Periodic Reporting for period 1 - THRIVE (TUMOUR-HOST INTERACTIONS IN LIVER CANCER OF CHILDHOOD AND ADULTS)
Período documentado: 2023-12-01 hasta 2025-05-31
Liver cancer is a major health concern, with 1 million new cases annually and ranking as the 3rd leading cause of cancer-related deaths worldwide. In 2020, ~87,000 Europeans were diagnosed with liver cancer and ~78,000 died from it. Hepatocellular carcinoma (HCC) is the most common type of liver cancer in adults (~90% of cases), while hepatoblastoma (HB) is the most common liver tumour in children. Both remain poorly understood.
HCC is aggressive and difficult to cure (~30% of patients are cured) with a median survival under 2 years in advanced HCC treated with immune therapies. In HB, ~25% children progress after surgery and chemotherapy, and half of survivors suffer severe long-term side effects.
Despite recent advances in understanding liver cancer pathogenesis and mechanisms of treatment action, these have not significantly improved patient care. To address this, the THRIVE Consortium, brings together leading experts to unravel tumour-host interactions driving cancer predisposition, find biomarkers of treatment response, to ultimately find better therapies.
Our EU Consortium, comprising 13 partners from 8 countries, will 1) define molecular features of cancer predisposition and at-risk populations, 2) develop a complete human liver cancer atlas of tumour, immune, stromal cells and intra-tumoral microbiomes, 3) identify artificial intelligence (AI)-based and molecular markers of response to treatments, 4) create preclinical platforms for discovery or repurposing of therapies, and 5) maximize social impact by integrating Social Sciences and Humanities (SSH), delivering accessible and reusable data/tools and informing policymakers and healthcare professionals.
HCC is aggressive and difficult to cure (~30% of patients are cured) with a median survival under 2 years in advanced HCC treated with immune therapies. In HB, ~25% children progress after surgery and chemotherapy, and half of survivors suffer severe long-term side effects.
Despite recent advances in understanding liver cancer pathogenesis and mechanisms of treatment action, these have not significantly improved patient care. To address this, the THRIVE Consortium, brings together leading experts to unravel tumour-host interactions driving cancer predisposition, find biomarkers of treatment response, to ultimately find better therapies.
Our EU Consortium, comprising 13 partners from 8 countries, will 1) define molecular features of cancer predisposition and at-risk populations, 2) develop a complete human liver cancer atlas of tumour, immune, stromal cells and intra-tumoral microbiomes, 3) identify artificial intelligence (AI)-based and molecular markers of response to treatments, 4) create preclinical platforms for discovery or repurposing of therapies, and 5) maximize social impact by integrating Social Sciences and Humanities (SSH), delivering accessible and reusable data/tools and informing policymakers and healthcare professionals.
Among the 4 scientific objectives of THRIVE, we first aimed to identify molecular features linked to cancer risk and early development (1st objective). In this regard, we identified that 17% of children with HB show mosaic genetic alterations in the 11p15.5 locus in their liver. These alterations occurred before common cancer-related mutations (e.g. CTNNB1 mutations) and may represent a pre-cancerous stage. Spatial transcriptomics and single-nucleus RNA sequencing revealed that these regions exhibit a distinct tumour microenvironment. Overall, our findings suggest early 11p15.5 alterations may help identify high-risk patients.
To build a detailed cellular map of liver cancer (2nd objective), we used single-cell RNA sequencing and defined immune cell signatures, to dissect the immune cells types in HCC, uncovering intratumoral heterogeneity. In paediatric HB, we identified liver progenitor and immune cold tumour subtypes linked to poorer outcomes. Using single-cell multiomics, we showed that certain HB cell clones are resistant to chemotherapy, linking clonal evolution to response.
To identify molecular markers of treatment response (3rd objective), by integrating single-cell and bulk RNA sequencing data, we uncovered two mechanisms driving response patterns to atezolizumab + bevacizumab, the current standard of care treatment in advanced HCC: one driven by immune activity and the other by angiogenesis. We also uncovered two resistance mechanisms, involving either immunosuppressive myeloid cells or TGF-/Notch activation. Further, we developed an AI method to predict HCC molecular subtypes from standard H&E tumour slides, laying the foundation for future models predicting response to immunotherapies. For HB, we identified molecular features linked to chemotherapy resistance.
To discover or repurpose affordable therapies (4th objective), we developed innovative pre-clinical models: (a) a mouse model mimicking Metabolic and Alcohol-Associated Liver Disease (Met-ALD); (b) HCC and HB patient-derived organoids (PDOs); and (c) a PDO integrating bacteria+immune cells to better simulate the tumour environment. Also, in collaboration with CRUK, we helped develop 27 genetically engineered, immunocompetent mouse models recapitulating 4 HCC molecular classes. Drug screening in these models revealed cladribine as a promising candidate enhancing therapy efficacy. Finally, in a MASH-HCC mouse model, we showed NRP1 blockade enhances immunotherapy efficacy.
To build a detailed cellular map of liver cancer (2nd objective), we used single-cell RNA sequencing and defined immune cell signatures, to dissect the immune cells types in HCC, uncovering intratumoral heterogeneity. In paediatric HB, we identified liver progenitor and immune cold tumour subtypes linked to poorer outcomes. Using single-cell multiomics, we showed that certain HB cell clones are resistant to chemotherapy, linking clonal evolution to response.
To identify molecular markers of treatment response (3rd objective), by integrating single-cell and bulk RNA sequencing data, we uncovered two mechanisms driving response patterns to atezolizumab + bevacizumab, the current standard of care treatment in advanced HCC: one driven by immune activity and the other by angiogenesis. We also uncovered two resistance mechanisms, involving either immunosuppressive myeloid cells or TGF-/Notch activation. Further, we developed an AI method to predict HCC molecular subtypes from standard H&E tumour slides, laying the foundation for future models predicting response to immunotherapies. For HB, we identified molecular features linked to chemotherapy resistance.
To discover or repurpose affordable therapies (4th objective), we developed innovative pre-clinical models: (a) a mouse model mimicking Metabolic and Alcohol-Associated Liver Disease (Met-ALD); (b) HCC and HB patient-derived organoids (PDOs); and (c) a PDO integrating bacteria+immune cells to better simulate the tumour environment. Also, in collaboration with CRUK, we helped develop 27 genetically engineered, immunocompetent mouse models recapitulating 4 HCC molecular classes. Drug screening in these models revealed cladribine as a promising candidate enhancing therapy efficacy. Finally, in a MASH-HCC mouse model, we showed NRP1 blockade enhances immunotherapy efficacy.
THRIVE advanced understanding of tumour-host interactions in liver cancer through the development of single-cell based immune signatures, revealing the complex immune heterogeneity in HCC. In HB, we identified (a) ‘liver progenitor’ and ‘immune cold’ subtypes linked to poor outcomes and (b) chemotherapy-resistant cell clones. Collectively, these findings provide unprecedented insight into liver cancer, enabling tailored therapies.
THRIVE also delivered advances in treatment response biomarkers. Approved immunotherapy biomarkers have limited predictive value in HCC. Here we identified mechanisms of response and resistance to immune therapies, and associated biomarkers. Also, we developed an AI method to predict HCC molecular subtypes from standard pathology slides, a first step towards AI-based response predictions.
For HB, we identified molecular features linked to chemoresistance, offering biomarkers to refine management. Together, these findings improve our understanding of treatment response and resistance, advancing personalized medicine.
We also progressed suggesting innovative and affordable liver cancer treatments. Cancer therapies are costly and benefit only a subset of patients which underscores an urgent need for better treatment options. Addressing this, we (a) developed novel preclinical tools for testing novel or repurposed drugs, and (b) proposed new strategies to enhance treatment responses. Overall, our work helps overcome resistance and improve patient outcomes.
Finally, we identified markers to detect populations at risk of developing liver cancer, supporting early surveillance and detection in clinical settings. In HB, known risk factors (inherited syndromes, low birth weight) account only a minority of cases. We went further by identifying early genetic alterations that may trigger cancer development. These findings offer new opportunities for identifying high-risk patients where current tools are limited.
THRIVE also delivered advances in treatment response biomarkers. Approved immunotherapy biomarkers have limited predictive value in HCC. Here we identified mechanisms of response and resistance to immune therapies, and associated biomarkers. Also, we developed an AI method to predict HCC molecular subtypes from standard pathology slides, a first step towards AI-based response predictions.
For HB, we identified molecular features linked to chemoresistance, offering biomarkers to refine management. Together, these findings improve our understanding of treatment response and resistance, advancing personalized medicine.
We also progressed suggesting innovative and affordable liver cancer treatments. Cancer therapies are costly and benefit only a subset of patients which underscores an urgent need for better treatment options. Addressing this, we (a) developed novel preclinical tools for testing novel or repurposed drugs, and (b) proposed new strategies to enhance treatment responses. Overall, our work helps overcome resistance and improve patient outcomes.
Finally, we identified markers to detect populations at risk of developing liver cancer, supporting early surveillance and detection in clinical settings. In HB, known risk factors (inherited syndromes, low birth weight) account only a minority of cases. We went further by identifying early genetic alterations that may trigger cancer development. These findings offer new opportunities for identifying high-risk patients where current tools are limited.