129P - Computational approaches for enhancing the efficacy of cancer immunotherapy

Background

Cancer immunotherapy has revolutionized cancer treatment by harnessing the immune system to target tumors. However, its clinical success is limited by low response rates, typically ∼20%. To address the challenge, we developed a transcriptome-based computational framework aimed at improving the efficacy of immunotherapy.

Methods

We established a computational framework to analyze similarities between transcriptome patterns associated with responsiveness or resistance to cancer immunotherapy. We then applied this computational framework to two applications: identifying predictive biomarkers for immunotherapy response and discovering synergistic compounds for combination therapy with immunotherapy. To identify predictive biomarkers, we investigated genomic and epigenomic changes and the transcriptional impact of gene knockouts or knockdowns that mimic the transcriptome pattern of responders. To identify synergistic compounds for combination therapy, we screened compounds in silico that reverse gene signatures associated with resistance to immunotherapy (e.g., T cell exclusion signature, cancer immune resistance program, and nivolumab resistance signature. Finally, we evaluated our computational results through in vitro and in vivo studies.

Results

Using this approach, we found genomic biomarkers that could enhance immunotherapy response. We also discovered three types of chemical compounds for combination therapy: I) known immunotherapy agents (POC verification), Ⅱ) unknown immunotherapy agents with known MoA (drug repurposing), Ⅲ) four clusters with new chemical structures (new drug candidates). We further studied a compound cluster from (Ⅲ) by reverse-docking and measuring structural similarity to identify potential drug targets. We finally demonstrated that co-administration of the compound with anti-PD1 significantly improved survival in a syngeneic mouse model.

Conclusions

We successfully identified both predictive biomarkers and novel synergistic compounds that enhance the efficacy of cancer immunotherapy. These findings hold promise for improving patient outcomes and developing more effective combination therapies. The newly identified compounds are now progressing through preclinical development.

Legal entity responsible for the study

The authors.

Funding

Korea Institute of Science and Technology Information (KISTI) (K24L2M1C4-01) The National Research Foundation of Korea (NRF-2022R1C1C1006162).

Disclosure

All authors have declared no conflicts of interest.