Abstract 1066: OncoKBTM, MSK’s precision oncology knowledge base: 2024 updates
OncoKB, Memorial Sloan Kettering Cancer Center’s (MSK) precision oncology knowledge base, contains evidence-based information about the oncogenic effect and therapeutic implications of somatic alterations in cancer. OncoKB currently includes annotation for >8000 alterations in 900 cancer-associated genes and is the only somatic cancer variant database partially recognized by the US-FDA. OncoKB supports variant interpretation in the cBioPortal for Cancer Genomics, is used to annotate >17,000 MSK patient sequencing reports annually and is publicly available through its website (www.oncokb.org). Access to OncoKB, including its web-based API, is freely available to users in an academic setting, while users from commercial and hospital settings require a fee-based license. OncoKB’s Therapeutic Levels of Evidence system classifies variants based on tumor type-specific sensitivity or resistance to targeted therapies. To date, OncoKB includes 53 Level 1 genes as well as MSI-H and TMB-H (included in the FDA drug label), 7 Level 2 genes (included in professional guidelines), 14 Level 3A genes (predictive of response in well-powered clinical studies), 10 Level 4 genes (predictive of response based on compelling biological evidence), and 12 R1/R2 resistance genes. In 2024, OncoKB updated its content to include notable precision oncology drug development changes. OncoKB promoted BRAF fusions to Level 1 following inclusion as patient eligibility criteria in the FDA drug label for tovorafenib (low-grade glioma). Additionally, OncoKB included KRAS G12C in colorectal cancer and IDH1 mutations in myelodysplastic syndromes as Level 1 following FDA approval of adagrasib + cetuximab and ivosidenib, respectively. NCCN guidelines for colorectal cancer and small bowel cancer listed immune-checkpoint inhibitors for tumors harboring POLE or POLD1 oncogenic exonuclease domain missense mutations, making them Level 2 in these indications. Lastly, novel biomarkers including FBXW7 and PPP2R1A alterations (endometrial and ovarian cancer), SMARCA4 mutations (non-small cell lung cancer and esophageal adenocarcinoma) and MTAP deletions (all solid tumors) were included in OncoKB based on compelling preclinical and emerging clinical evidence in association with lunresertib + camonsertib, PRT3789, and AMG193 and MRTX1719, respectively. In sum, 17 novel clinically actionable biomarkers (Levels 1-4) and 26 follow-on precision oncology therapies for existing leveled biomarkers were added to OncoKB in 2024. Additionally in 2024, OncoKB prepared to incorporate germline variants into the knowledge base, a feature expected to be publicly available in 2025. OncoKB also implemented major software updates to support data integration into the EPIC platform. Future OncoKB efforts are focused on whole genome/exome curation, inclusion of biomarkers for non-NGS-based precision oncology therapies, and the development of a clinical trial matching system. Citation Format: Moriah H. Nissan, Sarah P. Suehnholz, Hongxin Zhang, Ritika Kundra, Calvin Lu, Amanda Dhaneshwar, Nicole Fernandez, Kelly Cavender, Benjamin Preiser, John Konecny, Kseniya Petrova, Mark Ewalt, Maria E. Arcila, Marc Ladanyi, Michael F. Berger, Anoop Balakrishnan-Rema, Aijazuddin Syed, A. Rose Brannon, Ahmet Dogan, Diana Mandelker, Zsofia Stadler, Alexander Drilon, David B. Solit, Ross Levine, Nikolaus Schultz, Debyani Chakravarty. OncoKBTM, MSK’s precision oncology knowledge base: 2024 updates [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1066.
Authors
- Aijazuddin Syed (ORCID: https://orcid.org/0009-0007-8702-7984)
- Debyani Chakravarty (ORCID: https://orcid.org/0000-0001-8629-5732)
- Ritika Kundra (ORCID: https://orcid.org/0000-0001-6723-2859)
- Sarah P. Suehnholz
- Kseniya Petrova
- Amanda Dhaneshwar (ORCID: https://orcid.org/0000-0003-1887-1771)
- Ahmet Doǧan (ORCID: https://orcid.org/0000-0001-6576-5256)
- Ross L. Levine (ORCID: https://orcid.org/0000-0002-7884-1905)
- Nikolaus Schultz (ORCID: https://orcid.org/0000-0002-0131-4904)
- Michael F. Berger (ORCID: https://orcid.org/0000-0003-3882-5000)
- Marc Ladanyi (ORCID: https://orcid.org/0000-0001-9055-7213)
- Jan Konečný (ORCID: https://orcid.org/0000-0003-3934-5690)
- Maria E. Arcila (ORCID: https://orcid.org/0000-0001-5824-6554)
- Calvin Lu
- Diana L. Mandelker (ORCID: https://orcid.org/0000-0003-4154-0567)
- Alexander Edward Dela Cruz Drilon (ORCID: https://orcid.org/0000-0001-6806-9061)
- Nicole Fernandez (ORCID: https://orcid.org/0000-0002-5374-8659)
- Mark D. Ewalt (ORCID: https://orcid.org/0000-0003-4245-752X)
- Zsofia Kinga Stadler (ORCID: https://orcid.org/0000-0002-6985-2864)
- Moriah H. Nissan (ORCID: https://orcid.org/0000-0001-5852-3419)
- A. Rose Brannon (ORCID: https://orcid.org/0000-0003-4610-0149)
- Benjamin Preiser
- Hongxin Zhang (ORCID: https://orcid.org/0000-0003-2239-5974)
- David B. Solit (ORCID: https://orcid.org/0000-0002-6614-802X)
- Anoop Balakrishnan-Rema
- Kelly Cavender
Institutions
- Memorial Sloan Kettering Cancer Center (US)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1158/1538-7445.am2025-1066
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00