Targeting Pin1 renders pancreatic cancer eradicable by synergizing with immunochemotherapy.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by notorious resistance to current therapies attributed to inherent tumor heterogeneity and highly desmoplastic and immunosuppressive tumor microenvironment (TME). Unique proline isomerase Pin1 regulates multiple cancer pathways, but its role in the TME and cancer immunotherapy is unknown. Here, we find that Pin1 is overexpressed both in cancer cells and cancer-associated fibroblasts (CAFs) and correlates with poor survival in PDAC patients. Targeting Pin1 using clinically available drugs induces complete elimination or sustained remissions of aggressive PDAC by synergizing with anti-PD-1 and gemcitabine in diverse model systems. Mechanistically, Pin1 drives the desmoplastic and immunosuppressive TME by acting on CAFs and induces lysosomal degradation of the PD-1 ligand PD-L1 and the gemcitabine transporter ENT1 in cancer cells, besides activating multiple cancer pathways. Thus, Pin1 inhibition simultaneously blocks multiple cancer pathways, disrupts the desmoplastic and immunosuppressive TME, and upregulates PD-L1 and ENT1, rendering PDAC eradicable by immunochemotherapy.

Antigen dominance hierarchies shape TCF1+ progenitor CD8 T cell phenotypes in tumors.

CD8 T cell responses against different tumor neoantigens occur simultaneously, yet little is known about the interplay between responses and its impact on T cell function and tumor control. In mouse lung adenocarcinoma, we found that immunodominance is established in tumors, wherein CD8 T cell expansion is predominantly driven by the antigen that most stably binds MHC. T cells responding to subdominant antigens were enriched for a TCF1+ progenitor phenotype correlated with response to immune checkpoint blockade (ICB) therapy. However, the subdominant T cell response did not preferentially benefit from ICB due to a dysfunctional subset of TCF1+ cells marked by CCR6 and Tc17 differentiation. Analysis of human samples and sequencing datasets revealed that CCR6+ TCF1+ cells exist across human cancers and are not correlated with ICB response. Vaccination eliminated CCR6+ TCF1+ cells and dramatically improved the subdominant response, highlighting a strategy to optimally engage concurrent neoantigen responses against tumors.

Translocon Declogger Ste24 Protects against IAPP Oligomer-Induced Proteotoxicity.

Aggregates of human islet amyloid polypeptide (IAPP) in the pancreas of patients with type 2 diabetes (T2D) are thought to contribute to ß cell dysfunction and death. To understand how IAPP harms cells and how this might be overcome, we created a yeast model of IAPP toxicity. Ste24, an evolutionarily conserved protease that was recently reported to degrade peptides stuck within the translocon between the cytoplasm and the endoplasmic reticulum, was the strongest suppressor of IAPP toxicity. By testing variants of the human homolog, ZMPSTE24, with varying activity levels, the rescue of IAPP toxicity proved to be directly proportional to the declogging efficiency. Clinically relevant ZMPSTE24 variants identified in the largest database of exomes sequences derived from T2D patients were characterized using the yeast model, revealing 14 partial loss-of-function variants, which were enriched among diabetes patients over 2-fold. Thus, clogging of the translocon by IAPP oligomers may contribute to ß cell failure.

Basal dynamics of p53 reveal transcriptionally attenuated pulses in cycling cells.

The tumor suppressor p53 is activated by stress and leads to cellular outcomes such as apoptosis and cell-cycle arrest. Its activation must be highly sensitive to ensure that cells react appropriately to damage. However, proliferating cells often encounter transient damage during normal growth, where cell-cycle arrest or apoptosis may be unfavorable. How does the p53 pathway achieve the right balance between high sensitivity and tolerance to intrinsic damage? Using quantitative time-lapse microscopy of individual human cells, we found that proliferating cells show spontaneous pulses of p53, which are triggered by an excitable mechanism during cell-cycle phases associated with intrinsic DNA damage. However, in the absence of sustained damage, posttranslational modifications keep p53 inactive, preventing it from inducing p21 expression and cell-cycle arrest. Our approach of quantifying basal dynamics in individual cells can now be used to study how other pathways in human cells achieve sensitivity in noisy environments.

Scaling up single-cell RNA-seq data analysis with CellBridge workflow.

SUMMARY: Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of gene expression at the individual cell level, unraveling unprecedented insights into cellular heterogeneity. However, the analysis of scRNA-seq data remains a challenging and time-consuming task, often demanding advanced computational expertise, rendering it impractical for high-volume environments and applications. We present CellBridge, an automated workflow designed to simplify the standard procedures entailed in scRNA-seq data analysis, eliminating the need for specialized computational expertise. CellBridge utilizes state-of-the-art computational methods, integrating a range of advanced functionalities, covering the entire process from raw unaligned sequencing reads to cell type annotation. Hence, CellBridge accelerates the pace of discovery by seamlessly enabling insights into vast volumes of scRNA-seq data, without compromising workflow control and reproducibility. AVAILABILITY AND IMPLEMENTATION: The source code, detailed documentation, and materials required to reproduce the results are available on GitHub and archived in Zenodo. For the CellBridge pre-processing step (v1.0.0), access the GitHub repository at https://github.com/Sanofi-Public/PMCB-ToBridge and the Zenodo archive at https://zenodo.org/records/10246161. For the CellBridge processing step (v1.0.0), visit the GitHub repository at https://github.com/Sanofi-Public/PMCB-CellBridge and the Zenodo archive at https://zenodo.org/records/10246046.

The IL-4-IL-4Rα axis modulates olfactory neuroimmune signaling to induce loss of smell.

IL-4 and IL-13 have non-redundant effects in olfaction, with loss of smell in mice evoked only by intranasal administration of IL-4, but not IL-13. IL-4-evoked pathophysiological effects on olfaction is independent of compromised structural integrity of the olfactory neuroepithelium. IL-4-IL-4Rα signaling modulates neuronal crosstalk with immune cells, suggesting a functional link between olfactory impairment and neuroinflammation. Abbreviations: IL, interleukin; KO, knock-out; wk, week; WT, wild-type.

BACKGROUND: Loss of smell is a part of the diagnostic criteria for CRSwNP. Although the mechanistic understanding is poor, inhibition of IL­4Rα and IL­4/IL­13 signaling improves loss of smell in CRSwNP patients. In the present study, we compare the effects of IL­4, IL­13, and IL­4Rα blockade on murine olfaction and identify the underlying pathophysiological mechanisms of loss of smell. METHODS: To evaluate the effects of IL­4 and IL­13 on olfactory function, we administered these cytokines intranasally to BALB/c mice for 5 consecutive days and assessed their latency to find hidden food. Calcium uptake assays were conducted to measure olfactory neuronal activity in vitro and ex vivo. We also performed histological analyses, proteomics, bulk RNA sequencing, and single­cell RNA sequencing to assess the impact of IL­4, IL­13, and IL­4Rα blockade on the olfactory epithelium and to identify potential molecular or cellular correlations with smell loss in human CRSwNP patients. RESULTS: Here, we provide evidence for non­redundant effects of IL­4 and IL­13 in olfaction, with loss of smell in mice evoked by intranasal administration of IL­4, not IL­13. We demonstrate that an IL­4­IL­4Rα axis has a direct functional impact on murine olfactory sensory neurons and evokes inflammatory cell infiltration and pathophysiologic modulation of unique molecular signatures in olfactory epithelium without compromising structural integrity. Furthermore, single­cell analysis of olfactory epithelium reveals that IL­4­IL­4Rα signaling modulates neuronal crosstalk with mast cells, macrophages, and NK cells, suggesting a functional link between olfactory impairment and neuroinflammation. CONCLUSION: Collectively, this study suggests that an IL­4­IL­4Rα signaling axis plays a unique pathophysiological role in olfactory dysfunction via direct effect on neurons and modulation of neuroimmune interactions.

Activation and control of p53 tetramerization in individual living cells.

Homo-oligomerization is found in many biological systems and has been extensively studied in vitro. However, our ability to quantify and understand oligomerization processes in cells is still limited. We used fluorescence correlation spectroscopy and mathematical modeling to measure the dynamics of the tetramers formed by the tumor suppressor protein p53 in single living cells. Previous in vitro studies suggested that in basal conditions all p53 molecules are bound in dimers. We found that in resting cells p53 is present in a mix of oligomeric states with a large cell-to-cell variation. After DNA damage, p53 molecules in all cells rapidly assemble into tetramers before p53 protein levels increase. We developed a model to understand the connection between p53 accumulation and tetramerization. We found that the rapid increase in p53 tetramers requires a combination of active tetramerization and protein stabilization, however tetramerization alone is sufficient to activate p53 transcriptional targets. This suggests triggering tetramerization as a mechanism for activating the p53 pathway in cancer cells. Many other transcription factors homo-oligomerize, and our approach provides a unique way for probing the dynamics and functional consequences of oligomerization.

Constant rate of p53 tetramerization in response to DNA damage controls the p53 response.

The dynamics of the tumor suppressor protein p53 have been previously investigated in single cells using fluorescently tagged p53. Such approach reports on the total abundance of p53 but does not provide a measure for functional p53. We used fluorescent protein-fragment complementation assay (PCA) to quantify in single cells the dynamics of p53 tetramers, the functional units of p53. We found that while total p53 increases proportionally to the input strength, p53 tetramers are formed in cells at a constant rate. This breaks the linear input-output relation and dampens the p53 response. Disruption of the p53-binding protein ARC led to a dose-dependent rate of tetramers formation, resulting in enhanced tetramerization and induction of p53 target genes. Our work suggests that constraining the p53 response in face of variable inputs may protect cells from committing to terminal outcomes and highlights the importance of quantifying the active form of signaling molecules in single cells.

GENIX enables comparative network analysis of single-cell RNA sequencing to reveal signatures of therapeutic interventions.

Single-cell RNA sequencing (scRNA-seq) has transformed our understanding of cellular responses to perturbations such as therapeutic interventions and vaccines. Gene relevance to such perturbations is often assessed through differential expression analysis (DEA), which offers a one-dimensional view of the transcriptomic landscape. This method potentially overlooks genes with modest expression changes but profound downstream effects and is susceptible to false positives. We present GENIX (gene expression network importance examination), a computational framework that transcends DEA by constructing gene association networks and employing a network-based comparative model to identify topological signature genes. We benchmark GENIX using both synthetic and experimental datasets, including analysis of influenza vaccine-induced immune responses in peripheral blood mononuclear cells (PBMCs) from recovered COVID-19 patients. GENIX successfully emulates key characteristics of biological networks and reveals signature genes that are missed by classical DEA, thereby broadening the scope of target gene discovery in precision medicine.

Pure estrogen receptor antagonists potentiate capecitabine activity in ESR1-mutant breast cancer.

The ESR1 ligand binding domain activating mutations are the most prevalent genetic mechanism of acquired endocrine resistance in metastatic hormone receptor-positive breast cancer. These mutations confer endocrine resistance that remains estrogen receptor (ER) dependent. We hypothesized that in the presence of the ER mutations, continued ER blockade with endocrine therapies that target mutant ER is essential for tumor suppression even with chemotherapy treatment. Here, we conducted comprehensive pre-clinical in vitro and in vivo experiments testing the efficacy of adding fulvestrant to fluorouracil (5FU) and the 5FU pro-drug, capecitabine, in models of wild-type (WT) and mutant ER. Our findings revealed that while this combination had an additive effect in the presence of WT-ER, in the presence of the Y537S ER mutation there was synergy. Notably, these effects were not seen with the combination of 5FU and selective estrogen receptor modulators, such as tamoxifen, or in the absence of intact P53. Likewise, in a patient-derived xenograft (PDX) harboring a Y537S ER mutation the addition of fulvestrant to capecitabine potentiated tumor suppression. Moreover, multiplex immunofluorescence revealed that this effect was due to decreased cell proliferation in all cells expressing ER and was not dependent on the degree of ER expression. Taken together, these results support the clinical investigation of the combination of ER antagonists with capecitabine in patients with metastatic hormone receptor-positive breast cancer who have experienced progression on endocrine therapy and targeted therapies, particularly in the presence of an ESR1 activating mutation.

A marker gene-based method for identifying the cell-type of origin from single-cell RNA sequencing data.

Single-cell RNA sequencing (scRNA-seq) experiments provide opportunities to peer into complex tissues at single-cell resolution. However, insightful biological interpretation of scRNA-seq data relies upon precise identification of cell types. The ability to identify the origin of a cell quickly and accurately will greatly improve downstream analyses. We present Sargent, a transformation-free, cluster-free, single-cell annotation algorithm for rapidly identifying the cell types of origin based on cell type-specific markers. We demonstrate Sargent's high accuracy by annotating simulated datasets. Further, we compare Sargent performance against expert-annotated scRNA-seq data from human organs including PBMC, heart, kidney, and lung. We demonstrate that Sargent retains both the flexibility and biological interpretability of cluster-based manual annotation. Additionally, the automation eliminates the labor intensive and potentially biased user annotation, producing robust, reproducible, and scalable outputs.•Sargent is a transformation-free, cluster-free, single-cell annotation algorithm for rapidly identifying the cell types of origin based on cell type-specific markers.•Sargent retains both the flexibility and biological interpretability of cluster-based manual annotation.•Automation eliminates the labor intensive and potentially biased user annotation, producing robust, reproducible, and scalable outputs.

Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data.

New highly-multiplexed imaging technologies have enabled the study of tissues in unprecedented detail. These methods are increasingly being applied to understand how cancer cells and immune response change during tumor development, progression, and metastasis, as well as following treatment. Yet, existing analysis approaches focus on investigating small tissue samples on a per-cell basis, not taking into account the spatial proximity of cells, which indicates cell-cell interaction and specific biological processes in the larger cancer microenvironment. We present Visinity, a scalable visual analytics system to analyze cell interaction patterns across cohorts of whole-slide multiplexed tissue images. Our approach is based on a fast regional neighborhood computation, leveraging unsupervised learning to quantify, compare, and group cells by their surrounding cellular neighborhood. These neighborhoods can be visually analyzed in an exploratory and confirmatory workflow. Users can explore spatial patterns present across tissues through a scalable image viewer and coordinated views highlighting the neighborhood composition and spatial arrangements of cells. To verify or refine existing hypotheses, users can query for specific patterns to determine their presence and statistical significance. Findings can be interactively annotated, ranked, and compared in the form of small multiples. In two case studies with biomedical experts, we demonstrate that Visinity can identify common biological processes within a human tonsil and uncover novel white-blood cell networks and immune-tumor interactions.

Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma.

Lymphocytes are key for immune surveillance of tumors, but our understanding of the spatial organization and physical interactions that facilitate lymphocyte anti-cancer functions is limited. We used multiplexed imaging, quantitative spatial analysis, and machine learning to create high-definition maps of lung tumors from a Kras/Trp53-mutant mouse model and human resections. Networks of interacting lymphocytes ("lymphonets") emerged as a distinctive feature of the anti-cancer immune response. Lymphonets nucleated from small T cell clusters and incorporated B cells with increasing size. CXCR3-mediated trafficking modulated lymphonet size and number, but T cell antigen expression directed intratumoral localization. Lymphonets preferentially harbored TCF1+ PD-1+ progenitor CD8+ T cells involved in responses to immune checkpoint blockade (ICB) therapy. Upon treatment of mice with ICB or an antigen-targeted vaccine, lymphonets retained progenitor and gained cytotoxic CD8+ T cell populations, likely via progenitor differentiation. These data show that lymphonets create a spatial environment supportive of CD8+ T cell anti-tumor responses.

Scope2Screen: Focus+Context Techniques for Pathology Tumor Assessment in Multivariate Image Data.

Inspection of tissues using a light microscope is the primary method of diagnosing many diseases, notably cancer. Highly multiplexed tissue imaging builds on this foundation, enabling the collection of up to 60 channels of molecular information plus cell and tissue morphology using antibody staining. This provides unique insight into disease biology and promises to help with the design of patient-specific therapies. However, a substantial gap remains with respect to visualizing the resulting multivariate image data and effectively supporting pathology workflows in digital environments on screen. We, therefore, developed Scope2Screen, a scalable software system for focus+context exploration and annotation of whole-slide, high-plex, tissue images. Our approach scales to analyzing 100GB images of 109 or more pixels per channel, containing millions of individual cells. A multidisciplinary team of visualization experts, microscopists, and pathologists identified key image exploration and annotation tasks involving finding, magnifying, quantifying, and organizing regions of interest (ROIs) in an intuitive and cohesive manner. Building on a scope-to-screen metaphor, we present interactive lensing techniques that operate at single-cell and tissue levels. Lenses are equipped with task-specific functionality and descriptive statistics, making it possible to analyze image features, cell types, and spatial arrangements (neighborhoods) across image channels and scales. A fast sliding-window search guides users to regions similar to those under the lens; these regions can be analyzed and considered either separately or as part of a larger image collection. A novel snapshot method enables linked lens configurations and image statistics to be saved, restored, and shared with these regions. We validate our designs with domain experts and apply Scope2Screen in two case studies involving lung and colorectal cancers to discover cancer-relevant image features.

Temporal and spatial topography of cell proliferation in cancer.

Proliferation is a fundamental trait of cancer cells, but its properties and spatial organization in tumours are poorly characterized. Here we use highly multiplexed tissue imaging to perform single-cell quantification of cell cycle regulators and then develop robust, multivariate, proliferation metrics. Across diverse cancers, proliferative architecture is organized at two spatial scales: large domains, and smaller niches enriched for specific immune lineages. Some tumour cells express cell cycle regulators in the (canonical) patterns expected of freely growing cells, a phenomenon we refer to as 'cell cycle coherence'. By contrast, the cell cycles of other tumour cell populations are skewed towards specific phases or exhibit non-canonical (incoherent) marker combinations. Coherence varies across space, with changes in oncogene activity and therapeutic intervention, and is associated with aggressive tumour behaviour. Thus, multivariate measures from high-plex tissue images capture clinically significant features of cancer proliferation, a fundamental step in enabling more precise use of anti-cancer therapies.

Multimodal platform for assessing drug distribution and response in clinical trials.

BACKGROUND: Response to targeted therapy varies between patients for largely unknown reasons. Here, we developed and applied an integrative platform using mass spectrometry imaging (MSI), phosphoproteomics, and multiplexed tissue imaging for mapping drug distribution, target engagement, and adaptive response to gain insights into heterogeneous response to therapy. METHODS: Patient-derived xenograft (PDX) lines of glioblastoma were treated with adavosertib, a Wee1 inhibitor, and tissue drug distribution was measured with MALDI-MSI. Phosphoproteomics was measured in the same tumors to identify biomarkers of drug target engagement and cellular adaptive response. Multiplexed tissue imaging was performed on sister sections to evaluate spatial co-localization of drug and cellular response. The integrated platform was then applied on clinical specimens from glioblastoma patients enrolled in the phase 1 clinical trial. RESULTS: PDX tumors exposed to different doses of adavosertib revealed intra- and inter-tumoral heterogeneity of drug distribution and integration of the heterogeneous drug distribution with phosphoproteomics and multiplexed tissue imaging revealed new markers of molecular response to adavosertib. Analysis of paired clinical specimens from patients enrolled in the phase 1 clinical trial informed the translational potential of the identified biomarkers in studying patient's response to adavosertib. CONCLUSIONS: The multimodal platform identified a signature of drug efficacy and patient-specific adaptive responses applicable to preclinical and clinical drug development. The information generated by the approach may inform mechanisms of success and failure in future early phase clinical trials, providing information for optimizing clinical trial design and guiding future application into clinical practice.

HSF1 phase transition mediates stress adaptation and cell fate decisions.

Under proteotoxic stress, some cells survive whereas others die. The mechanisms governing this heterogeneity in cell fate remain unknown. Here we report that condensation and phase transition of heat-shock factor 1 (HSF1), a transcriptional regulator of chaperones1,2, is integral to cell-fate decisions underlying survival or death. During stress, HSF1 drives chaperone expression but also accumulates separately in nuclear stress bodies called foci3-6. Foci formation has been regarded as a marker of cells actively upregulating chaperones3,6-10. Using multiplexed tissue imaging, we observed HSF1 foci in human tumours. Paradoxically, their presence inversely correlated with chaperone expression. By live-cell microscopy and single-cell analysis, we found that foci dissolution rather than formation promoted HSF1 activity and cell survival. During prolonged stress, the biophysical properties of HSF1 foci changed; small, fluid condensates enlarged into indissoluble gel-like arrangements with immobilized HSF1. Chaperone gene induction was reduced in such cells, which were prone to apoptosis. Quantitative analysis suggests that survival under stress results from competition between concurrent but opposing mechanisms. Foci may serve as sensors that tune cytoprotective responses, balancing rapid transient responses and irreversible outcomes.

The first Autumn School on Proteostasis: from molecular mechanisms to organismal consequences.

The first Autumn School on Proteostasis was held at the Mediterranean Institute for Life Sciences (MedILS) in Split, Croatia, from November 12th-16th, 2018, bringing together 44 graduate students and postdoctoral fellows and 22 principal investigators from around the world. This meeting was geared towards providing students with an in-depth understanding of the field of proteostasis, with the aim of broadening their perspectives of the field. Session topics covered multiple aspects of cellular and organismal proteostasis, including fundamental principles, responses to heat shock, aging and disease, and protein folding, misfolding, and degradation. The structure of the meeting and the restricted number of participants afforded the students and postdocs the opportunity to interact with principal investigators to discuss not only their latest research, but also their career prospects and progression in a close, supportive environment.

Rebalancing Protein Homeostasis Enhances Tumor Antigen Presentation.

PURPOSE: Despite the accumulation of extensive genomic alterations, many cancers fail to be recognized as "foreign" and escape destruction by the host immune system. Immunotherapies designed to address this problem by directly stimulating immune effector cells have led to some remarkable clinical outcomes, but unfortunately, most cancers fail to respond, prompting the need to identify additional immunomodulatory treatment options.Experimental Design: We elucidated the effect of a novel treatment paradigm using sustained, low-dose HSP90 inhibition in vitro and in syngeneic mouse models using genetic and pharmacologic tools. Profiling of treatment-associated tumor cell antigens was performed using immunoprecipitation followed by peptide mass spectrometry. RESULTS: We show that sustained, low-level inhibition of HSP90 both amplifies and diversifies the antigenic repertoire presented by tumor cells on MHC-I molecules through an IFNγ-independent mechanism. In stark contrast, we find that acute, high-dose exposure to HSP90 inhibitors, the only approach studied in the clinic to date, is broadly immunosuppressive in cell culture and in patients with cancer. In mice, chronic non-heat shock-inducing HSP90 inhibition slowed progression of colon cancer implants, but only in syngeneic animals with intact immune function. Addition of a single dose of nonspecific immune adjuvant to the regimen dramatically increased efficacy, curing a subset of mice receiving combination therapy. CONCLUSIONS: These highly translatable observations support reconsideration of the most effective strategy for targeting HSP90 to treat cancers and suggest a practical approach to repurposing current orally bioavailable HSP90 inhibitors as a new immunotherapeutic strategy.See related commentary by Srivastava and Callahan, p. 6277.

Highly multiplexed immunofluorescence images and single-cell data of immune markers in tonsil and lung cancer.

In this data descriptor, we document a dataset of multiplexed immunofluorescence images and derived single-cell measurements of immune lineage and other markers in formaldehyde-fixed and paraffin-embedded (FFPE) human tonsil and lung cancer tissue. We used tissue cyclic immunofluorescence (t-CyCIF) to generate fluorescence images which we artifact corrected using the BaSiC tool, stitched and registered using the ASHLAR algorithm, and segmented using ilastik software and MATLAB. We extracted single-cell features from these images using HistoCAT software. The resulting dataset can be visualized using image browsers and analyzed using high-dimensional, single-cell methods. This dataset is a valuable resource for biological discovery of the immune system in normal and diseased states as well as for the development of multiplexed image analysis and viewing tools.