Tissue from a patient (a) with microsatellite instability (MSI) and a patient...
Tissue from a patient (a) with microsatellite instability (MSI) and a patient (b) with microsatellite stability (MSS).

Source: Jakob Nikolas Kather

News • Microsatellites

Stomach and colorectal cancer: AI identifyies patients for immunotherapy

Changes in certain sections of the genetic material of cancer cells, so-called microsatellites, can provide an important indication of whether immunotherapy may be successful in a patient with stomach or colorectal cancer.

Scientists from Uniklinik RWTH Aachen, the German Cancer Research Center (DKFZ), the German Cancer Consortium (DKTK) and the National Center for Tumor Diseases Heidelberg (NCT) have developed an adaptive algorithm that can predict instability in microsatellites based directly on images of tissue samples. This could help to potentially identify patients at an early stage who could benefit from immunotherapy. The research results were published in the journal Nature Medicine.

With our approach, we have the potential to test any patient with colorectal cancer for MSI automatically and cost-effectively, which allows us to provide the option of immunotherapy to a larger group of colorectal cancer patients

Jakob Nikolas Kather

Only a small number of patients with stomach or colorectal cancer respond to immunotherapy. Some tumors lead to changes in the genetic material, and this can cause mutations in the sections of the genome, referred to as “microsatellites”, that are frequently replicated. This microsatellite instability (MSI) is a characteristic for distinguishing between different cancers of the gastrointestinal tract and determines whether patients with these diseases are able to respond well to immunotherapy with checkpoint inhibitors. Detecting these properties usually requires a genetic or immunohistochemical test, which requires additional costs and is not always automatically performed for every patient in clinical practice.

The scientists in Aachen and Heidelberg, in collaboration with international colleagues, showed that a computer-aided adaptable algorithm, based on the concept of “deep learning”, enables MSI to be directly diagnosed based on routinely available images of tissue samples without the need for additional laboratory tests. “With our approach, we have the potential to test any patient with colorectal cancer for MSI automatically and cost-effectively, which allows us to provide the option of immunotherapy to a larger group of colorectal cancer patients,” says Jakob Nikolas Kather, physician and scientist at the Clinic for Gastroenterology, Metabolic Diseases and Internal Intensive Care (Medical Clinic III) at Uniklinik RWTH Aachen and member of staff at DKFZ and NCT Heidelberg. “This makes it possible to identify patients who may otherwise never be considered for immunotherapy. However, this approach must be reviewed in prospective studies,” adds Dirk Jäger, Medical and Executive Director of the Department of Medical Oncology at NCT Heidelberg.


Source: NCT Heidelberg

07.06.2019

Read all latest stories

Related articles

Photo

News • Biomarker-based predictions

AI drives precision oncology for colorectal cancer

For the first time, researchers show that AI-based predictions can deliver comparable results to clinical tests on biopsies of patients with colorectal cancer (CRC).

Photo

News • Mysterious mechanism solved

Researchers discover how bowel cancer 'blinds' the immune system

A mystery which has stumped bowel cancer researchers for decades, has been solved by scientists at the Cancer Research UK Beatson Institute and University of Glasgow.

Photo

News • AI approach to colorectal tumors

Deep learning identifies molecular patterns of cancer

A new AI platform can analyze genomic data extremely quickly, picking out key patterns to classify different types of colorectal tumors and improve the drug discovery process.

Related products

Advanced intelligent Clear-IQ Engine for MR

Artificial Intelligence

Canon · Advanced intelligent Clear-IQ Engine for MR

Canon Medical Systems Europe B.V.
CT Image Reconstruction

Artificial Intelligence

Canon · CT Image Reconstruction

Canon Medical Systems Europe B.V.
HIT Automation Platform

Artificial Intelligence

Canon · HIT Automation Platform

Canon Medical Systems Europe B.V.
REiLI

Artificial Intelligence

Fujifilm · REiLI

FUJIFILM Europe GmbH
Vantage Elan NX Edition

1.5 Tesla

Canon · Vantage Elan NX Edition

Canon Medical Systems Europe B.V.
3DQuorum SmartSlices

Artificial Intelligence

Hologic · 3DQuorum SmartSlices

Hologic, Inc.
Subscribe to Newsletter