53 research outputs found
Antibodies against endogenous retroviruses promote lung cancer immunotherapy
Funding Information: We are grateful for assistance from the Advanced Light Microscopy, Advanced Sequencing, Experimental Histopathology, Biological Research, Cell Services, Proteomics, Flow Cytometry and Scientific Computing facilities at the Francis Crick Institute. The TRACERx study (ClinicaTtrials.gov: NCT01888601) is sponsored by University College London (UCL/12/0279) and has been approved by an independent research ethics committee (13/LO/1546). TRACERx is funded by Cancer Research UK (C11496/A17786) and is coordinated through the Cancer Research UK and University College London Cancer Trials Centre, which has a core grant from CRUK (C444/A15953). We gratefully acknowledge the patients and relatives who participated in the TRACERx study. We thank all site personnel, investigators, funders and industry partners who supported the generation of the data within this study. The results shown here are in whole or part based on data generated by the TCGA Research Network ( http://cancergenome.nih.gov ). The GTEx Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health and by NCI, NHGRI, NHLBI, NIDA, NIMH and NINDS. This work was supported by the Francis Crick Institute (CC2097, CC2088, CC2041 and CC2044), which receives its core funding from Cancer Research UK, the UK Medical Research Council and the Wellcome Trust. For the purpose of open access, the author has applied a CC BY public copyright licence to any author accepted manuscript version arising from this submission. This work was also supported by the Cancer Research UK Lung Cancer Centre of Excellence and the CRUK City of London Centre Award (C7893/A26233) as well as by the University College London Experimental Cancer Medicine Centre. This project has received funding from the European Research Council (ERC) under the European Unionâs Horizon 2020 research and innovation programme (grant agreement no. 101018670). C.S. is a Royal Society Napier Research Professor (RSRP\R\210001). C.S. is funded by Cancer Research UK (TRACERx (C11496/A17786), PEACE (C416/A21999) and CRUK Cancer Immunotherapy Catalyst Network); the Cancer Research UK Lung Cancer Centre of Excellence (C11496/A30025); the Rosetrees Trust and the Butterfield and Stoneygate Trusts; the Novo Nordisk Foundation (ID16584); the Royal Society Professorship Enhancement Award (RP/EA/180007); the National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre; the Cancer Research UKâUniversity College London Centre; the Experimental Cancer Medicine Centre; the Breast Cancer Research Foundation (US); and the Mark Foundation for Cancer Research Aspire Award (grant no. 21-029-ASP). This work was supported by a Stand Up To CancerâLUNGevityâAmerican Lung Association Lung Cancer Interception Dream Team Translational Research Grant (grant no. SU2C-AACR-DT23-17 to S. M. Dubinett and A. E. Spira). Stand Up To Cancer is a division of the Entertainment Industry Foundation. Research grants are administered by the American Association for Cancer Research, the scientific partner of SU2C. C.S. is in receipt of an ERC Advanced Grant (PROTEUS) from the ERC under the European Unionâs Horizon 2020 research and innovation programme (grant agreement no. 835297). K.S.S.E. was supported by the European Unionâs Horizon 2020 research and innovation programme under Marie SkĆodowska-Curie grant agreement no. 838540 and the Royal Society (RF\ERE\210216). A.F. has received funding from the European Unionâs Horizon 2020 research and innovation programme under Marie SkĆodowska-Curie grant agreement no. 892360. S.d.C.T. was funded in part by a Marie SkĆodowska-Curie Individual Fellowship from the European Union (MSCA-IF-2015-EF-ST 703228-iGEMMdev). T.K. is supported by the JSPS Overseas Research Fellowships Program (202060447). S.-H.L. is supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant no. HR20C0025), and a National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT) (grant no. 2020R1A2C3006535). C.M.-R. is supported by the Rosetrees Trust (M630) and by the Wellcome Trust. A.M.F. is supported by Stand Up To Cancer (SU2C-AACR-DT23-17). M.A.B. is supported by Cancer Research UK and the Rosetrees Trust. K.L. is funded by the UK Medical Research Council (MR/P014712/1 and MR/V033077/1), the Rosetrees Trust and Cotswold Trust (A2437), and Cancer Research UK (C69256/A30194). N.J.B. is a fellow of the Lundbeck Foundation (R272-2017-4040) and acknowledges funding from the Aarhus University Research Foundation (AUFF-E-2018-7-14) and the Novo Nordisk Foundation (NNF21OC0071483). N. McGranahan is a Sir Henry Dale Fellow, jointly funded by the Wellcome Trust and the Royal Society (grant no. 211179/Z/18/Z), and also receives funding from Cancer Research UK, Rosetrees and the NIHR BRC at University College London Hospitals, and the Cancer Research UKâUniversity College London Experimental Cancer Medicine Centre. M.J.-H. is a CRUK Career Establishment Awardee and has received funding from CRUK, the IASLC International Lung Cancer Foundation, the Lung Cancer Research Foundation, the Rosetrees Trust, UKI NETs, the NIHR and the NIHR UCLH Biomedical Research Centre. Funding Information: We are grateful for assistance from the Advanced Light Microscopy, Advanced Sequencing, Experimental Histopathology, Biological Research, Cell Services, Proteomics, Flow Cytometry and Scientific Computing facilities at the Francis Crick Institute. The TRACERx study (ClinicaTtrials.gov: NCT01888601) is sponsored by University College London (UCL/12/0279) and has been approved by an independent research ethics committee (13/LO/1546). TRACERx is funded by Cancer Research UK (C11496/A17786) and is coordinated through the Cancer Research UK and University College London Cancer Trials Centre, which has a core grant from CRUK (C444/A15953). We gratefully acknowledge the patients and relatives who participated in the TRACERx study. We thank all site personnel, investigators, funders and industry partners who supported the generation of the data within this study. The results shown here are in whole or part based on data generated by the TCGA Research Network (http://cancergenome.nih.gov). The GTEx Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health and by NCI, NHGRI, NHLBI, NIDA, NIMH and NINDS. This work was supported by the Francis Crick Institute (CC2097, CC2088, CC2041 and CC2044), which receives its core funding from Cancer Research UK, the UK Medical Research Council and the Wellcome Trust. For the purpose of open access, the author has applied a CC BY public copyright licence to any author accepted manuscript version arising from this submission. This work was also supported by the Cancer Research UK Lung Cancer Centre of Excellence and the CRUK City of London Centre Award (C7893/A26233) as well as by the University College London Experimental Cancer Medicine Centre. This project has received funding from the European Research Council (ERC) under the European Unionâs Horizon 2020 research and innovation programme (grant agreement no. 101018670). C.S. is a Royal Society Napier Research Professor (RSRP\R\210001). C.S. is funded by Cancer Research UK (TRACERx (C11496/A17786), PEACE (C416/A21999) and CRUK Cancer Immunotherapy Catalyst Network); the Cancer Research UK Lung Cancer Centre of Excellence (C11496/A30025); the Rosetrees Trust and the Butterfield and Stoneygate Trusts; the Novo Nordisk Foundation (ID16584); the Royal Society Professorship Enhancement Award (RP/EA/180007); the National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre; the Cancer Research UKâUniversity College London Centre; the Experimental Cancer Medicine Centre; the Breast Cancer Research Foundation (US); and the Mark Foundation for Cancer Research Aspire Award (grant no. 21-029-ASP). This work was supported by a Stand Up To CancerâLUNGevityâAmerican Lung Association Lung Cancer Interception Dream Team Translational Research Grant (grant no. SU2C-AACR-DT23-17 to S. M. Dubinett and A. E. Spira). Stand Up To Cancer is a division of the Entertainment Industry Foundation. Research grants are administered by the American Association for Cancer Research, the scientific partner of SU2C. C.S. is in receipt of an ERC Advanced Grant (PROTEUS) from the ERC under the European Unionâs Horizon 2020 research and innovation programme (grant agreement no. 835297). K.S.S.E. was supported by the European Unionâs Horizon 2020 research and innovation programme under Marie SkĆodowska-Curie grant agreement no. 838540 and the Royal Society (RF\ERE\210216). A.F. has received funding from the European Unionâs Horizon 2020 research and innovation programme under Marie SkĆodowska-Curie grant agreement no. 892360. S.d.C.T. was funded in part by a Marie SkĆodowska-Curie Individual Fellowship from the European Union (MSCA-IF-2015-EF-ST 703228-iGEMMdev). T.K. is supported by the JSPS Overseas Research Fellowships Program (202060447). S.-H.L. is supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant no. HR20C0025), and a National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT) (grant no. 2020R1A2C3006535). C.M.-R. is supported by the Rosetrees Trust (M630) and by the Wellcome Trust. A.M.F. is supported by Stand Up To Cancer (SU2C-AACR-DT23-17). M.A.B. is supported by Cancer Research UK and the Rosetrees Trust. K.L. is funded by the UK Medical Research Council (MR/P014712/1 and MR/V033077/1), the Rosetrees Trust and Cotswold Trust (A2437), and Cancer Research UK (C69256/A30194). N.J.B. is a fellow of the Lundbeck Foundation (R272-2017-4040) and acknowledges funding from the Aarhus University Research Foundation (AUFF-E-2018-7-14) and the Novo Nordisk Foundation (NNF21OC0071483). N. McGranahan is a Sir Henry Dale Fellow, jointly funded by the Wellcome Trust and the Royal Society (grant no. 211179/Z/18/Z), and also receives funding from Cancer Research UK, Rosetrees and the NIHR BRC at University College London Hospitals, and the Cancer Research UKâUniversity College London Experimental Cancer Medicine Centre. M.J.-H. is a CRUK Career Establishment Awardee and has received funding from CRUK, the IASLC International Lung Cancer Foundation, the Lung Cancer Research Foundation, the Rosetrees Trust, UKI NETs, the NIHR and the NIHR UCLH Biomedical Research Centre. Publisher Copyright: © 2023, The Author(s).Peer reviewedPublisher PD
Expansion of airway basal epithelial cells from primary human non-small cell lung cancer tumors
Pre-clinical non-small cell lung cancer (NSCLC) models are poorly representative of the considerable inter- and intra-tumor heterogeneity of the disease in patients. Primary cell-based in vitro models of NSCLC are therefore desirable for novel therapy development and personalized cancer medicine. Methods have been described to generate rapidly proliferating epithelial cell cultures from multiple human epithelia using 3T3-J2 feeder cell culture in the presence of Y-27632, a RHO-associated protein kinase (ROCK) inhibitor, in what are known as "conditional reprograming conditions" (CRC) or 3T3+Y. In some cancer studies, variations of this methodology have allowed primary tumor cell expansion across a number of cancer types but other studies have demonstrated the preferential expansion of normal epithelial cells from tumors in such conditions. Here, we report our experience regarding the derivation of primary NSCLC cell cultures from 12 lung adenocarcinoma patients enrolled in the Tracking Cancer Evolution through Therapy (TRACERx) clinical study and discuss these in the context of improving the success rate for in vitro cultivation of cells from NSCLC tumors. This article is protected by copyright. All rights reserved
RAS oncogenic activity predicts response to chemotherapy and outcome in lung adenocarcinoma.
Activating mutations in KRAS occur in 32% of lung adenocarcinomas (LUAD). Despite leading to aggressive disease and resistance to therapy in preclinical studies, the KRAS mutation does not predict patient outcome or response to treatment, presumably due to additional events modulating RAS pathways. To obtain a broader measure of RAS pathway activation, we developed RAS84, a transcriptional signature optimised to capture RAS oncogenic activity in LUAD. We report evidence of RAS pathway oncogenic activation in 84% of LUAD, including 65% KRAS wild-type tumours, falling into four groups characterised by coincident alteration of STK11/LKB1, TP53 or CDKN2A, suggesting that the classifications developed when considering only KRAS mutant tumours have significance in a broader cohort of patients. Critically, high RAS activity patient groups show adverse clinical outcome and reduced response to chemotherapy. Patient stratification using oncogenic RAS transcriptional activity instead of genetic alterations could ultimately assist in clinical decision-making
Interpretability of radiomics models is improved when using feature group selection strategies for predicting molecular and clinical targets in clear-cell renal cell carcinoma: insights from the TRACERx Renal study
BACKGROUND: The aim of this work is to evaluate the performance of radiomics predictions for a range of molecular, genomic and clinical targets in patients with clear cell renal cell carcinoma (ccRCC) and demonstrate the impact of novel feature selection strategies and sub-segmentations on model interpretability. METHODS: Contrast-enhanced CT scans from the first 101 patients recruited to the TRACERx Renal Cancer study (NCT03226886) were used to derive radiomics classification models to predict 20 molecular, histopathology and clinical target variables. Manual 3D segmentation was used in conjunction with automatic sub-segmentation to generate radiomics features from the core, rim, high and low enhancing sub-regions, and the whole tumour. Comparisons were made between two classification model pipelines: a Conventional pipeline reflecting common radiomics practice, and a Proposed pipeline including two novel feature selection steps designed to improve model interpretability. For both pipelines nested cross-validation was used to estimate prediction performance and tune model hyper-parameters, and permutation testing was used to evaluate the statistical significance of the estimated performance measures. Further model robustness assessments were conducted by evaluating model variability across the cross-validation folds. RESULTS: Classification performance was significant (pââ0.1. Five of these targets (necrosis on histology, presence of renal vein invasion, overall histological stage, linear evolutionary subtype and loss of 9p21.3 somatic alteration marker) had AUROCâ>â0.8. Models derived using the Proposed pipeline contained fewer feature groups than the Conventional pipeline, leading to more straightforward model interpretations without loss of performance. Sub-segmentations lead to improved performance and/or improved interpretability when predicting the presence of sarcomatoid differentiation and tumour stage. CONCLUSIONS: Use of the Proposed pipeline, which includes the novel feature selection methods, leads to more interpretable models without compromising prediction performance. TRIAL REGISTRATION: NCT03226886 (TRACERx Renal
The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma
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
A clonal expression biomarker associates with lung cancer mortality
An aim of molecular biomarkers is to stratify patients with cancer into disease subtypes predictive of outcome, improving diagnostic precision beyond clinical descriptors such as tumor stage1. Transcriptomic intratumor heterogeneity (RNA-ITH) has been shown to confound existing expression-based biomarkers across multiple cancer types2,3,4,5,6. Here, we analyze multi-region whole-exome and RNA sequencing data for 156 tumor regions from 48 patients enrolled in the TRACERx study to explore and control for RNA-ITH in non-small cell lung cancer. We find that chromosomal instability is a major driver of RNA-ITH, and existing prognostic gene expression signatures are vulnerable to tumor sampling bias. To address this, we identify genes expressed homogeneously within individual tumors that encode expression modules of cancer cell proliferation and are often driven by DNA copy-number gains selected early in tumor evolution. Clonal transcriptomic biomarkers overcome tumor sampling bias, associate with survival independent of clinicopathological risk factors, and may provide a general strategy to refine biomarker design across cancer types
Pulmonary venous circulating tumor cell dissemination before tumor resection and disease relapse
Approximately 50% of patients with early-stage non-small-cell lung cancer (NSCLC) who undergo surgery with curative intent will relapse within 5 years1,2. Detection of circulating tumor cells (CTCs) at the time of surgery may represent a tool to identify patients at higher risk of recurrence for whom more frequent monitoring is advised. Here we asked whether CellSearch-detected pulmonary venous CTCs (PV-CTCs) at surgical resection of early-stage NSCLC represent subclones responsible for subsequent disease relapse. PV-CTCs were detected in 48% of 100 patients enrolled into the TRACERx study3, were associated with lung-cancer-specific relapse and remained an independent predictor of relapse in multivariate analysis adjusted for tumor stage. In a case study, genomic profiling of single PV-CTCs collected at surgery revealed higher mutation overlap with metastasis detected 10 months later (91%) than with the primary tumor (79%), suggesting that early-disseminating PV-CTCs were responsible for disease relapse. Together, PV-CTC enumeration and genomic profiling highlight the potential of PV-CTCs as early predictors of NSCLC recurrence after surgery. However, the limited sensitivity of PV-CTCs in predicting relapse suggests that further studies using a larger, independent cohort are warranted to confirm and better define the potential clinical utility of PV-CTCs in early-stage NSCLC
Antibodies against endogenous retroviruses promote lung cancer immunotherapy
B cells are frequently found in the margins of solid tumours as organized follicles in ectopic lymphoid organs called tertiary lymphoid structures (TLS)1,2. Although TLS have been found to correlate with improved patient survival and response to immune checkpoint blockade (ICB), the underlying mechanisms of this association remain elusive1,2. Here we investigate lung-resident B cell responses in patients from the TRACERx 421 (Tracking Non-Small-Cell Lung Cancer Evolution Through Therapy) and other lung cancer cohorts, and in a recently established immunogenic mouse model for lung adenocarcinoma3. We find that both human and mouse lung adenocarcinomas elicit local germinal centre responses and tumour-binding antibodies, and further identify endogenous retrovirus (ERV) envelope glycoproteins as a dominant anti-tumour antibody target. ERV-targeting B cell responses are amplified by ICB in both humans and mice, and by targeted inhibition of KRAS(G12C) in the mouse model. ERV-reactive antibodies exert anti-tumour activity that extends survival in the mouse model, and ERV expression predicts the outcome of ICB in human lung adenocarcinoma. Finally, we find that effective immunotherapy in the mouse model requires CXCL13-dependent TLS formation. Conversely, therapeutic CXCL13 treatment potentiates anti-tumour immunity and synergizes with ICB. Our findings provide a possible mechanistic basis for the association of TLS with immunotherapy respons
Determinants of anti-PD-1 response and resistance in clear cell renal cell carcinoma
ADAPTeR is a prospective, phase II study of nivolumab (anti-PD-1) in 15 treatment-naive patients (115 multiregion tumor samples) with metastatic clear cell renal cell carcinoma (ccRCC) aiming to understand the mechanism underpinning therapeutic response. Genomic analyses show no correlation between tumor molecular features and response, whereas ccRCC-specific human endogenous retrovirus expression indirectly correlates with clinical response. TÂ cell receptor (TCR) analysis reveals a significantly higher number of expanded TCR clones pre-treatment in responders suggesting pre-existing immunity. Maintenance of highly similar clusters of TCRs post-treatment predict response, suggesting ongoing antigen engagement and survival of families of TÂ cells likely recognizing the same antigens. In responders, nivolumab-bound CD8+ TÂ cells are expanded and express GZMK/B. Our data suggest nivolumab drives both maintenance and replacement of previously expanded TÂ cell clones, but only maintenance correlates with response. We hypothesize that maintenance and boosting of a pre-existing response is a key element of anti-PD-1 mode of action
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Determinants of anti-PD-1 response and resistance in clear cell renal cell carcinoma.
ADAPTeR is a prospective, phase II study of nivolumab (anti-PD-1) in 15 treatment-naive patients (115 multiregion tumor samples) with metastatic clear cell renal cell carcinoma (ccRCC) aiming to understand the mechanism underpinning therapeutic response. Genomic analyses show no correlation between tumor molecular features and response, whereas ccRCC-specific human endogenous retrovirus expression indirectly correlates with clinical response. TÂ cell receptor (TCR) analysis reveals a significantly higher number of expanded TCR clones pre-treatment in responders suggesting pre-existing immunity. Maintenance of highly similar clusters of TCRs post-treatment predict response, suggesting ongoing antigen engagement and survival of families of TÂ cells likely recognizing the same antigens. In responders, nivolumab-bound CD8+ TÂ cells are expanded and express GZMK/B. Our data suggest nivolumab drives both maintenance and replacement of previously expanded TÂ cell clones, but only maintenance correlates with response. We hypothesize that maintenance and boosting of a pre-existing response is a key element of anti-PD-1 mode of action
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