Treatment with immune checkpoint inhibitors (ICI) has demonstrated clinical benefit for a wide range of cancer types. Because only a subset of patients experience clinical benefit, there is a strong need for biomarkers that are easily accessible across diverse practice settings. Here, in a retrospective cohort study of 1714 patients with 16 different cancer types treated with ICI, we show that higher neutrophil-to-lymphocyte ratio (NLR) is significantly associated with poorer overall and progression-free survival, and lower rates of response and clinical benefit, after ICI therapy across multiple cancer types. Combining NLR with tumor mutational burden (TMB), the probability of benefit from ICI is significantly higher (OR = 3.22; 95% CI, 2.26-4.58; P < 0.001) in the NLR low/TMB high group compared to the NLR high/TMB low group. NLR is a suitable candidate for a cost-effective and widely accessible biomarker, and can be combined with TMB for additional predictive capacity.

BACKGROUND. Adenoid cystic carcinoma (ACC) is a rare malignancy arising in salivary glands and other sites, characterized by high rates of relapse and distant spread. Recurrent/metastatic (R/M) ACCs are generally incurable, due to a lack of active systemic therapies. To improve outcomes, deeper understanding of genetic alterations and vulnerabilities in R/M tumors is needed. METHODS. An integrated genomic analysis of 1,045 ACCs (177 primary, 868 R/M) was performed to identify alterations associated with advanced and metastatic tumors. Intratumoral genetic heterogeneity, germline mutations, and therapeutic actionability were assessed. RESULTS. Compared with primary tumors, R/M tumors were enriched for alterations in key Notch (NOTCH1, 26.3% vs. 8.5%; NOTCH2, 4.6% vs. 2.3%; NOTCH3, 5.7% vs. 2.3%; NOTCH4, 3.6% vs. 0.6%) and chromatin-remodeling (KDM6A, 15.2% vs. 3.4%; KMT2C/MLL3, 14.3% vs. 4.0%; ARID1B, 14.1% vs. 4.0%) genes. TERT promoter mutations (13.1% of R/M cases) were mutually exclusive with both NOTCH1 mutations (q = 3.3 × 10^–4) and MYB/MYBL1 fusions (q = 5.6 × 10^–3), suggesting discrete, alternative mechanisms of tumorigenesis. This network of alterations defined 4 distinct ACC subgroups: MYB⁺NOTCH1⁺, MYB⁺/other, MYB^WTNOTCH1⁺, and MYB^WTTERT⁺. Despite low mutational load, we identified numerous samples with marked intratumoral genetic heterogeneity, including branching evolution across multiregion sequencing. CONCLUSION. These observations collectively redefine the molecular underpinnings of ACC progression and identify further targets for precision therapies. FUNDING. Adenoid Cystic Carcinoma Research Foundation, Pershing Square Sohn Cancer Research grant, the PaineWebber Chair, Stand Up 2 Cancer, NIH R01 CA205426, the STARR Cancer Consortium, NCI R35 CA232097, the Frederick Adler Chair, Cycle for Survival, the Jayme Flowers Fund, The Sebastian Nativo Fund, NIH K08 DE024774 and R01 DE027738, and MSKCC through NIH/NCI Cancer Center Support Grant (P30 CA008748).

Predicting whether a patient with cancer will benefit from immune checkpoint inhibitors (ICIs) without resorting to advanced genomic or immunologic assays is an important clinical need. To address this, we developed and evaluated SCORPIO, a machine learning system that utilizes routine blood tests (complete blood count and comprehensive metabolic profile) alongside clinical characteristics from 9,745 ICI-treated patients across 21 cancer types. SCORPIO was trained on data from 1,628 patients across 17 cancer types from Memorial Sloan Kettering Cancer Center. In two internal test sets comprising 2,511 patients across 19 cancer types, SCORPIO achieved median time-dependent area under the receiver operating characteristic curve (AUC(t)) values of 0.763 and 0.759 for predicting overall survival at 6, 12, 18, 24 and 30 months, outperforming tumor mutational burden (TMB), which showed median AUC(t) values of 0.503 and 0.543. Additionally, SCORPIO demonstrated superior predictive performance for predicting clinical benefit (tumor response or prolonged stability), with AUC values of 0.714 and 0.641, compared to TMB (AUC = 0.546 and 0.573). External validation was performed using 10 global phase 3 trials (4,447 patients across 6 cancer types) and a real-world cohort from the Mount Sinai Health System (1,159 patients across 18 cancer types). In these external cohorts, SCORPIO maintained robust performance in predicting ICI outcomes, surpassing programmed death-ligand 1 immunostaining. These findings underscore SCORPIO’s reliability and adaptability, highlighting its potential to predict patient outcomes with ICI therapy across diverse cancer types and healthcare settings.