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Background Neoadjuvant immune checkpoint inhibitors (ICIs) have improved survival outcomes compared with chemotherapy in resectable non-small cell lung cancer (NSCLC). However, the impact of actionable genomic alterations (AGAs) on the efficacy of neoadjuvant ICIs remains unclear. We report the influence of AGAs on treatment failure (TF) in patients with resectable NSCLC treated with neoadjuvant ICIs.Methods Tumor molecular profiles were obtained from patients with stage I–IIIA resectable NSCLC (American Joint Committee on Cancer seventh edition) treated with either neoadjuvant nivolumab (N, n=23) or nivolumab+ipilimumab (NI, n=21) followed by surgery in a previously reported phase-2 randomized study (NCT03158129). TF was defined as any progression of primary lung cancer after neoadjuvant ICI therapy in patients without surgery, radiographic and/or biopsy-proven primary lung cancer recurrence after surgery, or death from possibly treatment-related complications or from primary lung cancer since randomization. Tumors with AGAs (n=12) were compared with tumors without AGAs and non-profiled squamous cell carcinomas (non-AGAs+NP SCC, n=20).Results With a median follow-up of 60.2 months, the overall TF rate was 34.1% (15/44). Tumor molecular profiling was retrospectively obtained in 47.7% (21/44) of patients and select AGAs were identified in 12 patients: 5 epidermal growth factor receptor (EGFR), 2 KRAS, 1 ERBB2, and 1 BRAF mutations, 2 anaplastic lymphoma kinase (ALK) and 1 RET fusions. The median time to TF in patients with AGAs was 24.7 months (95% CI: 12.6 to 40.4), compared with not reached (95% CI: not evaluable (NE)–NE) in the non-AGAs+NP SCC group. The TF risk was higher in AGAs (HR: 5.51, 95% CI: 1.68 to 18.1), and lower in former/current smokers (HR: 0.24, 95% CI: 0.08 to 0.75). The odds of major pathological response were 4.71 (95% CI: 0.49 to 45.2) times higher in the non-AGAs+NP SCC group, and the median percentage of residual viable tumor was 72.5% in AGAs compared with 33.0% in non-AGS+NP SCC tumors.Conclusions Patients with NSCLC harboring select AGAs, including EGFR and ALK alterations, have a higher risk for TF, shorter median time to TF, and diminished pathological regression after neoadjuvant ICIs. The suboptimal efficacy of neoadjuvant chemotherapy-sparing, ICI-based regimens in this patient subset underscores the importance of tumor molecular testing prior to initiation of neoadjuvant ICI therapy in patients with resectable NSCLC.

Abstract While we recognize the prognostic importance of clinicopathological measures and circulating tumor DNA (ctDNA), the independent contribution of quantitative image markers to prognosis in non-small cell lung cancer (NSCLC) remains underexplored. In our multi-institutional study of 394 NSCLC patients, we utilize pre-treatment computed tomography (CT) and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) to establish a habitat imaging framework for assessing regional heterogeneity within individual tumors. This framework identifies three PET/CT subtypes, which maintain prognostic value after adjusting for clinicopathologic risk factors including tumor volume. Additionally, these subtypes complement ctDNA in predicting disease recurrence. Radiogenomics analysis unveil the molecular underpinnings of these imaging subtypes, highlighting downregulation in interferon alpha and gamma pathways in the high-risk subtype. In summary, our study demonstrates that these habitat imaging subtypes effectively stratify NSCLC patients based on their risk levels for disease recurrence after initial curative surgery or radiotherapy, providing valuable insights for personalized treatment approaches.

Background Resistance to immune checkpoint inhibitors and targeted treatments for cancer is common; thus, novel immunotherapy agents are needed. Urelumab is a monoclonal antibody agonist that binds to CD137 receptors expressed on T cells. Here, we report two studies that evaluated urelumab in combination with cetuximab or nivolumab in patients with select, advanced solid tumors.Methods CA186-018: Patients with metastatic colorectal cancer or metastatic squamous cell carcinoma of the head and neck (SCCHN) were treated in a dose-evaluation phase with urelumab 0.1 mg/kg (urelumab-0.1) every 3 weeks (Q3W)+cetuximab 250 mg/m2 (cetuximab-250) weekly; and in a dose-expansion phase with urelumab 8 mg flat dose (urelumab-8) Q3W+cetuximab-250 weekly. CA186-107: The dose-escalation phase included patients with previously treated advanced solid tumors (or treated or treatment-naive melanoma); patients received urelumab 3 mg flat dose (urelumab-3) or urelumab-8 every 4 weeks+nivolumab 3 mg/kg (nivolumab-3) or 240 mg (nivolumab-240) every 2 weeks. In the expansion phase, patients with melanoma, non-small cell lung cancer, or SCCHN were treated with urelumab-8+nivolumab-240. Primary endpoints were safety and tolerability, and the secondary endpoint included efficacy assessments.Results CA186-018: 66 patients received study treatment. The most frequent treatment-related adverse events (TRAEs) were fatigue (75%; n=3) with urelumab-0.1+cetuximab-250 and dermatitis (45%; n=28) with urelumab-8+cetuximab-250. Three patients (5%) discontinued due to TRAE(s) (with urelumab-8+cetuximab-250). One patient with SCCHN had a partial response (objective response rate (ORR) 5%, with urelumab-8+cetuximab-250).CA186-107: 134 patients received study treatment. Fatigue was the most common TRAE (32%; n=2 with urelumab-3+nivolumab-3; n=1 with urelumab-8+nivolumab-3; n=40 with urelumab-8+nivolumab-240). Nine patients (7%) discontinued due to TRAE(s) (n=1 with urelumab-3+nivolumab-3; n=8 with urelumab-8+nivolumab-240). Patients with melanoma naive to anti-PD-1 therapy exhibited the highest ORR (49%; n=21 with urelumab-8+nivolumab-240). Intratumoral gene expression in immune-related pathways (CD3, CD8, CXCL9, GZMB) increased on treatment with urelumab+nivolumab.Conclusions Although the addition of urelumab at these doses was tolerable, preliminary response rates did not indicate an evident additive benefit. Nevertheless, the positive pharmacodynamics effects observed with urelumab and the high response rate in treatment-naive patients with melanoma warrant further investigation of other anti-CD137 agonist agents for treatment of cancer.Trial registration numbers NCT02110082; NCT02253992.

Traditional feature dimension reduction methods have been widely used to uncover biological patterns or structures within individual spatial transcriptomics data. However, these methods are designed to yield feature representations that emphasize patterns or structures with dominant high variance, such as the normal tissue spatial pattern in a precancer setting. Consequently, they may inadvertently overlook patterns of interest that are potentially masked by these high-variance structures. Herein we present our graph contrastive feature representation method called CoCo-ST (Comparing and Contrasting Spatial Transcriptomics) to overcome this limitation. By incorporating a background data set representing normal tissue, this approach enhances the identification of interesting patterns in a target data set representing precancerous tissue. Simultaneously, it mitigates the influence of dominant common patterns shared by the background and target data sets. This enables discerning biologically relevant features crucial for capturing tissue-specific patterns, a capability we showcased through the analysis of serial mouse precancerous lung tissue samples., Competing Interests: Competing interests The authors declare no competing interests.

Integrative multi-omics analysis provides deeper insight and enables better and more realistic modeling of the underlying biology and causes of diseases than does single omics analysis. Although several integrative multi-omics analysis methods have been proposed and demonstrated promising results in integrating distinct omics datasets, inconsistent distribution of the different omics data, which is caused by technology variations, poses a challenge for paired integrative multi-omics methods. In addition, the existing discriminant analysis-based integrative methods do not effectively exploit correlation and consistent discriminant structures, necessitating a compromise between correlation and discrimination in using these methods. Herein we present PAN-omics Discriminant Analysis (PANDA), a joint discriminant analysis method that seeks omics-specific discriminant common spaces by jointly learning consistent discriminant latent representations for each omics. PANDA jointly maximizes between-class and minimizes within-class omics variations in a common space and simultaneously models the relationships among omics at the consistency representation and cross-omics correlation levels, overcoming the need for compromise between discrimination and correlation as with the existing integrative multi-omics methods. Because of the consistency representation learning incorporated into the objective function of PANDA, this method seeks a common discriminant space to minimize the differences in distributions among omics, can lead to a more robust latent representations than other methods, and is against the inconsistency of the different omics. We compared PANDA to 10 other state-of-the-art multi-omics data integration methods using both simulated and real-world multi-omics datasets and found that PANDA consistently outperformed them while providing meaningful discriminant latent representations. PANDA is implemented using both R and MATLAB, with codes available at https://github.com/WuLabMDA/PANDA., Competing Interests: Competing interests The authors declare no competing interests. Additional Declarations: There is NO Competing Interest.