new study published in Nature Communications.
Specifically, the tool can aid doctors in identifying a “molecular diagnosis” based on a patient’s tumor and cancer characteristics, Kun-Hsing Yu, MD, PhD, the study’s senior author and an assistant professor of biomedical informatics at Harvard Medical School, Boston, said in an interview.
The Multi-omics Multi-cohort Assessment (MOMA) “successfully identified indicators of how aggressive a tumor was and how likely it was to behave in response to a particular treatment,” as well as patients’ overall and disease-free survival, noted Harvard Medical School in a press release. “Based on an image alone, the model also pinpointed characteristics associated with the presence or absence of specific genetic mutations – something that typically requires genomic sequencing of the tumor.”
The researchers designed the tool to offer “transparent reasoning,” so that if a clinician asks it why it made a certain prediction, it would be able to explain its reasoning and the variables it used, the press release noted.
“We first allow AI to explore any correlation, and then we try to explain those correlations using existing pathology terms that experts will be able to understand,” Dr. Yu said in an interview.
Although the tool is freely available to clinicians and researchers, it’s not yet ready for clinical use. When it is, the tool has the potential to provide timely, accurate decision support based on tumor imaging.
Colorectal cancer is the second most common cause of death from cancer in the United States, with more than 53,000 deaths each year, and the patient population has been gradually skewing younger over the past 2 decades.
Although clinicians already use histopathology and genetic analysis to guide treatment, the process can take several days or weeks in some areas, and these services may not be available in all parts of the world.
“Currently, a clinician has to send a [tissue] sample from the tumor specimen to genomic sequencing labs and wait for a week, sometimes up to 3 or more weeks, to get genomic sequencing results,” Dr. Yu said. That means a patient’s anxiety grows as they wait to find out which treatments might benefit them or how they might respond to a particular treatment.
Additionally, current knowledge for predicting patient survival, beyond considering the patient’s cancer stage, age, and general health status, is limited, Dr. Yu said.
The MOMA platform was trained on information from 1,888 patients with colorectal cancer from three national cohorts: 628 patients from The Cancer Genome Atlas (TCGA) program, 927 patients from the Nurses’ Health Study with Health Professionals Follow-Up Study (NHS-HPFS), and 333 patients from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial.
During the training, they fed the model information about the patients’ age, sex, cancer stage, and outcomes, as well as their tumors’ “multi-omic” information: the cancers’ genomic, epigenetic, protein, and metabolic profiles. Researchers showed the AI model digital, whole-slide histopathology images of tumor samples and asked it to look for visual markers related to tumor types, genetic mutations, epigenetic alterations, disease progression, and patient survival with the goal of enabling the platform to detect patterns that are indiscernible to the human eye.
They then tested the MOMA platform’s ability to interpret images by feeding it new tumor sample images from different patients and asking it to predict their survival and progression-free survival.
The researchers found that the tool successfully identified overall survival outcomes in patients with stage I or II cancer in the TCGA cohort, which they further validated with the NHS-HPFS and PLCO cohorts. The platform revealed that “dense clusters of adenocarcinoma cells are highly indicative of worse overall survival outcomes” and that the interaction of cancer cells with smooth muscle cells in cancerous areas predicted poorer overall survival.
MOMA was slightly more effective in predicting progression-free survival for stage I and stage II colorectal cancer across all three cohorts.
“Compared with the overall survival prediction, our progression-free survival model puts more emphasis on infiltrating lymphocytes and regions associated with extracellular mucin in its prediction,” the authors noted.
Prediction of overall survival and progression-free survival for stage III colorectal cancer showed similar levels of accuracy, they noted.
The tool also successfully assessed patients’ likely response to immunotherapy using predictions of microsatellite instability, since high MSI indicates a better response to immune checkpoint inhibitors.
MOMA outperformed a different machine-learning algorithm in predicting the copy number alterations and other features related to cancer development, and it predicted the likelihood of a BRAF mutation, which is linked to poorer prognosis.