Measuring multiple parameters of CD8+ tumor-infiltrating lymphocytes in human cancers by image analysis

Keith E Steele, Tze Heng Tan, René Korn, Karma Dacosta, Charles Brown, Michael Kuziora, Johannes Zimmermann, Brian Laffin, Moritz Widmaier, Lorenz Rognoni, Ruben Cardenes, Katrin Schneider, Anmarie Boutrin, Philip Martin, Jiping Zha, Tobias Wiestler, Keith E Steele, Tze Heng Tan, René Korn, Karma Dacosta, Charles Brown, Michael Kuziora, Johannes Zimmermann, Brian Laffin, Moritz Widmaier, Lorenz Rognoni, Ruben Cardenes, Katrin Schneider, Anmarie Boutrin, Philip Martin, Jiping Zha, Tobias Wiestler

Abstract

Background: Immuno-oncology and cancer immunotherapies are areas of intense research. The numbers and locations of CD8+ tumor-infiltrating lymphocytes (TILs) are important measures of the immune response to cancer with prognostic, pharmacodynamic, and predictive potential. We describe the development, validation, and application of advanced image analysis methods to characterize multiple immunohistochemistry-derived CD8 parameters in clinical and nonclinical tumor tissues.

Methods: Commercial resection tumors from nine cancer types, and paired screening/on-drug biopsies of non-small-cell lung carcinoma (NSCLC) patients enrolled in a phase 1/2 clinical trial investigating the PD-L1 antibody therapy durvalumab (NCT01693562), were immunostained for CD8. Additional NCT01693562 samples were immunostained with a CD8/PD-L1 dual immunohistochemistry assay. Whole-slide scanning was performed, tumor regions were annotated by a pathologist, and images were analyzed with customized algorithms using Definiens Developer XD software. Validation of image analysis data used cell-by-cell comparison to pathologist scoring across a range of CD8+ TIL densities of all nine cancers, relying primarily on 95% confidence in having at least moderate agreement regarding Lin concordance correlation coefficient (CCC = 0.88-0.99, CCC_lower = 0.65-0.96).

Results: We found substantial variability in CD8+ TILs between individual patients and across the nine types of human cancer. Diffuse large B-cell lymphoma had several-fold more CD8+ TILs than some other cancers. TIL densities were significantly higher in the invasive margin versus tumor center for carcinomas of head and neck, kidney and pancreas, and NSCLC; the reverse was true only for prostate cancer. In paired patient biopsies, there were significantly increased CD8+ TILs 6 weeks after onset of durvalumab therapy (mean of 365 cells/mm2 over baseline; P = 0.009), consistent with immune activation. Image analysis accurately enumerated CD8+ TILs in PD-L1+ regions of lung tumors using the dual assay and also measured elongate CD8+ lymphocytes which constituted a fraction of overall TILs.

Conclusions: Validated image analysis accurately enumerates CD8+ TILs, permitting comparisons of CD8 parameters among tumor regions, individual patients, and cancer types. It also enables the more complex digital solutions needed to better understand cancer immunity, like analysis of multiplex immunohistochemistry and spatial evaluation of the various components comprising the tumor microenvironment.

Trial registration: ClinicalTrials.gov identifier: NCT01693562 . Study code: CD-ON-MEDI4736-1108. Interventional study (ongoing but not currently recruiting). Actual study start date: August 29, 2012. Primary completion date: June 23, 2017 (final data collection date for primary outcome measure).

Keywords: CD8; Cancer immunotherapy; Image analysis; Immuno-oncology; Tumor microenvironment; Tumor-infiltrating lymphocytes.

Conflict of interest statement

Ethics approval and consent to participate

The clinical study (NCT01693562) from which the data in this report were obtained was carried out in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines. The study protocol, amendments, and participant informed consent document were approved by the appropriate institutional review boards.

Consent for publication

No individual data were used in this study.

Competing interests

KES, KD, CB, MK, AB, and PM are employees of MedImmune and own stock and/or stock options in AstraZeneca. THT, RK, J. Zimmerman, BL, MW, LR, RC, KS, and TW are employees of Definiens AG. J. Zha is an employee of NGM Biopharmaceuticals and at the time of this study was an employee of MedImmune and owned stock and/or stock options in AstraZeneca.

Publisher’s Note

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

Figures

Fig. 1
Fig. 1
Image analysis (IA) scheme and manual annotations of tumor regions. Key processes in the overall IA scheme leading to data production are depicted (a). Tumor regions of images of nonclinical samples were manually annotated by a pathologist to partition invasive margin (IM) from tumor center (TC). Shown is an NSCLC image (b) to which annotations (c) were applied, along with excluded areas (EA) used to omit necrotic areas or other interfering histological features
Fig. 2
Fig. 2
Validation of image analysis (IA) classification and enumeration of CD8+ TILs. Unclassified images (a) were examined by pathologists at high magnification, and immunolabeled TILs were annotated in purple (b). The IA software then characterized each cell (c) as true positive (blue), false positive (red), or false negative (orange). For clinical trial NCT01693562, concordance between IA and each of three pathologists (d) was determined. For nonclinical samples, concordance between IA and one pathologist was determined and compared, using three statistical measures for all nine tumor types (e)
Fig. 3
Fig. 3
Image analysis of CD8+ TILs in PD-L1–positive and –negative tumor. Serial sections of tumor specimens of 24 non-enrolled NSCLC patients of clinical trial NCT01693562 were immunostained for CD8 alone (a, b, c) and with a CD8/PD-L1 dual immunostain (d, e, f). CD8+ TILs were immunolabeled brown in the mono stain (a) and purple in the dual stain (d), with PD-L1 labeled brown. IA detected CD8+ TILs as blue in the mono stain (b) and lavender in the dual stain (e). IA further classified tumor cells (c, yellow areas) in the mono stain or PD-L1+ cells (f, red areas) in the dual stain; darker shades of red represent more intense PD-L1 expression. IA determined the numbers of CD8+ TILs in the two stains to be comparable (g); Pearson (PCC), Spearman (SCC), and Lin (CCC) concordance values are shown
Fig. 4
Fig. 4
CD8 tumor landscape. The density of CD8+ TILs in the tumor area (tumor center and invasive margin combined) of individual nonclinical specimens are shown as dots and grouped as violin plots for each cancer indication. The median density of CD8+ TILs is also shown (bars) for each indication
Fig. 5
Fig. 5
Paired density plot of CD8+ TILs in tumor center (TC) and invasive margin (IM) across cancer indications (1/mm2). CD8+ TILs in the TC and IM were compared. For each nonclinical specimen, CD8+ TIL densities were determined separately in annotated tumor regions as shown in Fig. 1c. Individual TC scores are plotted as dots and connected to the applicable IM score by a line. Median values for each are shown. For each tumor type, the Wilcoxon T values denote the degree of statistical difference between TC and IM CD8+ TIL densities. Some specimens without an identifiable IM are represented by isolated dots for TC (eg, pancreatic carcinoma). DLBCL is not shown because CD8+ TILs were not enumerated in the IM, as explained in the text
Fig. 6
Fig. 6
CD8+ TIL densities as a measure of durvalumab pharmacodynamic activity in clinical trial NCT01693562. Enumeration of CD8+ TILs by image analysis was performed on matched sets of pretreatment and on-therapy (±6 weeks) specimens of 25 NSCLC patients. Tumor was manually annotated by a pathologist. The density of CD8+ TILs in paired sets was compared using a two-sided paired t test. Of 25 patients, 20 had increased CD8+ TILs during therapy, with an average increase of 365 cells/mm2 (P = 0.009, 95% confidence interval = 101.3–628.5)

References

    1. Leventakos K, Mansfield AS. Advances in the treatment of non-small cell lung cancer: focus on nivolumab, pembrolizumab, and atezolizumab. BioDrugs. 2016;30(5):397–405.
    1. Tang T, Eldabaje R, Yang L. Current status of biological therapies for the treatment of metastatic melanoma. Anticancer Res. 2016;36(7):3229–3241.
    1. Topalian SL, Taube JM, Anders RA, Pardoll DM. Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nat Rev Cancer. 2016;16(5):275–287.
    1. Chen DS, Mellman I. Elements of cancer immunity and the cancer-immune set point. Nature. 2017;541(7637):321–330.
    1. Fridman WH, Pages F, Sautes-Fridman C, Galon J. The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer. 2012;12(4):298–306.
    1. Khalil DN, Smith EL, Brentjens RJ, Wolchok JD. The future of cancer treatment: immunomodulation, CARs and combination immunotherapy. Nat Rev Clin Oncol. 2016;13(5):273–290.
    1. Gnjatic S, Bronte V, Brunet LR, et al. Identifying baseline immune-related biomarkers to predict clinical outcome of immunotherapy. J Immunother Cancer. 2017;5:44.
    1. National Human Genome Research Institute. The Cancer Genome Atlas. 2017; . Accessed 5 May 2017.
    1. Caie PD, Zhou Y, Turnbull AK, Oniscu A, Harrison DJ. Novel histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reporting. Oncotarget. 2016;7(28):44381–44394.
    1. Carey CD, Gusenleitner D, Lipschitz M, et al. Topological analysis reveals a PD-L1 associated microenvironmental niche for reed-Sternberg cells in Hodgkin lymphoma. Blood. 2017;130(22):2420–2430.
    1. Carstens JL, Correa de Sampaio P, Yang D, et al. Spatial computation of intratumoral T cells correlates with survival of patients with pancreatic cancer. Nat Commun. 2017;8:15095.
    1. Feng Z, Bethmann D, Kappler M, et al. Multiparametric immune profiling in HPV- oral squamous cell cancer. JCI Insight. 2017;2(14):e93652. 10.1172/jci.insight.93652.
    1. Gorris MAJ, Halilovic A, Rabold K, et al. Eight-color multiplex immunohistochemistry for simultaneous detection of multiple immune checkpoint molecules within the tumor microenvironment. J Immunol. 2018;200(1):347–354.
    1. Rimm DL, Han G, Taube JM. A prospective,multi-institutional, pathologist-based assessment of 4 immunohistochemistry assays for PD-L1 expression in non–small-cell lung cancer. JAMA Oncol. 2017;3(8):1051–8.
    1. Apetoh L, Smyth MJ, Drake CG, et al. Consensus nomenclature for CD8+ T cell phenotypes in cancer. Oncoimmunology. 2015;4(4):e998538.
    1. Jackute J, Zemaitis M, Pranys D, et al. The prognostic influence of tumor infiltrating Foxp3(+)CD4(+), CD4(+) and CD8(+) T cells in resected non-small cell lung cancer. J Inflamm. 2015;12:63.
    1. Koebel CM, Vermi W, Swann JB, et al. Adaptive immunity maintains occult cancer in an equilibrium state. Nature. 2007;450(7171):903–907.
    1. Hay CM, Sult E, Huang Q, et al. Targeting CD73 in the tumor microenvironment with MEDI9447. Oncoimmunology. 2016;5(8):e1208875.
    1. Lee Y, Auh SL, Wang Y, et al. Therapeutic effects of ablative radiation on local tumor require CD8+ T cells: changing strategies for cancer treatment. Blood. 2009;114(3):589–595.
    1. Mattarollo SR, Loi S, Duret H, Ma Y, Zitvogel L, Smyth MJ. Pivotal role of innate and adaptive immunity in anthracycline chemotherapy of established tumors. Cancer Res. 2011;71(14):4809–4820.
    1. Ascierto PA, Capone M, Urba WJ, et al. The additional facet of immunoscore: immunoprofiling as a possible predictive tool for cancer treatment. J Transl Med. 2013;11:54.
    1. Boheim K, Denz H, Boheim C, Glassl H, Huber H. An immunohistologic study of the distribution and status of activation of head and neck tumor infiltrating leukocytes. Arch Otorhinolaryngol. 1987;244(2):127–132.
    1. Dieu-Nosjean MC, Giraldo NA, Kaplon H, Germain C, Fridman WH, Sautes-Fridman C. Tertiary lymphoid structures, drivers of the anti-tumor responses in human cancers. Immunol Rev. 2016;271(1):260–275.
    1. Galon J, Pages F, Marincola FM, et al. The immune score as a new possible approach for the classification of cancer. J Transl Med. 2012;10:1.
    1. Marrogi AJ, Munshi A, Merogi AJ, et al. Study of tumor infiltrating lymphocytes and transforming growth factor-beta as prognostic factors in breast carcinoma. Int J Cancer. 1997;74(5):492–501.
    1. Naito Y, Saito K, Shiiba K, et al. CD8+ T cells infiltrated within cancer cell nests as a prognostic factor in human colorectal cancer. Cancer Res. 1998;58(16):3491–3494.
    1. Teng MW, Ngiow SF, Ribas A, Smyth MJ. Classifying cancers based on T-cell infiltration and PD-L1. Cancer Res. 2015;75(11):2139–2145.
    1. Bethmann D, Feng Z, Fox BA. Immunoprofiling as a predictor of patient’s response to cancer therapy-promises and challenges. Curr Opin Immunol. 2017;45:60–72.
    1. Pages F, Berger A, Camus M, et al. Effector memory T cells, early metastasis, and survival in colorectal cancer. N Engl J Med. 2005;353(25):2654–2666.
    1. Fortis SP, Sofopoulos M, Sotiriadou NN, et al. Differential intratumoral distributions of CD8 and CD163 immune cells as prognostic biomarkers in breast cancer. J Immunother Cancer. 2017;5:39.
    1. Bottai G, Raschioni C, Losurdo A, et al. An immune stratification reveals a subset of PD-1/LAG-3 double-positive triple-negative breast cancers. Breast Cancer Res. 2016;18(1):121.
    1. Feichtenbeiner A, Haas M, Buttner M, Grabenbauer GG, Fietkau R, Distel LV. Critical role of spatial interaction between CD8(+) and Foxp3(+) cells in human gastric cancer: the distance matters. Cancer Immunol Immunother. 2014;63(2):111–119.
    1. Muller P, Rothschild SI, Arnold W, et al. Metastatic spread in patients with non-small cell lung cancer is associated with a reduced density of tumor-infiltrating T cells. Cancer Immunol Immunother. 2016;65(1):1–11.
    1. Park JH, Powell AG, Roxburgh CS, Horgan PG, McMillan DC, Edwards J. Mismatch repair status in patients with primary operable colorectal cancer: associations with the local and systemic tumour environment. Br J Cancer. 2016;114(5):562–570.
    1. Baine MK, Turcu G, Zito CR, et al. Characterization of tumor infiltrating lymphocytes in paired primary and metastatic renal cell carcinoma specimens. Oncotarget. 2015;6(28):24990–25002.
    1. Djenidi F, Adam J, Goubar A, et al. CD8+CD103+ tumor-infiltrating lymphocytes are tumor-specific tissue-resident memory T cells and a prognostic factor for survival in lung cancer patients. J Immunol. 2015;194(7):3475–3486.
    1. Mella M, Kauppila JH, Karihtala P, et al. Tumor infiltrating CD8+ T lymphocyte count is independent of tumor TLR9 status in treatment naive triple negative breast cancer and renal cell carcinoma. Oncoimmunology. 2015;4(6):e1002726.
    1. Zhu J, Wen H, Ju X, Bi R, Zuo W, Wu X. Clinical significance of programmed death ligand 1 and intra-tumoral CD8+ T lymphocytes in ovarian carcinosarcoma. PLoS One. 2017;12(1):e0170879.
    1. Halama N, Michel S, Kloor M, et al. Localization and density of immune cells in the invasive margin of human colorectal cancer liver metastases are prognostic for response to chemotherapy. Cancer Res. 2011;71(17):5670–5677.
    1. Baatz M, Zimmermann J, Blackmore CG. Automated analysis and detailed quantification of biomedical images using Definiens cognition network technology. Comb Chem High Throughput Screen. 2009;12(9):908–916.
    1. Arteta C, Lempitsky V, Noble JA, Zisserman A. Learning to detect cells using non-overlapping extremal regions. Med Image Comput Comput Assist Interv. 2012;15(Pt 1):348–356.
    1. Chen T, Chef'dhotel C. Deep learning-based automatic immune cell detection for immunohistochemistry images. In: Wu G, Zhang D, Zhou L, editors. Machine learning in medical imaging. Switzerland: Springer; 2014. pp. 17–24.
    1. Mualla F, Scholl S, Sommerfeldt B, Maier A, Hornegger J. Automatic cell detection in bright-field microscope images using SIFT, random forests, and hierarchical clustering. IEEE Trans Med Imaging. 2013;32(12):2274–2286.
    1. Niazi MKK, Satoskar AA, Gurcan MN. An automated method for counting cytotoxic T-cells from CD8 stained images of renal biopsies. Proccedings Volume 8676, Medical Imaging 2013: Digital Pathology; 867606. 10.1117/12.2007977. Available at .
    1. Parvin B, Yang Q, Han J, Chang H, Rydberg B, Barcellos-Hoff MH. Iterative voting for inference of structural saliency and characterization of subcellular events. IEEE Trans Image Process. 2007;16(3):615–623.
    1. Xin Q, Xing F, Foran DJ, Yang L. Robust segmentation of overlapping cells in histopathology specimens using parallel seed detection and repulsive level set. IEEE Trans Biomed Eng. 2012;59(3):754–765.
    1. Brieu N, Pauly O, Zimmermann J, Binnig G, Schmidt G. Slide-specific models for segmentation of differently stained digital histopathology whole slide images. Slide-specific models for segmentation of differently stained digital histopathology whole slide images. San Diego, CA: International Society for Optics and Photonics; 2016.
    1. Powers DMW. Evaluation: from precision, recall and F-measure to ROC, Informedness, Markedness & Correlation. J Mach Learn Technol. 2011;2(1):37.
    1. Efron B. Better bootstrap confidence intervals. J Am Stat Assoc. 1987;82(397):171–185.
    1. r Core Team. A language and environment for statistical computing. 2016; . Accessed 5 May 2017.
    1. Stevenson M, Nunes T, Heuer C, et al. epiR: tools for the analysis of epidemiological data. 2017; . Accessed 5 May 2017.
    1. Warnes GR, Bolker B, Gorjanc G, et al. gdata: Various R programming tools for data manipulation. 2017; . Accessed 18 July 2017.
    1. Wickham H. Scales: scale functions for visualization. 2016; . Accessed 18 July 2017.
    1. Wickham H. ggplot2: Elegant graphics for data analysis. 2009; . Accessed 5 May 2017.
    1. Lin LI. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989;45(1):255–268.
    1. Pearson K. Note on regression and inheritance in the case of two parents. Proc R Soc London. 1895;58:240–242.
    1. Spearman C. The proof and measurement of association between two things. Am J Psychol. 1904;15(1):72–101.
    1. McBride GB. Using statistical methods for water quality management: issues, problems, and solutions. New York: Wiley; 2005.
    1. Enwere EK, Kornaga EN, Dean M, et al. Expression of PD-L1 and presence of CD8-positive T cells in pre-treatment specimens of locally advanced cervical cancer. Mod Pathol. 2017;30(4):577–586.
    1. Nowicki TS, Akiyama R, Huang RR, et al. Infiltration of CD8 T cells and expression of PD-1 and PD-L1 in synovial sarcoma. Cancer Immunol Res. 2017;5(2):118–126.
    1. Ritter AT, Asano Y, Stinchcombe JC, et al. Actin depletion initiates events leading to granule secretion at the immunological synapse. Immunity. 2015;42(5):864–876.
    1. Ohgami RS, Zhao S, Natkunam Y. Large B-cell lymphomas poor in B cells and rich in PD-1+ T cells can mimic T-cell lymphomas. Am J Clin Pathol. 2014;142(2):150–156.
    1. Rizvi J, Brahmer JR, Ou SHI, et al. Safety and clinical activity of MEDI4736, an anti-programmed cell death-ligand 1 (PD-L1) antibody, in patients with non-small cell lung cancer. J Clin Oncol. 2015;33(15_suppl):8032–8032.
    1. Lesokhin AM, Ansell SM, Armand P, et al. Nivolumab in patients with relapsed or refractory hematologic malignancy: preliminary results of a phase Ib study. J Clin Oncol. 2016;34(23):2698–2704.
    1. Maia MC, Hansen AR. A comprehensive review of immunotherapies in prostate cancer. Crit Rev Oncol Hematol. 2017;113:292–303.
    1. Danaher P, Warren S, Dennis L, et al. Gene expression markers of tumor infiltrating leukocytes. J Immunother Cancer. 2017;5:18.
    1. Bhat P, Leggatt G, Matthaei KI, Frazer IH. The kinematics of cytotoxic lymphocytes influence their ability to kill target cells. PLoS One. 2014;9(5):e95248.
    1. Rudd CE. The reverse stop-signal model for CTLA4 function. Nat Rev Immunol. 2008;8(2):153–160.
    1. Althammer S, Steele K, Rebelatto M, et al. Combinatorial CD8+ and PD-L1+ cell densities correlate with response and improved survival in non-small cell lung cancer (NSCLC) patients treated with durvalumab. J Immunother Cancer. 2016;4(suppl 2):91.

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