Geospatial immune variability illuminates differential evolution of lung adenocarcinoma


Remarkable progress in molecular analyses has improved our understanding of the evolution of cancer cells toward immune escape1,2,3,4,5. 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.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: The computational pathology deep-learning pipeline for dissecting heterogeneous NSCLC tumors.
Fig. 2: Geospatial heterogeneity of lymphocytic infiltration in the TRACERx cohort.
Fig. 3: Evolution of immune escape and survival analyses in TRACERx and LATTICe-A.
Fig. 4: Association of spatial histology with genetic alterations relevant to immune surveillance.

Data availability

The digital pathology images from the TRACERx study generated or analyzed during this study are not publicly available and restrictions apply to their use. A test subset of such digital pathology images are available through the Cancer Research UK and UCL Cancer Trials Centre ( for noncommercial research purposes and access will be granted upon review of a project proposal that will be evaluated by a TRACERx data access committee and entering into an appropriate data access agreement, subject to any applicable ethical approvals. Digital pathology images for LATTICe-A samples with expert pathologist’s annotations used for validation are available: Request for data access for the remaining LATTICe-A samples can be submitted to J.L.Q.

Code availability

The deep-learning pipeline for digital pathology image analysis is available for noncommercial research purposes at All code used for statistical analyses of image data was developed in R (v.3.5.1) and is available at


  1. 1.

    Rosenthal, R. et al. Neoantigen-directed immune escape in lung cancer evolution. Nature 567, 479–485 (2019).

    CAS  Article  Google Scholar 

  2. 2.

    Morris, L. G. T. & Chan, T. A. Lung cancer evolution: what’s immunity got to do with it? Cancer Cell 35, 711–713 (2019).

    CAS  Article  Google Scholar 

  3. 3.

    Morris, L. G. T. et al. Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival. Oncotarget 7, 10051–10063 (2016).

    Article  Google Scholar 

  4. 4.

    Milo, I. et al. The immune system profoundly restricts intratumor genetic heterogeneity. Sci. Immunol. 3, eaat1435 (2018).

    Article  Google Scholar 

  5. 5.

    Jia, Q. et al. Local mutational diversity drives intratumoral immune heterogeneity in non-small cell lung cancer. Nat. Commun. 9, 5361 (2018).

    CAS  Article  Google Scholar 

  6. 6.

    Jamal-Hanjani, M. et al. Tracking the evolution of non-small cell lung cancer. N. Engl. J. Med. 376, 2109–2121 (2017).

    CAS  Article  Google Scholar 

  7. 7.

    Hendry, S. et al. Assessing tumor-infiltrating lymphocytes in solid tumors. Adv. Anat. Pathol. 24, 311–335 (2017).

    Article  Google Scholar 

  8. 8.

    Moore, D. A. et al. In situ growth in early lung adenocarcinoma may represent precursor growth or invasive clone outgrowth—a clinically relevant distinction. Mod. Pathol. 32, 1095–1105 (2019).

    CAS  Article  Google Scholar 

  9. 9.

    Whittaker, K. A. & Rynearson, T. A. Evidence for environmental and ecological selection in a microbe with no geographic limits to gene flow. Proc. Natl Acad. Sci. USA 114, 2651–2656 (2017).

    CAS  Article  Google Scholar 

  10. 10.

    Shafer, A. B. A. & Wolf, J. B. W. Widespread evidence for incipient ecological speciation: a meta-analysis of isolation-by-ecology. Ecol. Lett. 16, 940–950 (2013).

    Article  Google Scholar 

  11. 11.

    Costa, A. et al. Fibroblast heterogeneity and immunosuppressive environment in human breast cancer. Cancer Cell 33, 463–479 (2018).

    CAS  Article  Google Scholar 

  12. 12.

    Öhlund, D. et al. Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer. J. Exp. Med. 214, 579–596 (2017).

    Article  Google Scholar 

  13. 13.

    Salmon, H. et al. Matrix architecture defines the preferential localization and migration of T cells into the stroma of human lung tumors. J. Clin. Invest. 122, 899–910 (2012).

    CAS  Article  Google Scholar 

  14. 14.

    Thomas, D. A. & Massagué, J. TGF-β directly targets cytotoxic T cell functions during tumor evasion of immune surveillance. Cancer Cell 8, 369–380 (2005).

    CAS  Article  Google Scholar 

  15. 15.

    Joyce, J. A. & Fearon, D. T. T cell exclusion, immune privilege, and the tumor microenvironment. Science 348, 74–80 (2015).

    CAS  Article  Google Scholar 

  16. 16.

    Sorokin, L. The impact of the extracellular matrix on inflammation. Nat. Rev. Immunol. 10, 712–723 (2010).

    CAS  Article  Google Scholar 

  17. 17.

    Chen, D. S. & Mellman, I. Elements of cancer immunity and the cancer–immune set point. Nature 541, 321–330 (2017).

    CAS  Article  Google Scholar 

  18. 18.

    McGranahan, N. et al. Allele-specific HLA loss and immune escape in lung cancer evolution. Cell 171, 1259–1271 (2017).

    CAS  Article  Google Scholar 

  19. 19.

    McGranahan, N. et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 351, 1463–1469 (2016).

    CAS  Article  Google Scholar 

  20. 20.

    Yuan, Y. Modelling the spatial heterogeneity and molecular correlates of lymphocytic infiltration in triple-negative breast cancer. J. R. Soc. Interface 12, 20141153 (2015).

    Article  Google Scholar 

  21. 21.

    Thomas, A., Liu, S. V., Subramaniam, D. S. & Giaccone, G. Refining the treatment of NSCLC according to histological and molecular subtypes. Nat. Rev. Clin. Oncol. 12, 511–526 (2015).

    CAS  Article  Google Scholar 

  22. 22.

    Hammerman, P. S. et al. Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519–525 (2012).

    CAS  Article  Google Scholar 

  23. 23.

    Collisson, E. A. et al. Comprehensive molecular profiling of lung adenocarcinoma: the Cancer Genome Atlas Research Network. Nature 511, 543–550 (2014).

    CAS  Article  Google Scholar 

  24. 24.

    Keren, L. et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387 (2018).

    CAS  Article  Google Scholar 

  25. 25.

    Giesen, C. et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11, 417–422 (2014).

    CAS  Article  Google Scholar 

  26. 26.

    Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981 (2018).

    CAS  Article  Google Scholar 

  27. 27.

    Raza, S. E. A. et al. Micro-net: a unified model for segmentation of various objects in microscopy images. Med. Image Anal. 52, 160–173 (2019).

    Article  Google Scholar 

  28. 28.

    Sirinukunwattana, K. et al. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35, 1196–1206 (2016).

    Article  Google Scholar 

  29. 29.

    Abadi, M. et al. TensorFlow: a system for large-scale machine learning. 12th USENIX Conference on Operating Systems Design and Implementation 265–283 (2016).

  30. 30.

    Danaher, P. et al. Gene expression markers of tumor-infiltrating leukocytes. J. Immunother. Cancer 5, 18 (2017).

    Article  Google Scholar 

  31. 31.

    Holzinger, A. et al. Monoclonal antibody to thyroid transcription factor-1: production, characterization, and usefulness in tumor diagnosis. Hybridoma 15, 49–53 (1996).

    CAS  Article  Google Scholar 

  32. 32.

    Matoso, A. et al. Comparison of thyroid transcription factor-1 expression by 2 monoclonal antibodies in pulmonary and nonpulmonary primary tumors. Appl. Immunohistochem. Mol. Morphol. 18, 142–149 (2010).

    CAS  Article  Google Scholar 

  33. 33.

    Pelosi, G. et al. ΔNp63 (p40) and thyroid transcription factor-1 immunoreactivity on small biopsies or cellblocks for typing non-small cell lung cancer: a novel two-hit, sparing-material approach. J. Thorac. Oncol. 7, 281–290 (2012).

    Article  Google Scholar 

  34. 34.

    Heindl, A. et al. Relevance of spatial heterogeneity of immune infiltration for predicting risk of recurrence after endocrine therapy of ER+ breast cancer . J. Natl Cancer Inst. 110, 166–175 (2018).

    CAS  Article  Google Scholar 

  35. 35.

    Heindl, A. et al. Microenvironmental niche divergence shapes BRCA1-dysregulated ovarian cancer morphological plasticity. Nat. Commun. 9, 3917 (2018).

    Article  Google Scholar 

  36. 36.

    Nawaz, S., Heindl, A., Koelble, K. & Yuan, Y. Beyond immune density: critical role of spatial heterogeneity in estrogen receptor-negative breast cancer. Mod. Pathol. 28, 766–777 (2015).

    CAS  Article  Google Scholar 

  37. 37.

    Dubuc, B., Quiniou, J. F., Roques-Carmes, C., Tricot, C. & Zucker, S. W. Evaluating the fractal dimension of profiles. Phys. Rev. A 39, 1500–1512 (1989).

    CAS  Article  Google Scholar 

  38. 38.

    Moisy, F. & Jiménez, J. Geometry and clustering of intense structures in isotropic turbulence. J. Fluid Mech. 513, 111–133 (2004).

    Article  Google Scholar 

  39. 39.

    Francis, K. & Palsson, B. O. Effective intercellular communication distances are determined by the relative time constants for cyto/chemokine secretion and diffusion. Proc. Natl Acad. Sci. USA 94, 12258–12262 (1997).

    CAS  Article  Google Scholar 

Download references


This study is funded by a Cancer Research UK Career Establishment Award to Y.Y. (C45982/A21808). The TRACERx study ( NCT01888601) is sponsored by University College London (UCL) (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 coordinated through the Cancer Research UK and UCL Cancer Trials Centre. Y.Y. acknowledges additional support from Breast Cancer Now (2015NovPR638), Children’s Cancer and Leukaemia Group (CCLGA201906), National Institutes of Health (NIH) U54 CA217376 and R01 CA185138, CDMRP Breast Cancer Research Program Award BC132057, European Commission ITN (H2020-MSCA-ITN-2019), Wellcome Trust (105104/Z/14/Z) and The Royal Marsden/ICR National Institute of Health Research Biomedical Research Centre. C.S. is a Royal Society Napier Research Professor. This work was supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001169,FC001202), the UK Medical Research Council (FC001169, FC001202) and the Wellcome Trust (FC001169, FC001202). C.S. is funded by Cancer Research UK (TRACERx, PEACE and Cancer Research UK Cancer Immunotherapy Catalyst Network), the Cancer Research UK Lung Cancer Centre of Excellence, the Rosetrees Trust, NovoNordisk Foundation (ID16584) and the Breast Cancer Research Foundation. This research is supported by a Stand Up To Cancer‐LUNGevity-American Lung Association Lung Cancer Interception Dream Team Translational Research Grant (grant number: SU2C-AACR-DT23-17). Stand Up To Cancer is a program of the Entertainment Industry Foundation. Research grants are administered by the American Association for Cancer Research, the Scientific Partner of SU2C. C.S. receives funding from the European Research Council (ERC) under the European Union’s Seventh Framework Programme (FP7/2007-2013) Consolidator Grant (FP7-THESEUS-617844), European Commission ITN (FP7-PloidyNet 607722), an ERC Advanced Grant (PROTEUS) from the European Research Council under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement 835297) and Chromavision from the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement 665233). S.A.Q. is funded by a Cancer Research UK Senior Cancer Research Fellowship (C36463/A22246) and a Cancer Research UK Biotherapeutic Program Grant (C36463/A20764). S.L. is supported by the National Breast Cancer Foundation of Australia Endowed Chair and the Breast Cancer Research Foundation, New York. L.Z. has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement 846614. C.T.H. is funded by the UCL Biomedical Research Council. M.J.H. has received funding from Cancer Research UK, NIH Research, Rosetrees Trust and UKI NETs. We thank the members of the TRACERx and PEACE consortia for participating in this study. We thank the Tissue Image Analytics laboratory at the University of Warwick for their help in method implementation. We thank the Scientific Computing team at the Institute of Cancer Research for technical support. We also thank A. Teodósio and C. Ficken from the MRC Toxicology Unit Core Histology facility for their expert technical assistance.

Author information





R.R., M.J.-H. and S.V. are co-second authors. K.A. and S.E.A.R. contributed equally to this work. S.E.A.R. and K.A. developed the image processing and deep-learning pipeline and performed geospatial analysis. K.A. performed bioinformatics and statistical analyses. J.L.Q., R.S. and D.A.M. provided pathological expertise. M.J.-H. provided clinical expertise and patient characterization. S.V. performed histology sample generation and digitized H&E slides. A.A. generated and digitized IHC slides under the supervision of T.M. T.L. provided annotations for training and validating IHC analysis. N.M., R.R. and L.Z. assisted with genomic data integration. J.L.Q., R.S., S.L., M.A.B., D.A.M., C.T.H. and T.L. analyzed pathology TIL estimates. J.L.Q., L.O., M.S. and C. R. S. provided data and advice for LATTICe-A. Y.Y., N.M., J.L.Q., C.S., A.H. and S.A.Q. provided data analysis support and supervision. K.A., R.R., N.M., C.S. and Y.Y. wrote the manuscript with input from all authors. Y.Y. and C.S. jointly conceived and supervised the study.

Corresponding authors

Correspondence to John Le Quesne or Charles Swanton or Yinyin Yuan.

Ethics declarations

Competing interests

Y.Y. has received speaker’s bureau honoraria from Roche and consulted for Merck & Co., Inc. C.S. receives grant support from Pfizer, AstraZeneca, BMS, Roche-Ventana, Boehringer-Ingelheim and Ono Pharmaceutical. C.S. has consulted for Pfizer, Novartis, GlaxoSmithKline, MSD, BMS, Celgene, AstraZeneca, Illumina, Genentech, Roche-Ventana, GRAIL, Medicxi and the Sarah Cannon Research Institute. C.S. is a shareholder of Apogen Biotechnologies, Epic Bioscience, GRAIL and has stock options in and is co-founder of Achilles Therapeutics. M.A.B. is a consultant for Achilles Therapeutics. S.L. receives research funding to her institution from Novartis, Bristol Meyers Squibb, Merck, Roche-Genentech, Puma Biotechnology, Pfizer, Eli Lilly and Seattle Genetics. S.L. has acted as consultant (not compensated) to Seattle Genetics, Pfizer, Novartis, BMS, Merck, AstraZeneca and Roche-Genentech. S.L. has acted as consultant (paid to her institution) to Aduro Biotech, Novartis and G1 Therapeutics. D.A.M. has received speaker’s fees from AstraZeneca. M.J.H. is a member of the Advisory Board for Achilles Therapeutics.

Additional information

Peer review information Saheli Sadanand was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 CONSORT diagrams for TRACERx 100 and LATTICe-A histology cohorts and patient characteristics.

a. TRACERx CONSORT diagram to illustrate sample collection and analysis of regional and diagnostic histology samples, as well as the overlap with RNA-seq and DNA-seq studies. b. TRACERx patient characteristics for the histology cohort. c. LATTICe-A CONSORT diagram (n = 970 LUAD patients). Legends for ‘type of the analysis’ correspond to panel a. d. Demographics and clinical patient characteristics for TRACERx (top three panels) and LATTICe-A (bottom three panels) showing the distribution of age (colored by sex), distribution of smoking pack years and the proportion of patients in each pathological stage. Horizontal lines indicate the median value.

Extended Data Fig. 2 Validation of the automated single-cell classification for H&E.

a. Scatter plot showing the correlation between H&E-based adjacent-tumor lymphocytes/stromal and pathology TIL estimates in diagnostic samples (n = 98 diagnostic slides/patients). b. Scatter plots showing the correlations between H&E-based cancer cellularity estimate and ASCAT/VAF purity scores (n = 238 regions; 83 patients). c. Scatter plot showing the correlation between H&E-based estimate of lymphocyte percentage among all cells and RNA-seq-based CD8 signature using the Danaher et al. method30 (n = 142 regions; 56 patients). d. Scatter plot showing the correlation between H&E-based estimate of lymphocyte percentage among all cells and CD8+ cell percentage in IHC in the diagnostic samples (n = 100 diagnostic slides/patients). e. Scatter plots showing the correlation between H&E-based lymphocyte percentage versus pathological scores of overall lymphocytic cell fraction, and adjacent-tumor lymphocytes/stromal versus pathology TIL estimates in an external cohort (LATTICe-A, n = 80 diagnostic slides/patients). f. Illustrative example to show the spatial alignment of TTF1/CD45/SMA-stained IHC and H&E images obtained using sequential staining on the same tissue microarray section for biological validation. g. Scatter plot showing the correlation between stromal cell percentage determined by H&E and SMA+ cell percentage per LUAD image tiles of size 100µm2 (n = 144). The experiment was conducted once using one TMA (n = 33 cores/patients). The shading indicates 95% confidence interval.

Extended Data Fig. 3 Distribution of regional lymphocytic infiltration according to pathological stage.

All available patients’ data have been used in this figure except for the s.d. tests, where patients with a single tumor region were excluded. Patients without pathological staging information from the LATTICe-A cohort were also removed. a, b, c, top row: TRACERx and bottom row: LATTICe-A. Horizontal lines indicate the median value. a. Distribution of the s.d. of regional lymphocyte percentage for LUAD and LUSC patients in TRACERx (n = 69), and LUAD in LATTICe-A (n = 814). b. Distribution of the s.d. of regional lymphocyte percentage across pathological stages (n = 69 for TRACERx, 814 for LATTICe-A). c. Distribution of regional mean of lymphocyte percentage across stages (n = 79 for TRACERx, 827 for LATTICe-A). d. No significant difference among stages with respect to s.d. (n = 69 for TRACERx, 814 for LATTICe-A) or mean (n = 79 for TRACERx, 827 for LATTICe-A) of regional lymphocytic infiltration. Left panel, TRACERx and right panel, LATTICe-A. Correction for multiple testing was applied in d, for each cohort individually. A two-sided, non-parametric, unpaired, Wilcoxon signed-rank test was used; each dot represents a patient; the mean value is annotated with a large dot; the median value is represented by a thick horizontal line; minimum and maximum values are indicated by the extreme points; the first and third quantiles are represented by the box edges; and the violin shape shows the data distribution as a kernel density estimation.

Extended Data Fig. 4 Validation of immune phenotype classification.

a. The proposed immune classification imposed on density plot showing distribution of lymphocyte percentage. The middle zone corresponds to the intermediate phenotype, red zone for immune hot and blue zone for immune cold. Black dash line shows the median. This classification was validated after applying small perturbations to the thresholds to re-classify regional immune phenotypes, illustrated as grey dash lines: no intermediate zone, s.d./2 above and below the median, s.d./3 and s.d./6. b. Forest plots to show repeated multivariate Cox regression tests for the number of immune cold regions using these new classifications (n = 79 patients), after accounting for stage, total number of samples, upper quartile of clonal neoantigens determined for LUAD and LUSC individually, and other clinical parameters. Box plots showing difference in genomic distance for pairs of hot regions compared with pairs of cold regions for LUAD and LUSC separately (LUAD: n = 45 hot pairs, 45 cold pairs for no intermediate zone; n = 19 hot, 25 cold for s.d./2; n = 25 hot, 33 cold for s.d./3; n = 32 hot, 41 cold for s.d./6. LUSC: n = 32 hot pairs, 54 cold pairs for no intermediate zone; n = 19 hot, 27 cold for s.d./2; n = 19 hot, 37 cold for s.d./3; n = 27 hot, 41 cold for s.d./6.). AIC, Akaike’s information criterion. c. Box plots showing significant difference in CD8 RNA-seq signature using the Danaher method between regions of hot and cold phenotype across all classification schemes (n = 219 for s.d./4; 275 for no intermediate zone; 173 for s.d./2; 204 for s.d./3; 237 for s.d./6). d. Distribution and difference of lymphocytic infiltration for LUAD versus LUSC regions in TRACERx (n = 275 regions; 85 patients) as well as distribution for LUAD in LATTICe-A (n = 4,324 samples; 970 patients). Horizontal lines in the distribution plots indicate mean values. For statistical comparisons among groups, a two-sided, non-parametric, unpaired, Wilcoxon signed-rank test was used, unless stated otherwise.

Extended Data Fig. 5 Concordance between histology deep-learning and RNA-seq immune classification.

a. Box plot showing the difference in pathology TIL estimates between immune hot and immune cold regions (n = 219). Pathology TIL estimates score fraction of stroma containing TILs, whereas immune classification was defined based on the percentage of lymphocytes in all cells within a slide. b. A confusion matrix to compare RNA-seq and deep-learning histology immune classifications (discarding immune intermediate regions, n = 109 regions (57 LUAD, 37 LUSC, 15 other histology subtypes); 52 patients). The p-value was generated using a two-sided Fisher’s exact test for overlap. c. Box plot showing the difference in the fraction of immune hotspots36 in regions where the two classifications are in agreement (n = 78; labeled as ‘In agreement’) against the discrepant regions (n = 31, labeled as ‘Discrepant’). Each dot represents a region, the median value is indicated by a thick horizontal line; minimum and maximum values are indicated by the extreme points; and the first and third quantiles are represented by the box edges. d. Box plots to support the overall consistency between H&E-deep-learning and RNA-seq methods by comparing different immune scores as well as ASCAT tumor purity between immune hot/high and cold/low tumor regions (all P-values < 0.0001). Top row, H&E-deep-learning immune classification (n = 219; except the ASCAT purity box plot n = 186 regions), bottom row, RNA-seq-derived immune classification (n = 142; except the ASCAT purity box plot, n = 141 regions). For statistical comparisons among groups, a two-sided, non-parametric, unpaired, Wilcoxon signed-rank test was used, unless stated otherwise.

Extended Data Fig. 6 Genomic and survival analysis of tumor regions according to immune phenotypes.

a. Box plot showing the difference in genomic distances for pairs of immune hot versus immune cold regions within the same LUSC patients (n = 59 pairs). A two-sided, non-parametric, unpaired, Wilcoxon signed-rank test was used. b. Forest plots to show the univariate prognostic value for the number of immune low regions (both as continuous and dichotomized at the median (≤1 versus >1)), or the number of immune high regions, using the immune classification generated by RNA-seq-based infiltrating immune cell populations1 in 64 TRACERx tumors (41 LUAD, 16 LUSC and 7 other histology subtypes). c. Forest plots showing multivariate Cox regression analyses in both TRACERx (n = 79 patients; LUAD and LUSC combined) and LATTICe-A (n = 651 LUAD patients representing a subset with complete stage and smoking pack years data) with the number of immune cold regions dichotomized at the median (≤1 versus >1). This remains significant when the number of immune cold regions was replaced as a continuous variable, in the same multivariate model (P = 0.019 in TRACERx and < 0.001 in LATTICe-A, for the number of immune cold regions). Clonal neoantigens were dichotomized using the upper quartile, determined individually for LUAD and LUSC tumors1. d. The same test in c when tumor size (in mm) was also controlled in the multivariate model in LATTICe-A. This test also remained significant for a bigger group of patients with complete stage data, but missing pack years information (n = 815, P < 0.001, HR = 1.4[1.1-1.8]). e. Forest plots to compare the prognostic value of regional immune scores as well as diagnostic H&E and IHC scores for relapse-free survival in TRACERx (n = 79 patients, LUAD and LUSC combined). Wherever possible, these immune features were tested in LATTICe-A (n = 970 patients). To compare the prognostic value of the number of immune cold region with other immune features, LATTICe-A comparisons were conducted in Cox multivariate regression models to include every immune feature after correcting for the number of immune cold regions in the same model. Each variable’s HR is plotted with a 95% confidence interval; all P-values were adjusted for multiple testing; and the size of the circles denotes –log10(P). For the sake of visualization, a minor adjustment was made to the HR for the number of cold regions/total number of regions in LATTICe-A from 0.88[0.57-1.3] to 0.99[0.97-1.3]. Regional lymphocyte% (s.d.) represents the s.d. of lymphocyte percentage among samples within a tumor. f. Forest plots using Cox multivariate regression analysis showing that the prognostic value of the number of immune cold regions was independent of: 1) genetic measure, subclonal copy number alteration (subclonal CNA, obtained from6); 2) tumor cellularity from DNA-seq-based ASCAT purity, 3) tumor cellularity measured by deep-learning-based cancer cell percentage. g. Kaplan Meier curves to illustrate the difference in relapse-free survival for TRACERx patients including other histology types (n = 85; representing all TRACERx patients in the multiregion histology cohort) with high and low number of immune cold regions, dichotomized by its median value. Log-rank P = 0.0017. h. Forest plot using Cox regression for the multivariate survival analysis for the number of immune cold regions in TRACERx including patients with other histology subtypes (n = 85). AIC, Akaike’s information criterion.

Extended Data Fig. 7 Fractal dimension and relationships with stromal cells.

a. Distribution of the average minimum Euclidean distance between a stromal cell to its neighboring cancer cell. For every stromal cell in a tumor region slide, the minimum distance to nearest cancer cell was computed. This distance was then averaged for all identified stromal cells in every region to plot the distribution (n = 275 regions; 85 patients). b. Distribution of the fractal dimension of the cancer-stroma cell interface for histology types in the TRACERx cohort (n = 275 regions; 85 patients). c. Box plots to show the difference in fractal dimension between immune hot and cold regions in TRACERx LUAD (n = 113) and LUSC (n = 84). d. Box plots showing the difference in stromal cell percentage between immune hot and cold regions in all (n = 219), LUAD (n = 113), and LUSC (n = 84). e. Scatter plots showing the correlation between fractal dimension and percentage of cells that are stromal or cancer in all tumor regions (n = 275 regions; 85 patients). This shows that fractal dimension was independent of cancer cell composition, with only a weak correlation with stromal cell percentage and no correlation with tumor cellularity. f. Box plots showing the difference in fractal dimension between LUAD tumor regions harboring an LOH event for HLA type A (n = 106), type B (n = 113), type C (n = 108) versus regions that do not, adjusted for multiple comparisons with the corresponding test in Fig. 4c. g. The same test in f repeated for LUSC tumor regions (n = 87) for HLA of any type. h. Box plots showing the difference in tumor-level fractal dimension using the maximum value of regional measures between LUAD tumors (n = 48) harboring a single LOH event for any HLA type, HLA type A, type B and type C versus tumors that do not, independent of predicted clonal neoantigens. Each p-value was generated using a multiple regression linear model and was also adjusted for multiple testing correction. i. The same test in h repeated for LUSC tumors (n = 29) for HLA of any type. For statistical comparisons among groups, a two-sided, non-parametric, unpaired, Wilcoxon signed-rank test was used, unless stated otherwise.

Extended Data Fig. 8 Relationship of immune subsets and spatial TILs in LUAD.

a. Spearman’s correlations between immune scores in diagnostic slides and genetic measures including predicted neoantigens and HLALOH in LUAD patients (n = 46). ITLR: intra-tumor lymphocytes to total cancer cell ratio. Only significant correlations after multiple testing are highlighted (Rho = 0.37, P = 0.035). b. Examples of registered H&E and IHC tiles. The green cross denotes a manually placed landmark repeated 238 times on pairs of H&E-IHC image tiles. The Euclidean distance (difference in x,y coordinates) was computed between the two landmarks which was then c. shown as a distribution to represent the accuracy of the registration (n = 249 total H&E-IHC image tiles, maximum five landmarks per a pair of tiles). The average distance between matching landmarks was 9.57 µm and the distribution is within the expected range of maximum distance between four serial sections (16 µm). d. Box plots to illustrate the difference in percentage of immune cell subsets among adjacent, intra and distal-tumor lymphocytes (n = 20 image tiles), a non-parametric, paired Wilcoxon test was used.

Extended Data Fig. 9 Summary of immune and genomic features in NSCLC.

An extended heatmap showing all immune variables described in TRACERx across all patients (n = 275 regions; 85 patients), along with genomic measures and clinical parameters. Each column represents a tumor, grouped by their histologic subtype. Tumor regions (illustrated as dots) were assigned to immune hot, immune cold and intermediate phenotypes based on percentage of lymphocytes in all cells following H&E-based deep-learning analysis. Cancer-stromal fractal dimension was defined using the maximum fractal dimension in regions of a patient, using the median as cut-off to determine high and low groups.

Supplementary information

Supplementary Information

STROBE checklist, Supplementary Table 6, and Supplementary Figs. 1–20.

Reporting Summary

Supplementary Tables 1–5 and 7.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

AbdulJabbar, K., Raza, S.E.A., Rosenthal, R. et al. Geospatial immune variability illuminates differential evolution of lung adenocarcinoma. Nat Med (2020).

Download citation

Further reading