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Targeting PD-L1 and TIGIT could restore intratumoral CD8 T cell function in human colorectal cancer

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Abstract

Microsatellite stable colorectal cancers (MSS-CRC) are resistant to anti-PD-1/PD-L1 therapy but the combination of immune checkpoints inhibitors (ICI) could be a clue to reverse resistance. Our aim was to evaluate ex vivo the capacity of the combination of atezolizumab (anti-PD-L1) and tiragolumab (anti-TIGIT) to reactivate the immune response of tumor infiltrating lymphocytes (TILs) in MSS-CRC. We analysed CRC tumor tissue and the associated blood sample in parallel. For each patient sample, extensive immunomonitoring and cytokine production were tested. We generated an ex vivo assay to study immune reactivity following immune stimulation with checkpoint inhibitors of tumor cell suspensions. Three microsatellite instable (MSI) and 13 MSS-CRC tumors were analysed. To generalize our observations, bioinformatics analyses were performed on public data of single cell RNA sequencing of CRC TILs and RNA sequencing data of TCGA. Atezolizumab alone could only reactivate T cells from MSI tumors. Atezolizumab and tiragolumab reactivated T cells in 46% of MSS-CRC samples. Reactivation by ICK was observed in patients with higher baseline frequency of Th1 and Tc1 cells, and was also associated with higher baseline T cell polyfunctionality and higher CD96 expression. We showed that a high frequency of CD96 expression on T cells could be a surrogate marker of atezolizumab and tiragolumab efficacy. Together these data suggest that the association of atezolizumab and tiragolumab could restore function of CD4 and CD8 TILs in MSS-CRC and could be tested in a clinical trial in colorectal cancer patients with MSS status.

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Abbreviations

CRC:

ColoRectal Cancer

CTLA-4:

Cytotoxic T-Lymphocyte-Associated protein 4

CTV:

Cell Trace Violet

FBS:

Foetal Bovine Serum

ICK:

Immune ChecKpoint

ICI:

Immune Checkpoint Inhibitors

IFNγ:

InterFeroN gamma

IL-2:

InterLeukin-2

LAG3:

Lymphocyte-Activation Gene 3

mAbs:

Monoclonal antibodies

mCRC:

Metastatic ColoRectal Cancer

MFI:

Median Fluorescence Intensity

MSS:

MicroSatellite Stable

MSI:

MicroSatellite Instable

NK:

Natural Killer

NSCLC:

Non-Small Cell Lung Cancer

PBMC:

Peripheral Blood Mononuclear Cells

PBS:

Phosphate-Buffered Saline

PD-1:

Programmed cell Death protein 1

PD-L1:

Programmed cell Death protein Ligand 1

TIGIT:

T cell Immunoreceptor with Ig and ITIM domains

Th1:

T helper 1

TILs:

Tumor Infiltrating Lymphocytes

TNFα:

Tumor Necrosis Factor alpha

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Acknowledgements

This study was carried out in partnership with the Roche Institute, which supplied us with atezolizumab and tiragolumab. We would like to thank Dr. Laurent Arnould and his anatomopathology laboratory for their help in the recovery of the tumor samples. Flow cytometry analyses were performed on Beckman Coulter Cytoflex. Thanks to Olivier Jaen for his help in setting up the immunomonitoring panels.

Funding

This research was supported and granted by Genentech, Inc.

Author information

Authors and Affiliations

  1. Platform of Transfer in Biological Oncology, Georges François Leclerc Cancer Center-UNICANCER, 1 rue du Professeur Marion, 21000, Dijon, France

    Marion Thibaudin, Emeric Limagne, Léa Hampe, Elise Ballot, Caroline Truntzer & Francois Ghiringhelli

  2. UMR INSERM 1231, 7 Boulevard Jeanne d’Arc, 21000, Dijon, France

    Marion Thibaudin, Emeric Limagne, Léa Hampe, Elise Ballot, Caroline Truntzer & Francois Ghiringhelli

  3. Genomic and Immunotherapy Medical Institute, Dijon University Hospital, 14 rue Paul Gaffarel, 21000, Dijon, France

    Marion Thibaudin, Emeric Limagne, Léa Hampe, Elise Ballot, Caroline Truntzer & Francois Ghiringhelli

  4. University of Burgundy-Franche Comté, Maison de l’université Esplanade Erasme, 21000, Dijon, France

    Marion Thibaudin, Emeric Limagne, Léa Hampe, Elise Ballot, Caroline Truntzer & Francois Ghiringhelli

  5. Department of Medical Oncology, Georges François Leclerc Cancer Center-UNICANCER, 1 rue du Professeur Marion, Dijon, 21000, Dijon, France

    Francois Ghiringhelli

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  1. Marion Thibaudin
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Contributions

MT participated in the design of the study, the establishment of patient collection and draft the manuscript. LH performed patient collection, carried out the ex vivo and flow cytometry analysis and participated in analyse obtained data. EB and CT carried out all bioinformatics analysis. EL participated in the design of the study and helped to draft the manuscript. FG conceived of the study, and participated in its design and coordination and draft the manuscript. All authors read and approved the final manuscript.

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Correspondence to Francois Ghiringhelli.

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Supplementary Information

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Supplementary file1 (XLSX 11 kb)

262_2022_3182_MOESM2_ESM.pptx

Supplementary file2Supplementary figure 1: Gating strategy for “Lymphoid cell analysis” panel. The gating strategy was done on a tumour cell suspension sample and is performed in the same way on blood samples. The expression of CD226, TIGIT and PD1 was studied in the same way as on CD8 T cells on NK cells. Expression of naive/memory subpopulations dependent on CD45RA and CCR7 expression are studied in the same way as CD8 T cells on CD4 T cells, as well as the expression of CD226, TIGIT and PD1. The expression of CD96 is positive for all CD4 and CD8 T cells, so we analyzed the median intensity fluorenscence of this marker on our populations of interest. Supplementary figure 2: Gating strategy for “Myeloid cell analysis” panel. The gating strategy was done on a blood sample and is performed in the same way on tumour samples. The expression of PD-L1, CD111, CD112 and CD155 markers was studied in each of the following populations: granulocytes (CD15+), macrophages (CD14+ CD163+), monocytic MDSCs (CD14+ CD15- CD163- HLA-DRlow) and granulocytic MDSCs (CD14+ CD15+). Supplementary figure 3: Gating strategy for “Lymphocyte function analysis” panel. The gating strategy is developed on the CD3+ CD45- blood cell population. The same gating strategy was applied to the CD3+ CD45+ population corresponding to TILs. TILs was pre-labeled with an anti-CD45 antibody so that blood and TILs could be analyzed in the same tube. For the study of cytokine expression, we first analysed the expression of TNFα and IFNγ on conventional CD4 T cells (T CD4conv), regulatory CD4 T cells (Treg), conventional CD8 T cells (T CD8conv) and regulatory CD8 T cells (T CD8reg) and then the expression of Granzyme B and IL-2 on each subpopulation. Secondly, we analysed within the CD4conv T cells each of the following subpopulations: Th1 (CD4+ Foxp3- IFNγ+ IL-17A-), Th17 (CD4+ Foxp3- IFNγ- IL-17A+), Th1/17 (CD4+ Foxp3- IFNγ+ IL-17A+) and Th2 (CD4+ Foxp3- IFNγ- IL-17A- IL-4+) cells. We performed the same analysis within CD8conv T cells: Tc1, Tc17, Tc1/17 and Tc2 cells. For each of these populations, we studied the expression of the cytokines TNFα and IL-2 as shown for Th1 in this gating strategy. Supplementary figure 4: Gating strategy for “Ex vivo restimulation analysis”. Supplementary figure 5: Double blockade of PD-L1 and TIGIT restore tumor infiltration lymphocytes function in some cancer patients with MSS CRC. Tumor cell suspensions from 16 patients with cancer colorectal cancers were thawed and after removing dead cells were labelled with 1µM of CellTrace Violet. Then, the cells were plated at 5.105 cells per well and stimulated with anti-CD3 antibody (10 ng/mL) in the presence or absence of 10 µg/mL of anti-PD-L1 (clone 6E11) and/or anti-TIGIT (clone 10A7) during 5 days. Cells were also stimulated with 5 µg/mL of anti-CD3 and anti-CD28 as positive control experiments. 20 hours before the end of the culture, Brefeldin A was added to block transport processes during cell activation and allow the staining of intracellular cytokines. After 5 days of culture, to study the effect of immune checkpoints blockade antibodies on the proliferation and cytotoxic capacities of TILs, tumor cell suspensions were stained with anti-CD3, anti-CD4, anti-CD8, anti-IFNγ and anti-TNFα and analysed by flow cytometry. A. The percentage of TNFα expression in CD4 (left) and CD8 (right) is studied for the following 3 conditions: anti-CD3, anti-CD3 plus anti-PD-L1 and anti-CD3 plus anti-PD-L1 + anti-TIGIT. Patients classified as responders to double immunotherapy are shown in colour. B. The percentage of IFNγ expression in CD4 (left) and CD8 (right) comparing the anti-CD3 plus anti-TIGIT condition to anti-CD3 alone is depicted. Supplementary figure 6: Description of the cell composition in the blood and tumour of patients. Fresh tumor tissues and associated blood samples for each patient (n=16) were stained and then analysed by flow cytometry. A. Box plots showing the frequency of CD45+ and CD45- populations in patients’ blood and tumor. B. Box plots showing the frequency of CD3+, CD4+ and CD8+ T cells, NK and NKT cells, CD11b+; CD14+, CD15+ cells and gMDSC population in patients’ blood and tumor. Supplementary figure 7: Association between baseline lymphoid cell number and response to comboimmunotherapy. Fresh tumor tissues and associated blood samples for each patient were taken on the day of surgery. After tumor dissociation, 1.106 tumor cell suspension were stained with anti-CD45, resuspended in 50 µL of whole blood sample and then transferred to a tube containing PMA, Ionomycin and Brefeldin A for 3 hours. After activation, cells were fixed, permeabilized and stained with anti-IFNγ, anti-TNFα, anti-GranzymeB, anti-IL-4, anti-Foxp3, anti-IL-17A, anti-CD3, anti-CD4, anti-CD8 and anti-IL-2 antibodies and analysed by flow cytometry. A-B. Box plots showing the different combinations of expression of the 4 cytokines IFNγ, TNFα, Granzyme B (GrB) and IL-2 in CD4 (CD45- CD3+ CD4+) (A) and CD8 (CD45- CD3+ CD8+) (B) T cells in blood patients’ depending on the responder (R) versus non responder (NR) status. C. The frequency of Granzyme B (GrzB) expression according to responder (R) or non-responder status is depicted in tumor samples. Statistical difference is determined by a Mann–Whitney test. ns = no significant, * = p< 0.05 and ** = p< 0.01. Supplementary figure 8: Association between baseline nectin expression and response to comboimmunotherapy. Fresh tumor tissues and associated blood samples for each patient were taken on the day of surgery. After tumor dissociation, 1.106 tumor cell suspension and 100 µL of whole blood sample were stained with anti-DNAM-1 (CD226), anti-CD56, anti-CD96, anti-CD45, anti-TIGIT, anti-PD-1, anti-CD3, anti-CD45RA, anti-CD8, anti-CCR7, anti-Tim-3 and anti-CD4 antibodies and with a mortality marker DRAQ7 and then analysed by flow cytometry. A-B. Box plots showing the frequency of CD226+, CD226-, TIGIT+ and TIGIT- populations in the blood (A) and tumor (B) samples by comparing expression on CD4, CD8 and NK populations. C-D. Box plots showing the different combinations of expression of the 2 nectins CD226 and TIGIT within CD4 T cells and CD8 T cells in patient blood (C) and tumor (D) according to responder (R) or non responder (NR) status. Statistical difference is determined by a Mann–Whitney test. ns = no significant and * = p< 0.05, ** = p<0.01, *** = p<0.001 and **** = p<0.0001. Supplementary figure 9: Association between baseline nectin expression and response to comboimmunotherapy. Box plots showing the median fluorescence intensity (MFI) of CD96 marker in CD4 T cells and CD8 T cells in patient blood depending on the responder (R) or non responder (NR) status. Statistical difference is determined by a Mann–Whitney test. ns = no significant and * = p< 0.05. Supplementary figure 10: Association between baseline myeloid cell number and response to comboimmunotherapy. Fresh tumor tissues and associated blood samples for each patient were taken on the day of surgery. After tumor dissociation, 1.106 tumor cell suspension and 100 µL of whole blood sample were stained with anti-CD11b, anti-HLA-DR, anti-CD111, anti-CD155, anti-CD112, anti-PD-L1, anti-CD163, anti-CD15, anti-CD14, anti-CD3, anti-CD19, anti-CD20, anti-CD56, anti-Galectin-9 and anti-CD45 antibodies and with a mortality marker DRAQ7 and then analysed by flow cytometry. The frequency of CD111, CD112 and CD155 positive populations in macrophages (CD14+ CD163+), mMDSC (CD14+ CD163- HLA-DRlow), gMDSC (CD15+ CD14+) and granulocytes (CD15+) in tumor samples according to responder (R) or non-responder (NR) status is depicted. Statistical difference is determined by a Mann–Whitney test. ns = no significant and * = p< 0.05. Supplementary figure 11: CD96 expression in lymphoid cells characterized a particular CRC subtype. A-B. Kaplan-Meier curves for overall survival with patients stratified according to the level of CD96 RNAseq expression, respectively for patients with high (A) or low (B) level of the metagene. (PPTX 3848 kb)

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Thibaudin, M., Limagne, E., Hampe, L. et al. Targeting PD-L1 and TIGIT could restore intratumoral CD8 T cell function in human colorectal cancer. Cancer Immunol Immunother 71, 2549–2563 (2022). https://doi.org/10.1007/s00262-022-03182-9

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