology is a technical limitation in the segmentation of nuclei and cellular components particularly in heterogeneously stained cancer tissue images. To improve the detection and specificity of single-cell segmentation in hematoxylin-stained images (which can be utilized for recently reported 12-biomarker chromogenic sequential multiplex IHC), we adapted a segmentation algorithm previously developed for hematoxlin and eosin-stained images, where morphological features are extracted based on Gabor-filtering, followed by stacking of image pixels into n-dimensional feature space and unsupervised clustering of individual pixels. Our proposed method showed improved sensitivity and specificity in comparison with standard segmentation methods. Replacing previously proposed methods with our method in multiplex IHC/image cytometry analysis, we observed higher detection of cell lineages including relatively rare TH 17 cells, further enabling sub-population analysis into TH 1-like and TH 2-like phenotypes based on T-bet and GATA3 expression. Interestingly, predominance of TH 2-like TH 17 cells was associated with human papilloma virus (HPV)-negative status of oropharyngeal squamous cell carcinoma of head and neck, known as a poor-prognostic subtype in comparison with HPV-positive status. Furthermore, TH 2-like TH 17 cells in HPV-negative head and neck cancer tissues were spatiotemporally correlated with CD66b+ granulocytes, presumably associated with an immunosuppressive microenvironment. Our cell segmentation method for multiplex IHC/image cytometry potentially contributes to in-depth immune profiling and spatial association, leading to further tissue-based biomarker exploration. © 2019 International Society for Advancement of Cytometry.

Lipid droplets (LDs) are implicated in conditions of lipid and protein dysregulation. The fat storage-inducing transmembrane (FIT; also known as FITM) family induces LD formation. Here, we establish a model system to study the role of the Saccharomyces cerevisiae FIT homologues (ScFIT), SCS3 and YFT2, in the proteostasis and stress response pathways. While LD biogenesis and basal endoplasmic reticulum (ER) stress-induced unfolded protein response (UPR) remain unaltered in ScFIT mutants, SCS3 was found to be essential for proper stress-induced UPR activation and for viability in the absence of the sole yeast UPR transducer IRE1. Owing to not having a functional UPR, cells with mutated SCS3 exhibited an accumulation of triacylglycerol within the ER along with aberrant LD morphology, suggesting that there is a UPR-dependent compensatory mechanism that acts to mitigate lack of SCS3. Additionally, SCS3 was necessary to maintain phospholipid homeostasis. Strikingly, global protein ubiquitylation and the turnover of both ER and cytoplasmic misfolded proteins is impaired in ScFITΔ cells, while a screen for interacting partners of Scs3 identifies components of the proteostatic machinery as putative targets. Together, our data support a model where ScFITs play an important role in lipid metabolism and proteostasis beyond their defined roles in LD biogenesis. This article has an associated First Person interview with the first author of the paper.

Lipid droplets (LDs) have long been regarded as inert cytoplasmic organelles with the primary function of housing excess intracellular lipids. More recently, LDs have been strongly implicated in conditions of lipid and protein dysregulation. The fat storage inducing transmembrane (FIT) family of proteins comprises of evolutionarily conserved endoplasmic reticulum (ER)-resident proteins that have been reported to induce LD formation. Here, we establish a model system to study the role of S. cerevisiae FIT homologues (ScFIT), SCS3 and YFT2, in proteostasis and stress response pathways. While LD biogenesis and basal ER stress-induced unfolded protein response (UPR) remain unaltered in ScFIT mutants, SCS3 was found to be essential for proper stress-induced UPR activation and for viability in the absence of the sole yeast UPR transducer IRE1. Devoid of a functional UPR, scs3 mutants exhibited accumulation of triacylglycerol within the ER along with aberrant LD morphology, suggesting a UPR-dependent compensatory mechanism for LD maturation. Additionally, SCS3 was necessary to maintain phospholipid homeostasis. Strikingly, the absence of the ScFIT proteins results in the downregulation of the closely-related Heat Shock Response (HSR) pathway. In line with this observation, global protein ubiquitination and the turnover of both ER and cytoplasmic misfolded proteins is impaired in ScFIT cells, while a screen for interacting partners of Scs3 identifies components of the proteostatic machinery as putative targets. Taken together, these suggest that ScFIT proteins may modulate proteostasis and stress response pathways with lipid metabolism at the interface between the two cellular processes.

Successful cancer treatment continues to elude modern medicine and its arsenal of therapeutic strategies. Therapy resistance is driven by significant tumor heterogeneity, complex interactions between malignant, microenvironmental and immune cells and cross talk between signaling pathways. Advances in molecular characterization technologies such as next generation sequencing have helped unravel this network of interactions and have vastly affected how cancer is diagnosed and treated. However, the translation of complex genomic analyses to pathological diagnosis remains challenging using conventional immunofluorescence (IF) staining, which is typically limited to 2-5 antigens. Numerous strategies to increase distinct antigen detection on a single sample have been investigated, but all have deleterious effects on the tissue limiting the maximum number of biomarkers that can be imaged on a single sample and none can be seamlessly integrated into routine clinical workflows. To facilitate ready integration into clinical histopathology, we have developed a novel cyclic IF (cycIF) technology based on antibody conjugated oligonucleotides (Ab-oligos). In situ hybridization of complementary oligonucleotides (oligos) facilitates biomarker labeling for imaging on any conventional fluorescent microscope. We have validated a variety of oligo configurations and their respective signal removal strategies capable of diminishing fluorescent signal to levels of autofluorescence before subsequent staining cycles. Robust signal removal is performed without the employment of harsh conditions or reagents, maintaining tissue integrity and antigenicity for higher dimensionality immunostaining of a single sample. Our platform Ab-oligo cycIF technology uses conventional fluorophores and microscopes, allowing for dissemination to a broad audience and congruent integration into clinical histopathology workflows.

Mechanistic studies of pancreatic disease progression using animal models require objective and quantifiable assessment of tissue changes among animal cohorts. Disease state quantification, however, relies heavily on tissue immunostaining, which can be expensive, labor- and time-intensive, and all too often produces uneven staining that is prone to variable interpretation between experts and inaccurate quantification. Here we develop a fully automated semantic segmentation tool using deep learning for the rapid and objective quantification of histologic features using hematoxylin and eosin (H&E) stained pancreatic tissue sections acquired from murine pancreatic cancer models. The tool was successfully trained to segment and quantify multiple histopathologic features of pancreatic pre-cancer evolution, including normal acinar structures, the ductal phenotype of acinar-to ductal metaplasia (ADM), dysplasia, and the expanding stromal compartment. Disease quantifications produced by our computational tool were highly correlated to the results obtained by immunostaining markers of normal and diseased tissue (DAPI, amylase, and cytokeratins; correlation score= 0.9, 0.95, and 0.91, respectively) and were able to accurately reproduce immunostain patterns. Moreover, our tool was able to distinguish ADM from dysplasia, which are not reliably distinguished by immunostaining, and avoid the pitfalls of uneven or poor-quality staining. Using this tool, we quantified the changes in histologic feature abundance for murine cohorts with oncogenic Kras-driven disease at 2 months and 5 months of age (n=12, n=13). The calculated changes in histologic feature abundance were consistent with biological expectations, showing an expansion of the stromal compartment, a reduction of normal acinar tissue, and an increase in both ADM and dysplasia as disease progresses (p= 2e-6, 6e-7, 4e-4, and 3e-5, respectively). These results demonstrate the tool’s efficacy for accurate and rapid quantification of multiple histologic features using an objective and automated platform. Our tool promises to rapidly accelerate and improve the quantification of altered pancreatic disease progression in animal studies.

Ho et al. identified pathways, beyond lipid metabolism, that are required to maintain ER integrity and, when disrupted, activate the UPR by lipid bilayer stress through a sensor in Ire1. The resulting downstream transcriptional program differs from proteotoxic stress-induced UPR., Membrane integrity at the endoplasmic reticulum (ER) is tightly regulated, and its disturbance is implicated in metabolic diseases. Using an engineered sensor that activates the unfolded protein response (UPR) exclusively when normal ER membrane lipid composition is compromised, we identified pathways beyond lipid metabolism that are necessary to maintain ER integrity in yeast and in C. elegans. To systematically validate yeast mutants that disrupt ER membrane homeostasis, we identified a lipid bilayer stress (LBS) sensor in the UPR transducer protein Ire1, located at the interface of the amphipathic and transmembrane helices. Furthermore, transcriptome and chromatin immunoprecipitation analyses pinpoint the UPR as a broad-spectrum compensatory response wherein LBS and proteotoxic stress deploy divergent transcriptional UPR programs. Together, these findings reveal the UPR program as the sum of two independent stress responses, an insight that could be exploited for future therapeutic intervention., Graphical Abstract

Mechanisms of immune suppression in peripheral tissues counteract protective immunity to prevent immunopathology and are coopted by tumors for immune evasion. While lymphatic vessels facilitate T cell priming, they also exert immune suppressive effects in lymph nodes at steady-state. Therefore, we hypothesized that peripheral lymphatic vessels acquire suppressive mechanisms to limit local effector CD8+ T cell accumulation in murine skin. We demonstrate that nonhematopoietic PD-L1 is largely expressed by lymphatic and blood endothelial cells and limits CD8+ T cell accumulation in tumor microenvironments. IFNγ produced by tissue-infiltrating, antigen-specific CD8+ T cells, which are in close proximity to tumor-associated lymphatic vessels, is sufficient to induce lymphatic vessel PD-L1 expression. Disruption of IFNγ-dependent crosstalk through lymphatic-specific loss of IFNγR boosts T cell accumulation in infected and malignant skin leading to increased viral pathology and tumor control, respectively. Consequently, we identify IFNγR as an immunological switch in lymphatic vessels that balances protective immunity and immunopathology leading to adaptive immune resistance in melanoma.

Mechanisms of therapeutic resistance and vulnerability evolve in metastatic cancers as tumor cells and extrinsic microenvironmental influences change during treatment. To support the development of methods for identifying these mechanisms in individual people, here we present an omic and multidimensional spatial (OMS) atlas generated from four serial biopsies of an individual with metastatic breast cancer during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata that includes treatment times and doses, anatomic imaging, and blood-based response measurements to clinical and exploratory analyses, which includes comprehensive DNA, RNA, and protein profiles; images of multiplexed immunostaining; and 2- and 3-dimensional scanning electron micrographs. These data report aspects of heterogeneity and evolution of the cancer genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples of how integrative analyses of these data reveal potential mechanisms of response and resistance and suggest novel therapeutic vulnerabilities.

Objective and quantifiable assessment of tissue pathology is necessary to study mechanistic disease progression; however, current quantification methods based on tissue staining have many drawbacks including cost, time, labor, batch effects, as well as uneven staining which can result in misinterpretation and investigator bias. Here we present VISTA, an automated deep learning tool for semantic segmentation and quantification of histologic features from hematoxylin and eosin (H&E) stained pancreatic tissue sections. VISTA is trained to identify four key tissue types in developing murine PDAC samples: normal acinar, acinar-to-ductal metaplasia (ADM), dysplasia, and other normal tissue. Predicted segmentations were quantitatively evaluated against pathologist annotation with Dice Coefficients, achieving scores of 0.79, 0.70, 0.79 for normal acinar, ADM, and dysplasia, respectively. Predictions were evaluated against biological ground truth using the mean structural similarity index to immunostainings amylase and pan-keratin (0.925 and 0.920, respectively). The total area of feature prediction was also correlated to the area of immunostaining in whole tissue sections using spearman correlation (0.86, 0.97, and 0.92 for DAPI, amylase, and cytokeratins, respectively). Importantly, our tool is not only able to predict staining information, but it is able to distinguish between ADM and dysplasia, which are not reliably distinguished with common immunostaining methods, showing VISTA’s potential to expand research beyond what is capable with current standards. As a use case example of VISTA, we quantified abundance of histologic features in murine cohorts with oncogenic Kras-driven disease. We observed stromal expansion, a reduction in normal acinar, and an increase in both ADM and dysplasia as the disease progresses, which matches known biology. Since VISTA is an automated algorithm, it can accelerate histological analysis and improve the consistency of quantification between laboratories and investigators. This work has been published in Nature Scientific Reports, and the code is available on github at https://github.com/GelatinFrogs/MicePan-Segmentation. Citation Format: Luke Ternes, Ge Huang, Christian Lanciault, Guillaume Thibault, Rachelle Riggers, Joe Gray, John Muschler, Young Hwan Chang. VISTA: VIsual Semantic Tissue Analysis for pancreatic disease quantification in murine cohorts [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr PO-014.

Recent developments in large format electron microscopy have enabled generation of images that provide detailed ultrastructural information on normal and diseased cells and tissues. Analyses of these images increase our understanding of cellular organization and interactions and disease-related changes therein. In this manuscript, we describe a workflow for two-dimensional (2D) and three-dimensional (3D) imaging, including both optical and scanning electron microscopy (SEM) methods, that allow pathologists and cancer biology researchers to identify areas of interest from human cancer biopsies. The protocols and mounting strategies described in this workflow are compatible with 2D large format EM mapping, 3D focused ion beam-SEM and serial block face-SEM. The flexibility to use diverse imaging technologies available at most academic institutions makes this workflow useful and applicable for most life science samples. Volumetric analysis of the biopsies studied here revealed morphological, organizational and ultrastructural aspects of the tumor cells and surrounding environment that cannot be revealed by conventional 2D EM imaging. Our results indicate that although 2D EM is still an important tool in many areas of diagnostic pathology, 3D images of ultrastructural relationships between both normal and cancerous cells, in combination with their extracellular matrix, enables cancer researchers and pathologists to better understand the progression of the disease and identify potential therapeutic targets.

Lipid droplets (LDs) are implicated in conditions of lipid and protein dysregulation. The fat storage inducing transmembrane (FIT) family induces LD formation. Here, we establish a model system to study the role of S. cerevisiae FIT homologues (ScFIT), SCS3 and YFT2, in proteostasis and stress response pathways. While LD biogenesis and basal endoplasmic reticulum (ER) stress-induced unfolded protein response (UPR) remain unaltered in ScFIT mutants, SCS3 was found essential for proper stress-induced UPR activation and for viability in the absence of the sole yeast UPR transducer IRE1. Devoid of a functional UPR, muted SCS3 exhibited accumulation of triacylglycerol within the ER along with aberrant LD morphology, suggesting a UPR-dependent compensatory mechanism. Additionally, SCS3 was necessary to maintain phospholipid homeostasis. Strikingly, global protein ubiquitination and the turnover of both ER and cytoplasmic misfolded proteins is impaired in ScFITΔ cells, while a screen for interacting partners of Scs3 identifies components of the proteostatic machinery as putative targets. Together, our data support a model where ScFITs play an important role in lipid metabolism and proteostasis beyond their defined roles in LD biogenesis.

Therapeutic resistance plagues cancer outcomes, challenging treatment particularly in aggressive disease. A unique method to decipher drug interactions with their targets and inform therapy is to employ fluorescence-based screening tools; however, to implement productive screening assays, adequate model systems must be developed. Patient-derived pancreatic cancer models (e.g., cell culture, patient-derived xenograft mouse models, and organoids) have been traditionally utilized to predict personalized therapeutic response. However, cost, long read out times and the inability to fully recapitulate the tumor microenvironment have rendered most models incompatible with clinical decision making for pancreatic ductal adenocarcinoma (PDAC) patients. Tumor explant cultures, where patient tissue can be kept viable for up to weeks, have garnered interest as a platform for delivering personalized therapeutic prediction on a clinically relevant timeline. To fully explore this ex vivo platform, a series of studies were completed to quantitatively compare in vivo models with tumor explants, examining gemcitabine therapeutic efficacy, small molecule uptake and drug-target engagement using a novel fluorescently-labeled gemcitabine conjugate. This initial work shows promise for patient-specific therapeutic selection, where tumor explant drug distribution and response recapitulated the in vivo behavior and could provide a valuable platform for understanding mechanisms of therapeutic response and resistance.

Recent advances in multiplexed imaging technologies promise to improve the understanding of the functional states of individual cells and the interactions between the cells in tissues. This often requires compilation of results from multiple samples. However, quantitative integration of information between samples is complicated by variations in staining intensity and background fluorescence that obscure biological variations. Failure to remove these unwanted artifacts will complicate downstream analysis and diminish the value of multiplexed imaging for clinical applications. Here, to compensate for unwanted variations, we automatically identify negative control cells for each marker within the same tissue and use their expression levels to infer background signal level. The intensity profile is normalized by the inferred level of the negative control cells to remove between-sample variation. Using a tissue microarray data and a pair of longitudinal biopsy samples, we demonstrated that the proposed approach can remove unwanted variations effectively and shows robust performance., Chang et al. develop an analytical method called RESTORE to control for variations due to technical artifacts in multiplexed imaging. They test their method on a CycIF stained tissue microarray dataset and biopsies processed at different times. Their method can improve the applicability of imaging techniques in diagnostics and inference using unbiased clustering methods.

SUMMARYMembrane integrity at the endoplasmic reticulum (ER) is tightly regulated and is implicated in metabolic diseases when compromised. Using an engineered sensor that exclusively activates the unfolded protein response (UPR) during aberrant ER membrane lipid composition, we identified pathways beyond lipid metabolism that are necessary to maintain ER integrity in yeast and are conserved in C. elegans. To systematically validate yeast mutants disrupting ER membrane homeostasis, we identified a lipid bilayer stress (LBS) sensing switch in the UPR transducer protein Ire1, located at the interface of the amphipathic and transmembrane helices. Furthermore, transcriptome and chromatin immunoprecipitation (ChIP) analyses pinpoint the UPR as a broad-spectrum compensatory pathway in which LBS and proteotoxic stress-induced UPR deploy divergent transcriptional programs. Together, these findings reveal the UPR program as the sum of two independent stress events and could be exploited for future therapeutic intervention.

Immunotherapy harnesses the power of the adaptive immune system and has revolutionized the field of oncotherapy, as novel therapeutic strategies have been introduced into clinical use. The development of immune checkpoint inhibitors has led to durable control of disease in a subset of advanced cancer patients, such as those with melanoma and non-small cell lung cancer. However, predicting patient responses to therapy remains a major challenge, due to the remarkable genomic, epigenetic, and microenvironmental heterogeneity present in each tumor. Breast cancer (BC) is the most common cancer in women, where hormone receptor-positive (HR+; estrogen receptor and/or progesterone receptor) BC comprises the majority (50%) and has better prognosis, while a minority (20%) are triple negative BC (TNBC), which has an aggressive phenotype. There is a clinical need to identify predictors of late recurrence in HR+ BC and predictors of immunotherapy outcomes in advanced TNBC. Tumor-infiltrating lymphocytes (TILs) have recently been shown to predict late recurrence in HR+, counter to the findings that TILs confer good prognosis in TNBC and human epidermal growth factor receptor 2 positive (HER2+) subtypes. Furthermore, the spatial arrangement of TILs also appears to have prognostic value, with dense clusters of immune cells predicting poor prognosis in HR+ and good prognosis in TNBC. Whether TIL clusters in different breast cancer subtypes represent the same or different landscapes of TILs is unknown and may have treatment implications for a significant portion of breast cancer patients. Current histopathological staining technology is not sufficient for characterizing the ensembles of TILs and their spatial patterns, in addition to tumor and microenvironmental heterogeneity. However, recent advances in cyclic immunofluorescence enable differentiation of the subsets based on TILs, tumor heterogeneity, and microenvironment composition between good and poor responders. A computational framework for understanding the importance of the spatial relationships between TILs and tumor cells in cancer tissues, which will allow for quantitative interpretation of cyclic immunostaining, is also under development. This chapter will explore the workflow for a newly developed cyclic multiplexed-immunofluorescence (cmIF) assay, which has been optimized for formalin-fixed. paraffin-embedded tissues and developed to process digital images for quantitative single-cell based spatial analysis of tumor heterogeneity and microenvironment, including immune cell composition.

Immunotherapy harnesses the power of the adaptive immune system and has revolutionized the field of oncotherapy, as novel therapeutic strategies have been introduced into clinical use. The development of immune checkpoint inhibitors has led to durable control of disease in a subset of advanced cancer patients, such as those with melanoma and non-small cell lung cancer. However, predicting patient responses to therapy remains a major challenge, due to the remarkable genomic, epigenetic, and microenvironmental heterogeneity present in each tumor. Breast cancer (BC) is the most common cancer in women, where hormone receptor-positive (HR+; estrogen receptor and/or progesterone receptor) BC comprises the majority (>50%) and has better prognosis, while a minority (

Pathologists rely on clinical information, tissue morphology, and sophisticated molecular diagnostics to accurately infer the metastatic origin of secondary liver cancer. In this paper, we introduce a deep learning approach to identify spatially localized regions of cancerous tumor within hematoxylin and eosin stained tissue sections of liver cancer and to generate predictions of the cancer’s metastatic origin. Our approach achieves an accuracy of 90.2% when classifying metastatic origin of whole slide images into three distinct classes, which compares favorably to an established clinical benchmark by three board-certified pathologists whose accuracies ranged from 90.2% to 94.1% on the same prediction task. This approach illustrates the potential impact of deep learning systems to leverage morphological and structural features of H&E stained tissue sections to guide pathological and clinical determination of the metastatic origin of secondary liver cancers.

Recent developments in large format electron microscopy have enabled generation of images that provide detailed ultrastructural information on normal and diseased cells and tissues. Analyses of these images increase our understanding of cellular organization and interactions and disease-related changes therein. In this manuscript, we describe a workflow for two-dimensional (2D) and three-dimensional (3D) imaging, including both optical and scanning electron microscopy (SEM) methods, that allow pathologists and cancer biology researchers to identify areas of interest from human cancer biopsies. The protocols and mounting strategies described in this workflow are compatible with 2D large format EM mapping, 3D focused ion beam-SEM and serial block face-SEM. The flexibility to use diverse imaging technologies available at most academic institutions makes this workflow useful and applicable for most life science samples. Volumetric analysis of the biopsies studied here revealed morphological, organizational and ultrastructural aspects of the tumor cells and surrounding environment that cannot be revealed by conventional 2D EM imaging. Our results indicate that although 2D EM is still an important tool in many areas of diagnostic pathology, 3D images of ultrastructural relationships between both normal and cancerous cells, in combination with their extracellular matrix, enables cancer researchers and pathologists to better understand the progression of the disease and identify potential therapeutic targets.

Phospholipid homeostasis in biological membranes is essential to maintain functions of organelles such as the endoplasmic reticulum. Phospholipid perturbation has been associated to cellular stress responses. However, in most cases, the implication of membrane lipid changes to homeostatic cellular response has not been clearly defined. Previously, we reported that Saccharomyces cerevisiae adapts to lipid bilayer stress by upregulating several protein quality control pathways such as the endoplasmic reticulum-associated degradation (ERAD) pathway and the unfolded protein response (UPR). Surprisingly, we observed certain ER-resident transmembrane proteins, which form part of the UPR programme, to be destabilised under lipid bilayer stress. Among these, the protein translocon subunit Sbh1 was prematurely degraded by membrane stiffening at the ER. Moreover, our findings suggest that the Doa10 complex recognises free Sbh1 that becomes increasingly accessible during lipid bilayer stress, perhaps due to the change in ER membrane properties. Premature removal of key ER-resident transmembrane proteins might be an underlying cause of chronic ER stress as a result of lipid bilayer stress.

Background: The transition from ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) is an evolutionary bottleneck where progression occurs only in 30% of patients. Whilst the genetic drivers of this transition remain poorly understood, we have previously shown that immune escape is a key event. In this study, we profile the evolutionary trajectory of matched pure DCIS and IDC in the context of the immune microenvironment. Methods: We have evaluated changes in copy number profiles, mutational profiles, expression and neoantigen load in 6 cases of matched pure DCIS and IDC using exome and RNA sequencing. We have integrated this information with topologic assessment of H&E images and cyclic immunofluorescence. Results: We provide evidence for an evolutionary bottleneck during DCIS to IDC in matched patient samples, showing that copy number aberrations are early events, but low overlap in mutational profiles. Variation in immune composition and spatial orientation can arise as early as in DCIS and are subtype specific. Tumor-specific copy number changes including loss of MHC-I presentation machinery or changes at cytokine rich loci specifically in ER− tumors could contribute to a more immunosuppressive environment in IDC. Oncogenic hotspot mutations can present as neoantigens yet are paradoxically conserved during the DCIS-to-IDC transition. We suggest these mutations have a secondary immune-modulatory function or may be present in normal tissue, escaping immune surveillance as early as in DCIS. Conclusions: We show both genomic and microenvironmental differences in matched pure DCIS and recurrent IDC, highlighting that progression is shaped by both tumor and immune system at this evolutionary bottleneck. Citation Format: Anne Trinh, Carlos R. Gil Del Alcazar, Sachet A. Shukla, Koei Chin, Young Hwan Chen, Guillaume Thibault, Jennifer Eng, Bojana Jovanovic, C. Marcelo Aldaz, So Yeon Park, Joon Jong, Catherine Wu, Joe Gray, Kornelia Polyak. The genomic landscape of the in situ to invasive ductal breast carcinoma transition shaped by the immune system [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-059.

Triple negative breast cancer (TNBC) lacks expression of hormone receptors (ER and PR) and HER2 and is characterized by aggressive disease with poor outcomes. Recent work suggests that TNBC also has a high degree of intratumoral heterogeneity, as measured by lineage differentiation status. This heterogeneity may impact therapeutic response, as it has been shown that treatment with PI3K/mTOR (BEZ235) or MEK (trametinib) inhibitors can drive TNBC cells into more homogeneous states, but that the surviving cells are resistant to the targeted therapy. In this study, we sought to understand how the microenvironment impacts differentiation state heterogeneity and response to targeted therapeutics in HCC1143 cells using our microenvironment microarray (MEMA) platform. Under low serum growth conditions, we found that several ligands could drive the growth of HCC1143, particularly EGF family ligands like AREG and EGF. With respect to differentiation state and heterogeneity, EGF and TGFB1 drove HCC1143 cells into a more mesenchymal like state, with increased expression of VIM and decreased expression of KRT14. In contrast, BMP2 led to higher levels of KRT14 and lower levels of VIM, leading to a more basal-like state. We also grew HCC1143 on MEMA with trametinib treatment. Here we found that combinations of collagen-based substrates and NRG1, HGF, and EGF ligands all led to higher cell counts and EdU incorporation rates compared to PBS-control treated cells. However, the levels of resistance conferred by the microenvironment was less than we had previously seen in HER2 positive MEMA, as the GR50 values (dose required to inhibit growth by 50%) only increased modestly (18 nM for untreated cells, 40 nM for NRG1, 45 nM for HGF). Interestingly, in that HER2 positive MEMA study, we identified HGF and NRG1 as potent resistance factors to lapatinib, but that they functioned in a subtype specific manner. HGF was effective in basal subtype cells and NRG1 in luminal, but not vice versa. We postulated that the modest resistance we observed was due to ligands acting on subsets of cells. We thus treated cells with a combination of NRG1 plus HGF, and found that this resulted in increased resistance (GR50= 91 nM). Imaging showed that trametinib drove HCC1143 cells to a homogenous KRT14 positive state, but surprisingly, addition of ligands reverted the cells to a more heterogeneous state that was resistant to trametinib. These data demonstrate that the microenvironment can impact the differentiation state of TNBC cells and is also capable of conferring resistance within subsets of the heterogeneous cell populations. Citation Format: Rebecca Smith, Kaylyn Devlin, Moqing Liu, Tiera Liby, David Kilburn, Elmar Bucher, Damir Sudar, Guillaume Thibault, Mark Dane, Joe Gray, Laura Heiser, James E. Korkola. The impact of the microenvironment on heterogeneity and trametinib response in HCC1143 triple negative breast cancer cells [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1870.

Spatially-resolved molecular profiling by immunostaining tissue sections is a key feature in cancer diagnosis, subtyping, and treatment, where it complements routine histopathological evaluation by clarifying tumor phenotypes. In this work, we present a deep learning-based method called speedy histological-to-immunofluorescent translation (SHIFT) which takes histologic images of hematoxylin and eosin-stained tissue as input, then in near-real time returns inferred virtual immunofluorescence (IF) images that accurately depict the underlying distribution of phenotypes without requiring immunostaining of the tissue being tested. We show that deep learning-extracted feature representations of histological images can guide representative sample selection, which improves SHIFT generalizability. SHIFT could serve as an efficient preliminary, auxiliary, or substitute for IF by delivering multiplexed virtual IF images for a fraction of the cost and in a fraction of the time required by nascent multiplexed imaging technologies.KEY POINTSSpatially-resolved molecular profiling is an essential complement to histopathological evaluation of cancer tissues.Information obtained by immunofluorescence imaging is encoded by features in histological images.SHIFT leverages previously unappreciated features in histological images to facilitate virtual immunofluorescence staining.Feature representations of images guide sample selection, improving model generalizability.

Chemically reinforced essential fatty acids (FAs) promise to fight numerous age-related diseases including Alzheimer’s, Friedreich’s ataxia and other neurological conditions. The reinforcement is achieved by substituting the atoms of hydrogen at the bis-allylic methylene of these essential FAs with the isotope deuterium. This substitution leads to a significantly slower oxidation due to the kinetic isotope effect, inhibiting membrane damage. The approach has the advantage of preventing the harmful accumulation of reactive oxygen species (ROS) by inhibiting the propagation of lipid peroxidation while antioxidants potentially neutralize beneficial oxidative species. Here, we developed a model system to mimic the human dietary requirement of omega-3 in Caenorhabditis elegans to study the role of deuterated polyunsaturated fatty acids (D-PUFAs). Deuterated trilinolenin [D-TG(54:9)] was sufficient to prevent the accumulation of lipid peroxides and to reduce the accumulation or ROS. Moreover, D-TG(54:9) significantly extended the lifespan of worms under normal and oxidative stress conditions. These findings demonstrate that D-PUFAs can be used as a food supplement to decelerate the aging process, resulting in extended lifespan. MOE (Min. of Education, S’pore) Published version

This study has brought together image processing, clustering and spatial pattern analysis to quantitatively analyze hematoxylin and eosin-stained (H&E) tissue sections. A mixture of tumor and normal cells (intratumoral heterogeneity) as well as complex tissue architectures of most samples complicate the interpretation of their cytological profiles. To address these challenges, we develop a simple but effective methodology for quantitative analysis for H&E section. We adopt comparative analyses of spatial point patterns to characterize spatial distribution of different nuclei types and complement cellular characteristics analysis. We demonstrate that tumor and normal cell regions exhibit significant differences of lymphocytes spatial distribution or lymphocyte infiltration pattern.

Lipid droplets (LD) have increasingly become a major topic of research in recent years following its establishment as a highly dynamic organelle. Contrary to the initial view of LDs being passive cytoplasmic structures for lipid storage, studies have provided support on how they act in concert with different organelles to exert functions in various cellular processes. Although lipid dysregulation resulting from aberrant LD homeostasis has been well characterised, how this translates and contributes to cancer progression is poorly understood. This review summarises the different paradigms on how LDs function in the regulation of cellular stress as a contributing factor to cancer progression. Mechanisms employed by a broad range of cancer cell types in differentially utilising LDs for tumourigenesis will also be highlighted. Finally, we discuss the potential of targeting LDs in the context of cancer therapeutics.

Aging is one of the most critical risk factors for the development of metabolic syndromes1. Prominent metabolic diseases, namely type 2 diabetes and insulin resistance, have a strong association with endoplasmic reticulum (ER) stress2. Upon ER stress, the unfolded protein response (UPR) is activated to limit cellular damage by adapting to stress conditions and restoring ER homeostasis3,4. However, adaptive genes upregulated from the UPR tend to decrease with age5. Although stress resistance correlates with increased longevity in a variety of model organisms, the links between the UPR, ER stress resistance, and longevity remain poorly understood. Here, we show that supplementing bacteria diet with 2% glucose (high glucose diet, HGD) in post-reproductive 7-day-old (7DO)C. eleganssignificantly extend their lifespan in contrast to shortening the lifespan of reproductive 3-day-old (3DO) animals. The insulin-IGF receptor DAF-2 and its immediate downstream target, phosphoinositide 3-kinase (PI3K) AGE-1, were found to be critical factors in extending the lifespan of 7DO worms on HGD. The downstream transcription factor forkhead box O (FOXO) DAF-16 did not extend the lifespan of 7DO worms on HGD in contrast of its previously reported role in modulating lifespan of 3DO worms6. Furthermore, we identified that UPR activation through the highly conserved ATF-6 and PEK-1 sensors significantly extended the longevity of 7DO worms on HGD but not through the IRE-1 sensor. Our results demonstrate that HGD extends lifespan of post-reproductive worms in a UPR-dependent manner but independently of FOXO. Based on these observations, we hypothesise that HGD activates the otherwise quiescent UPR in aged worms to overcome age-related stress and to restore ER homeostasis. In contrast, young adult animals subjected to HGD leads to unresolved ER stress, conversely leading to a deleterious stress response.