metastasis

Weak Cell Adhesion is a Prognostic Signature of Invasive Cancer

Project Summary Despite early detection, low-grade and localized breast cancers such as ductal carcinoma in situ (DCIS) can relapse in up to 20% of cases despite standard of care. For DCIS, relapse affects over 12,000 U.S. women annually and has increased 60% in the last 40 years. Current diagnostic assessments including histopathological markers often miss early disseminating cells, lack specificity, or cannot distinguish cancer from non-cancer cells in the stroma. Hence there is an unmet need for cancer diagnostic technologies that employ radically different characterization methods. For example, significant physical differences exist between metastasizing and benign breast cancer cells, owing to metastasizing cells detaching from the primary tumor, migrating through the surrounding stroma, intravasating and extravasating, and ultimately engrafting in distant tissues. We recently demonstrated that cancer cells with weaker adhesion migrate faster and metastasize more frequently in murine breast cancer models than strongly adherent cells. In a small pilot study of human breast tumors, we also observed that the abundance of weakly adherent (WA) cells scales with disease severity; subpopulations from invasive carcinomas were the least adherent. However, a subset of DCIS cases displayed much less adhesion, suggesting that these patients may have a tumor subpopulation that progresses to metastatic disease despite standard-of-care treatment. Weak adhesion is a defining physical characteristic of tumors, but to establish their role in initiation, metastasis, and patient outcomes, we will leverage model systems and our newly patented adhesion technology to answer these fundamental questions of cancer biology and clinical translation. To understand the impact of adhesion on cancer progression, we will evaluate the tumor-initiating potential of WA versus strongly adherent (SA) tumor cells in a murine breast cancer model before confirming how weak adhesion advantages cells to cause secondary disease using bioengineered in vitro models. In dissecting the stages of metastasis where WA cells exhibit advantages, e.g., recapitulating stromal niche, transendothelial migration, and tissue-specific colonization, we will identify mechanisms that enable WA cells to thrive and evaluate therapeutic targets that disrupt these pathways. Finally, we will analyze the adhesion profiles of resected tumors and stroma from 80 breast cancer patients with DCIS or invasive disease. Adhesion data will be correlated with conventional assessment methods and ultimately with patient outcomes, e.g., disease-free and progression-free intervals. We anticipate that the DCIS subpopulation that aligns with the adhesion signature of invasive carcinomas will have shorter intervals and survival time. This integrated study design bridges mouse models, mechanistic bioengineering assays, and human samples to clarify the metastatic potential and prognostic value of WA breast cancer cells. Our use of mouse models in this grant is required to study the interactions among tumor cells, immune cells, vasculature, and stromal tissues that drive tumor formation in vivo. Bioengineered in vitro systems lack the complexity to ask such questions and using injected tumor cells is not possible in humans.

Multimodal computational models for early prediction of peritoneal recurrence in gastric cancer

ABSTRACT Gastric cancer represents a significant disease burden and is a leading cause of cancer-related deaths in the United States and globally. Approximately 80% of gastric cancer patients are diagnosed at an advanced stage, with the peritoneum being the most common site of relapse (peritoneal recurrence) after radical surgery. Nearly 50% of patients with advanced-stage gastric cancer develop peritoneal recurrence post-surgery, resulting in a median survival of only 3–6 months and a markedly reduced quality of life. Early peritoneal recurrence is primarily characterized by micro-metastasis, which traditional imaging techniques struggle to detect due to the small size of metastatic nodules. Predicting the likelihood and timing of peritoneal recurrence is crucial for identifying at- risk patients, enabling timely interventions that could improve survival rates and quality of life. Unfortunately, reliable predictive biomarkers and models for peritoneal recurrence in gastric cancer are lacking in clinical practice, highlighting an urgent need for innovative predictive tools. This proposal aims to develop and validate novel predictive models for early peritoneal recurrence in gastric cancer, leveraging advanced deep learning techniques and multimodal integration of clinical, radiological (CT), and histopathological (hematoxylin and eosin, H&E) data. In Aim 1, we will develop a rational approach for predicting peritoneal recurrence by creating a novel deep learning multimodal method guided by genomics knowledge. Additionally, we will integrate both deep learning-extracted features and traditional hand-crafted radiomics features with clinical data to improve prediction accuracy. Aim 2 focuses on developing a robust prediction model of peritoneal recurrence utilizing a pre-trained foundation model from large-scale H&E image data. Aim 3 will combine CT, H&E, and clinical data to further enhance predictive capabilities, employing an innovative cross-modal collaborative optimization approach for multimodal data integration. All models will be trained and internally validated using a retrospective cohort from Atrium Health Wake Forest Baptist Comprehensive Cancer Center and externally validated in two independent cohorts from additional institutions to ensure robustness across populations and imaging protocols. Additionally, we will compare our models with existing methods, including clinical staging and alternative fusion strategies. If successful, these models will enhance risk stratification and prediction of peritoneal recurrence in gastric cancer patients, significantly improving survival rates and quality of life by identifying those likely to develop peritoneal recurrence post-surgery and facilitating timely intervention. Furthermore, they can help avoid the risk of complications and extra medical costs associated with overtreatment. Since the information is derived from routinely examined CT, H&E and clinical data, they could be seamlessly integrated into current clinical workflows. The AI technology developed through this project has the potential to benefit underserved populations in low- resource settings and reduce healthcare disparities in the U.S.

A NOVEL GEMM TO ELUCIDATE THE ROLE OF CHAF1A IN NEUROBLASTOMA DEVELOPMENT

PROJECT SUMMARY: This proposal focuses on the fundamental understanding on how the CHAF1A oncogene drives molecular mechanisms, cellular signaling, and metabolic processes in the oncogenesis of neuroblastoma (NB). NB is an aggressive pediatric cancer, which accounts for 15% of pediatric cancer mortalities. High-risk NB is thought to arise from a small number of recurrent genetic alterations that block the ability of neural crest cells (NCCs) to differentiate. To assess the molecular mechanisms governing NC differentiation, our laboratory has established a definitive role of the epigenetic regulator CHAF1A in blocking NC differentiation and driving NB oncogenesis. In this proposal, we will determine the impact of CHAF1A on NB initiation and progression. To accomplish this goal, we propose to develop a novel CHAF1A-driven genetically-engineered mouse model (GEMM) of NB and test the impact of CHAF1A on NB incidence, histology and metastasis, and the tumor immune microenvironment (TIME). We hypothesize that CHAF1A will increase de novo incidence of NB, reduce mouse survival, and promote a suppressive TIME. By developing a novel GEMM of NB and employing innovative technology (including ATAC-seq, lipidomics, and scRNA-seq), we will: 1- elucidate the role of CHAF1A in NB tumor initiation and progression; and 2- determine the impact of CHAF1A on MYCN-induced oncogenesis. These findings will provide a novel view on the molecular mechanisms driving NB initiation, and will have high clinical implications, informing future differentiation-based interventions for high-risk NBs.

Dual mRNA Therapeutics for Liver Metastatic Uveal Melanoma

Abstract Uveal melanoma (UM) is the most common primary intraocular cancer in adults, accounting for approximately 70% of all ocular malignancies. Current treatments for primary UM include surgical tumor removal, transpupillary thermotherapy, and radiotherapy. Unfortunately, both surgical enucleation and brachytherapy have shown similar survival outcomes and carry an equivalent risk of metastasis. While the survival rate for patients with primary, non-metastatic UM is relatively high, metastatic uveal melanoma (MUM), especially when it spreads to the liver, remains universally fatal. The liver is the first site of metastasis in 80 to 90 percent of cases, and about 50 percent of UM patients develop liver metastases within 15 years of initial diagnosis. Median survival following liver metastasis is only 5 to 7 months, with an almost zero percent five-year survival rate. Currently, no available therapy significantly improves outcomes for patients with liver MUM. This R21 project addresses this urgent unmet need by developing liver-tropic mRNA therapeutics targeting two key drivers of MUM progression and metastasis: (1) constitutive activation of Gαq/11 caused by single-point mutations, and (2) loss-of-function mutations in BAP1. Both alterations occur in over 80 percent of UM patients and are associated with poor prognosis. We hypothesize that inhibition of constitutively active Gαq/11 and/or restoration of BAP1 tumor suppressor function will significantly suppress MUM progression and improve survival outcomes. Aim 1 focuses on delivering mRNA encoding a novel protein trap designed to specifically inhibit constitutively active Gαq/11 and its downstream oncogenic signaling pathways. Aim 2 seeks to restore wild-type BAP1, which is mutated or lost in approximately 84 percent of MUM cases, through liver-tropic mRNA delivery using a liver MUM model established via splenic inoculation. We will also evaluate the potential synergy between Gαq/11 inhibition and BAP1 restoration. The success of this project will not only advance our understanding of the disease mechanisms underlying MUM but also provide clinically viable strategies for treating liver metastases in uveal melanoma.

Metastatic recurrence in colorectal cancer arises from residual EMP1+ cells

PHGDH heterogeneity potentiates cancer cell dissemination and metastasis