Literature DB >> 32461698

Geospatial immune variability illuminates differential evolution of lung adenocarcinoma.

Khalid AbdulJabbar1,2, Shan E Ahmed Raza1,2, Rachel Rosenthal3,4, Mariam Jamal-Hanjani3,5, Selvaraju Veeriah3,4, Ayse Akarca6, Tom Lund7, David A Moore3,6, Roberto Salgado8,9, Maise Al Bakir4, Luis Zapata1,2, Crispin T Hiley3,4, Leah Officer10, Marco Sereno11, Claire Rachel Smith11, Sherene Loi9, Allan Hackshaw12, Teresa Marafioti6, Sergio A Quezada13, Nicholas McGranahan3,14, John Le Quesne15,16,17, Charles Swanton18,19,20, Yinyin Yuan21,22.   

Abstract

Remarkable progress in molecular analyses has improved our understanding of the evolution of cancer cells toward immune escape1-5. However, the spatial configurations of immune and stromal cells, which may shed light on the evolution of immune escape across tumor geographical locations, remain unaddressed. We integrated multiregion exome and RNA-sequencing (RNA-seq) data with spatial histology mapped by deep learning in 100 patients with non-small cell lung cancer from the TRACERx cohort6. Cancer subclones derived from immune cold regions were more closely related in mutation space, diversifying more recently than subclones from immune hot regions. In TRACERx and in an independent multisample cohort of 970 patients with lung adenocarcinoma, tumors with more than one immune cold region had a higher risk of relapse, independently of tumor size, stage and number of samples per patient. In lung adenocarcinoma, but not lung squamous cell carcinoma, geometrical irregularity and complexity of the cancer-stromal cell interface significantly increased in tumor regions without disruption of antigen presentation. Decreased lymphocyte accumulation in adjacent stroma was observed in tumors with low clonal neoantigen burden. Collectively, immune geospatial variability elucidates tumor ecological constraints that may shape the emergence of immune-evading subclones and aggressive clinical phenotypes.

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Year:  2020        PMID: 32461698      PMCID: PMC7610840          DOI: 10.1038/s41591-020-0900-x

Source DB:  PubMed          Journal:  Nat Med        ISSN: 1078-8956            Impact factor:   53.440


  54 in total

Review 1.  Genetic and non-genetic clonal diversity in cancer evolution.

Authors:  James R M Black; Nicholas McGranahan
Journal:  Nat Rev Cancer       Date:  2021-03-16       Impact factor: 60.716

Review 2.  Deep learning in histopathology: the path to the clinic.

Authors:  Jeroen van der Laak; Geert Litjens; Francesco Ciompi
Journal:  Nat Med       Date:  2021-05-14       Impact factor: 53.440

3.  Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology.

Authors:  Kaustav Bera; Ian Katz; Anant Madabhushi
Journal:  JCO Clin Cancer Inform       Date:  2020-11

Review 4.  Tumour immunotherapy: lessons from predator-prey theory.

Authors:  Phineas T Hamilton; Bradley R Anholt; Brad H Nelson
Journal:  Nat Rev Immunol       Date:  2022-05-05       Impact factor: 53.106

5.  Spatial proteomic characterization of HER2-positive breast tumors through neoadjuvant therapy predicts response.

Authors:  Katherine L McNamara; Jennifer L Caswell-Jin; Rohan Joshi; Zhicheng Ma; Eran Kotler; Gregory R Bean; Michelle Kriner; Zoey Zhou; Margaret Hoang; Joseph Beechem; Jason Zoeller; Michael F Press; Dennis J Slamon; Sara A Hurvitz; Christina Curtis
Journal:  Nat Cancer       Date:  2021-04-08

6.  Using DNA sequencing data to quantify T cell fraction and therapy response.

Authors:  Robert Bentham; Kevin Litchfield; Thomas B K Watkins; Emilia L Lim; Rachel Rosenthal; Carlos Martínez-Ruiz; Crispin T Hiley; Maise Al Bakir; Roberto Salgado; David A Moore; Mariam Jamal-Hanjani; Charles Swanton; Nicholas McGranahan
Journal:  Nature       Date:  2021-09-08       Impact factor: 49.962

Review 7.  Co-dependencies in the tumor immune microenvironment.

Authors:  Peiwen Chen; Prasenjit Dey
Journal:  Oncogene       Date:  2022-07-11       Impact factor: 8.756

8.  Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning.

Authors:  Yongju Lee; Jeong Hwan Park; Sohee Oh; Kyoungseob Shin; Jiyu Sun; Minsun Jung; Cheol Lee; Hyojin Kim; Jin-Haeng Chung; Kyung Chul Moon; Sunghoon Kwon
Journal:  Nat Biomed Eng       Date:  2022-08-18       Impact factor: 29.234

9.  Computational pathology reveals unique spatial patterns of immune response in H&E images from COVID-19 autopsies: preliminary findings.

Authors:  Germán Corredor; Paula Toro; Kaustav Bera; Dylan Rasmussen; Vidya Sankar Viswanathan; Christina Buzzy; Pingfu Fu; Lisa M Barton; Edana Stroberg; Eric Duval; Hannah Gilmore; Sanjay Mukhopadhyay; Anant Madabhushi
Journal:  J Med Imaging (Bellingham)       Date:  2021-07-13

Review 10.  Promises and challenges of adoptive T-cell therapies for solid tumours.

Authors:  Matteo Morotti; Ashwag Albukhari; Abdulkhaliq Alsaadi; Mara Artibani; James D Brenton; Stuart M Curbishley; Tao Dong; Michael L Dustin; Zhiyuan Hu; Nicholas McGranahan; Martin L Miller; Laura Santana-Gonzalez; Leonard W Seymour; Tingyan Shi; Peter Van Loo; Christopher Yau; Helen White; Nina Wietek; David N Church; David C Wedge; Ahmed A Ahmed
Journal:  Br J Cancer       Date:  2021-03-29       Impact factor: 7.640

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