Research suggests that five features predict response to checkpoint inhibitor (CPI) chemotherapy across a wide range of cancers.
Results in Genetics of naturecould help personalize cancer treatment by identifying those most likely to benefit from immunotherapy.
They could also expand the use of immunotherapy, as several patient groups who would not currently be considered candidates have been flagged as likely to benefit from the treatment.
The researchers believe the five features provide a frame of reference for current and future biomarkers of CPI response and survival.
“To date, much research has focused on identifying and reporting individual biomarkers, but our results suggest that many of these biomarkers may be different versions of the same underlying factors,” said researcher Abel González-Pérez, PhD, a bioinformatician at the Institute of Science and Technology in Barcelona, Spain.
Proton pump inhibitors (PPIs) have had a major impact on cancer treatment, but patient response to treatment is highly variable and treatment can be associated with serious immune-related adverse events.
It is becoming increasingly clear that response to CPIs and subsequent survival depend on latent factors that include characteristics of the tumor, its microenvironment, and the host.
Biomarkers identified in different studies may in fact reflect different measurements of these underlying aspects of the tumor, making it difficult to determine how many independent markers there truly are.
For example, the expression of genes and gene sets previously identified as biomarkers may collectively reflect the degree of tumor infiltration by cytotoxic cells.
To investigate further, the researchers analyzed thousands of molecular and clinical features in 479 patients with metastatic cancer who participated in the Center for Personalized Cancer Treatment study.
Participants received therapy with anti-PD1/PDL1 antibodies or a combination of anti-PD1/PDL1 and anti-CTLA4 antibodies.
Analysis of genomic, transcriptomic, and clinical data revealed that all significantly associated features could be classified into one of five independent latent factors that are relevant to all cancer types.
These included tumor mutation burden (TMB), effective T cell infiltration, whether patients had received any prior treatment, transforming growth factor beta (TGF-β) activity in the tumor microenvironment, and tumor proliferative potential.
Their association with CPI response and survival was confirmed in six independent cohorts comprising 1,491 individuals and covering six major cancer types.
The researchers examined how five hidden factors combined using multivariate machine learning models to predict response, overall survival, or progression-free survival in the original cohort of patients.
For patients in the HMF cohort who did not receive CPI inhibitors, these data revealed that a significant proportion of patients with skin (35%), bladder (42%), and lung (16%) tumors were highly likely to respond to treatment.
Patients with other metastatic malignancies, some of which would not typically be considered candidates for CPI, were also identified as potential responders.
For example, 4% of patients with breast cancer, 3% with colon cancer, 19% with renal tumors, and 15% with liver cancer were shown to be highly likely to respond to CPIs.
“This study represents an important step in understanding how different tumor characteristics affect treatment response,” said researcher Nuria Lopez-Bigas, MD, also of the Barcelona Institute of Science and Technology.
“We hope that in the future, these five factors will be incorporated into clinical practice and will serve to guide treatment decisions.”
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