Faculty Bibliography

BACKGROUND:edition of the PM staging system. METHODS:edition data. Overall survival (OS) was calculated by the Kaplan-Meier method and differences in OS assessed by the log-rank test. RESULTS:edition analyses. CONCLUSION/CONCLUSIONS:Given reproducible prognostication by Psum, size criteria will be incorporated into cT1-T3 categories in the 9th edition. Current cT4 category and all pT descriptors will be maintained, with reclassification of fissural invasion as pT2.

Recent statistics on lung cancer, including the steady decline of advanced diseases and the dramatically increasing detection of early-stage diseases and indeterminate pulmonary nodules, mark the significance of a comprehensive understanding of early lung carcinogenesis. Lung adenocarcinoma (ADC) is the most common histologic subtype of lung cancer, and atypical adenomatous hyperplasia is the only recognized preneoplasia to ADC, which may progress to adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) and eventually to invasive ADC. Although molecular evolution during early lung carcinogenesis has been explored in recent years, the progress has been significantly hindered, largely due to insufficient materials from ADC precursors. Here, we employed state-of-the-art deep learning and artificial intelligence techniques to robustly segment and recognize cells on routinely used hematoxylin and eosin histopathology images and extracted 9 biology-relevant pathomic features to decode lung preneoplasia evolution. We analyzed 3 distinct cohorts (Japan, China, and United States) covering 98 patients, 162 slides, and 669 regions of interest, including 143 normal, 129 atypical adenomatous hyperplasia, 94 AIS, 98 MIA, and 205 ADC. Extracted pathomic features revealed progressive increase of atypical epithelial cells and progressive decrease of lymphocytic cells from normal to AAH, AIS, MIA, and ADC, consistent with the results from tissue-consuming and expensive molecular/immune profiling. Furthermore, pathomics analysis manifested progressively increasing cellular intratumor heterogeneity along with the evolution from normal lung to invasive ADC. These findings demonstrated the feasibility and substantial potential of pathomics in studying lung cancer carcinogenesis directly from the low-cost routine hematoxylin and eosin staining.

INTRODUCTION/BACKGROUND:The International Association for the Study of Lung Cancer developed an international pleural mesothelioma database to improve staging. Data entered from 1995 to 2009 (training data set) were analyzed previously to evaluate supplemental prognostic factors. We evaluated these factors with new clinical data to determine whether the previous models could be improved. METHODS:Patients entered into the database from 2009 to 2019 (validation cohort) were assessed for the association between previous prognosticators and overall survival using Cox proportional hazards regression with bidirectional stepwise selection. Additional variables were analyzed and models were compared using Harrell's C-index. RESULTS:The training data set included 3101 patients and the validation cohort, 1733 patients. For the multivariable pathologic staging model applied to the training cohort, C-index was 0.68 (95% confidence interval [CI]: 0.656-0.705). For the validation data set (n = 497), C-index was 0.650 (95% CI: 0.614-0.685), and pathologic stage, histologic diagnosis, sex, adjuvant therapy, and platelet count were independently associated with survival. Adding anemia to the model increased the C-index to 0.652 (95% CI: 0.618-0.686). A basic presentation model including all parameters before staging yielded a C-index of 0.668 (95% CI: 0.641-0.695). In comparison, the European Organization for Research and Treatment of Cancer model yielded C-indices of 0.550 (95% CI: 0.511-0.589) and 0.577 (95% CI: 0.550-0.604) for pathologic staging and presentation models, respectively. CONCLUSIONS:Although significant predictors differed slightly, the International Association for the Study of Lung Cancer training model performed well in the validation set and better than the model of the European Organization for Research and Treatment of Cancer. International collaboration is critical to improve outcomes in this rare disease.

Lung cancer treatment has benefited greatly through advancements in immunotherapies. However, immunotherapy often fails in patients with specific mutations like KEAP1, which are frequently found in lung adenocarcinoma. We established an antigenic lung cancer model and used it to explore how Keap1 mutations remodel the tumor immune microenvironment. Using single-cell technology and depletion studies, we demonstrate that Keap1-mutant tumors diminish dendritic cell and T cell responses driving immunotherapy resistance. This observation was corroborated in patient samples. CRISPR-Cas9-mediated gene targeting revealed that hyperactivation of the NRF2 antioxidant pathway is responsible for diminished immune responses in Keap1-mutant tumors. Importantly, we demonstrate that combining glutaminase inhibition with immune checkpoint blockade can reverse immunosuppression, making Keap1-mutant tumors susceptible to immunotherapy. Our study provides new insight into the role of KEAP1 mutations in immune evasion, paving the way for novel immune-based therapeutic strategies for KEAP1-mutant cancers.

Approximately 30% of early-stage lung adenocarcinoma patients present with disease progression after successful surgical resection. Despite efforts of mapping the genetic landscape, there has been limited success in discovering predictive biomarkers of disease outcomes. Here we performed a systematic multi-omic assessment of 143 tumors and matched tumor-adjacent, histologically-normal lung tissue with long-term patient follow-up. Through histologic, mutational, and transcriptomic profiling of tumor and adjacent-normal tissue, we identified an inflammatory gene signature in tumor-adjacent tissue as the strongest clinical predictor of disease progression. Single-cell transcriptomic analysis demonstrated the progression-associated inflammatory signature was expressed in both immune and non-immune cells, and cell type-specific profiling in monocytes further improved outcome predictions. Additional analyses of tumor-adjacent transcriptomic data from The Cancer Genome Atlas validated the association of the inflammatory signature with worse outcomes across cancers. Collectively, our study suggests that molecular profiling of tumor-adjacent tissue can identify patients at high risk for disease progression.

BACKGROUND/UNASSIGNED:Immune microenvironment plays a critical role in cancer from onset to relapse. Machine learning (ML) algorithm can facilitate the analysis of lab and clinical data to predict lung cancer recurrence. Prompt detection and intervention are crucial for long-term survival in lung cancer relapse. Our study aimed to evaluate the clinical and genomic prognosticators for lung cancer recurrence by comparing the predictive accuracy of four ML models. METHODS/UNASSIGNED:A total of 41 early-stage lung cancer patients who underwent surgery between June 2007 and October 2014 at New York University Langone Medical Center were included (with recurrence, n=16; without recurrence, n=25). All patients had tumor tissue and buffy coat collected at the time of resection. The CIBERSORT algorithm quantified tumor-infiltrating immune cells (TIICs). Protein-protein interaction (PPI) network and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to unearth potential molecular drivers of tumor progression. The data was split into training (75%) and validation sets (25%). Ensemble linear kernel support vector machine (SVM) ML models were developed using optimized clinical and genomic features to predict tumor recurrence. RESULTS/UNASSIGNED:emerged as prominent hub genes. Prediction models integrating clinicopathological prognostic factors, tumor gene expression (45 genes), and buffy coat gene expression (47 genes) yielded varying receiver operating characteristic (ROC)-area under the curves (AUCs): 62.7%, 65.4%, and 59.7% in the training set, 58.3%, 83.3%, and 75.0% in the validation set, respectively. Notably, merging gene expression with clinical data in a linear SVM model led to a significant accuracy boost, with an AUC of 92.0% in training and 91.7% in validation. CONCLUSIONS/UNASSIGNED:Using ML algorithm, immune gene expression data from tumor tissue and buffy coat may enhance the precision of lung cancer recurrence prediction.

OBJECTIVE:Early-stage lung adenocarcinoma is treated with local therapy alone, although patients with grade 3 stage I lung adenocarcinoma have a 50% 5-year recurrence rate. Our objective is to determine if analysis of the tumor microenvironment can create a predictive model for recurrence. METHODS:Thirty-four patients with grade 3 stage I lung adenocarcinoma underwent surgical resection. Digital spatial profiling was used to perform genomic (n = 31) and proteomic (n = 34) analyses of pancytokeratin positive and negative tumor cells. K-means clustering was performed on the top 50 differential genes and top 20 differential proteins, with Kaplan-Meier recurrence curves based on patient clustering. External validation of high-expression genes was performed with Kaplan-Meier plotter. RESULTS:There were no significant clinicopathologic differences between patients who did (n = 14) and did not (n = 20) have recurrence. Median time to recurrence was 806 days; median follow-up with no recurrence was 2897 days. K-means clustering of pancytokeratin positive genes resulted in a model with a Kaplan-Meier curve with concordance index of 0.75. K-means clustering for pancytokeratin negative genes was less successful at differentiating recurrence (concordance index 0.6). Genes upregulated or downregulated for recurrence were externally validated using available public databases. Proteomic data did not reach statistical significance but did internally validate the genomic data described. CONCLUSIONS:Genomic difference in lung adenocarcinoma may be able to predict risk of recurrence. After further validation, stratifying patients by this risk may help guide who will benefit from adjuvant therapy.

BACKGROUND:Cross-species immunological incompatibilities have hampered pig-to-human xenotransplantation, but porcine genome engineering recently enabled the first successful experiments. However, little is known about the immune response after the transplantation of pig kidneys to human recipients. We aimed to precisely characterise the early immune responses to the xenotransplantation using a multimodal deep phenotyping approach. METHODS:We did a complete phenotyping of two pig kidney xenografts transplanted to decedent humans. We used a multimodal strategy combining morphological evaluation, immunophenotyping (IgM, IgG, C4d, CD68, CD15, NKp46, CD3, CD20, and von Willebrand factor), gene expression profiling, and whole-transcriptome digital spatial profiling and cell deconvolution. Xenografts before implantation, wild-type pig kidney autografts, as well as wild-type, non-transplanted pig kidneys with and without ischaemia-reperfusion were used as controls. FINDINGS:cells. Both xenografts showed increased expression of genes biologically related to a humoral response, including monocyte and macrophage activation, natural killer cell burden, endothelial activation, complement activation, and T-cell development. Whole-transcriptome digital spatial profiling showed that antibody-mediated injury was mainly located in the glomeruli of the xenografts, with significant enrichment of transcripts associated with monocytes, macrophages, neutrophils, and natural killer cells. This phenotype was not observed in control pig kidney autografts or in ischaemia-reperfusion models. INTERPRETATION:Despite favourable short-term outcomes and absence of hyperacute injuries, our findings suggest that antibody-mediated rejection in pig-to-human kidney xenografts might be occurring. Our results suggest specific therapeutic targets towards the humoral arm of rejection to improve xenotransplantation results. FUNDING:OrganX and MSD Avenir.

Genetically modified xenografts are one of the most promising solutions to the discrepancy between the numbers of available human organs for transplantation and potential recipients. To date, a porcine heart has been implanted into only one human recipient. Here, using 10-gene-edited pigs, we transplanted porcine hearts into two brain-dead human recipients and monitored xenograft function, hemodynamics and systemic responses over the course of 66 hours. Although both xenografts demonstrated excellent cardiac function immediately after transplantation and continued to function for the duration of the study, cardiac function declined postoperatively in one case, attributed to a size mismatch between the donor pig and the recipient. For both hearts, we confirmed transgene expression and found no evidence of cellular or antibody-mediated rejection, as assessed using histology, flow cytometry and a cytotoxic crossmatch assay. Moreover, we found no evidence of zoonotic transmission from the donor pigs to the human recipients. While substantial additional work will be needed to advance this technology to human trials, these results indicate that pig-to-human heart xenotransplantation can be performed successfully without hyperacute rejection or zoonosis.