Immunogenomics of Colorectal Cancer Response to Checkpoint Blockade: Analysis of the KEYNOTE 177 Trial and Validation Cohorts

doi:10.1053/j.gastro.2021.06.064

. 2021 Oct;161(4):1179-1193.

doi: 10.1053/j.gastro.2021.06.064. Epub 2021 Jun 29.

Immunogenomics of Colorectal Cancer Response to Checkpoint Blockade: Analysis of the KEYNOTE 177 Trial and Validation Cohorts

Michele Bortolomeazzi¹, Mohamed Reda Keddar¹, Lucia Montorsi¹, Amelia Acha-Sagredo¹, Lorena Benedetti¹, Damjan Temelkovski¹, Subin Choi¹, Nedyalko Petrov², Katrina Todd², Patty Wai³, Johannes Kohl³, Tamara Denner⁴, Emma Nye⁴, Robert Goldstone⁵, Sophia Ward⁵, Gareth A Wilson⁶, Maise Al Bakir⁶, Charles Swanton⁶, Susan John⁷, James Miles⁸, Banafshe Larijani⁹, Victoria Kunene¹⁰, Elisa Fontana¹¹, Hendrik-Tobias Arkenau¹², Peter J Parker¹³, Manuel Rodriguez-Justo¹⁴, Kai-Keen Shiu¹⁵, Jo Spencer¹⁶, Francesca D Ciccarelli¹⁷

Affiliations

¹ Cancer Systems Biology Laboratory, The Francis Crick Institute, London, United Kingdom; School of Cancer and Pharmaceutical Sciences, King's College London, London, United Kingdom.
² Biomedical Research Centre, Guy's and St. Thomas' National Health Service Trust, London, United Kingdom.
³ State-Dependent Neural Processing Laboratory, The Francis Crick Institute, London, United Kingdom.
⁴ Experimental Histopathology, The Francis Crick Institute, London, United Kingdom.
⁵ Advanced Sequencing Facility, The Francis Crick Institute, London, United Kingdom.
⁶ Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, United Kingdom; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, United Kingdom.
⁷ School of Immunology and Microbial Sciences, King's College London, London, United Kingdom.
⁸ FASTBASE Solutions S.L, Derio, Spain.
⁹ FASTBASE Solutions S.L, Derio, Spain; Cell Biophysics Laboratory, Ikerbasque, Basque Foundation for Science, Research Centre for Experimental Marine Biology and Biotechnology & Biophysics Institute, University of the Basque Country, Leioa, Bizkaia, Spain; Centre for Therapeutic Innovation, Cell Biophysics Laboratory, Department of Pharmacy and Pharmacology & Department of Physics, University of Bath, Bath, United Kingdom.
¹⁰ Medical Oncology, University Hospitals Birmingham National Health Service Foundation Trust, Birmingham, United Kingdom.
¹¹ Drug Development Unit, Sarah Cannon Research Institute UK, London, United Kingdom.
¹² Drug Development Unit, Sarah Cannon Research Institute UK, London, United Kingdom; Department of Oncology, University College Hospital, London, United Kingdom.
¹³ School of Cancer and Pharmaceutical Sciences, King's College London, London, United Kingdom; Protein Phosphorylation Laboratory, The Francis Crick Institute, London, United Kingdom.
¹⁴ Department of Histopathology, University College London Cancer Institute, London, United Kingdom.
¹⁵ Department of Gastrointestinal Oncology, University College London Hospital National Health Service Foundation Trust, London, United Kingdom.
¹⁶ School of Immunology and Microbial Sciences, King's College London, London, United Kingdom. Electronic address: jo.spencer@kcl.ac.uk.
¹⁷ Cancer Systems Biology Laboratory, The Francis Crick Institute, London, United Kingdom; School of Cancer and Pharmaceutical Sciences, King's College London, London, United Kingdom. Electronic address: francesca.ciccarelli@crick.ac.uk.

PMID: 34197832
PMCID: PMC8527923
DOI: 10.1053/j.gastro.2021.06.064

Immunogenomics of Colorectal Cancer Response to Checkpoint Blockade: Analysis of the KEYNOTE 177 Trial and Validation Cohorts

Michele Bortolomeazzi et al. Gastroenterology. 2021 Oct.

. 2021 Oct;161(4):1179-1193.

doi: 10.1053/j.gastro.2021.06.064. Epub 2021 Jun 29.

Authors

Affiliations

¹ Cancer Systems Biology Laboratory, The Francis Crick Institute, London, United Kingdom; School of Cancer and Pharmaceutical Sciences, King's College London, London, United Kingdom.
² Biomedical Research Centre, Guy's and St. Thomas' National Health Service Trust, London, United Kingdom.
³ State-Dependent Neural Processing Laboratory, The Francis Crick Institute, London, United Kingdom.
⁴ Experimental Histopathology, The Francis Crick Institute, London, United Kingdom.
⁵ Advanced Sequencing Facility, The Francis Crick Institute, London, United Kingdom.
⁶ Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, United Kingdom; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, United Kingdom.
⁷ School of Immunology and Microbial Sciences, King's College London, London, United Kingdom.
⁸ FASTBASE Solutions S.L, Derio, Spain.
⁹ FASTBASE Solutions S.L, Derio, Spain; Cell Biophysics Laboratory, Ikerbasque, Basque Foundation for Science, Research Centre for Experimental Marine Biology and Biotechnology & Biophysics Institute, University of the Basque Country, Leioa, Bizkaia, Spain; Centre for Therapeutic Innovation, Cell Biophysics Laboratory, Department of Pharmacy and Pharmacology & Department of Physics, University of Bath, Bath, United Kingdom.
¹⁰ Medical Oncology, University Hospitals Birmingham National Health Service Foundation Trust, Birmingham, United Kingdom.
¹¹ Drug Development Unit, Sarah Cannon Research Institute UK, London, United Kingdom.
¹² Drug Development Unit, Sarah Cannon Research Institute UK, London, United Kingdom; Department of Oncology, University College Hospital, London, United Kingdom.
¹³ School of Cancer and Pharmaceutical Sciences, King's College London, London, United Kingdom; Protein Phosphorylation Laboratory, The Francis Crick Institute, London, United Kingdom.
¹⁴ Department of Histopathology, University College London Cancer Institute, London, United Kingdom.
¹⁵ Department of Gastrointestinal Oncology, University College London Hospital National Health Service Foundation Trust, London, United Kingdom.
¹⁶ School of Immunology and Microbial Sciences, King's College London, London, United Kingdom. Electronic address: jo.spencer@kcl.ac.uk.
¹⁷ Cancer Systems Biology Laboratory, The Francis Crick Institute, London, United Kingdom; School of Cancer and Pharmaceutical Sciences, King's College London, London, United Kingdom. Electronic address: francesca.ciccarelli@crick.ac.uk.

PMID: 34197832
PMCID: PMC8527923
DOI: 10.1053/j.gastro.2021.06.064

Abstract

Background & aims: Colorectal cancer (CRC) shows variable response to immune checkpoint blockade, which can only partially be explained by high tumor mutational burden (TMB). We conducted an integrated study of the cancer tissue and associated tumor microenvironment (TME) from patients treated with pembrolizumab (KEYNOTE 177 clinical trial) or nivolumab to dissect the cellular and molecular determinants of response to anti- programmed cell death 1 (PD1) immunotherapy.

Methods: We selected multiple regions per tumor showing variable T-cell infiltration for a total of 738 regions from 29 patients, divided into discovery and validation cohorts. We performed multiregional whole-exome and RNA sequencing of the tumor cells and integrated these with T-cell receptor sequencing, high-dimensional imaging mass cytometry, detection of programmed death-ligand 1 (PDL1) interaction in situ, multiplexed immunofluorescence, and computational spatial analysis of the TME.

Results: In hypermutated CRCs, response to anti-PD1 immunotherapy was not associated with TMB but with high clonality of immunogenic mutations, clonally expanded T cells, low activation of Wnt signaling, deregulation of the interferon gamma pathway, and active immune escape mechanisms. Responsive hypermutated CRCs were also rich in cytotoxic and proliferating PD1⁺CD8 T cells interacting with PDL1⁺ antigen-presenting macrophages.

Conclusions: Our study clarified the limits of TMB as a predictor of response of CRC to anti-PD1 immunotherapy. It identified a population of antigen-presenting macrophages interacting with CD8 T cells that consistently segregate with response. We therefore concluded that anti-PD1 agents release the PD1-PDL1 interaction between CD8 T cells and macrophages to promote cytotoxic antitumor activity.

Keywords: Anti-PD1 Immunotherapy; CD8 T cells; Interferon Gamma; Tumor Mutational Burden; Wnt Signaling.

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Figures

**Figure 1**
Study design and quantification of tumor heterogeneity. (A) Description of the study cohorts. Clinical benefit from the treatment was assessed with Response Evaluation Criteria In Solid Tumors 1.1. (B) Experimental design: 24 sequential slides from formalin-fixed paraffin-embedded (FFPE) CRC blocks before treatment were used for multiregional CD3 immunohistochemistry (*slides A, B, F, H*, and J), IMC (*slide C*), mIF (*slide D*), WES (*slides E1–E5*), RNA-seq (*slides G1–G5*), TCR-seq (*slides I1–I5*), and A-FRET detection of PD1-PDL1 interaction in situ (*slides K1–K2*). Multiple regions with variable CD3 infiltration were identified in *slide A* and projected to all other slides. (C) Quantification of CD3⁺ cells/mm² from immunohistochemistry staining in 60 regions of the discovery cohort using Qupath. Values were normalized within each patient. The *gray boxes* indicate missing measures. (D) TMB of 32 sequenced regions in the discovery cohort. The *dotted line* corresponds to the TMB threshold of hypermutated CRC (12 mutations/megabase pairs). (E) Correlation between CD3⁺ cells/mm² from immunohistochemistry staining of *slide F* (discovery) and *slide E* (validation) and TMB across samples. Average CD3⁺ cell density across multiple regions per slide is reported. For the discovery cohort, TMB was calculated as the average between the 2 sequenced regions. For the validation cohort, TMB was obtained from the FM1 test. Pearson correlation coefficient R and associated P value are shown. (F) Comparison of TMB between DB- and nDB-CRCs of the discovery cohort and (G) in hypermutated CRCs from the validation cohort (Supplementary Table 1) and published studies., , , , For^, response was unavailable and the overall survival from the start of immunotherapy was used to define DB (≥12 months) and nDB (<12 months). (H) Comparison of neoantigenic index (ratio of predicted immunogenic mutations over all nonsilent mutations) and (I) clonality of immunogenic mutations in 17 regions with >30% tumor purity (Supplementary Table 2). Regions with lower purity were excluded because of unreliable mutation clonality assessment. Results hold true even when using all regions (data not shown). (J) Comparison of productive clonality of TCR beta rearrangements between DB- and nDB-CRCs with available data (Supplementary Table 2). The number of patients in each tumor group is reported in *brackets*. Distributions were compared using the 2-sided Wilcoxon’s rank sum test. The *horizontal line* in the middle of each *box* indicates the median; the *top* and *bottom borders* of the box mark the 75th and 25th percentiles, respectively, and the *vertical lines* mark points within 1.5 the inter-quartile range.

**Figure 2**
Cancer and immune aberrations across CRC groups. (A) Representative enriched pathways in differentially expressed genes between hypermutated and non-hypermutated CRCs of the discovery cohort. The false discovery rate (FDR) was calculated using Benjamini-Hochberg correction. Proportions of immune-related pathways over all enriched pathways are reported as pie chart. Normalized enrichment scores (NES) from single sample Gene Set Enrichment Analysis (ssGSEA) of 68 transcriptional targets of the Wnt pathway^, between hypermutated and non-hypermutated CRCs from (B) the discovery cohort and (C) TCGA. Representative pathways enriched in differentially expressed genes between DB- and nDB-CRCs from the (D) discovery and (E) validation cohorts. (F) Representative IMC images of CRCs with mutated and wild-type (WT) B2M protein. Scale bar = 50 μm. Comparison of normalized tumor and stroma B2M⁺ areas between DB- and nDB-CRCs of the (G) discovery and (H) validation cohorts. Number of patients in each tumor group is reported in brackets. Distributions were compared using the 2-sided Wilcoxon’s rank sum test. IFN, interferon; MHC, major histocompatibility complex. The *horizontal line* in the middle of each *box* indicates the median; the *top* and *bottom borders* of the box mark the 75th and 25th percentiles, respectively, and the *vertical lines* mark minimum and maximum of all the data.

**Figure 3**
Comparison of T cells infiltrates between CRC groups. (A) IMC analysis workflow using SIMPLI. For each region, images of the markers used (Supplementary Table 5) were preprocessed to extract pixel intensities. Masks for tumor and stroma were derived and used for the pixel analysis. Each region was segmented into single cells that were assigned to tumor or stroma, phenotypically identified through expression of representative markers, and used for single cell clustering. (B) Comparison of normalized CD3⁺ areas and (C) CD3⁺ cells between DB and nDB-CRCs in the discovery cohort. Benjamini-Hochberg false discovery rate (FDR) correction was applied for testing over 5 immune populations. (D) Comparison of normalized CD3⁺ areas and (E) CD3⁺ cells between DB- and nDB-CRCs in the validation cohort. (F) Uniform Manifold Approximation and Projection (UMAP) map of 20,890 T cells in 38 regions from 16 CRCs of the discovery cohort. Cells were grouped in 13 clusters based on the expression of 12 phenotypic markers using Seurat (Supplementary Table 8) and colored according to the mean intensities of representative markers. The *circles* indicate the 2 clusters enriched in hypermutated CRCs. (G) Proportions of cluster 1 (CD8⁺GzB⁺ cells) and cluster 2 (CD8⁺Ki67⁺ cells) over the total T cells in hypermutated and non-hypermutated CRCs. Distributions were compared using the 2-sided Wilcoxon’s rank sum test. Benjamini-Hochberg FDR correction was applied for testing over 13 clusters. (H) IMC-derived images of tumor-associated markers (E-cadherin and pan-keratin) and CD8 and GzB or CD8 and Ki67 in 2 representative samples. *Scale bar* = 100 μm. (I) Comparisons of normalized CD8⁺GzB⁺ and CD8⁺Ki67⁺ areas between hypermutated and non-hypermutated CRCs. Distributions were compared using the 2-sided Wilcoxon’s rank sum test. Benjamini-Hochberg FDR correction was applied for testing over 25 combinations of T-cell markers (Supplementary Table 6). The *horizontal line* in the middle of each *box* indicates the median; the *top* and *bottom borders* of the box mark the 75th and 25th percentiles, respectively, and the *vertical lines* mark minimum and maximum of all the data.

**Figure 4**
Difference in CD74⁺ macrophages between DB- and nDB-CRCs. (A) Uniform Manifold Approximation and Projection (UMAP) map of 16,748 macrophages in 30 regions from 13 hypermutated CRCs in the discovery cohort. Cells were grouped in 9 clusters based on the expression of 11 phenotypic markers using Seurat (Supplementary Table 8) and colored according to the mean intensities of representative markers. The *circle* indicates the cluster enriched in DB-CRCs. (B) Proportions of cluster 3 (CD68⁺CD74⁺ cells) over the total macrophages in DB- and nDB-CRCs. Distributions were compared using the 2-sided Wilcoxon’s rank sum test. Benjamini-Hochberg false discovery rate (FDR) correction was applied for testing over 9 clusters. (C) IMC-derived images of CD74, CD16, and CD163 and tumor-associated markers (E-cadherin and pan-keratin) in 2 representative samples. *Scale bar* = 100 μm. (D) Comparisons of normalized CD74⁺ area between DB- and nDB-CRCs in the discovery, (E) validation, and (F) both cohorts using the 2-sided Wilcoxon’s rank sum test. For the discovery cohort, Benjamini-Hochberg FDR correction was applied for testing over 9 combinations of macrophage markers (Supplementary Table 6). (G) CD74⁺ macrophages in the validation and (H) combined cohorts were identified by applying a threshold of 0.1 CD74 expression to all macrophages after IMC image histologic inspection. Mean marker intensities in CD74⁺ and CD74⁻ macrophages are reported and normalized across all markers and cells. (I) Comparison of normalized of CD74⁺ macrophages between DB- and nDB-CRCs in the validation and (J) combined cohorts. Distributions were compared using the 2-sided Wilcoxon’s rank sum test. The number of patients in each tumor group is reported in brackets. The *horizontal line* in the middle of each *box* indicates the median; the *top* and *bottom borders* of the box mark the 75th and 25th percentiles, respectively, and the *vertical lines* mark minimum and maximum of all the data.

**Figure 5**
Functional characterization of CD68⁺CD74⁺ cells. (A) CD68⁺CD74⁺ cells in 10 DB-CRCs from both cohorts were identified by applying a threshold of 0.35 CD74 expression to all CD68⁺ cells after IMC image histologic inspection. (B) Mean marker intensities in CD68⁺CD74⁺ and CD68⁺CD74 cells. Values were normalized across all markers and cells. Marker distributions were compared with the 2-sided Wilcoxon’s rank sum test, and Benjamini-Hochberg false discovery rate (FDR) correction was applied to account for testing over 14 markers. Fold change between the mean expression in CD68⁺CD74⁺ and CD68⁺CD74 cells is reported. DC, dendritic cells. (C) Percentage of CD68⁺CD74⁺ cells expressing selected markers associated with antigen presentation and M1 and M2 phenotypes. (D) Uniform Manifold Approximation and Projection (UMAP) maps of 2726 CD68⁺CD74⁺ cells in 17 regions from 10 DB-CRCs. Cells were grouped in 6 clusters based on the expression of 16 phenotypic markers using Seurat and colored according to the mean intensities of representative markers. The *circle* indicates a CPDL1-expressing cluster. (E) Single-cell segmentation (*upper panel*) and IMC images (*lower panels*) of selected CD68⁺CD74⁺ cell-associated markers from a representative DB-CRC. The *right bottom panel* reports the combination of all the selected markers. *Scalebar* = 100 μm.

**Figure 6**
Interaction between CD74⁺ macrophages and GzB⁺Ki67⁺ CD8 T cells. (A) CD8⁺GzB⁺ and CD8⁺Ki67⁺ T cells in the validation and (B) combined cohorts were identified by applying a threshold of 0.05 GzB and 0.15 Ki67 expression to CD8 T cells, after IMC image histologic inspection. Markers of mean intensities in CD8⁺GzB⁺ or CD8⁺Ki67⁺ and CD8⁺GzB⁻ or CD8⁺Ki67^-T cells were normalized across all markers and cells. (C) Distance distributions of CD8⁺GzB⁺ or (D) CD8⁺Ki67⁺ to the nearest CD74⁺ macrophage in the discovery, validation, and combined cohorts. Distances between cells were divided into 1.1-μm bins, and the density curves fitting the histograms were measured. Distributions of PD1⁺ or PDL1⁺ and the rest of the cells were compared using the 2-sided Wilcoxon’s rank sum test. The *dashed lines* represent medians of the distributions. (E) High-resolution mIF image of a representative CRC with a highlighted cluster of CD74⁺ macrophages (*main image*) and their interactions with CD8⁺GzB⁺ and CD8⁺Ki67⁺ T cells (*zoom-ins*). The image was scanned at original magnification ×40. *Scale bars* = 10 μm. (F) Correlation between normalized T cells and macrophages in 26 DB- and nDB-CRCs of the discovery and validation cohorts. Pearson correlation coefficient R and associated P value are shown. (G) Ratios of normalized CD74⁺ macrophages, CD8⁺GzB⁺, and CD8⁺Ki67⁺ T cells between regions with high and low T-cell infiltration. For samples with more 2 regions, the total cells in the high or low regions were normalized and used to compute the ratio. (H) Comparisons of normalized of CD74⁺ macrophages between DB- and nDB-CRCs of the combined cohorts considering only high (*left*) and low (*right*) T-cell infiltration regions. Distributions were compared using the 2-sided Wilcoxon’s rank sum test. The *horizontal line* in the middle of each *box* indicates the median; the *top* and *bottom borders* of the box mark the 75th and 25th percentiles, respectively, and the *vertical lines* mark points within 1.5 the inter-quartile range.

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References

1. Ribas A., Wolchok J.D. Cancer immunotherapy using checkpoint blockade. Science. 2018;359:1350–1355. - PMC - PubMed
1. Le D.T., Uram J.N., Wang H. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med. 2015;372:2509–2520. - PMC - PubMed
1. Van den Eynde M., Mlecnik B., Bindea G. The link between the multiverse of immune microenvironments in metastases and the survival of colorectal cancer patients. Cancer Cell. 2018;34:1012–1026.e3. - PubMed
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[1] Ribas A., Wolchok J.D. Cancer immunotherapy using checkpoint blockade. Science. 2018;359:1350–1355. - PMC - PubMed

[2] Ribas A., Wolchok J.D. Cancer immunotherapy using checkpoint blockade. Science. 2018;359:1350–1355. - PMC - PubMed

[3] Le D.T., Uram J.N., Wang H. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med. 2015;372:2509–2520. - PMC - PubMed

[4] Le D.T., Uram J.N., Wang H. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med. 2015;372:2509–2520. - PMC - PubMed

[5] Van den Eynde M., Mlecnik B., Bindea G. The link between the multiverse of immune microenvironments in metastases and the survival of colorectal cancer patients. Cancer Cell. 2018;34:1012–1026.e3. - PubMed

[6] Van den Eynde M., Mlecnik B., Bindea G. The link between the multiverse of immune microenvironments in metastases and the survival of colorectal cancer patients. Cancer Cell. 2018;34:1012–1026.e3. - PubMed

[7] Ganesh K., Stadler Z.K., Cercek A. Immunotherapy in colorectal cancer: rationale, challenges and potential. Nat Rev Gastroenterol Hepatol. 2019;16:361–375. - PMC - PubMed

[8] Ganesh K., Stadler Z.K., Cercek A. Immunotherapy in colorectal cancer: rationale, challenges and potential. Nat Rev Gastroenterol Hepatol. 2019;16:361–375. - PMC - PubMed

[9] André T., Shiu K.-K., Kim T.W. Pembrolizumab in microsatellite-instability–high advanced colorectal cancer. N Engl J Med. 2020;383:2207–2218. - PubMed

[10] André T., Shiu K.-K., Kim T.W. Pembrolizumab in microsatellite-instability–high advanced colorectal cancer. N Engl J Med. 2020;383:2207–2218. - PubMed

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Immunogenomics of Colorectal Cancer Response to Checkpoint Blockade: Analysis of the KEYNOTE 177 Trial and Validation Cohorts

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Immunogenomics of Colorectal Cancer Response to Checkpoint Blockade: Analysis of the KEYNOTE 177 Trial and Validation Cohorts

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