Yet another Success Story: Machine Learning to predict immunotherapy response
Curator and Reporter: Dr. Premalata Pati, Ph.D., Postdoc
Immune-checkpoint blockers (ICBs) immunotherapy appears promising for various cancer types, offering a durable therapeutic advantage. Only a number of cases with cancer respond to this therapy. Biomarkers are required to adequately predict the responses of patients. This article evaluates this issue utilizing a system method to characterize the immune response of the anti-tumor based on the entire tumor environment. Researchers build mechanical biomarkers and cancer-specific response models using interpretable machine learning that predict the response of patients to ICB.
The lymphatic and immunological systems help the body defend itself by combating. The immune system functions as the body’s own personal police force, hunting down and eliminating pathogenic baddies.
According to Federica Eduati, Department of Biomedical Engineering at TU/e, “The immune system of the body is quite adept at detecting abnormally behaving cells. Cells that potentially grow into tumors or cancer in the future are included in this category. Once identified, the immune system attacks and destroys the cells.”
Immunotherapy and machine learning are combining to assist the immune system solve one of its most vexing problems: detecting hidden tumorous cells in the human body.
It is the fundamental responsibility of our immune system to identify and remove alien invaders like bacteria or viruses, but also to identify risks within the body, such as cancer. However, cancer cells have sophisticated ways of escaping death by shutting off immune cells. Immunotherapy can reverse the process, but not for all patients and types of cancer. To unravel the mystery, Eindhoven University of Technology researchers used machine learning. They developed a model to predict whether immunotherapy will be effective for a patient using a simple trick. Even better, the model outperforms conventional clinical approaches.
The outcomes of this research are published on 30th June, 2021 in the journal Patterns in an article entitled “Interpretable systems biomarkers predict response to immune-checkpoint inhibitors”.
The Study
- Characterization of the tumor microenvironment from RNAseq and prior knowledge
- Multi-task machine-learning models for predicting antitumor immune responses
- Identification of cancer-type-specific, interpretable biomarkers of immune responses
- EaSIeR is a tool to predict biomarker-based immunotherapy response from RNA-seq
Image Source: https://els-jbs-prod-cdn.jbs.elsevierhealth.com/cms/attachment/815c5676-4343-4b3e-a8db-92ceecf7a464/fx1_lrg.jpg
“Tumor also contains multiple types of immune and fibroblast cells which can play a role in favor of or anti-tumor, and communicates among themselves,” said Oscar Lapuente-Santana, a researcher doctoral student in the computational biology group. “We had to learn how complicated regulatory mechanisms in the micro-environment of the tumor affect the ICB response. We have used RNA sequencing datasets to depict numerous components of the Tumor Microenvironment (TME) in a high-level illustration.”
Using computational algorithms and datasets from previous clinical patient care, the researchers investigated the TME.
Eduati explained
While RNA-sequencing databases are publically available, information on which patients responded to ICB therapy is only available for a limited group of patients and cancer types. So, to tackle the data problem, we used a trick.
All 100 models learned in the randomized cross-validation were included in the EaSIeR tool. For each validation dataset, we used the corresponding cancer-type-specific model: SKCM for the melanoma Gide, Auslander, Riaz, and Liu cohorts; STAD for the gastric cancer Kim cohort; BLCA for the bladder cancer Mariathasan cohort; and GBM for the glioblastoma Cloughesy cohort. To make predictions for each job, the average of the 100 cancer-type-specific models was employed. The predictions of each dataset’s cancer-type-specific models were also compared to models generated for the remaining 17 cancer types.
From the same datasets, the researchers selected several surrogate immunological responses to be used as a measure of ICB effectiveness.
Lapuente-Santana stated
One of the most difficult aspects of our job was properly training the machine learning models. We were able to fix this by looking at alternative immune responses during the training process.
Some of the researchers employed the machine learning approach given in the paper to participate in the “Anti-PD1 Response Prediction DREAM Challenge.”
DREAM is an organization that carries out crowd-based tasks with biomedical algorithms. “We were the first to compete in one of the sub-challenges under the name cSysImmunoOnco team,” Eduati remarks.
The researchers noted,
We applied machine learning to seek for connections between the obtained system-based attributes and the immune response, estimated using 14 predictors (proxies) derived from previous publications. We treated these proxies as individual tasks to be predicted by our machine learning models, and we employed multi-task learning algorithms to jointly learn all tasks.
The researchers discovered that their machine learning model surpasses biomarkers that are already utilized in clinical settings to evaluate ICB therapies.
But why are Eduati, Lapuente-Santana, and their colleagues using mathematical models to tackle a medical treatment problem? Is this going to take the place of the doctor?
Eduati explains
Mathematical models can provide an overview of the interconnection between individual molecules and cells and at the same time predicting a particular patient’s tumor behavior. This implies that immunotherapy with ICB can be personalized in a patient’s clinical setting. The models can aid physicians with their decisions about optimum therapy, it is vital to note that they will not replace them.
Furthermore, the model aids in determining which biological mechanisms are relevant for the biological response.
The researchers noted
Another advantage of our concept is that it does not need a dataset with known patient responses to immunotherapy for model training.
Further testing is required before these findings may be implemented in clinical settings.
Main Source:
Lapuente-Santana, Ó., van Genderen, M., Hilbers, P. A., Finotello, F., & Eduati, F. (2021). Interpretable systems biomarkers predict response to immune-checkpoint inhibitors. Patterns, 100293. https://www.cell.com/patterns/pdfExtended/S2666-3899(21)00126-4
Other Related Articles published in this Open Access Online Scientific Journal include the following:
Inhibitory CD161 receptor recognized as a potential immunotherapy target in glioma-infiltrating T cells by single-cell analysis
Reporter: Dr. Premalata Pati, Ph.D., Postdoc
Immunotherapy may help in glioblastoma survival
Reporter and Curator: Dr. Sudipta Saha, Ph.D.
https://pharmaceuticalintelligence.com/2019/03/16/immunotherapy-may-help-in-glioblastoma-survival/
Deep Learning for In-silico Drug Discovery and Drug Repurposing: Artificial Intelligence to search for molecules boosting response rates in Cancer Immunotherapy: Insilico Medicine @John Hopkins University
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Machine Learning (ML) in cancer prognosis prediction helps the researcher to identify multiple known as well as candidate cancer diver genes
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AI System Used to Detect Lung Cancer
Reporter: Irina Robu, PhD
https://pharmaceuticalintelligence.com/2019/06/28/ai-system-used-to-detect-lung-cancer/
Cancer detection and therapeutics
Curator: Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2016/05/02/cancer-detection-and-therapeutics/
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