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Research Articles

CAMK1D Triggers Immune Resistance of Human Tumor Cells Refractory to Anti–PD-L1 Treatment

Valentina Volpin, Tillmann Michels, Antonio Sorrentino, Ayse N. Menevse, Gertrud Knoll, Madlen Ditz, Vladimir M. Milenkovic, Chih-Yeh Chen, Anchana Rathinasamy, Klaus Griewank, Michael Boutros, Sebastian Haferkamp, Mark Berneburg, Christian H. Wetzel, Anja Seckinger, Dirk Hose, Hartmut Goldschmidt, Martin Ehrenschwender, Mathias Witzens-Harig, Arpad Szoor, Gyorgy Vereb, Nisit Khandelwal and Philipp Beckhove
DOI: 10.1158/2326-6066.CIR-19-0608 Published September 2020

Abstract

The success of cancer immunotherapy is limited by resistance to immune checkpoint blockade. We therefore conducted a genetic screen to identify genes that mediated resistance against CTLs in anti–PD-L1 treatment–refractory human tumors. Using PD-L1–positive multiple myeloma cells cocultured with tumor-reactive bone marrow–infiltrating CTL as a model, we identified calcium/calmodulin-dependent protein kinase 1D (CAMK1D) as a key modulator of tumor-intrinsic immune resistance. CAMK1D was coexpressed with PD-L1 in anti–PD-L1/PD-1 treatment–refractory cancer types and correlated with poor prognosis in these tumors. CAMK1D was activated by CTL through Fas-receptor stimulation, which led to CAMK1D binding to and phosphorylating caspase-3, -6, and -7, inhibiting their activation and function. Consistently, CAMK1D mediated immune resistance of murine colorectal cancer cells in vivo. The pharmacologic inhibition of CAMK1D, on the other hand, restored the sensitivity toward Fas-ligand treatment in multiple myeloma and uveal melanoma cells in vitro. Thus, rapid inhibition of the terminal apoptotic cascade by CAMK1D expressed in anti–PD-L1–refractory tumors via T-cell recognition may have contributed to tumor immune resistance.

Introduction

Endogenous T-cell responses against tumor antigens occur frequently in a broad variety of cancer types (1–3). Although these T-cell responses correlate to improved patient prognoses (2, 4, 5), they often do not rescue patients from tumor progression. A major reason, lies in the capacity of tumor cells to regulate T-cell activity through expression of the immune-inhibitory ligand PD-L1. The latter stimulates the inhibitory receptor PD-1 expressed on effector T cells and reduces T-cell receptor signaling (6). PD-L1 expression in healthy and tumor tissues can be induced by inflammatory cytokines such as IFNγ by effector T cells (7–9) and serves as a mechanism to prevent autoimmune diseases (10). Consequently, blockade of PD-L1/PD-1 interactions by therapeutic antibodies has resulted in stunning immune rejection of tumors in many patients (11–14). Still, a significant proportion of patients with cancer lack responses to anti–PD-L1/PD-1 therapies (15–17) possibly due to impaired IFNγ responsiveness resulting in reduced PD-L1 expression, severe, and irreversible T-cell exhaustion, or PD-1-induced blockade of T-cell differentiation (18). However, because functional tumor-reactive T cells are found in many patients refractory to anti–PD-L1/PD-1 treatment (3, 5), these mechanisms may only explain immune response resistance in a minor fraction of cases. Additional immune regulatory interactions may impose protection against immune destruction. Several immune-inhibitory receptors such as TIM3 or VISTA, triggered by ligands expressed in tumors, are characterized (19, 20), but immune resistance is likely caused by more than immune regulatory ligands controlling T-cell activity such as tumor cell–intrinsic resistance mechanisms.

Multiple myeloma is a rarely curable B-cell malignancy characterized by the accumulation of malignant plasma cell clones in the bone marrow (21). In multiple myeloma, spontaneous cytotoxic T-cell responses against myeloma-associated antigens occur (1). Immune checkpoint molecules are expressed by myeloma cells and induce tumor-related immune suppression (22–24). PD-L1 is commonly expressed on malignant plasma cells (9) and high expression of PD-L1 associates with disease progression and is upregulated at relapse or in the refractory stage (25). Nevertheless, results of a phase I trial with PD-1–blocking antibodies reported no objective responses amongst the 27 treated patients with multiple myeloma (26). There is thus rationale to assume that other immune checkpoint molecules may play a role in tumor escape mechanisms. Various immunotherapeutic treatments are being tested in multiple myeloma, including antibodies against CD38 (e.g., daratumumab, isatuximab), SLAMF7 (elotuzumab), BCMA-CAR-T–based treatments or BCMA-T-cell bispecific antibodies (27–31).

Here, we performed a systematic search for genes that regulated immune responsiveness in tumor cells, using multiple myeloma as anti–PD-L1/PD-1 treatment unresponsive tumor model (26).

To identify genes that inhibit tumor immune destruction by CTL, we applied a high-throughput (HTP) genetic screen allowing the silencing of a multitude of genes and subsequently assessed tumor lysis by patient-derived marrow-infiltrating lymphocytes (MIL). We identified 90 genes that regulated immune responsiveness after cytotoxic T-cell attack. Among them, calcium/calmodulin-dependent protein kinase 1D (CAMK1D) was coexpressed with PD-L1 and protected against T-cell–induced tumor cell killing in multiple myeloma and other PD-L1–refractory human cancers.

Materials and Methods

Experimental model and subject details: Patients, healthy donors, and samples

Patients with previously untreated multiple myeloma (n = 332) or monoclonal gammopathy of unknown significance (MGUS; n = 22) at the University Hospitals of Heidelberg and Montpellier as well as 10 healthy normal donors were included in this study, which was approved by the ethics committee (#229/2003 and S-152/2010) after written informed consent. Patients were diagnosed, staged, and response to treatment assessed according to standard criteria (32–34).

Samples

Normal bone marrow plasma cells and myeloma cells from the aforementioned patients were purified using anti-CD138 microbeads (Miltenyi Biotec, Germany #130-051-301) from bone marrow aspirates published previously (31, 35). Peripheral CD27+ memory B cells (n = 11) were FACS sorted as described previously (36). The human myeloma cell lines U266, RPMI-8226, LP-1, OPM-2, SK-MM-2, AMO-1, JJN-3, NCI-H929, KMS-12-BM, KMS-11, KMS-12-PE, KMS-18, MM1.S, JIM3, KARPAS-620, L363, and ANBL6 were purchased from the German Collection of Microorganisms and Cell Cultures and the ATCC, the XG lines were generated at INSERM U1040 (37). KMM-1 cells were obtained from the National Institutes of Biomedical Innovation, Health, and Nutrition. Cell line identity was regularly assessed by DNA fingerprinting and compared with the initial sample. Cell lines were grown from initial of first passage aliquots on a regular basis. Mycoplasma contamination excluded by PCR-based assays, and EBV infection status by clinical routine PCR-based diagnostics. If not otherwise stated, cell lines used for expression profiling were assessed from initial or early passage aliquots. Polyclonal plasmablastic cells (n = 10) were generated as published previously (35, 38, 39). The human uveal melanoma cell line Mel270 was established, characterized, and provided by Prof. Griewank (University Hospital Essen, Essen, Germany; ref. 40). KMM-1-luc cells were generated after transfection with a pEGFP-luc plasmid (provided by Dr. Rudolf Haase, LMU Munich, Germany) and selected for the G418-resistant gene. Lipofectamine LTX with Plus reagent (Thermo Scientific #15338100) were used as transfection reagents according to the manufacturer's instructions. Transfected cells were selected for 14 days with G418-containing medium (0.6 mg/mL). KMM-1-luc cells were sorted twice for the expression of GFP by flow cytometry (with 87% and 100% purity, respectively) and cultured in the presence of 0.6 mg/mL G418. Cell sorting was conducted in collaboration with the DKFZ sorting core facility, using the FACSARIA II cell sorter (BD Biosciences) and data were analyzed using FlowJo (Tree Star). KMM-1, U266, and Mel270 were cultured under standard conditions in RPMI media supplemented with 10% FCS, 100 U/mL penicillin G, and 100 μg/mL streptomycin at 37°C in a humidified atmosphere under 5% CO2.

Isolation of peripheral blood mononuclear cells

Peripheral blood mononuclear cells (PBMC) were isolated from buffy coats of healthy donors via Biocoll Density Gradient Centrifugation (Biochrome). Briefly, buffy coats were diluted 1:10 in RPMI and added to 50 mL conical centrifuge tubes, containing 15 mL of biocoll solution. Density-gradient centrifugation was performed at 2,000 rpm for 20 minutes at room temperature using low brake. Afterwards, PBMCs were collected, washed twice with RPMI, and frozen in aliquots of 5 × 107 cells per vial using freezing media A-B (1:1; freezing medium A: 60% AB serum + 40% RPMI; freezing medium B: 80% AB serum + 20% DMSO).

MIL isolation

MILs were isolated from the bone marrow of a patient with multiple myeloma. Briefly, T cells were isolated from the negative fraction of CD138-sorted bone marrow cells using Untouched Human T cells Dynabeads (Invitrogen #11344D) following manufacturer's instructions. Cells were stained for anti-CD3 [pacific blue anti-human CD3 (Clone OKT3), BioLegend], anti-CD4 [APC/Cy7 mouse anti-human CD4 (clone RPA-T4), BD Biosciences] and anti-CD8 [pacific blue mouse anti-human CD8 (clone RPA-T8), BD Biosciences], tested for HLA-A2 positivity [APC mouse anti-human HLA-A2 Clone BB7.2 (RUO), BD Biosciences] via flow cytometry and subsequently expanded using the rapid expansion protocol as described below.

MIL expansion

MIL cultures were ex vivo expanded using a modified version of the Rapid Expansion Protocol (REP; refs. 41, 42). A total of 2 × 106 of freshly isolated MILs were diluted to 6 × 105 cell/mL in CLM supplemented with 3,000 U/mL rHuIL2 (Novartis Pharma). Cells were incubated in 25 cm2 tissue culture flask for 48 hours at 37°C and 5% CO2. An excess of irradiated allogeneic PBMCs from healthy donors were added as “feeder cells” to support the activation and propagation of T cells early during the REP (43). Thus, PBMCs from three different buffy coats (at a ratio of 1:1:1) were irradiated with 60 Gy (Gammacell 1000) and used as feeder cells to support MILs expansion. A total of 2 × 106 MILs were coincubated with 2 × 108 feeder cells (in a ratio 1:100) in 400 mL of MIL expansion medium (CLM/AIM-V) with 30 ng/mL OKT3 antibody (Thermo Scientific) and 3,000 IU/mL IL2 for 5 days in a G-Rex 100 cell culture flask. Afterwards, 250 mL of supernatant was replaced with 150 mL of fresh media and IL2 was replenished to keep the concentration at 3,000 IU/mL. On day 7, MILs were divided into three G-Rex 100 flasks in a final volume of 250 mL medium each and media was again replenished on day 11. On day 14 of the expansion, MILs were counted and frozen in aliquots of 4 × 107 cells/mL in freezing media A (60% AB serum and 40% RPMI1640) and B (80% AB serum and 20% DMSO).

Generation of flu-antigen–specific CD8+ T cells

For the generation of flu-specific CD8+ T cells (flu TC), PBMCs from HLA-A2 healthy donors were isolated as described above. Total CD8+ T cells were sorted from PBMCs by magnetic separation (Miltenyi Biotec, #130-096-495; day 0) according to the manufacturer's instructions and expanded in the presence of A2-matched flu peptide (GILGFVFTL) for 14 days.

The autologous and peptide-loaded CD8-negative fraction was irradiated with 60 Gray (Gy; IBL 437C Blood Irradiator) and used as feeder cells for 1 week. Afterwards, these cells were substituted with irradiated (60 Gy; IBL 437C Blood Irradiator) T2 cells and used as fresh feeder cells. On day 1 and day 8, 100 IU/mL IL2 (Novartis Pharma) and 5 ng/μL IL15 (R&D Systems) were added to the expansion. The percentage of flu-antigen–specific T cells was determined by pentamer staining (GILGFVFTL-APC, ProImmune #P007-0A-E) on day 7 and 14 via flow cytometry analysis according to the manufacturer's instructions. After antigen-specific expansion, flu TCs were sorted by FACS and expanded further for 14 days by using the rapid expansion protocol.

PCR and qPCR

Gene expression was measured using end-point PCR. Briefly, total RNA was isolated from cell pellets using the RNeasy Mini Kit (Qiagen #74106) according to the manufacturer's guidelines. RNA quality and concentration were analyzed using the Scan Drop (AnalytikJena). One microgram of RNA was reverse transcribed to complementary DNA (cDNA) using the QuantiTect Reverse Transcription Kit (Qiagen #205313) according to the manufacturer's protocol. Synthesized cDNA was amplified using conventional PCR. PCR samples were set up in a 25 μL volume using 2× MyTaq HS Red Mix (Bioline #BIO-25044), 500 nmol/L of gene-specific primer mix (Supplementary Table S1), and 100 ng of template cDNA. Water was added to the reaction mix instead of cDNA for contamination controls. The PCR program was set as the following: 95°C for 3 minutes, 35 cycles of three repetitive steps of denaturation (95°C for 30 seconds), annealing (60°C for 30 seconds) and extension (72°C for 30 seconds), and a final step at 72°C for 5 minutes. PCR products were run on a 2% agarose gel in Tris-acetate-EDTA (TAE) buffer (Thermo Fisher Scientific #B49) using a gel electrophoresis system (Thermo Scientific) and DNA bands were visualized using UV light of myECL Imager (Thermo Scientific).

Knockdown efficiency of siRNA sequences was measured by quantitative PCR (qPCR). For qPCR, 10 ng of template cDNA, 2× QuantiFast SYBR Green PCR Mix (Qiagen #204056) and 300 nmol/L of gene-specific primer mix (Supplementary Table S1) was used per 20 μL reaction and each sample was prepared in triplicates. Reactions were run using the QuantStudio 3 (Applied Biosystems). Gene expression was normalized to β-actin and results were shown as fold change. The analysis was performed using comparative Ct method.

Gene expression profiling

Gene expression profiling was performed using U133 2.0 plus arrays (Affymetrix) as published previously (31, 44, 45). Expression data are deposited in ArrayExpress under accession numbers E-MTAB-317.

Survival and correlation analysis using The Cancer Genome Atlas

Transcriptomic gene expression (RNASeqV2, RSEM) and clinical data from all available tumor entities was downloaded from The Cancer Genome Atlas [TCGA; using getTCGA function of TCGA2STAT (version 1.2) package for R; ref. 46]. Log2-normalized expression values for uveal melanoma (TCGA-UVM, 80 patients), ovarian cancer (TCGA-OV, 303 patients), stomach adenocarcinoma (TCGA-STAD), and stomach and esophageal carcinoma (TCGA-STES, 599 patients) were correlated (Pearson r) using the ggpubr package for R. Survival curves were generated using survminer package for R. FAS expression was cut at the median to generate Fas-high and -low sets. Similarly, CAMK1D expression was cut at the median for the Kaplan–Meier survival curves. Significance was calculated using the log-rank test.

Reverse siRNA transfection

Gene knockdown in tumor cells was induced using reverse siRNA transfection with Lipofectamine RNAiMAX (Thermo Scientific #13778-150). Briefly, 200 μL of 250 nmol/L siRNA solution (Supplementary Table S1) was added to each well of a 6-well plate. Four microliters of RNAiMAX transfection reagent was diluted in 196 μL of RPMI (Sigma-Aldrich) and incubated for 10 minutes at room temperature. Four-hundred microliters of additional RPMI was added and 600 μL of RNAiMAX mix was given to the siRNA-coated wells and incubated for 30 minutes at room temperature. A total of 3.5 × 105 KMM-1 (WT or luc) cells were resuspended in 1.2 mL of antibiotic-free RPMI culture medium supplemented with 10% FCS, seeded in the siRNA-RNAiMAX–containing wells, and incubated for 48 hours at 37°C, 5% CO2. Final siRNA concentration was 25 nmol/L in all cases.

Phospho-protein isolation

To isolate phosphorylated proteins from cells, tumor cells were pelleted at 0.5 × g for 5 minutes and washed once with PBS at 4°C. The cell pellets were lysed with one pellet volume of Phosphoplex Lysis Buffer (Merck Millipore #43-040) containing protease inhibitor cocktail (Cabliochem #539134, 1:100) and phosphatase inhibitor cocktail (Sigma-Aldrich #P0044, 1:100) at 4°C for 15 minutes on a rotator. Samples were centrifuged at 17,000 × g at 4°C for 15 minutes. Supernatants containing the protein lysates were collected into fresh tubes and quantified using the Pierce BCA Protein Assay Kit (Thermo Scientific #23225) according to the manufacturer's protocol.

Briefly, supernatants were diluted 1:5 in water and pipetted together with BCA standards into a 96-well plate. BCA solution A and B were mixed 50:1 and 200 μL of this mix was added to each well. After 30-minute incubation at 37°C, the absorbance at 562 nm was measured with the TECAN reader and the protein concentration of the samples was calculated using the standard curve. Proteins were stored at −20°C.

SDS-PAGE

Thirty micrograms of protein lysates were denaturated in 4× NuPAGE LDS Sample Buffer (Thermo Scientific #NP0008) containing 10% ß-mercaptoethanol (Sigma-Aldrich #M6250-100ML) at 70°C for 10 minutes. Samples were spun down and separated on NuPAGE 4%–12% Bis-Tris Gels (Thermo Scientific NP0321BOX) along with PageRuler Prestained Protein Ladder (Thermo Scientific #26616) and run at 115–150 V for 90 minutes using 1× MES running buffer (Life Technologies #NP0002).

Semi-dry Western blot analysis

Proteins were transferred from the gel to a polyvinylidene difluoride (PVDF) membrane (Millipore #ISEQ00010) using a semi-dry Western blot method. Briefly, the PVDF blotting membrane was activated in 100% methanol (Millipore) for 1 minutes and afterwards placed in Pierce 1-Step Transfer Buffer (Thermo Science #84731 × 5) until use. Blots were assembled from anode to cathode into the Pierce Power Blot cassette (Thermo Scientific) and run at 24 V for 10 minutes. Membranes were washed in 1× TBS and then placed in blocking solution (5% BSA/0.05% TBST) for 2 hours. Primary antibodies (anti-CAMK1D (Abcam #ab172618) 1:20,000, anti–caspase-3 (Abcam #ab32351) 1:750, anti–caspase-6 (Abcam #ab108335) 1:2,000, anti–caspase-7 (Thermo Scientific MA5-15159) 1:1,000, anti–caspase-3 (phospho S150; Abcam #ab59425) 1:850, anti–caspase-6 (phospho S257; Abcam #ab135543) 1:250, and sodium potassium ATPase (Abcam #ab76020; 1:20,000) were diluted in 5% BSA/0.05% TBST and kept on the membrane overnight at 4°C on a rotator. Membranes were then washed three times for 10 minutes with 1 % BSA/0.05% TBST. Afterwards, horseradish peroxidase (HRP)–conjugated secondary antibodies [anti-rabbit 1:4,000, Cell Signaling Technology (#7074) or anti-mouse 1:4,000, Cell Signaling Technology (#7076)] were added to 1% BSA/TBST and kept on the membrane at room temperature for 1 hour on a shaker. Thereafter, the membranes were washed for 10 minutes with 1% BSA/TBST, then TBST and finally with TBS. The blots were incubated with the Amersham ECL Western Blotting Detection Reagent (Reagent A and Reagent B, 1:1, GE Healthcare # RPN2109) for 4 minutes and the chemiluminescence was detected with myECL Imager (Thermo Scientific). Images were analyzed using the ImageJ software (Wayane Rasband).

Coimmunoprecipitation assay

For detection of direct protein–protein interaction, coimmunoprecipitation was performed. Briefly, 10 million tumor cells (10 × 106) were seeded in 10 cm2 petri dishes. The next day, cells were stimulated for 4 hours with 100 ng/mL rHuFasL (BioLegend #589404). Unstimulated cells were used as negative control. Afterwards, tumor cells were detached, resuspended in ice-cold TBS, and centrifuged at 400 × g for 6 minutes at 4°C. Supernatant was discarded, cell pellet was resuspended in 1.5 mL TBS, and centrifuged at 500 × g for 8 minutes at 4°C. Cell pellet was lysed with 1.5 mL lysis buffer (50 mmol/L Tris-HCl, 150 mmol/L NaCl, 0.5% NP40, or Triton-X) containing protease inhibitor (Millipore #539134-1 ML) and kept on a rotator for 1 hour at 4°C. Afterwards, cells were centrifuged for 20 minutes at 20,000 × g at 4°C. Supernatant was collected and centrifuged for further 5 minutes at 20,000 × g at 4°C. Protein-G agarose (Sigma-Aldrich) was washed with 1 mL TBS and centrifuged for 1 minute at 12,000 × g. One milliliter of cell supernatant containing cytoplasmatic proteins was added to 60 μL protein-G agarose (Sigma-Aldrich #11719416001), incubated with caspase-3 (1:50; Cell Signaling Technology, #9662), caspase-6 (1:50; Abcam #ab108335) or caspase-7 (1:100; Cell Signaling Technology, #9491) antibodies and incubated overnight on a rotator at 4°C. Ninety microliters of cell lysates were frozen at −20°C. The next day, the immunoprecipitated samples were centrifuged at 12,000 × g at 4°C for 1 minute. Supernatant was discarded and protein-G agarose was washed three times with lyses buffer and centrifuged at 12,000 × g at 4°C for 1 minute. 2× LDS containing 10% β-mercaptoethanol was added to the immunoprecipitated samples, while 4× LDS containing 10% β-mercaptoethanol was added to the lysates. Samples were denaturated for 10 minutes at 95°C on a thermocycler. Samples were spun down and separated on NuPAGE 4%–12% Bis-Tris Gels (Thermo Scientific #NP0335BOX) along with PageRuler Prestained Protein Ladder (Thermo Scientific #26616) and run at 115–150 V for 90 minutes. After electrophoresis, proteins were transferred on a PVDF membrane (Millipore). CAMK1D antibody (1:10,000) was diluted in 5% BSA/0.05% TBST and kept on the membrane overnight at 4°C on a rotator. Membranes were then washed three times for 10 minutes with 1% BSA/0.05% TBST. Afterwards, HRP-conjugated secondary antibodies (anti-rabbit 1:3,000) was added to 1% BSA/TBST and kept on the membrane at room temperature for 1 hour on a shaker. The membrane was washed. The blot was incubated with the ECL Detection Reagent (Reagent A and Reagent B, 1:1, GE Healthcare) for 4 minutes and the chemiluminescence was detected with myECL Imager (Thermo Scientific). Images were analyzed using the ImageJ software (Wayane Rasband).

Plasmid transfection

To generate KMM-1-luc cells, 3.5 × 105 KMM-1 WT cells were seeded in a 6-well plate and incubated at 37°C overnight. Fifteen microliters Lipofectamine LTX reagent were diluted in 150 μL Opti-MEM medium (Gibco). Simultaneously, 3.5 μg of pEGFP-Luc plasmid was diluted in 175 μL Opti-MEM medium and 3.5 μL of Plus Reagent was added. 150 μL of diluted DNA was added to 150 μL diluted Lipofectamine LTX (Life Technologies) reagent and incubated for 5 minutes at room temperature. DNA–lipid complex was then added to the growth medium of the myeloma cells. Cells were incubated at 37°C for 48 hours before investigation of transfection efficacy by flow cytometry.

Luciferase-based cytotoxicity assay

KMM-1-luc cells were reverse transfected with the desired siRNA sequences (Supplementary Table S1) in white 96-well plate (Perkin Elmer) and incubated for 48 hours at 37°C, 5% CO2. On the same day of transfection, MILs were thawed and treated with benzonase (100 IU/mL; Merck). Cell density was adjusted to 0.6 × 106 cells/mL in CLM supplemented with 3,000 IU/mL rhuIL2 (Novartis) for 48 hours. IL2 was depleted 24 hours before the coculture. Briefly, cells were collected, centrifuged at 1,400 rpm for 10 minutes, and resuspended in CLM at a concentration of 0.6 × 106 cells/mL. Flu TC were thawed 6 hours before coculture. For the cytotoxicity assays, MILs, flu TC, the supernatant of activated MILs, or rHuFasL were added to transfected tumor cells at desired effector-to-target (E:T) ratio/concentration, and incubated for 20 hours at 37°C, 5% CO2. For the viability setting, only CLM was added to the tumor cells. After coculture, supernatant was removed, remaining tumor cells were lysed using 40 μL/well of cell lysis buffer for 10 minutes. After tumor cell lysis, 60 μL/well of luciferase assay buffer was added and luciferase intensity was measured by using the Spark 20M plate reader (Tecan) with a counting time of 100 msec. Luciferase activities (relative luminescence units = RLUs) were either represented as raw luciferase values or as normalized data to scramble or unstimulated controls.

Real-time live-cell imaging assay

Target genes in KMM-1 or U266 tumor cells were knocked down with reverse siRNA transfection for 48 hours. The reverse siRNA transfection was performed using transparent 96-well microplates (TPP). In parallel, MILs were thawed and prepared as described previously in section MILs expansion. After 48 hours, MILs (E:T 10:1) or rHuFasL (100 ng/mL) were added to the target cells in CLM with YOYO-1 (final concentration 1:5,000) and cocultured at 37°C. For viability controls, the according amount of CLM with YOYO-1 (final concentration 1:5,000) was added. MILs or rHuFasL-mediated tumor lysis was imaged on the green channel using an IncuCyte ZOOM Live Cell Imager (ESSEN BioScience) for the indicated time points at a 10× magnification. Data were analyzed with the Incucyte ZOOM 2016A software by creating a top-hat filter-based mask for the calculation of the area of YOYO-1–incorporating cells (indicating dead cells).

ELISA

Tumor cells were transfected with the indicated siRNAs (Supplementary Table S1) in a 96-well plate. Afterwards, T cells were added at the indicated E:T ratio for 20 hours and 100 μL of supernatants were harvested for the detection of IFNγ (Human IFNγ ELISA Set; BD OptEIA #555142), IL2 (Human IL2 ELISA Set; BD OptEIA #555190), Granzyme B (Human Granzyme B ELISA development kit; Mabtech #3485-1H-20), and TNF (Human TNF ELISA Set; BD OptEIA #555212). Experiments were performed according to the manufacturer's instructions. Polyclonal stimulation (Dynabeads Human T-Activator CD3/CD28, Invitrogen #11131D) for 20 hours was used as positive control. Absorbance was measured at λ = 450 nm, taking λ = 570 nm as reference wavelength using the Spark microplate reader (TECAN).

Flow cytometry

Flow cytometry was used for the detection of proteins expressed on the plasma membrane of tumor and T cells. Intracellular staining was performed for the detection of caspase-3 (FITC Active Caspase-3 Apoptosis Kit, BD Biosciences, #550480) according to manufacturer's instructions. Tumor cells were detached from plates using PBS-EDTA, centrifuged at 500 × g for 5 minutes, and resuspended in FACS buffer (5 × 105 cells/tube). Live T cells and tumor cells were distinguished by using Live/Dead Fixable Yellow Dead Cell Stain (Thermo Scientific #L34959) according to manufacturer's instructions followed by blocking with kiovig (human plasma-derived immunoglobulin, Baxter) at a concentration of 100 μg/mL in FACS buffer (PBS, 2% FCS) for 15 minutes in the dark on ice. Samples were washed two times in FACS buffer and incubated with either fluorophore-conjugated primary antibodies or isotype control [APC anti-human CD274 (PD-L1; clone 29E.2A3), BioLegend #329707; Alexa Fluor 647 Mouse anti-human CCR9 (clone 112509 (RUO), BD Biosciences #557975; Brilliant Violet 421 anti-human CD95 (Fas; clone DX2), BioLegend #305623; PE anti-human CD95 (Fas; clone DX2), BD Biosciences #555674; APC anti-human CD261 (DR4, TRAIL-R1; clone DJR1), BioLegend #307207; PE anti-human CD262 (DR5, TRAIL-R2; clone DJR2), BioLegend #307405; Biotin anti-human CD120a (TNFR1; clone W15099A), BioLegend #369908; PE/Cy7 anti-human CD120b (TNFR2; clone 3G7A02), BioLegend #358411; PE/Cy7 anti-human CD279 (PD-1) antibody, BioLegend #329918; APC mouse anti-human CD178 (clone NOK-1), BD Biosciences #564262; PE anti-human CD253 (TRAIL; clone RIK2), BioLegend #308206; APC anti-human TNFα (clone Mab11), BioLegend #502912] for 20 minutes on ice in the dark. Afterwards, cells were washed twice, they were acquired with the FACSCanto II cell analyzer machine (BD Biosciences) or FACSLyrics Flow cytometer and data were analyzed using FlowJo (Tree Star).

Calcium imaging

KMM-1 cells grown on coverslips were washed with Ringer solution (118 mmol/L NaCl, 5 mmol/L KCl, 1.2 mmol/L MgCl2, 1.2 mmol/L Na2HPO4, 2 mmol/L NaH2PO4, 1.8 mmol/L CaCl2, 5 mmol/L glucose, 9.1 mmol/L HEPES, pH 7.4, with NaOH) and loaded with Fura-2-AM ester (Thermo Fisher Scientific) for 45 minutes. After 15 minutes, MILs or rHuFasL (50 ng/mL) was added to scr siRNA–transfected cells and recording of the intracellular free Ca2+ was continued for further 30 minutes. Experiments were performed using a ZEISS live cell imaging setup based on an inverse microscope (Axio Observer Z.1) equipped with Fluor 40×/1.3 objective lens (ZEISS). Fura 2-AM–loaded KMM-1 cells were illuminated with light of 340 nm or 380 nm (BP 340/30 HE, BP 387/15 HE) using a fast wavelength switching and excitation device (Lambda DG-4, Sutter Instrument), and fluorescence was detected at 510 nm (BP 510/90 HE and FT 409) using an AxioCam MRm LCD camera (ZEISS). Data were recorded and analyzed with ZEN 2012 software (ZEISS).

Generation of supernatants of activated MILs

For the generation of the supernatant of polyclonally activated MILs, 1 × 106 MILs were suspended in 1 mL of CLM collected in a 15 mL tube and stimulated with 25 μL of Dynabeads Human T-Activator CD3/CD28 (Thermo Scientific) for 20 hours. Afterwards, only the supernatant (100 μL/well) of activated T cells was added to knocked down tumor cells and incubated overnight at 37°C, 5% CO2. Luciferase-based cytotoxicity assay was performed as described above. Alternatively, MILs were stimulated with tumor cells at an E:T ratio of 10:1. After 20-hour coculture, plates were centrifuged at 450 × g for 5 minutes and 100 μL/well of the supernatant was collected for cytokines detection (ELISA).

Functional neutralization

For the functional neutralization experiment, anti-FasL (clone NOK-1, BioLegend #306409) or isotype control (Clone MOPC-21, BioLegend #400153) were preincubated with MILs for 1 hour at 37°C, 5% CO2. As negative control, antibodies were cultivated in the absence of T cells. Afterwards, antibody-containing supernatants were used to stimulate KMM-1-luc cells, which were reverse transfected with the indicated siRNAs (Supplementary Table S1). The final concentration of the neutralizing antibodies was 100 ng/mL for anti-FasL and isotype control. As positive control recombinant FasL protein (100 ng/mL, BioLegend #589404) was added to the tumor cells instead of T cells. Twenty hours after coculture, luciferase intensity was measured as described above.

Blocking assays

For the experiments using the anti-Calmodulin (W-7 hydrochloride; Tocris #0369) inhibitor, 1 × 104 KMM-1-luc (scr or CAMK1D-transfected) cells/well were seeded in white 96-well plates (Perkin Elmer) in 100 μL of RPMI 10% FCS. The small-molecule inhibitor was added at the indicated concentrations for 1 hour at 37°C, before 100 ng/mL rHuFasL or medium control was added. DMSO treatment served as negative control. After 20-hour stimulation, luciferase-based cytotoxicity assay was performed. For CAMK1D inhibition, 1 × 104 KMM-1-luc or 1 × 104 Mel270 cells/well were incubated overnight in a 96-well plate. QPP-A inhibitor (Merck Millipore; CAS 404828-08-6) was added at the indicated concentrations 1 hour before rHuFasL stimulation (100 ng/mL) or medium control. DMSO treatment served as negative control. After 20-hour stimulation, luciferase-based cytotoxicity assay was performed.

Luminex assays

Tumor cells were stimulated with rHuFasL (100 ng/mL) for 15 minutes, 30 minutes, 1, 2, 4, and 8 hours. Unstimulated cells served as controls. For the detection of intracellular phosphorylated analytes, a general pathway (MILLIPLEX MAP Multi-Pathway Magnetic Bead 9-Plex kit, Merck Millipore #48-680MAG) was used following manufacturer's instructions. For the detection of proteins involved in the activation of apoptosis the MILLIPLEX MAP Early Phase Apoptosis 7-plex-kit (Merck Millipore #48-669MAG) together with active caspase-3 Magnetic Bead MAPmate (Merck Millipore #46-604MAG) were used following manufacturer's instructions. Beads specific for GAPDH (MILLIPLEX MAP GAPDH Total Magnetic Bead MAPmate; Merck Millipore #46-667MAG) served as normalization control. Twenty micrograms of protein lysates were used for the detection of ERK/MAP kinase 1/2 (Thr185/Tyr187), Akt (Ser473), STAT3 (Ser727), JNK (Thr183/Tyr185), p70 S6 kinase (Thr412), NFκB (Ser536), STAT5A/B (Tyr694/699), CREB (Ser133), and p38 (Thr180/Tyr182) phosphorylated Akt (Ser473), JNK (Thr183/Tyr185), Bad (Ser112), Bcl-2 (Ser70), p53 (Ser46), cleaved caspase-8 (Asp384), cleaved caspase-9 (Asp315), and active caspase-3 (Asp175). The assay was performed according to the manufacturer's instructions and samples were measured using the MAGPIX Luminex instrument (Merck Millipore).

HTP RNAi screening

Primary RNAi screening

The primary RNAi screening was conducted using a sublibrary of the genome-wide siRNA library siGENOME (Dhamacon, GE Healthcare), which comprised 2,887 genes (1,288 genes for GPCR/kinase and 1,599 genes for custom library). The library was prepared in Prof. Boutros's group (DKFZ) as described in ref. 47. Each well contained a pool of four nonoverlapping siRNAs (SMARTpool) targeting the same gene. This arrayed screening approach was performed in duplicates and was adopted from Khandelwal and colleagues (48). The siRNA sequences of the genome-wide library were distributed in the 384-well plates and positive and negative siRNA controls were added in empty wells. The final concentration of all siRNA sequences was 25 nmol/L. Reverse transfection was performed as described in section reverse siRNA transfection. The readout was performed using Mithras LB 940 microplate reader with a counting time of 100 msec. 40 × 384-well plates were subjected to the luciferase-based screening assay performed on KMM-1-luc cells. 20 × 384-well plates were subjected to the luciferase-independent CellTiter-Glo (CTG) screening performed on luciferase-negative KMM-1 cells without the addition of MILs to exclude genes affecting cell viability. Briefly, for the readout, supernatant was removed in each well containing siRNA-transfected tumor cells, and 20 μL of the CTG reagent (prediluted 1:4 in RPMI) were added. After 15-minute incubation in the dark, plates were measured using the Mithras reader as described above.

For the screening analysis, the raw RLUs from the primary screening were processed using the cellHTS2 package in R/Bioconductor (49). Values from both conditions were quantile normalized against each other using the aroma.light package in R. Pearson correlation (r2) between the two replicate values was calculated for each setting. Differential scores (cytotoxicity vs. viability) were calculated using the LOESS local regression method. To identify candidate hits, the following thresholds were applied on the z-scores of the samples: for the viability setting, genes showing a z > +2.0 or z <− 2.0 were excluded. For the cytotoxicity setting, CCR9 was used as threshold score. In addition, genes having a z-score >+ 0.5 or <− 0.5 in the CTG-based viability screening were filtered out from the candidate list.

Secondary screening

For the secondary screening, a customized library (G-CUSTOM-223794) containing 128 genes from the primary screening was distributed in several 96-well plates along with positive and negative siRNA controls. Reverse transfection was performed. For the cytotoxicity setting MILs (10:1 ratio) or supernatant of anti-CD3/anti-CD28 magnetic beads activated MILs were added to knockdown tumor cells (1 × 104 cells/well). Instead, CLM medium was added to the viability plates. After 20 hours, luciferase-based readout was performed. RLUs were normalized to Mock control. Cytotoxicity/viability ratios were calculated according to the formula:

Cytotoxicity/viability ratio = (Norm. RLU cytotoxicity setting/Norm. RLU viability setting).

The hit-list was generated by including only hits with improved T-cell–mediated cytotoxicity over mock transfection, (Cytotoxicity/viability ratio < 1). Pearson's correlation was calculated with Microsoft Excel.

In vivo experiments

Camk1d knockout MC38 murine colorectal cells were generated using the CRISPR/Cas9 technique. In vivo experiments were performed in two cohorts of mice: C57BL6 (n = 12) and NOD/SCID gamma chain (NSG) mice (n = 12) were subcutaneously injected with 1 × 105 MC38 Camk1d KO (g3 clone 11) or 1 × 105 MC38 NTS (clone 12) cells each into the right and left flank of one mouse, respectively. Tumor growth was measured twice a week with a caliper and the volume was determined using the following formula: Tumor volume (mm3) = (Width2 × Length) × (π/6). Mice were sacrificed when tumors exceeded 1.5 cm in diameter.

Statistical analysis

For statistical analysis, GraphPad Prism software v6.0 (GraphPad Software) was used. If not stated, statistical differences between the control and the test groups were determined by using two-tailed unpaired Student t test. In all statistical tests, a P value ≤0.05 was considered significant with *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001; and ****, P ≤ 0.0001.

Results

Multiple myeloma cells expressed multiple genes that confer intrinsic resistance toward T-cell attack

To identify novel genes involved in immune escape mechanisms of PD-L1 unresponsive cancer cells, an HTP screening approach (48) was adapted. The HLA-A2–positive human multiple myeloma cell line KMM-1, expressing high levels of PD-L1 and lower levels of CCR9 (48), was used as a tumor model in this study. As a reporter system for tumor cell survival, we stably transfected KMM-1 cells with e-GFP-firefly luciferase, allowing for luminescence imaging to detect immune-mediated tumor cell destruction in an HTP format (Fig. 1A).

Figure 1.
Figure 1.

Assessment of immune checkpoint controls for the HTP screen. A, Representative FACS data of at least three independent experiments. Left, expression of HLA-A2, PD-L1, CCR9, and GFP on KMM-1 cells. Right, PD-1 expression on CD4+ and CD8+ MILs. Isotypes are shown in dark gray. B, IFNγ ELISA. MILs and Survivin T-cell (TC) clones were used as negative controls. Anti-CD3/anti-CD28–stimulated T cells were used as positive controls. C and E, Luciferase-based killing assay of siRNA-transfected KMM-1 cells upon MIL coculture. Statistical significance was calculated compared with scr1. D, qPCR analysis: knockdown efficiency of CCR9 and PD-L1 in KMM-1 cells. B–E, Graphs show mean ± SEM of at least two independent experiments. P values were calculated using unpaired two-tailed Student t test (*, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001). ns, not significant.

As a source of tumor-reactive T cells, we used marrow-infiltrating, PD-1–positive T cells (MILs) from an HLA-A2–matched patient (Fig. 1A; Supplementary Fig. S1A). These MILs were not terminally exhausted as they displayed strong IFNγ secretion after polyclonal stimulation (Fig. 1B), which exceeded that of tumor antigen–specific CD8+ cytotoxic Survivin T cells (48). MILs recognized KMM-1 tumor cells, despite high PD-L1 expression (Fig. 1B). However, their limited killing capacity (Supplementary Fig. S1B) suggested the presence of resistance mechanisms against T-cell–induced death.

Silencing of firefly-luciferase (siFLuc), ubiquitin C (siUBC), a gene essential for cell survival, or transfection with a mixture of siRNAs inducing cell death (siCD) resulted in strong reduction of luciferase expression, indicating appropriate gene silencing and sensitivity of the luciferase-based readout. This was also maintained upon coculture of siRNA-treated KMM-1 cells with MILs (Fig. 1C).

We next studied the effect of PD-L1 and CCR9 on KMM-1 cells (Fig. 1D). The knockdown of PD-L1 did not result in increased KMM-1 killing by MILs, despite high expression of PD-L1 on the tumors and of PD-1 on the MILs (Fig. 1E). In contrast, CCR9 silencing significantly improved tumor cell rejection (Fig. 1E), suggesting that PD-L1 did not play a decisive role in immune resistance of KMM-1 cells. We therefore used CCR9 as a positive control within the screen.

To this end, KMM-1 cells were transfected in a multiwell format with a siRNA library consisting of a pool of four nonoverlapping siRNAs per target per well, targeting a total of 2,887 genes (Supplementary Table S2) covering a broad spectrum of all gene families. The screening approach comprised a viability setup, in which we assessed the intrinsic viability effect of each gene knockdown, and a cytotoxicity setup, where siRNA-transfected tumor cells were cocultured with MILs (Supplementary Fig. S2).

Negative (scramble siRNA sequences, scr1 and scr2) and positive controls (siRNAs targeting luciferase and essential viability genes) were included as a reference to calculate the effect of gene knockdown on cell viability. Overall, the distribution of values across test replicates and setups was highly concordant showing no viability or cytotoxicity effect of scr siRNAs but robust signal reduction after FLuc and UBC knockdown (Fig. 2A). Calculated z-scores for the impact on cell viability and T-cell cytotoxicity (Fig. 2B) and for the relative impact on T-cell–mediated tumor cell lysis (LOESS score) of each gene, revealed 128 genes whose silencing improved tumor cell lysis by T cells to a higher degree than CCR9 (Fig. 2C). Among them, we found several genes with described immune regulatory function in multiple myeloma such as CD5 (50), FES (51), and PAK3 (52). PD-L1 did not have any effect on T-cell–mediated killing of multiple myeloma cells (Fig. 2B and C). The identification of these validated immune checkpoints in combination with good immune checkpoint control performance supported the robustness and sensitivity of the screen.

Figure 2.
Figure 2.

Performance of the HTP screen. A, Dot plots showing technical replicates plotted against each other of normalized and scored RLUs of KMM-1 cells transfected with control siRNAs. Luciferase-independent (left) and luciferase-based (right) screening assay performed on KMM-1-wt and -luc cells respectively. Blue dots, cytotoxicity setting. Red dots, viability setting. Pearson correlation (r2) between the two replicate values was calculated for each setting. B, Quadrant plot showing z-scores of gene knockdown KMM-1-luc cells after coculture with MILs (cytotoxicity z-score) or culture medium (viability z-score). C, Gene ranking diagram showing differential score between cytotoxicity and viability z-scores using local regression (LOESS) rank. Top, the potential negative immune modulators with a high LOESS score. Luciferase-based secondary screening. Knockdown tumor cells were cocultured with MILs (D) or supernatant (E) of anti-CD3/anti-CD28–activated MILs. RLUs were normalized to Mock control. Log2 scale of cytotoxicity/viability ratio is depicted. Experiments were performed in duplicates. Mean is shown.

For further validation, we subjected the 128 candidate hits to a secondary screening procedure using the same setup as for the HTP screen. Silencing of 90 candidates again increased T-cell–mediated killing of tumor cells, and only had little effect on intrinsic tumor cell viability, thus confirming their immune regulatory role in KMM-1 cells (Fig. 2D). The highest immune modulatory effect was elicited by the serine/threonine calcium/calmodulin-dependent protein kinase 1D (CAMK1D; Fig. 2B–D).

To determine whether the observed tumor cell killing was mediated by cytokines or other soluble proteins released by activated MILs, an additional setting was included. MILs were polyclonally stimulated with anti-CD3/anti-CD28 magnetic beads and only their cell culture supernatant was added to the tumor cells. In this setup, silencing of few genes had an impact on tumor cell lysis indicating a role in resistance toward T-cell–secreted cytotoxic cytokines (Fig. 2E). Most of the identified candidate genes, including CAMK1D, regulated tumor cell killing only upon direct interaction with T cells. Taken together, these results provided an indication that multiple myeloma cells express multiple immune regulatory genes, among them CAMK1D, that confer immune resistance after T-cell engagement.

CAMK1D protected PD-L1+ tumor cells against death receptor signaling by cytotoxic T cells

On the basis of the immune resistance phenotype associated with CAMK1D expression in our screens, we validated and characterized the immune regulatory role of CAMK1D. We first deconvoluted the pool of siCAMK1D to exclude potential dominant off-target effects of single siRNAs within the pool. Three of four siRNAs (s1, s2, and s3) and the pool of all siRNAs increased T-cell–mediated cytotoxicity, whereas no viability impact was detected (Fig. 3A). All siRNAs significantly reduced CAMK1D mRNA and protein expression (Fig. 3B and C). In a luciferase-independent assay, employing live-cell imaging, we confirmed an increase MIL-induced apoptosis of CAMK1D-deficient KMM-1 cells (Fig. 3D). This could be inhibited with MHC-I–blocking antibodies, indicating that tumor cell apoptosis was induced by MHC-I–restricted CD8+ MILs in a T-cell receptor–dependent manner (Fig. 3E). To corroborate this finding, we pulsed KMM-1 cells with an HLA-A2–restricted influenza (flu) peptide and cocultured them with PD-1–positive, flu peptide–specific CD8+ cytotoxic T cells (flu TC; Supplementary Fig. S3A). Again, siCAMK1D, but not PD-L1 silencing, resulted in a significant increase of T-cell–mediated tumor cell lysis (Fig. 3F; Supplementary Fig. S3B). These data demonstrated that CAMK1D-mediated resistance of KMM-1 cells toward antigen-specific T cells, independent of the T-cell source. CAMK1D-mediated immune protection also occurred in the PD-L1+, HLA-A2+ multiple myeloma cell line, U266 (Fig. 3G–I; Supplementary Fig. S3C). We therefore studied CAMK1D expression in a large cohort of CD138-purfied malignant plasma cells from multiple myeloma patients with monoclonal gammopathy of unknown significance (MGUS), human myeloma cell lines (HMCL), memory B cells (MBC), plasmablasts (PPC), and normal bone marrow plasma cells (BMPC). CAMK1D expression was highest in MBC, but was also expressed in all multiple myeloma, MGUS, PPC, and in 30 of 32 HMCL samples, with higher expression than normal bone marrow plasma cells (BMPC; Fig. 3J). Thus, these data indicated that CAMK1D was consistently expressed in human multiple myelomas and conferred resistance against cytotoxic T-cell attack.

Figure 3.
Figure 3.

Validation of siCAMK1D effect. A, KMM-1-luc cells were transfected using single (s1, s2, s3) or pooled siRNAs targeting CAMK1D. Scr and siCCR9 siRNAs were used as negative and positive controls, respectively. Tumor cell lysis was measured using the luciferase-based cytotoxicity assay. Values were normalized to scr in each setting. KMM-1 cells were transfected with the specified siRNAs sequences, and 48 hours later, mRNA expression was determined by qPCR (B) and protein expression was measured via Western blot analysis (C). The Sodium–Potassium ATPase was used as housekeeping gene. D, Live-cell imaging analysis. siRNA-transfected tumor cells were cocultured with MILs. YOYO-1 dye was added as an indicator of apoptosis (green color). The graph shows the green object counted (GCO). The experiment is representative of three independent experiments. Right, representative pictures of scr- and siCAMK1D-transfected KMM-1 cells stained with YOYO-1 and cocultured with MILs. E, Luciferase-based killing assay for detection of T-cell–mediated cytotoxicity in the presence of the indicated concentrations of anti–MHC-I antibody (red line) and IgG2a isotype as positive control (black line). Anti–MHC-I antibody was added to KMM-1 cells in the absence of T cells as negative control (gray line). F, KMM-1-luc were pulsed with 0.005 μg/mL of flu peptide 1 hour before flu TC coculture or medium control. Tumor lysis was measured by luciferase assay. G, Endpoint PCR analysis of CAMK1D expression in U266 cells. KMM-1 cells were used as positive control. H, qPCR analysis of CAMK1D knockdown efficiency in KMM-1 and U266 cells. I, Live-cell imaging analysis. Scr- and siCAMK1D-transfected U266 tumor cells were cocultured with MILs. No MILs, condition served as viability control. Tumor cell death was measured by the addition of YOYO-1. Columns show the GCO. J, CAMK1D expression by gene expression profiling in human MBC, PPC, BMPC, MGUS, multiple myeloma (MM), and HMCL (*, P < 0.05; **, P < 0.01; ***, P < 0.001). A and B, Graphs show mean ± SEM. Cumulative data of at least two independent experiments. D, Graph shows mean ± SEM. P value was calculated using paired two-tailed Student t test. E, Representative data of at least three independent experiments. Graph shows mean ± SD. F, H, and I, Representative data of at least two independent experiments. Graphs show mean ± SEM. P values were calculated using unpaired two-tailed Student t test (*, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001).

As classical immune checkpoint molecules expressed by tumor cells regulate T-cell activity mostly through engagement of inhibitory receptors (53), we wondered whether CAMK1D, being an intracellular kinase, might indirectly regulate T-cell activity. We therefore studied parameters of T-cell effector function upon contact with CAMK1D-proficient or -deficient KMM-1 cells, including the secretion of INFγ, Granzyme B, IL2, or TNFα.

Although we consistently detected increased T-cell–mediated tumor cell killing after CAMK1D knockdown in KMM-1 cells, functional analysis of T cells did not reveal any increased T-cell function after interaction with CAMK1D-deficient compared with wt tumor cells (Supplementary Fig. S3D). Therefore, we concluded that CAMK1D did not affect type 1 effector T-cell function and hypothesized that it may instead have regulated the sensitivity of tumor cells toward cytotoxic T-cell attack. Thus, we exposed KMM-1 cells to the cytotoxic agents FasL (rHuFasL), TRAIL (rHuTRAIL), or TNF (rHuTNF) commonly used by T cells to kill their target cells. The respective cell death–mediating receptors for FasL and TRAIL, Fas, DR4, and DR5 were highly expressed on KMM-1 cells while the TNF receptors TNFR1 and TNFR2 were not (Fig. 4A). Whereas CAMK1D-proficient KMM-1 cells were resistant against all tested cytotoxic agents, CAMK1D-deficient tumor cells were dramatically reduced after exposure to FasL and TRAIL (Fig. 4B). We also detected FasL on 28.2% and 16.1% of CD4+ and CD8+ MILs, respectively (Fig. 4C) and on 12.7% of flu TC (Supplementary Fig. S3E). TRAIL expression was detected only on 12.5% and 5.3% of CD4+ and CD8+ MILs, while membrane-bound TNF was hardly detectable (Fig. 4C). Neutralization of FasL by mAbs completely abrogated CAMK1D-induced protection against the cytotoxic activity of MILs (Fig. 4D). Thus, CAMK1D-mediated intrinsic tumor resistance against activated T cells by interfering with Fas-mediated death signaling. In line with this, U266 cells highly expressed Fas (Fig. 4E) and were protected by CAMK1D expression against Fas-mediated cell death similar to KMM-1 cells (Fig. 4F).

Figure 4.
Figure 4.

Effect of CAMK1D knockdown in different tumor entities. A, Representative FACS analysis of Fas, DR4, DR5, TNFR1, and TNFR2 expression in KMM-1 cells. Positive tumor cells are marked in orange. Isotype is shown in gray. B, Representative results of siRNA-transfected KMM-1-luc cells treated with recombinant FasL, TRAIL, or TNF. Luciferase activity was measured after 20 hours of treatment. C, Representative FACS analysis of FasL, TRAIL, and TNFα expression on CD4+ and CD8+ MILs. Isotypes are shown in gray. TC, T cell. D, Luciferase-based assay: scr- or siCAMK1D-transfected KMM-1 cells were cocultured with MILs in the presence of FasL-neutralizing (anti-FasL) antibody (Ab) or isotype control. Loss of luciferase activity was measured. E, FACS analysis of Fas expression (shown in orange) in U266 cells. Isotype is shown in gray. F, Live-cell imaging analysis. siRNA-transfected U266 cells were stained with YOYO-1 and treated with rHuFasL. The experiment is representative of three independent experiments and shows the green objects counted (GCO). G, Representative FACS analysis of Fas expression in PANC-1, MCF-7, Mel270, and KMM-1 cells. Positive tumor cells are marked in orange. Isotype is shown in gray. H, Endpoint PCR of CAMK1D in Mel270. KMM-1 cells were used as positive control. I, Live-cell imaging analysis of uveal melanoma cells transfected with siCAMK1D or scr siRNAs upon exposure to rHuFasL or culture medium as performed in F. The experiment is representative of two independent experiments. Values denote mean ± SEM. J, Kaplan–Meier curves displaying the correlation between CAMK1D expression and patients' survival probability in Fas-high and -low samples. Results were generated using TCGA clinical data. Significance was calculated using the log-rank test. K, Correlation between CAMK1D and PD-L1 in uveal melanoma. B, D, and F, Graphs show mean ± SD. B and D, P values were calculated using unpaired two-tailed Student t test. F and I, P value was calculated using paired two-tailed Student t test (**, P < 0.01; ***, P < 0.001; ****, P < 0.0001). ns, not significant.

Because Fas–FasL interactions represent a major cytotoxic mechanism, we tested whether CAMK1D also protected solid tumors against immune rejection. In the human cancer cell lines PANC-1 and MCF7, Fas expression was low. However, we found high Fas and CAMK1D expression in Mel270, a PD-L1+ human uveal melanoma cell line (Fig. 4G and H; Supplementary Fig. S4A). Uveal melanoma is a highly treatment-refractory and anti–PD-1–resistant subtype of melanoma (54). Silencing of CAMK1D significantly increased the cytolytic response of Mel270 toward FasL exposure (Fig. 4I), indicating that uveal melanomas exploited CAMK1D for resistance against T-cell attack. In contrast, CAMK1D silencing in the Fas-negative tumor cell lines PANC-1 and MCF-7 did not sensitize these cells toward T-cell killing (Supplementary Fig. S4B and S4C). These data provided rationale for CAMK1D inhibition only in the context of Fas-positive tumors to achieve significant antitumor immune response. We hypothesized that CAMK1D expression in uveal melanoma might protect those tumors with strong Fas expression against immune rejection. We therefore stratified patients in the TCGA database cohort according to CAMK1D and Fas. Kaplan–Meier analyses showed that overexpression of CAMK1D in Fas receptorhigh, but not in Fas receptorlow tumors correlated with poor patient prognosis (Fig. 4J). This suggested that CAMK1D exerted a tumor-protective effect only in the context of Fas activation during an immune response. Overexpression of CAMK1D and PD-L1 were tightly coregulated in uveal melanomas (Fig. 4K). Consequently, our study with PD-L1 expressing yet refractory tumor models shows that CAMK1D represented another level of immune resistance superseding the PD-L1 axis in mediating immune suppression.

Using the TCGA database, we studied CAMK1D and PD-L1 coregulation in other tumor entities that are largely unresponsive to anti–PD-1 treatment, specifically ovarian, stomach, and esophageal carcinomas (55, 56). As observed in uveal melanoma, CAMK1D, and PD-L1 were coexpressed and we detected significant correlations of CAMK1D and Fas receptor expression with poor outcomes (Supplementary Fig. S5A–S5F). Hence, CAMK1D is coregulated with PD-L1 and controls tumor rejection after Fas receptor activation in several anti–PD-1 treatment–refractory tumors.

CAMK1D regulated the activity of effector caspases-3, -6, and -7 after Fas activation

FasL binding to Fas receptor results in complex signaling events leading to a caspase cascade that initiates apoptosis (57); this binding also stimulates Ca2+ influx into the cytoplasm, which ultimately triggers CAMK1D activation (58). We speculated that CAMK1D might interfere with the apoptotic cascade to mediate its tumor-protective effect. Thus, we assessed the impact of CAMK1D expression on tumor cell killing in the absence of effector caspases. Silencing of each downstream effector caspase-3, -6, and -7 completely abrogated the increased lysis of CAMK1D-deficient tumor cells after FasL exposure (Fig. 5A and B). Thus, CAMK1D selectively regulated cellular sensitivity toward apoptotic cell death. These results demonstrated the necessity of simultaneous activity of all three effector caspases for efficient induction of apoptotic cell death after Fas activation.

Figure 5.
Figure 5.

CAMK1D regulation. A, Caspase-3, -6, and -7 knockdown in KMM-1 cells measured via endpoint PCR. B, Effector caspases were knocked down alone or in combination with CAMK1D and treated with rHuFasL or culture medium. Representative result of luciferase-based readout of three independent experiments. C, Intracellular calcium response in KMM-1 cells upon MIL coculture (top) and rHuFasL treatment (bottom). D, Representative picture of intracellular free Ca2+ measurement in KMM-1 scr–transfected cells before (top) and after (bottom) coculture with MILs or treatment with rHuFasL. E, KMM-1 cells were treated with different concentrations of CaM inhibitor, and tumor cell survival was measured by luciferase activity. F, scr- and siCAMK1D-transfected KMM-1 cells were treated as in E together with rHuFasL. G, KMM-1 (left) and Mel270 (right) cells were treated with the indicated concentration of QPP-A and exposed to rHuFasL or medium. Tumor cell survival was measured by luciferase activity. H, MC38-Camk1d KO and -NTS cells were each injected into C57BL6 and NSG mice. Graphs show mean ± SEM, and statistical significance was calculated using two-way ANOVA Bonferroni post hoc test. B, Graphs show mean ± SD. Statistical significance was calculated using unpaired, two-tailed Student t test. E–G, Experiments were performed in triplicates, and representative results of three independent experiments are shown. Graphs show mean ± SEM, and statistical significance was calculated using unpaired, two-tailed Student t test (*, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001; ****, P ≤ 0.0001). Casp, caspase; norm., normalized; ns, not significant.

CAMK1D activation depends on Ca2+/calmodulin (CaM) binding, allowing the CAMK kinase (CAMKK) to phosphorylate and fully activate CAMK1D (58, 59). We speculated that FasL-expressing MILs might trigger Ca2+ release and therefore compared intracellular Ca2+ in KMM-1 cells on single-cell level after exposure to MILs or rHuFasL. Shortly after treatment, both conditions induced an increase of intracellular Ca2+ (Fig. 5C and D).

W-7 hydrochloride inhibits Ca2+/calmodulin complexes (60) consequently impacting CAMK1D activation. Treatment with 5 μmol/L W-7 hydrochloride was not toxic to KMM-1 cells (Fig. 5E) and sharply recapitulated the effect of CAMK1D silencing on FasL-induced tumor cell apoptosis, suggesting that CAMK1D was the decisive target of calmodulin for mediating FasL resistance (Fig. 5F). Because both CAMK1D silencing and W-7 hydrochloride treatment only incompletely blocked CAMK1D, we explored whether their combination reduced cell viability after FasL exposure. This combinatorial treatment resulted in a 3-fold further increase of tumor cell killing (Fig. 5F). To corroborate these findings, we applied CAMK1D inhibitor (QPP-A) to multiple myeloma and uveal melanoma cell lines. The additional treatment with rHuFasL induced a significant loss of tumor cell viability, confirming that CAMK1D played a substantial role in conferring resistance toward apoptosis (Fig. 5G).

These results demonstrated that CAMK1D activation in cancer cells was (i) triggered by CTL via FasL-induced Ca2+ release and (ii) was required to control Fas-induced tumor cell apoptosis. To confirm the immune-resistant role of CAMK1D in vivo, we knocked out Camk1d in the murine colorectal cell line MC38 (Supplementary Fig. S6A). In vitro analysis of Camk1d-deficient tumor cells revealed their increased sensitivity toward FasL as well as TRAIL-mediated apoptosis (Supplementary Fig. S6B). Thus, we injected MC38-Camk1d KO and -NTS (nontargeting sequence) cells into the left and right flank of the same mouse of both immunodeficient NSG and immunocompetent C57BL6 mice. Camk1d KO and NTS tumors developed rapidly in a similar manner in NSG mice, whereas a significant difference was observed in C57BL6 mice where Camk1d-deficient tumors were significantly reduced (Fig. 5H). These data demonstrated that the immune system in immunocompetent mice was not able to reduce tumor outgrowth due to Camk1d expression.

To elucidate CAMK1D involvement in the Fas signaling cascade, we studied activation of caspase-8 and -9, the prototypic initiator caspases of the extrinsic and intrinsic apoptotic pathway, respectively (61). FasL-induced activation of both caspases was comparably effective in CAMK1D-proficient and -deficient KMM-1 cells (Fig. 6A and B). Consequently, we hypothesized that CAMK1D regulated the activity of downstream effector caspases. To this end, we first studied the activation of the central executioner caspase-3. We observed an increase in caspase-3 activation in CAMK1D-deficient KMM-1 cells after FasL treatment (Fig. 6C–E). In addition, we also detected increased cleavage of the effector caspase-6 and -7 in CAMK1D-deficient tumor cells (Fig. 6F; Supplementary Fig. S7A). The phosphorylation and activation of the transcription factor cAMP response element-binding protein (CREB) was increased in CAMK1D-proficient cells, which was responsible for the transcription of the antiapoptotic molecule Bcl-2 (Supplementary Fig. S7B). We also observed that at early time points (15 minutes, 30 minutes, and 1 hour) after rHuFasL stimulation, the phosphorylation of ERK1/2 was enhanced in wild-type cells, but not in CAMK1D knockdown cells (Supplementary Fig. S7B). The altered activation of the presented proteins implied that CAMK1D not only controlled activation and activity of effector caspases, but also induced the expression of antiapoptotic and mitogenic proteins leading to tumor cell resistance toward FasL-positive T cells. CAMK1D has thus far not been established as a regulator of effector caspase activity. In silico analysis predicted a binding motif for CAMK1D on caspase-3 and caspase-6 (Supplementary Fig. S7D). Notably, CAMK1D coimmunoprecipitated with caspase-3, caspase-6, and caspase-7 and the interaction increased upon rHuFasL treatment (Fig. 6G and H; Supplementary Fig. S7C). A direct CAMK1D/effector caspase interaction could result in stoichiometric inhibition of caspase cleavage by initiator caspases. Alternatively, the effector caspases may also serve as targets of CAMK1D kinase activity. Phosphorylation of inhibitory serine residues impedes caspases activation, proteolytic activity, and ultimately hampers apoptosis induction (62).

Figure 6.
Figure 6.

Pathways regulated by CAMK1D. A–C, Luminex assays measuring apoptosis proteins. CAMK1D-proficient and -deficient cells were stimulated with rHuFasL for the indicated time frames. Protein expression was normalized to GAPDH and compared with scr-unstimulated cells. The amount of cleaved caspase-8 (A), cleaved caspase-9 (B), and cleaved caspase-3 (C) was measured. Graphs show cumulative data of at least two independent experiments. MFI, mean fluorescence intensity. D, FACS analysis of scr- and siCAMK1D-transfected KMM-1 cells treated for the given time frames with rHuFasL. Gate marks active caspase-3–labeled cells. E and F, KMM-1 cells were treated as in A–C and full-length and cleaved caspase-3 (E) and caspase-6 (F) were measured via Western blot analysis. The Sodium–Potassium ATPase was used as housekeeping gene. Representative results of at least two independent experiments. G and H, Representative blots showing coimmunoprecipitation of CAMK1D with caspase-3 (G) and caspase-6 (H) upon 4-hour rHuFasL stimulation in KMM-1 cells. Unstimulated cells were used as negative control. Unstimulated and stimulated cell lysates were used as positive control for CAMK1D detection. IP, immunoprecipitated. I and J, Western blot measuring phosphorylated caspase-3 and caspase-6 upon rHuFasL stimulation. Quantification of phosphorylated caspase-3 (K) and phosphorylated caspase-6 (L) upon rHuFasL stimulation for the indicated time frames. Graphs show cumulative data of four independent experiments. A, B, K, and L, Graphs show mean ± SEM. C, Graph shows mean ± SD. A–C, K, and L, Statistical significance was calculated using unpaired, two-tailed Student t test (*, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001). casp, caspase; norm, normalized; Stim., stimulated; Unstim., unstimulated.

The inhibitory phosphorylation sites of caspase-3 (Ser150) and caspase-6 (Ser257; refs. 63, 64) were located in the kinase-function critical distance of up to 4 amino acids apart from the predicted binding site for CAMK1D (Supplementary Fig. S7D). We therefore wondered whether CAMK1D was able to phosphorylate Ser150 and Ser257 of caspase-3 and -6, respectively. CAMK1D-deficient KMM-1 cells had reduced phosphorylation of inhibitory serine residues on both caspase-3 and -6 already at steady–state conditions (Fig. 6I–L). In KMM-1 wt cells, phosphorylation transiently decreased 15–30 minutes after FasL treatment (which is attributed to transient stimulation of phosphatases; ref. 65), but recovered to prestimulation expression within 1 (caspase-3) to 4 hours (caspase-6). In contrast, caspase-3 and -6 phosphorylation was persistently low in CAMK1D-deficient KMM-1 cells, resulting in overall much lower caspase inactivation compared with CAMK1D wt cells. This demonstrated that CAMK1D was required for steady-state inactivation of effector caspases through phosphorylation and for the rapid restoration of caspase-3 and -6 phosphorylation after FasL stimulation.

CAMK1D, upon its activation through FasL, regulated activation and activity of all effector caspases after cytotoxic T-cell encounter. These results suggested that this effect was at least partially achieved by the inhibitory phosphorylation of the effector caspases.

Discussion

Despite noteworthy improvements in the field of immunotherapy, where immune checkpoint blockade (ICB) has broad clinical success (11, 13, 14, 66) a significant proportion of patients with cancer do not respond to ICB (15, 16, 67). Unknown immune checkpoint molecules might be employed by tumor cells to escape the antitumor immune response. Here, we used a multiple myeloma cell line to conduct a systematic search for genes controlling immune rejection in PD-L1–refractory human tumors. Among the identified genes, CAMK1D was chosen for further validation and mode of action analysis. CAMK1D expression is elevated in invasive carcinomas compared with carcinoma in situ and overexpression of CAMK1D in nontumorigenic breast epithelial cells increased proliferation and epithelial-to-mesenchymal transition (68). We reported a different role of CAMK1D in controlling the resistance of PD-L1+ tumor cells against apoptosis triggered by cytotoxic T cells. Tumor cell killing in CAMK1D-deficient cells was independent of the T-cell source, as both MILs and flu-specific CD8+ T cells were able to reproduce the same effect. Tumor cells can evade the immune system either by intrinsically increasing tumor cell resistance (69) or by hampering immune cell activation (53). Our data demonstrated that CAMK1D-deficient tumor cells did not enhance T-cell function. On the other hand, CAMK1D acted as central mediator of intrinsic tumor resistance toward CTL. Cytotoxic T cells eliminate tumor cells through the extrinsic apoptosis pathway, initiated by death receptor signaling, activating pro–caspase-8 (57, 70, 71), or by triggering the intrinsic pathway through the release of cytotoxic granules. This induces mitochondrial damage, apoptosome formation, and subsequent activation of pro–caspase-9 (72). Both initiator caspases activate the common executioner caspases-3, -6, and -7, which in turn cleave key intracellular substrates including endonucleases, thus irreversibly triggering the apoptotic cell death (73, 74). We observed that the initiator caspase-8 and -9 were not differentially affected in CAMK1D-proficient and -deficient tumor cells. Caspase-8 is inactivated upon phosphorylation of tyrosine-380, which leads to increased resistance to CD95-induced apoptosis (75). However, CAMK1D is a serine/threonine protein kinase and in silico analysis revealed no binding site between CAMK1D and caspase-8. Coexpression of death receptor ligands and cytotoxic granules enabled CTL to simultaneously trigger both apoptosis pathways, which differed from other common mechanisms of cell apoptosis, which generally trigger either one or the other pathway (76). Therefore, efficient resistance mechanisms against T-cell–induced apoptosis may target the common end route of both pathways. Consequently, inhibition of effector caspase activity may represent a hallmark of tumor immune resistance.

Caspases can be regulated by posttranslational modifications such as phosphorylation and ubiquitylation that can block caspases activation and activity (62). Indeed, phosphorylation of caspase-3 by p38 at Ser150, directly inhibits caspase-3, hindering Fas-induced apoptosis in neutrophils (63). Likewise, in the colon cancer cell line SW480, caspase-6 is inhibited by ARK5-phosphorylation, leading to the evasion of Fas-induced apoptosis (64). Caspase-7 can be inhibited posttranslationally by PAK2-mediated phosphorylation at Ser30, Thr173 and Ser239, which negatively regulates caspase-7 activity (77). We proposed a model where FasL stimulation increases calcium release from the ER, thereby binding and activating calmodulin, the upstream activator of CAMK1D. The binding of calmodulin to CAMK1D allows CAMKK to phosphorylate and fully activate CAMK1D. As a consequence, CAMK1D bound to the effector caspases inhibiting their activation acting as a direct stoichiometric inhibitor and by phosphorylation CAMK1D subsequently reduced the activity of the effector caspases. Moreover, activated CAMK1D translocated into the nucleus where it phosphorylated and activated CREB, leading to the transcription of Bcl-2. Thus, CAMK1D is an immune checkpoint molecule that interferes with tumor cell death, sustaining antiapoptotic pathways.

As CAMK1D is ubiquitously expressed, the pharmacologic inhibition may increase tumor susceptibility toward T-cell attack, but also impair T-cell activity. In line with this concern, blockade of the tyrosine kinase JAK2 sensitized multiple myeloma tumor cells to NK-cell attack (78); however, the function of NK and T cells was impaired in human myeloproliferative neoplasms (79–81). More studies must be conducted to clarify the impact of CAMK1D-targeted therapy on T cells. Nonetheless, CAMK1D remains a potential target for cancer immunotherapy, in particular, for those patients who experience relapse or demonstrate unresponsiveness to conventional therapies.

Our studies confirm the role of CAMK1D in vivo as a novel immune checkpoint molecule conferring resistance toward immune attack. It is conceivable that tumor cells exploit Fas signaling imposed by cytotoxic T cells to activate an apoptosis resistance mechanism targeting the final effector expression of both intrinsic and extrinsic apoptotic pathways resulting in an increased resistance to immune cell attack. In T-cell–infiltrated tumors, this mechanism may impact the treatment resistance of tumor cells, as CAMK1D may reduce the efficacy of antitumor treatments that directly or indirectly exploit the intrinsic apoptotic signaling pathways to trigger cancer cell death.

Disclosure of Potential Conflicts of Interest

V. Volpin reports grants from Deutsche Forschungsgemeinschaft (DFG, German Research Foundation; project number 324392634 - TRR 221) during the conduct of the study, personal fees from iOmx Therapeutics AG (advisor) outside the submitted work, as well as a patent for EP20175030 (intracellular kinase associated with resistance against T-cell–mediated cytotoxicity and uses thereof pending).

T. Michels reports personal fees from iOmx Therapeutics AG (advisor) outside the submitted work and a patent for EP20175030 (intracellular kinase associated with resistance against T-cell–mediated cytotoxicity and uses thereof pending).

A. Sorrentino reports personal fees from iOmx Therapeutics AG (advisor) outside the submitted work and a patent for EP20175030 (intracellular kinase associated with resistance against T-cell–mediated cytotoxicity and uses thereof pending).

M. Boutros reports grants from Cellzome/GlaxoSmithKline outside the submitted work and a patent for EP20175030 (intracellular kinase associated with resistance against T-cell–mediated cytotoxicity and uses thereof pending).

S. Haferkamp reports personal fees from Bristol-Myers Squibb and MSD and grants from DFG (HA8418) during the conduct of the study.

H. Goldschmidt reports the following potential conflicts of interest: grants, research support, nonfinancial support, and personal fees from Amgen, Bristol-Myers Squibb/Celgene, Janssen/Cilag, and Sanofi; grants, research support, and personal fees from Chugai; research support and nonfinancial support from Takeda; research support and personal fees from Novartis; and grants from the Dietmar-Hopp-Foundation and the Johns Hopkins University.

H. Goldschmidt reports the following potential conflicts of interest outside the submitted work: research support from Incyte, Molecular Partners, Merck Sharp and Dohme (MSD), and Mundipharma GmbH; nonfinancial support from Adaptive Biotechnology; and personal fees from Beupdated Helbig Consulting & Research AG Schweiz, Chop GmbH, Congress Culture Concept München; ConnectMedia Warschau/Polen; Tumorzentrum Munchen; FomF Gmbh; GlaxoSmithKline; GWT Forschung und Innovation Dresden; Kompetenznetz Maligne Lymphome (KML); MedConcept GmbH; Medical Communication GmbH; Münchner Leukämie Labor; New Concept Oncology; Omnia Med Deutschland; Onko Internetportal dkg-web GmbH; STIL Forschungs GmbH; Veranstaltungskonzept Gesundheit Metternich; Institut für Versorgungsforschung in der Onkologie GbR; and ArtTempi.

G. Vereb reports grants from National Research, Development and Innovation Office, Hungary (OTKA K119690) during the conduct of the study.

N. Khandelwal reports a patent for EP20175030 (intracellular kinase associated with resistance against T-cell–mediated cytotoxicity and uses thereof pending).

P. Beckhove reports grants from Deutsche Forschungsgemeinschaft (DFG, German Research Foundation; project number 324392634 - TRR 221) and other from iOmx Therapeutics AG (supply of small molecules) during the conduct of the study, other from iOmx Therapeutics AG (shareholder) outside the submitted work, as well as a patent for EP20175030 (intracellular kinase associated with resistance against T-cell–mediated cytotoxicity and uses thereof pending).

No potential conflicts of interest were disclosed by the other authors.

Authors' Contributions

V. Volpin: Conceptualization, resources, formal analysis, supervision, funding acquisition, investigation, visualization, writing–original draft, project administration, writing–review and editing. T. Michels: Conceptualization, software, formal analysis. A. Sorrentino: Conceptualization, investigation. A.N. Menevse: Resources, investigation. G. Knoll: Investigation. M. Ditz: Investigation. V.M. Milenkovic: Investigation. C.-Y. Chen: Investigation. A. Rathinasamy: Resources. K. Griewank: Resources. M. Boutros: Resources. S. Haferkamp: Resources. M. Berneburg: Resources. C.H. Wetzel: Resources. A. Seckinger: Formal analysis. D. Hose: Resources, formal analysis. H. Goldschmidt: Conceptualization. M. Ehrenschwender: Resources. M. Witzens-Harig: Resources. A. Szoor: Investigation. G. Vereb: Resources. N. Khandelwal: Conceptualization, resources. P. Beckhove: Conceptualization, supervision, funding acquisition, visualization, writing–original draft, project administration, writing–review and editing.

Acknowledgments

The authors thank Dr. Haase (LMU, Munich, Germany) for the pEGFP-Luc plasmid, Prof. Moldenhauer (DKFZ) for the MHC-I antibody, Martins Freire (iOmx Therapeutics AG) for the bulk MC38-Camk1d KO cells, and Soelch (RCI) for technical support of QPP-A testing. This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation; project number 324392634 - TRR 221) and the National Research, Development and Innovation Office, Hungary (OTKA K119690).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Footnotes

  • Note: Supplementary data for this article are available at Cancer Immunology Research Online (http://cancerimmunolres.aacrjournals.org/).

  • Cancer Immunol Res 2020;8:1163–79

  • Received August 8, 2019.
  • Revision received March 16, 2020.
  • Accepted July 9, 2020.
  • Published first July 14, 2020.

References

  1. 1.↵
    1. Choi C,
    2. Witzens M,
    3. Bucur M,
    4. Feuerer M,
    5. Sommerfeldt N,
    6. Trojan A,
    7. et al.
    Enrichment of functional CD8 memory T cells specific for MUC1 in bone marrow of patients with multiple myeloma. Blood 2005;105:2132–4.
    OpenUrlAbstract/FREE Full Text
  2. 2.↵
    1. Safi S,
    2. Yamauchi Y,
    3. Rathinasamy A,
    4. Stamova S,
    5. Eichhorn M,
    6. Warth A,
    7. et al.
    Functional T cells targeting tumor-associated antigens are predictive for recurrence-free survival of patients with radically operated non-small cell lung cancer. Oncoimmunology 2017;6:e1360458.
    OpenUrl
  3. 3.↵
    1. Schmitz-Winnenthal FH,
    2. Volk C,
    3. Z'Graggen K,
    4. Galindo L,
    5. Nummer D,
    6. Ziouta Y,
    7. et al.
    High frequencies of functional tumor-reactive T cells in bone marrow and blood of pancreatic cancer patients. Cancer Res 2005;65:10079–87.
    OpenUrlAbstract/FREE Full Text
  4. 4.↵
    1. Galon J,
    2. Costes A,
    3. Sanchez-Cabo F,
    4. Kirilovsky A,
    5. Mlecnik B,
    6. Lagorce-Pages C,
    7. et al.
    Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 2006;313:1960–4.
    OpenUrlAbstract/FREE Full Text
  5. 5.↵
    1. Reissfelder C,
    2. Stamova S,
    3. Gossmann C,
    4. Braun M,
    5. Bonertz A,
    6. Walliczek U,
    7. et al.
    Tumor-specific cytotoxic T lymphocyte activity determines colorectal cancer patient prognosis. J Clin Invest 2015;125:739–51.
    OpenUrlCrossRefPubMed
  6. 6.↵
    1. Pardoll DM
    . The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer 2012;12:252–64.
    OpenUrlCrossRefPubMed
  7. 7.↵
    1. Abiko K,
    2. Matsumura N,
    3. Hamanishi J,
    4. Horikawa N,
    5. Murakami R,
    6. Yamaguchi K,
    7. et al.
    IFN-gamma from lymphocytes induces PD-L1 expression and promotes progression of ovarian cancer. Br J Cancer 2015;112:1501–9.
    OpenUrlCrossRefPubMed
  8. 8.↵
    1. Alsaab HO,
    2. Sau S,
    3. Alzhrani R,
    4. Tatiparti K,
    5. Bhise K,
    6. Kashaw SK,
    7. et al.
    PD-1 and PD-L1 checkpoint signaling inhibition for cancer immunotherapy: mechanism, combinations, and clinical outcome. Front Pharmacol 2017;8:561.
    OpenUrlCrossRefPubMed
  9. 9.↵
    1. Zou W,
    2. Wolchok JD,
    3. Chen L
    . PD-L1 (B7-H1) and PD-1 pathway blockade for cancer therapy: mechanisms, response biomarkers, and combinations. Sci Transl Med 2016;8:328rv4.
    OpenUrlFREE Full Text
  10. 10.↵
    1. Yokosuka T,
    2. Takamatsu M,
    3. Kobayashi-Imanishi W,
    4. Hashimoto-Tane A,
    5. Azuma M,
    6. Saito T
    . Programmed cell death 1 forms negative costimulatory microclusters that directly inhibit T cell receptor signaling by recruiting phosphatase SHP2. J Exp Med 2012;209:1201–17.
    OpenUrlAbstract/FREE Full Text
  11. 11.↵
    1. Hodi FS,
    2. O'Day SJ,
    3. McDermott DF,
    4. Weber RW,
    5. Sosman JA,
    6. Haanen JB,
    7. et al.
    Improved survival with ipilimumab in patients with metastatic melanoma. N Engl J Med 2010;363:711–23.
    OpenUrlCrossRefPubMed
  12. 12.↵
    1. Slovin SF,
    2. Higano CS,
    3. Hamid O,
    4. Tejwani S,
    5. Harzstark A,
    6. Alumkal JJ,
    7. et al.
    Ipilimumab alone or in combination with radiotherapy in metastatic castration-resistant prostate cancer: results from an open-label, multicenter phase I/II study. Ann Oncol 2013;24:1813–21.
    OpenUrlCrossRefPubMed
  13. 13.↵
    1. Yang JC,
    2. Hughes M,
    3. Kammula U,
    4. Royal R,
    5. Sherry RM,
    6. Topalian SL,
    7. et al.
    Ipilimumab (anti-CTLA4 antibody) causes regression of metastatic renal cell cancer associated with enteritis and hypophysitis. J Immunother 2007;30:825–30.
    OpenUrlCrossRef
  14. 14.↵
    1. Topalian SL,
    2. Hodi FS,
    3. Brahmer JR,
    4. Gettinger SN,
    5. Smith DC,
    6. McDermott DF,
    7. et al.
    Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med 2012;366:2443–54.
    OpenUrlCrossRefPubMed
  15. 15.↵
    1. Bu X,
    2. Mahoney KM,
    3. Freeman GJ
    . Learning from PD-1 resistance: new combination strategies. Trends Mol Med 2016;22:448–51.
    OpenUrlCrossRef
  16. 16.↵
    1. Carbognin L,
    2. Pilotto S,
    3. Milella M,
    4. Vaccaro V,
    5. Brunelli M,
    6. Calio A,
    7. et al.
    Differential activity of nivolumab, pembrolizumab and MPDL3280A according to the tumor expression of programmed death-ligand-1 (PD-L1): sensitivity analysis of trials in melanoma, lung and genitourinary cancers. PLoS One 2015;10:e0130142.
    OpenUrlCrossRefPubMed
  17. 17.↵
    1. Hugo W,
    2. Zaretsky JM,
    3. Sun L,
    4. Song C,
    5. Moreno BH,
    6. Hu-Lieskovan S,
    7. et al.
    Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 2016;165:35–44.
    OpenUrlCrossRefPubMed
  18. 18.↵
    1. Nowicki TS,
    2. Hu-Lieskovan S,
    3. Ribas A
    . Mechanisms of resistance to PD-1 and PD-L1 blockade. Cancer J 2018;24:47–53.
    OpenUrlPubMed
  19. 19.↵
    1. Fourcade J,
    2. Sun Z,
    3. Benallaoua M,
    4. Guillaume P,
    5. Luescher IF,
    6. Sander C,
    7. et al.
    Upregulation of Tim-3 and PD-1 expression is associated with tumor antigen-specific CD8+ T cell dysfunction in melanoma patients. J Exp Med 2010;207:2175–86.
    OpenUrlAbstract/FREE Full Text
  20. 20.↵
    1. Wang L,
    2. Rubinstein R,
    3. Lines JL,
    4. Wasiuk A,
    5. Ahonen C,
    6. Guo Y,
    7. et al.
    VISTA, a novel mouse Ig superfamily ligand that negatively regulates T cell responses. J Exp Med 2011;208:577–92.
    OpenUrlAbstract/FREE Full Text
  21. 21.↵
    1. Kocoglu M,
    2. Badros A
    . The role of immunotherapy in multiple myeloma. Pharmaceuticals 2016;9:3.
    OpenUrl
  22. 22.↵
    1. Jing W,
    2. Gershan JA,
    3. Weber J,
    4. Tlomak D,
    5. McOlash L,
    6. Sabatos-Peyton C,
    7. et al.
    Combined immune checkpoint protein blockade and low dose whole body irradiation as immunotherapy for myeloma. J Immunother Cancer 2015;3:2.
    OpenUrlAbstract/FREE Full Text
  23. 23.↵
    1. Hallett WH,
    2. Jing W,
    3. Drobyski WR,
    4. Johnson BD
    . Immunosuppressive effects of multiple myeloma are overcome by PD-L1 blockade. Biol Blood Marrow Transplant 2011;17:1133–45.
    OpenUrlCrossRefPubMed
  24. 24.↵
    1. Benson DM Jr.,
    2. Bakan CE,
    3. Mishra A,
    4. Hofmeister CC,
    5. Efebera Y,
    6. Becknell B,
    7. et al.
    The PD-1/PD-L1 axis modulates the natural killer cell versus multiple myeloma effect: a therapeutic target for CT-011, a novel monoclonal anti-PD-1 antibody. Blood 2010;116:2286–94.
    OpenUrlAbstract/FREE Full Text
  25. 25.↵
    1. Tamura H,
    2. Ishibashi M,
    3. Yamashita T,
    4. Tanosaki S,
    5. Okuyama N,
    6. Kondo A,
    7. et al.
    Marrow stromal cells induce B7-H1 expression on myeloma cells, generating aggressive characteristics in multiple myeloma. Leukemia 2013;27:464–72.
    OpenUrlCrossRefPubMed
  26. 26.↵
    1. Lesokhin AM,
    2. Ansell SM,
    3. Armand P,
    4. Scott EC,
    5. Halwani A,
    6. Gutierrez M,
    7. et al.
    Nivolumab in patients with relapsed or refractory hematologic malignancy: preliminary results of a phase Ib study. J Clin Oncol 2016;34:2698–704.
    OpenUrlAbstract/FREE Full Text
  27. 27.↵
    1. Sonneveld P,
    2. Schmidt-Wolf IG,
    3. van der Holt B,
    4. El Jarari L,
    5. Bertsch U,
    6. Salwender H,
    7. et al.
    Bortezomib induction and maintenance treatment in patients with newly diagnosed multiple myeloma: results of the randomized phase III HOVON-65/GMMG-HD4 trial. J Clin Oncol 2012;30:2946–55.
    OpenUrlAbstract/FREE Full Text
  28. 28.↵
    1. Hulin C,
    2. Belch A,
    3. Shustik C,
    4. Petrucci MT,
    5. Duhrsen U,
    6. Lu J,
    7. et al.
    Updated outcomes and impact of age with lenalidomide and low-dose dexamethasone or melphalan, prednisone, and thalidomide in the randomized, phase III FIRST trial. J Clin Oncol 2016;34:3609–17.
    OpenUrlAbstract/FREE Full Text
  29. 29.↵
    1. Lokhorst HM,
    2. Plesner T,
    3. Laubach JP,
    4. Nahi H,
    5. Gimsing P,
    6. Hansson M,
    7. et al.
    Targeting CD38 with daratumumab monotherapy in multiple myeloma. N Engl J Med 2015;373:1207–19.
    OpenUrlCrossRefPubMed
  30. 30.↵
    1. Child JA,
    2. Morgan GJ,
    3. Davies FE,
    4. Owen RG,
    5. Bell SE,
    6. Hawkins K,
    7. et al.
    High-dose chemotherapy with hematopoietic stem-cell rescue for multiple myeloma. N Engl J Med 2003;348:1875–83.
    OpenUrlCrossRefPubMed
  31. 31.↵
    1. Seckinger A,
    2. Delgado JA,
    3. Moser S,
    4. Moreno L,
    5. Neuber B,
    6. Grab A,
    7. et al.
    Target expression, generation, preclinical activity, and pharmacokinetics of the BCMA-T cell bispecific antibody EM801 for multiple myeloma treatment. Cancer Cell 2017;31:396–410.
    OpenUrl
  32. 32.↵
    1. Greipp PR,
    2. Miguel JS,
    3. Durie BGM,
    4. Crowley JJ,
    5. Barlogie B,
    6. Bladé J,
    7. et al.
    International staging system for multiple myeloma. J Clin Oncol 2005;23:3412–20.
    OpenUrlAbstract/FREE Full Text
  33. 33.↵
    1. Durie BG
    . Staging and kinetics of multiple myeloma. Semin Oncol 1986;13:300–9.
    OpenUrlPubMed
  34. 34.↵
    1. Blade J,
    2. Samson D,
    3. Reece D,
    4. Apperley J,
    5. Bjorkstrand B,
    6. Gahrton G,
    7. et al.
    Criteria for evaluating disease response and progression in patients with multiple myeloma treated by high-dose therapy and haemopoietic stem cell transplantation. Myeloma Subcommittee of the EBMT. European Group for Blood and Marrow Transplant. Br J Haematol 1998;102:1115–23.
    OpenUrlCrossRefPubMed
  35. 35.↵
    1. Hose D,
    2. Moreaux J,
    3. Meissner T,
    4. Seckinger A,
    5. Goldschmidt H,
    6. Benner A,
    7. et al.
    Induction of angiogenesis by normal and malignant plasma cells. Blood 2009;114:128–43.
    OpenUrlAbstract/FREE Full Text
  36. 36.↵
    1. Moreaux J,
    2. Cremer FW,
    3. Reme T,
    4. Raab M,
    5. Mahtouk K,
    6. Kaukel P,
    7. et al.
    The level of TACI gene expression in myeloma cells is associated with a signature of microenvironment dependence versus a plasmablastic signature. Blood 2005;106:1021–30.
    OpenUrlAbstract/FREE Full Text
  37. 37.↵
    1. Zhang XG,
    2. Gaillard JP,
    3. Robillard N,
    4. Lu ZY,
    5. Gu ZJ,
    6. Jourdan M,
    7. et al.
    Reproducible obtaining of human myeloma cell lines as a model for tumor stem cell study in human multiple myeloma. Blood 1994;83:3654–63.
    OpenUrlAbstract/FREE Full Text
  38. 38.↵
    1. Corre J,
    2. Mahtouk K,
    3. Attal M,
    4. Gadelorge M,
    5. Huynh A,
    6. Fleury-Cappellesso S,
    7. et al.
    Bone marrow mesenchymal stem cells are abnormal in multiple myeloma. Leukemia 2007;21:1079–88.
    OpenUrlPubMed
  39. 39.↵
    1. Fuhler GM,
    2. Baanstra M,
    3. Chesik D,
    4. Somasundaram R,
    5. Seckinger A,
    6. Hose D,
    7. et al.
    Bone marrow stromal cell interaction reduces syndecan-1 expression and induces kinomic changes in myeloma cells. Exp Cell Res 2010;316:1816–28.
    OpenUrlCrossRefPubMed
  40. 40.↵
    1. Griewank KG,
    2. Yu X,
    3. Khalili J,
    4. Sozen MM,
    5. Stempke-Hale K,
    6. Bernatchez C,
    7. et al.
    Genetic and molecular characterization of uveal melanoma cell lines. Pigment Cell Melanoma Res 2012;25:182–7.
    OpenUrlCrossRefPubMed
  41. 41.↵
    1. Dudley ME,
    2. Wunderlich JR,
    3. Shelton TE,
    4. Even J,
    5. Rosenberg SA
    . Generation of tumor-infiltrating lymphocyte cultures for use in adoptive transfer therapy for melanoma patients. J Immunother 2003;26:332–42.
    OpenUrlCrossRef
  42. 42.↵
    1. Jin J,
    2. Sabatino M,
    3. Somerville R,
    4. Wilson JR,
    5. Dudley ME,
    6. Stroncek DF,
    7. et al.
    Simplified method of the growth of human tumor infiltrating lymphocytes in gas-permeable flasks to numbers needed for patient treatment. J Immunother 2012;35:283–92.
    OpenUrlCrossRef
  43. 43.↵
    1. Forget MA,
    2. Malu S,
    3. Liu H,
    4. Toth C,
    5. Maiti S,
    6. Kale C,
    7. et al.
    Activation and propagation of tumor-infiltrating lymphocytes on clinical-grade designer artificial antigen-presenting cells for adoptive immunotherapy of melanoma. J Immunother 2014;37:448–60.
    OpenUrlCrossRef
  44. 44.↵
    1. Hose D,
    2. Reme T,
    3. Hielscher T,
    4. Moreaux J,
    5. Meissner T,
    6. Seckinger A,
    7. et al.
    Proliferation is a central independent prognostic factor and target for personalized and risk adapted treatment in multiple myeloma. Haematologica 2011;96:87–95.
    OpenUrlAbstract/FREE Full Text
  45. 45.↵
    1. Seckinger A,
    2. Meißner T,
    3. Moreaux J,
    4. Depeweg D,
    5. Hillengass J,
    6. Hose K,
    7. et al.
    Clinical and prognostic role of annexin A2 in multiple myeloma. Blood 2012;120:1087–94.
    OpenUrlAbstract/FREE Full Text
  46. 46.↵
    1. Wan YW,
    2. Allen GI,
    3. Liu Z
    . TCGA2STAT: simple TCGA data access for integrated statistical analysis in R. Bioinformatics 2016;32:952–4.
    OpenUrlCrossRefPubMed
  47. 47.↵
    1. Gilbert DF,
    2. Erdmann G,
    3. Zhang X,
    4. Fritzsche A,
    5. Demir K,
    6. Jaedicke A,
    7. et al.
    A novel multiplex cell viability assay for high-throughput RNAi screening. PLoS One 2011;6:e28338.
    OpenUrlCrossRefPubMed
  48. 48.↵
    1. Khandelwal N,
    2. Breinig M,
    3. Speck T,
    4. Michels T,
    5. Kreutzer C,
    6. Sorrentino A,
    7. et al.
    A high-throughput RNAi screen for detection of immune-checkpoint molecules that mediate tumor resistance to cytotoxic T lymphocytes. EMBO Mol Med 2015;7:450–63.
    OpenUrlAbstract/FREE Full Text
  49. 49.↵
    1. Boutros M,
    2. Bras LP,
    3. Huber W
    . Analysis of cell-based RNAi screens. Genome Biol 2006;7:R66.
    OpenUrlCrossRefPubMed
  50. 50.↵
    1. Zhang C,
    2. Xin H,
    3. Zhang W,
    4. Yazaki PJ,
    5. Zhang Z,
    6. Le K,
    7. et al.
    CD5 binds to interleukin-6 and induces a feed-forward loop with the transcription factor STAT3 in B cells to promote cancer. Immunity 2016;44:913–23.
    OpenUrlCrossRefPubMed
  51. 51.↵
    1. Tiedemann RE,
    2. Zhu YX,
    3. Schmidt J,
    4. Yin H,
    5. Shi CX,
    6. Que Q,
    7. et al.
    Kinome-wide RNAi studies in human multiple myeloma identify vulnerable kinase targets, including a lymphoid-restricted kinase, GRK6. Blood 2010;115:1594–604.
    OpenUrlAbstract/FREE Full Text
  52. 52.↵
    1. Ye DZ,
    2. Field J
    . PAK signaling in cancer. Cell Logist 2012;2:105–16.
    OpenUrlCrossRefPubMed
  53. 53.↵
    1. Chen L,
    2. Flies DB
    . Molecular mechanisms of T cell co-stimulation and co-inhibition. Nat Rev Immunol 2013;13:227–42.
    OpenUrlCrossRefPubMed
  54. 54.↵
    1. Algazi AP,
    2. Tsai KK,
    3. Shoushtari AN,
    4. Munhoz RR,
    5. Eroglu Z,
    6. Piulats JM,
    7. et al.
    Clinical outcomes in metastatic uveal melanoma treated with PD-1 and PD-L1 antibodies. Cancer 2016;122:3344–53.
    OpenUrlPubMed
  55. 55.↵
    1. Doo DW,
    2. Norian LA,
    3. Arend RC
    . Checkpoint inhibitors in ovarian cancer: a review of preclinical data. Gynecol Oncol Rep 2019;29:48–54.
    OpenUrl
  56. 56.↵
    1. Raufi AG,
    2. Klempner SJ
    . Immunotherapy for advanced gastric and esophageal cancer: preclinical rationale and ongoing clinical investigations. J Gastrointest Oncol 2015;6:561–9.
    OpenUrlPubMed
  57. 57.↵
    1. Green DR,
    2. Ferguson TA
    . The role of Fas ligand in immune privilege. Nat Rev Mol Cell Biol 2001;2:917–24.
    OpenUrlCrossRefPubMed
  58. 58.↵
    1. Wozniak AL,
    2. Wang X,
    3. Stieren ES,
    4. Scarbrough SG,
    5. Elferink CJ,
    6. Boehning D
    . Requirement of biphasic calcium release from the endoplasmic reticulum for Fas-mediated apoptosis. J Cell Biol 2006;175:709–14.
    OpenUrlAbstract/FREE Full Text
  59. 59.↵
    1. Sakagami H,
    2. Kamata A,
    3. Nishimura H,
    4. Kasahara J,
    5. Owada Y,
    6. Takeuchi Y,
    7. et al.
    Prominent expression and activity-dependent nuclear translocation of Ca2+/calmodulin-dependent protein kinase Idelta in hippocampal neurons. Eur J Neurosci 2005;22:2697–707.
    OpenUrlCrossRefPubMed
  60. 60.↵
    1. Swulius MT,
    2. Waxham MN
    . Ca(2+)/calmodulin-dependent protein kinases. Cell Mol Life Sci 2008;65:2637–57.
    OpenUrlCrossRefPubMed
  61. 61.↵
    1. McIlwain DR,
    2. Berger T,
    3. Mak TW
    . Caspase functions in cell death and disease. Cold Spring Harb Perspect Biol 2013;5:a008656.
    OpenUrlAbstract/FREE Full Text
  62. 62.↵
    1. Parrish AB,
    2. Freel CD,
    3. Kornbluth S
    . Cellular mechanisms controlling caspase activation and function. Cold Spring Harb Perspect Biol 2013;5:a008672.
    OpenUrlAbstract/FREE Full Text
  63. 63.↵
    1. Alvarado-Kristensson M,
    2. Melander F,
    3. Leandersson K,
    4. Ronnstrand L,
    5. Wernstedt C,
    6. Andersson T
    . p38-MAPK signals survival by phosphorylation of caspase-8 and caspase-3 in human neutrophils. J Exp Med 2004;199:449–58.
    OpenUrlAbstract/FREE Full Text
  64. 64.↵
    1. Suzuki A,
    2. Kusakai G,
    3. Kishimoto A,
    4. Shimojo Y,
    5. Miyamoto S,
    6. Ogura T,
    7. et al.
    Regulation of caspase-6 and FLIP by the AMPK family member ARK5. Oncogene 2004;23:7067–75.
    OpenUrlCrossRefPubMed
  65. 65.↵
    1. Alvarado-Kristensson M,
    2. Andersson T
    . Protein phosphatase 2A regulates apoptosis in neutrophils by dephosphorylating both p38 MAPK and its substrate caspase 3. J Biol Chem 2005;280:6238–44.
    OpenUrlAbstract/FREE Full Text
  66. 66.↵
    1. Brahmer JR,
    2. Tykodi SS,
    3. Chow LQ,
    4. Hwu WJ,
    5. Topalian SL,
    6. Hwu P,
    7. et al.
    Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. N Engl J Med 2012;366:2455–65.
    OpenUrlCrossRefPubMed
  67. 67.↵
    1. Royal RE,
    2. Levy C,
    3. Turner K,
    4. Mathur A,
    5. Hughes M,
    6. Kammula US,
    7. et al.
    Phase 2 trial of single agent Ipilimumab (anti-CTLA-4) for locally advanced or metastatic pancreatic adenocarcinoma. J Immunother 2010;33:828–33.
    OpenUrlCrossRefPubMed
  68. 68.↵
    1. Bergamaschi A,
    2. Kim YH,
    3. Kwei KA,
    4. La Choi Y,
    5. Bocanegra M,
    6. Langerod A,
    7. et al.
    CAMK1D amplification implicated in epithelial-mesenchymal transition in basal-like breast cancer. Mol Oncol 2008;2:327–39.
    OpenUrlCrossRefPubMed
  69. 69.↵
    1. Pitti RM,
    2. Marsters SA,
    3. Lawrence DA,
    4. Roy M,
    5. Kischkel FC,
    6. Dowd P,
    7. et al.
    Genomic amplification of a decoy receptor for Fas ligand in lung and colon cancer. Nature 1998;396:699–703.
    OpenUrlCrossRefPubMed
  70. 70.↵
    1. Ashkenazi A
    . Targeting the extrinsic apoptosis pathway in cancer. Cytokine Growth Factor Rev 2008;19:325–31.
    OpenUrlCrossRefPubMed
  71. 71.↵
    1. Kischkel FC,
    2. Hellbardt S,
    3. Behrmann I,
    4. Germer M,
    5. Pawlita M,
    6. Krammer PH,
    7. et al.
    Cytotoxicity-dependent APO-1 (Fas/CD95)-associated proteins form a death-inducing signaling complex (DISC) with the receptor. EMBO J 1995;14:5579–88.
    OpenUrlCrossRefPubMed
  72. 72.↵
    1. Mellier G,
    2. Huang S,
    3. Shenoy K,
    4. Pervaiz S
    . TRAILing death in cancer. Mol Aspects Med 2010;31:93–112.
    OpenUrlCrossRefPubMed
  73. 73.↵
    1. Lavrik I,
    2. Krueger A,
    3. Schmitz I,
    4. Baumann S,
    5. Weyd H,
    6. Krammer PH,
    7. et al.
    The active caspase-8 heterotetramer is formed at the CD95 DISC. Cell Death Differ 2003;10:144–5.
    OpenUrlCrossRefPubMed
  74. 74.↵
    1. Nicholson DW,
    2. Ali A,
    3. Thornberry NA,
    4. Vaillancourt JP,
    5. Ding CK,
    6. Gallant M,
    7. et al.
    Identification and inhibition of the ICE/CED-3 protease necessary for mammalian apoptosis. Nature 1995;376:37–43.
    OpenUrlCrossRefPubMed
  75. 75.↵
    1. Powley IR,
    2. Hughes MA,
    3. Cain K,
    4. MacFarlane M
    . Caspase-8 tyrosine-380 phosphorylation inhibits CD95 DISC function by preventing procaspase-8 maturation and cycling within the complex. Oncogene 2016;35:5629–40.
    OpenUrlCrossRef
  76. 76.↵
    1. Danial NN,
    2. Korsmeyer SJ
    . Cell death: critical control points. Cell 2004;116:205–19.
    OpenUrlCrossRefPubMed
  77. 77.↵
    1. Li X,
    2. Wen W,
    3. Liu K,
    4. Zhu F,
    5. Malakhova M,
    6. Peng C,
    7. et al.
    Phosphorylation of caspase-7 by p21-activated protein kinase (PAK) 2 inhibits chemotherapeutic drug-induced apoptosis of breast cancer cell lines. J Biol Chem 2011;286:22291–9.
    OpenUrlAbstract/FREE Full Text
  78. 78.↵
    1. Bellucci R,
    2. Nguyen HN,
    3. Martin A,
    4. Heinrichs S,
    5. Schinzel AC,
    6. Hahn WC,
    7. et al.
    Tyrosine kinase pathways modulate tumor susceptibility to natural killer cells. J Clin Invest 2012;122:2369–83.
    OpenUrlCrossRefPubMed
  79. 79.↵
    1. Dunn GP,
    2. Sheehan KC,
    3. Old LJ,
    4. Schreiber RD
    . IFN unresponsiveness in LNCaP cells due to the lack of JAK1 gene expression. Cancer Res 2005;65:3447–53.
    OpenUrlAbstract/FREE Full Text
  80. 80.↵
    1. Parampalli Yajnanarayana S,
    2. Stubig T,
    3. Cornez I,
    4. Alchalby H,
    5. Schonberg K,
    6. Rudolph J,
    7. et al.
    JAK1/2 inhibition impairs T cell function in vitro and in patients with myeloproliferative neoplasms. Br J Haematol 2015;169:824–33.
    OpenUrlCrossRefPubMed
  81. 81.↵
    1. Schonberg K,
    2. Rudolph J,
    3. Vonnahme M,
    4. Parampalli Yajnanarayana S,
    5. Cornez I,
    6. Hejazi M,
    7. et al.
    JAK inhibition impairs NK cell function in myeloproliferative neoplasms. Cancer Res 2015;75:2187–99.
    OpenUrlAbstract/FREE Full Text
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CAMK1D Triggers Immune Resistance of Human Tumor Cells Refractory to Anti–PD-L1 Treatment
Valentina Volpin, Tillmann Michels, Antonio Sorrentino, Ayse N. Menevse, Gertrud Knoll, Madlen Ditz, Vladimir M. Milenkovic, Chih-Yeh Chen, Anchana Rathinasamy, Klaus Griewank, Michael Boutros, Sebastian Haferkamp, Mark Berneburg, Christian H. Wetzel, Anja Seckinger, Dirk Hose, Hartmut Goldschmidt, Martin Ehrenschwender, Mathias Witzens-Harig, Arpad Szoor, Gyorgy Vereb, Nisit Khandelwal and Philipp Beckhove
Cancer Immunol Res September 1 2020 (8) (9) 1163-1179; DOI: 10.1158/2326-6066.CIR-19-0608
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