Tumor Cell Populations Research Articles

Single Cell RNA Sequencing Before and After Light Chain Escape Reveals Intra-patient Multiple Myeloma Subpopulations with Divergent Osteolytic Gene Expression.

High-risk multiple myeloma (MM) is genomically unstable, comprised of heterogeneous populations of tumor cells that evolve over time. Light chain escape (LCE) is a clinical phenomenon observed when light chains rise separately from M-spike values, implying divergent tumor evolution. We sought to understand LCE by performing high depth transcriptomic and phenotypic studies. We performed single cell RNA sequencing and ex vivo drug sensitivity profiling on serial bone marrow biopsies from a patient with LCE at diagnosis, first relapse and relapsed/refractory timepoints. Single cell RNA sequencing uncovered distinct transcriptomic subpopulations with phenotypes that could be tracked separately by clinical serum light chain and M-spike values. Genes differentially expressed between subpopulations were assessed for generalizable effects on prognosis from the MMRF CoMMpass and GSE24080 datasets. Notably, the LCE subpopulation exhibited gene expression profile featuring prominent LAMP5-overexpression, which was associated with risk for osteolytic bone lesions. Ex vivo drug sensitivity testing displayed differential sensitivity of the subpopulations. Copy Number Variant inference showed that the transcriptomic subpopulation underlying LCE was related to a genetic subclone that evolved over time. Our findings illustrate that malignant subpopulations underly light chain escape in multiple myeloma. These studies implied that LCE and LAMP5 gene overexpression portends for increased risk of osteolytic bone disease and adverse prognosis, findings that were confirmed in the subset of patients in the CoMMpass database with light chain escape.

Innovative strategies to optimise colorectal cancer immunotherapy through molecular mechanism insights

BackgroundColorectal cancer (CRC) is a leading cause of cancer-related deaths globally. The heterogeneity of the tumor microenvironment significantly influences patient prognosis, while the diversity of tumor cells shapes its unique characteristics. A comprehensive analysis of the molecular profile of tumor cells is crucial for identifying novel molecular targets for drug sensitivity analysis and for uncovering the pathophysiological mechanisms underlying CRC.MethodsWe utilized single-cell RNA sequencing technology to analyze 13 tissue samples from 4 CRC patients, identifying key cell types within the tumor microenvironment. Intercellular communication was assessed using CellChat, and a risk score model was developed based on eight prognostic genes to enhance patient stratification for immunotherapeutic approaches. Additionally, in vitro experiments were performed on DLX2, a gene strongly associated with poor prognosis, to validate its potential role as a therapeutic target in CRC progression.ResultsEight major cell types were identified across the tissue samples. Within the tumor cell population, seven distinct subtypes were recognized, with the C0 FXYD5+ tumor cells subtype being significantly linked to cancer progression and poor prognosis. CellChat analysis indicated extensive communication among tumor cells, fibroblasts, and immune cells, underscoring the complexity of the tumor microenvironment. The risk score model demonstrated high accuracy in predicting 1-, 3-, and 5-year survival rates in CRC patients. Enrichment analysis revealed that the C0 FXYD5+ tumor cell subtype exhibited increased energy metabolism, protein synthesis, and oxidative phosphorylation, contributing to its aggressive behavior. In vitro experiments confirmed DLX2 as a critical gene associated with poor prognosis, suggesting its viability as a target for improving drug sensitivity.ConclusionIn summary, this study advances our understanding of CRC progression by identifying critical tumor subtypes, molecular pathways, and prognostic markers that can inform innovative strategies for predicting and enhancing drug sensitivity. These findings hold promise for optimizing immunotherapeutic approaches and developing new targeted therapies, ultimately aiming to improve patient outcomes in CRC.

Soft tissue sarcomas at the single-cell and spatial resolution: new markers and targets.

Soft tissue sarcomas (STS) are heterogeneous and aggressive tumors, originating in connective tissues embryologically derived from the mesenchyme. Due to their rarity, crucial information about their biology is still lacking. In recent years, single-cell and spatial analyses have opened up new horizons in oncology, leading to the possibility of characterizing the internal architecture of the tumor at the single-cell and spatial levels. This review summarizes the first results acquired through these revolutionary methods for different types of STS. We discuss tumor cell populations and their evolution, interactions between tumor cells and the microenvironment, new prognostic markers, and clinically important targets. Finally, we examine the challenges presented by the single-cell and spatial omics of STS and the future perspectives in this field.

Abstract C044: Single cell RNA-sequencing coupled with lineage tracing identifies novel clonal populations associated with immunotherapy resistance

Abstract Although immune checkpoint inhibitors (ICIs) have revolutionized melanoma treatment, many individuals either show no response or eventually develop acquired resistance. This variability could be linked to differences in the tumor immune microenvironment, pre-existing drug-resistant cells or treatment induced changes in melanoma cell states. Using single cell RNA sequencing (scRNA-seq) coupled with heritable barcodes, this study aims to lineage trace changes in melanoma transcriptional cell states and map changes in the tumor immune microenvironment to uncover if preexisting and/or therapy-induced melanoma transcriptional cell states lead to resistance to ICIs. In this study, we used YUMMER1.7 mouse melanoma cells which were transduced with barcodes under conditions that allow delivery of a single unique barcode to each cell, this served as a cell lineage tag. Each barcode is heritable and stably transcribed into RNA molecules so that individual barcoded cells can be matched with a gene expression profile from scRNA-seq outputs. The barcoded cells were subcutaneously injected into C57BL/6 mice, and once tumors were established, mice were treated with 3 cycles of anti-PD-1 and anti-CTLA-4 therapy. Lineage tracing and scRNA-seq analyses were performed on tumors harvested prior to treatment, early on treatment (Day 6), during minimal residual disease (Day 13) and upon relapse. Early on treatment (Day 6)- two tumour groups were harvested, those that responded well and others that only partially responded. Our results showed that during the early on treatment phase (Day 6) tumors that responded well and those that only partially responded displayed distinct barcodes. Additionally, prevalent barcoded cells identified at the minimal residual disease phase were also dominant upon relapse. Analysis of the transcriptional states is underway to determine if ICI therapy induces transcriptional heterogeneity in sensitive and tolerant cells and if a particular or several transcriptional states lead to relapse. This study will potentially identify predictive response biomarkers and vulnerabilities of ICI naïve and resistant cells that could be targeted to improve outcomes for melanoma patients. We will also identify neoadjuvant approaches, to remove the inherent heterogeneity within the tumor cell population, facilitating a more uniform sensitivity to ICIs. Citation Format: Reem Saleh, Riyaben Patel, Dane Vassiliadis, Fayrouz Hammal, Benjamin Blyth, Xin Du, Katie Fennell, Mark A. Dawson, Grant A. McArthur, Karen Sheppard. Single cell RNA-sequencing coupled with lineage tracing identifies novel clonal populations associated with immunotherapy resistance [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr C044.

Abstract B030: Productive T-cell-mediated immunoediting of developing sarcomas by distinct mechanisms

Abstract Immunoediting is the process where developing tumors are shaped by cells of the immune system. Tumors with low immunogenicity are likely to emerge after neoantigen-specific T cells eliminate the most immunogenic cell populations. However, direct interrogation of mechanisms by which this occurs has not possible due to the pre-emergent nature before the disease is verifiably established. By modifying a mouse model of soft-tissue sarcoma where emergent tumors are consistently edited, we developed a system to examine mechanisms underlying how T cells interact with pre-emergent tumor cell populations to suppress tumor emergence. This system involves production of neoantigen-expressing autochthonous tumors with a fluorescent reporter to define neoantigen-expression status of individual cells. Tumors emerge with lower penetrance and delayed onset in T cell-sufficient mice, while every tumor from T cell-deficient mice contain a significant fraction of neoantigen-negative tumor cells. Yet, ∼53% of T cell-sufficient mice never developed, which suggests that neoantigen silencing was necessary, but not sufficient for tumor emergence. Consistent with this, genetic removal of the neoantigen after malignant transformation shows that tumor development is only rescued when neoantigen loss occurs prior to the peak T cell response, occurring around 8 days post transformation. Moreover, clonality studies reveal the presence of T cells reduces the number clones that contribute to tumor emergence down to 1-2 clones per emerged tumor. Mechanistically, blockade of the T cell effector cytokine, IFNgamma, rescues the neoantigen-negative, but not neoantigen-expressing tumor cells, demonstrating that T cells control early tumors via at least two distinct mechanisms: IFNgamma-dependent suppression of neoantigen-negative tumors cells and IFNgamma-independent suppression of neoantigen-expressing cells. Thus, our findings support that neoantigen-negative cells are subjected to cytokine-mediated suppression from T cells responding to the neoantigen-positive population in the pre-emergent tumor microenvironment. This bystander suppression can prevent tumor emergence despite non-homogeneity in neoantigen expression. Citation Format: Brian G Hunt, Julie F. Cheung, Srividhya Venkatesan, Jennifer L. Loza, Kelli A. Connolly, Shudipto Wahed, Elaine Cheng, Ishan Bansal, Emily Borr, Wei Wei, Ivana William, Brittany Fitzgerald, Nikhil Joshi. Productive T-cell-mediated immunoediting of developing sarcomas by distinct mechanisms [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr B030.

VisRNA: Interactive web server for scRNA-seq data analysis to discover therapeutic targets for non-small cell lung cancer

Abstract. Motivation: Non-small cell lung cancer (NSCLC) is a leading cause of lung cancer diagnoses and mortality worldwide. Single-cell RNA sequencing (scRNA-seq) has transformed our molecular understanding of cancer by revealing gene dysregulation in individual cells. This granularity allows the discovery of new therapeutic targets, promising NSCLC treatment strategies. Method: The study introduces VisRNA, a Python-based web application for analyzing scRNA-seq data statistically and functionally. VisRNA uses advanced machine learning algorithms to reduce multiple dimensionalities in scRNA-seq data and automatically annotate cell types. This method allows gene expression analysis across NSCLC tumor cell populations. Results: VisRNA analysis of NSCLC scRNA-seq datasets identified 14 distinct cell types with distinct gene expression patterns. VisRNA found several significantly differentially expressed genes in these cell populations that could be therapeutic targets. The application ranked drug candidates by molecular docking simulation performance, indicating potential efficacy against targets. Conclusion: VisRNA is a powerful tool for identifying new therapeutic targets and drug candidates for NSCLC, highlighting a significant advancement in scRNA-seq data analysis. VisRNA helps understand the diseases molecular basis by examining gene expression in individual NSCLC tumor cells, which may lead to better treatments.

EPCO-19. MULTIOMIC PROFILING OF EPIGENETIC HETEROGENEITY IN GLIOBLASTOMA AT SINGLE-CELL RESOLUTION USING MULTIOMIC SINGLE-CELL CUT&TAG

Abstract INTRODUCTION Recent evidence suggests epigenetic heterogeneity may be the primary driver of cell phenotype and treatment resistance in glioblastoma (GBM). Epigenetic heterogeneity is defined by alterations of chromatin histone modifications with activating marks such as H3K27 acetylation and repressive marks such as H3K27 trimethylation. Combinations of these histone marks govern the activity of functional genomic elements such as enhancers or promoters. Single-cell (“sc-”) technologies have only recently been adapted to the study of epigenetic cell states and significant technical challenges have limited their widespread adoption, especially in complex tissues. Here, we report the development of an optimized method for performing tissue processing and simultaneous single-cell epigenetic and transcriptomic analyses in flash frozen GBM samples. METHODS Building on our prior work, we developed a new protocol, multiomic single-cell cleavage-under-targets and tagmentation, or “mscCUT&TAG”. We optimized nuclei isolation, cleanup and permeabilization from flash frozen GBM and reaction conditions for the scCUT&TAG portion of the assay. Critically, these changes maintained high-quality, intact nuclei in suspension while limiting well-described technical issues such as nuclei clumping and sample loss. Furthermore, we incorporated the ability to multiplex both patient samples and histone marks. RESULTS We applied 10X scRNA-seq, 10X Multiome (RNA+ATAC-seq), and mscCUT&TAG for five histone marks to the same GBM sample, enabling an integrated evaluation of the tumor’s transcriptome, chromatin accessibility and epigenetic regulation at single-cell resolution. mscCUT&TAG enabled the first assessment of single-cell genome segmentation in GBM. We integrated across modalities to identify specific tumor cell populations, cell-type specific coordinated changes in epigenetics and transcriptome, and define functional regulatory elements. CONCLUSIONS We developed a novel multiomic protocol, mscCUT&TAG, that allows for a comprehensive analysis of epigenetic heterogeneity in GBM. We anticipate this protocol will enhance our understanding of fundamental biology and potential therapeutic avenues in GBM.

NSDHL contributes to breast cancer stem-like cell maintenance and tumor-initiating capacity through TGF-β/Smad signaling pathway in MCF-7 tumor spheroid

BackgroundNAD(P)-dependent steroid dehydrogenase-like protein (NSDHL), which is involved in breast tumor growth and metastasis, has been implicated in the maintenance of cancer stem cells. However, its role in regulating breast cancer stem-like cells (BCSCs) remains unclear. We have previously reported the clinical significance of NSDHL in patients with estrogen receptor-positive (ER +) breast cancer. This study aimed to elucidate the molecular mechanisms by which NSDHL regulates the capacity of BCSCs in the ER + human breast cancer cell line, MCF-7.MethodsNSDHL knockdown suppressed tumor spheroid formation in MCF-7 human breast cancer cells grown on ultralow-attachment plates. RNA sequencing revealed that NSDHL knockdown induced widespread transcriptional changes in the MCF-7 spheroids. TGF-β signaling pathway was the most significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway (fold change ≥ 2, P ≤ 0.05) identified in NSDHL-knockdown MCF-7 spheroids compared with the control. In orthotopic tumor models injected with NSDHL-knockdown MCF-7 spheroids, tumor initiation and growth were strongly suppressed compared with those in the control.ResultsBCSC populations with CD44+/CD24- and CD49f+/EpCAM + phenotypes and high ALDH activity were decreased in NSDHL-knockdown MCF-7 spheroids and xenograft tumors relative to controls, along with decreased secretion of TGF-β1 and 3, phosphorylation of Smad2/3, and expression of SOX2. In RNA-sequencing data from The (TCGA) database, a positive correlation between the expression of NSDHL and SOX2 was found in luminal-type breast cancer specimens (n = 998). Our findings revealed that NSDHL plays an important role in maintaining the BCSC population and tumor-initiating capacity of ER-positive MCF-7 spheroids, suggesting that NSDHL is an attractive therapeutic target for eliminating BCSCs, thus preventing breast cancer initiation and progression.ConclusionsOur findings suggest that NSDHL regulates the BCSC/tumor-initiating cell population in MCF-7 spheroids and xenograft tumors.

Longitudinal Study of Transcriptomic Changes Occurring over Six Weeks of CHOP Treatment in Canine Lymphoma Identifies Prognostic Subtypes.

The majority of canine lymphoma patients treated with the standard of care, the CHOP chemotherapy protocol, initially achieve remission but eventually relapse with a multi-drug-resistant phenotype. This study assesses gene expression profiles of canine lymphoma tumor cell populations using RNA-Seq data from 15 matched patient samples taken prior to treatment and again six weeks into treatment with CHOP. Two distinct clusters were present in the t-SNE dimensionality reduction of the gene expression profiles. There was a significant difference in progression-free survival (PFS) between the cluster groups, with a median of 43.5 days in a group of six patients and 185 days in another group of nine patients. Comparing the group with shorter PFS to the group with longer PFS, we identified 265 significantly enriched GO:BP terms in 3874 significantly up-regulated genes and 740 significantly enriched GO:BP terms in 3236 significantly down-regulated genes. Comparing the six-week timepoint against the initial timepoint, in the group with longer PFS, we identified 277 significantly enriched GO:BP terms in 413 significantly up-regulated genes and 222 significantly enriched GO:BP terms in 267 significantly down-regulated genes. In the group with shorter PFS, we only identified 27 significantly differentially expressed genes, for this comparison. We found DNA damage response genes to be enriched in the down-regulated genes in both comparisons. These results identify and characterize two transcriptionally distinct groups of canine lymphoma patients with significantly different responses to CHOP chemotherapy.

Durvalumab Versus Chemotherapy as First-line Treatment for Metastatic NSCLC With Tumor PD-L1 Expression of 25% or Higher: Results From the Randomized Phase 3 PEARL Study

IntroductionPEARL (NCT03003962) is an open-label, phase 3 study comparing first-line durvalumab monotherapy with chemotherapy in patients with metastatic NSCLC (mNSCLC [EGFR/ALK wild type]) with programmed cell death ligand 1 (PD-L1) tumor cell (TC) membrane expression status of 25% or higher. We report the final analysis of PEARL. MethodsAdults (N = 669) with previously untreated stage IV mNSCLC were randomized (1:1) to durvalumab 20 mg/kg every four weeks or chemotherapy every three weeks for four to six cycles. Dual primary endpoints were overall survival (OS) in the PD-L1 TC of 25% and higher and the PD-L1 TC of 25% and higher low risk of early mortality populations. ResultsDurvalumab was associated with a numerical reduction in the risk of death versus chemotherapy in the 25% and higher PD-L1 TC population (OS hazard ratio [HR] = 0.84, 95% confidence interval [CI]: 0.71–0.99, p = 0.037; median OS 14.6 months, 95% CI: 12.2–16.9 versus 12.8 months, 95% CI: 10.1–14.7, respectively). In the 25% and higher PD-L1 TC low risk of early mortality population the OS hazard ratio for durvalumab versus chemotherapy was 0.96 (95% CI: 0.79–1.15, p = 0.628); median OS 14.6 months (95% CI: 12.6–17.2) versus 15.0 months (95% CI: 13.1–16.8), respectively. In the safety population, the incidence of grade 3/4 treatment-related adverse events was 15.5% (durvalumab) and 45.9% (chemotherapy). ConclusionsDurvalumab did not statistically significantly improve OS versus chemotherapy as first-line treatment in patients with mNSCLC and 25% and higher PD-L1 TC. The numerical improvement in OS was consistent with previous studies of first-line immune checkpoint inhibitor monotherapy in patients with mNSCLC.

A single-cell perspective on immunotherapy for pancreatic cancer: from microenvironment analysis to therapeutic strategy innovation.

Pancreatic cancer remains one of the most lethal malignancies, with conventional treatment options providing limited efficacy. Recent advancements in immunotherapy have offered new hope, yet the unique tumor microenvironment (TME) of pancreatic cancer poses significant challenges to its successful application. This review explores the transformative impact of single-cell technology on the understanding and treatment of pancreatic cancer. By enabling high-resolution analysis of cellular heterogeneity within the TME, single-cell approaches have elucidated the complex interplay between various immune and tumor cell populations. These insights have led to the identification of predictive biomarkers and the development of innovative, personalized immunotherapeutic strategies. The review discusses the role of single-cell technology in dissecting the intricate immune landscape of pancreatic cancer, highlighting the discovery of T cell exhaustion profiles and macrophage polarization states that influence treatment response. Moreover, it outlines the potential of single-cell data in guiding the selection of immunotherapy drugs and optimizing treatment plans. The review also addresses the challenges and prospects of translating these single-cell-based innovations into clinical practice, emphasizing the need for interdisciplinary research and the integration of artificial intelligence to overcome current limitations. Ultimately, the review underscores the promise of single-cell technology in driving therapeutic strategy innovation and improving patient outcomes in the battle against pancreatic cancer.

Stemness in solid malignancies: coping with immune attack.

Immunotherapy has become a key new pillar of cancer treatment, and this has sparked interest in understanding mechanisms of cancer immune evasion. It has long been appreciated that cancers are constituted by heterogeneous populations of tumour cells. This feature is often fuelled by specialized cells that have molecular programs resembling tissue stem cells. Although these cancer stem cells (CSCs) have capacity for unlimited self-renewal and differentiation, it is increasingly evident that some CSCs are capable of achieving remarkable immune resistance. Given that most immunotherapy regiments have overlooked CSC-specific immune-evasive mechanisms, many current treatment strategies often lead to cancer relapse. This Review focuses on advancements in understanding how CSCs in solid tumours achieve their unique immune-evasive properties, enabling them to drive tumour regrowth. Moreover, as cancers often arise from tissue stem cells that acquired oncogenic mutations, we discuss how tissue stem cells undergoing malignant transformation activate intrinsic immune-evasive mechanisms and establish close interactions with suppressive immune cells to escape immune surveillance. In addition, we summarize how in advanced disease stages, CSCs often hijack features of normal stem cells to resist antitumour immunity. Finally, we provide insights in how to design a new generation of cancer immunotherapies to ensure elimination of CSCs.

Understanding the development of enzalutamide resistance based on a functional single-cell approach.

Single-cell RNA-Sequencing reveals heterogeneities of tumor cell populations. In most cases, however, the functional significance of the observed heterogeneity is not tested. In this study, we first identified a possible therapy-resistant prostate cancer cell subpopulation with scRNA-Seq, then confirmed the resistant phenotype with single cell and colony - based cloning and functional testing. In addition, we also identified a therapeutic vulnerability of the resistant cells.

Tumor Heterogeneity Shapes Survival Dynamics in Drug-Treated Cells, Revealing Size-Drifting Subpopulations.

The goal of this project was to demonstrate that subpopulations of cells in tumors can uniquely fluctuate in size in response to environmental conditions created during drug treatment, thereby acting as a dynamic "rheostat" to create a favorable tumor environment for growth. The cancer modeling used for these studies was subpopulations of melanoma cells existing in cultured and tumor systems that differed in aldehyde dehydrogenase (ALDH) activity. However, similar observations were found in other cancer types in addition to melanoma, making them applicable broadly across cancer. The approach was designed to show that either ALDHhigh and ALDHlow subpopulations rapidly epigenetically transition between stem-cell-like high into nonstem-like low production states to create an environment during drug treatment that would enable optimal cellular proliferation and tumor expansion to facilitate drug resistance. The controlled experiments showed proportional changes in each cell population to reach an evolutionarily stable equilibrium mediated by the needed levels of ALDH enzyme activity. Mechanistically, cell population size changes served to functionally move the aldehyde and the resulting reactive oxygen species (ROS) levels to those compatible with optimal cellular proliferation with population fluctuations dependent on the levels of drug induced tumor stress. This is the first report documenting fluctuations in the sizes of cell populations in tumors to cooperatively assist in drug resistance development.

Generalized Hamiltonian and Lagrangian aspects of a model for virus–tumor interaction in oncolytic virotherapy

We analyze the generalized Hamiltonian structure of a system of first‐order ordinary differential equations for the Jenner et al. system (Letters in Biomathematics 5 (2018), no. S1, S117–S136). The system of equations is used for modeling the interaction of an oncolytic virus with a tumor cell population. Our analysis is based on the existence of a Jacobi last multiplier and a time‐dependent first integral. Suitable conditions on the model parameters allow for the reduction of the problem to a planar system of equations, and the time‐dependent Hamiltonian flows are described. The geometry of the Hamiltonian flows is also investigated using the symplectic and cosymplectic methods.

Two distinct epithelial-to-mesenchymal transition programs control invasion and inflammation in segregated tumor cell populations

Epithelial-to-mesenchymal transition (EMT) triggers cell plasticity in embryonic development, adult injured tissues and cancer. Combining the analysis of EMT in cell lines, embryonic neural crest and mouse models of renal fibrosis and breast cancer, we find that there is not a cancer-specific EMT program. Instead, cancer cells dedifferentiate and bifurcate into two distinct and segregated cellular trajectories after activating either embryonic-like or adult-like EMTs to drive dissemination or inflammation, respectively. We show that SNAIL1 acts as a pioneer factor in both EMT trajectories, and PRRX1 drives the progression of the embryonic-like invasive trajectory. We also find that the two trajectories are plastic and interdependent, as the abrogation of the EMT invasive trajectory by deleting Prrx1 not only prevents metastasis but also enhances inflammation, increasing the recruitment of antitumor macrophages. Our data unveil an additional role for EMT in orchestrating intratumor heterogeneity, driving the distribution of functions associated with either inflammation or metastatic dissemination.

Single-cell analysis of the multiple myeloma microenvironment after γ-secretase inhibition and CAR T-cell therapy

AbstractChimeric antigen receptor (CAR) T cells and bispecific antibodies targeting B-cell maturation antigen (BCMA) have significantly advanced the treatment of relapsed and refractory multiple myeloma. Resistance to BCMA-targeting therapies, nonetheless, remains a significant challenge. BCMA shedding by γ-secretase is a known resistance mechanism, and preclinical studies suggest that inhibition may improve anti-BCMA therapy. Leveraging a phase 1 clinical trial of the γ-secretase inhibitor (GSI), crenigacestat, with anti-BCMA CAR T cells (FCARH143), we used single-nuclei RNA sequencing and assay for transposase-accessible chromatin sequencing to characterize the effects of GSI on the tumor microenvironment. The most significant impacts of GSI involved effects on monocytes, which are known to promote tumor growth. In addition to observing a reduction in the frequency of nonclassical monocytes, we also detected significant changes in gene expression, chromatin accessibility, and inferred cell-cell interactions after exposure to GSI. Although many genes with altered expression are associated with γ-secretase–dependent signaling, such as Notch, other pathways were affected, indicating GSI has far-reaching effects. Finally, we detected monoallelic deletion of the BCMA locus in some patients with prior exposure to anti-BCMA therapy, which significantly correlated with reduced progression-free survival (PFS; median PFS, 57 vs 861 days). GSIs are being explored in combination with the full spectrum of BCMA-targeting agents, and our results reveal widespread effects of GSI on both tumor and immune cell populations, providing insight into mechanisms for enhancing BCMA-directed therapies.

Parameter estimation from single patient, single time-point sequencing data of recurrent tumors.

In this study, we develop consistent estimators for key parameters that govern the dynamics of tumor cell populations when subjected to pharmacological treatments. While these treatments often lead to an initial reduction in the abundance of drug-sensitive cells, a population of drug-resistant cells frequently emerges over time, resulting in cancer recurrence. Samples from recurrent tumors present as an invaluable data source that can offer crucial insights into the ability of cancer cells to adapt and withstand treatment interventions. To effectively utilize the data obtained from recurrent tumors, we derive several large number limit theorems, specifically focusing on the metrics that quantify the clonal diversity of cancer cell populations at the time of cancer recurrence. These theorems then serve as the foundation for constructing our estimators. A distinguishing feature of our approach is that our estimators only require a single time-point sequencing data from a single tumor, thereby enhancing the practicality of our approach and enabling the understanding of cancer recurrence at the individual level.

8801 Towards Safe and Efficacious Medical Therapy for Patients with Clinically Non-Functioning Pituitary Tumors

Abstract Disclosure: D. Zhang: None. M. Bergsneider: None. W. Kim: None. M.B. Wang: None. H. Vinters: None. A.P. Heaney: None. Clinically non-functioning pituitary tumors (CNFTs) account for one-third of all pituitary adenomas. They do not cause clinical and/or biochemical evidence of tumor-related hormone hypersecretion, grow insidiously and typically present with mass effect symptoms such as headache and visual field deficits. Surgical resection is the current standard of care for CNFTs but complete resection is infrequently achieved due to their size and invariable invasion of locally adjacent structures. CNFT remnant tumor regrowth rates approach 50% and these patients may need repeat surgery and/or radiotherapy (RT). The latter is quite effective in achieving tumor control, but carries a risk of hypopituitarism of ∼ 40% at 10 years. Although dopamine agonists and somatostatin receptor ligands have been explored as medical therapy, there are currently no consistently effective medical treatments for CNFTs. There is a clear unmet need for novel safe and effective medical therapies for these comparatively common tumors. In a highly innovative approach we have used patient-derived ex vivo 3D tumoroid human CNFTs in an automated high throughput compound screen (HTS) to identify novel tumoricidal and growth inhibitory compounds for CNFTs. To date, we have screened ∼4,000 compounds and have identified 13 potential hits that inhibited tumor cell proliferation (a reduction of 76±15%) and induced apoptosis (induction rate of 8.2 ± 6.6) in the CNFT tumoroids respectively. The hit compounds to date fall into 3 categories based on their targets, namely Na+/K+-ATPase inhibitors (n=6), reactive oxygen species (ROS) regulators (n=4), and cell cycle inhibitors (n=3). Representative “hit” compounds from each group identified in our primary screen were rescreened in triplicate in 20 concentrations from 25µM to 50pM at 2-fold dilutions. Dose-response curves characterized compound doses that inhibited cell viability by 50% (IC50) and ranged from 13nM to 103nM. Two of these 3 compounds also induced 2-3-fold apoptosis at 0.1uM with the final compound displaying a potent 5-10-fold pro-apoptotic effect at 0.15uM concentration. In parallel, we used single cell RNA sequencing to validate targets that emerged from our HTS and to characterize the mechanism of action (MOA) of these hit compounds. As proof of concept, several of the known drug targets were noted to be amongst the highest differentially expressed genes in the CNFT tumor cell population. This approach validating a candidate target in conjunction with demonstrable activity of a drug for that same target in CNFTs may simultaneously provide us with a back-bone molecule to further develop with structural activity relationship analysis and/or allow us to individualize drug choice in patients based on immunocytochemical tumor target expression. Presentation: 6/1/2024

Integration of single-cell sequencing and bulk transcriptome data develops prognostic markers based on PCLAF+ stem-like tumor cells using artificial neural network in gastric cancer

BackgroundGastric cancer stem cells (GCSCs) are important tumour cells involved in tumourigenesis and gastric cancer development. However, their clinical value remains unclear due to the limitations of the available technologies. This study aims to explore the clinical significance of GCSCs, their connection to the tumour microenvironment, and their underlying molecular mechanisms. MethodsStem-like tumour cells were identified by mining single-cell transcriptomic data from multiple samples. Integrated analysis of single-cell and bulk transcriptome data was performed to analyse the role of stem-like tumour cells in predicting clinical outcomes by introducing the intermediate variable mRNA stemness degree (SD). Consensus clustering analysis was performed to develop an SD-related molecular classification strategy to assess the clinical characteristics in gastric cancer. A prognostic model was constructed using a customized approach that comprehensively considered SD-related gene signatures based on an artificial neural network. ResultsBy analysing single-cell data and validating immunofluorescence results, we identified a PCLAF+ stem-like tumour cell population in GC. By calculating SD, we observed that PCLAF+ stem-like tumour cells were associated with poor prognosis and certain clinical features. The SD was negatively correlated with the abundance of most immune cell types. Furthermore, we proposed an SD-related classification method and prognostic model. In addition, the customised prognostic model can be used to predict whether a patient respond to PD-1/PD-L1 immunotherapy. ConclusionWe identified a cluster of stem-like cells and elucidated their clinical significance, highlighting the possibility of their use as immunotherapeutic targets.