Tumor Architecture Research Articles

Single-cell sequencing depicts tumor architecture and empowers clinical decision in metastatic conjunctival melanoma

Conjunctival melanoma (CoM) is a potentially devastating tumor that can lead to distant metastasis. Despite various therapeutic strategies for distant metastatic CoM, the clinical outcomes remain unfavorable. Herein, we performed single-cell RNA sequencing (scRNA-seq) of 47,017 cells obtained from normal conjunctival samples (n = 3) and conjunctival melanomas (n = 7). Notably, we noticed a higher abundance of cancer-associated fibroblasts (CAFs) in tumor microenvironment (TME), correlated with enhanced angiogenic capacity and increased VEGFR expression in distal metastatic CoM. Additionally, we observed a significant decrease in the proportion of total CD8+ T cells and an increase in the proportion of naive CD8+ T cells, contributing to a relatively quiescent immunological environment in distal metastatic CoM. These findings were confirmed through the analyses of 70,303 single-cell transcriptomes of 7 individual CoM samples, as well as spatially resolved proteomes of an additional 10 samples of CoMs. Due to the increase of VEGFR-mediated angiogenesis and a less active T cell environment in distal metastatic CoMs, a clinical trial (ChiCTR2100045061) has been initiated to evaluate the efficacy of VEGFR blockade in combination with anti-PD1 therapy for patients with distant metastatic CoM, showing promising tumor-inhibitory effects. In conclusion, our study uncovered the landscape and heterogeneity of the TME during CoM tumorigenesis and progression, empowering clinical decisions in the management of distal metastatic CoM. To our knowledge, this is the initial exploration to translate scRNA-seq analysis to a clinical trial dealing with cancer, providing a novel concept by accommodating scRNA-seq data in cancer therapy.

Deep learning based brain tumour architecture for weight sharing optimization in federated learning

Deep learning based brain tumour architecture for weight sharing optimization in federated learning

An optimised patient-derived explant platform for breast cancer reflects clinical responses to chemotherapy and antibody-directed therapy

Breast Cancer is the most common cancer among women globally. Despite significant improvements in overall survival, many tumours are refractory to therapy and so novel approaches are required to improve patient outcomes. We have evaluated patient-derived explants (PDEs) as a novel preclinical platform for breast cancer (BC) and implemented cutting-edge digital pathology and multi-immunofluorescent approaches for investigating biomarker changes in both tumour and stromal areas at endpoint. Short-term culture of intact fragments of BCs as PDEs retained an intact immune microenvironment, and tumour architecture was augmented by the inclusion of autologous serum in the culture media. Cell death/proliferation responses to FET chemotherapy in BC-PDEs correlated significantly with BC patient progression-free survival (p = 0.012 and p = 0.0041, respectively) and cell death responses to the HER2 antibody therapy trastuzumab correlated significantly with HER2 status (p = 0.018). These studies show that the PDE platform combined with digital pathology is a robust preclinical approach for informing clinical responses to chemotherapy and antibody-directed therapies in breast cancer. Furthermore, since BC-PDEs retain an intact tumour architecture over the short-term, they facilitate the preclinical testing of anti-cancer agents targeting the tumour microenvironment.

Beyond histology: A tissue algorithm predictive of post-surgical recurrence in hepatocellular carcinomas, including TERT promoter mutation.

Surgical resection for hepatocellular carcinoma (HCC) is burdened with a high recurrence rate and a lack of reliable prognostic factors. The aim of this study was to integrate the HCC pathological features with gene mutations to improve the prognostic role of pathological analysis.This is a monocentric prospective study, including 67 patients resected for HCC. All clinical data and histological features were collected, including tumor grade, architecture, margins, microvascular invasion, and microscopic portal vascular invasion (MPVI). Next-generation sequencing (NGS) was performed using a laboratory-developed multi-gene panel, allowing to amplify 330 amplicons (21.77kb), covering the relevant targets for solid tumor analysis. The most represented mutations were TERT promoter (n = 41, 61.2%), TP53 (n = 18, 26.9%) and CTNNB1 (n = 17, 25.4%). At follow-up, 13 (19.4%) patients experienced HCC recurrence: at multivariate analysis, tumor dimensions (p = 0.040), MPVI (p = 0.010), and TERT mutation (p = 0.034) correlated with recurrence. Dimensions ≥ 4.5cm (very close to AJCC stage pT3; 9 recurrences, p = 0.041, odd-ratio = 3.7), MPVI (9 recurrences, p = 0.062, OR = 3.3), and TERT (11 recurrences, p = 0.049, OR = 4.4) correlated with disease-free survival also at univariate analysis. The concomitant occurrence of these three variables was present in 7 cases, among which 5 recurred (p = 0.002, OR = 15.94).In conclusion, NGS analysis in resected HCC could not only be used for future therapies but should be integrated with histopathology to predict the risk of tumor recurrence after surgical resection: TERT mutation is among the strongest predictors of tumor recurrence, together with tumor stage (dimensions) and the occurrence of MPVI, which should always be reported separately from the classic MVI.

Abstract PO3-28-08: Triple Hormone Receptor Signatures as Novel Prognostic Markers in Breast Cancer

Abstract Background Tumor phenotype across various cancers, including breast cancer, is predominantly shaped by a synergy between genomic alterations and the cell-of-origin from which the tumor emerges. These elements collectively influence crucial aspects like tumor aggressiveness, treatment response, and patient prognosis. While existing research highlights that the cell-of-origin often leaves a lasting imprint on the molecular architecture of tumors, leveraging this information for actionable clinical insights has proven challenging. Specifically, in the realm of breast cancer, utilizing cellular ancestry signatures could be a key factor in tailoring effective therapeutic strategies and improving patient outcomes. Methods In this study, we developed a unique signature anchored in the expression levels of triple hormone receptors (THR)—namely androgen (AR), estrogen (ER), and vitamin D (VDR)—in normal breast cells. Building on this, we formulated two distinct mRNA markers, THR-50 and THR-70, aimed at categorizing breast tumors based on their THR expression profiles. These markers were rigorously validated across 65 independent breast cancer studies, involving a total of 6,679 patients, utilizing Kaplan-Meier survival curves, Cox proportional hazards models, and unsupervised clustering analyses. Results Our findings indicate that both THR-50 and THR-70 are robustly associated with overall survival and recurrence-free survival in breast cancer patients across all datasets evaluated. Importantly, these THR signatures demonstrate broad applicability across various breast cancer subtypes, grades, and treatment phases—unlike conventional prognostic markers that tend to be subtype-specific. Additionally, the THR signatures have revealed four unique patient clusters with divergent survival outcomes, three of which are ER-positive and one ER-negative. When augmented with an immune-based signature (i20), THR-70 identifies a specific ER-negative breast cancer subgroup with a highly favorable prognosis, comparable to ER-positive subtypes, as well as another ER-negative subgroup characterized by exceedingly poor survival outcomes. Conclusion The THR-based cellular ancestry signatures open a new avenue in understanding the intricacies of breast cancer biology. These signatures offer a robust and stable framework for developing other prognostic markers, thus providing an enhanced stratification of existing breast cancer categories as well as creating new classifications. Citation Format: Mohamed Omar, Chuck Harrell, Rulla Tamimi, Luigi Marchionni, Tan Ince. Triple Hormone Receptor Signatures as Novel Prognostic Markers in Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-28-08.

Abstract PO1-26-04: Patient-derived organotypic tissue cultures for organoid isolation and probing unique metabolic features of individual breast cancer patients

Abstract Patient-derived organotypic tissue cultures (PD-OTC) are unique models for probing how individual patient’s tumor microenvironment (TME) influences cancer development and treatment responses. They retain the patient tumor architecture and TME, that are difficult to recapitulate in other models, while affording flexible treatment options and comparison of cancer (CA) versus matched non-cancer (NC) tissue responses to treatments. We have developed a culturing method for establishing PD-OTC of breast cancer patients that exhibit CA tissue growth and extensive metabolic reprogramming between CA versus matched NC tissues while enabling patient-derived organoids (PDO) and fibroblasts to be isolated. Pairs of CA and NC tissues freshly resected from six patients bearing early-stage estrogen receptor/progesterone receptor positive ductal carcinoma were sliced at 750 µm, embedded in Matrigel, and cultured in a 6-well plate with a Biopore membrane insert and custom medium at 37°C/5% CO2 with gentle rocking (16-40 mg wet weight; n=3). Excess tissue slices were cryopreserved and kept at -196 °C. Culture media were sampled periodically for metabolite analysis by NMR. Microscopic examination revealed significant tissue, organoid-like, and fibroblast outgrowth after 1-1.5 month of culturing. Two to three days prior to the harvest of two patients’ (CZ16 and 17) OTC, the culture medium was replaced with [U-2H]-glucose + [U-13C,15N]-glutamine-containing medium to allow the two stable isotope tracers to be metabolized, followed by Stable Isotope-Resolved Metabolomic (SIRM) analysis by NMR and ion chromatography coupled with ultra high-resolution Fourier transform mass spectrometry (IC-UHRFTMS). Two separate pieces of the OTC were cut and each was assayed for glucose uptake and mitochondrial membrane potential by live fluorescence spectroscopy via a portable custom-built fluorescence microscope, and by histochemical analysis. Histological analysis showed that both CA and NC OTC of all patients maintained their structural integrity. SIRM analysis of two patients’ OTC revealed active catabolic and anabolic metabolism including glucose uptake/glycolysis, the Krebs cycle/mitochondrial membrane potential, the pentose phosphate pathway (PPP), gluconeogenesis, glycogen synthesis, and purine/pyrimidine/sugar nucleotides synthesis. The CA OTC of both patients showed enhanced glucose uptake, glycolysis, mitochondrial membrane potential/Krebs cycle, PPP, glycogen synthesis, and purine nucleotide synthesis, when compared with the matched NC OTC. Treatment-naïve CA OTC of CZ16 showed much more significant metabolic reprogramming (NAD+ metabolism, in particular) than CA OTC of CZ17 who has undergone neoadjuvant treatment, chemotherapy, and hormone therapy prior to surgery. Distinct metabolic features of breast CA tissues can be exploited as therapeutic targets. Organoids and fibroblasts were isolated from CA and/or NC OTC cultures and their characterization is in progress. When revived, cryopreserved OTC exhibited active metabolism and tissue/organoid/fibroblast outgrowth similarly to fresh OTC. In conclusion, our culturing method enables PD-OTC models to be established efficiently for early-stage breast cancer patients for downstream functional studies including live cell imaging and SIRM analysis. Organoids and fibroblasts can also be derived from < 20 mg of tissues for further studies. Future direction includes therapeutic studies on these patient-derived models to better understand and predict individual patient’s responses to therapy. Citation Format: Teresa Fan, Carlos Goncalves, Jing Yan, Jahid Islam, Penghui Lin, Mohamed Kaddah, Richard Higashi, Andrew Lane, Caigang Zhu. Patient-derived organotypic tissue cultures for organoid isolation and probing unique metabolic features of individual breast cancer patients [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-26-04.

Abstract PS03-08: Spatial biomechanics determines fate in breast cancer

Abstract Background: With the recent advancements of spatial omics, it has become increasingly recognized that spatial analysis the of tumor architecture is key to decipher the heterogenous intratumoral relationships between different tumor components and provide better understanding of cancer therapy resistance, as well as help to identify potential targets for personalized therapy. The challenge of the discovery and implementation of such biomarkers still lays in the technological and methodological difficulties, and their translation into clinical practice. In parallel a major advancement in cancer research has been the recognition of the importance of cancer biomechanical properties in cancer progression, metastasis, and therapy response. Methods: We developed a new computational platform to identify biomechanical drivers of cancer outcome from spatial omics data. We used it to identify recurring spatial patterns of these markers within the tumor microenvironment, and define the spatial scale of heterogeneity of such patterns. Our dataset consists in 700 primary breast cancer baseline samples analyzed with two 30-marker imaging mass cytometry panels to identify cancer, immune, and stromal cells and structures and their biomechanical states. Our patient cohort includes 20+ years of follow up, with up to 6 samples per patient from 64 patients alive 12 years post diagnosis, and 49 patients lost to breast cancer deaths. Patients were treated with surgery, radiation, chemotherapy, endocrine therapy, or combinations of these post-surgery. Based on this approach, we derived a new three-parameter quantitative metric of preferential spatial co-localization between different cells or structures, and we use this metric to stratify patients into responders and non-responders with the aid of single and multivariate survival analysis. Finally, we include a correlation with Atomic Force Microscopy to identify the mechanical signature of these driving patterns in breast cancer. Results: Our work enabled the identification of tumor-immune-stromal spatial patterns that drive breast cancer outcome and their spatial scale. Among our results, we were able to further define spatial regions of hypoxia-driven EMT. We found these regions to be usually on the scale of 50 mm radius, and enriched in presence of unspecified immune cells. They are also more frequent in ER+ areas, with high NaKATPase activity. Our analysis also shed light on the controversy on the role of fibroblasts. We have found that the presence of a strong, spatially structured stroma, in fibroblast-rich regions is a strong predictor of positive outcome. Similarly, in our analysis collagen cross-linking emerges as a positive control factor in Vimentin-rich microenvironments, offering new interpretation to the role of the ECM structure. Importantly, our first principle approach allows for the identification of driving structures beyond heterogeneity, and thus enables easy extension to additional datasets. Finally, when correlating these patterns with Atomic force microscopy measurements, we can clearly see that patterns predictive of poor survival present a clear heterogeneous stiffness signature, as opposed to a very homogenous good survival pattern signature. Conclusion: These results confirm the importance of spatial distribution of biomechanical drivers in cancer, offering new avenues of physics-based therapeutic targets. Our results also offer a solid base to inform machine learning algorithms on how to significantly parametrize spatial patterns in breast cancer for clinical significance. Furthermore, we demonstrate for the first time the use of Atomic Force Microscopy as a single biomarker for these patterns in breast cancer with a direct correlation based on pathology matching. Citation Format: Sara Nizzero, Maria Pelaez Soni, Gregory Zaugg, Mariam Gachechiladze, Yitian Xu, Licheng Zhang, Junjun Zheng, Brian Menegaz, Lee B Jordan, Colin A Purdie, Philip R Quinlan, Chandandeep Nagi, Karla A Sepulveda, Philipp Oertle, Tobias A Appenzeller, Marko Loparic, Zhihui Wang, Shu-Hsia Chen, Vittorio Cristini, Marija Plodinec, Alastair Thompson. Spatial biomechanics determines fate in breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS03-08.

Abstract PS16-10: SPATIAL IMMUNE CELL DISTRIBUTION CAN REFINE PROGNOSIS IN EARLY-STAGE ER+/HER2-/LN- BREAST CANCER

Abstract Lymphocytic infiltrate is a known prognostic biomarker in estrogen receptor(ER)-negative breast cancers (BCs). Comparatively ER+ disease is putatively cold, however, there exists an infiltrate-rich subset of ER+ tumours with significant spatial heterogeneity and unknown clinical impact. Using serial sections taken from early-stage, ER+/HER2- breast tumours of Irish patients enrolled in the TAILORx trial (n=450), we aimed to investigate the prognostic potential of markers encompassing tumour architecture. Immunohistochemistry for Ki67 and CD45 (leukocyte common-antigen), and staining for haematoxylin and eosin was applied to all sections [1]. Classifiers for stromal fraction (SF), stromal-infiltrate (sTIL), Ki67-LI, and their spatial relationships within the tumour were generated using QuPath [2] and R Studio. Trained marker classifiers were validated against an expert pathologist (R2 of M-Score to: CD45%: 0.968, Ki67-LI: 0.814, sTIL: 0.864) to define observed lymphocytes as tumour or stromal-infiltrating, retain only tumour Ki67+ density, and to investigate SF. Cohort mean SF was 67.69% (range 16.35 – 98.29%), with marginally significant differences in recurrence prediction for cohort high v low SF by mean (c-index = 0.58, p< 0.032), and luminal A disease (p=0.037) only. sTILs did not differ significantly between high/low mean SF (Mann-Whitney p=0.6201), though sTIL was prognostic in the OncotypeDx intermediate Recurrence Score (RS) category overall (p=0.0076). This trend appeared strongest in intermediate RS patients receiving chemo-endocrine therapy (HT+CT) (p < 0.0001) vs endocrine therapy (HT) alone (p=0.86). Investigating the effects of prescribed therapy on sTIL-derived recurrence risk also revealed significant trends in those patients receiving HT+CT only (p < 0.00001) vs HT alone (p=0.26). Spatial analysis of sTILs suggest that tumours become more immune excluded as Oncotype Dx RS increases - particularly from intermediate to high RS (Wilcoxon Intermediate v High: p=0.0077), with significant differences in survival for high/low tumour-immune hotspots observed in Intermediate RS, HT+CT treated patients (p=0.0052). Further spatial analysis suggests that high normalised frequency of infiltrate [method adapted from 3] (within 7um of tumour cells) have negative prognostic impact for premenopausal patients (p=0.017), and inter-sectional analysis identifies niche tumour environments where high Ki67-LI colocalize with immune infiltrates[methods adapted from 4, 5], most notable in Intermediate RS tumours.. Overall these data suggest immune-spatial subsets can be used to refine risk stratification of ER+ breast cancer.

Exploring Contribution of HMGA2 to the Aggressive Tumor Structure in Urothelial Carcinoma to Identify Prospective Clinical Applications

For patients with advanced upper tract urothelial carcinoma (UTUC), the five-year survival rate is approximately 20-40%. In previous real-world data analyses, we observed that UTUC patients with a sessile tumor architecture tend to exhibit more aggressive behavior and concurrently have poorer outcomes in terms of metastasis-free and cancer-specific survival. This suggests that tumor architecture may serve as a crucial prognostic factor for predicting outcomes. We utilized various tumor tissue specimens, categorizing them based on tumor architecture into papillary and sessile groups. RNA sequencing was used to analyze the two groups, and significant differences in the expression levels of high mobility group AT-hook 2 (HMGA2) were found. In the more aggressive sessile tumor architecture, HMGA2 exhibited higher expression levels. HMGA2 is currently known to participate in various biological processes, including the cell cycle, DNA damage repair, apoptosis, and epithelial-mesenchymal transition. Additionally, HMGA2 is abnormally regulated in several human cancers, such as lung, breast, ovarian, and bladder cancers. Elevated expression of HMGA2 is associated with a higher risk of tumor recurrence and cancer progression. Hence, we hypothesize that HMGA2 may regulate tumor cell epithelial-mesenchymal transition through the PI3K-Akt cell signaling pathway, influencing tumor architecture and cancer prognosis. Attempting to downregulate HMGA2 should theoretically enhance the effectiveness of chemotherapeutic drugs in achieving an anti-cancer effect. We discovered that inhibiting the expression of HMGA2 using siRNA in UTUC cells, such as BFTC909, significantly reduces cell proliferation and migration. Moreover, the addition of an HMGA2 inhibitor known as suramin enhances cytotoxic effects and decreases cell migration. Our preliminary findings provide evidence indicating a correlation between high levels of HMGA2 expression and aggressive tumor architecture in UTUC. We designed UTUC patient-derived organoids (PDO). The test results revealed that the organoids, as indicated by HE stain and Ki67 immunohistochemical (IHC) staining, closely resembled their corresponding parental tumors. Currently, it appears that the organoids have retained the original tumor structure and protein molecular expression characteristics. The PDO model is hoped to serve as a valuable tool in aiding the selection of suitable therapeutic strategies as clinical applications. This study was funded by the Chang Gung Medical Foundation, Taiwan (CMRPG8M0131, and CMRPG8M0132) and the Ministry of Science and Technology, Taiwan (MOST 111-2314-B-182A-140). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Establishment of patient-derived organoids for guiding personalized therapies in breast cancer patients.

Breast cancer has become the most commonly diagnosed cancer. The intra- and interpatient heterogeneity induced a considerable variation in treatment efficacy. There is an urgent requirement for preclinical models to anticipate the effectiveness of individualized drug responses. Patient-derived organoids (PDOs) can accurately recapitulate the architecture and biological characteristics of the origin tumor, making them a promising model that can overtake many limitations of cell lines and PDXs. However, it is still unclear whether PDOs-based drug testing can benefit breast cancer patients, particularly those with tumor recurrence or treatment resistance. Fresh tumor samples were surgically resected for organoid culture. Primary tumor samples and PDOs were subsequently subjected to H&E staining, immunohistochemical (IHC) analysis, and whole-exome sequencing (WES) to make comparisons. Drug sensitivity tests were performed to evaluate the feasibility of this model for predicting patient drug response in clinical practice. We established 75 patient-derived breast cancer organoid models. The results of H&E staining, IHC, and WES revealed that PDOs inherited the histologic and genetic characteristics of their parental tumor tissues. The PDOs successfully predicted the patient's drug response, and most cases exhibited consistency between PDOs' drug susceptibility test results and the clinical response of the matched patient. We conclude that the breast cancer organoids platform can be a potential preclinical tool used for the selection of effective drugs and guided personalized therapies for patients with advanced breast cancer.

Abstract 3771: 3D correlated multi-scale cellular and molecular architecture of solid tumors

Abstract A solid tumor presents heterogeneous 3D structures at tissue, cell, and molecule levels. To develop advanced cancer diagnostics and therapies, it is important to understand in-depth cellular and molecular architecture of a tumor at multiple scales. Here we introduce novel optical microscopy methodologies for in-depth 3D spatial analysis of tumor tissues: 1) LED photobleaching-mediated 3D multiplex immunofluorescence (IF) microscopy, 2) Correlated 3D multi-resolution optical microscopy, and 3) 3D tissue region-based cell type-selective multi-omics. We built a high-power LED light irradiator with a broad emission spectrum (490-700 nm) that bleaches a broad wavelength of fluorescence signals simultaneously throughout an optically cleared, 400 µm-thick tumor tissue. Cyclic workflow involving IF staining, optical tissue clearing, 3D confocal microscopy, and LED photobleaching enables visualization of multiple cell types in the same whole 400 µm-thick tumor tissue after computational 3D image registration. The constructed multiplex image offers comprehensive 3D cellular landscape of a tumor. We also developed a method for tracking regions of interest (ROIs) in the middle of a large tumor tissue using a UV-activated visible dye in light sheet microscopy. Light sheet microscopy allows imaging an optically cleared large-sized tumor tissue at a low spatial, tissue resolution. To mark the ROI positions on a tumor, the dye is infused into agarose gel surrounding the tumor tissue and be activated and present purple-colored lines where are exposed to UV light sheets in light sheet microscopy. The tumor tissue was physically sectioned at the marked ROI positions using a vibratome after reversing tissue clearing process. Sequential IF staining and high-resolution confocal microscopy permit for visualizing detailed cellular compositions of the ROIs in the tumor. The tissue and cellular-resolution 3D images from light sheet and confocal microscopy are correlatively integrated to display ‘Google Earth’-like multi-scale view of a tumor tissue. To further analyze the ROIs of a tumor at molecular level, we have developed optical microscope-mediated fluorescence lithography methods that allow fluorescently labeling of a selected cell type in ROIs of an optically cleared 3D tumor tissue. After wash free from clearing solution and dissociation of the tumor into cells, fluorescence-marked, intact cells from the ROIs are collected through flow cell sorting. Proteins and mRNAs are extracted from the collected cells and sent to LC-MS/MS analysis and RNA sequencing. The spatial multi-omics method provides in-depth molecular information of selected cells in the ROIs of a tumor. We believe all these 3D microscopy methods will provide powerful spatial analysis tools for better understanding cellular and molecular architecture of solid tumors. Citation Format: Steve Seung-Young Lee, Yi-Chien Wu, Jingtian Zheng, Elie Abi Khalil, Samuel Wang, Alexander Lippert, Joshua Plank. 3D correlated multi-scale cellular and molecular architecture of solid tumors [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 3771.

Abstract 4407: Simultaneous spatial epigenomic and transcriptomic analysis of gastric adenocarcinoma reveals regulatory patterns governing tumor and microenvironment architecture at the cellular level

Abstract Recent advances in spatial transcriptomics and spatial proteomics have enabled increasingly complex questions on the nature of gene regulation and expression in cellular subtypes in tumor tissue and the tumor microenvironment. However, most spatial omics techniques do not profile the epigenomic landscape responsible for downstream gene expression. Furthermore, current spatial technologies have yet to profile the epigenome and transcriptome simultaneously, and thus it remains a challenge to correlate multi-omics data across sections of extremely heterogenous tumor tissue. Recently, co-profiling of spatial epigenomics and transcriptomics using principles of Deterministic Barcoding in Tissue for spatial omics sequencing (DBiT-seq) has been demonstrated on normal brain tissue. Joint spatial profiling of chromatin states and whole transcriptome in tissue allows for parallel characterization of gene regulation programs across all cell types, while preserving the tissue architecture for greater understanding of the cellular environment. Here we present the first application of spatial ATAC-seq and spatial transcriptomics on the same tissue section to characterize the tumor microenvironment of an invasive gastric adenocarcinoma (GAC) and adjacent normal tissue. GAC is the fifth most common cancer and commonly exhibits mutations in epigenetic modifiers, including ARID1A and MLL1-4. Distinct spatial clusters representing different cell subtypes were identified via both spatial chromatin accessibility and spatial transcriptomics. Spatial ATAC-seq profiling of accessible regulatory elements correlated well with RNA expression of target genes. Spatial patterns of transcription factor motif accessibility also correlated well with the observed transcriptional program of tumor tissue. When compared to adjacent normal tissue, spatial co-profiling of chromatin accessibility and the transcriptome revealed that the epigenetic landscape is significantly altered in tumorigenesis of GAC. Future work will focus on development of co-profiling of histone modifications and the transcriptome to enable the study of another layer of the epigenomic landscape, especially as targeting epigenetic modifiers such as EZH2 has been identified as a potential therapeutic strategy in GACs. Overall, we present a solution to profile multiple layers of gene regulation and expression with spatial context, which can be applied to most tumor types for better understanding of tumorigenesis and the consequences of new targeted therapies. Citation Format: Katelyn J. Noronha, Jennifer M. Garbarino, Daniel Massucci, Abigail R. Tyree, Colin Ng. Simultaneous spatial epigenomic and transcriptomic analysis of gastric adenocarcinoma reveals regulatory patterns governing tumor and microenvironment architecture at the cellular level [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 4407.

Abstract 4298: Predicting tumor evolution from digital histology using AI

Abstract Diverse clinical presentations of clear cell renal cell carcinoma (ccRCC) confound clinical decision making, leading to over and undertreatment. Clonal evolution of ccRCC proceeds through distinct trajectories characterised by differing levels of genomic intratumoral heterogeneity (gITH) and chromosomal complexity (weighted genomic instability index, wGII). However, accurate evaluation of these indices requires multiregional profiling of fresh tumour; cost prohibitive and logistically challenging in the clinical setting. Clinical histopathology workflows routinely capture multiple tumour areas enabling the use artificial intelligence (AI) to predict tumour evolutionary features directly from clinical grade H&E whole slide image (WSIs). ccRCC displays profound genetic and histological ITH but the link between these entities remains unclear. We leverage the TRACERx Renal cohort, comprising 1485 WSIs from 81 tumours to predict WGII and gITH and to gain insights into the relationship between genetic and histological ITH. Critically, each WSI is associated with a wGII and gITH label derived from a closely linked fresh tumour sample. For both prediction tasks, we extracted meaningful features for each WSI using self-supervised representation learning “MoCo”. Since high wGII confers poor prognosis we focussed on predicting binary stratification label of high wGII or low wGII (relative to the cohort median). First we predicted wGII as a continuous variable using a supervised multiple instance learning regression model trained on the MoCo features, and then classified the predicted wGII into “high” or “low” achieving 0.80 AUROC. To predict gITH we postulated that the degree of gITH would correlate with histological ITH. Using an unsupervised clustering of refined MoCo features we defined 24 histological clusters. The number of computationally derived histological clusters within a single tumour positively correlated with gITH (pearson’s 0.56). We used the number of clusters to classify WSIs into prognostic binary groups of high or low gITH (relative to the cohort median) achieving an AUROC of 0.80. To understand the biological relationship between histological and genetic ITH we pathologically characterised the histological clusters: a pathologist annotated WSIs with tumour architecture and cytomorphology. Image tiles were associated with the annotations using spatial coordinates, illuminating phenotypic traits of different evolutionary trajectories and providing an interpretability framework for our AI pipelines. Since the tumour evolutionary course dictates disease progression tempo, applying evolutionary classification in clinic can fundamentally improve patient care. Here, for the first time, we provide a framework to translate fundamental evolutionary principles underpinning tumour biology and clinical progression into a prognostic computational pathology biomarker possible to clinically implement. Citation Format: Charlotte E. Spencer, Axel Camara, Auriane Riou, Lewis Au, Jose I. Lopez, Zayd Tippu, Charles Maussion, Kenneth Ho, Amy Strange, Emma Nye, Veronique Birault, Lydwine Van-praet, Kim Edmonds, Eleanor Carlyle, Steve Hazell, Sarah Rudman, James Larkin, Samra Turajlic. Predicting tumor evolution from digital histology using AI [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 4298.

Abstract 533: Attenuating EZH2-mediated metastasis and tumor growth by pharmacological-based approaches using three-dimensional in vitro culture models

Abstract Triple Negative breast cancer (TNBC) accounts for 10-15% of all breast cancers where patients encounter poor clinical outcomes. Treatment modalities for TNBC are conventional cytotoxic chemotherapy and radiation leads to 35%-40% relapse within 5 years of diagnosis. There is a growing need for more effective targeted therapeutics to inhibit TNBC growth. The role of EZH2 in maintaining tumor growth and metastasis in TNBC is well documented. EZH2 promotes breast tumor-initiating cell expansion (BITC) and cancer progression by impairing the DNA damage repair process favoring activation of oncogenes. Despite the advancement in the discovery of inhibitors for EZH2 that attenuate its catalytic activity, resistance to these small molecules limits their use in solid tumors. The neurotransmitter dopamine via its D1 receptor activation in TNBC cell lines induces apoptosis and autophagy, as well as inhibits the invasion and regress in mammary tumors in vivo. Here we hypothesize that the presence of dopamine D1 receptor agonist (A77636) enhances the efficacy of EZH2 inhibitors (GSK126) to inhibit in vitro TNBC tumor growth and metastasis. To test the efficacy of the combination we employed a 3D culture system of MDA-MB-231 cells encapsulated in calcium-alginate microgels seeded from a microfluidic droplet generator. We also employed a 3D organ-on-chip-based microphysiological (MPS) platform (SynTumor) to study the effect of the drug combination on metastasis in vitro. The SynTumor MPS devices replicate the pathophysiological architecture of native vascularized breast tumors. The combination treatment enhanced a significant reduction of tumor spheroid diameter compared to the vehicle and indicated spheroid regression over treatment. Furthermore, the combination therapy induced necrosis and caused complete inhibition of EZH2 expression. The knockdown of EZH2 inhibited tumor spheroid formation and displayed cytoskeleton de-arrangement. In the microfluidic SynTumor model, circulating tumor cell numbers were reduced by half at 96 hrs after EZH2 combination treatment. Our data indicate that the combinatorial effect of DRD1 agonist and EZH2 inhibitor efficiently attenuates the EZH2-mediated in vitro tumor growth and metastasis (This work is supported by DOD: W81XWH2010065, for Eswar Shankar). Citation Format: xilal Rima, Gautam Sarathy, Chunyu Hu, Divya S. Patel, Deborah Ramsey, Lara Rizotto, Dario Palmeri, Gwen Fewell, Bhuvaneswari Ramaswamy, Eduardo Reátegu, Eswar Shankar. Attenuating EZH2-mediated metastasis and tumor growth by pharmacological-based approaches using three-dimensional in vitro culture models [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 533.

Abstract 4257: A novel 3D co-culture platform for patient-specific immune-oncology treatment

Abstract Lung cancer is the leading cause of global cancer-related mortality resulting in ~1.8 million deaths. annually. Over the past decade, the contribution of tumor microenvironment (TME) in lung cancer progression has been highly appreciated and provided significant improvements of survival outcomes for patients. That includes the complexity of the tumor immune microenvironment (TIME) playing a critical role in cancer progression. However, drug resistance usually arises and there is an urgent need for novel immune cell-based therapies. 3D tumor cell structures that recapitulate the architecture and functionality of the original tumor have revolutionized the understanding of disease mechanisms and testing potential therapeutics. However, there are certain drawbacks that need to be overcome. For example, these models mainly consist of only the epithelial compartment and lack signals from the microenvironment. Recent advances with engineering co-culture systems with immune cells at Champions Oncology help to emulate cell-cell and immune cell infiltration. We have successfully established a biobank of TumorGraft3D (CTG3Ds) derived from non-small cell lung cancer (NSCLC) patient-derived xenografts as well as their autologous and allogeneic immune cells (TILs, PBMCs) to establish a proprietary co-culture platform to investigate disease mechanisms and test potential therapeutics. In brief, surgical resections from patients were used to generate patient-derived xenografts organoids, and matched immune cells were expanded using standard rapid expansion protocol. The combination of a comprehensive staining system and high-content imaging as an endpoint allowed for 3D confocal analysis and deep evaluation of cancer-immune cellular interaction at the single cell level. Our pipeline of image analysis not only differentiates cancer-immune cell spatial distribution and cellular death but also allows for the evaluation of tumor infiltration and drug cytotoxicity. The characterization of the platform showed proliferation of the cancer cells and functional immune profiling by flow cytometry. The molecular profile of the organoids composing the platform showed a tight overlap with the original tumor highlighting the clinical relevance of the model. In addition, this novel co-culture platform has been tested with several NSCLC models to test the efficacy of various therapeutics such as immune-checkpoint inhibitors (ICIs) in reproducible, cost-effective, and patient-specific manner. In summary, Champion’s novel co-culture platform allows screening of novel immune targeting agents to determine impacts tumor cytotoxicity. Furthermore, our lung cancer tumorGraft3D platform serves as a powerful tool to facilitate the practice of precision medicine by investigating a variety of therapeutic modalities, alone or in combination, to identify patient-specific treatment strategies. Citation Format: Taylor Light, Samaneh Kamali, Mara Gilardi, Abhay Andar, Karin Abarca-Heidemann, Maria Mancini. A novel 3D co-culture platform for patient-specific immune-oncology treatment [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 4257.

Abstract 3803: Spatial architecture of tumor and immune cell lineages in syngeneic mouse tumor tissues

Abstract Immune checkpoint blockade (ICB) therapies have demonstrated great clinical benefit in a variety of malignancies, but there are many patients that do not respond. Despite many preclinical and clinical studies, the mechanism of ICB is not completely understood. Development of novel therapies targeting other immunosuppressive mechanisms or combining ICB with conventional chemo or radio therapies have the potential to increase the number of patients that achieve durable responses. However, the optimal therapy or combination of therapies for a patient is likely influenced by which immunosuppressive mechanisms exist within the tumor microenvironment (TME) and the phenotype and function of tumor-infiltrating immune cells. To delineate the mechanisms of immunosuppression we selected FFPE tissue sections from three syngeneic murine cancer models and performed tumor-immune profiling to spatially understand the contributions of both immune cells and cancer cells. Mouse reactive antibodies targeting specific cellular populations will help identify the location of immune cells within the tumor site and can be used for proof-of-concept studies. This approach can lead to novel insights to synergize anti-tumor activity of ICB therapies and will help to identify differences between responders and non-responders. The Cell DIVE Multiplex Imaging Solution allows probing and imaging of dozens of biomarkers on a whole single tissue section with an iterative staining and dye inactivation workflow. At its core, Cell DIVE is a precise and adaptable open multiplexing solution that enables flexibility in antibody selection of biomarker panels used in a multiplexed imaging study. Cell Signaling Technology (CST®) has a broad portfolio of IHC-validated antibodies to detect key proteins in the TME, enabling immune cell detection and phenotyping in tissue. CST offers off the shelf (OTS), ready-to-ship antibody conjugates that have been verified to work on Cell DIVE and offers custom conjugation of IHC-validated antibodies to fluorophores and other detection reagents. CST employs a rigorous approach to IHC validation, followed by verification on the Cell DIVE platform to ensure successful detection of proteins. Here, we demonstrate multiplexed Cell DIVE imaging using a novel panel of dozens of CST biomarker antibodies across multiple mouse syngeneic tumor types. In summary, multiplexed whole slide imaging using validated antibodies and Aivia AI-based image processing software enables spatially resolved tissue analysis of the tumor microenvironment, including new insights into spatial biology. Citation Format: ARINDAM BOSE, Lisa Arvidson, Emily Quann Alonzo, Michael J. Smith, Katie O. White, Richard A. Heil-Chapdelaine, Gabriella Spang, Matthew Norton, Samuel Jensen, Vasundhara Agrawal. Spatial architecture of tumor and immune cell lineages in syngeneic mouse tumor tissues [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 3803.

Abstract 4172: Pathology supervised approaches for ROI placement and segmentation to overcome caveats and pitfalls in high-plex spatial profiling

Abstract Background: Tissue-based high-plex platforms evaluate numerous biomarkers in limited tissue sections. Certain tissue features and assay technical limitations may jeopardize the accuracy of this analysis. In this study, we aim to determine tissue specific features that influence image-based region of interest (ROI) selection and segmentation for spatial profiling of carcinomas, and to develop pathology approaches to overcome them. Methodology: A total of 317 samples from different carcinoma types processed with the GeoMx Digital spatial profiling protein assay protocol were assessed to identify deviations from original strategies for ROI placement and segmentation (Table 1). ROI were selected by a pathologist. The initial strategy for all cases was to place rectangles ROIs of 660*785 µm in tumor area and to segment based on morphology biomarkers as indicated in Table 1. Strategies to overcome technical and tissue specific features were recorded and quantified. Results: A total of 2068 ROIs and 4422 AOIs were evaluated. In all the cases, tissue displayed specific features that limited planned ROI placement and segmentation. Table 1 describes these features by tissue type and profiling strategies. A total of 970 ROIs were solved using different annotation type or changes in segmentation strategy, 584 bypassed the limitations by changing the processing protocol; and 322 were not solved due to non-compliance with assay requirements. Conclusions: We identified tissue features that limit the planned ROI selection and segmentation in spatial high-plex profiling, mainly due to tissue processing, distinct histology, type of sample, and presence of adjacent non-neoplastic tissue. A deep understanding of tumor architecture, biology and platform related technical limitations is needed to select areas of analysis for spatial profiling. Table 1. ROI and segmentation strategy methodology and tissue specific features with pathology appro Histological type (Profiling segments) Anal Carcinoma (Tu/TME) Lung NSCLC (Tu/TME) Lung NSCLC TMA (Tu/Tcells/Mϕ) Lung NSCLC TMA (Tu/TME) CRC MT Tu/Immune Breast Ca Tu/Immune/else Pathology Approach (ROIs) Methodology Samples 48 33 80 80 6 70 Biopsy type Incisional (Core/other), Whole tissue Incisional (Core/other)Whole tissue TMA TMA Whole tissue Core Morphology markers/mIF Channels Syto13: FITC/525 PanCK: Cy3/568 CD45: Texas red/615 CD68: Cy5/666 Syto13: FITC/525PanCK: Cy3/568CD20: Texas red/615CD3: Cy5/666 Syto13: FITC/525 PanCK: Cy3/568 CD68: Texas red/615 CD3: Cy5/666 Syto13: FITC/525 PanCK: Cy3/568 CD68: Texas red/615 CD3: Cy5/666 Syto13: FITC/525 PanCK: CD45: Texas red/615 Syto13: FITC/525 PanCK: Cy3/568 CD45: Texas red/615 Strategy for segmentation Tumor (PanCK+) TME (PanCK-) Tumor (PanCK+)TME (PanCK-) Tumor (PanCK+) T cell (CD3+) Mϕ (CD68+) Tumor (PanCK+) TME (PanCK-) Tumor (PanCK+) Immune (CD45+) Tumor (PanCK+) Immune (CD45+) Else (PanCK-CD45-) Total ROIs/AOIs 224/435 144/252 80/218 82/162 944/1573 594/1782 Tissue specific features (ROI number/percentage) Folds (ROIs) 10 1 0 0 9 5 Draw Polygons (25) Adjacent* Non-Tumoral tissue 22 7 9 7 388 40 Draw Polygons (473) Tissue Derived Autofluorescence 3 105 0 1 0 45 Draw Polygons (13). New segment to exclude autofluorescence (141) Sample Size 26 19 1 2 5 138 Polygons (191) Necrosis 13 0 1 1 217 6 Polygons (238) Biological absence of morphology marker 10 0 0 0 0 5 Polygons non segmented for Tu and TME (15) Solid tumors with scant TME 0 21 0 0 0 9 Polygons non segmented for Tu and TME (30) Eosin autofluorescence 0 0 0 0 0 584 Stained new section with Validated PanCK in Cy5/666 channel (584) Failure to reach the minimum number of cell requirements 13 36 22 2 249 43 Excluded segments (322) New scan to select more areas (43) Citation Format: Sharia Hernandez, Claudio J. Arrechedera, Pedro Rocha, Larisa Kostousov, Sean Barnes, Khan Khaja, Luisa M. Solis-Soto. Pathology supervised approaches for ROI placement and segmentation to overcome caveats and pitfalls in high-plex spatial profiling [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 4172.

Abstract 1494: Clonal dominance defines metastatic dissemination in pancreatic cancer

Abstract Tumors represent ecosystems where subclones compete during tumor growth. While extensively investigated, a comprehensive picture of the interplay of clonal lineages during dissemination is still lacking. Using patient-derived pancreatic cancer cells, we created orthotopically-implanted clonal replica tumors to trace clonal dynamics of unperturbed tumor expansion and dissemination. This model revealed the multifaceted nature of tumor growth, with rapid changes in clonal fitness leading to continuous reshuffling of tumor architecture and alternating clonal dominance as a distinct feature of cancer growth. Regarding dissemination, a large fraction of tumor lineages could be found at secondary sites each having distinctive organ growth patterns as well as numerous undescribed behaviors such as abortive colonization. Paired analysis of primary and secondary sites revealed fitness as major contributor to dissemination. From the analysis of pro- and non-metastatic isogenic subclones, we identified a transcriptomic signature able to identify metastatic cells in human tumors and predict patients’ survival. Citation Format: I-Lin Ho, Chieh-Yuan Li, Fuchenchu Wang, Li Zhao, Jingjing Liu, Er-Yen Yen, Charles A. Dyke, Rutvi Shah, Zhaoliang Liu, Ali Osman Çetin, Francesca Citron, Sergio Attanasio, Virgina Giuliani, Tim Heffernan, Kim-Anh Do, Gaetano Gargiulo, Giulio Draetta, Alessandro Carugo, Ruitao Lin, Andrea Viale. Clonal dominance defines metastatic dissemination in pancreatic cancer [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 1494.

Clonal dominance defines metastatic dissemination in pancreatic cancer.

Tumors represent ecosystems where subclones compete during tumor growth. While extensively investigated, a comprehensive picture of the interplay of clonal lineages during dissemination is still lacking. Using patient-derived pancreatic cancer cells, we created orthotopically implanted clonal replica tumors to trace clonal dynamics of unperturbed tumor expansion and dissemination. This model revealed the multifaceted nature of tumor growth, with rapid changes in clonal fitness leading to continuous reshuffling of tumor architecture and alternating clonal dominance as a distinct feature of cancer growth. Regarding dissemination, a large fraction of tumor lineages could be found at secondary sites each having distinctive organ growth patterns as well as numerous undescribed behaviors such as abortive colonization. Paired analysis of primary and secondary sites revealed fitness as major contributor to dissemination. From the analysis of pro- and nonmetastatic isogenic subclones, we identified a transcriptomic signature able to identify metastatic cells in human tumors and predict patients' survival.

Abstract A101: A novel ovarian cancer organotypic tumor slice culture model

Abstract A101: A novel ovarian cancer organotypic tumor slice culture model