By Marta Rybczynski
Researchers at Johns Hopkins Kimmel Cancer Center and Bloomberg-Kimmel Institute for Cancer Immunotherapy have developed a computer model that could help clinicians quickly distinguish patients who will respond to immunotherapy for treating cancer, and who will not.
According to a recent study published in Nature, the “MANAscore” algorithm outperformed other RNA sequencing-based methods in identifying immune cells, highlighting its potential as a faster, more cost-effective alternative to current processes used to identify cells targeted by immunotherapy.
“We’re not the first to come up with one of these models,” said lead study author Kelly Smith, “but what sets ours apart is that it uses only three genes, while the most commonly used model requires more than 200 genes.”
Challenges of immunotherapy
Immunotherapies like PD-1 inhibitors have proven highly effective at treating cancer, but not all patients respond equally. Identifying T cell clones that drive successful immunotherapy responses is still a major challenge.
However, understanding the markers on MANA-specific T cells can provide insights into how these cells function in the tumour environment, and by knowing which markers are linked to their activity or exhaustion, researchers may be able to better predict how well these T cells might respond to cancer immunotherapies.
Superior performance
The team analysed tumour samples from 15 patients with non-small-cell lung cancer (NSCLC) who received neoadjuvant anti-PD-1 therapy with Bristol Myers Squibb’s Opdivo (nivolumab) and three patients with melanoma who received personalised neoantigen vaccines.
Investigators showed that the MANAscore model could identify the immune cells targeted by immune checkpoint inhibitor therapies, and helped them identify differences associated with patient response to immunotherapy as well. In addition, they found that the algorithm works better than other methods.
“The stem-like characteristics of T cells are critical because they enable self-renewal and long-term persistence,” said lead author Zhen Zeng, adding that these characteristics allow for sustained immune responses.
The team is continuing to develop a clinical test that uses multispectral immunofluorescence panels to identify the three-gene signature of immune therapy-responding T cells.
