AIRIaL: Artificial Intelligence and Resistance Imaging in Lung Cancer
Lead Research Organisation:
King's College London
Department Name: Imaging & Biomedical Engineering
Abstract
Lung cancer is the most common cause of cancer death world-wide (~1.6 million deaths/year). Unfortunately, of all people who have lung cancer, only 5 out of 100 are living ten years after diagnosis. The main reason for this low survival rate is that standard lung cancer therapies fail due to the presence of cancer that does not respond to these treatments.
In this early-stage project performed in mice, we will develop new medical imaging tests to detect therapy resistance in lung cancer. Using this imaging test, we can predict if the cancer will respond to the prescribed treatment, and if so, how well. In the future, knowing early on about how a patient will respond to treatment will enable the clinical team to switch to alternative options if necessary. This will avoid patients being given potentially toxic treatments that will not benefit them. At present doctors have to wait for CT scans about 3 months after treatment starts to be able to tell if it is working or not.
We will create a computer programme (using artificial intelligence) which is able to automatically read lung scans and more accurately predict whether the cancer is resistant to treatment. Then, we will use engineered materials to deliver drugs only to the resistant cancer cells. At this stage we will test these new drugs and computer program in mice to see if this new approach is effective. By delivering the drug just to the cancer itself, we hope to reduce side effects for patients and significantly improve how well the treatment works. Finally, we will determine if our computer programme can monitor changes in cancer over time, which will provide insight into how treatment resistance occurs as this is currently not well-understood. Ultimately, we hope that the results of this project will enable doctors to tailor the treatment for lung cancer to the individual patient. Moreover, it might lead to the development of new treatments for this devastating disease.
In this early-stage project performed in mice, we will develop new medical imaging tests to detect therapy resistance in lung cancer. Using this imaging test, we can predict if the cancer will respond to the prescribed treatment, and if so, how well. In the future, knowing early on about how a patient will respond to treatment will enable the clinical team to switch to alternative options if necessary. This will avoid patients being given potentially toxic treatments that will not benefit them. At present doctors have to wait for CT scans about 3 months after treatment starts to be able to tell if it is working or not.
We will create a computer programme (using artificial intelligence) which is able to automatically read lung scans and more accurately predict whether the cancer is resistant to treatment. Then, we will use engineered materials to deliver drugs only to the resistant cancer cells. At this stage we will test these new drugs and computer program in mice to see if this new approach is effective. By delivering the drug just to the cancer itself, we hope to reduce side effects for patients and significantly improve how well the treatment works. Finally, we will determine if our computer programme can monitor changes in cancer over time, which will provide insight into how treatment resistance occurs as this is currently not well-understood. Ultimately, we hope that the results of this project will enable doctors to tailor the treatment for lung cancer to the individual patient. Moreover, it might lead to the development of new treatments for this devastating disease.
Technical Summary
Most lung cancer deaths result from ineffective treatment of stage 3 and 4 disease. Currently, there is no satisfactory way to identify patients that will not respond to standard-of-care treatments. Positron emission tomography (PET) imaging offers a potential solution to this clinical problem through the non-invasive assessment of molecular processes that underpin therapy-resistance. The identification of cancer patients that are refractory to treatment will allow the use of innovative second-line therapies that have the potential to improve patient response and survival.
Here, we will combine expertise in biological, physical, data, and medical sciences to identify and treat therapy-resistant non-small cell lung cancer (NSCLC). We have developed a PET radiotracer, [18F]FSPG, that can non-invasively detect therapy-resistant tumours in multiple models of NSCLC. [18F]FSPG is taken up by the amino acid transporter xCT, which provides the rate-limiting precursor for glutathione biosynthesis and is upregulated >10-fold in therapy-resistant NSCLC. Through innovations in artificial intelligence and machine learning, minimal preclinical [18F]FSPG imaging datasets will be used to identify, segment, and extract quantitative parameters from therapy-resistant tumours; far outperforming conventional methods of annotation. Concurrently, we will develop molecularly imprinted nanoparticles (nanoMIPs) that bind xCT to deliver a targeted payload of drug to these resistant tumours. An iterative selection process will be employed to manufacture double imprinted fluorescent nanoMIPs that bind both xCT and doxorubicin to produce therapeutic agents with excellent selectivity and potency. Finally, these innovative technologies will be combined to image, detect, and treat multifocal therapy-resistant disease in a genetically engineered mouse model of NSCLC. Together, this novel treatment platform will support our mission to provide treatment options where currently there are none.
Here, we will combine expertise in biological, physical, data, and medical sciences to identify and treat therapy-resistant non-small cell lung cancer (NSCLC). We have developed a PET radiotracer, [18F]FSPG, that can non-invasively detect therapy-resistant tumours in multiple models of NSCLC. [18F]FSPG is taken up by the amino acid transporter xCT, which provides the rate-limiting precursor for glutathione biosynthesis and is upregulated >10-fold in therapy-resistant NSCLC. Through innovations in artificial intelligence and machine learning, minimal preclinical [18F]FSPG imaging datasets will be used to identify, segment, and extract quantitative parameters from therapy-resistant tumours; far outperforming conventional methods of annotation. Concurrently, we will develop molecularly imprinted nanoparticles (nanoMIPs) that bind xCT to deliver a targeted payload of drug to these resistant tumours. An iterative selection process will be employed to manufacture double imprinted fluorescent nanoMIPs that bind both xCT and doxorubicin to produce therapeutic agents with excellent selectivity and potency. Finally, these innovative technologies will be combined to image, detect, and treat multifocal therapy-resistant disease in a genetically engineered mouse model of NSCLC. Together, this novel treatment platform will support our mission to provide treatment options where currently there are none.
Publications

Garg S
(2024)
Electroactive Molecularly Imprinted Polymer Nanoparticles (eMIPs) for Label-free Detection of Glucose: Toward Wearable Monitoring.
in Small (Weinheim an der Bergstrasse, Germany)

Greenwood HE
(2023)
Imaging the master regulator of the antioxidant response in non-small cell lung cancer with positron emission tomography.
in bioRxiv : the preprint server for biology
Title | AI tool to assess therapy resistance |
Description | AI/deep learning method to identify therapy resistant cancer from PET imaging scans. |
Type Of Material | Technology assay or reagent |
Year Produced | 2023 |
Provided To Others? | No |
Impact | None yet as still in development. |
Title | Imaging agent to detect therapy resistant cancer - FSPG |
Description | Development of a novel positron emission tomography agents that binds to the amino acid transporter, xCT. Results in the identification of therapy resistant cancer in living subjects. |
Type Of Material | Technology assay or reagent |
Year Produced | 2023 |
Provided To Others? | Yes |
Impact | Clinical trial initiated (NCT05889312) funded by RoseTrees and GST Charity. Adoption of imaging methods by others. |
Title | NanoMIP for xCT |
Description | Specific nanoparticle for the binding and treatment of therapy-resistant cancer |
Type Of Material | Technology assay or reagent |
Year Produced | 2023 |
Provided To Others? | No |
Impact | N/A as still in development. |
Description | AI assessment of therapy resistant cancer |
Organisation | University of Oxford |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Creation of a new imaging agent to detect therapy resistant cancer. Imaging across eight different tumour types to provide extensive dataset for AI assessment. |
Collaborator Contribution | AI model creation and evaluation of our imaging dataset. |
Impact | Multidisciplinary research: Incorporates tumour biology, imaging research, and deep learning/mathematical modelling. Outputs are pending. |
Start Year | 2024 |
Description | Creation of nanoMIPs targeting therapy resistant cancer |
Organisation | University of Manchester |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Generate know-how regarding lung cancer biology, tumour models, and therapeutic targets. Evaluate the resulting therapeutics. |
Collaborator Contribution | Development of a novel nanoMIP that targets a biomarker, xCT, expressed in therapy-resistant tumours. |
Impact | Multidisciplinary research: incorporating cancer biology, imaging research, medicinal and polymer chemistry. |
Start Year | 2023 |
Title | First in class ALDH1A1 radiotracer |
Description | The development of a first-in-class novel ALDH1A1 imaging agent for the non-invasive assessment of drug resistance in vivo. |
Type | Diagnostic Tool - Imaging |
Current Stage Of Development | Initial development |
Year Development Stage Completed | 2020 |
Development Status | Under active development/distribution |
Impact | This PET radiotracer is able to identify, for the very first time, ALDH1A1 expression which is linked to cancer stem cells which are inherently chemo-resistant. Next-generation ALDH1A1 imaging tools have the potential to stratify patients into responders and non-responders, with the aim to dynamically track and recommend alternate treatments. |
Description | Interactive talk and workshop "Radioactivity - the overlooked cancer remedy" at the Thriving Minds symposium, Sancton Wood School, Cambridge, hosted by Lucy Hawking. |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Schools |
Results and Impact | 80 pupils from across the country attended our interactive 2h workshop on the medical uses of radioactivity. The pupils gained a new understanding of the benefits, not just the risks of radiation, it's use in everyday life, and how it can be used to treat cancer patients. There was a lively discussion in this interactive workshop, which included practical activities, such as locating where the radioactive substance was inside a representation of a human body. The event was covered by the Cambridge local press and we got very positive feedback from the teachers, students, and organiser Lucy Hawking (Stephen Hawking's daughter). |
Year(s) Of Engagement Activity | 2023 |