AI-SEED-RCC - using generative AI to Spatially map Evolution, Environment, and Drug responses to Renal Cell Carcinoma
Lead Research Organisation:
UNIVERSITY OF CAMBRIDGE
Abstract
Context
Kidney cancers behave very differently between patients. Some patients have slow-growing tumours that are restricted to the kidney and respond well to treatment, whilst others experience aggressive disease that spreads to other organs and is unresponsive to treatment. It is currently very difficult to predict which patients will do well and which will not. This means some people receive treatments they don’t need, while others miss the chance for early intervention.
This Programme, AI-SEED-RCC, brings together researchers from the University of Cambridge, the Wellcome Sanger Institute and University College London. AI-SEED-RCC is based on the idea that cancer is not just a problem of the faulty cells that become tumours ("the seed"), but also of the environment around those faulty cells ("the soil"), such as immune cells and blood vessels. Exactly how "seed” and "soil" interact across patients is a mystery. We seek to tackle this using cutting-edge technologies, including artificial intelligence (AI), spatial genomics and advanced imaging, with aligned data from patients, to understand how kidney cancers grow and change after treatment.
What problem are we solving?
Despite progress in cancer care, there are three major gaps that still need to be addressed in kidney cancer, which when solved, will be key to designing better treatments:
Lack of precision: It is difficult to know which early tumours will become dangerous and which will not, leading to overtreatment or missed opportunities to act early.
Treatment resistance: Many patients stop responding to existing drugs, in part because tumour biology can adapt over time.
Limited understanding of the tumour environment: It is unclear how cancer cells interact with the vasculature supplying these tumours and the immune cells which attempt to fight them.
What will we do?
This project has three main aims:
Create tumour maps: We will use spatial genomics and AI to analyse 100 kidney tumours from untreated patients, creating detailed maps that track how cancer cells, their genetics changes and their surroundings behave and change as the disease progresses.
Track responses to treatment: Using samples from unique clinical trials, we will study how tumours respond to t after therapy, helping to explain why some respond and others do not.
Predict and test new treatments: Using AI tools, we will simulate how alterations to "the seed" or "the soil" influence kidney cancer, which will inform tests of newer and smarter anti-cancer drugs in cutting edge laboratory-based models.
What difference will this make?
Our tools, data, and results will be shared openly with the global research community. By decoding the “seed” from the “soil” of kidney cancer, this Programme could transform our understanding of how kidney cancer grows and responds to treatment by:
Generating rich information about the cells that drive untreated kidney cancer, helping to identify which patients need treatment and which can safely be monitored.
Leveraging information on the genetic and cellular features of a patient’s tumour to match the best available therapies for them.
Supporting the development of new treatments that target the vasculature and immune cells that support tumours, to inhibit or reverse tumour growth.
Kidney cancers behave very differently between patients. Some patients have slow-growing tumours that are restricted to the kidney and respond well to treatment, whilst others experience aggressive disease that spreads to other organs and is unresponsive to treatment. It is currently very difficult to predict which patients will do well and which will not. This means some people receive treatments they don’t need, while others miss the chance for early intervention.
This Programme, AI-SEED-RCC, brings together researchers from the University of Cambridge, the Wellcome Sanger Institute and University College London. AI-SEED-RCC is based on the idea that cancer is not just a problem of the faulty cells that become tumours ("the seed"), but also of the environment around those faulty cells ("the soil"), such as immune cells and blood vessels. Exactly how "seed” and "soil" interact across patients is a mystery. We seek to tackle this using cutting-edge technologies, including artificial intelligence (AI), spatial genomics and advanced imaging, with aligned data from patients, to understand how kidney cancers grow and change after treatment.
What problem are we solving?
Despite progress in cancer care, there are three major gaps that still need to be addressed in kidney cancer, which when solved, will be key to designing better treatments:
Lack of precision: It is difficult to know which early tumours will become dangerous and which will not, leading to overtreatment or missed opportunities to act early.
Treatment resistance: Many patients stop responding to existing drugs, in part because tumour biology can adapt over time.
Limited understanding of the tumour environment: It is unclear how cancer cells interact with the vasculature supplying these tumours and the immune cells which attempt to fight them.
What will we do?
This project has three main aims:
Create tumour maps: We will use spatial genomics and AI to analyse 100 kidney tumours from untreated patients, creating detailed maps that track how cancer cells, their genetics changes and their surroundings behave and change as the disease progresses.
Track responses to treatment: Using samples from unique clinical trials, we will study how tumours respond to t after therapy, helping to explain why some respond and others do not.
Predict and test new treatments: Using AI tools, we will simulate how alterations to "the seed" or "the soil" influence kidney cancer, which will inform tests of newer and smarter anti-cancer drugs in cutting edge laboratory-based models.
What difference will this make?
Our tools, data, and results will be shared openly with the global research community. By decoding the “seed” from the “soil” of kidney cancer, this Programme could transform our understanding of how kidney cancer grows and responds to treatment by:
Generating rich information about the cells that drive untreated kidney cancer, helping to identify which patients need treatment and which can safely be monitored.
Leveraging information on the genetic and cellular features of a patient’s tumour to match the best available therapies for them.
Supporting the development of new treatments that target the vasculature and immune cells that support tumours, to inhibit or reverse tumour growth.