Heterogeneous Cancer Cell Populations Research Articles

Abstract 548: Exploiting pronounced selection pressure from therapeutic bottlenecks to identify orthogonal immunotherapy targets

Abstract Evolutionary processes play a crucial role in the development of cancer, where tumor cells continuously adapt, leading to the dominance of cell populations over their non-tumor counterparts, evading treatment and ultimately resulting in death. Effective cancer therapies induce significant and targeted selection pressures. During and in response to the induced bottleneck by therapy, the heterogeneous cancer cell population either diminishes or evolves, causing a subsequent selective sweep and the emergence of cells with resistance traits. However, this bottleneck phase presents a unique opportunity to predictably reduce the diversity of tumor cells. In this context, we propose a framework to quantify the selection on variants before, during, and after the therapeutic bottleneck, enhancing our understanding of the mechanisms underlying the acquisition of therapeutic resistance. Furthermore, we combine measures of selection intensity with the prediction of neoantigens in a patient cohort to identify potential alternative targets for immunotherapy. The outcome is a strategy that exploits the predictability introduced by the primary therapeutic bottleneck to eliminate the remaining tumor cell population. Finally, we illustrate the efficacy of this strategy in the context of several cancer types and empirically identify potential cancer type specific and pan-cancer immunotherapy targets orthogonal to primary, first-line therapies. Citation Format: J. Nic Fisk, Jeffrey P. Townsend. Exploiting pronounced selection pressure from therapeutic bottlenecks to identify orthogonal immunotherapy targets [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 548.

Allele-specific transcriptional effects of subclonal copy number alterations enable genotype-phenotype mapping in cancer cells

Subclonal copy number alterations are a prevalent feature in tumors with high chromosomal instability and result in heterogeneous cancer cell populations with distinct phenotypes. However, the extent to which subclonal copy number alterations contribute to clone-specific phenotypes remains poorly understood. We develop TreeAlign, which computationally integrates independently sampled single-cell DNA and RNA sequencing data from the same cell population. TreeAlign accurately encodes dosage effects from subclonal copy number alterations, the impact of allelic imbalance on allele-specific transcription, and obviates the need to define genotypic clones from a phylogeny a priori, leading to highly granular definitions of clones with distinct expression programs. These improvements enable clone-clone gene expression comparisons with higher resolution and identification of expression programs that are genomically independent. Our approach sets the stage for dissecting the relative contribution of fixed genomic alterations and dynamic epigenetic processes on gene expression programs in cancer.

Mathematical modeling of drug resistance in heterogeneous cancer cell populations

Drug resistance is one of the most intractable issues associated with cancer treatment in clinical practice. Mathematical models provide an analytic framework for facilitating the understanding of resistance evolution dynamics and the design of cancer clinical trial. In this paper, we develop an elementary, compartmental mathematical model for absolute drug resistance, focusing on the effects of point mutations in genetic drivers of malignancy. A set of ordinary differential equations (ODEs) is used to describe the dynamics of competing heterogeneous cancer cell populations while taking account of pharmacokinetics. All possible equilibria and their local geometric properties are analyzed, with the result suggests that the system exhibits bistable dynamics. The existence of optimal treatment time is discussed. To identify the critical parameters which influence cellular dynamics, we also perform parameter sensitivity analysis. Finally, numerical simulations are presented to verify the feasibilities of our analytical results and to find that the pre-existence of resistant cell phenotypes contributes more than resistant mutants generated during the treatment phase.

Molecular profiling and specific targeting of gemcitabine-resistant subclones in heterogeneous pancreatic cancer cell populations.

Chemotherapy is pivotal in the multimodal treatment of pancreatic ductal adenocarcinoma (PDAC). Technical advances unveiled a high degree of inter- and intratumoral heterogeneity. We hypothesized that intratumoral heterogeneity (ITH) impacts response to gemcitabine treatment and demands specific targeting of resistant subclones. Using single cell-derived cell lines (SCDCLs) from the classical cell line BxPC3 and the basal-like cell line Panc-1, we addressed the effect of ITH on response to gemcitabine treatment. Individual SCDCLs of both parental tumor cell populations showed considerable heterogeneity in response to gemcitabine. Unsupervised PCA including the 1,000 most variably expressed genes showed a clustering of the SCDCLs according to their respective sensitivity to gemcitabine treatment for BxPC3, while this was less clear for Panc-1. In BxPC3 SCDCLs, enriched signaling pathways EMT, TNF signaling via NfKB, and IL2STAT5 signaling correlated with more resistant behavior to gemcitabine. In Panc-1 SCDCLs MYC targets V1 and V2 as well as E2F targets were associated with stronger resistance. We used recursive feature elimination for Feature Selection in order to compute sets of proteins that showed strong association with the response to gemcitabine. The optimal protein set calculated for Panc-1 comprised fewer proteins in comparison to the protein set determined for BxPC3. Based on molecular profiles, we could show that the gemcitabine-resistant SCDCLs of both BxPC3 and Panc-1 are more sensitive to the BET inhibitor JQ1 compared to the respective gemcitabine-sensitive SCDCLs. Our model system of SCDCLs identified gemcitabine-resistant subclones and provides evidence for the critical role of ITH for treatment response in PDAC. We exploited molecular differences as the basis for differential response and used these for more targeted therapy of resistant subclones.

Single-cell third-generation sequencing-based multi-omics uncovers gene expression changes governed by ecDNA and structural variants in cancer cells.

Cancer cells often exhibit large-scale genomic variations, such as circular extrachromosomal DNA (ecDNA) and structural variants (SVs), which have been highly correlated with the initiation and progression of cancer. Currently, no adequate method exists to unveil how these variations regulate gene expression in heterogeneous cancer cell populations at a single-cell resolution. Here, we developed a single-cell multi-omics sequencing method, scGTP-seq, to analyse ecDNA and SVs using long-read sequencing technologies. We demonstrated that our method can efficiently detect ecDNA and SVs and illustrated how these variations affect transcriptomic changes in various cell lines. Finally, we applied and validated this method in a clinical sample of hepatocellular carcinoma (HCC), demonstrating a feasible way to monitor the evolution of ecDNA and SVs during cancer progression.

Migration speed of captured breast cancer subpopulations correlates with metastatic fitness.

The genetic alterations contributing to migration proficiency, a phenotypic hallmark of metastatic cells required for colonizing distant organs, remain poorly defined. Here, we used single-cell magneto-optical capture (scMOCa) to isolate fast cells from heterogeneous human breast cancer cell populations, based on their migratory ability alone. We show that captured fast cell subpopulations retain higher migration speed and focal adhesion dynamics over many generations as a result of a motility-related transcriptomic profile. Upregulated genes in isolated fast cells encoded integrin subunits, proto-cadherins and numerous other genes associated with cell migration. Dysregulation of several of these genes correlates with poor survival outcomes in people with breast cancer, and primary tumors established from fast cells generated a higher number of circulating tumor cells and soft tissue metastases in pre-clinical mouse models. Subpopulations of cells selected for a highly migratory phenotype demonstrated an increased fitness for metastasis.

Cell facilitation promotes growth and survival under drug pressure in breast cancer

The interplay of positive and negative interactions between drug-sensitive and resistant cells influences the effectiveness of treatment in heterogeneous cancer cell populations. Here, we study interactions between estrogen receptor-positive breast cancer cell lineages that are sensitive and resistant to ribociclib-induced cyclin-dependent kinase 4 and 6 (CDK4/6) inhibition. In mono- and coculture, we find that sensitive cells grow and compete more effectively in the absence of treatment. During treatment with ribociclib, sensitive cells survive and proliferate better when grown together with resistant cells than when grown in monoculture, termed facilitation in ecology. Molecular, protein, and genomic analyses show that resistant cells increase metabolism and production of estradiol, a highly active estrogen metabolite, and increase estrogen signaling in sensitive cells to promote facilitation in coculture. Adding estradiol in monoculture provides sensitive cells with increased resistance to therapy and cancels facilitation in coculture. Under partial inhibition of estrogen signaling through low-dose endocrine therapy, estradiol supplied by resistant cells facilitates sensitive cell growth. However, a more complete blockade of estrogen signaling, through higher-dose endocrine therapy, diminished the facilitative growth of sensitive cells. Mathematical modeling quantifies the strength of competition and facilitation during CDK4/6 inhibition and predicts that blocking facilitation has the potential to control both resistant and sensitive cancer cell populations and inhibit the emergence of a refractory population during cell cycle therapy.

Intratumor heterogeneity and cell secretome promote chemotherapy resistance and progression of colorectal cancer

The major underlying cause for the high mortality rate in colorectal cancer (CRC) relies on its drug resistance, to which intratumor heterogeneity (ITH) contributes substantially. CRC tumors have been reported to comprise heterogeneous populations of cancer cells that can be grouped into 4 consensus molecular subtypes (CMS). However, the impact of inter-cellular interaction between these cellular states on the emergence of drug resistance and CRC progression remains elusive. Here, we explored the interaction between cell lines belonging to the CMS1 (HCT116 and LoVo) and the CMS4 (SW620 and MDST8) in a 3D coculture model, mimicking the ITH of CRC. The spatial distribution of each cell population showed that CMS1 cells had a preference to grow in the center of cocultured spheroids, while CMS4 cells localized at the periphery, in line with observations in tumors from CRC patients. Cocultures of CMS1 and CMS4 cells did not alter cell growth, but significantly sustained the survival of both CMS1 and CMS4 cells in response to the front-line chemotherapeutic agent 5-fluorouracil (5-FU). Mechanistically, the secretome of CMS1 cells exhibited a remarkable protective effect for CMS4 cells against 5-FU treatment, while promoting cellular invasion. Secreted metabolites may be responsible for these effects, as demonstrated by the existence of 5-FU induced metabolomic shifts, as well as by the experimental transfer of the metabolome between CMS1 and CMS4 cells. Overall, our results suggest that the interplay between CMS1 and CMS4 cells stimulates CRC progression and reduces the efficacy of chemotherapy.

Epithelial-mesenchymal Transition Factor and Its Association With Mammographic Density and Breast Cancer Prognosis.

The tumor microenvironment influences tumor progression, invasion, and metastasis. This study determined the expression levels of epithelial-mesenchymal transition (EMT) factors according to zone and their correlation with mammographic breast density and investigated the prognostic value of EMT factors. The clinical and pathological data of invasive carcinoma and ductal carcinoma in situ were reviewed. Primary breast tissue samples were evaluated using immunohistochemistry (IHC) staining of the EMT-associated markers, including α-SMA, vimentin, MMP-9, and CD34. The expression levels were analyzed in three areas: the tumor center, interface, and distal zones. EMT factors were correlated with mammographic breast density and oncologic outcomes. An overall EMT phenotype conversion from positive to negative was seen in 55.7% of α-SMA- and 34.4% of MMP-9-positive cells between the tumor center and interface zones, which was significantly different (p<0.05). Most changes in EMT expression from the center to the distal zone were from positive to negative, but 23.0% of CD34-expressing cells showed negative to positive conversion. The proportion of α-SMA, vimentin, and MMP-9 expression was higher in the non-dense breast group compared to the dense breast group in the interface and distal zones (p<0.05). CD34 expression in the distal zone was an independent favorable prognostic factor for disease-free survival (p=0.039). The differential expression of EMT markers in each zone suggests heterogeneous cancer cell populations within each zone of breast cancer. EMT factor expression can also interplay between breast density stroma and geographical tumor zone.

Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data

Tumor heterogeneity is an important driver of treatment failure in cancer since therapies often select for drug-tolerant or drug-resistant cellular subpopulations that drive tumor growth and recurrence. Profiling the drug-response heterogeneity of tumor samples using traditional genomic deconvolution methods has yielded limited results, due in part to the imperfect mapping between genomic variation and functional characteristics. Here, we leverage mechanistic population modeling to develop a statistical framework for profiling phenotypic heterogeneity from standard drug-screen data on bulk tumor samples. This method, called PhenoPop, reliably identifies tumor subpopulations exhibiting differential drug responses and estimates their drug sensitivities and frequencies within the bulk population. We apply PhenoPop to synthetically generated cell populations, mixed cell-line experiments, and multiple myeloma patient samples and demonstrate how it can provide individualized predictions of tumor growth under candidate therapies. This methodology can also be applied to deconvolution problems in a variety of biological settings beyond cancer drug response.

Cell migration capacity alone can be hereditary and predict metastatic burden.

Among all alterations that cells need for colonizing distant organs, migratory proficiency is widely accepted as a key characteristic of highly metastatic cells. However, investigations are almost exclusively based on functional inhibition or genetic knock-out of candidate proteins involved in cell migration to study their impact on in vivo metastasis. Importantly, there is no antibody or gene that allows the labeling of highly migratory cells. Identification and capture of live individual cells in a heterogenous ensemble typically rely on genetic manipulations that encode fluorescent probes. Thus, a precise understanding of how several molecular components interact to yield the phenotype of interest is a prerequisite to distinguish and isolate such target cells. Single cell magneto-optical capture (scMOCa) has the capacity to tag and manipulate cells based on visual phenotypes to subsequently identify and characterize novel molecules that modulate these phenotypes. Here, we capture single cells from a highly heterogeneous triple-negative breast cancer cell population based solely on the analysis of their migration trajectories. We isolate and expand intact cells to generate novel populations defined by their travelled distance alone. We demonstrate that the progeny of these cells stably retains their migratory phenotype and exhibits a dramatically different transcriptomic profile when compared to the parental counterpart. Comparison of RNA sequencing of fast and parental cell populations reveals a motility-related transcriptomic profile that highlights Biological Adhesion Gene Ontology terms. Fast cells possess an increased dynamics and distinct morphology of focal adhesions. Finally, primary tumors established from fast cells generate a higher number of circulating tumor cells and soft tissue metastases in vivo, resulting in shorter time to clinical endpoint following tumor resection. Thus, selection for a highly migratory phenotype alone can confer increased fitness for specific steps in the metastatic cascade.

Hypoxia-resistance heterogeneity in tumours: the impact of geometrical characterization of environmental niches and evolutionary trade-offs. A mathematical approach

In the study of cancer evolution and therapeutic strategies, scientific evidence shows that a key dynamics lies in the tumour-environment interaction. In particular, oxygen concentration plays a central role in the determination of the phenotypic heterogeneity of cancer cell populations, whose qualitative and geometric characteristics are predominant factors in the occurrence of relapses and failure of eradication. We propose a mathematical model able to describe the eco-evolutionary spatial dynamics of tumour cells in their adaptation to hypoxic microenvironments. As a main novelty with respect to the existing literature, we combine a phenotypic indicator reflecting the experimentally-observed metabolic trade-off between the hypoxia-resistance ability and the proliferative potential with a 2d geometric domain, without the constraint of radial symmetry. The model is settled in the mathematical framework of phenotype-structured population dynamics and it is formulated in terms of systems of coupled non-linear integro-differential equations. The computational outcomes demonstrate that hypoxia-induced selection results in a geometric characterization of phenotypic-defined tumour niches that impact on tumour aggressiveness and invasive ability. Furthermore, results show how the knowledge of environmental characteristics provides a predictive advantage on tumour mass development in terms of size, shape, and composition.

Heterogeneity beyond tumor heterogeneity—SULF2 involvement in Wnt/β-catenin signaling activation in a heterogeneous side population of liver cancer cells

Heterogeneity beyond tumor heterogeneity—SULF2 involvement in Wnt/β-catenin signaling activation in a heterogeneous side population of liver cancer cells

234P Epithelial-mesenchymal transition and cancer stem cells: Missing link in oral squamous cell carcinoma

Cancer stem cells (CSCs) are a heterogeneous population of cancer cells with characteristics similar to normal stem cells, including the ability for self-renewal and differentiation. CSCs can differentiate into tumor-supporting cells potentially enabling metastasis, relapse, and chemoresistance. Complex networks involving several transcription factors, such as Nanog, Oct4, SOX2, and various miRNAs, have been identified to regulate CSC properties. The EMT is a process in which epithelial cells change their morphology, lose their polarity and acquire the migratory properties of mesenchymal cells.

Variant calling enhances the identification of cancer cells in single-cell RNA sequencing data.

Single-cell RNA-sequencing is an invaluable research tool that allows for the investigation of gene expression in heterogeneous cancer cell populations in ways that bulk RNA-seq cannot. However, normal (i.e., non tumor) cells in cancer samples have the potential to confound the downstream analysis of single-cell RNA-seq data. Existing methods for identifying cancer and normal cells include copy number variation inference, marker-gene expression analysis, and expression-based clustering. This work aims to extend the existing approaches for identifying cancer cells in single-cell RNA-seq samples by incorporating variant calling and the identification of putative driver alterations. We found that putative driver alterations can be detected in single-cell RNA-seq data obtained with full-length transcript technologies and noticed that a subset of cells in tumor samples are enriched for putative driver alterations as compared to normal cells. Furthermore, we show that the number of putative driver alterations and inferred copy number variation are not correlated in all samples. Taken together, our findings suggest that augmenting existing cancer-cell filtering methods with variant calling and analysis can increase the number of tumor cells that can be confidently included in downstream analyses of single-cell full-length transcript RNA-seq datasets.

Link between glucose metabolism and epithelial-to-mesenchymal transition drives triple-negative breast cancer migratory heterogeneity

SummaryIntracellular and environmental cues result in heterogeneous cancer cell populations with different metabolic and migratory behaviors. Although glucose metabolism and epithelial-to-mesenchymal transition have previously been linked, we aim to understand how this relationship fuels cancer cell migration. We show that while glycolysis drives single-cell migration in confining microtracks, fast and slow cells display different migratory sensitivities to glycolysis and oxidative phosphorylation inhibition. Phenotypic sorting of highly and weakly migratory subpopulations (MDA+, MDA−) reveals that more mesenchymal, highly migratory MDA+ preferentially use glycolysis while more epithelial, weakly migratory MDA− utilize mitochondrial respiration. These phenotypes are plastic and MDA+ can be made less glycolytic, mesenchymal, and migratory and MDA− can be made more glycolytic, mesenchymal, and migratory via modulation of glucose metabolism or EMT. These findings reveal an intrinsic link between EMT and glucose metabolism that controls migration. Identifying mechanisms fueling phenotypic heterogeneity is essential to develop targeted metastatic therapeutics.

Catulin reporter marks a heterogeneous population of invasive breast cancer cells with some demonstrating plasticity and participating in vascular mimicry

Breast cancer is the most commonly diagnosed cancer in women worldwide. The activation of partial or more complete epithelial–mesenchymal transition in cancer cells enhances acquisition of invasive behaviors and expands their generation of cancer stem cells. Increased by EMT plasticity of tumor cells could promote vascular mimicry, a newly defined pattern of tumor microvascularization by which aggressive tumor cells can form vessel-like structures themselves. VM is strongly associated with a poor prognosis, but biological features of tumor cells that form VM remains unknown. Here we show that catulin is expressed in human BC samples and its expression correlates with the tumor progression. Ablation of catulin in hBC cell lines decreases their invasive potential in the 3D assays. Using a novel catulin promoter based reporter we tracked and characterized the small population of invasive BC cells in xenograft model. RNAseq analysis revealed enrichment in genes important for cellular movement, invasion and interestingly for tumor-vasculature interactions. Analysis of tumors unveiled that catulin reporter marks not only invasive cancer cells but also rare population of plastic, MCAM positive cancer cells that participate in vascular mimicry. Ablation of catulin in the xenograft model revealed deregulation of genes involved in cellular movement, and adhesive properties with striking decrease in CD44 which may impact stemness potential, and plasticity of breast cancer cells. These findings show directly that some plastic tumor cells can change the fate into endothelial-like, expressing MCAM and emphasize the importance of catulin in this process and breast cancer progression.

Abstract 1005: Ecological interactions in breast cancer: Cell cooperation within heterogeneous populations promotes growth and survival under drug pressure

Abstract The interplay of positive and negative interactions between drug sensitive and resistant cells determines the efficacy of treatment in heterogeneous cancer cell populations. While competition between different cell lineages within tumors has been considered in cancer evolution and progression, cooperative interactions receive far less study. We have developed in vitro ER+ breast cancer model systems to investigate cell-cell communication in heterogeneous cancer populations under selective drug pressure and probe for differences in growth, resistance, and interactions, such as cooperation, between cell types. Using cells resistant and sensitive to ribociclib (CDK 4/6 inhibitor), we have compared cell population growth in monoculture and coculture under different treatments. Furthermore, with the application of mathematical models, we have unraveled the contributions of proliferation rates, treatment effects, competition, and cooperation. Under ribociclib treatment, resistant cells confer an increased survival benefit to sensitive cells when cocultured together. Treatment-conditioned media taken from monocultured resistant cells and used to supplement sensitive cells reproduces this advantage, suggesting that secretion of some factor from resistant cells manifest the effect. Resistant cells may promote this growth by supplying estradiol as LC-MS/MS assays detected increased estradiol levels in coculture media compared with monoculture media under ribociclib pressure. This suggests estrogen-dependent sensitive cells benefit from the increase in local estradiol from resistant cells under treatment in coculture, resulting in cooperation. scRNA sequencing data between populations also revealed broad dysregulation of cell cycle pathways in resistant cells with cocultured sensitive cells shifting toward this resistant phenotype. Western blot detection in conjunction with scRNAseq data also supports an increased expression of estradiol conversion enzyme, HSD17β1, in resistant cells and increased expression of estradiol inactivating enzyme, HSD17β8, in sensitive cells shedding mechanistic insight into the estrogen-induced survival benefit provided by resistant cells. Proliferation of drug sensitive cells by resistant cells in co-culture is also enhanced through addition of estradiol, and blocked by small molecule inhibitors targeting the estrogen signaling pathway. These studies show that cooperation imparts drug resistance to normally sensitive cells and promotes survival, which highlights how interactions within heterogenous cell populations can shape the dynamics of growth and resistance. From a therapeutic standpoint, uncovering the mechanisms and consequences of cooperative cell-cell interactions may lead to strategic advancements for promoting drug response by targeting these influential interactions. Citation Format: Rena Emond, Jason Griffiths, Vince K. Grolmusz, Rachel Sousa, Jinfeng Chen, Eric M. Castaneda, Andrea H. Bild, Frederick R. Adler. Ecological interactions in breast cancer: Cell cooperation within heterogeneous populations promotes growth and survival under drug pressure [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1005.

Plasticity Enables Cooperation among Heterogeneous Cancer Cell Populations to Support Metastatic Fitness.

The invasive progression of cancer known as metastasis remains strongly associated with morbidity and lethality. New meaningful therapeutic interventions could be derived from a better understanding of the underlying processes driving cancer cell seeding and proliferation at secondary sites. Emerging findings regarding the heterogeneity of cancer cells observed in metastases have led us to revisit concepts surrounding metastatic fitness. Novel model systems to study the markers of cancer stem cell plasticity and their evolution during metastatic growth have uncovered that dynamic and heterogeneous cancer cell populations are observed during metastatic disease progression. Heinz and colleagues studied the heterogeneity of colorectal carcinomas, where primary tumors evolve alongside an epithelium well characterized for its self-renewing stem cell population. Their work revealed a functional dynamic interplay in the organization of the metastatic lesions as they transition from stagnating to expanding nodules, wherein the heterogeneous mixture of cancer cell stem cells with more differentiated cancer cells is essential for metastatic outgrowth. Their work supports that dynamic YAP signaling enables the growth-permissive heterogeneous composition of the metastatic nodule, in contrast with growth-restricted homogeneous compositions. See related article by Heinz et al., p. 1953.

High-dimensionality reduction clustering of complex carbohydrates to study lung cancer metabolic heterogeneity.

The tumor microenvironment contains a heterogeneous population of stromal and cancer cells that engage in metabolic crosstalk to ultimately promote tumor growth and contribute to progression. Due to heterogeneity within solid tumors, pooled mass spectrometry workflows are less sensitive at delineating unique metabolic perturbations between stromal and immune cell populations. Two critical, but understudied, facets of glucose metabolism are anabolic pathways for glycogen and N-linked glycan biosynthesis. Together, these complex carbohydrates modulate bioenergetics and protein-structure function, and create functional microanatomy in distinct cell populations within the tumor heterogeneity. Herein, we combine high-dimensionality reduction and clustering (HDRC) analysis with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) and demonstrate its ability for the comprehensive assessment of tissue histopathology and metabolic heterogeneity in human FFPE sections. In human lung adenocarcinoma (LUAD) tumor tissues, HDRC accurately clusters distinct regions and cell populations within the tumor microenvironment, including tumor cells, tumor-infiltrating lymphocytes, cancer-associated fibroblasts, and necrotic regions. In-depth pathway enrichment analyses revealed unique metabolic pathways are associated with each distinct pathological region. Further, we highlight the potential of HDRC analysis to study complex carbohydrate metabolism in a case study of lung cancer disparity. Collectively, our results demonstrate the promising potentials of HDRC of pixel-based carbohydrate analysis to study cell-type and regional-specific stromal signaling within the tumor microenvironment.