SCORPIO predicting treatment response

Can AI Help Predict Which Cancer Patients Should Be Treated with Immunotherapy? February 26, 2025, by Sharon Reynolds

A class of immunotherapy drugs called immune checkpoint inhibitors have proven transformative for people with some types of cancer. In a small number of patients with advanced forms of melanoma, lung cancer, and other tumor types, deposits of cancer scattered throughout their bodies simply melt away. In notable but rare cases, the cancer never comes back.

However, in most patients immune checkpoint inhibitors don’t have any effect on the cancer or, if they do, the cancer returns. So, because these drugs can have serious and sometimes deadly side effects, as well as a high price tag, researchers have been racing to find ways to identify the patients who are most likely to benefit from these treatments.

In a new study, researchers tested an artificial intelligence–based tool that may be able to meet this need. The tool, called SCORPIO, was more accurate than Food and Drug Administration–approved tests in predicting whether patients’ tumors would shrink following treatment with immune checkpoint inhibitors and how long they were likely to live after the treatment, according to findings published January 6 in Nature Medicine.

According to the study's lead investigator, Diego Chowell, Ph.D., of the Icahn School of Medicine at Mount Sinai, what's particularly noteworthy about SCORPIO is that it's "constructed using information pulled only from routine blood tests and patients’ medical records."

It's becoming increasingly important to develop these sorts of predictive tools, said Eytan Ruppin, M.D., Ph.D., of NCI’s Center for Cancer Research. 

“In many patients, you can consider a few different types of drugs,” explained Dr. Ruppin, who has developed similar models but was not involved with the current study. “So, we’d like to know which one a patient’s cancer is most likely to respond to, to help us carefully weigh the benefits versus the risks and side effects.” 

Building SCORPIO from the basics

To build and test SCORPIO, Dr. Chowell and his colleagues used data from nearly 10,000 people who had been treated with one or a combination of immune checkpoint inhibitors at Mount Sinai and Memorial Sloan Kettering Cancer Center in New York.

A total of 21 cancer types were included in SCORPIO’s development. The most common types were melanoma and cancers of the bladder, liver, lung, and kidney.

The researchers initially built SCORPIO using data from about 2,000 patients treated at Memorial Sloan Kettering Cancer Center. They found that simple clinical factors such as age, sex, body mass index, and measurements from standard blood “panels” were key factors that could predict survival and tumor response after immune checkpoint inhibitor treatment.

The team then tested the new model’s performance using several validation sets of data collected from patients in two other large real-world cohorts and participants in 10 clinical trials who had been treated with immune checkpoint inhibitors. SCORPIO accurately predicted survival, with performance ranging from 72% to 76% in different patient groups over the next two and a half years. 

“That’s a remarkable performance, just using routine blood tests and basic [patient] data,” said Dr. Chowell. The model could also distinguish between people who were more or less likely to benefit from treatment with an immune checkpoint inhibitor. This included a broad range of responses, from tumors that grew to those that remained stable (i.e., neither shrinking nor growing) and those that were completely eliminated.

SCORPIO was much better than TMB at predicting which patients would live longer after treatment.

“And [SCORPIO] performed better in the real-world cohorts than in the clinical trial cohorts,” Dr. Chowell explained. This finding, he said, shows that incorporating data from a wide variety of treatment scenarios and a diverse variety of people with different health profiles made the tool more accurate than if it had been built from clinical trial data alone.

What’s next for SCORPIO?

Dr. Chowell’s team will next be working with hospitals and clinics to test how well SCORPIO performs in cancer patients who haven’t yet received an immune checkpoint inhibitor (although the score will not be used to determine whether they should get one).

The team is also building a cloud-based platform for the tool that will be publicly accessible. LORIS is publicly available on NCI’s website. 

Researchers working on models to predict the response to various treatments are also looking for other information that can be added to further increase the predictive power of these models, Dr. Ruppin explained. 

“It’s possible that we’re reaching a plateau [of what we can predict using data from routine blood tests], because tumors are complicated. But that merits further exploration and only time will tell.”

Latest Research on Soft Sarcoma Detection

Recent studies report substantial progress in soft tissue sarcoma (STS) detection, with a focus on non-invasive liquid biopsies, dual-time PET imaging, and artificial intelligence-based diagnostics. These innovations address longstanding difficulties in distinguishing rare sarcomas from benign masses such as lipomas. 

Recent Diagnostic Advancements

• Liquid Biopsies and cfDNA: Given the lack of widely validated blood biomarkers for STSs, current research emphasises circulating tumour DNA (ctDNA) and cell-free DNA (cfDNA). Recent studies demonstrate that ctDNA analysis reliably monitors high-risk patients and detects tumour recurrence or metastasis with accuracy comparable to standard radiological imaging.

• AI-Assisted Imaging Analysis: Convolutional Neural Networks (CNNs) are currently trained on large thoracic imaging datasets to detect metastatic nodules, often less than 1 cm in size, using conventional X-rays and CT scans. Incorporating these tools into clinical workflows enables faster and more precise nodule detection.

• Dual-Time Point PET Scans: Studies involving FDG PET-CT indicate that assessing lesions 4 hours after injection, rather than the standard 30 minutes, improves differentiation between malignant and benign tissues. This approach leverages the peak metabolic uptake characteristic of malignant STSs.

• Epigenetic Biomarker Signatures: Researchers are investigating DNA methylation patterns as diagnostic fingerprints. Similar to approaches used in brain tumours, these cell-specific epigenetic modifications present a promising avenue for early detection of sarcomas that are challenging to biopsy.