Deep learning unlocks personalised cancer therapy
14 August 2023
Engineers and cancer researchers have harnessed the power of machine learning technology to predict immune-boosting proteins.
Machine learning technology developed by a team of Johns Hopkins engineers and cancer researchers can accurately predict cancer-related protein fragments that may trigger an immune system response.
If validated in clinical trials, the technology could help scientists overcome a major hurdle to developing personalised immunotherapies and vaccines.
In a new study, investigators from Johns Hopkins Biomedical Engineering, the Johns Hopkins Institute for Computational Medicine, the Johns Hopkins Kimmel Cancer Center and the Bloomberg~Kimmel Institute for Cancer Immunotherapy show that their deep learning method, called BigMHC, can identify protein fragments on cancer cells that elicit a tumour cell-killing immune response, an essential step in understanding response to immunotherapy and in developing personalised cancer therapies.
“Cancer immunotherapy is designed to activate a patient’s immune system to destroy cancer cells,” says Rachel Karchin, PhD, Professor of Biomedical Engineering, Oncology And Computer Science, and a core member of the Institute for Computational Medicine.
“A critical step in the process is immune system recognition of cancer cells through T cell binding to cancer-specific protein fragments on the cell surface.”
The cancer protein fragments that elicit this tumour-killing immune response may originate from changes in the genetic makeup of cancer cells (or mutations), called mutation-associated neoantigens.
Each patient’s tumour has a unique set of such neoantigens that determine tumour foreignness, in other words, how different the tumour makeup is compared to self.
Scientists can identify which mutation-associated neoantigens a patient’s tumour has by analysing the genome of the cancer. Determining those which are most likely to trigger a tumour-killing immune response could enable scientists to develop personalised cancer vaccines or customised immune therapies as well as inform patient selection for these therapies.
However, current methods for identifying and validating immune response-triggering neoantigens are time-consuming and costly, as these typically rely on labour-intense, wet laboratory experiments.
Because neoantigen validation is so resource intensive, there are little data to train deep learning models. To address this, the researchers trained BigMHC, a set of deep neural networks, in a two-stage process called transfer learning.
First, BigMHC learned to identify antigens that are presented at the cell surface, an early stage of the adaptive immune response for which many data are available.
Then, BigMHC was fine-tuned by learning a later stage, T-cell recognition, for which little data exist. In this manner, the researchers leveraged massive data to build a model of antigen presentation, and refined this model to predict immunogenic antigens.
The researchers tested BigMHC on a large independent data set, and showed that it was better at predicting antigen presentation than other methods. They further tested BigMHC on data from study co-author Kellie Smith, PhD, Associate Professor Of Oncology at the Bloomberg-Kimmel Institute for Cancer Immunotherapy, and found that BigMCH significantly outperformed seven other methods at identifying neoantigens that trigger a T-cell response.
“BigMHC has outstanding precision at predicting immunogenic neoantigens,” says Karchin.
“There is an urgent, unmet clinical need to tailor cancer immunotherapy to the subset of patients most likely to benefit, and BigMHC can shed light into cancer features that drive tumour foreignness, thus triggering an effective anti-tumour immune response,” says study co-author Valsamo “Elsa” Anagnostou, M.D., PhD.
The team is now expanding its efforts in testing BigMHC in several immunotherapy clinical trials to determine if it can help scientists sift through hundreds of thousands of neoantigens to filter down to those most likely to provoke an immune response.
“The hope is that BigMHC could guide cancer immunologists as they develop immunotherapies that can be used for multiple patients, or develop personalised vaccines that would boost a patient’s immune response to kill their cancer cells,” says lead author Benjamin Alexander Albert.
Karchin and her team believe BigMHC and machine learning-based tools like it can help clinicians and cancer researchers efficiently and cost-effectively sift through vast amounts of data needed to develop more personalised approaches to cancer treatment. “Deep learning has an important role to play in clinical cancer research and practice,” Karchin says.