Machine learning identifies clinical tumor mutation landscape pathways of resistance to checkpoint inhibitor therapy in NSCLC
In summary, we found several genomic markers and pathways that provide insight into biological mechanisms affecting response to CPI therapy. The analyses identified novel targets and biomarkers that have the potential to provide candidates for combination therapies or patient enrichment strategies, which could increase response rates to CPI therapy in patients with NSCLC.
Authors
- Bruno Gomès (ORCID: https://orcid.org/0009-0003-7414-5333)
- Vitalay Fomin (ORCID: https://orcid.org/0000-0002-8382-3445)
- WeiQing Venus So (ORCID: https://orcid.org/0000-0002-8416-1455)
- James J. Cai (ORCID: https://orcid.org/0000-0002-8081-6725)
- R Barbieri
- Jehad Charo (ORCID: https://orcid.org/0000-0002-5409-9160)
- Tingting Tu (ORCID: https://orcid.org/0009-0009-9563-9723)
- Kenley Hiller-Bittrolff
- Elina Koletou (ORCID: https://orcid.org/0000-0003-4539-5117)
Institutions
- Roche (Switzerland) (CH)
- Capgemini (France) (FR)
- La Roche College (US)
Publication Details
- Journal
- Journal for ImmunoTherapy of Cancer
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1136/jitc-2024-009092
- Citations
- 9
- Primary Topic
- Cancer Genomics and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 8.43
Funders
- Roche