Grand techniques for cancer research: a Cancer Grand Challenges update
The global research initiative co-founded in 2020 by Cancer Research UK (London, UK) and the National Cancer Institute (MD, USA), Cancer Grand Challenges, which aims to accelerate high-impact research through global team science, has set its 5th round of challenges.
Here, we catch up with members from two Cancer Grand Challenge teams, NexTGen and PROSPECT to find out how they are getting on and how they are going about their work, from pioneering new software developments to the utilization of organoids and LC-MS.
NexTGen with Team Lead Catherine Bollard and Future Leader Zach Harpaz
NexTGen is tackling the solid tumours in children challenge. By building a deep understanding of how solid cancers develop in children, applying advanced technologies and performing progressive clinical studies, the team aims to produce effective CAR T-cell therapies for children. In this article, we delve into the pioneering software the team is developing for CAR T-cell discovery automation.
Catherine Bollard (left; The George Washington University, D.C., USA) is the Co-team lead of NexTGen alongside Matin Pule (University College London, UK). She leads clinical and research efforts to fight cancer by strengthening the immune system using adoptive cell therapies.
Zach Harpaz (right; NYU Langone Health, NY, USA), is a data scientist and software engineer in the Cancer Grand Challenges NexTGen team. Zach is focused on designing and coding for LogicFinder, an AI tool that leverages publicly available data to automate the discovery of logic-gated CAR T-cell therapy targets. He also aids with the analysis of the data it generates and develops updates required by the team, ensuring that they have a reliable, automated system to explore potential logic-gated CAR T-cell targets.
What can you share so far about the progress of the team and any discoveries made?
Catherine: Team NexTGen is preparing to launch three parallel clinical trials of next-generation CAR T-cell therapies specifically developed for children with hard-to-treat sarcomas and brain tumors. These trials will run across centralized sites in the US and UK, enabling shared insights and real-time refinement. The team has made strong preclinical progress, identifying novel tumor-specific targets, such as DLK1 and Galectin-3, characterizing barriers in the tumor microenvironment, and engineering advanced CAR T cells using innovative approaches like Boolean logic gating, peptide-centric designs and novel cytokine modulation strategies.
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What is LogicFinder, and how does it work?
Zach: LogicFinder is primarily focused on solving the challenge of solid tumors, which are notoriously hard to treat. Traditional CAR T-cell therapies work well for blood cancers but can be dangerous for solid tumors since it is very difficult to identify single antigen targets that are solely expressed in the cancer cells and absent on the healthy cells. Logic-gated CAR T cells offer a way to overcome this challenge by using precise targeting rules; they are only activated when tumor-specific antigen combinations are detected, helping to avoid damage to healthy tissue. LogicFinder identifies combinations of genetic markers that are uniquely expressed in tumors and absent in healthy tissues.
The tool pulls publicly available datasets containing single-cell RNA sequencing data, CRISPR data, and bulk RNA sequencing data from databases, analyzes them, and automatically generates CAR T-cell designs tailored to a specific tumor type. It predicts which combinations of targets could activate CAR T-cells only in the presence of the cancer, minimizing collateral damage. It even generates the actual genetic sequences used for the CAR T construct. It’s a big step toward scalable, AI-driven personalized cancer therapy.
Any tips for using the tool?
Zack: While the tool is still in the process of being published, one tip I’d give is to start with cancer types that LogicFinder is able to automatically analyze using publicly available data, because the tool is optimized to automatically retrieve and analyze that data, without any need for data input from the user. We’ve designed the interface to be as user friendly as possible.
What impact do you believe LogicFinder could have?
Zach: LogicFinder has the potential to unlock personalized, effective therapies for cancers that are currently very challenging to treat, especially in children. By automating the search for logic-gated target combinations, LogicFinder makes it easy to test and develop CAR T-cell therapies on a much larger scale than before. The end goal is to transform tumor sequencing data into an actionable treatment plan within hours, which would be a significant jump for personalized medicine. This kind of breakthrough aligns closely with the goals of Cancer Grand Challenges: to drive bold, collaborative science that has real-world impact. For patients, this could mean safer, more effective treatments with fewer long-term side effects.
Cancer Grand Challenges: taming T-cell receptors and targeting the undruggable with new technologies
Gemma Balmer-Kemp, Head of Research for Cancer Grand Challenges, discusses some of the latest teams to receive funding from Cancer Grand Challenges, the challenges they are addressing in T-cell receptor binding prediction and the drivers of pediatric solid tumors.
Team PROSPECT with Andy Chan
Andy Chan (left) is a physician-scientist at Massachusetts General Hospital (MA, USA) and co-lead of the Cancer Grand Challenges team PROSPECT alongside Yin Cao (Washington University in St. Louis, MO, USA).
Team PROSPECT is a collection of multidisciplinary researchers tackling Cancer Grand Challenge’s early-onset challenge. The team aims to address the global rise in the incidence of early-onset colorectal cancer (EOCRC), by understanding the pathways, risk factors, and molecules involved in its development. They aim to go beyond correlation, to understand the causes and mechanisms behind this rise, while developing innovative interventions to prevent it.
What can you share so far about the progress of the team and any discoveries made?
PROSPECT will use data from cohorts around the world, bringing together close to 10 million individuals and over 2,700 EOCRC cases. In our first year, the team has been developing the required protocols and regulatory agreements to allow this complex international endeavor, as well as working hard on its data harmonisation efforts. We have also optimised and standardised blood, tissue, and stool sample collection across all sites to maximize the data that precious biospecimens can yield. We plan to use these specimens for exposomics, genomics, epigenomics, immune profiling and the study of tissue architecture and the microbiome.
In January, my co-team lead Yin Cao contributed to an important update on colorectal cancer rates and trends in younger versus older age groups globally, led by the American Cancer Society. This work emphasised the urgency of the problem, as the data revealed that EOCRC is also on the rise in middle-income countries across the globe and is no longer limited only to high-income countries.
What techniques are proving vital to these investigations?
We’ll be using state-of-the-art bioanalytical techniques including, exposomics, microbiomics, and epigenomics on blood and tissue samples to detect changes that occur in the body over time and to rigorously identify differences between people who develop EOCRC and those who do not.
We’ll also be using innovative organoid and life course animal models to understand how specific risk factors identified from human data increase susceptibility to, or drive the progression of, EOCRC over time.
Standardising protocols across sites and optimizing sample collection will be critical to enable as much information as possible to be extracted, and for data to be reliably integrated and compared.
What will you explore next?
Exciting findings have emerged from Mutographs and OPTIMISTICC, other Cancer Grand Challenges teams, which have seen an association between mutational signatures caused by the genotoxin colibactin, produced by pks+ E.coli, and EOCRC. We have Emily Balskus (Harvard University, MA, USA) on our team, whose lab identified the structure of colibactin, so we are well placed to explore whether colibactin indeed plays a causal role in EOCRC development.
The use of liquid chromatography-mass spectrometry experiments in combination with multiplexed imaging of transcripts, proteins, and metabolites is something that we are very excited about. This should enable us to identify potential exposures in people developing EOCRC, providing clues to get us get closer to the causes of the rise in rates of this cancer.
