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Updated: Sep 28, 2025

Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Serial changes in liquid biopsy-derived variant allele frequency predict immune checkpoint inhibitor responsiveness
Shumei Kato1, Bing Li2, Jacob J Adashek3
1Center for Personalized Cancer Therapy and Division of Hematology and Oncology, Department of Medicine, UC San Diego Moores Cancer Center, La Jolla, CA, USA.
Abstract:
Major immunotherapy challenges include a limited number of predictive biomarkers and the unusual imaging features post-therapy, such as pseudo-progression, which denote immune infiltrate-mediated tumor enlargement. Such phenomena confound clinical decision-making, since the cancer may eventually regress, and the patient should stay on treatment. We prospectively evaluated serial, blood-derived cell-free DNA (cfDNA) (baseline and 2-3 weeks post-immune checkpoint inhibitors [ICIs]) for variant allele frequency (VAF) and blood tumor mutation burden (bTMB) changes (next-generation sequencing) (N = 84 evaluable patients, diverse cancers). Low vs. high cfDNA-derived average adjusted ΔVAF (calculated by a machine-learning model) was an independent predictor of higher clinical benefit rate (stable disease ≥6 months/complete/partial response) (69.2% vs. 22.5%), and longer median progression-free (10.1 vs. 2.25 months) and overall survival (not reached vs. 6.1 months) (all P < .001, multivariate). bTMB changes did not correlate with outcomes. Therefore, early dynamic changes in cfDNA-derived VAF were a powerful predictor of pan-cancer immunotherapy outcomes.
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