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Selective multi-kinase inhibition sensitizes mesenchymal pancreatic cancer to immune checkpoint blockade by remodeling the tumor microenvironment

Nature Cancer volume 3, pages 318–336 (2022)Cite this article

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Abstract

KRAS-mutant pancreatic ductal adenocarcinoma (PDAC) is highly immunosuppressive and resistant to targeted and immunotherapies. Among the different PDAC subtypes, basal-like mesenchymal PDAC, which is driven by allelic imbalance, increased gene dosage and subsequent high expression levels of oncogenic KRAS, shows the most aggressive phenotype and strongest therapy resistance. In the present study, we performed a systematic high-throughput combination drug screen and identified a synergistic interaction between the MEK inhibitor trametinib and the multi-kinase inhibitor nintedanib, which targets KRAS-directed oncogenic signaling in mesenchymal PDAC. This combination treatment induces cell-cycle arrest and cell death, and initiates a context-dependent remodeling of the immunosuppressive cancer cell secretome. Using a combination of single-cell RNA-sequencing, CRISPR screens and immunophenotyping, we show that this combination therapy promotes intratumor infiltration of cytotoxic and effector T cells, which sensitizes mesenchymal PDAC to PD-L1 immune checkpoint inhibition. Overall, our results open new avenues to target this aggressive and therapy-refractory mesenchymal PDAC subtype.

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Fig. 1: Resistance to MEK inhibition in vitro and in vivo.
Fig. 2: Systematic combination drug screens identify new therapies for nonglandular mesenchymal PDAC.
Fig. 3: Genetic screens uncover nintedanib targets that sensitize mesenchymal PDAC toward trametinib.
Fig. 4: The combination treatment prolongs survival and reprograms the TME in vivo.
Fig. 5: The combination treatment enhances tumor immune infiltration specifically in the mesenchymal subtype.
Fig. 6: The T/N combination sensitizes mesenchymal PDAC toward anti-PD-L1 ICB.
Fig. 7: ScRNA-seq analysis reveals context-specific responses of tumor cells and their microenvironment on combination drug treatment.
Fig. 8: The combination therapy induces a T-cell-mediated anti-tumor immune response in mesenchymal PDAC.

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Data availability

The RNA-seq dataset has been deposited in the EBIArrayExpress repository with accession no. E-MTAB-11187. The MS kinobead pulldown and the MS secretomics data have been deposited in the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository with accession nos. PXD023267 and PXD027877, respectively. The scRNA-seq data have been deposited in the EBIArrayExpress repository with accession no. E-MTAB-9954. The human pancreatic cancer data were derived from previous studies and are available in the supplementary information of the respective publications6,7,8. All other data have been provided as supplementary tables or source data files. Mice and cell lines are available from the corresponding author on reasonable request. Key resources are listed in Supplementary Table 8. Source data are provided with this paper.

Code availability

Analyses were performed using open-source software, and in-house scripts in R v.3.6.2 and Python v.3.8.3, which are available from the corresponding author on reasonable request. Codes are provided via the GitHub repository at the following link: https://github.com/stefanie-baerthel/combinatorial_treatment_analysis.

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Acknowledgements

We thank H. Nakhei, T. Jacks, D. Tuveson, M. Baccarini, R. Schmid and A. Bradley for providing transgenic animals, and the TUM animal facility and imaging core facility of the Department of Nuclear Medicine, Klinikum rechts der Isar, for excellent technical support. The present study was supported by the German Cancer Consortium (DKTK), Deutsche Forschungsgemeinschaft (DFG SA 1374/4-2, SFB 824 C09 to G.S. and D.S., SFB 1321 Project-ID 329628492 P06 to D.S. and M.S.S, P11 to M.S.R. and D.S., and S01 to D.S., M.R., R.R. and G.S., and SFB 1371 Project-ID 395357507 P12 to D.S.), the Wilhelm Sander-Stiftung (2020.174.1), and the European Research Council (ERC CoG No. 648521, to D.S.).

Author information

Author notes

  1. These authors contributed equally: Chiara Falcomatà, Stefanie Bärthel.

Authors and Affiliations

  1. Division of Translational Cancer Research, German Cancer Research Center and German Cancer Consortium, Heidelberg, Germany

    Chiara Falcomatà, Stefanie Bärthel, Jeannine Heetmeyer, Bianca Teodorescu, Oliver Reichert, Constantin Schmitt, Kathrin Grabichler, Andrea Coluccio, Fabio Boniolo, Christian Veltkamp, Magdalena Zukowska, Angelica Arenas Vargas, Woo Hyun Paik, Moritz Jesinghaus & Dieter Saur

  2. Chair of Translational Cancer Research and Institute of Experimental Cancer Therapy, Klinikum rechts der Isar, School of Medicine, Technische Universität München, Munich, Germany

    Chiara Falcomatà, Stefanie Bärthel, Jeannine Heetmeyer, Bianca Teodorescu, Oliver Reichert, Constantin Schmitt, Kathrin Grabichler, Andrea Coluccio, Fabio Boniolo, Christian Veltkamp, Magdalena Zukowska, Angelica Arenas Vargas, Woo Hyun Paik, Moritz Jesinghaus & Dieter Saur

  3. Center for Translational Cancer Research (TranslaTUM), School of Medicine, Technical University of Munich, Munich, Germany

    Chiara Falcomatà, Stefanie Bärthel, Sebastian A. Widholz, Juan José Montero, Thorsten Kaltenbacher, Jeannine Heetmeyer, Bianca Teodorescu, Oliver Reichert, Constantin Schmitt, Kathrin Grabichler, Andrea Coluccio, Fabio Boniolo, Christian Veltkamp, Magdalena Zukowska, Angelica Arenas Vargas, Woo Hyun Paik, Moritz Jesinghaus, Roman Maresch, Rupert Öllinger, Tim Ammon, Olga Baranov, Roland Rad, Marc Schmidt-Supprian & Dieter Saur

  4. Institute of Molecular Oncology and Functional Genomics, School of Medicine, Technische Universität München, Munich, Germany

    Sebastian A. Widholz, Juan José Montero, Thorsten Kaltenbacher, Roman Maresch, Rupert Öllinger, Olga Baranov & Roland Rad

  5. German Cancer Consortium, Heidelberg, Germany

    Sebastian A. Widholz, Christian Schneeweis, Juan José Montero, Thorsten Kaltenbacher, Roman Maresch, Rupert Öllinger, Tim Ammon, Olga Baranov, Bernhard Kuster, Maximilian Reichert, Roland Rad, Marc Schmidt-Supprian & Günter Schneider

  6. Department of Internal Medicine II, Klinikum rechts der Isar, Technische Universität München, Munich, Germany

    Christian Schneeweis, Andrea Coluccio, Maximilian Reichert, Roland Rad, Günter Schneider & Dieter Saur

  7. Institute for Medical Microbiology, Immunology and Hygiene, Technische Universität München, Munich, Germany

    Albulena Toska, Jonas Mir & Michael Flossdorf

  8. Experimental Systems Immunology Laboratory, Max Planck Institute of Biochemistry, Martinsried, Germany

    Jonathan J. Swietlik, Jing-Yuan Cheng & Felix Meissner

  9. Institute of Pathology, Technische Universität München, Munich, Germany

    Moritz Jesinghaus & Katja Steiger

  10. Institute of Pathology, University Hospital Marburg, Marburg, Germany

    Moritz Jesinghaus

  11. Institute of Experimental Hematology, School of Medicine, Technical University of Munich, Munich, Germany

    Tim Ammon & Marc Schmidt-Supprian

  12. Institute of Medical Psychology, Faculty of Medicine, LMU Munich, Munich, Germany

    Maria S. Robles

  13. Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany

    Julia Rechenberger & Bernhard Kuster

  14. Institute of Innate Immunity, Department of Systems Immunology and Proteomics, Medical Faculty, University of Bonn, Bonn, Germany

    Felix Meissner

  15. Center for Protein Assemblies, Technische Universität München, Garching, Germany

    Maximilian Reichert

  16. University Medical Center Göttingen, Department of General, Visceral and Pediatric Surgery, Göttingen, Germany

    Günter Schneider

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  1. Chiara Falcomatà
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C.F., S.B., S.A.W., C.S., J.J.M., A.T., J.M., T.K., J.H., J.J.S., B.T., O.R., C.S., K.G., A.C., C.V., M.Z., A.A.V., W.H.P., R.M., R.Ö., T.A. and J.R. performed the research. M.S.R., B.K., K.S., F.M., M.R., M.F., R.R., M.S.S. and D.S. contributed new reagents and analytical tools. C.F., S.B., S.A.W., C.S., J.M., J.J.S., J.C., A.C., F.B., M.J., K.S., O.B., J.R, F.M., M.F., R.R., M.S.S., G.S. and D.S. analyzed the data. C.F. and D.S. wrote the paper.

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Correspondence to Dieter Saur.

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The authors declare that they have no competing interests. Correspondence and requests for materials should be addressed to D.S.

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Nature Cancer thanks Michael Hemann and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Assessment of differential pharmacologic and genetic dependencies and signaling pathway activities in PDAC subtypes.

a, Clonogenic assay of two hPDAC cell lines (top) and two mPDAC cell cultures (bottom) treated with the MEK inhibitor trametinib. The shown cell lines represent the drug-response of the epithelial and mesenchymal subtypes to trametinib treatment. b, c, β-score distribution of CRISPR/Cas9 genome wide negative-selection (viability) screens performed in one classical (6075, panel (b)) and one mesenchymal (9091, panel (c)) mPDAC cell line. Highlighted in yellow, for the classical line, and blue, for the mesenchymal line, are the β-scores of KRAS and the core genes involved in direct KRAS downstream signaling. d, CRISPR/Cas9 dependency scores of KRAS and core genes involved in direct KRAS downstream signaling. The dependency scores of all hPDAC cell lines were obtained from the DepMap database and are shown in grey. Dependency scores corresponding to classical and mesenchymal cell lines included in the T/N drug screen are represented in the yellow and blue violin plots. Data were obtained from the CRISPR dataset and analyzed through the DepMap release DepMap 21Q2 Public (https://depmap.org/portal/download/). e, f, Mesenchymal (9091) and classical (8661) PDAC cell cultures were used to generate site-specific phospho-array datasets (Phospho Explorer antibody microarray, Full Moon Biosystems). Phospho-array data (supplementary table 1) were used to test for the enrichment of differentially phosphorylated sites between classical and mesenchymal mPDAC cell lines. Functionally grouped networks with reactome terms as nodes, showing pathways overrepresented in classical (e) and mesenchymal (f) cells are represented with the ClueGO plugin of Cytoscape. Only the pathways with an adjusted p value (calculated by two-sided hypergeometric test, Bonferroni corrected) ≤ 0.05 are depicted. The node size represents the term enrichment significance.

Source data

Extended Data Fig. 2 Genetic depletion of Mek1/2 in established PDAC.

a, Genetic strategy to delete Mek1 and Mek2 by 4-hydroxytamoxifen (4OHT)-mediated CreERT2 activation. Pdx1-Flp;FSF-KrasG12D/+;FSF-R26CAG-CreERT2/+ mice were crossed with mice harboring loxP-flanked Mek1 and Mek2 alleles. This allowed MEK1/2 deletion in established PDAC by tamoxifen administration in vitro and in vivo after orthotopic transplantation. b, Genotyping PCR of PDAC cells with indicated genotypes to analyze recombination of the floxed Mek1 allele. Non-recombined mutant, recombined mutant and wild-type PCR products are indicated on the right side. Representative gel of three independent experiments. c, Western blot analysis of MEK1 and MEK2 expression in primary PDAC cell cultures with indicated genotypes after 4 days of tamoxifen (4-OHT) and vehicle (ethanol, EtOH) treatment. HSP90 served as loading control. Representative gel of three independent experiments. d, Clonogenic assays of mPDAC cells with indicated genotypes. Control cells treated with vehicle (ethanol; EtOH) are shown in the upper row, 4-OHT treated cells in the lower row. e, Schematic representation of the experimental set-up to test the effect of Mek1/2 knockout in vivo by tamoxifen administration using syngeneic immunocompetent PDAC models. mPDAC cells with conditional Mek1lox/lox;Mek2lox/lox alleles were used for the orthotopic transplantation experiments. f, Waterfall plot showing tumor response of vehicle and tamoxifen treated animals after one week of treatment (fold-change compared to baseline before treatment based on MRI-volumetric measurements, y-axis). Each bar represents one mouse. P values calculated with two-tailed unpaired t test. g, Kaplan-Meier survival curve of vehicle and tamoxifen treated PDAC models. Number of mice is indicated in the corresponding panels. P value was calculated with log-rank (Mantel-Cox) test. h, Representative images of HE and IHC for MEK1, MEK2 and KI67 of tissue sections of tumors from orthotopically transplanted Mek1lox/lox;Mek2lox/lox models treated with vehicle or tamoxifen. Representative pictures of three independent experiments. Scale bars, 100 µm.

Source data

Extended Data Fig. 3 Pharmacologic and genetic modulation of drug sensitivity in classical and mesenchymal PDAC cell cultures.

a, b, Clonogenic assays of a representative human (left) and mouse (right) PDAC cell culture showing antagonism to the trametinib/nintedanib (T/N) combination. Cell cultures were treated with indicated concentrations of T/N. c, Western blot of phospho-ERK and ERK in T/N (10 nM trametinib + 2 µM nintedanib) and vehicle treated classical and mesenchymal primary mPDAC cell lines. HSP90 served as loading control. Classical cell lines are marked in yellow, mesenchymal in blue. Representative gels of three independent experiments. d, Clonogenic assays using increased drug concentrations of the T/N combination of three of the most antagonistic cell lines, as depicted in Fig. 2, panel (g). e, Doxycycline-induced overexpression of KRASG12D in mouse PDAC cells. 2259 mPDAC cells representative of the classical subtype was transduced with lentivirus carrying doxycycline-inducible KRASG12D or GFP-control expression constructs. KRASG12D or GFP expression were induced by doxycycline (100 ng/ml) for one or 14 days. f, Western blot of phospho-ERK and total ERK in cells overexpressing KRASG12D or GFP for one day. HSP90 served as loading control. g, Expression of the marker gene Cdh1 for epithelial cell differentiation was evaluated by qRT-PCR (normalized to Cyclophilin B). Data are shown as mean ± SD; n = 3 biological replicates. P value was calculated with two-tailed unpaired t test. h, Representative picture of three independent experiments of the morphological changes of PDAC cells upon KRASG12D induction for one or 14 days of doxycycline treatment. Scale bars, 200 µm. i, Representative clonogenic assays of mPDAC cells treated with the indicated concentrations of trametinib and nintedanib upon KRASG12D (right panel) or GFP (left panel) overexpression. j, Bliss synergy scores for the mPDAC cell line treated with the combination of trametinib and nintedanib upon KRASG12D or GFP overexpression.

Source data

Extended Data Fig. 4 Kinobead-based proteomic identification of the trametinib and nintedanib targets and treatment-induced changes in the phosphoproteome of classical and mesenchymal PDAC.

a, b, Representative pictures of the target space of trametinib (a) and nintedanib (b) for 2259 PDAC cells. A phylogenetic tree of all kinases for the 2259 primary mouse PDAC cell culture is shown. The indicated circle sizes indicate potency (apparent dissociation constants (Kdapp)), the color code specifies protein-drug interaction with the designated or other targets. Arrows highlight the identified targets. c, Radar plot showing the overlay of the pKd (−log10Kd) for the targets of nintedanib in the 6 PDAC cell cultures tested. PDAC cells of the classical (n = 4) and mesenchymal (n = 2) subtype are indicated with the color code. d, Heatmap showing the differentially expressed genes between epithelial and mesenchymal cell cultures identified as targets of nintedanib. The color code indicates the Z score. e, Volcano plots representing the change in gene expression of the nintedanib targets (in blue) upon trametinib treatment. The x-axis log2 fold change (treated/control), the y-axis shows the per test adjusted p values, which were calculated by differential expression test (using the DESeq2 package). A gene was considered to be differentially expressed with a Benjamini-Hochberg adjusted p-value of 0.05 and an absolute fold change >1. f, g, Mesenchymal (9091) and classical (8661) PDAC cell cultures were used to generate site-specific phospho-array datasets (Phospho Explorer antibody microarray, Full Moon Biosystems). The cell lines were analyzed at basal condition and in presence of T/N (trametinib 10 nM + nintedanib 2 μM). Phospho-array data (Supplementary Table 3) were used to test for the decrease of differentially phosphorylated sites between T/N and vehicle (DMSO) treated classical and mesenchymal mPDAC cells. Functionally grouped maps, obtained with the ClueGO plugin of Cytoscape, representing pathways overrepresented in mesenchymal (f) and classical (g) mPDAC upon T/N treatment are shown. Only the pathways with an adjusted p value (calculated by two-sided hypergeometric test, Bonferroni corrected) ≤ 0.05 are represented. The node size represents the term enrichment significance.

Extended Data Fig. 5 Pharmacologic assessment of nintedanib targets.

a, Combinatorial drug screen on mesenchymal hPDAC cell line MiaPaca2 and mPDAC cell line 9091, as shown in Fig. 2, panel (b). The MEK inhibitor trametinib was used in fixed concentration and combined with 418 additional drugs under preclinical and clinical investigation. Highlighted in orange are the drugs in the high-throughput drug screen showing overlapping targets with nintedanib. b, Venn diagrams showing the target overlap between the drugs identified in (a) and nintedanib (see Supplementary Table 4) as reported from the ProteomicsDB database (https://www.proteomicsdb.org). c, Venn diagrams showing the target overlap between nintedanib and additional drugs with an overlapping target profile chosen for further target assessment. The overlapping targets are listed below each figure. The target information was downloaded from the ProteomicsDB database (https://www.proteomicsdb.org). d, Representative clonogenic assays of mesenchymal mPDAC cell cultures treated with trametinib in combination with the drugs shown in (c) as compared to nintedanib. The cell lines were treated with the indicated concentrations of trametinib and the indicated experimental drug. e, Bliss synergy scores of clonogenic assays shown in (d) integrated with cell morphology for the treated mPDAC cell cultures (classical subtype depicted in yellow and mesenchymal in blue).

Source data

Extended Data Fig. 6 Genetic screens to identify relevant nintedanib targets sensitizing mesenchymal PDAC towards trametinib.

a, Western blot of Cas9 expression in the clonal cell lines used for CRISPR/Cas9 screens. β-Actin served as loading control. b, Editing efficiency at the Hprt locus. c, Relative viability upon 6-Thioguanine treatment to validate Cas9 function in Hprt proficient and deficient Cas9 cells (mean ± SD; n = 3 biological replicates). d, Relative cell growth (y-axis), assessed by cell counting, in the presence of different concentrations of trametinib (mean ± SD; n = 3 biological replicates). The pink line indicates the trametinib concentration used for the CRISPR/Cas9 screens. e, Phospho-ERK, ERK and Cas9 Western blots of clones used for CRISPR/Cas9 screens. Cells were treated with DMSO or trametinib (1.25 nM, 2.5 nM, 5 nM, 10 nM and 20 nM) for 4 days. HSP90 served as loading control. f, Focused CRISPR/Cas9-based genetic screening in mesenchymal mPDAC cells 8248 and 8570. Trametinib sensitivity (x-axis) represents β-scores calculated as sgRNA representation difference between trametinib-treated cells and their initial representation. Differential sensitivity (y-axis) indicates β-score differences between trametinib- and DMSO-treated arms. In red, genes presenting differential sensitivity ≤ −0.25. g, Network visualization of normalized gene expression (assessed by RNA-seq) of nintedanib targets shown in Fig. 3 (d). h, Lentiviral CRISPR/Cas9-mediated deletion of selected top-scoring nintedanib targets in 8248 and 8570 cells. Knock-out cells were treated with trametinib (5 nM) or DMSO and viability was assessed through clonogenic assays. i, Quantification of panel (h). Data are normalized to DMSO-treated non-targeting controls (mean ± SD; n = 3 biological replicates). The dashed line represents the mean of trametinib-treated non-targeting controls. j, Editing efficiency of each sgRNA used in Fig. 2 (f and g) and in panels (h) and (i) of this figure. k, Combinatorial deletion of nintedanib targets via ribonucleoprotein (RNP) electroporation. Mesenchymal 8248 and 8570 knock-out cells were treated with trametinib (5 nM) or DMSO and viability was assessed through clonogenic assays. l, Quantification of panel (k). Data are normalized to DMSO-treated non-targeting controls (mean ± SD; n = 3 biological replicates). Dashed line represents the mean of trametinib-treated non-targeting controls. The shown gels are representative of three independent experiments.

Source data

Extended Data Fig. 7 Characterization of context-specific changes of the tumor vasculature and the adaptive immune system in classical and mesenchymal PDAC subtypes upon therapy.

a, Orthotopically transplanted tumors of the indicated subtypes were treated with vehicle (control) and the T/N combination. Representative images of immunofluorescence stainings of tissue sections for P-selectin (upper panel) and α-SMA (lower panel) (magenta). CD31 was used to detect endothelial cells (green). DAPI was used for nuclear staining (blue). Scale bars, 25 µm. b, c, Quantification of the P-selectin+ vessels (b) and α-SMA + vessels (c) of the immunofluorescence stainings depicted in (a). Individual tumors are shown as single dots in the graph (classical: control n = 3, T/N n = 4; mesenchymal: control n = 4, T/N n = 5). d, Orthotopically transplanted tumors of the indicated subtypes were treated with vehicle (control) and the indicated drugs and drug combinations, explanted, single cell suspended and analyzed by flow cytometry. Panel (d) shows the staining for CD45 + cells. Individual tumors are shown as single points in the graph. e, f, Graphs representing the percentage of CD4 + (e) and CD8 + (f) cells in the PDAC control cohort and in the different treatment conditions as analyzed by flow cytometry. Single points represent individual tumors. g, Left, scheme of the in vivo experimental strategy using orthotopic PDAC cell transplantations into T cell deficient CD3ε knockout (KO) mice. Right, representative FACS plot of immunodeficient CD3ε-KO and wild-type C57BL/6 mice, highlighting the lack of T cells in the CD3ε-KO animals. h, Representative MRI picture of vehicle (control) and T/N treated PDAC bearing CD3ε-KO mice before (week 2) and after 1 week treatment (week 3). P values in (b), (c), (d) and (f) were calculated with two-tailed unpaired t test. T: trametinib, N: nintedanib, T/N: trametinib+nintedanib.

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Extended Data Fig. 8 Characterization of context-specific changes of the innate immune system in classical and mesenchymal PDAC subtypes upon therapy.

a, Pie charts representative of the fraction of innate immune cell populations in PDAC from vehicle control and mice that received the combination (T/N) for both classical and mesenchymal orthotopically transplanted tumors as analyzed by flow cytometry. The number of tumors per condition analyzed is depicted in the corresponding panel. b, Graphs representing the percentage of Ly6G- CD11b + F4/80+ macrophages in PDAC of the control cohort and in the treatment conditions as analyzed by flow cytometry. Single points represent individual tumors. c, Representative immunofluorescence staining for CD80/CD68 and ARG1/CD68 cells in both classical and mesenchymal tumors treated with the T/N combination therapy or vehicle as control. Scale bars, 50 µm. d, Quantification of the M1-like polarization macrophage markers CD80/CD68 + and INOS/CD68 + from the immunofluorescence stainings depicted in (c). Individual tumors are shown as single points in the graph (classical: control n = 5, T/N n = 5; mesenchymal: control n = 4, T/N n = 5). e, Quantification of the M2-like polarization macrophage markers ARG1/CD68 + and MRC1/CD68 + from the staining depicted in (c). Individual tumors are shown as single points in the graph (classical: control n = 5, T/N n = 5; mesenchymal: control n = 4, T/N n = 5). f, Graphs representing the percentage of Ly6G + CD11b + neutrophils in the control cohort and in the treatment conditions as analyzed by flow cytometry. Single points represent individual tumors. P values in (d) and (e) were calculated with two-tailed unpaired t test. T: trametinib, N: nintedanib, T/N: trametinib+nintedanib.

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Extended Data Fig. 9 scRNA-seq reveals treatment-induced changes in TME cell subpopulations and activation of the DNA damage pathway in cancer cells.

a, Dotplot displaying marker gene expression across each identified cluster of cancer cells and corresponding tumor microenvironment for both classical and mesenchymal tumors. The clusters are indicated on the y axis and the main markers for each identified population are indicated on the x axis. b, Left, UMAP plot showing all identified cell populations within the scRNA-seq experiment. Middle, UMAP plot showing classical (yellow) and mesenchymal (blue) tumors from all treatment and vehicle groups. Right, UMAP plot showing the treatment-induced changes in cell type composition among the identified cell populations across subtypes. Lower part, UMAP density plots showing distribution of annotated clusters upon treatment, cell numbers for each condition are integrated below. c, UMAP plot showing the identified tumor cell clusters. The expression of Cdh1 and Krt18, epithelial markers, and of Cdh2 and Vim, mesenchymal markers, across treatment conditions are shown below. d, Heatmap of the most differentially expressed genes from the gene expression signature in Fig. 7 across subtypes and treatment conditions. e, Gene set enrichment analysis (GSEA) of scRNA-seq data of cancer cells reveals enrichment of DNA damage in both classical and mesenchymal tumors upon treatment with the T/N combination. NES and FDR-q values are indicated. f, Representative images of immunohistochemical staining for γH2AX of control and T/N treated tumor sections for both classical and mesenchymal subtypes. Scale bar, 70 µm. g, Quantification of γH2AX positive cells in (f). Individual tumors are shown as single points in the graph (classical: control n = 6, T/N n = 5; mesenchymal: control n = 8, T/N n = 7). P values were calculated with two-tailed unpaired t test. Endo cells: endothelial cells. T/N: trametinib+nintedanib. T/N + aPD-L1: trametinib+nintedanib+anti PD-L1 antibody.

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Extended Data Fig. 10 Context-dependent reprogramming of the cancer cell derived secretome and cancer associated fibroblasts (CAFs) by the T/N combination therapy.

a, Volcano plots highlighting the changes in secreted factors upon T/N treatment in classical (left) and mesenchymal (right) PDAC cells. The x-axis shows log2 fold change (treated/control), the y-axis the per test adjusted p values, which were calculated by differential expression test (two-sided t test). b, Circos plot showing the key communication signals from tumor cells to T cell subtypes, tumor cells and acinar cells in classical mPDAC. The ligand protein expression fold change, identified from secretome experiments, between T/N and control is shown in the middle. Normalized receptor expression levels obtained from scRNA-seq data are shown in the outer concentric circles. c, UMAP plot highlighting the whole population of CAF cells identified in classical and mesenchymal tumors. d, Left, UMAP plot showing the CAF population across different treatment conditions in classical tumors. Right, UMAP plots displaying the identified CAF clusters and resulting subpopulations for classical tumors. e, UMAP plots of the CAF cluster displaying selected marker gene expression. f, Heatmap displaying expression of selected genes in CAFs across the identified clusters. The y axis shows the selected marker genes, the x axis represents each of the identified clusters in (d). g, Violin plot showing Tgfb1 expression by myoCAFs across the different treatment conditions. h, Proportion of CAF subtypes in the indicated different treatment conditions. CAF subpopulations were identified in the fibroblast cell clusters and annotated with the markers described in (f). T/N: trametinib+nintedanib, T/N + aPD-L1: trametinib+nintedanib+anti PD-L1 antibody.

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Supplementary information

Supplementary Information

Supplementary Fig. 1 FACS gating strategy.

Reporting Summary

Supplementary Table

Supplementary Tables 1–8.

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Falcomatà, C., Bärthel, S., Widholz, S.A. et al. Selective multi-kinase inhibition sensitizes mesenchymal pancreatic cancer to immune checkpoint blockade by remodeling the tumor microenvironment. Nat Cancer 3, 318–336 (2022). https://doi.org/10.1038/s43018-021-00326-1

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