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PURPOSE: Co-occurring mutations in KEAP1 and STK11KRAS have emerged as determinants of survival outcomes in non-small cell lung cancer (NSCLC) patients treated with immunotherapy. However, these mutational contexts identify a fraction of non-responders to immune checkpoint inhibitors. We hypothesized that KEAP1 wild-type tumors recapitulate the transcriptional footprint of KEAP1 mutations, and that this KEAPness phenotype can determine immune responsiveness with higher precision compared to mutation-based models. EXPERIMENTAL DESIGN: The TCGA was used to infer the KEAPness phenotype and explore its immunological correlates at the pan-cancer level. The association between KEAPness and survival outcomes was tested in two independent cohorts of advanced NSCLC patients treated with immunotherapy and profiled by RNA-Seq (SU2C n=153; OAK/POPLAR n=439). The NSCLC TRACERx421 multi-region sequencing study (tumor regions n=947) was used to investigate evolutionary trajectories. RESULTS: KEAPness-dominant tumors represented 50% of all NSCLCs and were associated with shorter progression-free survival (PFS) and overall survival (OS) compared to KEAPness-free cases in independent cohorts of NSCLC patients treated with immunotherapy (SU2C PFS P=0.042, OS P=0.008; OAK/POPLAR PFS P=0.0014, OS P<0.001). Patients with KEAPness tumors had survival outcomes comparable to those with KEAP1-mutant tumors. In the TRACERx421, KEAPness exhibited limited transcriptional intratumoral heterogeneity and immune exclusion, resembling the KEAP1-mutant disease. This phenotypic state occurred across genetically divergent tumors, exhibiting shared and private cancer genes under positive selection when compared to KEAP1-mutant tumors. CONCLUSIONS: We identified a KEAPness phenotype across evolutionary divergent tumors. KEAPness outperforms mutation-based classifiers as a biomarker of inferior survival outcomes in NSCLC patients treated with immunotherapy.
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Cancer is a major cause of global mortality, both in affluent countries and increasingly in developing nations. Many patients with cancer experience reduced life expectancy and have metastatic disease at the time of death. However, the more precise causes of mortality and patient deterioration before death remain poorly understood. This scarcity of information, particularly the lack of mechanistic insights, presents a challenge for the development of novel treatment strategies to improve the quality of, and potentially extend, life for patients with late-stage cancer. In addition, earlier deployment of existing strategies to prolong quality of life is highly desirable. In this Roadmap, we review the proximal causes of mortality in patients with cancer and discuss current knowledge about the interconnections between mechanisms that contribute to mortality, before finally proposing new and improved avenues for data collection, research and the development of treatment strategies that may improve quality of life for patients.
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Neoplasias , Qualidade de Vida , Humanos , Neoplasias/mortalidade , Neoplasias/psicologia , Causas de Morte , Expectativa de VidaRESUMO
Patient-derived xenograft (PDX) models are widely used in cancer research. To investigate the genomic fidelity of non-small cell lung cancer PDX models, we established 48 PDX models from 22 patients enrolled in the TRACERx study. Multi-region tumor sampling increased successful PDX engraftment and most models were histologically similar to their parent tumor. Whole-exome sequencing enabled comparison of tumors and PDX models and we provide an adapted mouse reference genome for improved removal of NOD scid gamma (NSG) mouse-derived reads from sequencing data. PDX model establishment caused a genomic bottleneck, with models often representing a single tumor subclone. While distinct tumor subclones were represented in independent models from the same tumor, individual PDX models did not fully recapitulate intratumor heterogeneity. On-going genomic evolution in mice contributed modestly to the genomic distance between tumors and PDX models. Our study highlights the importance of considering primary tumor heterogeneity when using PDX models and emphasizes the benefit of comprehensive tumor sampling.
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Carcinoma Pulmonar de Células não Pequenas , Heterogeneidade Genética , Neoplasias Pulmonares , Camundongos Endogâmicos NOD , Camundongos SCID , Carcinoma Pulmonar de Células não Pequenas/genética , Carcinoma Pulmonar de Células não Pequenas/patologia , Humanos , Animais , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/patologia , Camundongos , Feminino , Sequenciamento do Exoma , Genômica/métodos , Masculino , Ensaios Antitumorais Modelo de Xenoenxerto , Xenoenxertos , Modelos Animais de Doenças , Idoso , Pessoa de Meia-IdadeRESUMO
Understanding the role of the tumor microenvironment (TME) in lung cancer is critical to improving patient outcomes. We identified four histology-independent archetype TMEs in treatment-naïve early-stage lung cancer using imaging mass cytometry in the TRACERx study (n = 81 patients/198 samples/2.3 million cells). In immune-hot adenocarcinomas, spatial niches of T cells and macrophages increased with clonal neoantigen burden, whereas such an increase was observed for niches of plasma and B cells in immune-excluded squamous cell carcinomas (LUSC). Immune-low TMEs were associated with fibroblast barriers to immune infiltration. The fourth archetype, characterized by sparse lymphocytes and high tumor-associated neutrophil (TAN) infiltration, had tumor cells spatially separated from vasculature and exhibited low spatial intratumor heterogeneity. TAN-high LUSC had frequent PIK3CA mutations. TAN-high tumors harbored recently expanded and metastasis-seeding subclones and had a shorter disease-free survival independent of stage. These findings delineate genomic, immune, and physical barriers to immune surveillance and implicate neutrophil-rich TMEs in metastasis. SIGNIFICANCE: This study provides novel insights into the spatial organization of the lung cancer TME in the context of tumor immunogenicity, tumor heterogeneity, and cancer evolution. Pairing the tumor evolutionary history with the spatially resolved TME suggests mechanistic hypotheses for tumor progression and metastasis with implications for patient outcome and treatment. This article is featured in Selected Articles from This Issue, p. 897.
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Neoplasias Pulmonares , Microambiente Tumoral , Humanos , Neoplasias Pulmonares/imunologia , Neoplasias Pulmonares/patologia , Neoplasias Pulmonares/genética , Microambiente Tumoral/imunologia , Linfócitos T/imunologia , Células Mieloides/imunologia , Feminino , Masculino , Evasão da Resposta ImuneRESUMO
The last 50 years have witnessed extraordinary developments in understanding mechanisms of carcinogenesis, synthesized as the hallmarks of cancer. Despite this logical framework, our understanding of the molecular basis of systemic manifestations and the underlying causes of cancer-related death remains incomplete. Looking forward, elucidating how tumors interact with distant organs and how multifaceted environmental and physiological parameters impinge on tumors and their hosts will be crucial for advances in preventing and more effectively treating human cancers. In this perspective, we discuss complexities of cancer as a systemic disease, including tumor initiation and promotion, tumor micro- and immune macro-environments, aging, metabolism and obesity, cancer cachexia, circadian rhythms, nervous system interactions, tumor-related thrombosis, and the microbiome. Model systems incorporating human genetic variation will be essential to decipher the mechanistic basis of these phenomena and unravel gene-environment interactions, providing a modern synthesis of molecular oncology that is primed to prevent cancers and improve patient quality of life and cancer outcomes.
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Neoplasias , Humanos , Carcinogênese , Microbiota , Neoplasias/genética , Neoplasias/patologia , Neoplasias/terapia , Obesidade/complicações , Qualidade de VidaRESUMO
While there is a great clinical need to understand the biology of metastatic cancer in order to treat it more effectively, research is hampered by limited sample availability. Research autopsy programmes can crucially advance the field through synchronous, extensive, and high-volume sample collection. However, it remains an underused strategy in translational research. Via an extensive questionnaire, we collected information on the study design, enrolment strategy, study conduct, sample and data management, and challenges and opportunities of research autopsy programmes in oncology worldwide. Fourteen programmes participated in this study. Eight programmes operated 24 h/7 days, resulting in a lower median postmortem interval (time between death and start of the autopsy, 4 h) compared with those operating during working hours (9 h). Most programmes (n = 10) succeeded in collecting all samples within a median of 12 h after death. A large number of tumour sites were sampled during each autopsy (median 15.5 per patient). The median number of samples collected per patient was 58, including different processing methods for tumour samples but also non-tumour tissues and liquid biopsies. Unique biological insights derived from these samples included metastatic progression, treatment resistance, disease heterogeneity, tumour dormancy, interactions with the tumour micro-environment, and tumour representation in liquid biopsies. Tumour patient-derived xenograft (PDX) or organoid (PDO) models were additionally established, allowing for drug discovery and treatment sensitivity assays. Apart from the opportunities and achievements, we also present the challenges related with postmortem sample collections and strategies to overcome them, based on the shared experience of these 14 programmes. Through this work, we hope to increase the transparency of postmortem tissue donation, to encourage and aid the creation of new programmes, and to foster collaborations on these unique sample collections. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Autopsia , Oncologia , Neoplasias , Humanos , Neoplasias/patologia , Neoplasias/mortalidade , Oncologia/métodos , Animais , Pesquisa Translacional BiomédicaRESUMO
The introduction of the International Association for the Study of Lung Cancer grading system has furthered interest in histopathological grading for risk stratification in lung adenocarcinoma. Complex morphology and high intratumoral heterogeneity present challenges to pathologists, prompting the development of artificial intelligence (AI) methods. Here we developed ANORAK (pyrAmid pooliNg crOss stReam Attention networK), encoding multiresolution inputs with an attention mechanism, to delineate growth patterns from hematoxylin and eosin-stained slides. In 1,372 lung adenocarcinomas across four independent cohorts, AI-based grading was prognostic of disease-free survival, and further assisted pathologists by consistently improving prognostication in stage I tumors. Tumors with discrepant patterns between AI and pathologists had notably higher intratumoral heterogeneity. Furthermore, ANORAK facilitates the morphological and spatial assessment of the acinar pattern, capturing acinus variations with pattern transition. Collectively, our AI method enabled the precision quantification and morphology investigation of growth patterns, reflecting intratumoral histological transitions in lung adenocarcinoma.