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1. Computed tomography-based radiomics for the differential diagnosis of pneumonitis in stage IV non-small cell lung cancer patients treated with immune checkpoint inhibitors.

Author

Tohidinezhad F, Bontempi D, Zhang Z, Dingemans AM, Aerts J, Bootsma G, Vansteenkiste J, Hashemi S, Smit E, Gietema H, Aerts HJ, Dekker A, Hendriks LEL, Traverso A, and De Ruysscher D

Subjects
Humans, Immune Checkpoint Inhibitors adverse effects, Diagnosis, Differential, Tomography, X-Ray Computed methods, Carcinoma, Non-Small-Cell Lung diagnostic imaging, Carcinoma, Non-Small-Cell Lung drug therapy, Lung Neoplasms diagnostic imaging, Lung Neoplasms drug therapy, Pneumonia chemically induced, Pneumonia diagnostic imaging
Abstract

Introduction: Immunotherapy-induced pneumonitis (IIP) is a serious side-effect which requires accurate diagnosis and management with high-dose corticosteroids. The differential diagnosis between IIP and other types of pneumonitis (OTP) remains challenging due to similar radiological patterns. This study was aimed to develop a prediction model to differentiate IIP from OTP in patients with stage IV non-small cell lung cancer (NSCLC) who developed pneumonitis during immunotherapy., Methods: Consecutive patients with metastatic NSCLC treated with immunotherapy in six centres in the Netherlands and Belgium from 2017 to 2020 were reviewed and cause-specific pneumonitis events were identified. Seven regions of interest (segmented lungs and spheroidal/cubical regions surrounding the inflammation) were examined to extract the most predictive radiomic features from the chest computed tomography images obtained at pneumonitis manifestation. Models were internally tested regarding discrimination, calibration and decisional benefit. To evaluate the clinical application of the models, predicted labels were compared with the separate clinical and radiological judgements., Results: A total of 556 patients were reviewed; 31 patients (5.6%) developed IIP and 41 patients developed OTP (7.4%). The line of immunotherapy was the only predictive factor in the clinical model (2nd versus 1st odds ratio = 0.08, 95% confidence interval:0.01-0.77). The best radiomic model was achieved using a 75-mm spheroidal region of interest which showed an optimism-corrected area under the receiver operating characteristic curve of 0.83 (95% confidence interval:0.77-0.95) with negative and positive predictive values of 80% and 79%, respectively. Good calibration and net benefits were achieved for the radiomic model across the entire range of probabilities. A correct diagnosis was provided by the radiomic model in 10 out of 12 cases with non-conclusive radiological judgements., Conclusion: Radiomic biomarkers applied to computed tomography imaging may support clinicians making the differential diagnosis of pneumonitis in patients with NSCLC receiving immunotherapy, especially when the radiologic assessment is non-conclusive., Competing Interests: Conflict of interest statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Published
2023
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2. Defining the biological basis of radiomic phenotypes in lung cancer.

Author

Grossmann P, Stringfield O, El-Hachem N, Bui MM, Rios Velazquez E, Parmar C, Leijenaar RT, Haibe-Kains B, Lambin P, Gillies RJ, and Aerts HJ

Subjects
Adenocarcinoma diagnostic imaging, Adenocarcinoma pathology, Adenocarcinoma radiotherapy, Biomarkers, Tumor metabolism, Carcinoma, Squamous Cell diagnostic imaging, Carcinoma, Squamous Cell pathology, Carcinoma, Squamous Cell radiotherapy, Clinical Decision-Making, Female, Humans, Immunohistochemistry, Lung Neoplasms pathology, Male, Prognosis, Tomography, X-Ray Computed methods, Diagnostic Imaging methods, Lung Neoplasms diagnostic imaging, Lung Neoplasms radiotherapy, Phenotype, Radiometry methods
Abstract

Medical imaging can visualize characteristics of human cancer noninvasively. Radiomics is an emerging field that translates these medical images into quantitative data to enable phenotypic profiling of tumors. While radiomics has been associated with several clinical endpoints, the complex relationships of radiomics, clinical factors, and tumor biology are largely unknown. To this end, we analyzed two independent cohorts of respectively 262 North American and 89 European patients with lung cancer, and consistently identified previously undescribed associations between radiomic imaging features, molecular pathways, and clinical factors. In particular, we found a relationship between imaging features, immune response, inflammation, and survival, which was further validated by immunohistochemical staining. Moreover, a number of imaging features showed predictive value for specific pathways; for example, intra-tumor heterogeneity features predicted activity of RNA polymerase transcription (AUC = 0.62, p=0.03) and intensity dispersion was predictive of the autodegration pathway of a ubiquitin ligase (AUC = 0.69, p < 10 -4 ). Finally, we observed that prognostic biomarkers performed highest when combining radiomic, genetic, and clinical information (CI = 0.73, p<10 -9 ) indicating complementary value of these data. In conclusion, we demonstrate that radiomic approaches permit noninvasive assessment of both molecular and clinical characteristics of tumors, and therefore have the potential to advance clinical decision-making by systematically analyzing standard-of-care medical images.

Published
2017
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3. Inter-scan and inter-observer tumour volume delineation variability on cone beam computed tomography in patients treated with stereotactic body radiation therapy for early-stage non-small cell lung cancer.

Author

Hou Y, Lee S, Agrawal V, Romano J, Baldini EH, Chen AB, Kozono DE, Killoran JH, Wagar M, Hacker FL, Aerts HJ, Lewis JH, and Mak RH

Subjects
Carcinoma, Non-Small-Cell Lung diagnostic imaging, Female, Humans, Lung diagnostic imaging, Lung pathology, Lung Neoplasms diagnostic imaging, Male, Observer Variation, Reproducibility of Results, Carcinoma, Non-Small-Cell Lung pathology, Carcinoma, Non-Small-Cell Lung radiotherapy, Cone-Beam Computed Tomography methods, Lung Neoplasms pathology, Lung Neoplasms radiotherapy, Radiosurgery methods, Tumor Burden
Abstract

Introduction: Quantification of volume changes on cone beam computed tomography (CBCT) during lung stereotactic body radiation therapy (SBRT) for non-small cell lung cancer (NSCLC) may provide a useful radiological marker for radiation response and for adaptive treatment planning. This study quantifies inter-scan and inter-observer variability in tumour volume delineation on CBCT., Methods: Three clinicians independently contoured the primary gross tumour volume (GTV) manually on CBCTs taken immediately before SBRT treatment (pre) and after the same SBRT treatment (post) for 19 NSCLC patients. Relative volume differences (RVD) were calculated between the pre- and post-CBCTs for a given treatment and between any two of three observers for a given CBCT. Coefficient of variation (CV) was used to quantitatively measure and compare the extent of variability., Results: Inter-observer variability had a significantly higher CV of 0.15 ± 0.13 compared to inter-scan CV of 0.03 ± 0.04 with P < 0.0001. The greatest variability was observed with tumours (<2 cm in diameter) versus larger tumours with 95% limit of agreement (LOA) (Mean ± Standard Deviation) of 1.90% ± 19.55% vs. -0.97% ± 12.26% for inter-scan RVD and 29.99% ± 73.84% vs. 9.37% ± 29.95% for inter-observer RVD respectively., Conclusions: Inter-observer variability was greater than inter-scan variability for tumour volume delineation on CBCT with greatest variability for small tumours (<2 cm in diameter). LOA for inter-scan variability (~12%) helps defines a threshold for clinically meaningful tumour volume change during SBRT treatment for tumours with diameter greater than 2 cm, with larger thresholds needed for smaller tumours., (© 2016 The Royal Australian and New Zealand College of Radiologists.)

Published
2017
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4. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Author

Zwanenburg, Alex, Vallières, Martin, Abdalah, Mahmoud A, Aerts, Hugo JWL, Andrearczyk, Vincent, Apte, Aditya, Ashrafinia, Saeed, Bakas, Spyridon, Beukinga, Roelof J, Boellaard, Ronald, Bogowicz, Marta, Boldrini, Luca, Buvat, Irène, Cook, Gary JR, Davatzikos, Christos, Depeursinge, Adrien, Desseroit, Marie-Charlotte, Dinapoli, Nicola, Dinh, Cuong Viet, Echegaray, Sebastian, El Naqa, Issam, Fedorov, Andriy Y, Gatta, Roberto, Gillies, Robert J, Goh, Vicky, Götz, Michael, Guckenberger, Matthias, Ha, Sung Min, Hatt, Mathieu, Isensee, Fabian, Lambin, Philippe, Leger, Stefan, Leijenaar, Ralph TH, Lenkowicz, Jacopo, Lippert, Fiona, Losnegård, Are, Maier-Hein, Klaus H, Morin, Olivier, Müller, Henning, Napel, Sandy, Nioche, Christophe, Orlhac, Fanny, Pati, Sarthak, Pfaehler, Elisabeth AG, Rahmim, Arman, Rao, Arvind UK, Scherer, Jonas, Siddique, Muhammad Musib, Sijtsema, Nanna M, Socarras Fernandez, Jairo, Spezi, Emiliano, Steenbakkers, Roel JHM, Tanadini-Lang, Stephanie, Thorwarth, Daniela, Troost, Esther GC, Upadhaya, Taman, Valentini, Vincenzo, van Dijk, Lisanne V, van Griethuysen, Joost, van Velden, Floris HP, Whybra, Philip, Richter, Christian, and Löck, Steffen

Subjects
Biomedical Imaging, Cancer, Bioengineering, Clinical Trials and Supportive Activities, Clinical Research, Good Health and Well Being, Biomarkers, Calibration, Fluorodeoxyglucose F18, Humans, Image Processing, Computer-Assisted, Lung Neoplasms, Magnetic Resonance Imaging, Phantoms, Imaging, Phenotype, Positron-Emission Tomography, Radiopharmaceuticals, Reproducibility of Results, Sarcoma, Software, Tomography, X-Ray Computed, Medical and Health Sciences, Nuclear Medicine & Medical Imaging
Abstract

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

Published
2020

5. Semi‐automated pulmonary nodule interval segmentation using the NLST data

Author

Balagurunathan, Yoganand, Beers, Andrew, Kalpathy‐Cramer, Jayashree, McNitt‐Gray, Michael, Hadjiiski, Lubomir, Zhao, Bensheng, Zhu, Jiangguo, Yang, Hao, Yip, Stephen SF, Aerts, Hugo JWL, Napel, Sandy, Cherezov, Dmitrii, Cha, Kenny, Chan, Heang‐Ping, Flores, Carlos, Garcia, Alberto, Gillies, Robert, and Goldgof, Dmitry

Subjects
Medical and Biological Physics, Engineering, Physical Sciences, Biomedical Engineering, Cancer, Biomedical Imaging, Bioengineering, Aged, Automation, Female, Humans, Image Processing, Computer-Assisted, Lung Neoplasms, Male, Mass Screening, Middle Aged, Software, Tomography, X-Ray Computed, change in volume segmentation, CT lung, lung nodule segmentation, volume estimate, Other Physical Sciences, Oncology and Carcinogenesis, Nuclear Medicine & Medical Imaging, Biomedical engineering, Medical and biological physics
Abstract

PurposeTo study the variability in volume change estimates of pulmonary nodules due to segmentation approaches used across several algorithms and to evaluate these effects on the ability to predict nodule malignancy.MethodsWe obtained 100 patient image datasets from the National Lung Screening Trial (NLST) that had a nodule detected on each of two consecutive low dose computed tomography (LDCT) scans, with an equal proportion of malignant and benign cases (50 malignant, 50 benign). Information about the nodule location for the cases was provided by a screen capture with a bounding box and its axial location was indicated. Five participating quantitative imaging network (QIN) institutions performed nodule segmentation using their preferred semi-automated algorithms with no manual correction; teams were allowed to provide additional manually corrected segmentations (analyzed separately). The teams were asked to provide segmentation masks for each nodule at both time points. From these masks, the volume was estimated for the nodule at each time point; the change in volume (absolute and percent change) across time points was estimated as well. We used the concordance correlation coefficient (CCC) to compare the similarity of computed nodule volumes (absolute and percent change) across algorithms. We used Logistic regression model on the change in volume (absolute change and percent change) of the nodules to predict the malignancy status, the area under the receiver operating characteristic curve (AUROC) and confidence intervals were reported. Because the size of nodules was expected to have a substantial effect on segmentation variability, analysis of change in volumes was stratified by lesion size, where lesions were grouped into those with a longest diameter of

Published
2018

6. Body composition and lung cancer-associated cachexia in TRACERx

Author

Al-Sawaf, Othman, Weiss, Jakob, Skrzypski, Marcin, Lam, Jie Min, Karasaki, Takahiro, Zambrana, Francisco, Kidd, Andrew C, Frankell, Alexander M, Watkins, Thomas BK, Martinez-Ruiz, Carlos, Puttick, Clare, Black, James RM, Huebner, Ariana, Al Bakir, Maise, Sokac, Mateo, Collins, Susie, Veeriah, Selvaraju, Magno, Neil, Naceur-Lombardelli, Cristina, Prymas, Paulina, Toncheva, Antonia, Ward, Sophia, Jayanth, Nick, Salgado, Roberto, Bridge, Christopher P, Christiani, David C, Mak, Raymond H, Bay, Camden, Rosenthal, Michael, Sattar, Naveed, Welsh, Paul, Liu, Ying, Perrimon, Norbert, Popuri, Karteek, Beg, Mirza Faisal, McGranahan, Nicholas, Hackshaw, Allan, Breen, Danna M, O'Rahilly, Stephen, Birkbak, Nicolai J, Aerts, Hugo JWL, Jamal-Hanjani, Mariam, Swanton, Charles, Lester, Jason F, Bajaj, Amrita, Nakas, Apostolos, Sodha-Ramdeen, Azmina, Ang, Keng, Tufail, Mohamad, Chowdhry, Mohammed Fiyaz, Scotland, Molly, Boyles, Rebecca, Rathinam, Sridhar, Wilson, Claire, Marrone, Domenic, Dulloo, Sean, Fennell, Dean A, Matharu, Gurdeep, Shaw, Jacqui A, Riley, Joan, Primrose, Lindsay, Boleti, Ekaterini, Cheyne, Heather, Khalil, Mohammed, Richardson, Shirley, Cruickshank, Tracey, Price, Gillian, Kerr, Keith M, Benafif, Sarah, Gilbert, Kayleigh, Naidu, Babu, Patel, Akshay J, Osman, Aya, Lacson, Christer, Langman, Gerald, Shackleford, Helen, Djearaman, Madava, Kadiri, Salma, Middleton, Gary, Leek, Angela, Hodgkinson, Jack Davies, Totten, Nicola, Montero, Angeles, Smith, Elaine, Fontaine, Eustace, Granato, Felice, Doran, Helen, Novasio, Juliette, Rammohan, Kendadai, Joseph, Leena, Bishop, Paul, Shah, Rajesh, Moss, Stuart, Joshi, Vijay, Crosbie, Philip, Gomes, Fabio, Brown, Kate, Carter, Mathew, Chaturvedi, Anshuman, Priest, Lynsey, Oliveira, Pedro, Lindsay, Colin R, Blackhall, Fiona H, Krebs, Matthew G, Summers, Yvonne, Clipson, Alexandra, Tugwood, Jonathan, Kerr, Alastair, Rothwell, Dominic G, Kilgour, Elaine, Dive, Caroline, Schwarz, Roland F, Kaufmann, Tom L, Wilson, Gareth A, Rosenthal, Rachel, Van Loo, Peter, Szallasi, Zoltan, Kisistok, Judit, Diossy, Miklos, Demeulemeester, Jonas, Bunkum, Abigail, Stewart, Aengus, Magness, Alastair, Rowan, Andrew, Karamani, Angeliki, Chain, Benny, Campbell, Brittany B, Castignani, Carla, Bailey, Chris, Abbosh, Christopher, Weeden, Clare E, Lee, Claudia, Richard, Corentin, Hiley, Crispin T, Moore, David A, Pearce, David R, Karagianni, Despoina, Biswas, Dhruva, Levi, Dina, Hoxha, Elena, Cadieux, Elizabeth Larose, Lim, Emilia L, Colliver, Emma, Nye, Emma, Gronroos, Eva, Galvez-Cancino, Felip, Athanasopoulou, Foteini, Gimeno-Valiente, Francisco, Kassiotis, George, Stavrou, Georgia, Mastrokalos, Gerasimos, Zhai, Haoran, Lowe, Helen L, Matos, Ignacio Garcia, Goldman, Jacki, Reading, James L, Herrero, Javier, Rane, Jayant K, Nicod, Jerome, Hartley, John A, Peggs, Karl S, Enfield, Katey SS, Selvaraju, Kayalvizhi, Thol, Kerstin, Litchfield, Kevin, Ng, Kevin W, Chen, Kezhong, Dijkstra, Krijn, Grigoriadis, Kristiana, Thakkar, Krupa, Ensell, Leah, Shah, Mansi, Duran, Marcos Vasquez, Litovchenko, Maria, Sunderland, Mariana Werner, Hill, Mark S, Dietzen, Michelle, Leung, Michelle, Escudero, Mickael, Angelova, Mihaela, Tanic, Miljana, Sivakumar, Monica, Kanu, Nnennaya, Chervova, Olga, Lucas, Olivia, Pich, Oriol, Hobson, Philip, Pawlik, Piotr, Stone, Richard Kevin, Bentham, Robert, Hynds, Robert E, Vendramin, Roberto, Saghafinia, Sadegh, Lopez, Saioa, Gamble, Samuel, Ung, Seng Kuong Anakin, Quezada, Sergio A, Vanloo, Sharon, Zaccaria, Simone, Hessey, Sonya, Boeing, Stefan, Beck, Stephan, Bola, Supreet Kaur, Denner, Tamara, Marafioti, Teresa, Mourikis, Thanos P, Spanswick, Victoria, Barbe, Vittorio, Lu, Wei-Ting, Hill, William, Liu, Wing Kin, Wu, Yin, Naito, Yutaka, Ramsden, Zoe, Veiga, Catarina, Royle, Gary, Collins-Fekete, Charles-Antoine, Fraioli, Francesco, Ashford, Paul, Clark, Tristan, Forster, Martin D, Lee, Siow Ming, Borg, Elaine, Falzon, Mary, Papadatos-Pastos, Dionysis, Wilson, James, Ahmad, Tanya, Procter, Alexander James, Ahmed, Asia, Taylor, Magali N, Nair, Arjun, Lawrence, David, Patrini, Davide, Navani, Neal, Thakrar, Ricky M, Janes, Sam M, Hoogenboom, Emilie Martinoni, Monk, Fleur, Holding, James W, Choudhary, Junaid, Bhakhri, Kunal, Scarci, Marco, Hayward, Martin, Panagiotopoulos, Nikolaos, Gorman, Pat, Khiroya, Reena, Stephens, Robert CM, Wong, Yien Ning Sophia, Bandula, Steve, Sharp, Abigail, Smith, Sean, Gower, Nicole, Dhanda, Harjot Kaur, Chan, Kitty, Pilotti, Camilla, Leslie, Rachel, Grapa, Anca, Zhang, Hanyun, AbdulJabbar, Khalid, Pan, Xiaoxi, Yuan, Yinyin, Chuter, David, MacKenzie, Mairead, Chee, Serena, Alzetani, Aiman, Cave, Judith, Scarlett, Lydia, Richards, Jennifer, Ingram, Papawadee, Austin, Silvia, Lim, Eric, De Sousa, Paulo, Jordan, Simon, Rice, Alexandra, Raubenheimer, Hilgardt, Bhayani, Harshil, Ambrose, Lyn, Devaraj, Anand, Chavan, Hema, Begum, Sofina, Buderi, Silviu, Kaniu, Daniel, Malima, Mpho, Booth, Sarah, Nicholson, Andrew G, Fernandes, Nadia, Shah, Pratibha, Proli, Chiara, Hewish, Madeleine, Danson, Sarah, Shackcloth, Michael J, Robinson, Lily, Russell, Peter, Blyth, Kevin G, Dick, Craig, Le Quesne, John, Kirk, Alan, Asif, Mo, Bilancia, Rocco, Kostoulas, Nikos, and Thomas, Mathew

Subjects
Male, Proteomics, Cachexia, Lung Neoplasms, Antigens, Neoplasm, Carcinoma, Non-Small-Cell Lung, Body Weight, Body Composition, Humans, Neoplasm Recurrence, Local, Muscle, Skeletal, Neoplasm Proteins
Abstract

Cancer-associated cachexia (CAC) is a major contributor to morbidity and mortality in individuals with non-small cell lung cancer. Key features of CAC include alterations in body composition and body weight. Here, we explore the association between body composition and body weight with survival and delineate potential biological processes and mediators that contribute to the development of CAC. Computed tomography-based body composition analysis of 651 individuals in the TRACERx (TRAcking non-small cell lung Cancer Evolution through therapy (Rx)) study suggested that individuals in the bottom 20th percentile of the distribution of skeletal muscle or adipose tissue area at the time of lung cancer diagnosis, had significantly shorter lung cancer-specific survival and overall survival. This finding was validated in 420 individuals in the independent Boston Lung Cancer Study. Individuals classified as having developed CAC according to one or more features at relapse encompassing loss of adipose or muscle tissue, or body mass index-adjusted weight loss were found to have distinct tumor genomic and transcriptomic profiles compared with individuals who did not develop such features. Primary non-small cell lung cancers from individuals who developed CAC were characterized by enrichment of inflammatory signaling and epithelial-mesenchymal transitional pathways, and differentially expressed genes upregulated in these tumors included cancer-testis antigen MAGEA6 and matrix metalloproteinases, such as ADAMTS3. In an exploratory proteomic analysis of circulating putative mediators of cachexia performed in a subset of 110 individuals from TRACERx, a significant association between circulating GDF15 and loss of body weight, skeletal muscle and adipose tissue was identified at relapse, supporting the potential therapeutic relevance of targeting GDF15 in the management of CAC. ispartof: NATURE MEDICINE vol:29 issue:4 ispartof: location:United States status: Published online

Published
2023

7. The evolution of non-small cell lung cancer metastases in TRACERx

Author

Al Bakir, Maise, Huebner, Ariana, Martinez-Ruiz, Carlos, Grigoriadis, Kristiana, Watkins, Thomas BK, Pich, Oriol, Moore, David A, Veeriah, Selvaraju, Ward, Sophia, Laycock, Joanne, Johnson, Diana, Rowan, Andrew, Razaq, Maryam, Akther, Mita, Naceur-Lombardelli, Cristina, Prymas, Paulina, Toncheva, Antonia, Hessey, Sonya, Dietzen, Michelle, Colliver, Emma, Frankell, Alexander, Bunkum, Abigail, Lim, Emilia L, Karasaki, Takahiro, Abbosh, Christopher, Hiley, Crispin T, Hill, Mark S, Cook, Daniel E, Wilson, Gareth A, Salgado, Roberto, Nye, Emma, Stone, Richard Kevin, Fennell, Dean A, Price, Gillian, Kerr, Keith M, Naidu, Babu, Middleton, Gary, Summers, Yvonne, Lindsay, Colin R, Blackhall, Fiona H, Cave, Judith, Blyth, Kevin G, Nair, Arjun, Ahmed, Asia, Taylor, Magali N, Procter, Alexander James, Falzon, Mary, Lawrence, David, Navani, Neal, Thakrar, Ricky M, Janes, Sam M, Papadatos-Pastos, Dionysis, Forster, Martin D, Lee, Siow Ming, Ahmad, Tanya, Quezada, Sergio, Peggs, Karl S, Van Loo, Peter, Dive, Caroline, Hackshaw, Allan, Birkbak, Nicolai J, Zaccaria, Simone, Jamal-Hanjani, Mariam, McGranahan, Nicholas, Swanton, Charles, Lester, Jason F, Bajaj, Amrita, Nakas, Apostolos, Sodha-Ramdeen, Azmina, Ang, Keng, Tufail, Mohamad, Chowdhry, Mohammed Fiyaz, Scotland, Molly, Boyles, Rebecca, Rathinam, Sridhar, Wilson, Claire, Marrone, Domenic, Dulloo, Sean, Matharu, Gurdeep, Shaw, Jacqui A, Riley, Joan, Primrose, Lindsay, Boleti, Ekaterini, Cheyne, Heather, Khalil, Mohammed, Richardson, Shirley, Cruickshank, Tracey, Benafif, Sarah, Gilbert, Kayleigh, Patel, Akshay J, Osman, Aya, Lacson, Christer, Langman, Gerald, Shackleford, Helen, Djearaman, Madava, Kadiri, Salma, Leek, Angela, Hodgkinson, Jack Davies, Totten, Nicola, Montero, Angeles, Smith, Elaine, Fontaine, Eustace, Granato, Felice, Doran, Helen, Novasio, Juliette, Rammohan, Kendadai, Joseph, Leena, Bishop, Paul, Shah, Rajesh, Moss, Stuart, Joshi, Vijay, Crosbie, Philip, Gomes, Fabio, Brown, Kate, Carter, Mathew, Chaturvedi, Anshuman, Priest, Lynsey, Oliveira, Pedro, Krebs, Matthew G, Clipson, Alexandra, Tugwood, Jonathan, Kerr, Alastair, Rothwell, Dominic G, Kilgour, Elaine, Aerts, Hugo JWL, Schwarz, Roland F, Kaufmann, Tom L, Rosenthal, Rachel, Szallasi, Zoltan, Kisistok, Judit, Sokac, Mateo, Diossy, Miklos, Demeulemeester, Jonas, Stewart, Aengus, Magness, Alastair, Karamani, Angeliki, Chain, Benny, Campbell, Brittany B, Castignani, Carla, Bailey, Chris, Puttick, Clare, Weeden, Clare E, Lee, Claudia, Richard, Corentin, Pearce, David R, Karagianni, Despoina, Biswas, Dhruva, Levi, Dina, Hoxha, Elena, Larose Cadieux, Elizabeth, Gronroos, Eva, Galvez-Cancino, Felip, Athanasopoulou, Foteini, Gimeno-Valiente, Francisco, Kassiotis, George, Stavrou, Georgia, Mastrokalos, Gerasimos, Zhai, Haoran, Lowe, Helen L, Matos, Ignacio, Goldman, Jacki, Reading, James L, Black, James RM, Herrero, Javier, Rane, Jayant K, Nicod, Jerome, Lam, Jie Min, Hartley, John A, Enfield, Katey SS, Selvaraju, Kayalvizhi, Thol, Kerstin, Litchfield, Kevin, Ng, Kevin W, Chen, Kezhong, Dijkstra, Krijn, Thakkar, Krupa, Ensell, Leah, Shah, Mansi, Vasquez, Marcos, Litovchenko, Maria, Werner Sunderland, Mariana, Leung, Michelle, Escudero, Mickael, Angelova, Mihaela, Tanic, Miljana, Sivakumar, Monica, Kanu, Nnennaya, Chervova, Olga, Lucas, Olivia, Al-Sawaf, Othman, Hobson, Philip, Pawlik, Piotr, Bentham, Robert, Hynds, Robert E, Vendramin, Roberto, Saghafinia, Sadegh, Lopez, Saioa, Gamble, Samuel, Ung, Seng Kuong Anakin, Vanloo, Sharon, Boeing, Stefan, Beck, Stephan, Bola, Supreet Kaur, Denner, Tamara, Marafioti, Teresa, Mourikis, Thanos P, Spanswick, Victoria, Barbe, Vittorio, Lu, Wei-Ting, Hill, William, Liu, Wing Kin, Wu, Yin, Naito, Yutaka, Ramsden, Zoe, Veiga, Catarina, Royle, Gary, Collins-Fekete, Charles-Antoine, Fraioli, Francesco, Ashford, Paul, Clark, Tristan, Borg, Elaine, Wilson, James, Patrini, Davide, Martinoni Hoogenboom, Emilie, Monk, Fleur, Holding, James W, Choudhary, Junaid, Bhakhri, Kunal, Scarci, Marco, Hayward, Martin, Panagiotopoulos, Nikolaos, Gorman, Pat, Khiroya, Reena, Stephens, Robert CM, Wong, Yien Ning Sophia, Bandula, Steve, Sharp, Abigail, Smith, Sean, Gower, Nicole, Dhanda, Harjot Kaur, Chan, Kitty, Pilotti, Camilla, Leslie, Rachel, Grapa, Anca, Zhang, Hanyun, AbdulJabbar, Khalid, Pan, Xiaoxi, Yuan, Yinyin, Chuter, David, MacKenzie, Mairead, Chee, Serena, Alzetani, Aiman, Scarlett, Lydia, Richards, Jennifer, Ingram, Papawadee, Austin, Silvia, Lim, Eric, De Sousa, Paulo, Jordan, Simon, Rice, Alexandra, Raubenheimer, Hilgardt, Bhayani, Harshil, Ambrose, Lyn, Devaraj, Anand, Chavan, Hema, Begum, Sofina, Buderi, Silviu, Kaniu, Daniel, Malima, Mpho, Booth, Sarah, Nicholson, Andrew G, Fernandes, Nadia, Shah, Pratibha, Proli, Chiara, Hewish, Madeleine, Danson, Sarah, Shackcloth, Michael J, Robinson, Lily, Russell, Peter, Dick, Craig, Le Quesne, John, Kirk, Alan, Asif, Mo, Bilancia, Rocco, Kostoulas, Nikos, and Thomas, Mathew

Subjects
Clonal Evolution, Cohort Studies, Evolution, Molecular, Lung Neoplasms, Carcinoma, Non-Small-Cell Lung, Disease Progression, Humans, Neoplasm Metastasis, Neoplasm Recurrence, Local, Clone Cells
Abstract

Metastatic disease is responsible for the majority of cancer-related deaths1. We report the longitudinal evolutionary analysis of 126 non-small cell lung cancer (NSCLC) tumours from 421 prospectively recruited patients in TRACERx who developed metastatic disease, compared with a control cohort of 144 non-metastatic tumours. In 25% of cases, metastases diverged early, before the last clonal sweep in the primary tumour, and early divergence was enriched for patients who were smokers at the time of initial diagnosis. Simulations suggested that early metastatic divergence more frequently occurred at smaller tumour diameters (less than 8 mm). Single-region primary tumour sampling resulted in 83% of late divergence cases being misclassified as early, highlighting the importance of extensive primary tumour sampling. Polyclonal dissemination, which was associated with extrathoracic disease recurrence, was found in 32% of cases. Primary lymph node disease contributed to metastatic relapse in less than 20% of cases, representing a hallmark of metastatic potential rather than a route to subsequent recurrences/disease progression. Metastasis-seeding subclones exhibited subclonal expansions within primary tumours, probably reflecting positive selection. Our findings highlight the importance of selection in metastatic clone evolution within untreated primary tumours, the distinction between monoclonal versus polyclonal seeding in dictating site of recurrence, the limitations of current radiological screening approaches for early diverging tumours and the need to develop strategies to target metastasis-seeding subclones before relapse. ispartof: NATURE vol:616 issue:7957 ispartof: location:England status: Published online

Published
2023

8. The evolution of lung cancer and impact of subclonal selection in TRACERx

Author

Frankell, Alexander M, Dietzen, Michelle, Al Bakir, Maise, Lim, Emilia L, Karasaki, Takahiro, Ward, Sophia, Veeriah, Selvaraju, Colliver, Emma, Huebner, Ariana, Bunkum, Abigail, Hill, Mark S, Grigoriadis, Kristiana, Moore, David A, Black, James RM, Liu, Wing Kin, Thol, Kerstin, Pich, Oriol, Watkins, Thomas BK, Naceur-Lombardelli, Cristina, Cook, Daniel E, Salgado, Roberto, Wilson, Gareth A, Bailey, Chris, Angelova, Mihaela, Bentham, Robert, Martinez-Ruiz, Carlos, Abbosh, Christopher, Nicholson, Andrew G, Le Quesne, John, Biswas, Dhruva, Rosenthal, Rachel, Puttick, Clare, Hessey, Sonya, Lee, Claudia, Prymas, Paulina, Toncheva, Antonia, Smith, Jon, Xing, Wei, Nicod, Jerome, Price, Gillian, Kerr, Keith M, Naidu, Babu, Middleton, Gary, Blyth, Kevin G, Fennell, Dean A, Forster, Martin D, Lee, Siow Ming, Falzon, Mary, Hewish, Madeleine, Shackcloth, Michael J, Lim, Eric, Benafif, Sarah, Russell, Peter, Boleti, Ekaterini, Krebs, Matthew G, Lester, Jason F, Papadatos-Pastos, Dionysis, Ahmad, Tanya, Thakrar, Ricky M, Lawrence, David, Navani, Neal, Janes, Sam M, Dive, Caroline, Blackhall, Fiona H, Summers, Yvonne, Cave, Judith, Marafioti, Teresa, Herrero, Javier, Quezada, Sergio A, Peggs, Karl S, Schwarz, Roland F, Van Loo, Peter, Miedema, Daniel M, Birkbak, Nicolai J, Hiley, Crispin T, Hackshaw, Allan, Zaccaria, Simone, Jamal-Hanjani, Mariam, McGranahan, Nicholas, Swanton, Charles, Bajaj, Amrita, Nakas, Apostolos, Sodha-Ramdeen, Azmina, Ang, Keng, Tufail, Mohamad, Chowdhry, Mohammed Fiyaz, Scotland, Molly, Boyles, Rebecca, Rathinam, Sridhar, Wilson, Claire, Marrone, Domenic, Dulloo, Sean, Matharu, Gurdeep, Shaw, Jacqui A, Riley, Joa, Primrose, Lindsay, Cheyne, Heather, Khalil, Mohammed, Richardson, Shirley, Cruickshank, Tracey, Gilbert, Kayleigh, Patel, Akshay J, Osman, Aya, Lacson, Christer, Langman, Gerald, Shackleford, Helen, Djearaman, Madava, Kadiri, Salma, Leek, Angela, Hodgkinson, Jack Davies, Totten, Nicola, Montero, Angeles, Smith, Elaine, Fontaine, Eustace, Granato, Felice, Doran, Helen, Novasio, Juliette, Rammohan, Kendadai, Joseph, Leena, Bishop, Paul, Shah, Rajesh, Moss, Stuart, Joshi, Vijay, Crosbie, Philip, Gomes, Fabio, Brown, Kate, Carter, Mathew, Chaturvedi, Anshuman, Priest, Lynsey, Oliveira, Pedro, Lindsay, Colin R, Clipson, Alexandra, Tugwood, Jonathan, Kerr, Alastair, Rothwell, Dominic G, Kilgour, Elaine, Aerts, Hugo JWL, Kaufmann, Tom L, Szallasi, Zoltan, Kisistok, Judit, Sokac, Mateo, Diossy, Miklos, Demeulemeester, Jonas, Stewart, Aengus, Magness, Alastair, Rowan, Andrew, Karamani, Angeliki, Chain, Benny, Campbell, Brittany B, Castignani, Carla, Weeden, Clare E, Richard, Corentin, Pearce, David R, Karagianni, Despoina, Levi, Dina, Hoxha, Elena, Larose Cadieux, Elizabeth, Nye, Emma, Gronroos, Eva, Galvez-Cancino, Felip, Athanasopoulou, Foteini, Gimeno-Valiente, Francisco, Kassiotis, George, Stavrou, Georgia, Mastrokalos, Gerasimos, Zhai, Haoran L, Lowe, Helen L, Matos, Ignacio, Goldman, Jacki, Reading, James L, Rane, Jayant K, Lam, Jie Min, Hartley, John A, Enfield, Katey SS, Selvaraju, Kayalvizhi, Litchfield, Kevin, Ng, Kevin W, Chen, Kezhong, Dijkstra, Krijn, Thakkar, Krupa, Ensell, Leah, Shah, Mansi, Vasquez, Marcos, Litovchenko, Maria, Werner Sunderland, Mariana, Leung, Michelle, Escudero, Mickael, Tanic, Miljana, Sivakumar, Monica, Kanu, Nnennaya, Chervova, Olga, Lucas, Olivia, Al-Sawaf, Othman, Hobson, Philip, Pawlik, Piotr, Stone, Richard Kevin, Hynds, Robert E, Vendramin, Roberto, Saghafinia, Sadegh, Lopez, Saioa, Gamble, Samuel, Ung, Seng Kuong Anakin, Vanloo, Sharon, Boeing, Stefan, Beck, Stephan, Bola, Supreet Kaur, Denner, Tamara, Mourikis, Thanos P, Spanswick, Victoria, Barbe, Vittorio, Lu, Wei-Ting, Hill, William, Wu, Yin, Naito, Yutaka, Ramsden, Zoe, Veiga, Catarina, Royle, Gary, Collins-Fekete, Charles-Antoine, Fraioli, Francesco, Ashford, Paul, Clark, Tristan, Borg, Elaine, Wilson, James, Procter, Alexander James, Ahmed, Asia, Taylor, Magali N, Nair, Arjun, Patrini, Davide, Martinoni Hoogenboom, Emilie, Monk, Fleur, Holding, James W, Choudhary, Junaid, Bhakhri, Kunal, Scarci, Marco, Hayward, Martin, Panagiotopoulos, Nikolaos, Gorman, Pat, Khiroya, Reena, Stephens, Robert CM, Wong, Yien Ning Sophia, Bandula, Steve, Sharp, Abigail, Smith, Sean, Gower, Nicole, Dhanda, Harjot Kaur, Chan, Kitty, Pilotti, Camilla, Leslie, Rachel, Grapa, Anca, Zhang, Hanyun, AbdulJabbar, Khalid, Pan, Xiaoxi, Yuan, Yinyin, Chuter, David, MacKenzie, Mairead, Chee, Serena, Alzetani, Aiman, Scarlett, Lydia, Richards, Jennifer, Ingram, Papawadee, Austin, Silvia, De Sousa, Paulo, Jordan, Simon, Rice, Alexandra, Raubenheimer, Hilgardt, Bhayani, Harshil, Ambrose, Lyn, Devaraj, Anand, Chavan, Hema, Begum, Sofina, Buderi, Silviu, Kaniu, Daniel, Malima, Mpho, Booth, Sarah, Fernandes, Nadia, Shah, Pratibha, Proli, Chiara, Danson, Sarah, Robinson, Lily, Dick, Craig, Kirk, Alan, Asif, Mo, Bilancia, Rocco, Kostoulas, Nikos, and Thomas, Mathew

Subjects
Lung Neoplasms, Treatment Outcome, DNA Copy Number Variations, Mutagenesis, Carcinoma, Non-Small-Cell Lung, Mutation, Smoking, Humans, Adenocarcinoma of Lung, Neoplasm Recurrence, Local, Phylogeny
Abstract

Lung cancer is the leading cause of cancer-associated mortality worldwide1. Here we analysed 1,644 tumour regions sampled at surgery or during follow-up from the first 421 patients with non-small cell lung cancer prospectively enrolled into the TRACERx study. This project aims to decipher lung cancer evolution and address the primary study endpoint: determining the relationship between intratumour heterogeneity and clinical outcome. In lung adenocarcinoma, mutations in 22 out of 40 common cancer genes were under significant subclonal selection, including classical tumour initiators such as TP53 and KRAS. We defined evolutionary dependencies between drivers, mutational processes and whole genome doubling (WGD) events. Despite patients having a history of smoking, 8% of lung adenocarcinomas lacked evidence of tobacco-induced mutagenesis. These tumours also had similar detection rates for EGFR mutations and for RET, ROS1, ALK and MET oncogenic isoforms compared with tumours in never-smokers, which suggests that they have a similar aetiology and pathogenesis. Large subclonal expansions were associated with positive subclonal selection. Patients with tumours harbouring recent subclonal expansions, on the terminus of a phylogenetic branch, had significantly shorter disease-free survival. Subclonal WGD was detected in 19% of tumours, and 10% of tumours harboured multiple subclonal WGDs in parallel. Subclonal, but not truncal, WGD was associated with shorter disease-free survival. Copy number heterogeneity was associated with extrathoracic relapse within 1 year after surgery. These data demonstrate the importance of clonal expansion, WGD and copy number instability in determining the timing and patterns of relapse in non-small cell lung cancer and provide a comprehensive clinical cancer evolutionary data resource. ispartof: NATURE vol:616 issue:7957 ispartof: location:England status: Published online

Published
2023

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