Activity-based urinary biomarkers of response and resistance to checkpoint blockade immunotherapy

Abstract

Immune checkpoint blockade (ICB) therapy has transformed the clinical care of cancer, yet the majority of patients do not derive clinical benefit and responders can acquire resistance to therapy. Noninvasive biomarkers to indicate early on-treatment response and resistance mechanisms are needed to improve patient management. We engineer activity-based synthetic biomarkers called immune sensors for monitoring checkpoint blockade therapy (INSIGHT), which comprise a library of mass-barcoded peptides conjugated to ICB antibodies (e.g., αPD1). INSIGHT allows detection of in vivo T cell and tumor protease activity by quantification of cleaved peptide fragments that have cleared into urine. αPD1-sensor conjugates monitoring the T cell protease granzyme B (GzmB) retained target binding and were capable of sensing T cell killing of cancer cells. In syngeneic tumors, systemic administration of these conjugates resulted in therapeutic efficacy comparable to unconjugated antibodies and produced elevated reporter signals in urine indicative of tumor responses by the second dose. To differentiate resistant tumors, we analyzed the transcriptomes of ICB-treated tumors for protease signatures of response and resistance and developed a multiplexed library of mass-barcoded protease sensors. This library enabled us to build machine learning classifiers based on urine signals that detected and stratified two mechanisms of resistance, B2m and Jak1 loss-of-function mutations. Our data demonstrates the potential of INSIGHT for early on-treatment response assessment and classification of refractory tumors based on resistance mechanisms.

Interfacing Biomaterials with Synthetic T Cell Immunity

“…Upon sensing proteases, the substrates are cleaved, releasing mass‐encoded reporters into urine for multiplexed analysis by LC/MS–MS. [ 55,61,62 ] Ab, antibody; pMHC, peptide‐major histocompatibility complex; PDMS, polydimethylsiloxane; PET, positron emission tomography; NP, nanoparticle; LC/MS–MS, liquid chromatography and tandem mass spectrometry.…”

“…This opportunity to challenge current limits of detection has enabled the use of synthetic biomarkers for early disease detection in cancer and other pathological conditions. [ 54–63 ]…”

“…[ 152 ] Therefore, the development of synthetic biomarkers to sense GzmB activity in vivo has the potential for early detection of T cell‐mediated conditions such as transplant rejection and tumor responses to cancer immunotherapy. [ 55,62 ] To develop GzmB nanosensors for detection of T cell activity at the onset of acute cellular rejection, Mac et al. decorated iron oxide nanoparticles with peptide substrates specific for GzmB.…”

“…In this approach, GzmB peptide substrates were directly coupled to therapeutic checkpoint antibodies (e.g., α PD1, α CTLA4) to harness the biological functions of therapeutic antibodies while sensing anti‐tumor T cell activity at the same time points of ICB treatment. [ 55 ] In two syngeneic tumor models, ICB antibody‐GzmB sensor conjugates produced elevated reporter signals in urine of mice responding to therapy, allowing noninvasive detection of therapeutic responses as early as the second dose of treatment (AUROC = 0.92–1.00) and before changes in tumor burden were detected. The increases in reporter signal correlated with observed increases in GzmB+ CD8+ TILs by flow cytometry, suggesting that GzmB sensor conjugates could detect anti‐tumor T cell activity at the onset of therapy response through production of urinary reporters.…”

“…extended ICB response assessment by using a mass‐barcoded library of synthetic biomarkers to monitor both immune‐ and tumor‐associated proteases for classification of refractory tumors based on resistance mechanisms. [ 55 ] In mouse models of resistance, gene knockout of B2m or Jak1 allowed for evasion of CD8+ T cell‐mediated tumor control, leading to resistance to checkpoint inhibitors. Administration of a multiplexed library of synthetic biomarkers enabled development of machine learning classifiers based on urinary signatures that stratified B2m from Jak1 resistance with high accuracy (AUROC ≥ 0.9).…”