Purpose: To understand the role of nuclear factor erythroid-2–related factor 2 (Nrf2) and Kelch-like ECH-associated protein 1 (Keap1) in non–small cell lung cancer (NSCLC), we studied their expression in a large series of tumors with annotated clinicopathologic data, including response to platinum-based adjuvant chemotherapy. Experimental Design: We determined the immunohistochemical expression of nuclear Nrf2 and cytoplasmic Keap1 in 304 NSCLCs and its association with patients' clinicopathologic characteristics, and in 89 tumors from patients who received neoadjuvant (n = 26) or adjuvant platinum-based chemotherapy (n = 63). We evaluated NFE2L2 and KEAP1 mutations in 31 tumor specimens. Results: We detected nuclear Nrf2 expression in 26% of NSCLCs; it was significantly more common in squamous cell carcinomas (38%) than in adenocarcinomas (18%; P < 0.0001). Low or absent Keap1 expression was detected in 56% of NSCLCs; it was significantly more common in adenocarcinomas (62%) than in squamous cell carcinomas (46%; P = 0.0057). In NSCLC, mutations of NFE2L2 and KEAP1 were very uncommon (2 of 29 and 1 of 31 cases, respectively). In multivariate analysis, Nrf2 expression was associated with worse overall survival [P = 0.0139; hazard ratio (HR), 1.75] in NSCLC patients, and low or absent Keap1 expression was associated with worse overall survival (P = 0.0181; HR, 2.09) in squamous cell carcinoma. In univariate analysis, nuclear Nrf2 expression was associated with worse recurrence-free survival in squamous cell carcinoma patients who received adjuvant treatment (P = 0.0410; HR, 3.37). Conclusions: Increased expression of Nrf2 and decreased expression of Keap1 are common abnormalities in NSCLC and are associated with a poor outcome. Nuclear expression of Nrf2 in malignant lung cancer cells may play a role in resistance to platinum-based treatment in squamous cell carcinoma. Clin Cancer Res; 16(14); 3743–53. ©2010 AACR.

Objective: Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. Materials and Methods: In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. Results: We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. Discussion and Conclusion: With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.

Immunotherapy targeting T cells is increasingly utilized to treat solid tumors including non-small cell lung cancer (NSCLC). This requires a better understanding of the T cells in the lungs of patients with NSCLC. Here, we report T cell repertoire analysis in a cohort of 236 early-stage NSCLC patients. T cell repertoire attributes are associated with clinicopathologic features, mutational and immune landscape. A considerable proportion of the most prevalent T cells in tumors are also prevalent in the uninvolved tumor-adjacent lungs and appear specific to shared background mutations or viral infections. Patients with higher T cell repertoire homology between the tumor and uninvolved tumor-adjacent lung, suggesting a less tumor-focused T cell response, exhibit inferior survival. These findings indicate that a concise understanding of antigens and T cells in NSCLC is needed to improve therapeutic efficacy and reduce toxicity with immunotherapy, particularly adoptive T cell therapy.

Tumor-infiltrating B and plasma cells (TIB) are prevalent in lung adenocarcinoma (LUAD); however, they are poorly characterized. We performed paired single-cell RNA and B-cell receptor (BCR) sequencing of 16 early-stage LUADs and 47 matching multiregion normal tissues. By integrative analysis of ∼50,000 TIBs, we define 12 TIB subsets in the LUAD and adjacent normal ecosystems and demonstrate extensive remodeling of TIBs in LUADs. Memory B cells and plasma cells (PC) were highly enriched in tumor tissues with more differentiated states and increased frequencies of somatic hypermutation. Smokers exhibited markedly elevated PCs and PCs with distinct differentiation trajectories. BCR clonotype diversity increased but clonality decreased in LUADs, smokers, and with increasing pathologic stage. TIBs were mostly localized within CXCL13+ lymphoid aggregates, and immune cell sources of CXCL13 production evolved with LUAD progression and included elevated fractions of CD4 regulatory T cells. This study provides a spatial landscape of TIBs in early-stage LUAD. Significance: While TIBs are highly enriched in LUADs, they are poorly characterized. This study provides a much-needed understanding of the transcriptional, clonotypic states and phenotypes of TIBs, unraveling their potential roles in the immunopathology of early-stage LUADs and constituting a road map for the development of TIB-targeted immunotherapies for the treatment of this morbid malignancy. This article is highlighted in the In This Issue feature, p. 2483