Automated Analysis of Lymphocytic Infiltration,Tumor Budding, and Their Spatial RelationshipImproves Prognostic Accuracy in ColorectalCancerInes P. Nearchou1, Kate Lillard2, Christos G. Gavriel1, Hideki Ueno3,David J. Harrison1, and Peter D. Caie1
Abstract
Both immune profiling and tumor budding significantlycorrelate with colorectal cancer patient outcome but are tra-ditionally reported independently. This study evaluated theassociation and interaction between lymphocytic infiltrationand tumor budding, coregistered on a single slide, in order todetermine a more precise prognostic algorithm for patientswith stage II colorectal cancer. Multiplexed immunofluores-cence and automated image analysis were used for the quan-tification of CD3þCD8þ T cells, and tumor buds (TBs), acrosswhole slide images of three independent cohorts (trainingcohort: n¼ 114, validation cohort 1: n¼ 56, validation cohort2: n ¼ 62). Machine learning algorithms were used for featureselection and prognostic risk model development. High num-bers of TBs [HR¼ 5.899; 95% confidence interval (CI) 1.875–
18.55], low CD3þ T-cell density (HR¼ 9.964; 95%CI, 3.156–31.46), and low mean number of CD3þCD8þ T cells within50 mm of TBs (HR ¼ 8.907; 95% CI, 2.834–28.0) were asso-ciated with reduced disease-specific survival. A prognosticsignature, derived from integrating TBs, lymphocyte infiltration,and their spatial relationship, reported amore significant cohortstratification (HR ¼ 18.75; 95% CI, 6.46–54.43), than TBs, alymphocytic infiltration score, or pT stage. This was confirmedin two independent validation cohorts (HR ¼ 12.27; 95% CI,3.524–42.73; HR ¼ 15.61; 95% CI, 4.692–51.91). The inves-tigation of the spatial relationship between lymphocytes andTBs within the tumor microenvironment improves accuracy ofprognosis of patients with stage II colorectal cancer through anautomated image analysis and machine learning workflow.
IntroductionTumor–node–metastasis (TNM) staging remains the gold stan-
dard for stratification of colorectal cancer patients into prognosticsubgroups and is fundamental for treatment selection (1, 2).Surgical resection without the use of adjuvant treatment resultsin long-termdisease-free survival inmost stage II colorectal cancercases. However, 20% of patients experience disease-specificdeath (3). In addition, some stage III patients experience betteroutcomes than some stage II patients. This is in spite of revisionsto the TNM guidelines, which, for example, subdivide pT stage IIinto three categories (4). Alongside pT stage, tumordifferentiationaids the pathologist in substratifying patients into high or low riskof tumor progression; however, grade is highly subjective (5).
Tumor budding, which morphologically resembles small, poorlydifferentiated clusters of cells (1–4 cells) disseminating at theinvasive margin (IM), has consistently been correlated with poorcolorectal cancer prognosis (6–8).
Both TNMstaging and tumor budding concentrate solely on thetumor, but it is nowwell established that the host interaction, suchas immune infiltrate, within the tumor microenvironment (TME)plays a crucial role in tumor progression (9, 10). Specifically, incolorectal cancer, the pioneering studies of Galon and colleaguesdemonstrated that CD3þ and CD8þ T-cell infiltration was anindependent prognostic factor predicting patient survival (9, 11).Tumor buds (TBs) are hypothesized to represent a highly invasivetumor subpopulation, especially if they also have the ability toevade the host immune response (6–8). The interaction betweenimmune response and TBs may therefore hold vital informationpertaining to the tumor's potential to metastasize.
Previous research has demonstrated the ability to automaticallyquantify CD3þ and CD8þ T cells within the TME, thereby achiev-ing a greater level of standardization than with subjective, manualreporting (9). Similarly, we have previously shown that tumorbudding can be standardized through automated image analy-sis (12, 13). Although the automatic quantification of bothimmune infiltrate and tumor budding has been associated withpatient prognosis, they have not been studied together. Further-more, by coregistration on the same tissue section, it is possible toaccurately study the spatial interaction with each other. We applysuch an approach in this study to investigate if quantifying bothfeatures alongside their spatial interactionallows for amorepreciseand accurate prognosis for stage II colorectal cancer patients.
1Quantitative and Digital Pathology, School of Medicine, University of St.Andrews, St. Andrews, UK. 2Indica Labs, Inc., Corrales, New Mexico, USA.3Department of Surgery, National Defense Medical College, Saitama, Japan.
Note: Supplementary data for this article are available at Cancer ImmunologyResearch Online (http://cancerimmunolres.aacrjournals.org/).
Corrected online July 3, 2019.
�IMMUNOSCORE is a registered trademark owned by INSERM.
Corresponding Author: Ines P. Nearchou, School of Medicine, University of StAndrews, St Andrews, North Haugh, Scotland KY16 9TF, UK. Phone: 44-1334-463603; Fax: 44-1334-467470; E-mail: [email protected]
Materials and MethodsPatient material and clinical data
This study included three independent cohorts of stage IIcolorectal cancer patients who underwent surgical resection inEdinburgh, UK, hospitals over the years 2002–2004 (n ¼ 170)and the National Defense Medical College Hospital (NDMCH),Japan, over the years 2006–2011 (n ¼ 62). The formalin-fixedparaffin-embedded (FFPE) block that contained the deepest can-cer invasion for each patient was selected after the review ofhematoxylin and eosin (H&E) slides. Cancer blocks from Edin-burgh hospitals comprised specimens of cross-sectional cut,whereas the ones from the NDMCH were of longitudinal cut.Associated clinical data included features from the originalpathology report, such as TNM staging and differentiation as wellas detailed follow-up information including disease-specific sur-vival (DSS): up to 11.5 years for the Edinburgh cohort and 8.6years for the Japanese cohort. One hundred fourteen consecutivepatients who underwent surgical resection in Edinburgh hospitalsover the years 2002–2003 were used as a training cohort for thisstudy. All available patients who underwent surgical resection inEdinburgh hospitals in the following year (2004) were used as afirst validation cohort (n¼ 56).We then used a similar number ofpatients who underwent surgical resection in theNDMCH, Japan,over the years 2006–2011 as an international validation set(n ¼ 62). The clinicopathologic data of all the cohorts are shownin Table 1. This study was conducted in accordance with theDeclaration of Helsinki. Ethical approval was obtained afterreview by the NHS Lothian NRS BioResource, REC-approvedResearch Tissue Bank (REC approval ref: 13/ES/0126), grantedby East of Scotland Research Ethics Service and by the EthicsCommittee of the National Defense Medical College (approvalref: No. 2992). The ethical approval permitted us to use archivaldiagnostic samples once they were deidentified for the purposesof the research.
Immunofluorescence and image captureImmunohistochemistry (IHC) antibody optimization. We selectedexternally validated specific primary antibodies: primary anti-body against CD8þ cytotoxic T cells (monoclonal mouse anti-human CD8, M7103; Dako) was previously used for the inter-national validation of the consensus IMMUNOSCORE�� byPages and colleagues (14), primary antibody against pan T cells(polyclonal rabbit anti-human CD3, A045201-2, Dako) used byHarter and colleagues (15), in assessment of tumor-infiltratinglymphocytes in human brain metastases and for pan-cytokeratin(PanCK; mouse monoclonal anti-human cytokeratin, M351501-2, Dako) previously used by Koelzer and colleagues (16), for theassessment of TBs in colorectal cancer.
In order to assess the in-house antibody performance, eachantibody was individually assessed by brightfield IHC. The pri-mary antibodies against CD3 and CD8 T cells were each testedindividually on 3-mm thick serial sections of normal tonsil FFPEtissue, known to have high lymphocytic cell concentration. Forassessment of PanCK primary antibody, a colorectal cancer opti-mizing tissue microarray, consisting of 100 cores taken fromprimary tissue blocks, was used. Expression of all cell markerswas detected using 3,30-diaminobenzidine (DAB) chromogen(K3468; Dako). Nuclei were counterstained with hematoxylin(RBA-4213-00A; CellPath). In parallel, H&E (RBB-0100-00A;CellPath) stainingwas performed on serial sections and specificity
of antibodies from the IHC was assessed and compared visuallyby an experienced pathologist (D.J. Harrison) and research scien-tists (I.P. Nearchou and P.D. Caie). No CD8-positive but CD3-negative cells were present, giving confidence to the antibodies'specificity. Positive and negative controls were used in each IHCstaining; human tonsil FFPE tissue without primary antibody forCD3 and CD8 T cells and a colorectal cancer optimizing tissuemicroarray without primary antibody for PanCK. Western blotanalysis was also used to assess the specificity of the PanCKantibody using colorectal cancer cell lines.
Immunofluorescence staining optimization. Once specificity ofantibodies was confirmed in brightfield, each target was assessedby a uniplex immunofluorescence. Uniplex immunofluorescencewas performed on serial sections of the colorectal cancer opti-mizing tissue microarray (specified above) with the use of indi-vidual tyramide signal amplification (TSA) fluorescence kits. Afterdeparaffinization, slides were placed in amicrowaveable pressurecooker containing boiling antigen retrieval (AR) buffer (0.1 Msodium citrate pH6) and microwaved for 5minutes at 75�C.Slides were allowed to cool down in running tap water for 20minutes at room temperature and were then rinsed with 0.1%phosphate-buffered saline-Tween 20 (PBS-T). PBS-T was used forall washing steps. Tissues were then blocked with 3% hydrogenperoxide for 5 minutes and Dako serum-free protein block(X090930-2, Dako) was then applied for 10 minutes. Slides werethen incubated for 30 minutes with the same antibodies as forIHC at these dilutions: CD3 (dilution 1:400), CD8 (dilution1:200), and PanCK (dilution 1:100). Slides were then washed(3 � 5 minutes). The slides that had previously been incubatedwith CD3 or CD8 antibodies were then incubated in predilutedanti-mouse or anti-rabbit HRP-conjugated secondary antibodies(K400111-2 and K400311-2, respectively; Dako) for 30 minutes.Following three more washes of 5 minutes, slides were incubatedfor 10 minutes with either TSA fluorescein (FITC) for CD3antibody visualization or TSA Cyanine 5 for CD8 antibodyvisualization (NEL741B001KT andNEL745B001KT, respectively;PerkinElmer) at 1:100dilution. The slide that hadbeenpreviouslyincubated with PanCK antibody was then incubated in anti-mouse AlexaFluor 555 (A21422, Thermo Fisher Scientific) inantibody diluent (S080983-2, Dako) at 1:100 dilution for 30minutes. Finally, following a washing step, the slides were coun-terstained for 10 minutes with Hoechst 33342 (H3570, ThermoFisher Scientific) at 1:20 dilution in deionized water. Sectionswere thendehydrated in 80%ethanol andmountedwith ProLongGold Antifade Reagent (P36930, Thermo Fisher Scientific). Opti-mal antibody dilutions in the uniplex immunofluorescence slideswere chosen to ensure best signal to noise and specific stainingpattern when digitized on the Zeiss Axioscan.z1 whole slidescanner (Zeiss). Fluorescence slides were compared with serialsections of brightfield DAB slides using the same antibodies.Positive and negative controls were included in each stainingrun. The positive controls for the CD3 and CD8 antibodies werethe tonsil tissue as specified above.
Once the appropriate dilution of primary antibodies, second-ary antibodies, and fluorophores were found under unipleximmunofluorescence, we then assessed them multiplexed on asingle tissue section. In order to eliminate species cross reactivity,microwave strippingwas performed between theCD8 and PanCKstaining. Microwave stripping was performed as follows. ARbuffer was heated in a pressure cooker in the microwave for 12
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minutes prior to adding the slides in the solution. Slides werethen microwaved for 17 minutes using the autodefrost function.Sections were then allowed to cool down in running tap water for20 minutes and washed for 5 minutes. The immunofluorescenceprotocol utilized to label the study samples was performed asabove using a Dako Link48 Autostainer (Dako). The first run oflabeling included the primary antibody against CD8 prior toantibody stripping, and labeling with a primary antibody cocktailof CD3 and PanCK prior to counterstaining with Hoechst. Themultiplexed section was compared with the uniplex labeled serialsections and we confirmed that there was no increase or decreasein the level of specific signals or nonspecific background signals.
Whole slide fluorescence images were captured with a 20�objective using an Axioscan.Z1 (Zeiss). Exposure times for
Hoechst, Cy3, Cy5, and FITC channels were 12, 120, 8, and 25ms,respectively.
Digital image analysisDigital whole slide fluorescence images, in Zeiss' .czi file
format, were uploaded into HALO Next-Generation Image Anal-ysis software (version 2.1.1637.18; IndicaLabs) for image analy-sis. Full algorithm workflow and settings are shown below:
Nuclear segmentation. All nuclei in the whole slide image wereautomatically segmented using the commercially availableHigh-Plex FL (version 2.0) module within the HALO Next-Generation Image Analysis software. Optimized module para-meters for nuclei detection included dye weights (Hoechst
Table 1. Univariate Cox regression analysis for clinicopathologic data, LIS, TB number, and TBISI for all three cohorts
Training cohort (n ¼ 114) Validation cohort 1 (n ¼ 56) Validation cohort 2 (n ¼ 62)Features Freq (%) HR (95% CI) P Freq (%) HR (95% CI) P Freq (%) HR (95% CI) P
nucleus weight ¼ 4, Cy5 nucleus weight ¼ 0.232, FITCnucleus weight ¼ 0.232), nuclear contrast threshold (0.5),minimum nuclear intensity (0.079), nuclear segmentationaggressiveness (0.65), and using the default nuclear size set-ting (1–500 mm2). All samples were analyzed using thesespecific settings.
Lymphocytic infiltration analysis. Within the same software mod-ule in which we segmented the nuclei (High-Plex FL), cells werethen classified as CD3 (FITC) or CD8 (Cy5) positive based ondye nucleus-positive threshold (Cy5 ¼ 0.132, FITC ¼ 0.15),
cytoplasm-positive threshold (Cy5 ¼ 0.075, FITC ¼ 0.5) andmembrane-positive threshold (Cy5 ¼ 0.075, FITC ¼ 0.5).The algorithm settings were kept constant across all patients.Next, the algorithm automatically quantified the number ofeach lymphocyte classification across the whole slide image. Theflood tool was used to identify the invasive front. An IM wascreated that consisted of a width span of 500 mm in and out fromthe invasive front. The tumor core area (CT) was set to be thewhole tumor area excluding the 500 mm before the invasive front(Fig. 1A). Lymphocytes (CD3þ andCD8þ T cells) infiltrating boththe CT and the IM of the tumor were automatically quantified
Figure 1.
Digital image analysis method. A,Full tissue area (yellow line) andinvasive front (green line) areoutlined; pancytokeratin (PanCK),CD3þ, and CD8þ cells annotated ingreen, yellow, and red, respectively;IM region is highlighted in green andthe tumor core region in blue (CT);B, detection and classification oflymphocyte cell type (CD3þ cells inyellow, CD8þ cells in red and theircolocalization in orange mask); C,full tissue area (yellow line) andinvasive front (green line) areoutlined, PanCK, CD3þ, and CD8þ
cells annotated in green, yellow, andred, respectively; area of TBquantification is highlighted ingreen; D, tumor to stromasegmentation and PanCK cellquantification within the tumorareas; E, proximity analysis oflymphocytes to TBs. CD3þ cells areshown in blue, CD8þ cells in orange,and TBs in gray. Proximity line seriesis shown for lymphocytes within50 mmof TBs.
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(Fig. 1B) and their densities (cells/mm2) were exported to assesstheir prognostic significance.
Lymphocytic infiltration score (LIS) evaluation. LIS was performedbased on the methodology described previously by Galon andcolleagues (9). Using the lymphocytic infiltration analysis results,CD3þ and CD8þ lymphocytes were counted in both the IM andCT, resulting in the reporting of the following features: CD3þ
T cells in the CT, CD3þ T cells in the IM, CD8þ cells in the CT, andCD8þ T cells in the IM. Optimal cutoff points for the four featureswere calculated based on survival data of the training cohort(Supplementary Table S1). Values above theoptimal cutoff pointsfor each feature were scored 1, whereas values below were scored0. A LIS was then calculated by the sum of all scores for eachpatient sample. Finally, similarly to Liu and colleagues (17),patients were stratified into two groups: high LIS > 2 and lowLIS � 2.
Tumor budding analysis. The PanCK intensity within each wholeslide image was initially assessed using the AreaQuantification FLv1.2 module without any changes to the commercially availablemodule thresholds. Patient samples were grouped into two cat-egories based on their resulting PanCK intensity: low (average dyeintensity < 2.16� 10�2) and high (average dye intensity > 2.16�10�2). Using samples from within each PanCK group (high orlow), machine learning classifiers were trained to segment tumorfrom nontumor regions. TwoHigh-Plex FL (version 2.0)moduleswere next created: one using the classifier trained from low PanCKpatient samples and one from the high PanCK patient samples.Patient samples with an average PanCK intensity below 2.16 �10�2 were analyzed to segment tumor from nontumor using theclassifier termed "low PanCK" and the other samples with theclassifier termed "high PanCK." These modules were run within a1,000-mm border inward from the invasive front, termed thetumor-budding region of interest (TBROI; Fig. 1C). Within thetumor regions detected, nuclei were detected using the method-ology described above, and cancer cells were classified based onPanCK positivity in nucleus (0.1), cytoplasm (0.08), and mem-brane (0.05; Fig. 1D). These cancer cell classification thresholdswere kept constant across the entire cohort of images. TBs wereclassified as tumor clusters containing up to 4 PanCKþ cells. TBdensity (TBs/mm2) and number were then exported from thealgorithm. Mucin pools and areas of necrosis were excluded.
Spatial analysis of lymphocytes and TBs. Spatial coordinates ofthe lymphocytes and TBs were imported into a spatial plotwithin the HALO software. From this plot, the Spatial Analysisalgorithm was utilized to calculate the number of CD3þ andCD8þ T cells within a range between 0 and 100 mm radii ofTBs (Fig. 1E) in consecutively increasing 10 mm steps, thereforecreating 11 classes: 0–10 mm, 0–20 mm, 0–30 mm, and so on.All classes of radius size were significantly associated withsurvival; however, after assessing the prognostic significance ofiterative groupings of varying radius sizes, there was a statisticaldifference observed between the 0–50 mm and the 0–100 mmgroupings. The classes were therefore merged into two catego-ries: 0–50 mm and 0–100 mm radii of TBs. Finally, the numberand densities of CD3þ and CD8þ T cells within these distanceswere established.
All features exported from the image analysis are listed inSupplementary Table S2.
Statistical analysisAll statistical analyses, unless otherwise stated, were performed
in RStudio 1.1.419 running R 3.4.3 (18, 19). Continuous imageanalysis data, categorical clinical data, and survival data wereloaded into R. The relationship between lymphocyte distributionpatterns, TBs, and patient characteristics was assessed using the c2
test. For comparison with categorical patient data, image analysisfeatures were divided into low and high groups according tooptimal cutoff points based on survival data. To assess therelationship between lymphocytes and TBs, Spearman correlationcoefficients (r) were applied on continuous data. All r ¼ 0 valuesindicated no association between features, r > 0 indicated apositive association where as one variable increases, so does theother, and r < 0 indicated a negative association where asone variable increases the other decreases. Associations betweenfeatures were assessed on the training cohort.
Univariate Cox regression was performed on both the categor-ical clinical and continuous image analysis data. All P < 0.05values were considered statistically significant.
The least absolute shrinkage and selection operator (LASSO)penalized Cox proportional hazard regression was performed foridentification of significant features (20), using the glmnet pack-age (21). The significant features identified by LASSO were theninputted into a random forest (RF; n ¼ 500) decision-tree modelusing the randomForest package (22)andwere rankedaccording totheirmean decrease Gini coefficient. Featureswith amean decreaseGini of above 4 were taken forward for further analysis. Optimalcutoff points for these four features were calculated, and iterativecombinations of these features were tested. The model with mostpredictive value was then selected, termed the "tumor bud-immuno spatial index" (TBISI). In order to avoid overfitting duringthe development of the model, validation was performed for bothLASSO Cox regression model (10-fold cross-validation) and RF(out of the bag). Univariate Cox regression was performed for theTBISI, pT stage, TB number, and LIS, and a bootstrap resamplingtechnique was applied (number of samples ¼ 1,000) in SPSS24 (23). Multivariate Cox regression with a forward stepwisemethod and the receiver operating characteristic (ROC) curve werealso applied on to compare the TBISI, pT stage, TB number, and LISin SPSS 24. The prognostic value of TBISI was then assessed on twoindependent validation cohorts. All validations were performedusing the fixed optimal cutoff points of the training cohort. Alloptimal cutoff points for the categorization of image analysisfeatures including tumor budding, the LIS, and for the develop-ment of the TBISI were based onDSS andwere calculated using thesurvminer package (24). All Kaplan–Meier (KM) curves wereplotted using the survival package (25). The P values from KMcurves were corrected for false discovery rate (FDR) using theBenjamini–Hochberg procedure to account for multiple hypoth-esis testing (26).
Study workflow is illustrated in Supplementary Fig. S1.
ResultsPatient characteristics
The clinicopathologic characteristics of all three cohorts of stageII colorectal cancer patients are shown in Table 1. This studycohort comprised a training cohort of 114 stage II colorectalcancer patients, of which 57 were female and 57 were male. Thevalidation cohorts included 34 male and 22 female patients invalidation cohort 1 and 42 male and 20 female patients in
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validation cohort 2. The range of patients' age was 37–96 yearsold. There were 43 right-sided colon carcinomas, 38 left-sided,and 33 rectal cancers in the training cohort and 29 and 14 right-sided colon carcinomas, 9 and 22 left-sided, and 18 and 26 rectalcancers in validation cohorts 1 and 2, respectively.
Correlations of patient characteristics with lymphocyte densityand TB number
Automated image analysis was performed to quantify TBs(within TBROI), CD3þ cell, and CD8þ T-cell densities (withinIM and CT). Optimal cutoff points based on survival data wereused to categorize image analysis features of the training cohortinto low and high groups (Supplementary Table S1). TB numberand density were highly correlated with each other (correlationcoefficient¼ 0.7466; P < 2.2� 10�16); therefore, TB number wasselected to be used for correlation assessments. The relationshipbetween patient characteristics and both lymphocyte distributionpatterns and TB number was then assessed. A high density ofCD3þ T cells at the IM was associated with no extramural lym-phovascular invasion (EMLVI; P ¼ 0.049), whereas in the CT, itwas correlated with moderate differentiation status and nonmu-cinous histologic type (P ¼ 0.007 and P ¼ 0.023, respectively). Ahigh density of CD8þ cells at the IM and across the whole tumorsection (WTS) was significantly associated with female gender(P ¼ 0.016 and P ¼ 0.030, respectively). Finally, advanced pTstage was significantly associated with a high TB number at theTBROI (P ¼ 0.031; Table 2).
Relationship between tumor budding and lymphocyte densityTB number was tested for association with lymphocyte densi-
ties of the training cohort, using the Spearman correlation coef-ficient (Fig. 2). This indicated a weak negative relationshipbetween TB number and all lymphocytic cell densities (r < 0)regardless of distribution pattern (i.e., IM, CT, WTS) or lympho-cytic cell subpopulations (CD3þ or CD8þ). However, a higherdensity of CD3þ T cells at the IM, CD8þ T cells at the IM, andWTSwere significantly correlated with a lower TB number (P ¼ 0.016,P¼ 0.037, and P¼ 0.041, respectively; Supplementary Table S3).
Survival analysis: Clinicopathologic dataTo assess the prognostic significance of the categorical clinico-
pathologic data of the training cohort, univariate Cox regressionwas applied. KM survival analysis was used to further assess anysignificant prognostic features from the clinicopathologic data ofthe training cohort (Fig. 3).UnivariateCox regression showed thatonly pT stage (HR ¼ 4.143; 95% CI, 1.480–11.570; P ¼ 0.006)was significantly associated with DSS (Table 1). KM survivalfunction was then applied and showed that patients of T4 stagehave poor DSS (73% survived) compared with T3 stage patients(91% survived; P ¼ 0.003; Fig. 3A).
Survival analysis: Image analysis featuresTo explore the prognostic effects of the quantified lymphocytic
distribution patterns, TB number and density within the trainingcohort, we performed univariate Cox regression analysis. Thisanalysis was also performed for the spatial relationships betweenlymphocytes and TBs. The following specific continuous featureswere calculated and exported from the image analysis algorithm:(i) in IM, CT, and WTS; densities of CD3þ and CD8þ T cells andratio of CD3þ to CD8þ T cells; (ii) TB number and density at theTBROI, (iii)within 0–50mmand0–100mmradius of TBs; number
and mean number of CD3þ and CD8þ T cells. The densities ofboth CD3þ and CD8þ T cells were significantly associated withDSS (P < 0.05). TB number and density conferred prognosticsignificance (HR ¼ 1.001; 95% CI, 1.000–1.001; P ¼ 0.0004 andHR¼ 1.030; 95%CI, 1.000–1.060; P¼ 0.0446, respectively). KManalysis showed TB number to be a significant prognostic factor;95% of patients in the low tumor budding group survivedcompared with the high tumor budding group, in which 73%of patients survived (P ¼ 0.0006; Fig. 3D). Furthermore, a highmean number of lymphocytes near TBs was significant in pre-dicting DSS for stage II colorectal cancer patients (P < 0.05;Supplementary Table S2).
Significant feature selectionAll clinicopathologic and image analysis data of the training
cohort (listed in Supplementary Table S2) were input for a LASSOpenalized Cox proportional hazard regression. This was per-formed to identify the features that add significant value to theprediction of DSS over time and therefore to reduce the datadimensionality. The analysis reported that there were eight sig-nificant candidate features (CD3þ T-cell density at WTS, meanCD3þCD8þ T-cell number within 0–50 mm of TBs, TB number,CD8þ T-cell density in CT, pT stage, EMLVI, age, and differenti-ation). The eight features that added value to themodel were usedas further input into a RF decision-tree model for optimal prog-nostic feature selection. The RFmodel reported four features witha mean decrease Gini of above 4 (CD3þ T-cell density in WTS,mean CD3þCD8þ T-cell number within 0–50 mm of TBs, TBnumber, CD8þ T-cell density in CT; Table 3).
Development of TBISIOptimal cutoff points for these four features (CD3þ T-cell
density in WTS, mean CD3þCD8þ T-cell number within 0–50mm of TBs, TB number, CD8þ T-cell density in CT) werecalculated for the training cohort using the patient survivaldata (389.6 cells/mm2, 4.1, 1104.0, and 114.7 cells/mm2,respectively). The least significant feature was then removedin an iterative process until the removal of a feature decreasedthe prognostic value of the model. This led to the creation of theTBISI consisting of three features: CD3þ T-cell density in WTS,mean CD3þCD8þ T-cell number within 0–50 mm of TBs, andTB number. Patients with CD3þ T-cell density in WTS andmeanCD3þCD8þ T-cell number within 0–50 mm of TBs below thecutoff point and TB number above the cutoff point wereclassified as a "high risk" of disease-specific death group; theremainder of the patients were classified as a "low-risk" group.KM curves and bootstrap univariate Cox regression P valueswere used to determine the significance of this index. Thecreated TBISI was highly significant in the prediction of dis-ease-specific death from both KM analysis and Cox regression(HR ¼ 18.75; 95% CI, 6.46–54.43; P ¼ 1.202 � 10�13; Fig. 3J).KM and univariate Cox-regression analyses were also appliedto pT stage (HR ¼ 4.143; 95% CI, 1.48–11.57; P ¼ 0.0033;Fig. 3A), TB number (HR ¼ 5.899, 95% CI, 1.875–18.55; P ¼0.0006; Fig. 3D) and LIS (HR ¼ 7.02; 95% CI, 2.49–19.84; P ¼1.922 � 10�05; Fig. 3G). Multivariate Cox regression model(forward stepwise) revealed that this TBISI was the only featurethat added value to the model when pT stage, TB number, LIS,and TBISI were included (Supplementary Table S4). Finally, theROC curve showed that the TBISI had the greatest area underthe curve (area ¼ 0.785; 95% CI, 0.629–0.941) when compared
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with pT stage or each of the model's component individually(Supplementary Fig. S2).
Validation of tumor budding, LIS, and TBISIThe TBISI developed from the training set, alongside the
LIS and TBs, were evaluated on two independent validationcohorts: validation cohort 1 patients from Edinburgh hospitals(n ¼ 56) and international validation cohort 2 patients from theNDMCH, Japan (n ¼ 62). Cutoff points calculated from thetraining set data were directly applied to the two validationcohorts. Univariate Cox regression and KM analyses were calcu-lated using the patient data from the two validation cohorts andused to assess the clinicopathologic data, TB number, LIS, andthe model TBISI (Table 1). pT stage was significantly associatedwith patient survival in validation cohort 1 (HR¼ 5.00; 95% CI,1.52–16.46; P ¼ 0.008; Fig. 3B), but not in the Japanese valida-tion cohort 2 (Fig. 3C). TB number was significant in bothvalidation cohorts 1 and 2 (HR ¼ 5.29; 95% CI, 1.14–24.50;P ¼ 0.033 and HR ¼ 8.816; 95% CI, 2.70–28.82; P ¼ 0.0003;Fig. 3E and F, respectively), whereas the LIS was only significantin validation cohort 1 (HR ¼ 4.72; 95% CI, 1.02–21.87; P ¼0.047) (Fig. 3H), although trended to significance in validationcohort 2 (Fig. 3I).
TBISI was highly significant on both validation cohorts; vali-dation cohort 1 with HR¼ 12.27; 95% CI, 3.52–42.73; P¼ 8.2�10�05 and in validation cohort 2 with HR¼ 15.61; 95%CI, 4.69–51.91; P ¼ 7.42 � 10�06 (Fig. 3K and L, respectively). The TBISIdemonstrated an increase in hazard ratio and significance in bothvalidation cohorts than when analyzing the sum of its partsindependently.
DiscussionRisk assessment and thus treatment choices for stage II
colorectal cancer patients are in need of improvement. A growingawareness exists that the TME contributes to cancer differentiation,progression, invasion, andmetastasis (27–29). In colorectal cancer,the two most promising candidate prognostic features involve twodistinct aspects of the TME: tumor budding (2, 30–32) and theIMMUNOSCORE�� (9, 10, 14, 33). Tumor budding representssmall clusters of infiltrating cancer cells within the IM, whereas theIMMUNOSCORE�� quantifies tumor-infiltrating lymphocytes(TILs) within both the IM and the CT. However, these features arepredominantly assessed independently of eachother. Thequestion,therefore, arises as to whether tumor budding and lymphocyticinfiltration combined augment their individual prognostic signif-icance and whether their association and spatial relationship addsfurther prognostic value for stage II colorectal cancer patients. Thework presented here utilizes automated image analysis to quantifyand compare TBs and TILs coregistered to the same tissue section.
We report four major findings: first, we have shown significantassociations between clinicopathologic factors, CD3þ CD8þ
T-cell densities, and TB number; second, we have shown thatLIS and tumor budding are strong independent prognostic factorsfor DSS in stage II colorectal cancer; third, we have demonstratedthat the spatial interaction between lymphocytes and TBs holdsadditional prognostic significance; and finally, a standardizedprognostic index was created combining all the above featuresto achieve a higher significant cohort stratification than anyindividual prognostic feature tested.
Emerging technologies in tissue analysis have allowed furtherunderstanding of the role of the TME in disease progression.Automated image analysis has been shown to have a high poten-tial for unbiased and reproducible assessment of histopathologicfeatures in colorectal cancer, such as for TBs, lymphatic vesselinvasion (12), and TILs (14), as well as in other disease types(34, 35). To an extent, this is assisted by the advancement ofacquiringmultiparametric data froma single tissue section,whichcan provide a more complete understanding of the underlyingmechanismswithin the TME. Even though somemultiplex IHCorimmunofluorescence methodologies have shown the ability ofhigh-level multiplexing on a single FFPE tissue sample, theanalysis may be restricted to manually selected regions of interestor through their applications on tissue microarrays (36). Suchmethodologies can also be associated with long acquisition timesfor whole tissue sections and sequential stitching of image tilesinto one large image (37). In this study, we quantified TILs andTBs coregistered to a single whole slide section through the use offour-plexed immunofluorescence, whole slide scanning, andautomated image analysis. This methodology permitted thecolocalization of the four tissue markers, required to quantifythe featuresmeasured in this study, across thewhole tissue sectionand at single-cell resolution. Spatial coordinates of each cell typewere exported from the same tissue section ensuring more accu-rate analysis than is achieved from coregistering serial sections.This methodology therefore conserves precious tissue, reducesscanning time and cost, while also allowing the objective analysisof the entire TME and its cellular interactions.
Our results showed that the presence of high CD3þ T-celldensity correlated with favorable clinicopathologic features, atthe IM with the absence of EMLVI and at the CT with moderatedifferentiation status and nonmucinous histologic type. These
Figure 2.
Spearman correlation matrix for lymphocyte densities and TB numbers.Spearman correlation coefficient is shown for all relationships. A coefficientwith eitherþ1 (blue), 0 (white), or�1 (red) value indicates a perfectassociation, no association, and a perfect negative association of ranks,respectively.
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Kaplan–Meier survival analysis for pT stage, TB number, lymphocytic infiltration score (LIS), and TBISI for training cohort, validation cohort 1, and validationcohort 2. A, pT stage for training cohort, (B) pT stage for validation cohort 1, and (C) pT stage for validation cohort 2. D, TB number for training cohort, (E) TBnumber for validation cohort 1, and F, TB number for validation cohort 2; High represents the group of patients with TB number above the optimal cutoff point(1104.0). Low represents the group of patients with TB number below the cutoff point. G, LIS for training cohort; H, LIS for validation cohort 1; and I, LIS forvalidation cohort 2. High LIS represents the group of patients with a LIS > 2, and low LIS represents the group of patients with a LIS� 2. J, TBISI for trainingcohort; K, TBISI for validation cohort 1; and L, TBISI for validation cohort 2. High risk represents the group of patients who have CD3þ density inWTS andmeanCD3þCD8þ cell number within 0–50 mmof TBs below the cutoff point (389.6 cells/mm2 and 4.1, respectively) and TB number above the cutoff point (1104.0).Low risk represents all other patients.
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data concur well with prognostic analyses on the propitiousimpact of overall TIL densities in colorectal cancer patients (9, 10).We also found that a high CD8 T-cell density was correlated withthe female gender, a finding that has previously been shown ininvasive colorectal cancer by Jang (38).However, themechanismsunderlying this observation remain unknown. An elevated num-ber of TBs was significantly associated with advanced pT stage,correlating favorably with previous literature showing TBs beingassociated with adverse prognostic features (39–41). These find-ings highlight the impairing effect T cells have on tumor progres-sion, while suggesting that high tumor budding cancers harbor amore aggressive phenotype.
Our data indicate a significant inverse relationship between thedensities of TILs and TB number, which substantiates the work ofZlobec and colleagues (42), who found the absence of TILs to beassociated with the presence of tumor budding. This may suggestthat during the transition of TBs from epithelial-to-mesenchymalphenotype, they might occupy a niche that may not elicit animmune response. Indeed, it has been suggested that TBs loseMHC expression during this transition (7), and others have shownthat TB positivity is significantly associated with PD-L1 positivi-ty (43). The data presented here add further support to the conceptof TBs having the ability to evade the antitumoral host responseand thereby embark on the first steps of the metastatic cascade.
Despite repeated evidence of tumor budding conferring prog-nostic significance in colorectal cancer (8, 12, 41, 44), its routineclinical assessmenthasnot yet been establisheddue to the lackof astandardized methodology of assessment. Most studies investi-gating tumor budding rely upon the use ofmanual quantification,often based on subjective selection of high magnification regionsof interest (2). The use of cytokeratin IHC has previously beenshown to improve the detection of TBs as well as increasinginterobserver reproducibility (30). Furthermore, cytokeratin IHChas been recommended by the International Tumor Bud Con-sensus Conference to help TB quantification in challenging casessuch as tumor regions with strong inflammation or rupturedglands (2). In the current study, tumor budding was identifiedby the use of cytokeratin immunofluorescence and quantified byautomated and standardized image analysis across the entireTBROI. Previous studies have highlighted the difficulty in thequalitative assessment of TBs in circumstances of gland fragmen-tation, stromal cells mimicking TBs, inflammation (45), micro-vesicles, and membrane fragments that might be cytokeratin-positive (30). In order to address these concerns, we ensured thepresence of whole nuclei (n ¼ 1–4) for the TB identification and
only counted the isolated clusters of cells as buds.Quantifying TBsat the whole TBROI has the advantage of being representative ofthe whole IM rather than a manually selected region of interest.However, our methodology has the potential to "dilute" thesignificance of TBs represented by hotspots due to the tumor'sinnate heterogeneity. Like previous semiquantitative studies(6, 41), our quantification methodology reported TB numberand density at the TBROI to be significantly correlated to poorpatient survival.
The IMMUNOSCORE��, proposed by Galon and collea-gues (9, 46, 47), has been validated in a large internationalcolon cancer study (14). It is not only a promising prognosticindex (9, 11), but it has been shown to be superior to TNMstagingwhen assessing colorectal cancer prognosis (46, 48). This scoreallows for the quantification of the in situ lymphocytic infiltrationby the chromogenic labeling of two sequential tissue sections: onewithCD3þT cells andonewithCD8þT cells andquantified in tworegions (IM and CT). The LIS created in this study, quantifiedlymphocytic infiltration from multiplexed immunofluorescenceacross a single tissue section. Our LIS results compared favorablywith those of Galon and colleagues (9), showing that a highdensity of TILs was correlated with better survival outcomes instage II colorectal cancer.
In order to reflect the dynamic processes present at the tumorfront, T lymphocytes in close (50 and 100 mm)proximity of TBs ina two-dimensional plot were quantified. Our results revealed thata high number of T lymphocytes near TBs were associated withbetter survival. This supports the theory that TBs represent aheterogeneous subpopulation of cells and, although hypothe-sized to be more invasive than well-differentiated tumor glands,retain the potential to be recognized by immune-surveillancemechanisms. This process has previously been described as "nip-ping in the bud" (42). This theory was also supported by a studyon the microenvironment of TBs by Lugli and colleagues (49),reporting high lymphocyte to TB ratio as a good prognostic factorin patients with colorectal cancer, and by Lang-Schwarz andcolleagues (50), reporting the combination of both TILs andtumor budding predicting survival in colorectal cancer. Due tothe high complexity and heterogeneity within the whole tissueslide, spatial mapping of specific features by eye is difficult,subjective, and time consuming. Selected regions of interest tostudy this interplay between different features and cells might notrepresent the spatial relationships present in thewhole tissue.Here,we propose a rapid and completely unbiased method capturingevery single event at the single-cell resolution and therefore betterreporting on the interactions among TBs and TILs seen at thewholeIM. Furthermore, such methodology can reveal specific patternsthat may advance our understanding of the interactions occurringbetween cancer cells and their microenvironment.
Multiple features reported from the clinicopathologic reportand by automated image analysis demonstrated prognostic sig-nificance. In order to reduce the dimensionality of the data andidentify the independently significant features, a Cox regressionmodel with LASSO regularization was applied to all 32 reportedfeatures in this study. This reported a reduced and independentlysignificant set of eight features: four from the clinicopathologicreport and four from image analysis. This reduced feature set wasinput for a RF machine learning model, which consisted of 500trees. The advantage of feature selection through RF is the thor-ough testing of all significant features across multiple modelswhile building in validation. This machine learning application
Table 3. LASSO penalized Cox proportional hazard regression and RF Ginicoefficients for the significant features
Coefficients
Features LASSOMeandecrease Gini
CD3þ density in WTS �0.0006 5.7932Mean CD3þCD8þ number within 0–50 mm of TB �0.8951 5.4673TB number 0.0004 4.7092CD8þ density in CT �0.0004 4.4586pT 0.8394 1.3724EMLVI �0.8623 1.1499Age 0.5791 1.1071Differentiation 0.4967 0.9728
NOTE: Features with Mean decrease Gini above 4 are shown in bold.Abbreviations: WTS, whole tumor section; CT, tumor core; EMLVI, extramurallymphovascular invasion.
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reported only four features with a high mean decrease Ginicoefficient, all of which were exported from image analysis. Inorder to avoid overfitting of our model on the training cohort, weincluded multiple validation steps within the workflow. Throughthis, we demonstrate that a model in which patients with acombined low CD3þ T-cell density in WTS, low mean numberof TILs within 50 mm proximity to TBs, and high TB numberconferred a survival disadvantage compared with any other com-bination. When a multivariate forward stepwise Cox regressionincluding pT stage, TB number, LIS, and TBISI was applied, theonly feature reported to significantly add value to the model wasthe TBISI, whereas pT stage, TB number, and LISwere not includedin the equation. The new prognostic model of TBISI was thensuccessfully validated across two independent cohorts, the secondof which included patients with different genetic background,tumor genetic features, and gut microbiota, compared with thefirst cohort. This model and its cutoff points did not need to bealtered for the Japanese validation cohort despite the fact that theirtissue was sectioned longitudinally compared with the othercohorts that were cut at a cross-section. pT stage in the Japanesecohort, unlike both Edinburgh ones, was not significantly corre-lated with DSS in univariate analysis. This reflects the heteroge-neous nature of stage II colorectal cancer disease and furtherhighlights the need for additional methodologies, such as theTBISI reported here, to more accurately stratify these patients forboth prognostic reasons and potentially identifying patients whomay benefit from adjuvant therapy.
In conclusion, this study reports an automated image analysis–based workflowwith the ability to quantify the tumor-infiltratingimmune cells; total T cells (CD3þ) and cytotoxic T cells (CD8þ),and TBs at the invasive front, across a single tissue section.Additionally, through a machine learning approach, our resultsshow evidence that the spatial associationof lymphocytes andTBshas a high prognostic significance in stage II colorectal cancerpatients. This combinational prognostic model demonstratedaugmented accuracy and precision over that gained by eithercharacterizing the lymphocytic infiltration or tumor budding inisolation. Furthermore, the final model contained no clinical
parameters, thus demonstrating the benefit of automated profil-ing of the TME to provide a more precise and accurate prognosis.The methodology completely removes human subjectivity andnegates the need for experienced staff to spend valuable timequantifying complex features across large areas of a whole slidesection. This research can serve as a base for future studies on theprognostic significance of the interplay between different celltypes within the patient's heterogeneous and heterotypic TME.
Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.
Authors' ContributionsConception and design: I.P. Nearchou, P.D. CaieDevelopment of methodology: I.P. Nearchou, P.D. CaieAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): I.P. Nearchou, C.G. Gavriel, H. Ueno,D.J. Harrison, P.D. CaieAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): I.P. NearchouWriting, review, and/or revision of the manuscript: I.P. Nearchou, K. Lillard,H. Ueno, D.J. Harrison, P.D. CaieAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): I.P. Nearchou, K. LillardStudy supervision: D.J. Harrison, P.D. Caie
AcknowledgmentsThe authors would like to acknowledge Frances Rae from Tissue Governance
NHS Lothian and Dr. Yoshiki Kajiwara from the National Defense MedicalCollege for securing ethics and patient follow-up data as well as InHwaUmandMustafa Elshani from the School of Medicine, University of St. Andrews, forcutting the tissue sections. Funding for the study was provided by MedicalResearch Scotland, and Indica Labs, Inc., provided in-kind resource.
The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.
Received June 8, 2018; revised November 15, 2018; accepted January 24,2019; published first March 7, 2019.
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Correction: Automated Analysis ofLymphocytic Infiltration, TumorBudding, andTheir Spatial Relationship ImprovesPrognostic Accuracy in Colorectal Cancer
In the original version of this article (1) and its supplementary data, the mannerin which the authors referred to registered trademark IMMUNOSCORE wasincorrect. This error has been corrected in the latest online HTML and PDFversions of the article, as well as in the supplementary data. The authors regretthis error.
Reference1. Nearchou IP, Lillard K, Gavriel CG, Ueno H, Harrison DJ, Caie PD. Automated analysis of
lymphocytic infiltration, tumor budding, and their spatial relationship improves prognosticaccuracy in colorectal cancer. Cancer Immunol Res 2019;7:609–20.
Published first August 1, 2019.Cancer Immunol Res 2019;7:1381doi: 10.1158/2326-6066.CIR-19-0440�2019 American Association for Cancer Research.
2019;7:609-620. Published OnlineFirst March 7, 2019.Cancer Immunol Res Ines P. Nearchou, Kate Lillard, Christos G. Gavriel, et al. Colorectal Cancerand Their Spatial Relationship Improves Prognostic Accuracy in Automated Analysis of Lymphocytic Infiltration, Tumor Budding,
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