Oral Presentation 38th Lorne Cancer Conference 2026

The Evolving Microenvironment: What Drives Checkpoint Response? (136302)

Melvin Chin 1 2 3
  1. University of Western Australia, Crawley, WA
  2. The Kids Research Instutite, Nedlands, WA, Australia
  3. Sir Charles Gairdner Hospital, Western Australia, WA, Australia

Immune checkpoint blockade has transformed outcomes across multiple tumour types, yet durable responses remain limited to a subset of patients. Static biomarkers on pre-treatment tumour samples such as PD-L1 expression and tumour mutational burden fail to capture the dynamic nature of the tumour microenvironment and its evolution during treatment. Understanding how the microenvironment changes, and what drives these changes, is critical to improving patient selection and therapeutic strategies.

This presentation provides a narrative review examining dynamic determinants of checkpoint response. Using mesothelioma as a model system, we draw on preclinical murine studies, translational chemoimmunotherapy cohorts, and single-cell transcriptomic approaches to explore how the tumour microenvironment evolves before and during therapy.

The microenvironment is not static. Throughout treatment, immune populations expand and contract, shift phenotype, and acquire markers of activation or exhaustion. Emerging evidence highlights that the temporal kinetics of immune signalling matter. Transient, tightly regulated type I interferon activation distinguishes responders from non-responders, while chronic signalling may drive resistance. In peripheral blood, stem-like CD8+ effector memory populations expand in responders, and these changes are coupled to the transcriptional state of the tumour itself. Together, these findings suggest that tracking immune dynamics across tumour and blood may better predict response than static pre-treatment measurements.

A key focus is the value of translational approaches. Murine models allow precise dissection of temporal immune dynamics, and it is encouraging that the principle of dynamic biomarker change extends to clinical cohorts despite the heterogeneity inherent in human disease. Collectively, this points toward improved response prediction and new opportunities for intervention.

  1. Zemek RM, Chin WL, Fear VS, Wylie B, Casey TH, Forbes C, Tilsed CM, Boon L, Guo BB, Bosco A, Forrest ARR, Millward MJ, Nowak AK, Lake RA, Lassmann T, Lesterhuis WJ. Temporally restricted activation of IFNβ signaling underlies response to immune checkpoint therapy in mice. Nat Commun. 2022;13(1):4895.
  2. Chin WL, Cook AM, Chee J, Principe N, Hoang TS, Kidman J, Hmon KPW, Yeow Y, Jones ME, Hou R, Denisenko E, McDonnell AM, Hon CC, Moody J, Anderson D, Yip S, Cummins MM, Stockler MR, Kok PS, Brown C, John T, Kao SC-H, Karikios DJ, O'Byrne KJ, Hughes BGM, Lake RA, Forrest ARR, Nowak AK, Lassmann T, Lesterhuis WJ. Coupling of response biomarkers between tumor and peripheral blood in patients undergoing chemoimmunotherapy. Cell Rep Med. 2025;6(1):101882.
  3. Chin WL, Zemek RM, Tilsed CM, Forrest ARR, Fear VS, Forbes C, Boon L, Bosco A, Guo BB, Millward MJ, Nowak AK, Lake RA, Lesterhuis WJ, Lassmann T. Time-course RNAseq data of murine AB1 mesothelioma and Renca renal cancer following immune checkpoint therapy. Sci Data. 2024;11(1):448.