bioRxivBioengineeringPreprint
Activity-based urinary biomarkers of response and resistance to checkpoint blockade immunotherapy
Q. D. Mac, C. Xu, J. R. Bowen, A. Sivakumar, H. Phuengkham, F.-Y. Su, S. Z. Stentz, H. Sim, A. M. Harris, T. T. Li, P. Qiu, G. A. Kwong
Abstract
Immune checkpoint blockade (ICB) therapy has transformed cancer treatment, yet most patients do not derive clinical benefit and responders can acquire resistance to therapy. Noninvasive biomarkers are needed to indicate early on-treatment response and resistance mechanisms. Here we developed ImmuNe Sensors for monItorinG cHeckpoint blockade Therapy (INSIGHT), which comprises a library of mass-barcoded peptide substrates conjugated to PD1 antibodies, as therapeutic sensors. Following systemic administration, INSIGHT carries out the dual role of reinvigorating T cell function and profiling T cell and tumor proteases by the release of cleaved peptides into urine for noninvasive detection by mass spectrometry. We show that an PD1 therapeutic sensor for Granzyme B discriminates early treatment responses before tumor volumes significantly diverge from isotype controls in murine models of colorectal cancer. To differentiate mechanisms of resistance by multivariate analysis, we design a mass-barcoded, 14-plex INSIGHT library to profile proteases differentially expressed by tumors harboring B2m or Jak1 loss-of-function mutations. We find that binary classifiers trained on urine samples indicate response to PD-1 therapy as early as the start of the second dose, and discriminate B2m from Jak1 resistance with high sensitivity and specificity (AUROCs > 0.9). Our data supports the use of activity-based biomarkers for early on-treatment response assessment and classification of refractory tumors based on resistance mechanisms. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/420265v2_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@12ce799org.highwire.dtl.DTLVardef@118fcc5org.highwire.dtl.DTLVardef@17b227eorg.highwire.dtl.DTLVardef@115a7fc_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: mouse
- assay: proteomics
- research focus: drug response / drug discovery
- approach: deep learning
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