Long noncoding RNAs (lncRNA) play an important role in gene regulation in both normal tissues and cancer. Targeting lncRNAs is a promising therapeutic approach that has become feasible through the development of gapmer antisense oligonucleotides (ASO). Metastasis-associated lung adenocarcinoma transcript (Malat1) is an abundant lncRNA whose expression is upregulated in several cancers. Although Malat1 increases the migratory and invasive properties of tumor cells, its role in the tumor microenvironment (TME) is still not well defined. We explored the connection between Malat1 and the tumor immune microenvironment (TIME) using several immune-competent preclinical syngeneic Tp53-null triple-negative breast cancer (TNBC) mouse models that mimic the heterogeneity and immunosuppressive TME found in human breast cancer. Using a Malat1 ASO, we were able to knockdown Malat1 RNA expression resulting in a delay in primary tumor growth, decreased proliferation, and increased apoptosis. In addition, immunophenotyping of tumor-infiltrating lymphocytes revealed that Malat1 inhibition altered the TIME, with a decrease in immunosuppressive tumor-associated macrophages (TAM) and myeloid-derived suppressor cells (MDSC) as well as an increase in cytotoxic CD8+ T cells. Malat1 depletion in tumor cells, TAMs, and MDSCs decreased immunosuppressive cytokine/chemokine secretion whereas Malat1 inhibition in T cells increased inflammatory secretions and T-cell proliferation. Combination of a Malat1 ASO with chemotherapy or immune checkpoint blockade (ICB) improved the treatment responses in a preclinical model. These studies highlight the immunostimulatory effects of Malat1 inhibition in TNBC, the benefit of a Malat1 ASO therapeutic, and its potential use in combination with chemotherapies and immunotherapies.
Of increasing importance in the field of RNA biology is deciphering the function of long noncoding RNAs (lncRNA) and determining their utility as therapeutic targets. LncRNAs regulate a wide array of cellular functions, including nuclear organization, and transcriptional and posttranscriptional regulation, and are known to interact with miRNAs and effector proteins to alter gene expression (1–4). They also play a meaningful role in cancer progression and metastasis, and the expression of certain lncRNAs—for example, prostate cancer antigen 3—can be used as biomarkers for cancer diagnosis (5–7). LncRNAs are also known to regulate the immune response and can participate in immune cell activation and antigen presentation (8–10). The tumor microenvironment (TME) and the immune cells that make up the tumor immune microenvironment (TIME) are critical factors in cancer development and are potential targets for cancer treatment. Therefore, identifying and studying lncRNAs that can affect the TIME may lead to novel therapeutic strategies.
The highly invasive and aggressive nature of triple-negative breast cancer (TNBC; ref. 11) creates a challenge for effective treatment contributing to a lower progression-free survival (PFS) rate compared with other breast cancer subtypes (12). Immune checkpoint blockade (ICB) is now at the forefront of therapy and in combination with chemotherapy improves responses for patients with TNBC in both locally advanced and advanced stages of the disease (13, 14). The TNBC TIME is routinely comprised of a large number of tumor-associated macrophages (TAM) and myeloid-derived suppressor cells (MDSC) creating a highly immunosuppressive environment that promotes tumor-immune escape and invasion (15). This creates a challenge for therapies such as ICB and chemotherapy, and new approaches to help bolster immune infiltration and cytotoxicity are paramount for improving clinical responses.
Metastasis-associated lung adenocarcinoma transcript 1 (Malat1) is a lncRNA known to promote cell proliferation, invasion, and metastasis in numerous cancers (16–18). Breast cancers, including the TNBC subtype, have higher Malat1 expression compared with adjacent normal tissue (19). Other studies have shown that patients with breast cancer with higher expression of Malat1 have a lower recurrence-free survival as compared with those with lower or mid-levels of Malat1 expression (20, 21). Knockdown of Malat1 in a preclinical tumor model results in a decrease in the metastatic burden and increased differentiation (22). With respect to the TIME, Malat1 has been shown to promote angiogenesis and immunosuppression in certain cancers (23, 24). However, the intrinsic effects of Malat1 on immune cells and the TIME are still not well defined.
The primary goal of this study was to determine whether there could be a therapeutic benefit of Malat1 inhibition in TNBC and to explore any role Malat1 may have in regulating TIME dynamics. Targeting of Malat1 was made possible with the use of gapmer antisense oligonucleotides (ASO), which have been successful in preclinical models for targeted knockdown of lncRNAs (22) and may provide a future therapeutic option. Advances in the development of second generation ASOs have improved the stability of ASOs with limited toxicity leading to clinical trials in several diseases, including cancer (25–27). To study the efficacy of targeted knockdown using an ASO in TNBC, we used the syngeneic Tp53-null preclinical mouse models that have been extensively characterized both genetically and with respect to their TIME (28, 29). In these models, knockdown of Malat1 RNA expression in vivo delayed primary tumor growth and significantly decreased tumor volume, with decreased tumor cell proliferation and increased apoptosis. Furthermore, analysis of tumor-infiltrating lymphocytes (TIL) and in vitro coculture assays of myeloid cells and T cells revealed that Malat1 inhibition shifted the TIME to a more immunostimulatory state.
Materials and Methods
All Tp53-null tumor-derived cell lines were created in the Rosen Laboratory as previously reported (30) and are tested for Mycoplasma periodically using the Universal Mycoplasma Detection Kit (ATCC). Mycoplasma testing for these cell lines was last performed in April, 2019. T11 and T12 cells were cultured in DMEM/F12 with no phenol red (GeneDEPOT, CM018–300) and supplemented with 10% heat-inactivated FBS (GeneDEPOT, F0900–050), 5 μg/mL insulin (Sigma, I-5500), 10 ng/mL EGF (Sigma, SRP3196), 1 μg/mL hydrocortisone (Sigma, H0888), and 100 U/mL antibiotic–antimycotic (Thermo Fisher Scientific, 15240062). 2208 L cells were cultured in DMEM media (GenDEPOT, CM002–050) supplemented with 10% heat-inactivated FBS and 100 U/mL antibiotic–antimycotic. Cell lines were incubated in humidified incubator at 37°C with 5% CO2. When cells from frozen stock are thawed and plated new frozen stocks are created without further passages. Cells used for experiments are used within 5 passages of being thawed from frozen stocks. GFP-labeled T12 cells were generated by lentiviral transduction of GFP reporter plasmids as described previously (31). Briefly, GFP reporter plasmids were transfected together with psPAX2 and pMD2.G packaging plasmids into HEK293T cells using X-tremeGENE HP DNA transfection reagent (Sigma, 6366244001). 48 hours later, the viral supernatant was harvested and filtered by 0.45-μm-filter (VWR International, 76479–020). T12 cells were transduced with lentivirus and 8 μg/mL polybrene (Sigma, TR-1003). Two days later, GFP-positive cells were sorted using a SONY MA900 sorter to generate a reporter cell line.
All ASOs used for this study were generously supplied by Ionis and Flamingo Pharmaceuticals. These compounds are 3–10–3 cEt gapmers and the sequence of the ASO used are shown in Supplementary Table S1. To determine the concentration required for ≥50% Malat1 depletion, dosing experiments were performed in T11 and 2208 L cell lines and 250 and 750 nmol/L of ASO, respectively, were used for all subsequent tumor cell line–specific experiments (Supplementary Fig. S1A and S1B). In Claudin-low tumor cell lines (T11 and T12) an ASO transfection reagent was needed to achieve Malat1 knockdown.
T12 and T11 tumor cells were seeded in 6-well plates at a cell density between a total of 2.0 × 105 and 2.5 × 105 cells per well 24 hours before transfection. A transfection cocktail consisting of 250 μL Opti-MEM serum-free media (Thermo Fisher Scientific, 31985070), 15 μL oligo transfection reagent (Mirus Bio, MIR 2160), and 250 nmol/L of either Malat1 ASO or a nontargeted Scramble ASO per well was incubated for 20 minutes at room temperature and added drop-wise to cells. Cells were harvested for analysis by RT-qPCR 48 hours after transfection. 2208 L tumor cells were treated with 750 nmol/L of ASO in cell culture media without oligo transfection reagent.
RNA was harvested using either an RNeasy mini-isolation kit (Qiagen, 74104) or TRizol (Thermo Fisher Scientific, 15596018) and 1 μg of RNA was used to make cDNA using a High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, 4368814). 5 μg of cDNA was added to a cDNA cocktail containing 1x amfisure qGreen Q-PCR Master Mix (GenDEPOT, Q5602–005) and Malat1-specific reverse and forward primers (Forward: TCTTCTATTCTTGGGCTTCCTACT, Reverse: AAGCATCTTTAGAAGACAGAAAAG). Neat1 RNA expression (Forward: TGTTAAAGGCGCTTTGGAAG, Reverse: GCGGGGCTAAGTATAAAGGAG) was quantified as well. Malat1 and Neat1 expressions were normalized to Gapdh (Forward: CTGGAGAAACCTGCCAAGTAT, Reverse: GAGTTGCTGTTGAAGTCGCAG) expression in cells and the relative RNA expression was calculated using the ΔΔCt relative fold change method. All RT-qPCR experiments were performed on the StepOnePlus Real-Time PCR System (Thermo Fisher Scientific, 4376600).
All animal experiments were performed in the Baylor College of Medicine animal facility with 12-hour day or night cycles. Animal experiments were performed in accordance with the Baylor College of Medicine Animal Care and Use Committee (IACUC)-approved protocol (AN-504). Studies complied with all ethical regulations regarding animal research.
Single-agent ASO treatment
Tp53-null tumor pieces from either the Claudin-low models (T11 and T12) or the luminal-like model 2208 L were kept frozen in an established tumor bank in the Rosen Laboratory. For treatment experiments, frozen tumor pieces were thawed and implanted into the mammary fat pad of a 7- to 10-week-old donor wild-type (WT) BALB/c mouse (Envigo Laboratories). The tumor was harvested when it reached approximately 500 mm3 and implanted into the 4th mammary fat pad of 7- to 10-week-old WT BALB/c mice. Once tumors were palpable and ≤50 mm3 in volume, mice were randomized into a Malat1 ASO treatment group and a nontargeted Scramble ASO control group. For randomization, mice were weighed and had their tumor size measured using digital calipers. Mice were then assigned a number and numbers were randomly placed in two groups using Excel. Mice implanted with Claudin-low tumors received a 25 mg/kg subcutaneous injection of Malat1 ASO or a nontargeted Scramble ASO daily and mice implanted with the 2208 L tumors received a 50 mg/kg subcutaneous injection. The appropriate dosage was determined by a short 5-day ASO treatment of tumors to confirm Malat1 knockdown in the tumors. Mice were weighed and had their tumors measured and recorded every 2 to 3 days. Mice were sacrificed after 14 days for short treatment studies or once the tumor reached an approximate volume of 1,500 mm3 for survival studies. Tumor volume was calculated using length × width × width/2. Tumors were harvested and center sections were fixed with either 4% paraformaldehyde (Sigma, P6148) or 10% formalin (Thermo Fisher Scientific, 22–050–105), small sections were cryopreserved for RNA and protein extraction, and the remaining tumor was digested with 1 mg/mL Collagenase Type 1 (Sigma-Aldrich, 11088793001) for the isolation of TILs.
ASO treatment in T cell–deficient mice
To assess the effects of Malat1 ASO or a nontargeted Scramble ASO in T cell–deficient mice, 6- to 7-week-old nude mice (Hsd:Athymic Nude-Foxn1nu; Envigo Laboratories, 069) were implanted with 2208 L tumors and treated with 50 mg/kg of single-agent ASO once tumors were palpable. In addition, CD8+ T-cell depletion treatment studies were performed using, 6- to 7-week-old WT BALB/c mice implanted with 2208 L tumors and treated with blocking antibody (BioXcell; clone 2.43, BE0061) once every 4 days. Once tumors were palpable, mice were randomized into four groups and treated as follows: 250 μg anti-CD8α (BioXcell; clone 2.43, BE0061) + 50 mg/kg Scramble ASO, 250 μg anti-CD8α + 50 mg/kg Malat1 ASO, 250 μg IgG2b (BioXcell; clone LTF-2, BE0090) + 50 mg/kg Scramble ASO, and 250 μg IgG2b + 50 mg/kg Malat1 ASO. Mice were sacrificed after 14 days of treatment.
Chemotherapy/checkpoint and ASO combination treatment
For separate combination studies, 2208 L tumor pieces were implanted into WT BALB/c mice and 25 mg/kg of Carboplatin (Sigma-Aldrich, C2538) was injected intraperitoneally weekly in combination with daily injection of either Malat1 ASO or Scramble ASO control. Combination studies using 2208 L primary tumors treated with anti-PD1 checkpoint inhibitor were also performed and 10 mg/kg of anti-PD1 antibody (Bio X Cell Clone: RMP1–14, BE0146) was injected intraperitoneally once every 72 hours in combination with daily ASO injection. A small cohort of mice was treated with an IgG2b control (Bio X Cell Clone: MPC-11, BE0086). Mice were sacrificed after 5 or 14 days for short treatment studies or once the tumors reached an approximate volume of 1,500 mm3 for survival studies.
Isolation of TILs
Tumors were minced using a McIlwain tissue chopper and digested in digestion medium comprising 1 mg/mL Collagenase Type 1 and 1 μg/mL DNase (Sigma-Aldrich, 11284932001) in DMEM/F12 media with no additives for 2 hours at 37°C on a rocker. Digestions were then spun down at 1,500 rpm and the supernatant was discarded. Pellets were resuspended in PBS (Thermo Fisher Scientific, BP3994 or GenDEPOT, CA008–050) and underwent 3 short centrifugations, collecting the supernatant into a separate tube after each spin. The collected supernatant was then spun down and the pellet was resuspended in 1x RBC lysis buffer (BioLegend, 420301) and incubated in ice for 3 minutes. PBS was added after incubation to stop the reaction and spun down at 1,500 rpm. The pellet was resuspended in staining buffer (PBS with 2% FBS) and passed through a 40-μm mesh filter (VWR 352340). Live cells were counted with trypan blue (Bio-Rad, 1450021) using a Bio-Rad TC20 Cell Counter and 1 million cells were plated per panel in 96-well nontreated tissue culture plate (Thermo Fisher Scientific 50202148) for flow cytometry.
Fixed tumor sections were paraffin-embedded and 5-μm tissue sections were used for IHC staining.
Paraffin-embedded tissue sections were washed and permeabilized using xylene (Thermo Fisher Scientific, 22050283) and 100%, 95%, 80%, and 75% ethanol. Sections underwent antigen retrieval using a Tris-EDTA antigen retrieval buffer (10 mmol/L Trisbase, 1 mmol/L EDTA) for 20 minutes and were then rinsed three times in deionized (DI) water and PBS for 5 minutes each. Sections were then blocked in a hydrogen peroxide solution (Thermo Fisher Scientific, H323–500) for 10 minutes and further blocked for 1 hour in a blocking buffer consisting of 3% BSA (Thermo Fisher Scientific, NC1235497), 2% Goat serum (Sigma-Aldrich, G9023), and 0.01% Tween (Millipore Sigma, 11332465001) in PBS for rabbit antibodies or blocked using the M.O.M blocking buffer (Vector Laboratories, BMK-2202) for mouse or rat antibodies. After blocking, sections were incubated with primary antibodies [Phospho-histone 3 (1:1,000; Cell Signaling Technology, 9701S)], Cleaved Caspase 3 (1:1,000; Cell Signaling Technology, 9661S), CD8α (1:750; Thermo Fisher Scientific, 14–0081–82), Foxp3 (1:500; Cell Signaling Technology, 12653T), F4/80 (1:500; Cell Signaling Technology, 70076S), or S100a8 (1:5,000; Thermo Fisher Scientific, MAB3059) at 4°C overnight and the next day washed three times with PBS for 10 minutes each and incubated with horseradish peroxidase (HRP)–conjugated secondary antibody for rat (Vector Laboratories, PI-9400–1) or rabbit (Vector Laboratories, PI-1000–1) at 1:1,000 concentration for 1 hour at room temperature. Sections were then incubated with VECTASTAIN Elite ABC HRP Reagent (Vector Laboratories, PK7100) for 30 minutes, washed with PBS three times and treated with ImmPACT DAB peroxidase substrate (Vector Laboratories, sk-4105) before being rinsed with DI water. Sections were then counterstained with hematoxylin (Poly Scientific, S212A) and once dry, mounted in Poly-Mount Xylene (Poly Scientific, 24176–120). 3 to 5 microscopic images were taken for each slide using the Olympus BX40 light microscope and Magnafire camera at a 20× objective. Positive staining was quantified using ImageJ (ImageJ, RRID:SCR_003070; ref. 32). Briefly, images were converted to 8-bit and threshold was adjusted to remove background from each image. The percentage of the positive area of up to 3 representative images from each tissue section was calculated and 5 to 8 tissue sections were used for each treatment group.
A total of 1.0 × 106 TILs were stained with Live/Dead Fixable Yellow (Thermo Fisher Scientific L34968) at a 1:1,000 concentration for 30 minutes on ice, rinsed with staining buffer and then blocked with CD16/32 FCR blocker at a 1:100 concentration (BioLegend, 101319) for 30 minutes on ice. Cells were then stained with antibodies specific for cell surface markers at a previously validated concentration for 30 minutes on ice. Two panels of antibodies were used to immunophenotype TILs and they were as follows; Panel 1–Cd11c Pacific Blue (BioLegend, 117322), MHCII BV711 (BioLegend, 107643), CD86 FITC (BioLegend, 105006), CD11b PerCP CY 5.5 (BioLegend, 101227), Ly-6C PeCy7 (BioLegend, 128017), F4/80 APC (BioLegend, 123115), CD45 Alexa Fluor 700 (BioLegend, 103128), and Ly6-G APC/Fire 750 (BioLegend, 127651). Panel 2–CD45 Pacific Blue (BioLegend, 103126), CD3ϵ PerCP Cy 5.5 (BioLegend, 100218), CD45R (B220) PE-CY7 (BioLegend, 103221), CD8α Alexa Fluor 700 (BioLegend, 100730), CD49b (pan-NK cells) PE/Dazzle 594 (BioLegend, 108924), and CD4 APC/Fire 750 (BioLegend, 100460). After surface staining, cells were rinsed with PBS and fixed overnight at 4°C using a Foxp3/Transcription Factor Staining Buffer Set (Thermo Fisher Scientific, 00–5523–00). After fixation cells were blocked in permeabilization buffer (Thermo Fisher Scientific, 00–5523–00) with 2% rat serum (Sigma-Aldrich, R9759) for 30 minutes at room temperature and then stained with antibodies specific for intracellular markers. Panel 1 contained antibodies specific for Arginase PE (Thermo Fisher Scientific, 12–3697–82), CD206 PE/Dazzle 594 (BioLegend, 141732) for T12 TILs and Panel 2 antibodies specific for contained Ifnγ FITC (BioLegend, 505806), Foxp3 PE (BioLegend, 126403), and Granzyme B APC (BioLegend, 372203). After staining, cells were washed twice with permeabilization buffer, resuspended in PBS and data were collected using the Attune NxT flow cytometer. Flow samples were properly compensated and analyzed using FlowJo v10 software (FlowJo, RRID:SCR_008520).
Peripheral blood mononuclear cells
To validate depletion of CD8α+ cells, blood was collected retro-orbitally using capillary tubes (Thermo Fisher Scientific, 22- 362566) and red blood cells were lysed for 30 minutes using red lysis buffer (Thermo Fisher Scientific, 00–4333–57). Cells were blocked for 30 minutes using anti CD16/32 FCR blocker and then stained with CD3ϵ PerCP Cy 5.5 (BioLegend, 100218), CD8α Alexa Fluor 700 (BioLegend, 100730), and CD4 APC/Fire 750 (BioLegend, 100460) antibodies for 30 minutes on ice. After staining, cells were resuspended in PBS (GenDEPOT, CA008–050) containing NucBlue Live ReadyProbes (Thermo Fisher Scientific, R37605) and data were collected using the Attune NxT flow cytometer.
In vitro myeloid cells
Myeloid cells collected from either T12 or 2208 L primary tumors were stained with Live/Dead Fixable Yellow (Thermo Fisher Scientific L34968) at a 1:1,000 concentration for 30 minutes on ice, rinsed with staining buffer, and then blocked with CD16/32 FCR blocker at a 1:100 concentration (BioLegend, 101319) for 30 minutes on ice. Cells were then stained with cell surface markers for 30 minutes on ice. The surface markers analyzed were: MHCII BV711 (BioLegend, 107643), CD86 Alexa Fluor 700(BioLegend, 105024), CD11b PerCP CY 5.5 (BioLegend, 101227), Ly-6C PeCy7 (BioLegend, 128017), F4/80 APC (BioLegend, 123115), and Ly6-G APC/Fire 750 (BioLegend, 127651). Data were collected using the Attune NxT flow cytometer.
In vitro T cells
T cells collected from the spleen of WT BALB/c mice were stained with Live/Dead Fixable Yellow (Thermo Fisher Scientific L34968) at a 1:1,000 concentration for 30 minutes on ice, rinsed with staining buffer, and then blocked with CD16/32 FCR blocker at a 1:100 concentration (BioLegend, 101319) for 10 minutes on ice. Cells were then stained with antibodies specific for cell surface markers for 30 minutes on ice. The surface markers analyzed were: CD3ϵ PerCP Cy 5.5 (BioLegend, 100218), CD8α Alexa Fluor 700 (BioLegend, 100730), and CD4 APC/Fire 750 (BioLegend, 100460) After surface staining, cells were rinsed with PBS and fixed for 20 minutes at room temperature using a Foxp3/Transcription Factor Staining Buffer Set (Thermo Fisher Scientific, 00–5523–00). After fixation, cells were stained with antibodies specific for intracellular markers: Granzyme B APC (BioLegend, 372203) and Perforin PE (BioLegend, 154405). Data were collected using the Attune NxT flow cytometer.
In vitro T-cell proliferation assay
To assess changes to T-cell function, proliferation assays were performed on naïve splenocytes isolated from WT BALB/c mice. Red blood cells were lysed using 1X RBC lysis buffer and CD3+ T cells were enriched using negative selection of biotinylated antibodies B220, CD11b, and Gr-1 (#559971, BD Pharmingen, RRID:AB_10053179), magnetically sorted using the EasySep Mouse Biotin Positive Selection Kit (#18559, Stemcell), and stained with carboxyfluorescein diacetate succinimidyl ester (CFSE; Thermo Fisher Scientific, C34570). First, cells were washed with PBS and the pellet was resuspended in a CFSE labeling solution consisting of 1.5 μmol/L CFSE in 0.1% FBS and incubated at room temperature for 10 minutes. Equal volume of warmed FBS was added to solution and cells were further incubated at 37°C for 10 minutes. Cells were centrifuged at 1,500 rpm and washed with PBS + 2% FBS. Cells were then counted using trypan blue and plated in 24-well culture plates in T-cell media consisting of 10% FBS, 2 mmol/L Glutamax (Thermo Fisher Scientific 35050061), 5 mmol/L sodium pyruvate (Thermo Fisher Scientific 11360070), 5 mmol/L NEAA (Thermo Fisher Scientific 11–140–050), 55 μmol/L BME (Thermo Fisher Scientific 21985023) in RPMI media (GenDEPOT CM058–050). 10 ng/mL IL2 (BioLegend, 575404) was added to the culture every 2 to 3 days. To determine the concentration required for Malat1 depletion, dosing experiments were performed on stimulated T cells. Cells were either left unstimulated or stimulated at a 1:1 ratio using anti-CD3/anti-CD28–coated Dynabeads (Thermo Fisher Scientific, 11456D) and after 48 hours of incubation, 500 nmol/L of either Malat1 ASO or Scramble ASO was added to the culture medium and replenished every 48 hours. Two separate Malat1-targeting ASOs and two nontargeted Scramble ASO were tested. Cells were collected on days 3 and 5 and quantified using flow cytometry. Furthermore, intracellular staining of T-cell activation markers, Granzyme B APC (BioLegend, 372203) and Perforin (BioLegend, 154405) was performed. Data were collected using Attune NxT flow cytometer and analyzed using FlowJo v10 software.
In vitro T-cell suppression assay
To assess whether myeloid cell function was altered with Malat1 depletion, TAMs and MDSCs were isolated from tumor-bearing mice and cocultured with activated CD3+ T cells isolated from naïve splenocytes of BALB/c mice. T12 and 2208 L tumors were digested as previously described (see Isolation of TILs) and either TAMs or MDSCs were isolated from the harvested TILs using the EasySep F4/80+ positive selection kit (STEMCELL, 100–0659) and the EasySep CD11b GR1+ isolation kit (STEMCELL Technologies, 19867), respectively. Isolated myeloid cells were plated in 24-well culture plates at a 125k cell density in T-cell media supplemented with 20% conditioned media collected from the supernatant of either T12 or 2208 L tumor cells. Myeloid cells without T cells were cultured in 10% FBS, 55 μmol/L BME, and 100 U/mL antibiotic–antimycotic in RPMI media supplemented with 20% conditioned media. To determine the ASO concentration required for Malat1 depletion in myeloid cells, dosing experiments were performed in TAMs isolated from T12 primary tumors. To assess T-cell suppression, stimulated T cells stained with CFSE were added to myeloid cell cultures at myeloid cell:T-cell ratios of 0:5:1 and 1:1. Cocultures were treated with 250 nmol/L of ASO and incubated for 72 hours. T cells were collected, magnetically separated from the activation beads and CFSE dilution was analyzed using flow cytometry. Intracellular staining of the T-cell activation markers Granzyme B APC and Perforin PE was performed and quantified. Two separate Malat1-targeting ASOs and two nontargeted Scramble ASO were tested. Data were collected using Attune NxT Flow Cytometer and analyzed using FlowJo v10.
T-cell suppression was also assessed using GFP-targeted T cells isolated from the splenocytes of Just EGFP Death–Inducing (JEDI) mice (C57bL/6), cultured with a GFP-labeled T12 tumor-derived cell line, and with TAMs or MDSCs isolated from a T12 primary tumor at a 1:1:1 or 5:5:1 T-cell:TAM/MDSC:tumor cell ratio. JEDI mice carry a GFP-specific T-cell receptor transgene. JEDI mice for this study were kindly gifted by Dr. Brian Brown (Icahn School of Medicine at Mt. Sinai). Cocultures were treated with 250 nmol/L of ASO and incubated in the Incucyte S3 Live-Cell Analysis instrument where fluorescence and phase images were taken every 2 hours for approximately 72 hours. Myeloid cells were cultured with tumor cells without T cells as a control. After approximately 72 hours, T cells were collected from wells and stained with antibodies specific for surface markers—CD3ϵ PerCP Cy 5.5, CD8α Alexa Fluor 700, and CD4 APC/Fire—and intracellular T-cell activation markers—Granzyme B APC and Perforin PE. Residual tumor cells were collected from the wells and stained with cell death marker Annexin V (BioLegend, 64093). Data were collected using Attune NxT flow cytometer and analyzed using FlowJo v10 software.
In vitro T-cell function assay
To carry out T-cell functional studies, CD3+ T cells were isolated as previously described (see In vitro T-cell proliferation assay) from the spleen of JEDI mice and activated at a 1:1 ratio with anti-CD3/anti-CD28–coated Dynabeads for 72 hours in T-cell media. Activated T cells were magnetically separated from the beads and cultured with GFP-labeled T12 tumor cells at various effector:target (E:T) ratios (1:1, 5:1, and 10:1). Cocultures were treated with 500 nmol/L of ASO and incubated in the Incucyte S3 Live-Cell Analysis Instrument where fluorescence and phase images were taken every 2 hours for 24 to 48 hours at a 4× objective. After 48 hours, T cells were collected from wells and stained with antibodies specific for surface markers—CD3ϵ PerCP Cy 5.5 (BioLegend, 100218), CD8α Alexa Fluor 700 (BioLegend, 100730), and CD4 APC/Fire 750 (BioLegend, 100460)—and intracellular T-cell activation markers—Granzyme B APC (BioLegend, 372203) and Perforin PE (BioLegend, 154405). Residual tumor cells also were collected from the well and stained with the cell death marker Annexin V (BioLegend, 64093). Changes in GFP signal was calculated by dividing the total green object–integrated intensity (GCU × μm2/image) at the endpoint by the starting green object–integrated intensity (GCU × μm2/image). GFP signal was recorded using Incucyte S3. Data were collected using Attune NxT flow cytometer and analyzed using FlowJo v10 software. Supernatants from the different experimental groups were also collected for cytokine profiling.
To determine tumor intrinsic changes to the cytokine profile after Malat1 knockdown, cell supernatant, cell lysate, and tissue homogenate were assayed using the Mouse Cytokine/Chemokine 31-Plex Discovery Assay (Eve Technologies Corp.). Tissue homogenates were prepared using a tissue protein extraction reagent (Thermo Fisher Scientific, 78510) and cells were lysed using an NP-40 lysis buffer (50 mmol/L Tris-Base, 150 mmol/L NaCl, 1.0% NP-40, and cOmplete, EDTA-free Protease Inhibitor Cocktail; Sigma-Aldrich, 11873580001). Heat maps of differentially expressed cytokines or chemokines were generated using Morpheus (https://software.broadinstitute.org/morpheus). Supernatant of cocultures with tumor cells, JEDI T cells, and myeloid cells were assayed using a Mouse High Sensitivity T-Cell 18-Plex Discovery Assay Array (Eve Technologies Corp., MDHSTC18). Heat maps of differentially expressed cytokines or chemokines were generated using Morpheus.
Bone marrow and peripheral blood were collected from mice carrying orthotopic PyMT-N tumors (C57BL/6, Envigo), using previously described methods (29). RNA isolated from Hematopoietic cells using 10X Genomics Chromium protocol was subjected to single-cell RNA sequencing. The 10X Genomics Chromium system was used for library preparation and libraries were prepared per 10x Genomics guidelines. Sequencing adapters are provided by 10x Genomics. All sequencing results were pooled from three mice (three technical replicates). Raw sequencing files were imported into the 10x Genomics Cell Ranger toolkit (v3.1.0) for alignment, filtering, barcode counting, and UMI counting with default parameters. The mm10 (v3.0.0) genome was used for reads mapping. The Seurat (v3.2.3) package on R (v4.0.2) was used for downstream analysis. For quality control, we kept cells with less than 20,000 read counts and have less than 10% mitochondria genes. These parameters aimed to filter doublets and dead cells. We then filtered mitochondrial genes (genes start with mt), ribosomal protein genes (genes start with Rpl and Rps), and unspecified genes (genes start with Gm). Next, we used the SCTransform function in the Seurat package, which implemented regularized negative binomial regression to normalize raw Unique Molecular Identifier (UMI) counts. Variable genes identified by this method were used as inputs for principal component analysis (PCA). We applied Uniform Manifold Approximation and Projection analysis for visualization using the first 50 principal components identified by PCA. Cells were clustered by the shared nearest neighbor with the FindNeighbors and FindClusters functions in the Seurat package. Matured neutrophils are identified by expression of Ly6g, Camp, Ltf, and S100a9. Various clusters were ordered in pseudo-time using the Monocle 3 package (v1.0.0). In blood, two neutrophil clusters were identified, one before infiltration of tumors and the other after tumor infiltration according to pseudo-time, and were defined as “pre-” and “posttumor” clusters. The difference in Malat1 RNA expression of neutrophils before entering the tumor and neutrophils after exiting the tumor was visualized by a violin plot generated by Vlnplot function in Seurat package. The P value was calculated by FindAllMarkers function in Seurat package using a Wilcoxon Rank-Sum test. Single-cell sequencing data generated in this study are publicly available in Gene Expression Omnibus (GEO) using the accession number GSE222854.
Sample sizes for treatment studies were determined from preliminary short-term studies and availability of ASO, not statistically predetermined. All graphs and statistical analysis were performed using GraphPad Prism 9.4.1 (GraphPad Prism, RRID:SCR_002798). Two-way ANOVA with the Šídák's multiple comparisons test were used to determine significant differences in tumor volumes over time. Kaplan–Meier curves with Log-Rank (Mantel–Cox) test were used to determine change in survival between treatment groups. For all comparisons between two experimental groups an unpaired two-tailed Student t test was used. In grouped analysis, multiple Student t tests using the Holm–Šídák method were used and the P value was adjusted for multiple comparisons. With three or more experimental groups significance was determined using one-way ANOVA and Dunnett's multiple comparisons test with an adjusted P value for multiple comparisons. A P value of <0.05 was considered significant.
Single-cell sequencing data generated in this study are publicly available in GEO using the accession number GSE222854. For flow cytometry studies, raw data were generated at Baylor College of Medicine Cytometry and Cell Sorting Core. Derived data supporting the findings of this study are available from the corresponding author upon request. Other data generated in this study are available upon request from the corresponding author or can be found in the article and its Supplementary Files.
Single-agent Malat1 ASO delays primary tumor growth in TNBC preclinical mouse models
To determine the effect of Malat1 inhibition on mammary tumor progression in vivo, we used the established syngeneic Tp53-null mouse models derived from BALB/c mice that recapitulate the aggressive and heterogenous nature of TNBC (28). The neutrophil-enriched 2208 L luminal-like tumor subtype and macrophage-enriched T12 and T11 Claudin-low tumor subtypes were used because of their highly immunosuppressive myeloid compartments, which contribute to both tumorigenesis, metastasis, and resistance to available treatments (29). Gapmer ASOs allow for targeted degradation of Malat1 and have been shown to achieve knockdown with great efficiency in previous studies (22). WT BALB/c mice had Tp53-null tumor pieces implanted into the mammary fat pad and once palpable, either a Malat1-targeted ASO or a scramble control ASO was injected subcutaneously for five continuous days with a 2-day drug holiday (Fig. 1A). After 5 days of treatment, the Malat1 ASO caused an approximately 60%–75% depletion of Malat1 RNA expression that was maintained using the 5 days on 2 days off treatment regimen (Fig. 1B and C). Similarly, tumor-derived cell lines treated with Malat1 ASO for 48 hours had a significant decrease of Malat1 RNA expression (Supplementary Fig. S1C–S1E). Malat1 inhibition led to a delay in primary tumor growth for both T12 and 2208 L tumors, and after 14 days of treatment the tumor volume was significantly reduced in both models (Fig. 1D and E). In longer treatment studies with 2208 L and T11, Malat1 inhibition maintained delayed tumor progression as well as slightly prolonged survival in 2208 L tumor-bearing mice (Supplementary Fig. S1F–S1H). To ensure Malat1-specific knockdown, expression of a similar abundant lncRNA Neat1 was quantified and there was no significant change in both Claudin-low and luminal-like tumor subtypes (Supplementary Fig. S1I and S1J). There was no significant change in weight with mice undergoing ASO treatment in either tumor models (Supplementary Fig. S1K and S1L). IHC staining of tumor sections after harvest revealed that Malat1 ASO–treated tumors had decreased staining of the proliferative marker phospho-histone 3 and increased apoptosis, as evident by increased cleaved caspase 3 staining (Fig. 1F). These results demonstrate that specifically targeting Malat1 using an ASO and subsequent Malat1 inhibition results in decreased tumor volume due to increased cell death coupled with decreased cell proliferation.
Malat1 inhibition decreases the immunosuppressive myeloid cells in the TME
Malat1 has previously been shown to contribute to cancer progression by increasing both tumor cell proliferation and invasion (33, 34), but little is known about its effect on the TIME. To study changes to the TIME with Malat1 knockdown, TILs were isolated from T12 and 2208 L tumors treated with Malat1 ASO for 14 days and myeloid cell markers were quantified using flow cytometry (Fig. 2A and E; Supplementary Fig. S2A and S2B). Malat1-depleted T12 Claudin-low tumors, which are highly macrophage enriched, displayed a decrease in F4/80+ macrophages. In addition, of the macrophages present, there was a decrease in CD206+Arginase-1 (Arg-1)+F4/80+ cells, which are representative of immunosuppressive “M2-like” macrophages (Fig. 2B and D) Conversely, there was slight increase in CD86+F4/80+ cells with CD86 being an inflammatory marker present on “M1-like” macrophages (Fig. 2C). In 2208 L tumors, which are highly neutrophil enriched, Malat1 inhibition led to a decrease in Cd11b+Ly6ClowLy6Ghigh cells known as granulocytic MDSCs (G-MDSC), which are the most abundant granulocytes present (Fig. 2F). The monocytic MDSC (M-MDSC) compartment characterized by Cd11b+Ly6ChighLy6Glow showed no significant difference between treatment groups (Fig. 2G). Previous studies have shown that when one myeloid population is depleted, there may be a compensatory enrichment in a separate myeloid population (29), but with the Malat1 ASO treatment, we observed a decrease in Arg-1+ macrophages in 2208 L tumors as well (Fig. 2H). Tumor sections were stained with F4/80 and the neutrophil marker S100a8 to elucidate the spatial distribution of the myeloid cells. IHC revealed myeloid cells to be widely dispersed throughout the tumor with an abundance of macrophages present in T12 tumors and more neutrophils in 2208 L tumors, as expected. Malat1-depleted tumors had decreased staining of both F4/80 and S100a8 (Fig. 2I) in both tumor models.
To determine tumor-intrinsic effects that might contribute to decreased myeloid populations, tumor-derived T12 and 2208 L cell lines were treated with Malat1 ASO and the supernatants were collected for analysis using an inflammatory cytokine/chemokines array. Malat1 depletion in both cell lines altered the tumor secretory profiles. In T12 cancer cells, there was a decrease in chemokines known to recruit immunosuppressive macrophages and neutrophils to the tumor site (ref. 35; including a significant decrease in Ccl5; Fig. 2J). The inflammatory chemokines, Cxcl9 and Cxcl10 that help to recruit “M1-like” macrophages to the TME, were also decreased in Malat1 ASO–treated tumor cells providing evidence that Malat1 inhibition in tumor cells is not responsible for repolarizing macrophages to a more inflammatory phenotype. Malat1 ASO–treated 2208 L tumor cells had decreased secretion of immunosuppressive cytokines/chemokines similar to T12. Chemokines Cxcl1, Gm-csf, and Tnfα were reduced along with a significant decrease in Ccl5 and the cytokine IL6. These factors are known to bolster the recruitment and development of MDSCs (35–37) and support immunosuppression. Vegf, a promoter of angiogenesis and myeloid cell enrichment (36), was decreased in both cell lines (Fig. 2J). These analyses reveal that Malat1 inhibition reduces the frequency of immunosuppressive immune cells found in the TNBC microenvironment with concordant changes to the tumor cell's secretory profile resulting in decreased recruitment of myeloid cells to the primary tumor. However, because neither myeloid compartment was completely abolished, we next examined the function of the remaining myeloid cells present.
Malat1 inhibition decreases TAM and MDSC-immunosuppressive function
After establishing that Malat1 depletion in tumor cells can influence the recruitment of immunosuppressive myeloid cells to the TME, we next wanted to understand the effect that Malat1 inhibition had on TAM and MDSC function. Both TAMs and MDSCs extracted from T12 and 2208 L tumors, respectively, were found to have abundant Malat1 RNA expression comparable with tumor cells (Supplementary Figs. S3B and S4A). Single-cell sequencing studies showed that neutrophils collected from peripheral blood mononuclear cells before and after direct tumor cell contact displayed increased expression of Malat1 once encountering the tumor (Supplementary Fig. S4B), suggesting that Malat1 may play a role in the tumor education of neutrophils. This led us to take a more detailed look at the intrinsic effect that Malat1 depletion may have on myeloid cells. To determine whether the Malat1 ASO was able to efficiently knockdown Malat1 in TAMs isolated from T12 primary tumors and MDSCs isolated from 2208 L primary tumors, were cultured in vitro for 72 hours (Supplementary Figs. S3A and S4C) in the presence of 250 nmol/L ASO. In both cell types, Malat1 RNA expression was significantly decreased in vitro (Figs. 3A and 4A). In addition, TAMs extracted from T12 tumors (Fig. 3B) and MDSCs extracted 2208 L tumors (Fig. 4B) after 5 days of ASO treatment showed significant knockdown of Malat1 RNA expression confirming that this treatment regimen reduced Malat1 expression in myeloid cells as well as tumor cells. To determine changes in TAM function, TAMs isolated from T12 tumors were cultured in vitro with CD3/CD28-stimulated T cells isolated from the spleen of a WT BALB/c mouse. The ASO concentration of 250 nmol/L caused a 60% to 80% knockdown in TAMs (Supplementary Fig. S3C) but not in T cells (Supplementary Fig. S3D), and there was no change in proliferation when T cells were cultured alone with this concentration of ASO. T cells cultured with Malat1-depleted TAMs for 72 hours had increased cell proliferation, as evidenced by decreased CFSE expression (Fig. 3C and D; Supplementary Fig. S3E), with a significant increase in CD8+ T-cell proliferation and no change in CD4+ T-cell proliferation (Fig. 3E and F). Similarly, when MDSCs isolated from 2208 L tumors with decreased Malat1 RNA expression were co-cultured with activated T cells, CD8+ T-cell proliferation increased (Fig. 4C–F; Supplementary Fig. S4D). To assess the effect that Malat1-depleted myeloid cells had on T-cell killing capability, TAMs or MDSCs isolated from a T12 primary tumor were cultured with a GFP-labeled T12 tumor-derived cell line, and GFP-specific T cells isolated from the spleen of JEDI mouse at 1:1:1 and 5:5:1 T-cell:TAM:tumor cell ratios and a 1:1:1 T-cell:MDSC:tumor cell ratio in the presence of 250 nmol/L ASO. Pictures and the GFP signal of cells were taken every 2 hours using Incucyte (Figs. 3G and 4G). Residual cells were analyzed using flow cytometry (Supplementary Fig. S3F) and there was no change in the cell death marker, Annexin V, in Malat1 ASO–treated TAM cocultures (Fig. 3H). T cells were also collected and analyzed (Supplementary Fig. S3G), and we observed an increase in CD8+ cells with the cytotoxic T-cell markers Granzyme B and Perforin (Fig. 3I and J). Malat1-depleted MDSC cocultures had increased T-cell cytotoxicity, as evidenced by a significant increase in Annexin V+ tumor cells as well as an increase in both Granzyme B+CD8+ T cells and Perforin+CD8+ T cells (Fig. 4H–J). TAMs and MDSCs were also cultured with tumor cells without T cells as additional controls (Supplementary Figs. S3H and S4E). A chemokine/cytokine assay of supernatants collected in the 5:5:1 ratio for TAM and the 1:1:1 ratio for MDSC cocultures displayed a secretory profile similar to tumor cells where a decrease in immunosuppressive chemokines was observed, and in cocultures of both cell types there was a significant decrease in Il10 (Figs. 3K and 4K). In the presence of tumor cells, a decrease in Ifnγ secretion in Malat1 ASO–treated cultures was also observed, suggesting that Malat1 inhibition decreased the overall inflammatory signature in TAMs and MDSCs, which included a decrease in immunosuppressive factors. These results indicate a change in functionality of myeloid cells with Malat1 knockdown in TNBC. Furthermore, Malat1 inhibition within the primary TNBC TME helped to create a more immunostimulatory environment by decreasing the suppressive effects of TAMs and MDSCs on T cells, which may then allow for an increased T-cell response. These results led us to examine the effects on T cells within the TME after Malat1 ASO treatment.
Malat1 inhibition increases T-cell infiltration in the TME
Once we established the effect Malat1 knockdown had on myeloid populations in the TME, we wanted to investigate the effect Malat1 depletion had on T-cell infiltration using TILs isolated from T12 and 2208 L primary tumors and quantified using flow analysis (Fig. 5A; Supplementary Fig. S5A). In 2208 L tumors, we observed a significant increase in CD8+ T cells with a greater population of CD8+ T cells expressing the cytotoxic markers Granzyme B and Ifnγ (Fig. 5B–D). There was also a decrease in the CD4+ T-cell population, which coincided with a significant decrease in immunosuppressive regulatory Foxp3+CD4+ T cells (Tregs, regulatory T-cell; Fig. 5E and F). In T12 tumors there was a significant increase in Granzyme B+ T-cell infiltration, with no change in overall CD8+ T-cell infiltration (Fig. 5G and H). T12 tumors contained a much smaller population of CD4+ T cells and no significant change in Tregs was observed (Fig. 5,I and J). We performed CD8α and Foxp3 IHC staining of tumor sections to determine the spatial distribution of these cells. Many T cells were found in the stroma of 2208 L tumors (Supplementary Fig. S5B), although Malat1 ASO–treated tumors showed an increase in CD8α staining, with more cells found outside of the stroma as compared with Scramble ASO–treated tumors (Fig. 5K). T12 tumors also displayed a slight increase in CD8α staining in Malat1 ASO–treated tumors compared with Scramble (Fig. 5K). Foxp3 staining was quite sparse, however, quantification revealed a decrease in Foxp3 with Malat1 ASO treatment in both tumor models (Fig. 5K). This analysis of primary tumor TILs helps to support the conclusion that Malat1 inhibition creates a more immunostimulatory TME with increased cytotoxic T-cell infiltration. To determine the extent the increased immunostimulatory TME after Malat1 depletion had on tumor progression, we performed a series of treatment studies in T cell–deficient mice. 2208 L tumor pieces were implanted into the mammary fat pad of Nude athymic mice that do not have CD8+ or CD4+ T cells and treated with either Malat1 ASO or Scramble control. In this T cell–deficient model, there was no change in tumor growth between treatment groups even though Malat1 expression was significantly reduced with ASO treatment (Supplementary Fig. S5C and S5D). A pilot study using a CD8α-specific antibody that successfully ablated circulating CD8+ T cells (Supplementary Fig. S5E) also showed no significant change in primary tumor growth between treatment groups while still significantly decreasing Malat1 RNA expression in the tumor. Conversely, mice treated with IgG and Malat1 ASO had a decreased tumor volume, although due to small sample size, this was not statistically significant (Supplementary Fig. S5F–S5H). These results underscore the importance of the TME in tumor response in our mouse models, and that altering the immune microenvironment, in particular increasing T-cell infiltration, is enough to delay tumor progression.
Malat1 inhibition in T cells increases proliferation and cell cytotoxicity
Although there was an increase in Ifnγ in the tissue homogenate samples, Malat1 depletion in T12 and 2208 L tumor cells did not increase immunostimulatory cytokine secretion from cells (Supplementary Fig. S6A and S6B). This led us to investigate whether the increase in cytotoxic T-cell infiltration observed in vivo may be caused, in part, by intrinsic changes to the T cells. To study the effect of Malat1 depletion on T cells, we used in vitro culture and coculture experiments. CD3+ T cells were isolated from the spleen of WT BALB/c mice through negative selection and stimulated with CD3/CD28 activation beads for 3 to 5 days. Malat1 and Scramble ASOs (500 nm each) were added to wells on days 2 and 4 of culture. The ASO concentration used could effectively reduce Malat1 expression by at least 50% (Supplementary Fig. S6C). qPCR analysis revealed significant knockdown of Malat1 RNA expression with a greater depletion of Malat1 after 5 days of culture when T cells were treated twice with the ASO (Fig. 6A). To determine the effects of Malat1 knockdown on T-cell proliferation, T cells were stained with CFSE before being seeded. After 3 days, we observed reduced CFSE in Malat1 ASO–treated T cells as compared with the Scramble ASO–treated T cells (Fig. 6B; Supplementary Fig. S6D) signifying increased proliferation. Flow analysis of Malat1-depleted T cells cultured for 3 to 5 days showed a slight increase in CD8+ T-cell populations after 3 days of culture with the difference being less pronounced after 5 days (Fig. 6C). When examining cytotoxic markers, we saw a modest increase in Granzyme B+ and Perforin+ CD8+ cells after 3 days that was maintained after 5 days in culture (Fig. 6D and E).
To further investigate the effect of Malat1 inhibition on cell cytotoxicity, GFP-specific T cells isolated from the splenocytes of a WT JEDI mouse were cultured with GFP-labeled T12 cells at a 1:1 and 5:1 effector: target ratio for approximately 48 hours and monitored using Incuyte. At the 1:1 effector ratio, residual tumor cells were analyzed and Annexin V+ tumor cells were quantified. At the 5:1 effector ratio, there was a negligible amount of residual tumor cells collected so we chose to assess cell cytotoxicity by measuring the remaining GFP signal in each well. Malat1-depleted T cells showed increased tumor cell killing and a slight increase in Granzyme B and Perforin cytotoxic markers at the 5:1 ratio (Fig. 6F–J). Cocultures not treated with ASO, along with T cells cultured alone and tumor cells cultured alone were all used as control groups (Supplementary Fig. S6E and S6F). A cytokine assay of supernatant collected from these co-cultures revealed that Malat1 ASO–treated T cells had increased inflammatory cytokine secretions, including an increase in Ifnγ and Tnfα that was not seen in tumors or myeloid cells. Unlike in the myeloid-cell populations, there were no significant changes in immunosuppressive cytokine/chemokine secretion (Fig. 6K). These findings highlight the potential of improving T-cell cytotoxicity through Malat1 inhibition while also emphasizing the different role that Malat1 plays in effector T cells as compared with myeloid and tumor cells. An increase in inflammation was observed in T cells that was not seen in TAMs, MDSCs, or tumor cells. T cells treated with the Malat1 ASO could potentially be used in an ex vivo setting to improve therapeutic response.
Combination of Malat1 ASO with chemotherapy or ICB improves response in preclinical mouse models
The promising effects that the single-agent Malat1 ASO treatment had on 2208 L and T12 tumors, particularly the increased immunostimulatory effect seen in the TIME, led us to investigate potential benefit of combining the ASO with established clinical therapies such as chemotherapy and ICB. BALB/c mice implanted with 2208 L tumors were randomized into four separate treatment cohorts: Carboplatin at half the clinically relevant dose of 25 mg/kg with Scramble ASO, 25 mg/kg of carboplatin with Malat1 ASO, 10 mg/kg anti-PD1 with Scramble ASO, and 10 mg/kg anti-PD1 with Malat1 ASO. A small cohort of mice was treated with isotype-matched IgG antibody alone as a separate control for the ICB treatment groups. Mice were treated using an established treatment regimen (Fig. 7A). We observed that in both combination treatment groups there was a significant delay in primary tumor growth and tumor volume with a significant reduction of tumor weights at time of harvest (Fig. 7B and C). Although we did not achieve tumor stasis or regression with combination treatments, we were able to prolong mouse survival (Fig. 7D and E) in these highly aggressive tumors. Of note, mice treated with single-agent ICB did not have any response as compared with the IgG control, but in combination with the Malat1 ASO there was significant prolonged mouse survival.
Immunophenotyping the TILs isolated from the primary tumor in these treatment groups recapitulated the changes in T-cell populations seen with single-agent treatment, with an increase in cytotoxic Granzyme B+CD8+ T cells and a decrease in Tregs (Fig. 7F–M). Single-agent ICB-treated tumors had an increase in regulatory Foxp3+ CD4+ cells as compared with single-agent ASO or chemotherapy treatment groups, but Malat1 ASO used in combination with ICB significantly decreased this immunosuppressive T-cell population (Fig. 7l and M). IHC staining in these treatment groups supported what was observed in the TIL flow analysis with more CD8+ T cells found both inside and outside the stromal areas of the tumor in combination with Malat1 ASO as compared with single-agent treatment (Supplementary Fig. S7A). These results highlight the potential of using Malat1 ASO in combination with common clinically available therapies and that the immune infiltration seen in the TME may boost the efficacy of chemotherapy and ICB when used with the right staging parameters.
This study reports the efficacy of using a systemic gapmer ASO to successfully target a lncRNA and the effects Malat1 depletion have in two separate and quite different in vivo TNBC models. Previous studies have documented how Malat1 promotes aggressive and invasive behavior in cancer cells (35, 36), and this is suggested to occur primarily through its ability to act as a ceRNA, an RNA that can compete for miRNAs and sequester miRNAs from their intended target (38, 39). With respect to breast cancer, many in vitro studies have shown that Malat1 silencing decreases tumor cell proliferation, invasion, and revealed Malat1’s role in the epithelial-to-mesenchymal transition (21, 40–42). Arun and colleagues (22) previously have shown that treating the luminal B–like mouse model MMTV-PyMT with a Malat1 ASO decreased the lung metastatic burden as well as increased differentiation in the primary tumor to a more cystic phenotype. Our study revealed that depletion of Malat1 in two genetically distinct tumor models of TNBC using an ASO delays primary tumor growth, strengthening the claim that Malat1 promotes tumor propagation in TNBC. As a single-agent treatment, the Malat1 ASO decreased cell proliferation and increased apoptosis in tumors, but we also wanted to take advantage of these syngeneic mouse models and examine the effects Malat1 inhibition may have in reprogramming the TME. Moreover, we wanted to determine whether creating a more immunostimulatory TME could help improve response to treatment in these aggressive preclinical models. A major roadblock to treatment efficacy is the highly immunosuppressive TME found in TNBC and many solid tumors (43, 44). Though TNBC does display an increase T-cell infiltration as compared with hormone receptor–positive breast cancers (45, 46), checkpoint markers along with other immunosuppressive factors render these T cells ineffective in complete tumor clearance. ICB has proven to be an important option in TNBC especially when used in combination with chemotherapy that creates a more immunogenic TME (47). However, chemotherapy is still necessary for an increased clinical response and even with improved immune infiltration, immunosuppressive myeloid and stromal cells are still roadblocks that hinder efficacy. In our claudin-low and luminal-like TNBC models, there is an abundance of TAMs and MDSCs, respectively, that make immunomodulatory drugs like checkpoint inhibition and chemotherapy ineffective, contributing to the acquired resistance to treatment (29). In this study, we have demonstrated in in vivo TNBC models that Malat1 depletion through ASO silencing promotes an immunostimulatory TME.
The effects of Malat1 on immune cells have been previously studied in relation to infection, cardiovascular disease, and diabetes (48–50). In certain cancers, upregulated Malat1 expression increases angiogenesis and “M2-like” macrophage recruitment to the TME and silencing of Malat1 helps to relieve this immunosuppression (23, 51). Single-cell analysis of neutrophils before and after direct contact with the primary tumor in PyMT-N, a neutrophil enriched mouse model, shows increased Malat1 expression in neutrophils after coming in contact with tumor cells, revealing that part of the tumor education of neutrophils includes increasing their Malat1 expression. These observations provide evidence that Malat1 helps to confer the immunosuppressive capabilities of myeloid cells. We were able to determine that the depletion of Malat1 in tumor cells intrinsically alters chemokine and cytokine secretion and diminishes the recruitment signals that tumor cells produce to increase infiltration of TAMs and MDSCs to the tumor site. The intrinsic changes to the tumor cell secretory profile may contribute to the decrease in immunosuppressive TAMs and G-MDSCs that we observed in T12 and 2208 L primary tumors, respectively. Further studies indicate that there is significant knockdown of Malat1 RNA expression in TAMs as well GR1+ MDSCs following ASO treatment, and extensive in vitro coculture experiments with isolated TAMs and MDSCs illustrate that depleting Malat1 in myeloid cells diminishes their immunosuppressive function. This results in increased effector function of T cells when cultured with tumor cells and Malat1-depleted TAMs or MDSCs. Although we observed a decrease in numerous chemokines/cytokines responsible for myeloid cell recruitment and propagation with Malat1 knockdown in both TAMs and MDSCs, the specific decline of secreted IL10, which is known to hamper T-cell function (52, 53), may a play role in curtailing the immunosuppressive function of myeloid cells.
The effect of Malat1 on myeloid cell populations in the TME in vivo led us to investigate T-cell infiltration and function within the immune landscape because the T-cell response is paramount for the efficacy of immunomodulation therapies. A recent study in COVID-19 patients found that decreased Malat1 expression in T cells was linked to increased proliferative capabilities (54). However, there is little known about the effect Malat1 inhibition has on T-cell infiltration and function in cancer. In this study, we discovered that when mice are treated in vivo with the ASO regimen, there is an increase in CD8+ T-cell infiltration with a decrease in Tregs in the 2208 L model, and in both the T12 and 2208 L tumors there is increase in cytotoxic T cells. The cytokine/chemokine assays performed on tumor cells and myeloid cells did not reveal an increase in inflammatory markers that could explain the increase in infiltration and led us to look at the intrinsic effect Malat1 depletion had on T-cell function. Malat1 knockdown in T cells increased T-cell proliferation with an increase in cytotoxic CD8+ T-cell markers Granzyme B and Perforin. In addition, Malat1 ASO–treated GFP-targeting T cells cultured with GFP-labeled tumor cells revealed an increase in tumor cell killing with Malat1 depletion. Unlike tumor cells and myeloid cells, there was also increased Ifnγ and Tnfα in T cell–conditioned supernatants; however we observed variability within each treatment condition that we attribute to the use of primary cells. These alterations on immune populations with Malat1 ASO treatment suggest that the decrease in the immunosuppressive properties of tumor and myeloid cells coupled with increased cytotoxicity of T cells acts in synergy to improve the overall response observed in vivo.
To attribute the delay in primary tumor growth with Malat1 ASO treatment with the changes in the TME after Malat1 knockdown, we performed a series of treatment studies in T cell–deficient mice implanted with 2208 L tumors. In the athymic nude mouse model absent of any T cells, there was no delay in tumor growth with Malat1 ASO treatment even with an approximate 62% reduction of Malat1 RNA expression in the tumor, similar to the knockdown observed in BALB/c mice. Treatment of BALB/c mice with a CD8α-specific antibody along with ASO, also demonstrated no change in primary tumor growth, whereas tumors treated with the IgG control still responded to the Malat1 ASO. These results suggest that much of the response we see with single-agent Malat1 ASO is T-cell–mediated and may specifically be CD8+ T cell–mediated. Because of the large number of myeloid cells present compared with T cells in 2208 L tissue, we believe that the changes in myeloid cell recruitment and function after Malat1 depletion may allow for increased T-cell infiltration and cytotoxicity, contributing to the response observed.
To investigate whether changes in the TIME after Malat1 depletion would also improve the tumor response to immunomodulatory therapies, we combined the Malat1 ASO with anti-PD1 in the 2208 L model. The highly immunosuppressive neutrophil-enriched microenvironment in 2208 L tumors results in negligible response to checkpoint inhibition (29). In this study, combining Malat1 ASO with anti-PD1 led to a significant decrease in primary tumor growth, tumor volume, and weight. Furthermore, we were able to significantly prolong mouse survival with this combination treatment. The effect Malat1 depletion has on both immunosuppressive myeloid cells and T-cell infiltration may play a vital role in this improved response by promoting a more immunostimulatory microenvironment that improves checkpoint inhibition efficacy. Both IHC and flow analysis confirmed the increase in cytotoxic T-cell infiltration with combination treatment, similar to what we saw with the single-agent treatment. Of note, we also observed that ICB treatment causes an increase in regulatory CD4+ T cells that is significantly reduced when combined with Malat1 ASO. However, with the current treatment regimen we were not able to achieve tumor regression or establish PFS in these aggressive tumor models and further investigation into treatment scheduling and/or combination regimens, may provide an improved tumor response. Depleting Malat1 expression in tumors first, followed by treatment with chemotherapy or ICB may be a promising dosing regimen and remains a possible avenue of future investigation.
In more general terms, these discoveries of the effects of Malat1 depletion in the TNBC TME highlight the promising capabilities that targeting a lncRNA with an ASO provides. In the case of Malat1, which is a highly abundant lncRNA that has many possible binding partners, it was difficult to pinpoint a single effector molecule and signaling pathway responsible for the observed alterations of the immune microenvironment. Studies in the T cell–deficient mouse models point to T cells being integral to the tumor response. However, understanding whether this is due to intrinsic changes to T-cell function or the relief of immunosuppression on T cells will require further experimentation. We suggest that depleting Malat1 in vivo has led to pleiotropic effects in numerous TILs and tumor cells and these combined effects play a role in altering the immune landscape. However, single-cell sequencing of tumor cells and CD45+ cells as well as proteomics will be required in future studies to provide a better understanding of the crosstalk between tumor, myeloid, and T cells occurring within the TME after Malat1 knockdown. In this study, we chose to focus initially on the effects of Malat1 depletion at the primary tumor site; however, understanding the effects systemic Malat1 ASO treatment have on peripheral immune cells also should be investigated further. We provide proof of concept about the potential benefits of using an ASO therapy in vivo and how the effects on the TIME might improve tumor response to current therapeutics. At present, we are unaware of ongoing clinical trials targeting lncRNAs for cancer treatment as single agents or in combination with other drugs, and the primary focus of lncRNAs in cancer therapy has been on the direct effect of lncRNAs on tumor cells. Tumor progression and metastasis is a multifactorial process and the TME is an integral factor contributing to tumor growth and dissemination. The results reported in this study are only the tip of the iceberg and highlight the importance of understanding the mechanisms by which lncRNAs contribute to the TIME.
O. Adewunmi reports grants from National Cancer Institute (NCI) during the conduct of the study. No disclosures were reported by the other authors.
O. Adewunmi: Data curation, formal analysis, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing. Y. Shen: Data curation, formal analysis, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing. X.H.-F. Zhang: Resources, data curation, software, methodology, writing–review and editing. J.M. Rosen: Conceptualization, resources, software, supervision, funding acquisition, methodology, project administration, writing–review and editing.
The research for this study was funded by NCI R01 CA16303–46 and CURES supplement grant RCA016303K. This project was also supported by the Cytometry and Cell Sorting Core at Baylor College of Medicine with funding from the CPRIT Core Facility Support Award (CPRIT-RP180672), the NIH (CA125123 and RR024574), and the assistance of Joel M. Sederstrom. Special thanks to Sergio Aguirre, Clark Hamor, Nadia Leu, and Shirley Small for helping with rodent surgery and maintenance.
The publication costs of this article were defrayed in part by the payment of publication fees. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734.
Note: Supplementary data for this article are available at Cancer Immunology Research Online (http://cancerimmunolres.aacrjournals.org/).