Ignacio I. Wistuba

Distinct Spatial Immune Architectures in Tumor and Tumor-Adjacent Tissues of Early-Stage Non-Small Cell Lung Cancer

Background Lung cancer remains the leading cause of cancer-related deaths in the United States with over 124,000 estimated deaths for 2026. Previous studies have found that tumor-adjacent lung tissues may provide additional insight into the immune microenvironment of early-stage NSCLC. Methods Multiplex immunofluorescence (mIF) imaging was performed on 192 tissues from 101 early-stage non-small cell lung cancer patients including 91 matched tumor-adjacent pairs, using three mIF panels. Spatial analyses were performed to identify distinctions between tissues and identify associations with clinical and genomic features. Results Tumor tissue showed significantly higher densities of T cells, macrophages, B cells, and memory/regulatory populations than adjacent tissue (p<0.001). Tumors exhibited greater spatial heterogeneity, with higher prevalence of spatial patterning (47.7% vs. 31.2%) and more consistently localized organization, whereas adjacent tissue showed stronger individual-cell clustering. Pairwise colocalization (Ripley’s L-cross) revealed selective spatial segregation in tumors (including B-cells from memory/regulatory cells and among cytotoxic T-cell subsets) and reduced immune proximity to malignant cells relative to the strong immune-epithelial association in adjacent tissue. Spatial features were linked to genomic features or patient outcomes: tumor neoantigen burden exhibited a positive association with CD3+ T cells in the tumor, while colocalization between CD45RO + CD57 + GZMB + cells and CD57 + GZMB + cells was negatively associated with overall survival outside the tumor. Conclusion Tumor and adjacent tissues harbor distinct spatial immune architectures, with spatial features displaying associations with genomic features or patient outcomes. These findings highlight immune microenvironment reorganization and the importance of incorporating spatial context from both compartments into risk assessment in NSCLC.

The American Cancer Society National Lung Cancer Roundtable strategic plan: Challenges in clinical guideline recommendations for biomarker testing in lung cancer

Abstract LB285: RASH3D19 mediates RAS pathway activation and adaptive resistance to KRAS targeting drugs in cancer

Spatial Transcriptomics Reveals Tumor Microenvironment Transformation in Lung Adenocarcinoma

FOXM1-specific TCR-engineered T cells target non-small cell lung cancer

Nivolumab plus ipilimumab induce hyper-progression in renal medullary carcinoma: results of a phase II trial and preclinical evidence

Therapeutic options for patients with renal medullary carcinoma (RMC) are limited. Here we report the results of a phase II clinical trial (NCT03274258) of anti-PD1 nivolumab plus anti-CTLA4 ipilimumab in patients with RMC, with objective response rate as primary outcome. Enrollment was halted for futility at a prespecified interim analysis as all 10 treated patients experienced rapid disease progression. 5/10 met radiological criteria for hyperprogression and median progression-free survival (secondary outcome) was 1.38 months (95% confidence interval: 1.28, 1.60). In a post-hoc single-cell RNA sequencing analysis, data from patients with RMC before and after nivolumab plus ipilimumab treatment indicated that immune checkpoint therapy (ICT) triggered an interferon-γ response that induced a “myeloid mimicry” program in tumor cells, regulated by the CEBPB / p300 axis and linked to proliferation and hyperprogression. In preclinical experiments using an immunocompetent somatic mosaic genetically engineered mouse model of RMC, combination ICT accelerated tumor growth while activating myeloid-affiliated transcriptional circuits. Selective pharmacologic inhibition of p300 suppressed this program and restored sensitivity to ICT. These findings reveal an adaptive mechanism of resistance to ICT in RMC and support targeting master myeloid regulators to enable therapeutic benefit.

Tsaonpj Precision Oncology

MET (c-Met) protein overexpression is an emerging protein biomarker in non-small cell lung cancer

MET protein (also known as c-Met protein) overexpression (OE) is an emerging clinically relevant biomarker that is targetable with antibody-drug conjugates. c-Met protein's prevalence varies, depending on the different OE thresholds, antibodies, and detection methodologies used. Unlike MET genomic aberrations, MET protein OE is a protein biomarker requiring immunohistochemistry (IHC)-based testing. Herein, we summarize the current evidence on MET protein OE, including prognostic value, heterogeneity, and stability. We also provide key considerations for enabling optimal real-world testing of this IHC-based biomarker.

Multimodal spatial-omics reveal co-evolution of alveolar progenitors and proinflammatory niches in progression of lung precursor lesions

The co-evolution of different cell subsets in the progression of precursor lesions to lung adenocarcinoma (LUAD) is incompletely understood. We generated spatial transcriptomic maps of 56 human precursor lesions and LUADs from 25 patients and of an independent cohort of 36 lesions from 19 patients, analyzing a total of 486,519 spots and 5.4 million cells. We identify region-specific programs that distinguish precursors from LUADs. Spatially resolved clonal architectures reveal patient-specific heterogeneity in evolution of precursors to LUADs. We find epithelial alveolar progenitors expressing tumor-associated meta-programs and residing in niches enriched with proinflammatory subsets including IL1B high macrophages. Epithelial-proinflammatory niches are prevalent in precursor lesions but become less frequent in LUADs. These niches are conserved in mice and promote alveolar progenitor growth. Targeting inflammation alone or in combination with immune checkpoint blockade in precancerous phase reduces alveolar progenitors. Epithelial-inflammatory niches are stage-specific, shape early LUAD development and represent promising targets for interception.

Enhancing Pathology Knowledge for Biomedical Scientists: A Comprehensive Self-Paced Online Curriculum for Bridging Fundamental Concepts and Translational Applications (Preprint)

Machine-learning driven strategies for adapting immunotherapy in metastatic NSCLC

Immune checkpoint inhibitors (ICIs), either as monotherapy (ICI-Mono) or combined with chemotherapy (ICI-Chemo), improves survival in advanced non-small cell lung cancer (NSCLC). However, prospective guidance for choosing between these options remains limited, and single-feature biomarkers like PD-L1 prove inadequate. We develop a machine learning model using clinicogenomic data from four cohorts (MD Anderson n = 750; Mayo Clinic n = 80; Dana-Farber n = 1077; Stand Up To Cancer n = 393) to predict individual benefit from adding chemotherapy. Benefit scores are calculated using five distinct functions derived from 28 genomic and 6 clinical features. Our integrated model, A-STEP (Attention-based Scoring for Treatment Effect Prediction), estimates heterogeneous treatment effects and achieves the largest reduction in 3-month progression risk, improving weighted risk reduction by 13–23% over stand-alone models. A-STEP recommends treatment changes for over 50% of patients, most often favoring ICI-Chemo. In simulation on external cohort, patients treated in accordance with A-STEP recommendations show improved 2-year progression-free survival (HR = 0.60 for ICI-Mono treatment arm; HR = 0.58 for ICI-Chemo treatment arm). Predictive features include FBXW7, APC, and PD-L1. In this study, we demonstrate how machine learning can fill critical gaps in immunotherapy selection for NSCLC, by modeling treatment heterogeneity with real-world clinicogenomic data, driving precision medicine beyond conventional biomarker boundaries. The approval of first line immune checkpoint blockade (ICB) has improved outcomes for patients with metastatic non-small cell lung cancer (mNSCLC), however, whether patients would benefit more from ICB alone or alongside chemotherapy is unclear. Here, the authors develop a machine-learning based approach to help guide individual treatment selection patients with mNSCLC.

Supplementary Figure 2 from The Integrated Stress Response Pathway Coordinates Translational Control of Multiple Immune Checkpoints in Lung Cancer

Supplementary Figure 4 from The Integrated Stress Response Pathway Coordinates Translational Control of Multiple Immune Checkpoints in Lung Cancer

Supplementary Figure 1 from The Integrated Stress Response Pathway Coordinates Translational Control of Multiple Immune Checkpoints in Lung Cancer

Supplementary Figure 5 from The Integrated Stress Response Pathway Coordinates Translational Control of Multiple Immune Checkpoints in Lung Cancer

Supplementary Figure 6 from The Integrated Stress Response Pathway Coordinates Translational Control of Multiple Immune Checkpoints in Lung Cancer

Data from The Integrated Stress Response Pathway Coordinates Translational Control of Multiple Immune Checkpoints in Lung Cancer

Supplementary Figure 3 from The Integrated Stress Response Pathway Coordinates Translational Control of Multiple Immune Checkpoints in Lung Cancer

Intratumoral neutrophil-to-lymphocyte ratio is mirrored by circulating neutrophil-to-lymphocyte ratio in non-small cell lung cancer

Tumor-initiated emergency granulopoiesis results in expansion of the circulating neutrophil compartment and neutrophil recruitment into the tumor microenvironment (TME), which may in turn promote tumor progression. Although an elevated circulating neutrophil-to-lymphocyte ratio (cNLR) has repeatedly been demonstrated to be an adverse prognostic factor in patients with non-small cell lung cancer (NSCLC), whether this neutrophil expansion in circulation reflects a similar relative neutrophil abundance in the TME remains unclear. We sought to characterize the relationships between cNLR and the intratumoral neutrophil-to-lymphocyte ratio (tNLR), between tNLR and proteogenomic and immune features of NSCLC tumors, and between tNLR and prognosis. We analyzed tNLR (transcriptomic signatures) and cNLR in a prospectively-enrolled cohort of patients with NSCLC (stage IA-III) that was subjected to multifaceted immunoprofiling (ImmunogenomiC prOfiling of early-stage Non-small cell lung cancer (ICON), N=150). We examined the relationship between tNLR and genomic, transcriptomic, and proteomic features of NSCLC tumors in The Cancer Genome Atlas (TCGA) and ICON. Finally, tNLR was analyzed for associations with postoperative recurrence-free survival (ICON) and overall survival (TCGA). In the ICON cohort, tNLR was significantly positively correlated with cNLR, but there was no association between intratumoral and circulating neutrophils or lymphocytes alone. High tNLR was associated with poor postoperative recurrence-free survival, and multivariate analysis indicated tNLR was a stronger driver of outcomes than cNLR. Mutations in KEAP1 , STK11 , PTEN , PI3K , and TSC2 were associated with an increased tNLR. Tumors with elevated tNLR were marked by proteomic and transcriptomic features indicative of increased cell cycle, receptor tyrosine kinase, and YAP signaling, as well as immunosuppression (reduced IFNG and GZMB expression). Flow cytometry and multiplex immunofluorescence confirmed reduced CD8 + granzyme B + T cells in the TME of tumors with high tNLR. Finally, TCGA confirmed associations between tNLR with prognosis, mutational status, and proteomic/transcriptomic features, and further showed that tNLR is prognostically relevant in multiple solid cancers. tNLR is mirrored by NLR in circulation (cNLR) in NSCLCs. High tNLR is associated with an immunosuppressed TME phenotype and poor prognosis across multiple cancers. These findings support ongoing investigations of the utility of cNLR and tNLR as clinical biomarkers in the context of patients with NSCLC treated with immune checkpoint inhibitor therapies.

Phase II basket trial of Dual Anti-CTLA-4 and anti-PD-1 blockade in Rare Tumors (DART) SWOG S1609: pancreatic neuroendocrine neoplasm (PNEN) cohort

PURPOSE: umors (DART) studied the efficacy of ipilimumab combined with nivolumab across multiple rare tumor types. We report the results of the pancreatic neuroendocrine neoplasm (PNEN) cohort. EXPERIMENTAL DESIGN: Treatment consisted of ipilimumab 1 mg/kg intravenously every 6 weeks with nivolumab 240 mg intravenously every 2 weeks. The primary endpoint was overall response rate (ORR) (Response Evaluation Criteria In Solid TumorsRECIST V.1.1). Secondary endpoints include progression-free survival (PFS), overall survival (OS), and toxicity. Clinical benefit rate (includes ORR plus stable disease (SD)>6 months was examined. Correlative studies were performed. The trial was conducted by the National Cancer Institute/Southwest Oncology Group Early Therapeutics and Rare Cancers Committee and opened at >1,000 sites. RESULTS: 19 patients with PNEN were enrolled. The median number of lines of prior therapy was 2 (range: 0-4). The ORR was 11% (2/19 patients); the clinical benefit rate (CBR; stable disease >6 months+partial response+complete response), 26% (5/19). The median PFS was 3 months; median OS, 24 months. The longest PFSs were 26 (intermediate grade PNEN), 31 (low grade) and 39+months (intermediate grade). The most common toxicities were fatigue (47% of patients) and aspartate aminotransferase (AST) elevation (32%); the most common grade 3/4 immune-related adverse event (AE) was AST (32%) and bilirubin elevation (26%), with no grade 5 events. Programmed death-ligand 1 expression by chromogenic immunohistochemistry (N=12 patients assessed) did not associate with ORR; tumor mutation burden (TMB) was high in three patients; one of the two patients with partial remission (PFS=26 months) had high TMB (150 mutations/mb). Peripheral effector memory T-cell activation (N=11 patients assessed by cytometry by time-of-flight with 5 having longitudinal analysis) was associated with response, though the number of patients evaluated was limited. CONCLUSIONS: Low-dose ipilimumab plus nivolumab demonstrated an 11% ORR and 26% CBR (includes SD>6 months) in patients with refractory PNEN, with durable benefit (>2 years) in 3 (16%) patients. TRIAL REGISTRATION NUMBER: NCT02834013.

Spatial and multiomics analysis of human and mouse lung adenocarcinoma precursors reveals TIM-3 as a putative target for precancer interception

How tumor microenvironment shapes lung adenocarcinoma (LUAD) precancer evolution remains poorly understood. Spatial immune profiling of 114 human LUAD and LUAD precursors reveals a progressive increase of adaptive response and a relative decrease of innate immune response as LUAD precursors progress. The immune evasion features align the immune response patterns at various stages. TIM-3-high features are enriched in LUAD precancers, which decrease in later stages. Furthermore, single-cell RNA sequencing (scRNA-seq) and spatial immune and transcriptomics profiling of LUAD and LUAD precursor specimens from 5 mouse models validate high TIM-3 features in LUAD precancers. In vivo TIM-3 blockade at precancer stage, but not at advanced cancer stage, decreases tumor burden. Anti-TIM-3 treatment is associated with enhanced antigen presentation, T cell activation, and increased M1/M2 macrophage ratio. These results highlight the coordination of innate and adaptive immune response/evasion during LUAD precancer evolution and suggest TIM-3 as a potential target for LUAD precancer interception.