Targeting TM4SF1 exhibits therapeutic potential via inhibition of cancer stem cells

Identification and characterization of TM4SF1+ tumor self-seeded cells

Tumor self-seeding is a process whereby circulating tumor cells (CTCs) recolonize the primary tumor, which promotes tumor growth, angiogenesis, and invasion. However, the detailed nature and functions of tumor self-seeded cells (TSCs) have not been well defined due to challenges in tracking and isolating TSCs. Here, we report an accurate animal model using photoconvertible tagging to recapitulate the spontaneous process of tumor self-seeding and identify TSCs as a subpopulation of primary tumor cells with enhanced invasiveness and survival. We demonstrate transmembrane-4-L-six-family-1 (TM4SF1) as a marker of TSCs, which promotes migration, invasion, and anchorage-independent survival in cancer cells. By analyzing single-cell RNA sequencing datasets, we identify a potential TSC population with a metastatic profile in patients with cancer, which is detectable in early-stage disease and expands during cancer progression. In summary, we establish a framework to study TSCs and identify emerging cell targets with diagnostic, prognostic, or therapeutic potential in cancers.

Breast cancer malignancy is governed by regulation of the macroH2A2/TM4SF1 axis, the AKT/NF-κB pathway, and elevated MMP13 expression

The histone variant, macroH2A (mH2A) influences gene expression through epigenetic regulation. Tumor suppressive function of mH2A isoforms has been reported in various cancer types, but few studies have investigated the functional role of mH2A2 in breast cancer pathophysiology. This study aimed to determine the significance of mH2A2 in breast cancer development and progression by exploring its downstream regulatory mechanisms. Knockdown of mH2A2 facilitated the migration and invasion of breast cancer cells, whereas its overexpression exhibited the opposite effect. In vivo experiments revealed that augmenting mH2A2 expression reduced tumor growth and lung metastasis. Microarray analysis showed that TM4SF1 emerged as a likely target linked to mH2A2 owing to its significant suppression in breast cancer cell lines where mH2A2 was overexpressed among the genes that exhibited over twofold upregulation upon mH2A2 knockdown. Suppressing TM4SF1 reduced the migration, invasion, tumor growth, and metastasis of breast cancer cells in vitro and in vivo. TM4SF1 depletion reversed the increased aggressiveness triggered by mH2A2 knockdown, suggesting a close interplay between mH2A2 and TM4SF1. Our findings also highlight the role of the mH2A2/TM4SF1 axis in activating the AKT/NF-κB pathway. Consequently, activated NF-κB signaling leads to increased expression and secretion of MMP13, a potent promoter of metastasis. In summary, we propose that the orchestrated regulation of the mH2A2/TM4SF1 axis in conjunction with the AKT/NF-κB pathway and the subsequent elevation in MMP13 expression constitute pivotal factors governing the malignancy of breast cancer.

Lineage-specific intolerance to oncogenic drivers restricts histological transformation

Lung adenocarcinoma (LUAD) and small cell lung cancer (SCLC) are thought to originate from different epithelial cell types in the lung. Intriguingly, LUAD can histologically transform into SCLC after treatment with targeted therapies. In this study, we designed models to follow the conversion of LUAD to SCLC and found that the barrier to histological transformation converges on tolerance to Myc, which we implicate as a lineage-specific driver of the pulmonary neuroendocrine cell. Histological transformations are frequently accompanied by activation of the Akt pathway. Manipulating this pathway permitted tolerance to Myc as an oncogenic driver, producing rare, stem-like cells that transcriptionally resemble the pulmonary basal lineage. These findings suggest that histological transformation may require the plasticity inherent to the basal stem cell, enabling tolerance to previously incompatible oncogenic driver programs.

Machine-Learning Identifies a Strategy for Differentiation Therapy in Solid Tumors

BACKGROUND

Although differentiation therapy can cure some hematologic malignancies, its curative potential remains unrealized in solid tumors. This is because conventional computational approaches succumb to the thunderous noise of inter-/intratumoral heterogeneity. Using colorectal cancers (CRCs) as an example, here we outline a machine learning(ML)-based approach to track, differentiate, and selectively target cancer stem cells (CSCs).

METHODS

A transcriptomic network was built and validated using healthy colon and CRC tissues in diverse gene expression datasets (~5,000 human and >300 mouse samples). Therapeutic targets and perturbation strategies were prioritized using ML, with the goal of reinstating the expression of a transcriptional identifier of the differentiated colonocyte, CDX2, whose loss in poorly differentiated (CSC-enriched) CRCs doubles the risk of relapse/death. The top candidate target was then engaged with a clinical-grade drug and tested on 3 models: CRC lines in vitro, xenografts in mice, and in a prospective cohort of healthy (n = 3) and CRC (n = 23) patient-derived organoids (PDOs).

RESULTS

The drug shifts the network predictably, induces CDX2 and crypt differentiation, and shows cytotoxicity in all 3 models, with a high degree of selectivity towards all CDX2-negative cell lines, xenotransplants, and PDOs. The potential for effective pairing of therapeutic efficacy (IC50) and biomarker (CDX2-low state) is confirmed in PDOs using multivariate analyses. A 50-gene signature of therapeutic response is derived and tested on 9 independent cohorts (~1700 CRCs), revealing the impact of CDX2-reinstatement therapy could translate into a ~50% reduction in the risk of mortality/recurrence.

CONCLUSIONS

Findings not only validate the precision of the ML approach in targeting CSCs, and objectively assess its impact on clinical outcome, but also exemplify the use of ML in yielding clinical directive information for enhancing personalized medicine.

Single-cell RNA sequencing reveals cancer stem-like cells and dynamics in tumor microenvironment during cholangiocarcinoma progression

Cholangiocarcinoma is a malignancy of the bile ducts that is driven by activities of cancer stem-like cells and characterized by a heterogeneous tumor microenvironment. To better understand the transcriptional profiles of cancer stem-like cells and dynamics in the tumor microenvironment during the progression of cholangiocarcinoma, we performed single-cell RNA analysis on cells collected from three different timepoints of tumorigenesis in a YAP/AKT mouse model. Bulk RNA sequencing data from TCGA (The Cancer Genome Atlas program) and ICGC cohorts were used to verify and support the finding. In vitro and in vivo experiments were performed to assess the stemness of cancer stem-like cells. We identified Tm4sf1high malignant cells as cancer stem-like cells. Across timepoints of cholangiocarcinoma formation in YAP/AKT mice, we found dynamic change in cancer stem-like cell/stromal/immune cell composition. Nevertheless, the dynamic interaction among cancer stem-like cells, immune cells, and stromal cells at different timepoints was elaborated. Collectively, these data serve as a useful resource for better understanding cancer stem-like cell and malignant cell heterogeneity, stromal cell remodeling, and immune cell reprogramming. It also sheds new light on transcriptomic dynamics during cholangiocarcinoma progression at single-cell resolution.

Subcellular visualization: Organelle-specific targeted drug delivery and discovery

Organelles perform critical biological functions due to their distinct molecular composition and internal environment. Disorders in organelles or their interacting networks have been linked to the incidence of numerous diseases, and the research of pharmacological actions at the organelle level has sparked pharmacists' interest. Currently, cell imaging has evolved into a critical tool for drug delivery, drug discovery, and pharmacological research. The introduction of advanced imaging techniques in recent years has provided researchers with richer biological information for viewing and studying the ultrastructure of organelles, protein interactions, and gene transcription activities, leading to the design and delivery of precision-targeted drugs. Therefore, this reviews the research on organelles-targeted drugs based upon imaging technologies and development of fluorescent molecules for medicinal purposes. We also give a thorough analysis of a number of subcellular-level elements of drug development, including subcellular research instruments and methods, organelle biological event investigation, subcellular target and drug identification, and design of subcellular delivery systems. This review will make it possible to promote drug research from the individual/cellular level to the subcellular level, as well as give a new focus based on newly found organelle activities.

Dynamic heterogeneity of colorectal cancer during progression revealed clinical risk-associated cell types and regulations in single-cell resolution and spatial context

Background

Tumor heterogeneity is contributed by tumor cells and the microenvironment. Dynamics of tumor heterogeneity during colorectal cancer (CRC) progression have not been elucidated.

Methods

Eight single-cell RNA sequencing (scRNA-seq) data sets of CRC were included. Milo was utilized to reveal the differential abundance of cell clusters during progression. The differentiation trajectory was imputed by using the Palantir algorithm and metabolic states were assessed by using scMetabolism. Three spatial transcription sequencing (ST-seq) data sets of CRC were used to validate cell-type abundances and colocalization. Cancer-associated regulatory hubs were defined as communication networks affecting tumor biological behaviors. Finally, quantitative reverse transcription polymerase chain reaction and immunohistochemistry staining were performed for validation.

Results

TM4SF1⁺, SOX4⁺, and MKI67⁺ tumor cells; CXCL12⁺ cancer-associated fibroblasts; CD4⁺ resident memory T cells; Treg; IgA⁺ plasma cells; and several myeloid subsets were enriched in stage IV CRC, most of which were associated with overall survival of patients. Trajectory analysis indicated that tumor cells from patients with advanced-stage CRC were less differentiated, when metabolic heterogeneity showed a highest metabolic signature in terminal states of stromal cells, T cells, and myeloid cells. Moreover, ST-seq validated cell-type abundance in a spatial context and also revealed the correlation of immune infiltration between tertiary lymphoid structures and tumors followed by validation in our cohort. Importantly, analysis of cancer-associated regulatory hubs revealed a cascade of activated pathways including leukocyte apoptotic process, MAPK pathway, myeloid leukocyte differentiation, and angiogenesis during CRC progression.

Conclusions

Tumor heterogeneity was dynamic during progression, with the enrichment of immunosuppressive Treg, myeloid cells, and fibrotic cells. The differential state of tumor cells was associated with cancer staging. Assessment of cancer-associated regulatory hubs suggested impaired antitumor immunity and increased metastatic ability during CRC progression.