Spatial Genomics Research Articles

CNSC-65. ELECTROPHYSIOLOGICAL AND SPATIAL TRANSCRIPTOMIC PROFILING OF GLIOMA-INFILTRATED CORTEX REVEAL LAYER SPECIFIC ALTERATIONS

Abstract Specialized neocortical circuits selectively organize neuronal signals and encode features of cognitive processing via local and long-range connections. Since glioma-infiltrated cortex is excitable and can participate in cognitive processing, the underlying laminar structure and functionality may still be preserved despite infiltration. Moreover, since glioma cells remodel existing neural circuits and microenvironmental factors drive invasion and proliferation, neuron-glioma interactions may demonstrate layer specificity. As glioma-infiltrated cortex remains poorly understood, we sought to characterize these regions using electrophysiological, structural, and genomic approaches. We assessed the power spectra of normal-appearing and glioma-infiltrated cortex using magnetoencephalography and subdural high-density electrode arrays. Immunohistochemistry of formalin-fixed paraffin-embedded (FFPE) samples of infiltrated cortex enabled protein-level neuronal and glioma identification to determine laminar preservation and spatial invasion patterns. Spatial transcriptomics of FFPE tissues and single-nucleus RNA-sequencing were used to identify cell populations, genomic alterations, and cell-cell communication within and across samples at testing and validation sites across the US and Europe. Increased delta range (1-4 Hz) power and decreased power in the beta range (12-20 Hz), activity thought to originate from deep layers, were identified as robust features of infiltrated cortex which were maintained across glioma subtypes (n = 139 patients) and preserved in a validation cohort (n = 8 patients). Immunohistochemistry revealed greater tumor burden within deep laminae regardless of glioma subtype (n = 20 samples). Tissue proteomics and spatial genomics analyses performed using glioma-infiltrated cortical samples (n = 10 samples; 29,011 spots) confirmed routine preservation of laminar organization, greater tumor burden in deep layers, as well as layer specific differences in glioma-related expression programs. Cell-cell communication analyses demonstrated increased interactions in glioma-infiltrated cortex across layers. These findings suggest that laminar structure may be preserved in glioma-infiltrated cortex while remodeling of neural circuits alters spatiotemporal activity in a predictable manner and support a deep to superficial invasion pattern. Thus, investigating glioma infiltration using layer-centric approaches may facilitate novel insights into neuron-glioma interactions.

Macrophage‑driven pathogenesis in acute lung injury/acute respiratory disease syndrome: Harnessing natural products for therapeutic interventions (Review).

Acute lung injury (ALI) or acute respiratory distress syndrome (ARDS) is a common respiratory disease characterized by hypoxemia and respiratory distress. It is associated with high morbidity and mortality. Due to the complex pathogenesis of ALI, the clinical management of patients with ALI/ARDS is challenging, resulting in numerous post‑treatment sequelae and compromising the quality of life of patients. Macrophages, as a class of innate immune cells, play an important role in ALI/ARDS. In recent years, the functions and phenotypes of macrophages have been better understood due to the development of flow cytometry, immunofluorescence, single‑cell sequencing and spatial genomics. However, no macrophage‑targeted drugs for the treatment of ALI/ARDS currently exist in clinical practice. Natural products are important for drug development, and it has been shown that numerous natural compounds from herbal medicine can alleviate ALI/ARDS caused by various factors by modulating macrophage abnormalities. In the present review, the natural products from herbal medicine that can modulate macrophage abnormalities in ALI/ARDS to treat ALI/ARDS are introduced, and their mechanisms of action, discovered in the previous five years (2019‑2024), are presented. This will provide novel ideas and directions for further research, to develop new drugs for the treatment of ALI/ARDS.

Decoding the human prenatal immune system with single-cell multi-omics.

The human immune system is made up of a huge variety of cell types each with unique functions. Local networks of resident immune cells are poised to sense and protect against pathogen entry, whereas more widespread innate and adaptive immune networks provide first rapid, then long-lasting and targeted responses. However, how we develop such a diverse and complex system remains unknown. Studying human development directly has been challenging in the past, but recent advances in single-cell and spatial genomics, together with the co-ordinated efforts of the Human Cell Atlas and other initiatives, have led to new studies that map the development of the human immune system in unprecedented detail. In this Review, we consider the timings, transitions, cell types and tissue microenvironments that are crucial for building the human immune system. We also compare and contrast the human system with model species and in vitro systems, and discuss how an understanding of prenatal immune system development will improve our knowledge of human disease.

Integrated multimodal cell atlas of Alzheimer's disease.

Alzheimer's disease (AD) is the leading cause of dementia in older adults. Although AD progression is characterized by stereotyped accumulation of proteinopathies, the affected cellular populations remain understudied. Here we use multiomics, spatial genomics and reference atlases from the BRAIN Initiative to study middle temporal gyrus cell types in 84 donors with varying AD pathologies. This cohort includes 33 male donors and 51 female donors, with an average age at time of death of 88 years. We used quantitative neuropathology to place donors along a disease pseudoprogression score. Pseudoprogression analysis revealed two disease phases: an early phase with a slow increase in pathology, presence of inflammatory microglia, reactive astrocytes, loss of somatostatin+ inhibitory neurons, and a remyelination response by oligodendrocyte precursor cells; and a later phase with exponential increase in pathology, loss of excitatory neurons and Pvalb+ and Vip+ inhibitory neuron subtypes. These findings were replicated in other major AD studies.

Ovarian cancer-derived IL-4 promotes immunotherapy resistance.

Ovarian cancer is resistant to immunotherapy, and this is influenced by the immunosuppressed tumor microenvironment (TME) dominated by macrophages. Resistance is also affected by intratumoral heterogeneity, whose development is poorly understood. To identify regulators of ovarian cancer immunity, we employed a spatial functional genomics screen (Perturb-map), focused on receptor/ligands hypothesized to be involved in tumor-macrophage communication. Perturb-map recapitulated tumor heterogeneity and revealed that interleukin-4 (IL-4) promotes resistance to anti-PD-1. We find ovarian cancer cells are the key source of IL-4, which directs the formation of an immunosuppressive TME via macrophage control. IL-4 loss was not compensated by nearby IL-4-expressing clones, revealing short-range regulation of TME composition dictating tumor evolution. Our studies show heterogeneous TMEs can emerge from localized altered expression of cancer-derived cytokines/chemokines that establish immune-rich and immune-excluded neighborhoods, which drive clone selection and immunotherapy resistance. They also demonstrate the potential of targeting IL-4 signaling to enhance ovarian cancer response to immunotherapy.

A molecular switch from tumor suppressor to oncogene in ER+ve breast cancer: Role of androgen receptor, JAK-STAT, and lineage plasticity

Cancers develop resistance to inhibitors of oncogenes mainly due to target-centric mechanisms such as mutations and splicing. While inhibitors or antagonists force targets to unnatural conformation contributing to protein instability and resistance, activating tumor suppressors may maintain the protein in an agonistic conformation to elicit sustainable growth inhibition. Due to the lack of tumor suppressor agonists, this hypothesis and the mechanisms underlying resistance are not understood. In estrogen receptor (ER)-positive breast cancer (BC), androgen receptor (AR) is a druggable tumor suppressor offering a promising avenue for this investigation. Spatial genomics suggests that the molecular portrait of AR-expressing BC cells in tumor microenvironment corresponds to better overall patient survival, clinically confirming AR's role as a tumor suppressor. Ligand activation of AR in ER-positive BC xenografts reprograms cistromes, inhibits oncogenic pathways, and promotes cellular elasticity toward a more differentiated state. Sustained AR activation results in cistrome rearrangement toward transcription factor PROP paired-like homeobox 1, transformation of AR into oncogene, and activation of the Janus kinase/signal transducer (JAK/STAT) pathway, all culminating in lineage plasticity to an aggressive resistant subtype. While the molecular profile of AR agonist-sensitive tumors corresponds to better patient survival, the profile represented in the resistant phenotype corresponds to shorter survival. Inhibition of activated oncogenes in resistant tumors reduces growth and resensitizes them to AR agonists. These findings indicate that persistent activation of a context-dependent tumor suppressor may lead to resistance through lineage plasticity-driven tumor metamorphosis. Our work provides a framework to explore the above phenomenon across multiple cancer types and underscores the importance of factoring sensitization of tumor suppressor targets while developing agonist-like drugs.

Are we there yet? Exploring astrocyte heterogeneity one cell at a time

AbstractAstrocytes are a highly abundant cell type in the brain and spinal cord. Like neurons, astrocytes can be molecularly and functionally distinct to fulfill specialized roles. Recent technical advances in sequencing‐based single cell assays have driven an explosion of omics data characterizing astrocytes in the healthy, aged, injured, and diseased central nervous system. In this review, we will discuss recent studies which have furthered our understanding of astrocyte biology and heterogeneity, as well as discuss the limitations and challenges of sequencing‐based single cell and spatial genomics methods and their potential future utility.

Scalable imaging-free spatial genomics through computational reconstruction.

Tissue organization arises from the coordinated molecular programs of cells. Spatial genomics maps cells and their molecular programs within the spatial context of tissues. However, current methods measure spatial information through imaging or direct registration, which often require specialized equipment and are limited in scale. Here, we developed an imaging-free spatial transcriptomics method that uses molecular diffusion patterns to computationally reconstruct spatial data. To do so, we utilize a simple experimental protocol on two dimensional barcode arrays to establish an interaction network between barcodes via molecular diffusion. Sequencing these interactions generates a high dimensional matrix of interactions between different spatial barcodes. Then, we perform dimensionality reduction to regenerate a two-dimensional manifold, which represents the spatial locations of the barcode arrays. Surprisingly, we found that the UMAP algorithm, with minimal modifications can faithfully successfully reconstruct the arrays. We demonstrated that this method is compatible with capture array based spatial transcriptomics/genomics methods, Slide-seq and Slide-tags, with high fidelity. We systematically explore the fidelity of the reconstruction through comparisons with experimentally derived ground truth data, and demonstrate that reconstruction generates high quality spatial genomics data. We also scaled this technique to reconstruct high-resolution spatial information over areas up to 1.2 centimeters. This computational reconstruction method effectively converts spatial genomics measurements to molecular biology, enabling spatial transcriptomics with high accessibility, and scalability.

Computational Methods for Image Analysis in Craniofacial Development and Disease.

Observation is at the center of all biological sciences. Advances in imaging technologies are therefore essential to derive novel biological insights to better understand the complex workings of living systems. Recent high-throughput sequencing and imaging techniques are allowing researchers to simultaneously address complex molecular variations spatially and temporarily in tissues and organs. The availability of increasingly large dataset sizes has allowed for the evolution of robust deep learning models, designed to interrogate biomedical imaging data. These models are emerging as transformative tools in diagnostic medicine. Combined, these advances allow for dynamic, quantitative, and predictive observations of entire organisms and tissues. Here, we address 3 main tasks of bioimage analysis, image restoration, segmentation, and tracking and discuss new computational tools allowing for 3-dimensional spatial genomics maps. Finally, we demonstrate how these advances have been applied in studies of craniofacial development and oral disease pathogenesis.

CHCHD2 mutant mice display mitochondrial protein accumulation and disrupted energy metabolism.

Mutations in the mitochondrial cristae protein CHCHD2 lead to a late-onset autosomal dominant form of Parkinson's disease (PD) which closely resembles idiopathic PD, providing the opportunity to gain new insights into the mechanisms of mitochondrial dysfunction contributing to PD. To begin to address this, we used CRISPR genome-editing to generate CHCHD2 T61I point mutant mice. CHCHD2 T61I mice had normal viability, and had only subtle motor deficits with no signs of premature dopaminergic (DA) neuron degeneration. Nonetheless, CHCHD2 T61I mice exhibited robust molecular changes in the brain including increased CHCHD2 insolubility, accumulation of CHCHD2 protein preferentially in the substantia nigra (SN), and elevated levels of α-synuclein. Metabolic analyses revealed an increase in glucose metabolism through glycolysis relative to the TCA cycle with increased respiratory exchange ratio, and immune-electron microscopy revelated disrupted mitochondria in DA neurons. Moreover, spatial genomics revealed decreased expression of mitochondrial complex I and III respiratory chain proteins, while proteomics revealed increased respiratory chain and other mitochondrial protein-protein interactions. As such, the CHCHD2 T61I point-mutation mice exhibit robust mitochondrial disruption and a consequent metabolic shift towards glycolysis. These findings thus establish CHCHD2 T61I mice as a new model for mitochondrial-based PD, and implicate disrupted respiratory chain function as a likely causative driver.

Optics-free reconstruction of 2D images via DNA barcode proximity graphs.

Spatial genomic technologies include imaging- and sequencing-based methods (1-3). An emerging subcategory of sequencing-based methods relies on a surface coated with coordinate-associated DNA barcodes, which are leveraged to tag endogenous nucleic acids or cells in an overlaid tissue section (4-7). However, the physical registration of DNA barcodes to spatial coordinates is challenging, necessitating either high density printing of coordinate-specific oligonucleotides or in situ sequencing/probing of randomly deposited, oligonucleotide-bearing beads. As a consequence, the surface areas available to sequencing-based spatial genomic methods are constrained by the time, labor, cost, and instrumentation required to either print, synthesize or decode a coordinate-tagged surface. To address this challenge, we developed SCOPE (Spatial reConstruction via Oligonucleotide Proximity Encoding), an optics-free, DNA microscopy (8) inspired method. With SCOPE, the relative positions of randomly deposited beads on a 2D surface are inferred from the ex situ sequencing of chimeric molecules formed from diffusing "sender" and tethered "receiver" oligonucleotides. As a first proof-of-concept, we apply SCOPE to reconstruct an asymmetric "swoosh" shape resembling the Nike logo (16.75 × 9.25 mm). Next, we use a microarray printer to encode a "color" version of the Snellen eye chart for visual acuity (17.18 × 40.97 mm), and apply SCOPE to achieve optics-free reconstruction of individual letters. Although these are early demonstrations of the concept and much work remains to be done, we envision that the optics-free, sequencing-based quantitation of the molecular proximities of DNA barcodes will enable spatial genomics in constant experimental time, across fields of view and at resolutions that are determined by sequencing depth, bead size, and diffusion kinetics, rather than the limitations of optical instruments or microarray printers.

Optics-free Spatial Genomics for Mapping Mouse Brain Aging.

Spatial transcriptomics has revolutionized our understanding of cellular network dynamics in aging and disease by enabling the mapping of molecular and cellular organization across various anatomical locations. Despite these advances, current methods face significant challenges in throughput and cost, limiting their utility for comprehensive studies. To address these limitations, we introduce IRISeq (Imaging Reconstruction using Indexed Sequencing), a optics-free spatial transcriptomics platform that eliminates the need for predefined capture arrays or extensive imaging, allowing for the rapid and cost-effective processing of multiple tissue sections simultaneously. Its capacity to reconstruct images based solely on sequencing local DNA interactions allows for profiling of tissues without size constraints and across varied resolutions. Applying IRISeq, we examined gene expression and cellular dynamics in thirty brain regions of both adult and aged mice, uncovering region-specific changes in gene expression associated with aging. Further cell type-centric analysis further identified age-related cell subtypes and intricate changes in cell interactions that are distinct to certain spatial niches, emphasizing the unique aspects of aging in different brain regions. The affordability and simplicity of IRISeq position it as a versatile tool for mapping region-specific gene expression and cellular interactions across various biological systems.

Pathogenesis of Endometriosis and Endometriosis-Associated Cancers.

Endometriosis is a hormone-dependent, chronic inflammatory condition that affects 5-10% of reproductive-aged women. It is a complex disorder characterized by the growth of endometrial-like tissue outside the uterus, which can cause chronic pelvic pain and infertility. Despite its prevalence, the underlying molecular mechanisms of this disease remain poorly understood. Current treatment options are limited and focus mainly on suppressing lesion activity rather than eliminating it entirely. Although endometriosis is generally considered a benign condition, substantial evidence suggests that it increases the risk of developing specific subtypes of ovarian cancer. The discovery of cancer driver mutations in endometriotic lesions indicates that endometriosis may share molecular pathways with cancer. Moreover, the application of single-cell and spatial genomics, along with the development of organoid models, has started to illuminate the molecular mechanisms underlying disease etiology. This review aims to summarize the key genetic mutations and alterations that drive the development and progression of endometriosis to malignancy. We also review the significant recent advances in the understanding of the molecular basis of the disorder, as well as novel approaches and in vitro models that offer new avenues for improving our understanding of disease pathology and for developing new targeted therapies.

Optimizing the design of spatial genomic studies

Spatial genomic technologies characterize the relationship between the structural organization of cells and their cellular state. Despite the availability of various spatial transcriptomic and proteomic profiling platforms, these experiments remain costly and labor-intensive. Traditionally, tissue slicing for spatial sequencing involves parallel axis-aligned sections, often yielding redundant or correlated information. We propose structured batch experimental design, a method that improves the cost efficiency of spatial genomics experiments by profiling tissue slices that are maximally informative, while recognizing the destructive nature of the process. Applied to two spatial genomics studies—one to construct a spatially-resolved genomic atlas of a tissue and another to localize a region of interest in a tissue, such as a tumor—our approach collects more informative samples using fewer slices compared to traditional slicing strategies. This methodology offers a foundation for developing robust and cost-efficient design strategies, allowing spatial genomics studies to be deployed by smaller, resource-constrained labs.

ICARUS-LUNG01: A phase 2 study of datopotomab deruxtecan (Dato-DXd) in patients with previously treated advanced non-small cell lung cancer (NSCLC), with sequential tissue biopsies and biomarkers analysis to predict treatment outcome.

8501 Background: Few treatments with limited benefit are currently available after failure of targeted therapies, immunotherapy and platinum-based chemotherapy in patients (pts) with advanced NSCLC. Dato-DXd is an antibody-drug conjugate (ADC) composed of a TROP-2-directed monoclonal antibody linked to a topoisomerase I inhibitor via a peptide cleavable linker. In the phase 3 TROPION-Lung01 study, Dato-DXd demonstrated a statistically significant improvement of PFS over docetaxel in previously treated pts with advanced NSCLC. Here we report the results of ICARUS-Lung01 (NCT04940325), a multi-center, single-arm, phase 2 study evaluating activity, safety, and biomarkers of response/resistance to Dato-DXd in pretreated pts with advanced NSCLC. Methods: Intravenous Dato-DXd 6 mg/kg was given every 21 days to pts with advanced NSCLC, ECOG 0/1, who progressed on 1-3 lines of therapy (including actionable genomic alteration (AGA)-specific therapy if indicated). All pts underwent fresh tumor tissue biopsies at baseline, on-treatment (week 3 or 6) and end of treatment. Primary endpoint: investigator-assessed confirmed objective response rate (ORR). A set of translational analyses was performed to determine biomarkers associated with response/resistance, including TROP2 tumor membrane expression, TROP2-dynamics and spatial distribution (by AI-digital pathology), genomics, transcriptomic, spatial proteomics (by imaging mass cytometry) and CTCs. Results: A total of 100 pts received ≥1 dose: median age 60 years (26-83); 62% males, 89% smokers, 82% non-squamous (NSQ) histology, 23% AGA, median number of prior therapy lines 2 (1-5). At data cut-off (Dec 04, 2023), 92% discontinued therapy, 92 % had disease event, 67% died. Median treatment duration was 2.8 months (mos) (95%CI, 2.1-4.8), median follow-up 19.4 mos (95%CI, 18.2-20.4). Efficacy and safety results are shown in the table. Conclusions: In this heavily pretreated population, Dato-DXd showed similar efficacy and safety results to those reported in TROPION-Lung01. Patients with NSQ appeared to derive the greatest benefit. Translational analyses to determine biomarkers associated with response and/or resistance will be presented. Clinical trial information: NCT04940325 . [Table: see text]

Spatial genomics: mapping human steatotic liver disease.

Metabolic dysfunction-associated steatotic liver disease (MASLD, formerly known as non-alcoholic fatty liver disease) is a leading cause of chronic liver disease worldwide. MASLD can progress to metabolic dysfunction-associated steatohepatitis (MASH, formerly known as non-alcoholic steatohepatitis) with subsequent liver cirrhosis and hepatocellular carcinoma formation. The advent of current technologies such as single-cell and single-nuclei RNA sequencing have transformed our understanding of the liver in homeostasis and disease. The next frontier is contextualizing this single-cell information in its native spatial orientation. This understanding will markedly accelerate discovery science in hepatology, resulting in a further step-change in our knowledge of liver biology and pathobiology. In this Review, we discuss up-to-date knowledge of MASLD development and progression and how the burgeoning field of spatial genomics is driving exciting new developments in our understanding of human liver disease pathogenesis and therapeutic target identification.

Abstract LB333: Improved spatially resolved single-cell transcriptomic imaging in archival tissues with MERSCOPE

Abstract The advent of spatial transcriptomics has enabled a revolution in how complex tissues are studied. However, samples with lower quality RNA due to degradation, protein crosslinking, or high RNase content remain challenging for spatial transcriptomic measurement. In particular, formalin fixed, paraffin embedded (FFPE) tissues are the most widely used sample types in clinical and molecular diagnosis, yet they are notoriously difficult for single-cell transcriptomic analysis. To accurately profile the gene expression in FFPE samples in situ, a spatial transcriptomics technique with high detection efficiency and single molecule resolution is required. The Vizgen® MERSCOPE® Platform for spatial genomics is built on Multiplexed Error Robust in situ Hybridization (MERFISH) technology and directly profiles the transcriptome of intact tissues with high sensitivity in high-quality samples. Here we present an updated workflow to perform MERFISH in low and high-quality samples. We demonstrated its application in more than 5 FFPE sample types from mouse and human, including archival samples. For each tissue type, hundreds of thousands of cells were captured using the updated MERSCOPE Platform workflow, generating 100s million counts and their spatial information for profiled genes in each sample. The updated workflow involves streamlined sample preparation and chemistry optimization to improve sensitivity. MERSCOPE accurately profiled gene expression in situ and mapped cell types in archival human samples across a range of low and high RNA qualities. We compared the performance of MERSCOPE imaging using the updated protocol to the previous version and observed a significant increase in gene counts per 100 micron2 of tissue. We also demonstrated increased reproducibility between replicates with the streamlined workflow and chemistry. Furthermore, we demonstrated the updated workflow is compatible with simultaneous protein-based cell boundary staining. Finally, we constructed a spatially resolved single-cell atlas across low-quality archival breast and lung tumor types, mapped and cataloged different cell types within the tumor microenvironment, and systematically characterized the gene expression among cells. Spatially resolved transcriptomic profiling of low-quality samples at single-cell level provides enormous opportunities in cancer research. These improvements will enable new genomic inquiries into previously intractable tissues like FFPE, leading to new biological insights into cancer progression. Citation Format: Jiang He, Bin Wang, Justin He, Renchao Chen, Benjamin Patterson, Sudhir Tattikota, Timothy Wiggin, Lizi Maziashvili, Peter Reinhold, Manisha Ray, George Emanuel. Improved spatially resolved single-cell transcriptomic imaging in archival tissues with MERSCOPE [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB333.

Abstract 3875: Glioblastoma mutational profiles drive cancer cell signaling and immune evasion

Abstract Glioblastoma multiforme (GBM) is the most common malignant brain tumor in adults with a median survival below two years. Characterization of GBM through gene expression profiling and DNA sequencing has identified disease subgroups and commonly altered genes and pathways, including alterations in the genes encoding neurofibromin-1 (NF1) and the epidermal growth factor receptor (EGFR). NF1 and EGFR are involved in receptor tyrosine kinase (RTK), RAS/MEK/ERK, and PI3K signaling, and together, are genetically altered in over 60% of adult GBMs. Interestingly, NF1 and EGFR alterations in GBM are almost always mutually exclusive. Here, we designed a multimodal approach integrating spatial proteomics, transcriptomics, and genomics to measure cell composition, immune cell function, cancer cell signaling, cellular spatial organization, gene expression, transcriptional subtypes, and genome alterations in 80 NF1 and EGFR altered human GBMs with extensively curated treatment and survival data. We quantified the spatial expression of 42 proteins in over 31 million single cells measuring RTK/RAS/PI3K and Rb signaling and features of the TME, including 23 cell types covering tumor cells, immune cells, and brain resident cells. In addition to tumor genotype labels, we classified each GBM tumor by its transcriptional subtype (i.e., mesenchymal, classical, or proneural). We identified genotype-phenotype associations in GBM including hyperactive RAS signaling cellular niches, a macrophage-rich TME, and an epithelial-mesenchymal signature in NF1 altered tumors and hyperactive RTK signaling and an immune desert TME in EGFR amplified tumors. Furthermore, we observed spatial heterogeneity in cancer cell signaling patterns suggestive of the presence of different tumor cell clones within different regions of each GBM tumor. Together, we identify several genotype-associated features of GBMs, reinforcing the value of integrating spatial multi-omic data for generation of genotype-driven therapeutic hypotheses, with implications for GBM genotype-based therapeutic stratification. Citation Format: Maryam Pourmaleki, Brian D. Greenstein, Caitlin J. Jones, Subhiksha Nandakumar, Daniel A. Navarrete, Smrutiben Mehta, Carl Campos, Travis J. Hollmann, Nicholas D. Socci, Tejus Bale, Sohrab P. Shah, Ingo K. Mellinghoff. Glioblastoma mutational profiles drive cancer cell signaling and immune evasion [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3875.

Abstract B075: Spatial genomics identifies cancer cell cytokines regulating ovarian cancer immunity

Abstract Ovarian cancer (OvCa) is among the most aggressive and lethal cancers with a poor response to immune checkpoint blockade (ICB) despite its modest tumor mutational burden and expression of known cancer antigens. Consequently, it is critical to understand the molecular mechanisms of tumor immune escape and resistance to immunotherapies. Intercellular signaling between cancer cells and the tumor microenvironment (TME) is a crucial determinant of tumor immunity and given the immunosuppressive nature of OvCa TME, it is of utmost importance to dissect how this immunosuppressed TME is established. To begin to determine which cancer-cell genes may function cell externally in controlling of OvCa immunity, we employed a first-of-its-kind spatial functional genomics approach we developed, called Perturb-map, which enables dozens of CRISPR gene knock-out (KO) cells to be resolved in a tissue/tumor by imaging, along with cells in the local TME. We used Perturb-map to determine how 35 different genes, identified through ligand-receptor analysis of ovarian tumors, influenced tumor response to anti-PD-1 treatment in an OvCa mouse model. Strikingly, Il4 KO tumors were unique amongst the 35 gene KOs making the tumors significantly more responsive to anti-PD-1 treatment despite not impacting tumor growth in the absence of immunotherapy. Further experiments with single CRISPR KO tumors validated the Perturb-map finding, where Il4 KO combined with anti-PD-1 treatment resulted in a major reduction in tumor growth and a significant increase in survival. Cyclic immunofluorescence (CyCIF) multiplex imaging revealed that loss of cancer cell-derived IL-4 led to a significant reduction in pro-tumor macrophages and an increase in dendritic cell (DC) subsets as well as both progenitor and terminally exhausted CD8 T cells. Upon anti-PD-1 treatment, Il4 KO tumors had significant enrichment for activated CD8 T cells and B cells. Importantly, spatial analysis of the tumor samples identified enrichment of T follicular helper cells, B cells, and DCs in tertiary lymphoid structures in tumors with combined IL-4 loss and PD-1 inhibition. Further, we analyzed a human OvCa single-cell transcriptomics dataset and demonstrated that an IL-4 response signature is expressed in tumor-associated macrophages, indicating active IL-4 signaling in human ovarian tumors. Importantly, we employed a CRISPR-knock-in expression reporter in mouse tumors as well as immunohistochemistry of both human and mouse tumor samples to confirm that IL-4 is expressed by cancer cells. Thus, we identified cancer cell-secreted IL-4 as a potent regulator of OvCa TME and response to ICB. Given that an anti-IL-4-receptor antibody is already in clinical use to treat immune diseases, our findings have the potential to be translated to the clinical management of this dreadful disease. Citation Format: Gurkan Mollaoglu, Alexander Tepper, Hunter Potak, Luisanna Pia, Angelo Amabile, Chiara Falcomata, Jaime Mateus-Tique, Noam Rabinovich, Rachel Brody, Lindsay Browning, Jia-Ren Lin, Peter Sorger, Sandro Santagata, Miriam Merad, Alessia Baccarini, Brian Brown. Spatial genomics identifies cancer cell cytokines regulating ovarian cancer immunity [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B075.

Abstract B073: A spatially resolved single-cell tumor microenvironment of clinicomolecular subtypes of high-grade serous ovarian cancer

Abstract Background & objective: High grade serous ovarian cancer (HGSC) is the most lethal type of ovarian cancer malignancies. Around 50% of HGSCs are DNA-repair homologous recombination deficient (HRD) and 15% have CCNE1 gene amplification. Previous results have shown that tumor microenvironment (TME) can impact clinical prognosis in HGSC. In this study our objective was to characterize the interplay of the TME with HRD and CCNE1 amplification in HGSC and its association with clinical outcomes. Materials and methods: We collected TME spatial protein expression, genomics, and clinical data from 250 HGSC patients. We determined BRCA1/2 germline and somatic mutation status using targeted sequencing and promoter hyper-methylation. For the tumors without BRCA1/2 mutations, we performed shallow whole genome sequencing, and estimated the CCNE1 amplification and HRD status using shallowHRD and BRCAness copy number profiles. Immune cell infiltration was assessed also using IHC for CD8, CD20, CD68, and CD103. The single-cell spatial landscapes were analyzed for 1000 tumor cores from the tumor center and tumor periphery using cyclic immunofluorescence (tCycIF) imaging for 34 different protein markers. We categorized and spatially analyzed the single cells from the TME to distinct cellular subpopulations using the TRIBUS and scimap software. Results: In our study of the TME using highly multiplexed tissue imaging we identified a total of 4.8 million single cells of them 1.6 million were immune cells belonging to eight biological subpopulations. The immune cell proportion detected in each sample showed high concordance with the IHC estimation by a pathologist. Interestingly, the CD8+, CD20+, CD11c+, and CD15+ high immune cell infiltrations were higher in BRCA1/2 mutated as compared to the CCNE1 amplified tumors. High immune infiltration was associated with prolonged overall survival only in samples debulked before chemotherapy but not in samples debulked after chemotherapy. Using consensus clustering for cell protein expression and cell morphology, we identified five distinct tumoral and stromal cell clusters which showed significant heterogeneity among the tumor genotypes and association with clinical outcomes. Then, after clustering of the spatial single-cell proximity data, we identified 13 distinct recurrent cellular neighborhoods (RCNs). The two RCNs related to the tumor-stroma and tumor-immune interfaces exhibited the highest differences between HRD and CCNE1 samples. Finally, by using random forest machine learning classifiers of the cells and its neighborhoods, and using an approach to minimize overfitting, we were able to detect the features more representative in HRD samples, for example, MHC-I expression in cancer cells and in stromal cells. Conclusion: The integration of multi-omics data revealed distinct TMEs associated with HRD and clinical outcomes; this has the potential to be used to discover biomarkers for precision oncology in HGSCs. Citation Format: Fernando Perez-Villatoro, Lilian van Wagensveld, Aleksandra Shabanova, Siamak Hajizadeh, Julia Casado, Ella Anttila, Maaike A. van der Aa, Roy F.P.M. Kruitwagen, Gabe S. Sonke, Koen K. Van de Vijver, Peter K. Sorger, Hugo M. Horlings, Anniina Färkkilä. A spatially resolved single-cell tumor microenvironment of clinicomolecular subtypes of high-grade serous ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B073.