Tumor neoantigenicity assessment with CSiN score incorporates clonality and immunogenicity to predict immunotherapy outcomes

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Science Immunology  21 Feb 2020:
Vol. 5, Issue 44, eaaz3199
DOI: 10.1126/sciimmunol.aaz3199

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Neoantigen Number Crunching

Immunotherapy with anti–PD-1 and other checkpoint inhibitors is an important cancer treatment modality, but improved biomarkers are needed to better predict which patients will respond. Current computational approaches that assess tumor immunogenicity by deep sequencing of tumor samples to count mutations and predict neoantigen epitopes are unable to factor in clonal variation within tumors. Lu et al. developed an algorithm to calculate CSiN score, a metric that also integrates the distribution of mutations among tumor clones. Testing of CSiN score against other indices of tumor neoantigen burden revealed improved correlations with outcome and prognosis in cohorts of patients with tumor types known to be immunogenic. Calculation of CSiN scores from tumor genomics data may assist in selection of patients most likely to benefit from cancer immunotherapy.


Lack of responsiveness to checkpoint inhibitors is a central problem in the modern era of cancer immunotherapy. Tumor neoantigens are critical targets of the host antitumor immune response, and their presence correlates with the efficacy of immunotherapy treatment. Many studies involving assessment of tumor neoantigens principally focus on total neoantigen load, which simplistically treats all neoantigens equally. Neoantigen load has been linked with treatment response and prognosis in some studies but not others. We developed a Cauchy-Schwarz index of Neoantigens (CSiN) score to better account for the degree of concentration of immunogenic neoantigens in truncal mutations. Unlike total neoantigen load determinations, CSiN incorporates the effect of both clonality and MHC binding affinity of neoantigens when characterizing tumor neoantigen profiles. By analyzing the clinical responses in 501 treated patients with cancer (with most receiving checkpoint inhibitors) and the overall survival of 1978 patients with cancer at baseline, we showed that CSiN scores predict treatment response to checkpoint inhibitors and prognosis in patients with melanoma, lung cancer, and kidney cancer. CSiN score substantially outperformed prior genetics-based prediction methods of responsiveness and fills an important gap in research involving assessment of tumor neoantigen burden.

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