LCOR mediates interferon-independent tumor immunogenicity and responsiveness to immune-checkpoint blockade in triple-negative breast cancer

Pérez-Núñez, Iván; Rozalén, Catalina; Palomeque, José Ángel; Sangrador, Irene; Dalmau, Mariona; Comerma, Laura; Hernández-Prat, Anna; Casadevall, David; Menendez, Silvia; Liu, Daniel Dan; Shen, Minhong; Berenguer, Jordi; Ruiz, Irene Rius; Peña, Raul; Montañés, José Carlos; Albà, M. Mar; Bonnin, Sarah; Ponomarenko, Julia; Gomis, Roger R.; Cejalvo, Juan Miguel; Servitja, Sonia; Marzese, Diego M.; Morey, Lluis; Voorwerk, Leonie; Arribas, Joaquín; Bermejo, Begoña; Kok, Marleen; Pusztai, Lajos; Kang, Yibin; Albanell, Joan; Celià-Terrassa, Toni

doi:10.1038/s43018-022-00339-4

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Published: 17 March 2022

LCOR mediates interferon-independent tumor immunogenicity and responsiveness to immune-checkpoint blockade in triple-negative breast cancer

Iván Pérez-Núñez¹,
Catalina Rozalén¹,
José Ángel Palomeque¹,
Irene Sangrador¹,
Mariona Dalmau¹,
Laura Comerma²,
Anna Hernández-Prat¹,
David Casadevall¹,
Silvia Menendez¹,
Daniel Dan Liu ORCID: orcid.org/0000-0003-0100-5973³^nAff20,
Minhong Shen³,
Jordi Berenguer¹,
Irene Rius Ruiz ORCID: orcid.org/0000-0002-6097-7572⁴,
Raul Peña¹,
José Carlos Montañés⁵,
M. Mar Albà^5,6,
Sarah Bonnin⁷,
Julia Ponomarenko^7,8,
Roger R. Gomis ORCID: orcid.org/0000-0001-6473-2858^6,9,10,
Juan Miguel Cejalvo^10,11,
Sonia Servitja^10,12,
Diego M. Marzese¹³,
Lluis Morey ORCID: orcid.org/0000-0001-9851-4066^14,15,
Leonie Voorwerk¹⁶,
Joaquín Arribas ORCID: orcid.org/0000-0002-0504-0664^1,4,6,10,
Begoña Bermejo^10,11,
Marleen Kok ORCID: orcid.org/0000-0001-9043-9815^16,17,
Lajos Pusztai ORCID: orcid.org/0000-0001-9632-6686¹⁸,
Yibin Kang ORCID: orcid.org/0000-0002-1626-6730^3,19,
Joan Albanell ORCID: orcid.org/0000-0003-1239-4580^1,8,10,12 &
…
Toni Celià-Terrassa ORCID: orcid.org/0000-0002-0615-8744^1,10

Nature Cancer volume 3, pages 355–370 (2022)Cite this article

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Abstract

Ligand-dependent corepressor (LCOR) mediates normal and malignant breast stem cell differentiation. Cancer stem cells (CSCs) generate phenotypic heterogeneity and drive therapy resistance, yet their role in immunotherapy is poorly understood. Here we show that immune-checkpoint blockade (ICB) therapy selects for LCOR^low CSCs with reduced antigen processing/presentation machinery (APM) driving immune escape and ICB resistance in triple-negative breast cancer (TNBC). We unveil an unexpected function of LCOR as a master transcriptional activator of APM genes binding to IFN-stimulated response elements (ISREs) in an IFN signaling-independent manner. Through genetic modification of LCOR expression, we demonstrate its central role in modulation of tumor immunogenicity and ICB responsiveness. In TNBC, LCOR associates with ICB clinical response. Importantly, extracellular vesicle (EV) Lcor–messenger RNA therapy in combination with anti-PD-L1 overcame resistance and eradicated breast cancer metastasis in preclinical models. Collectively, these data support LCOR as a promising target for enhancement of ICB efficacy in TNBC, by boosting of tumor APM independently of IFN.

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Main

Immune-checkpoint blockade immunotherapy unleashes antitumor immune responses and has emerged as one of the most effective therapies in oncology. Nevertheless, most patients do not respond or acquire resistance to ICB, and this strategy is not active against all cancer types. The underlying tumor-intrinsic mechanisms of ICB resistance are thus still under intense investigation^1,2. Among these, phenotypic heterogeneity might help to explain how patients with similar tumor types still show varying sensitivity to ICB³.

Interferon signaling plays a central role in tumor immunosurveillance and immunotherapy response, in part by modulation of APM in tumor cells^3,4,5,6. APM includes major histocompatibility complex (MHC) class I genes (HLAs), aiding molecules (β2M), transporters (TAP complex and tapasin) and immunoproteasome genes (PSMBs) among others, which are the cellular effectors of antigen presentation allowing its recognition by the immune system⁷. Multiple studies and clinical reports have identified genetic alterations in antigen presentation components and the IFN pathway as major mechanisms of ICB resistance^2,8,9,10. Epigenetic factors and transactivators, such as EZH2, NF-κB and NLRC5, can also modulate MHC-I and other APM genes affecting tumor immunogenicity^11,12. Therefore, a better understanding of APM regulation in different tumor cell populations and phenotypes can help to elucidate the mechanisms underlying immunotherapy resistance.

Stem cell phenotypes, such as embryonic and slow-cycling adult stem cells, downregulate MHCs, favoring immune surveillance evasion^13,14. CSCs resemble their normal counterparts in many aspects¹⁵, and may co-opt similar immune-evasive phenotypes relevant in ICB resistance. CSCs actively interplay with immune-secreted cytokines, including IFNs^16,17,18, and some reports have shown low expression of MHCs and Transporter 1 (TAP1) in CSCs^19,20, suggesting a role in immune surveillance evasion. CSCs also express CD274 (PD-L1), a mechanism that contributes to immunosuppression²¹ but not to anti-PD-L1 ICB escape. Epithelial-to-mesenchymal transition, often linked to stemness, also promotes immunosuppressive features^22,23,24. In the context of immunotherapy resistance, skin squamous cell carcinoma (SCC) tumor-initiating cells (TICs) have been linked to adoptive cell transfer resistance by upregulation of CD80 (ref. ²⁵); and head and neck SCC CSCs have ICB resistance by downregulating the secretion of T cell-recruiting chemokines²⁶. However, the connection between stemness and APM pathway dysregulation leading to ICB escape, and the molecular mechanisms controlling it, are not established.

In the mammary gland, normal mammary stem cells (MaSCs) and CSCs are usually governed by differentiating cell fate determinants and stem cell transcription factors^{16,27,28,29,30,31}. LCOR is a differentiation factor sensitizing these to IFN, which drives intrinsic tumor cell differentiation and reduced tumor growth¹⁶. However, how LCOR intersects the IFN response has never been explored and may be critical to understanding cellular immunity. Therefore, we were interested in exploring their mechanistic connections with immunity and ICB resistance in TNBC. This is clinically relevant, because there is an urgent need to improve the efficacy of ICB in breast cancer, approved in TNBC with only limited clinical benefit to date^32,33. Here, we show that breast LCOR^low CSCs shut down both antigen processing and presentation, contributing to immune-checkpoint therapy resistance in TNBC. LCOR activates transcription of the APM independently of IFN, rendering CSCs visible and vulnerable to immune attack during ICB. Our results demonstrate the relevance of phenotypic heterogeneity and LCOR biology in APM regulation as an excellent therapeutic partner of ICB therapy.

Results

ICB resistance emerges from MaSC-like states

To understand breast cancer ICB resistance, we generated a preclinical immunocompetent syngeneic model of anti-PD-L1 ICB resistance in vivo. Mouse breast cancer 4TO7 cells were orthotopically implanted into the mammary fat pad (MFP). Once tumors had reached a size of 0.5 × 0.5 cm², mice were treated with anti-PD-L1. After an initial transient response, all tumors developed resistance to anti-PD-L1 treatment (Fig. 1a). Anti-PD-L1 immunotherapy-resistant tumors (IRTs) were harvested in cell culture and their resistance was confirmed again in vivo (Extended Data Fig. 1a).

**Fig. 1: CSCs lead immunoediting escape and ICB resistance.**

Transcriptomic analysis of IRT cells revealed loss of APM among the most downregulated Gene Ontology (GO) pathways, followed by an expected loss of IFN signaling (Fig. 1b,c). Interestingly, among upregulated pathways we found enrichment of stem cell signatures (Fig. 1b) and, using gene set variation analysis (GSVA), we verified an important enrichment of CSC-like signatures associated with breast cancer aggressiveness, such as ES_1 (ref. ²⁹), NOS_Targets²⁹ and breast_CSCs³⁴ (Fig. 1d). Moreover, chromatin immunoprecipitation (ChIP)–enrichment analysis (CHEA) ranked stem cell transcription factors (SC-TFs) most enriched in IRT (Extended Data Fig. 1b). We validated these transcriptomic findings by reverse transcription quantitative PCR with reverse transcription (RT–qPCR), showing that IRT cells are enriched for SC-TF genes Pou5f1 (Oct4), Sox2, Sox9 and Nanog and depleted in mammary differentiation factors—in particular, Lcor (Fig. 1e). Importantly, IRT cells also showed increase in the MaSC markers CD24⁺/CD29^hi (ref. ³⁵), a population that has also been reported in 4TO7 cells and others³⁶ (Fig. 1f). This cellular selection by ICB treatment was not observed when we disrupted antigen presentation using β2m knockout (KO) cells, consistent with absence of selective immune-mediated killing (Extended Data Fig. 1c). Of note, IRT cells maintained high Pd-l1 expression (Extended Data Fig. 1d), suggesting that they resisted anti-PD-L1 despite expressing the target. We propose that this resistance has emerged through downregulation of APM in CSCs, as suggested by the absence of CSC-like cell enrichment in β2m KO cells after treatment (Extended Data Fig. 1c). Further characterization showed that IRT cells possess increased tumor sphere formation ability across sphere generations in vitro (Extended Data Fig. 1e), despite no differences in cell cycle and with proliferation rates similar to the control condition, in both systems IRT and CD24^hi/CD29^hi CSCs (Extended Data Fig. 1f,g). To evaluate their TIC frequency in vivo, a hallmark of CSCs, we performed orthotopic MFP injections in limiting dilution assays (LDAs) in immunodeficient NOD-SCID Gamma (NSG) mice. IRT cells were enriched >tenfold for TIC frequency (Fig. 1g), confirming that CSC properties are selected by ICB therapy.

Next, we performed in vitro cytotoxic T lymphocyte (CTL) assays by coculture of AT3-OVA cells and OT-I CD8 T cells, which specifically recognize the OVA peptide 257-264 (SIINFEKL). After 3 days of coculture, surviving tumor cells that had evaded immune killing were enriched in CD24^lo/CD44^hi AT3 CSCs^37,38, particularly after anti-PD-L1 treatment (Fig. 1h). Accordingly, sorted AT3 and 4TO7 CSCs were resistant to immune-mediated cell killing compared to non-CSCs when cocultured with OT-I CD8 and JEDI CD8 T cells, respectively (Fig. 1i and Extended Data Fig. 1h). AT3 CSCs induced less antitumor T cell activity than non-CSCs (Extended Data Fig. 1i). We utilized the SORE6 CSC reporter system, which reports SOX2 and OCT4 activity³¹, in AT3-OVA and Py8119-OVA cells. We first validated SORE6 fidelity in our models, showing that SORE6⁺ expressed SC-TFs and had >sixfold higher TIC frequency in vivo when injected into the MFP in LDAs (Extended Data Fig. 1j,k). Similar to previous results, the residual surviving cell population in CTL assays was highly enriched in SORE6⁺ CSCs due to selective elimination of SORE6⁻ cells, an effect further enhanced by anti-PD-L1 treatment (Extended Data Fig. 1l). Overall, these results demonstrate the anti-PD-L1 ICB escape ability of CSC populations.

Despite the limitations in studying immune-specific interactions in human models, CD104^hi/CD44^hi CSCs isolated from MDA-MB-231 cells³⁹ were more resistant to anti-PD-L1 when cocultured with human peripheral blood mononuclear cells (PBMCs) in an allogenic in vitro setting (Extended Data Fig. 1m). Moreover, when immune-humanized mice with PBMCs were orthotopically injected with MDA-MB-231 cells, after 5 weeks tumors treated with anti-PD-L1 were enriched for the CD104^hi/CD44^hi CSC population (Extended Data Fig. 1n).

To assess the clinical significance of our preclinical IRT resistance model, we generated a breast cancer ‘ICB-resistance signature’ (IRS) using the top 300 IRT upregulated genes (Supplementary Table 1). We applied IRS to transcriptomic RNA sequencing (RNA-seq) data from the TONIC trial, which collected 53 metastatic TNBC cases treated with anti-PD1 after preinduction therapy⁴⁰. Gene set enrichment analysis (GSEA) showed that IRS was highly enriched in those patients not benefitting from ICB therapy (nonresponders) (Fig. 1j), validating its clinical value. Moreover, the nonresponding group was also enriched with stem cell-like signatures ES_1 (ref. ²⁹) and breast_CSCs³⁴ (Fig. 1k), consistent with our experimental observations. We also reanalyzed a single-cell RNA-seq dataset of 29 patients with breast cancer before and during anti-PD-1 therapy⁴¹. On-treatment tumors reduced LCOR and gained breast_CSC³⁴ expression (Extended Data Fig. 1o–q). Overall, these results support the existence of CSC immunoediting driving ICB resistance.

LCOR^low breast CSCs have reduced antigen-presenting ability

Based on the finding that ICB-resistant cells have a stem cell-like phenotype and low APM expression (Fig. 1b,c), we analyzed available MaSC transcriptomic profiles⁴². As expected, fetal mammary stem cells (fMaSCs) showed simultaneous downregulation of APM pathway genes, including immunoproteasome factors (PSMB8 and PSMB9), transporters (TAP1 and TAP2), β2M and MHC-I genes (Extended Data Fig. 2a).

To study the APM pathway in human CSCs, we generated patient-derived organoids (PDOs) from clinical samples and isolated CD24^lo/CD44^hi CSCs³⁷ from four different patients with TNBC (Fig. 2a). Our analysis confirmed that APM genes are downregulated in clinical CD24^lo/CD44^hi low-LCOR CSCs, by RT–qPCR analysis (Fig. 2a) and by flow cytometry of pan-HLAs-ABC and β2M (Fig. 2b). Additionally, three-dimensional (3D) PDO imaging with advanced light-sheet fluorescence microscopy (LSFM) also demonstrated the absence of pan-HLA staining in SORE6⁺ CSCs (Fig. 2c). In human mammary cell lines, HMLE CD24^lo/CD44^hi stem cells⁴³ and MDA-MB-231 CD104^hi/CD44^hi CSC populations³⁹ also showed downregulation of APM genes and LCOR compared to non-CSCs (Extended Data Fig. 2b–d), as well as in ALDH⁺ populations (Extended Data Fig. 2c,d). Accordingly, mouse 4TO7 CSCs and IRT cells also showed low Lcor and low APM (Extended Data Figs. 2c,e and 1e). Next, we generated a fused LCOR-green fluorescent protein (GFP) knock-in in MDA-MB-231 cells reporting the endogenous protein levels and localization of LCOR (Fig. 2d and Extended Data Fig. 2f,g). As expected, LCOR^low MDA-MB-231 cells were enriched in OCT4⁺/SOX2⁺ CSCs (SORE6⁺) (Extended Data Fig. 2h). We then used fluorescence-activated cell sorting (FACS) to isolate cells based on GFP (LCOR-GFP) levels and measured the expression of APM genes in LCOR^−/low and LCOR^high cells by RT–qPCR. APM gene expression progressively increased with increasing levels of LCOR (Fig. 2e). This was further validated by immunofluorescence (IF) of pan-HLA-ABC and LCOR-GFP (Fig. 2f). These results indicate reduced APM activity in human LCOR^low CSCs.

**Fig. 2: LCOR^low breast CSCs have low APM activity.**

To demonstrate impaired APM in CSCs, we isolated breast cancer cells based on APM activity using the OVA antigen-peptide presentation as readout. We ectopically expressed the full-length native chicken egg ovalbumin (OVA) in AT3 and Py8119 cells, which are H2-K1^b haplotype cells established from mouse breast cancer C57BL/6J PyMT tumors⁴⁴. Ectopic OVA is processed by the immunoproteasome generating the OVA_257–264 (SIINFEKL) peptide, transported and presented in a MHC-I H2-K1^b context⁴⁵. As we hypothesized, isolated OVA^−/low cells (Fig. 2g and Extended Data Fig. 2i) expressed higher levels of CSC genes (Oct4, Sox2, Sox9 and Nanog) compared to OVA^high cells as measured by RT–qPCR, and substantially lower levels of Lcor (Fig. 2h and Extended Data Fig. 2j). Accordingly, the AT3 CSC population CD24^lo/CD44^hi^37,38 is largely segregated in OVA^−/low AT3 cells by flow cytometry (Fig. 2i) whereas CSCs are only partially segregated by Pd-l1 (Extended Data Fig. 2k). Functional assays in both cell lines demonstrated increased tumor sphere formation (Extended Data Fig. 2l) and increased TIC capacity of OVA^−/low cells in orthotopic MFP LDAs in immunodeficient NSG mice in vivo (Fig. 2j and Extended Data Fig. 2m), reflecting an inherent tumor-initiation stem cell potential of OVA^−/low cells. Therefore, within tumor cell heterogeneity, LCOR^low CSCs have a defective APM system as an important immune-evasive property for ICB escape.

LCOR regulates APM independently of interferon signaling

LCOR is among the top downregulated mammary differentiation factors in our IRT model, and LCOR^low CSCs are associated with low APM. Here, we show expression correlation of LCOR and APM components in estrogen receptor (ER)-negative breast cancer cell lines of the Cancer Cell Line Encyclopedia (CCLE) (Fig. 3a). Moreover, patient stratification of the TNBC METABRIC dataset based on LCOR levels demonstrates a strong correlation between LCOR expression and both the APM pathway (Kyoto Encyclopedia of Genes and Genomes (KEGG): M16004) (Fig. 3b) and IRS (Fig. 3c). These results suggest that LCOR may play a key role in APM regulation.

**Fig. 3: LCOR regulates APM genes independently of IFN stimulation.**

To investigate the mechanistic link of LCOR with the APM pathway, we used LCOR gain-and-loss modifications in MDA-MB-231 and HMLE cells. Remarkably, ectopic LCOR expression induced APM pathway genes while LCOR-KD reduced their expression in both cell types (Fig. 3d and Extended Data Fig. 3a). Moreover, we measured the expression of different APM components via flow cytometry in these models. LCOR-KD cells phenocopy CSCs and display low APM as measured by the reduced 26S proteasome activity reporter pQCXIN/ZsGreen⁴⁶, and reduced expression of transporters (TAP1) and presenting molecules (β2M and HLA-ABC) (Fig. 3e–g and Extended Data Fig. 3b–d). As expected, the inverse results were obtained with LCOR-OE boosting antigen processing and presentation (Fig. 3d–g and Extended Data Fig. 3a–d,f).

Surprisingly, these effects were not dependent on IFN stimulation. Despite IFN-γ treatment enhancing the effects of LCOR on APM components, ruxolitinib—an inhibitor of JAK1/JAK2-mediated activation of STATs/IRFs—did not affect the ability of LCOR to induce different APM components in vitro (Fig. 3e–g and Extended Data Fig. 3b–d). Importantly, the effects of IFN-γ treatment were abrogated in LCOR-KD, suggesting that LCOR levels modulate sensitivity to IFN and its impact on the APM pathway (Fig. 3e–g and Extended Data Fig. 3b–d). To validate global APM activity, murine AT3-OVA and Py8119-OVA Lcor-KD cells showed reduced OVA presentation while Lcor-OE cells had high presentation in all three conditions, thus demonstrating the essential role of LCOR in modulating and priming APM activity (Fig. 3h and Extended Data Fig. 3e,f). Again, IFN-γ treatment was unable to increase OVA presentation in Lcor-KD cells. Overall, our data demonstrate that LCOR levels determine APM activity in tumor cells with and without IFN signals, highlighting a dominant role of LCOR in antigen presentation.

LCOR directly regulates APM factors through ISRE binding

To understand how LCOR regulates the APM pathway, we transduced MDA-MB-231 cells with ectopic expression of LCOR and LCOR mutant forms¹⁶, including a double-point mutation in the nuclear receptor (NR) binding domain (LSKLL to LSKAA) preventing binding to NRs and deletion of the HTH DNA-binding domain (ΔHTH), abolishing its putative binding to DNA. We verified the correct overexpression and nuclear localization of each variant (Extended Data Fig. 4a–c). Next, we performed chromatin immunoprecipitation sequencing (ChIP–seq) analysis. The highest cluster of peaks genome wide was found on the short arm of chromosome 6 (chr6) for the wild-type and LSKAA forms of LCOR, but not for the ΔHTH mutant (Fig. 4a). Importantly, this genomic region of approximately 8 Mb (chr6: 27,810,120–35,980,577) contains the MHC-I cluster⁴⁷ and all APM genes (Fig. 4a,b), except β2M located on chr15. Consistently, gene expression analysis in MDA-MB-231 cells showed that the wild-type and LSKAA forms of LCOR, but not the ΔHTH mutant, induce APM genes (Fig. 4a,b). Of note, LCOR peaks were located in gene regulatory elements (GREs) of these genes (Fig. 4c), including β2M, suggesting direct master regulation of the APM cluster, also validated by endogenous LCOR ChIP–qPCR using an LCOR-HA knock-in system (Extended Data Fig. 4d). Accordingly, ChIP–seq peak enrichment analysis ranked the APM pathway as the most highly enriched pathway among BioCarta biological processes (Fig. 4d). Interestingly, by analysis of available assay for transposase-accessible chromatin using sequencing (ATAC–seq) data, we observed that this region is shut down in fMaSCs⁴² (Extended Data Figs. 4e and 2a), reflecting a conserved gene regulatory mechanism of mammary cell immunogenicity. Therefore, we investigated the evolutionary conservation of LCOR in other species and found that it is highly conserved in all vertebrates, especially the NR and HTH domains, the latter being the only domain preserved beyond vertebrates (Extended Data Fig. 4f,g). Therefore, it is an ancient DNA-binding domain originating in prokaryotes as a transcription factor domain. These findings support a conserved role of LCOR in coordinating APM transcriptional regulation through its HTH domain.

**Fig. 4: LCOR transcriptional regulation of the APM genomic region through ISRE binding.**

Our transcriptomic and conservation analyses suggest that LCOR can act as a transcriptional activator. However, LCOR has previously been described as a transcriptional corepressor⁴⁸ and its function as a possible transcriptional activator had not yet been proven. Performing motif discovery analysis of LCOR ChIP–seq peaks using HOMER software revealed ISREs among the top-ranked prediction motifs—in particular, the interferon-regulatory factor 1 (IRF1) binding site (Fig. 4e), typical of interferon-stimulated genes and APM genes in antiviral responses⁴⁹. To demonstrate that LCOR can activate APM genes, we generated a promoter-reporter with six tandem repeats of the ISRE motif (6 × CAGTTTCACTTTCCC) upstream of a minimal CMV promoter, and also a mutated version (6 × CAGTGGCACGGTCCC) to discern specific LCOR binding (Fig. 4f). As expected, only LCOR and LSKAA, but not ΔHTH, increased reporter activity as quantified by red fluorescent protein (RFP) flow cytometry in MDA-MB-231 cells (Fig. 4g). Remarkably, no induction was detected when the ISRE sequence was mutated (Fig. 4g), showing that LCOR binds to ISREs and activates transcription. Next, we combined the ISRE reporter with the LCOR-GFP knock-in, generating a unique flow cytometry system to study ISRE activity dependent on endogenous LCOR regulation. We explored the effects of different conditions, including IFN-γ treatment and different IFN inhibitors—ruxolitinib (inhibitor of JAK1/JAK2) and BX-795 (TBK1 inhibitor blocking STING signaling)—to further extricate the interference by IRFs and LCOR on reporter activity. We observed that IFN-γ treatment increased ISRE in LCOR⁺ cells (medium and high), but not in LCOR⁻ cells. Moreover, ruxolitinib inhibited ISRE activity only in LCOR⁻ cells but not in LCOR⁺ cells, demonstrating that LCOR induction of ISRE is independent of IFN and essential for the transcriptional activation of ISRE-controlled genes (Fig. 4h). Accordingly, these treatments did not suppress ISRE induction in LCOR-OE cells (Extended Data Fig. 4h) and IFN-γ failed to induce ISRE in LCOR-KD cells (Extended Data Fig. 4i). This is consistent with the independent induction of APM activity (Fig. 3e–h).

We further confirmed the transcriptional activity of LCOR by performing RNA polymerase II subunit B (POLR2B) ChIP. POLR2B bound MHC-I genes only in the presence of LCOR (Extended Data Fig. 4j) but not in LCOR-KD cells (Extended Data Fig. 4k). Moreover, POLR2B is enriched with either LCOR-OE or LSKKA-OE but not with the ΔHTH mutant (Extended Data Fig. 4j), indicating the requirement of LCOR binding at MHC-I gene promoters. Overall, these results reveal that LCOR is a transcriptional activator of the APM pathway.

LCOR facilitates tumor-immune infiltration and killing

Next, we tested the effects of LCOR-mediated APM induction on tumor immunity. In vitro CTL assays of AT3-OVA and Py8119-OVA cells cocultured with CD8⁺ OT-I T cells, and of 4TO7-EGFP cocultured with CD8⁺ JEDI T cells, showed increased immune killing of Lcor-OE cells in both systems (Fig. 5a,b and Extended Data Fig. 5a,c). Knockdown and knockout of β2m in Lcor-OE cells rescued survival and evasion of T cell killing, demonstrating that this effect is dependent on antigen presentation capacity (Fig. 5c and Extended Data Fig. 5d). In addition, Lcor-KD cells avoided immune-mediated cell killing (Fig. 5d and Extended Data Fig. 5b,e), consistent with their reduced OVA presentation ability (Fig. 3h). Lcor-OE cells increased T cell activation as measured by CD69, while Lcor-KD reduced it (Fig. 5e). In tumor growth experiments, 4TO7 Lcor-OE cells showed higher Lcor-mediated reduction in immunocompetent mice (Balb/c) than in immunodeficient NSG mice (Extended Data Fig. 5f,g), indicating an elicited immune reaction due to LCOR-mediated immunogenicity. The inverse effect was true for Lcor-KD tumors (Extended Data Fig. 5h,i). 4TO7 Lcor-OE tumors showed a substantial increase in CD4 and CD8 infiltrating lymphocytes compared to control tumors as measured by immunohistochemistry (IHC) and flow cytometry (Fig. 5f,g), and a nonsignificant increase in CD45⁺ leukocytes (Fig. 5f and Extended Data Fig. 5j). The CD45⁺/CD3⁻ compartment showed no significant changes in dendritic cells (CD45⁺/CD3⁻/CD11c⁺/F4/80⁻) and macrophages (CD45⁺/CD3⁻/CD11b⁺/F4/80⁺) (Extended Data Fig. 5k,l). These results support a lymphocytic immunoreactive response to Lcor-OE tumors due to their high immunogenicity.

**Fig. 5: Tumor infiltration, immunogenicity and killing.**

We performed deconvolution xCell⁵⁰ analysis of the METABRIC dataset to estimate the immune content of 186 samples from patients with TNBC⁵¹. We generated immunophenotype clusters representative of the immune landscape: cluster 1, low-immune-infiltrated tumors; cluster 2, immunosuppressive populations; cluster 3, cytotoxic populations; and cluster 4, highly cytotoxic populations. Next, we analyzed LCOR levels in these different clusters and found higher expression in the cytotoxic clusters (Fig. 5h,i). LCOR-high tumors were also more enriched for CD4, CD8 and γδ T lymphocyte gene signatures (Extended Data Fig. 5m). Overall, these results support the premise that LCOR promotes immunogenicity and adaptive immune infiltration in TNBC, mediating antitumor immunity.

LCOR levels and ICB responsiveness in patients with TNBC

We performed LCOR IHC analysis on a small set of matched clinical TNBC samples before and after ICB treatment in the neoadjuvant setting. Two patients (P1 and P2) were treated with anti-PD-L1 atezolizumab plus polychemotherapy (abraxane and carboplatin) while two others (P3 and P4) were treated with anti-PD1 nivolumab plus SYK/FLT3 inhibitor. In all cases, LCOR expression was lower in residual disease (Fig. 6a). These data support the supposition that LCOR cells are eliminated by combinatorial neoadjuvant ICB therapy.

**Fig. 6: Preclinical and clinical implications of LCOR in combination with ICB therapy.**

We also explored the value of LCOR in ICB response in larger clinical trials. Analysis of the TONIC trial, which included 53 metastatic TNBC patient samples pretreatment⁴⁰, showed higher levels of LCOR in responders (Fig. 6b). Additionally, data from the phase-II I-SPY2 trial, using durvalumab, olaparib and neoadjuvant paclitaxel in patients with TNBC⁵², also showed higher levels of LCOR in responders (Fig. 6b). These findings demonstrate that LCOR levels are associated with response to ICB-containing combination therapy in TNBC. To assess whether these observations also apply in other cancer types, we examined a melanoma cohort treated with single-agent anti-PD-1, showing higher levels of LCOR in responders (Extended Data Fig. 6a). Moreover, our breast IRS overlaps with a melanoma ICB resistance signature⁵³, and patients with LCOR^high TNBC inversely correlated with the latter signature (Extended Data Fig. 6b–d). Overall, these results support the association of LCOR with ICB clinical benefit.

LCOR overcomes resistance to ICB leading to tumor eradication

Antigen presentation and PD-L1 are hallmarks of anti-PD-1/PD-L1 therapy response³. Lcor slightly increased Pd-l1 expression in 4TO7 and AT3 cells (Extended Data Fig. 6e,f), and showed positive correlation in patients with TNBC (Extended Data Fig. 6g). This may be explained by LCOR priming IFN sensitivity, which can induce PD-L1 (refs. ^3,5). Remarkably, the positive levels of PD-L1 in combination with the potent LCOR induction of APM sets an ideal tumor configuration for anti-PD-1/PD-L1 therapy. Therefore, we performed orthotopic MFP transplantation of syngeneic 4TO7 cells in immunocompetent mice, allowed tumors to reach a size of 0.5 × 0.5 cm², then initiated ICB therapy with anti-PD-L1 once per week. Control-nontreated and control-treated tumors continued growing; however, Lcor-OE tumors treated with anti-PD-L1 ICB totally regressed, with complete response (CR) by 20 days in all mice (Fig. 6d,e). Importantly, depletion of the CD4/CD8 compartment led to no response to ICB, validating that the observed CR to anti-PD-L1 in Lcor-OE tumors was mediated by the adaptive immune system. We performed up to five independent experiments and observed CR in 49 out of 50 Lcor-OE tumors (Fig. 6f). Of note, the only Lcor-OE tumor that did not respond had lost the ectopic expression of Lcor (Extended Data Fig. 6h). All 49 mammary glands with CR were tumor free after 2 months of anti-PD-L1 discontinuation. We followed up 15 of these mice for 1 year, and in none had tumors recurred and all had tumor-free glands, suggesting that we had irreversibly eradicated the tumors and cured these mice (Fig. 6f,g). Accordingly, 4TO7 Lcor-KD tumors demonstrated higher resistance to anti-PD-L1 compared to control 4TO7 cells (Fig. 6h). The AT3 syngeneic model in C57BL/6J mice also confirmed the CR of Lcor-OE tumors to ICB therapy, both in vivo (Extended Data Fig. 6i) and in vitro (Extended Data Fig. 6j).

To study the dominant role of LCOR over IFN signaling in vivo, we performed a MFP experiment comparing the effects of Lcor-OE and the IFN type-I inducer Poly (I:C)⁵⁴. The combination of Poly (I:C) treatment and anti-PD-L1 did not reach the efficiency shown by the Lcor + anti-PD-L1 condition (Extended Data Fig. 6k), again demonstrating the dominant role of LCOR in ICB therapy beyond IFN-mediated effects.

Next, we conducted preclinical lung metastasis assays using 4TO7 tail vein (TV) administration and allowed for the establishment and growth of lung metastases before starting anti-PD-L1 therapy (Fig. 6i). To achieve synchronous metastases among the different conditions, we injected threefold more Lcor-OE cells due to their inherent reduced tumorigenicity. 4TO7 Lcor-OE metastasis were cured after 4 weeks of anti-PD-L1 treatment in five out of six mice, while all control metastatic tumors progressed despite anti-PD-L1 exposure (Fig. 6i and Extended Data Fig. 6l). Overall, these preclinical assays demonstrate a conclusive curative response of anti-PD-L1 treatment mediated by the effects of LCOR on tumor cell immunogenicity, which represents a promising therapeutic target for early and advanced TNBC.

mRNA-based LCOR therapy in combination with ICB in vivo

Therapeutic mRNA delivery using nanoparticles has enabled the rapid development of highly effective vaccines against COVID-19 and may similarly revolutionize cancer therapy⁵⁵. Based on current knowledge on extracellular vesicles (EVs) and mRNA delivery⁵⁶, we designed a proof-of-concept approach to restore Lcor expression through the introduction of Lcor mRNA into tumor cells, and to combine this treatment with ICB. We ectopically expressed Lcor tagged with HA in HEK293T cells, which produce large quantities of EVs containing ectopic Lcor-HA mRNA transcripts (Fig. 7a). In vitro treatment of 4TO7 cells with Lcor-HA mRNA EVs showed incorporation and translation of the Lcor protein as detected by immunoblot using anti-HA (Fig. 7b), and corrected nuclear localization (Fig. 7c). Lcor EVs reduced the CSC population and upregulated APM genes in 4TO7 cells (Extended Data Fig. 7a,b), increasing CD8⁺ T cell-mediated killing (Extended Data Fig. 7c). Next, we designed a preclinical lung metastasis assay to test Lcor mRNA therapy. After 5 days of EV administration, lung metastases were already showing incorporation and translation of Lcor-HA protein in most tumor cells (Fig. 7d) as proof of principle in vivo. Next, mice serially treated with the combination of EV-based Lcor mRNA therapy and anti-PD-L1 showed significantly longer survival and complete elimination of lung metastasis compared with the EV-control and anti-PD-L1 therapy (Fig. 7e–g). These results suggest that LCOR mRNA therapy is a potential therapeutic partner of ICB therapy.

**Fig. 7: Lcor mRNA therapy with EVs in combination with ICB in breast cancer metastasis.**

Discussion

Our study provides integrative knowledge of TNBC ICB resistance, with insights into intratumoral phenotypic heterogeneity and a key molecular mechanism that controls tumor cell immune detection independently of IFN. We have shown how, in tumor heterogeneity, LCOR^low breast CSCs possess an abrogated APM gene program, representing a tumor-intrinsic mechanism of ICB resistance (Fig. 7h). Therefore, LCOR modulation represents a potential therapeutic strategy for improving the clinical benefit of immunotherapy in TNBC.

LCOR is known as a corepressor of agonist-activated NR signaling⁴⁸, and has been associated with reduced proliferation in different cancer types^57,58,59. In ER⁻ BC, although LCOR was reported as a tumor suppressor through mediation of IFN sensitivity¹⁶, it remains unknown what molecular mechanism explains the overlap of LCOR and genes downstream of IFN. Our findings mechanistically dissect their overlap and uncouple LCOR from IFN signaling. We demonstrate that LCOR is a transcription factor binding to ISREs—commonly found in APM genes and other genes downstream of IFN—activating their transcription with or without IFN inputs. Another conclusion from our study is that IFNs barely activate APM in the absence of LCOR, demonstrating that LCOR is essential for the transcriptional coordination of APM. Although the role of LCOR as a transcription factor has not been reported, previous reports are aligned with this function since it belongs to the transactivator factor homolog family Mblk1 (ref. ⁵⁷) and its ablation reduced specific gene expression⁶⁰. Our conservation analysis further supports the transcription factor function of LCOR, showing high conservation of the HTH DNA-binding domain in ancestral organisms⁵⁷. Therefore, we reveal a relevant activity of LCOR as a conserved transcription factor controlling cellular immunity.

We report a remarkable genomic binding zonation of LCOR on the MHC genomic region and APM genes of human chromosome 6 (ref. ⁴⁷). It is believed that these genes are evolutionarily clustered to favor highly coordinated regulation of cellular immunity⁴⁷. Our results suggest that the LCOR-HTH domain might be evolutionarily favored to orchestrate these genes, and thus cellular immunogenicity. Therefore, in a malignancy context with high genomic instability and neoantigens, LCOR-mediated immunogenicity promotes selective antitumor immunity while leaving healthy cells exempt from immune attack due to low neoantigen load. LCOR modulation thus represents an excellent opportunity to be exploited in immune-based strategies specifically targeted at elimination of cancer cells.

Cancer stem cells with defective APM fit immunoediting principles, in which a poorly immunogenic subpopulation is not eliminated but persists, self-renews and finally escapes due to immune-evasive properties and tumor initiation capability. Importantly, we demonstrate that LCOR^low CSCs exploit this mechanism by abrogating the entire APM system to escape ICB immunotherapy, suggesting that eradication of LCOR^low CSCs will prevent immunoediting escape and relapse and thus improving long-term ICB clinical benefit. Recent studies also support the belief that CSCs and tumor dedifferentiation are implicated in immunotherapy resistance^25,26,61, although in different cancer types and by mechanisms not related to the APM. In TNBC, recent data on ICB therapy show no efficiency in the mesenchymal subtype, which is enriched in stem cell-like phenotypes⁶². These evidences are aligned with our unique findings in regard to TNBC phenotypic heterogeneity and CSCs driving ICB resistance.

In breast cancer, we show that the LCOR–APM axis is highly relevant in triple-negative disease, is associated with ICB response and thus provides an opportunity to improve TNBC therapy. While most immunotherapies are designed to avoid immunosuppression or to potentiate immune system antitumoral activity, tumor antigen presentation is still critical for ICB response^3,6. A key translational breakthrough of our study is a potential therapy designed to specifically modulate antigen presentation in tumor cells using LCOR mRNA delivery in concert with ICB. The current focus on mRNA therapies, and development of nanotechnology for its therapeutic delivery, makes this strategy a compelling case for potential future cancer therapies. In summary, LCOR represents an unprecedented opportunity to reconfigure tumor immunity, constrain CSCs and exploit alongside ICB therapy. Future clinical studies are required to apply mRNA LCOR therapy in combination with ICB in patients with TNBC.

Methods

Ethical regulations

This study complies with all ethical regulations. Clinical patient samples have approval from the Ethical Committee of Clinical Investigation—Mar Park of Health, the institutional review boards at Vall d’Hebron Hospital and from the INCLIVA ethical committee. All individuals gave their informed consent before inclusion. All animal procedures presented in this study were approved by the Ethical Committee for Animal Research of the Barcelona Biomedical Research Park and by regulation from the Departament de Medi Ambient i Habitatge de la Generalitat de Catalunya (Catalonia Government). For all experimental procedures, euthanasia was applied once tumors reached a volume of 1,500 mm³ or when animal health was compromised.

Animal studies

For this study, mouse strains Balb/c, C57BL/6J, C57BL/6-Tg (TcraTcrb)1100Mjb/J (OT-1), Ptprc^a (TcrbTcra)Ln1Bdb H2^d/J Just Enhanced GFP (JEDI) and NOD.Cg-Prkdc^scid Il2rg^tm1Wjl/SzJ (NSG) were used. Tumor cells were orthotopically injected into the MFP using 1:1 PBS:Matrigel (Corning). For TIC evaluation, severely immunocompromised NSG female mice were orthotopically transplanted with series of LDA. After 2 weeks, tumor incidence was evaluated by palpation and TIC frequency calculated using extreme limiting dilution analysis (ELDA) software¹⁶. For tumor comparison between immunocompetent and immunodeficient strains, 5,000 AT3 or 4TO7 cells were transplanted into the MFP of ten glands of NSG and Balb/c or C57BL/6J mice. Tumor volume was measured twice weekly with digital calipers, and calculations were applied (π × length × width²/6). In ICB assays, mice were treated with either anti-human/mouse anti-PD-L1 (atezolizumab, clone SP142, Tecentriq) or anti-mouse anti-PD-L1 (BioXCell, clone 10F.9G2, catalog no. BE0101) versus vehicle. Treatments were initiated when tumor size reached 0.5 × 0.5 cm². Dose regimes were applied at 10 mg kg^–1 every 3 or 7 days (as indicated in the different experiments). Depletion of CD4 and CD8 was achieved using anti-CD4 and anti-CD8 neutralizing antibodies, respectively, at 400 μg per mouse once tumors reached 0.5 × 0.5 cm², followed by a weekly dose of 250 μg. For lung metastasis experiments, 50,000 transduced control and 150,000 Lcor-overexpressing 4TO7 cells were TV injected in Balb/c mice to obtain similar metastatic growth among conditions. After 1 week, metastases were treated with 10 mg kg^–1 anti-PD-L1 once weekly. Metastatic lesions were monitored by photon flux bioluminescence (BLI) imaging once per week, and data were collected using Live Image v.4.3.1 in a Perkin Elmer Living Image system. For the humanized xenograft model, NSG mice were orthotopically transplanted with 15,000 MDA-MB-231 cells. Once tumors reached 0.3 × 0.3 cm², animals were intraperitoneally injected with 10,000,000 peripheral blood mononuclear cells (PBMCs) from a healthy donor⁶³. When tumor size reached 0.5 × 0.5 cm², the animals were treated with either vehicle or human/mouse crossreactive anti-PD-L1 at 10 mg kg^–1 every 3 days.

Paraffin-embedded tissue samples and PDOs

Paraffin-embedded tissue samples were obtained from a total of four patients with early-stage TNBC pre and post treatment. Two patients were treated with polychemotherapy (abraxane + carboplatin) in combination with atezolizumab (NeoTRIP study) while the other two were treated with TAK649 in combination with anti-PD1 nivolumab (NCT02834247 study). Samples were obtained from the Hospital Clínic of Valencia. Hormonal receptor status was evaluated by IHC (ER- and PR- were defined as <1% positive stained nuclei), and HER2 was assessed by IHC and fluorescence in situ hybridization.

Small patient-derived tumor pieces (~1–5 cm³) were obtained from surgical resection of patients with TNBC at Hospital del Mar (Barcelona), with previous informed consent. Small tumor pieces were mechanically and enzymatically digested at 37 °C for 2 h in mammary epithelial cell growth medium¹⁶ supplemented with enzymes (Supplementary Table 2), followed by a short incubation with 5 mg ml^–1 dispase (Merck, catalog no. D4693), 0.1 mg ml^–1 DNase I (Merck, catalog no. D5025-150) and 0.25% trypsin (Cultek, catalog no. 25300-062). The digested tissue suspension was strained through a 100-μM filter, and single cells were cultured and expanded in vitro as previously described⁶⁴.

Cell lines, culture conditions and treatments

All cell lines used in this study—breast cancer cell lines (human MDA-MB-231 and mouse Py8119, 4TO7 and AT3)—were obtained from Y. Kang at Princeton University and cultured according to the American Type Culture Collection (ATCC). HMLE cells were obtained from R. Weinberg at MIT. HEK293T cells were obtained from ATCC. Cells were routinely checked for Mycoplasma and all were negative. For this study, anti-human/mouse anti-PD-L1 (atezolizumab, clone SP142, Tecentriq) was used in vitro at 10 μg ml^–1 for 72 h; recombinant mouse or human IFN-γ (R&D systems, human catalog no. 285-IF-100; mouse: catalog no. 485-MI-100) at 10 ng ml^–1 for 24 h; the JAK1-JAK2 inhibitor ruxolitinib (LC laboratories, catalog no. R-6688) at 1 μM for 24 h; and BX-795 (TBK1i) (Merck, catalog no. SML0694) at 0.5 nM for 24 h.

3D tumor sphere assay

Single cells were plated in Ultra-Low-attachment plates (Cultek, catalog no. 3473) in standard mammosphere medium (Supplementary Table 2)¹⁶. For multiple-generation sphere formation assay, tumor spheres were collected by centrifugation at 200g for 2 min and incubated with trypsin 0.25% for 5 min at 37 °C. Single cells were strained through a 40-μM filter, centrifuged at 300g for 2 min and reseeded at an equal number of cells for the next round of tumor sphere formation.

CTL assay

For mouse cell coculture assays, splenocytes from OT-I and JEDI mouse models were obtained. Spleens were mechanically minced, and single cells were incubated with ACK buffer (Fisher, catalog no. A1049201). The single-cell suspension was strained over a 70-μM filter and activated overnight with 2 μg ml^–1 SIINFEKL (Merck, catalog no. S7951) or HYLSTQSAL (Proimmune, catalog no. F198-2A-E) peptide for OT-1 and JEDI, respectively, in mouse splenocyte medium (Supplementary Table 2). CD8⁺ T cells were purified using the CD8a⁺ T cell Isolation kit (Milteny, catalog no. 130-104-075) for negative selection, then cocultured with tumor cells for 72 h with or without anti-human/mouse PD-L1 (ref. ⁶⁵) (atezolizumab, clone SP142, Tecentriq) at 10 μg ml^–1. For the T cell-killing assay with β2m knockdown, cell lines were transfected using lipofectamine 3000 (Life Technologies, catalog no. L3000015) with either 100 ρM siRNA negative control (Life Technologies, catalog no. AM4613) or 100 ρM Silencer Pre-designed siRNA against murine β2m (Life Technologies, catalog no. 160820). For human coculture assays, PBMCs were isolated by gradual centrifugation⁶³ of blood samples from healthy adult donors from the Tissue Bank of Catalonia, and cocultured with tumor cells in coculture medium (Supplementary Table 2). Cell viability or enrichment was determined after 72 h by the crystal violet method or flow cytometry, respectively.

EV isolation and in vitro/in vivo delivery assays

HEK293T cells were cultured in DMEM with 10% extracelular vesicles (EVs)-reduced fetal bovine serum (FBS) (Hyclone) and transfected with either control pLEX-HA vector (control condition) or mouse pLEX-Lcor-HA overexpression construct using Lipofectamine 3000. Medium was changed 24 h after transfection, and supernatants were collected at 48 and 72 h for EV isolation. Cells, debris and large vesicles were first removed by serial spinning at 500g for 10 min, 2,000g for 15 min and 10,000g for 30 min, followed by ultracentrifugation in a Beckman Coulter L-90K ultracentrifuge at 70,000g for 60 min. The resulting EV pellet was resuspended in PBS. EV RNA content was estimated by RNA isolation (Qiagen) and quantification with Nanodrop; protein content was measured by bicinchoninic acid assay. EVs were treated with 10 µg ml^–1 RNAse A (Fisher Scientific, catalog no. 12091021) and 5 µl of proteinase K at 20 mg ml^–1 (Fisher Scientific, catalog no. EO0491) for 30 min at 37 °C before assay. 4TO7 cells in culture were treated with 4 ng µl^–1 EV protein for 72 h at 37 °C. For experimental metastasis in vivo, 50,000 4TO7 cells were TV injected. Mice were treated with 10 mg kg^–1 anti-PD-L1 (atezolizumab, Tecentriq) and 8 µg of EV protein (in a total volume of 100 µl of PBS) from control or Lcor-HA EVs via retro-orbital venous sinus injection every 3 days, when BLI imaging (>2 × 10⁶ photon flux) showed metastatic colonization. Metastatic lesions were monitored by photon flux BLI, then lungs were harvested for anti-HA IHC and hematoxylin and eosin staining.

IF, LSFM and IHC analysis

For IF analysis of LCOR-OE and LCOR mutants, cells were seeded in coverslips, fixed for 1 h with methanol at –20 °C and washed with acetone. Samples were blocked for 30 min using blocking buffer (Supplementary Table 3), incubated for 2 h at room temperature with anti-HA, PBS washed and incubated for 1 h at room temperature with a secondary antibody Alexa Fluor 488 anti-rabbit. For IF analysis of LCOR-GFP knock-in reporter system and pan-HLA expression, cells were seeded, fixed for 15 min at room temperature with 4% paraformaldehyde (PFA), washed and blocked for 1 h at room temperature with blocking buffer. Cells were incubated with anti-pan-HLA-ABC for 2 h at room temperature, followed by incubation with an Alexa Fluor 647 anti-mouse. Images were taken using an upright Nikon Eclipse Ni-E fluorescence microscope (Nikon), and data were collected using Ni Setup Tool v.1.2.2. Fiji software was used for further analysis.

For imaging of living organoids transduced with the SORE6⁺ CSC reporter, we used the LS1 live light-sheet microscope system (Viventis). Individual organoids were incubated with anti-pan-HLA-ABC for 2 h at room temperature, washed five times with PBS and incubated with Alexa Fluor 647 anti-mouse for 1 h at room temperature. Hoechst 33342 (Fisher, catalog no. H3570) was used. 3D rendering of organoids was performed using the Clear Volume plugin in FIJI software.

For IHC analysis of infiltration by CD45⁺ and CD8⁺ T cells, paraffin-embedded mouse tumor samples were stained with an Auto stainer Plus (Dako) using the 3,3’-diaminobenzidine (DAB) staining method. Tissue sections (3 µm) were stained for CD8 and CD45 antibodies; necrotic areas were excluded from quantification. For LCOR evaluation in clinical samples, paraffin-embedded sections (3 µm) from tumor tissue blocks were stained with anti-LCOR at room temperature for 1 h, followed by incubation with an anti-rabbit Ig dextran polymer (Flex, Agilent). Sections were visualized with DAB and counterstained with hematoxylin. All incubations were performed on the Agilent Link platform. Nuclear immunoreactive score (nIRS) was used to evaluate LCOR expression in samples. nIRS provides a range between 0 and 12 as the product of positive cell proportion score (0–4) (0, 0%; 1, 1–30%; 2, 31–60%; and 3, >60%) and staining intensity score (0–3) (0, no reaction; 1, weak signal; 2, mild signal; and 3, strong signal). For confirmation of Lcor-HA delivery in metastatic lesions, metastatic lungs treated with control or Lcor-HA EVs were collected and fixed in 4% PFA overnight. Paraffin-embedded sections (3 µm) from tissue blocks were stained with anti-HA at room temperature for 2 h. For all IHC analyses performed in this study, slides were visualized and analyzed using either QuPath-0.2.0 (ref. ⁶⁶) or CellSens software.

Time-lapse confocal microscopy imaging

Stained tumor cells (CellTracker Deep Red; Fisher, catalog no. C34565) were cocultured with stained CD8⁺ T cells (CellTracker CM-Dil Dye; Fisher, catalog no. C7000) at a 1:1 ratio in coverslips µ-Slide 8 Well (Ibidi, catalog no. 80826) for 16 h in RPMI medium containing SYTOX green dye (Invitrogen, no. S7020). Forty random zones were selected and analyzed using Zeiss Cell Observer HS (Zeiss), taking images every 5 min in the green, red, deep red and phase contrast channels. Images were processed and visualized using FIJI software.

Evolutionary conservative analysis

LCOR homologous sequences from 12 representative vertebrate species were obtained using Ensembl (https://2020.ensembl.org/). We aligned the sequences with MAFFT⁶⁷ and displayed the alignments with the NCBI Multiple Sequence Alignment Viewer (https://www.ncbi.nlm.nih.gov/projects/msaviewer/, National Center for Biotechnology Information, 2020). We searched for conserved protein domains in LCOR using PFAM⁶⁸. Information on other proteins containing HTH-psq domains, and their domain organization, was also obtained from PFAM.

Viral production and transduction of cell lines

HEK293T cells were transfected with lentiviral plasmids jointly with pocket plasmid (VSVG) and gag-pol plasmid (pCMV-R8.91), following the standard lentiviral packaging protocol. Cell lines were transduced in six-well plates, with concentrated viruses in the appropriate medium for each cell line containing 8 μg ml^–1 Polybrene and selected with the corresponding antibiotic resistance.

ChIP and ChIP–seq library preparation

For ChIP–qPCR and sequencing, cells were grown on 150-mm² plates, fixed, lysed and sonicated for seven cycles of 30 min on/30 min off using Bioruptor Pico Tubes (Diagenode) with Sonication Beads (Diagenode) in a Bioruptor Sonicator (Diagenode). Samples were incubated overnight at 4 °C with rotation, with either 5 μg of anti-HA antibody or 5 μg of goat anti-rabbit IgG (R&D, no. SC-2025). Dynabeads Protein A for Immunoprecipitation (Fisher Scientific) was used for chromatin isolation. Immunoprecipitated chromatin was washed, eluted and purified using the Qiagen DNA purification kit (Qiagen). All primers for ChIP–qPCR used in this study are listed in Supplementary Table 4. For ChIP–seq, chromatin quality and quantity were checked using the Agilent Bioanalyzer. Libraries were sequenced (50-base pair (bp) single-end) on a HiSeq 2500 platform (Illumina). The quality of fastq files was checked with FastQC software (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Reads were aligned with the bowtie2 (ref. ⁶⁹) mapper to release 27 of the Homo sapiens Gencode version of the genome (GRCh38/hg38 assembly) (https://www.gencodegenes.org/human/release_27.html). Quality of the mapped files (BAM format) was checked with QualiMap. Peaks⁷⁰ on individual samples were identified using the MACS2 (ref. ⁷¹) program (narrow peaks with q < 0.1 were initially selected). Overlap of peaks across biological replicates was retrieved using the Bedtools⁷² suite of tools.

Knock-in generation

We generated two different LCOR knock-in types in MDA-MB-231 in the endogenous LCOR of human chromosome 10: LCOR tagged with GFP (LCOR-GFP) and LCOR tagged with HA (×3) (IRES-GFP). We cloned single-guide RNAs targeting the 3’ of exon 8 into the pX330-U6 plasmid⁷³ (sgRNAs shown in Supplementary Table 4). Insertion of each guide was checked by PCR and Sanger sequencing. To construct donor vectors for LCOR-GFP and LCOR-3 × HA-IRES-GFP knock-in, homology arms of 1 kb targeting the cutting site were amplified and inserted in a homology direct repair (HDR)-plasmid donor backbone. Plasmid sequences were verified by Sanger sequencing. MDA-MB-231 was cotransfected with lipofectamine 3000 with plasmids HDR-GFP-HDR or HDR-3 × HA-IRES-GFP-HDR and pX330-U6-sgRNA at a 10:1 ratio, respectively. Cells were harvested and single-cell sorted for GFP⁺. Single clones were tested by flow cytometry and Sanger sequencing.

β2m KO generation

For 4TO7-EGFP β2m KO generation, three independent guides (Supplementary Table 4) were annealed and cloned into digested pSpCas9(BB)-2A-GFP (px458) with FastDigest Bpil (Fisher Scientific, catalog no. FD1014). 4TO7 cells were transfected with lipofectamine 3000, stained for β2m using a mouse PE anti-β2m and sorted in single-cell, 96-well plates. β2m KO clones were validated by flow cytometry (Extended Data Fig. 8a), PCR and Sanger sequencing.

Molecular cloning and plasmids

LCOR-overexpression plasmids (controls pLEX-MCS and pLEX-HA; pLEX- mouse Lcor and human LCOR-HA, LCOR-LSKAA-HA and LCOR-ΔHTH⁻HA) and an OVA-overexpressing plasmid (pLEX-OVA-IRES-mCherry) were obtained from the laboratory of Y. Kang. pLEX-Lcor-HA was generated by PCR amplification of mouse Lcor complementary DNA and the addition of HA at the 3’ end, inserted together into pLEX-MCS after digestion with SpeI and AgeI (NEB). For gene knockdown assays, shRNA were purchased from Sigma-Aldrich for targeting of mouse gene Lcor (no. TRCN0000085107) and human gene LCOR (nos. TRCN0000016306 and TRCN0000436034). Other plasmids used were the SORE6 series³¹ including minCMV-GFP, minCMV-RFP, SORE6-minCMV-GFP and SORE6-minCMV-RFP, plus the proteasomal-activity pQCXIN/ZsGreen reporter⁴⁶. The ISRE reporter was generated by the introduction of ISRE sequences (six tandem repeats of the interferon-stimulated response element (ISRE) 5’-CAGTTTCACTTTCCC-’3) in front of the minimal CMV promoter (mCMVp)-RFP after removal of binding sites of the SORE6-minCMV-RFP construct³¹ using BstZ17I-HF and ClaI (NEB) restriction enzymes. A mutated version of the ISRE reporter was also designed by mutation of two thymines to guanines on two highly conserved thymine triplets (Supplementary Table 5). Sequence insertion was assessed by Sanger sequencing. Details of all constructs and recombinant DNA used can be found in Supplementary Table 6.

Flow cytometry analysis and cell sorting

Flow cytometry analysis data were collected on either a Fortessa Flow Cytometer (BD) or with LSRII Flow cytometry (BD) using FACSDiva v.9.0 software, and analyzed by FlowJo software v.10.8.1 (FlowJo). All gating strategies for Figs. 1–5 and Extended Data Figs. 1–7 are summarized in Extended Data Fig. 9. Cells were strained through a 70-μM filter, counted and diluted in PBS + 10% FBS buffer before proceeding with a general staining protocol using anti-SIINFEKL (OVA), anti-mouse or human PD-L1, intracellular staining anti-TAP1, proteasomal activity using the pQCXIN reporter system⁴⁶, pan-HLA-ABC, anti-mouse or human β2M, pan-H2-K^d/D^d, CSC SORE6 reporter³¹ and ISRE reporter in cell lines or organoids; and anti-CD69 in OT-1 CD8⁺ T cells. For analysis of tumor-infiltrating lymphocytes, tumors were disaggregated as described above. Cells were strained through a 70-μM filter, counted and diluted in PBS + 10% FBS buffer before proceeding with the staining protocol using anti-CD45, anti-CD3, anti-CD8, anti-CD4, anti-CD11c, anti-CD11b and anti-F4/80. For cell cycle analysis, cells were fixed with ice-cold methanol for 2 h at 4 °C, stained with propidium iodide buffer (Supplementary Table 3) and analyzed for DNA content by flow cytometry.

Either FacsAria (BD) or Influx (BD) cell sorter equipment was used to isolate CD24^lo/CD44^hi CSC populations from PDOs, which were then incubated with anti-CD24 and anti-CD44. For cell sorting from cell lines, cells were washed with PBS, trypsinized, counted and diluted. Poorly and highly immunogenic cells, and stable OVA-expressing AT3 and Py8119 cells, were incubated with anti-OVA and isolated gating for OVA^−/low and OVA^high populations. For SORE6 system validation in our mouse models, Py8119 and AT3 cells SORE6-pCMV-GFP were separated into GFP⁺ and GFP⁻ cells³¹. For CSC isolation from MDA-MB-231, cells were stained using anti-CD44 and anti-CD104 and sorted by CD44^hi/CD104^hi-enriched CSC and CD44^hi/CD104^lo-nonenriched CSC populations³⁹; ALDH⁻ was compared with ALDH⁺ using the Aldefluor Kit (Stemcell technologies) and gated using DEAB control to delimit the ALDH⁺ population, following the manufacturer’s instructions. HMLE cells were sorted by CD44^hi/CD24^lo-enriched CSC and CD44^lo/CD24^hi-nonenriched CSC populations⁴³. For CSC isolation in cell lines 4TO7 and AT3, CD24^hi/CD29^hi CSC and CD24^lo/CD29^lo non-CSC populations and CD44^hi/CD24^lo CSC and CD44^lo/CD24^hi non-CSC populations were isolated, respectively. Cells were incubated with anti-CD29, anti-CD24 and anti-CD44.

RT–qPCR analysis

Total mRNA was purified using the RNeasy Mini Kit (Qiagen) and reverse transcribed into cDNA using the High-Capacity cDNA Reverse Transcription Kit (Life Technologies). RT–qPCR was performed using LightCycler 480 SYBR Green I Master (Merck). Data were collected using QuantStudio 12 K Flex software. mRNA expression was normalized by the expression of GAPDH (RT–qPCR primers are listed in Supplementary Table 4).

Immunoblot analysis

Cells were lysed with RIPA buffer (Supplementary Table 3) and SDS Laemmli buffer (Supplementary Table 3). Primary antibodies were rabbit anti-HA and mouse monoclonal anti-β-actin; HRP-secondary antibodies against rabbit IgG and mouse IgG were used. Data were collected using Nine Alliance Q9 software in an UVITEC Cambridge system.

RNA-seq analysis

RNA was isolated from tumors using the Qiagen RNA extraction Kit. Poly-A sequencing was selected for library preparation, and samples were sequenced using the Illumina Hi-Seq 2500 platform with 1 × 50-bp settings at the Centre of Genomic Regulation (CRG). Quality checking of raw data (fastq files) was done with FastQC software (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Estimation of ribosomal RNA in the raw data was obtained with riboPicker⁷⁴. Raw reads were aligned with the STAR mapper⁷⁵ to the Mus musculus genome (Gencode release M24 of the GRMm38/mm10 assembly: https://www.gencodegenes.org/mouse/release_M24.html). The raw count of reads per gene and per sample was obtained with STAR (quantMode TranscriptomeSAM GeneCounts option). The R/Bioconductor package DESeq2 v.1.30.1 was used to assess differential expression between experimental groups (Wald statistical test plus false discovery rate (FDR) correction). Genes for which the sum of raw counts across all samples was <1 were discarded. Genes are considered differentially expressed if their absolute log₂ fold change was >1 and their adjusted P < 0.05. With these parameters, we generated an ICB-resistant signature taking the top 300 genes upregulated in IRT versus control cells.

RNA-seq reanalysis of fMaSCs, basal (Ba), luminal progenitor (LP) and luminal (L) cells was carried out based on a published dataset (GSE116386)⁴². This dataset was reanalyzed for mRNA expression of genes of interest using z-row score (z = (x – μ)/σ) where x is the raw score, μ is the population mean and σ is the population standard deviation.

Bioinformatic ATAC–seq analysis

Previously published ATAC–seq of fMaSCs, Ba, LP) and L cells (GSE116386)⁴² was obtained and analyzed using IGV_2.8.0 software. The various conditions were aligned, autoscaled and analyzed for the mouse MHC cluster (Chr17:33,681,276–38,548,659) region.

Clinical dataset analysis

Clinical assessment of LCOR expression was performed using RNA-seq transcriptomic data from the TONIC trial (no. EGAS0001003535)⁴⁰, which included 53 metastatic TNBC preinduced patient samples with nine responders (one CR and eight partial responses) and 44 nonresponders (one stable disease and 43 progression disease); RNA-seq transcriptomic data of stage I–III TNBC pretreated samples (durvalumab in combination with olaparib) from the I-SPY2 trial (no. GSE173839)⁵² with nine responders and 12 nonresponders; and RNA-seq transcriptomic data from 30 on-treatment metastatic melanoma biopsies with either pembrolizumab or nivolumab (no. phs001919)⁷⁶ including 13 patients with progression disease, three with stable disease, ten with partial response and two with CR. The METABRIC dataset was obtained from the open access cBioportal website (http://www.cbioportal.org/index.do), and mRNA expression data from 186 patients with TNBC were analyzed. METABRIC TNBC and TONIC trial data were also used in GSEA and GSVA.

GSEA, GO and ChEA

Transcriptomic and ChIP–seq data generated in this study were submitted and analyzed for GO and ChEA. For GO analysis, up- and downregulated genes in IRT cells and promoter/enhancer regions from LCOR ChIP–seq were interrogated for enriched pathways using BioCarta signatures. For ChEA⁷⁷, upregulated genes in IRT were interrogated for enriched transcription factors.

GSEA was used for correlation of signatures in stratified datasets, performing 1,000 random permutations of their labeled phenotypes and obtaining P and q values and normalized enrichment score (NES). Genes were ranked using the formula r(_g) = sign (FC_g) (1 – P_g), where r(g) is the enrichment score of each gene, FCg reflects median fold change (FC) between two conditions and Pg represents the P value of the Wilcoxon rank-sum test⁴⁰. The following datasets were used: METABRIC, in which samples were stratified as higher or lower than LCOR expression levels based on median expression after normality was confirmed; and the TONIC dataset⁴⁰, stratified by responders and nonresponders. IRT transcriptomic data were also compared to IRT control cells. Datasets were interrogated with different signatures: the public signature of embryonic stem cell-like (BENPORATH_ES_1)²⁹ with 379 genes; the LIU breast ER⁻ CSC signature (generated by median expression of ER⁻ breast CSCs isolated by CD24⁻/CD44⁺ from HCC1954, MC1, SUM149 and SUM159 cells)³⁴ with 164 UP genes qualifying as >2FC; the human Kyoto Encyclopedia of Genes and Genomes (KEGG: M16004) signature of APM with 88 APM-related genes; the mouse GO 0048002 signature of APM) with 76 APM-related genes; our generated ICB-resistant signature of 300 UP genes qualifying as >1FC and adjusted P > 0.05; and the previously described immunoresistant signature⁵³ in melanoma patients.

GSVA

The GSVA package was used to establish an enrichment score representing the enrichment of signatures across patients⁷⁸. GSVA was applied using the following datasets: IRT transcriptomic RNA-seq data, and METABRIC TNBC clinical data and transcriptomic data from 39 ER⁻ breast cancer cell lines from the CCLE. Datasets were interrogated with the following gene signatures: BENPORATH_ES_1 (ref. ²⁹); NOS (NANOG, OCT4, SOX2) targets²⁹; LIU breast ER⁻ CSC³⁴; APM signature (KEGG: M16004); our IRS; and the melanoma immunotherapy-resistant signature (Jerby-Arnon)⁵³.

Deconvolution and immunophenotype analysis

For estimation of immune infiltrating cells, the xCell algorithm (https://xcell.ucsf.edu/)⁵⁰ was used on METABRIC TNBC data. A nonhierarchical k-means cluster analysis of four clusters based on Euclidean distance was applied, and LCOR expression was assessed in the various clusters or immune phenotypes.

Single-cell RNA-seq analysis

Available count data from single-cell RNA-seq⁴¹ were downloaded in the form of an R-readable RDS file, along with corresponding metadata. Only cohort 1 (treatment-naive patients receiving anti-PD1 treatment, n = 29) was considered. Count data were preprocessed and analyzed with the Seurat R package⁷⁹. Low-quality cells were first filtered in order to have a number of features <6,000 and >200, and there should be <15% mitochondrially derived genes. Counts were then normalized with the global-scaling normalization method ‘LogNormalize’. The remaining analysis was performed on cancer cells only. Highly variable features were calculated and subsequently used to scale the data. The ‘addModuleScore’ Seurat function was used to calculate a score for each cell for LCOR expression and LIU breast ER⁻ CSC signature³⁴. The uniform manifold approximation and projection dimensional reduction technique was run, and cells were color coded according to that score. We then calculated the percentage of cells for which the given signature was expressed more highly than expected, in the pretreatment group on one side and in the on-treatment group on the other. We next used Wilcoxon’s paired test to check whether the fraction of this signature had significantly changed following treatment.

Statistical analysis and reproducibility

No statistical method was used to predetermine sample size in animal studies; rather, this was guided by pilot and previous studies. Randomization among litters was performed before injection time, with animals of a similar age and female sex. Researchers were not blinded to allocation during experimentation and outcome since it was necessary to know the treatment groups. Tumor initiation assays in vivo were monitored by a trained technician in a blinded fashion. For in vitro experiments no statistical method was used to predetermine sample size, all samples being analyzed equally, and thus no randomization was required. Tissue staining scores were determined by three independent researchers blinded to sample information. For all in vivo and in vitro experiments, independent biological replicates are indicated in the figure legends. Each result is represented by mean ± s.e.m. For all experiments, normality and variance equality were checked by Kolmogorov–Smirnov and Bartlett’s test, respectively. Statistical significance was determined by applying either a one- or two-tailed unpaired t-test or paired Wilcoxon signed-rank test for parametric, and Welsh’s t-test for nonparametric. For multiple independent groups, either two-/one-way analysis of variance (ANOVA) or the Mann–Whitney test was applied. Tests used to check for statistical differences are specified in the figure legends. For TIC, ELDA software was used with Pearson’s χ²-test. P value and Rho index were used to assess significance for correlations. For free-survival analysis, P log-rank tests were applied using GraphPad Prism. All other statistics were calculated with the R and R-studio interface (https://www.r-project.org/). All experiments were reproduced in independent biological experiments at least three times, unless indicated.

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

All ChIP–seq and RNA-seq data generated in this study have been deposited at the NCBI Gene Expression Omnibus under accession codes GSE163408 and GSE176580, respectively. Previous published datasets that were reanalyzed are available under origin accession codes EGAS00001004809 (ref. ⁴¹), GSE116386 (RNA-seq and ATAC–seq)⁴², EGAS0001003535 (ref. ⁴⁰), GSE173839 (ref. ⁵²) and phs001919 (ref. ⁷⁶). The gene sets used for GSEA and/or GSVA analysis can be found in the MSigDB database v.5.1 under code KEGG: M16004 and GO:0048002, respectively. GO analysis (http://geneontology.org/) and BioCarta analysis (http://www.biocarta.com/) were used. METABRIC TNBC RNA-seq data are available at the cBioportal website (http://www.cbioportal.org/index.do). Transcriptomic data from ER⁻BC cell lines are available at CCLE (https://sites.broadinstitute.org/ccle/). Source data for Figs. 1–7 and Extended Data Figs. 1–8 have been provided as Source data files. All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

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Acknowledgements

This work was supported by the Instituto de Salud Carlos III-FSE (nos. MS17/00037 and PI18/00014) and the Cancer Research Institute, Clinic and Laboratory Integration Program (grant no. CRI2477 to T.C.-T). We also thank the AECC LAB (grant no. LABAE19007CELI) and the FERO foundation (to T.C.-T). This work was also supported by ISCIII (CIBERONC nos. CB16/12/00481, CB16/12/00241, PI18/00006 and PI21/00002), Generalitat de Catalunya (no. 2017 SGR 507) and the European Community through the Regional Development Funding Program to J. Albanell. Y.K. is supported by the Brewster Foundation, the Breast Cancer Research Foundation, the Susan G. Komen Foundation and the American Cancer Society. J. Arribas is funded by the Breast Cancer Research Foundation (no. BCRF-20-08), Instituto de Salud Carlos III (no. PI19/01181), Asociación Española Contra el Cáncer (no. GCAEC19017ARRI) and Fundación BBVA (no. CAIMI VHIO-FBBVA 2018-2021). M.M.A. is funded by MICINN and the Spanish Government (no. PGC2018-094091-B-I00). D.C. was funded by Instituto de Salud Carlos III (no. JR1800003). S.M. is funded by PERIS (no. SLT006/17/00040). The wotk of R.R.G. is funded by MICINN, the Spanish Government (no. PID2019-104948RB-I00) and the BBVA Foundation. S.B and J.P. acknowledge support from the Spanish Ministry of Science, the EMBL partnership and the CESO and CERCA Program. The TONIC study was funded by BMS and the Dutch Cancer Society, to M.K. L.P. is funded by the Breast Cancer Research Foundation. We thank the Michelangelo Foundation and L. Gianni who authorized the use of NeoTRIP samples. We thank T. Morell, Cafè Plaça and Sa Pobla participants for donations. We thank A. Bigas and L. Espinosa for scientific discussions and critical advice. We thank A. Ribas for facilitating clinical datasets. We thank F. Olivares from the Hospital del Mar Pathology unit. We thank CRG/UPF for flow cytometry assistance, especially O. Fornas, E. Julià, A. Bote and E. Ramírez; and the CRG genomics and bioinformatics units for assistance. We thank J. Swoger and G. Shah from the EMBL-Barcelona Mesoscopic Imaging Facility for imaging assistance. The animal cartoons in Figs. 6d and 7d and Extended Data Fig. 6k were created with BioRender (https://biorender.com/). We thank L. M. Wakefield for sharing reagents for the SORE6-GFP reporter; and F. Pajonk for the pQCXIN/ZsGreen reporter.

Author information

Daniel Dan Liu
Present address: Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA

Authors and Affiliations

Cancer Research Program, Hospital del Mar Medical Research Institute, Barcelona, Spain
Iván Pérez-Núñez, Catalina Rozalén, José Ángel Palomeque, Irene Sangrador, Mariona Dalmau, Anna Hernández-Prat, David Casadevall, Silvia Menendez, Jordi Berenguer, Raul Peña, Joaquín Arribas, Joan Albanell & Toni Celià-Terrassa
Pathology Department, Hospital del Mar, Barcelona, Spain
Laura Comerma
Department of Molecular Biology, Princeton University, Princeton, NJ, USA
Daniel Dan Liu, Minhong Shen & Yibin Kang
Preclinical Research Program, Vall d’Hebron Institute of Oncology, Barcelona, Spain
Irene Rius Ruiz & Joaquín Arribas
Research Program on Biomedical Informatics, Hospital del Mar Medical Research Institute and Universitat Pompeu Fabra, Barcelona, Spain
José Carlos Montañés & M. Mar Albà
Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain
M. Mar Albà, Roger R. Gomis & Joaquín Arribas
Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain
Sarah Bonnin & Julia Ponomarenko
Universitat Pompeu Fabra, Barcelona, Spain
Julia Ponomarenko & Joan Albanell
Cancer Science Program, Institute for Research in Biomedicine, The Barcelona Institute of Science and Technology, Barcelona, Spain
Roger R. Gomis
Centro de Investigación Biomédica en Red de Oncología, Madrid, Spain
Roger R. Gomis, Juan Miguel Cejalvo, Sonia Servitja, Joaquín Arribas, Begoña Bermejo, Joan Albanell & Toni Celià-Terrassa
Medical Oncology Department, Hospital Clínico Universitario; Medicine Department, Universidad de Valencia, Spain, INCLIVA, Valencia, Spain
Juan Miguel Cejalvo & Begoña Bermejo
Medical Oncology Department, Hospital del Mar, Barcelona, Spain
Sonia Servitja & Joan Albanell
Fundació Institut d’Investigació Sanitària Illes Balears, Mallorca, Spain
Diego M. Marzese
Sylvester Comprehensive Cancer Center, Miami, FL, USA
Lluis Morey
Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
Lluis Morey
Division of Tumor Biology & Immunology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
Leonie Voorwerk & Marleen Kok
Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, the Netherlands
Marleen Kok
Breast Medical Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
Lajos Pusztai
Ludwig Institute for Cancer Research Princeton Branch, Princeton, NJ, USA
Yibin Kang

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Iván Pérez-Núñez
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Lluis Morey
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Leonie Voorwerk
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Joaquín Arribas
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Begoña Bermejo
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Marleen Kok
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Lajos Pusztai
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Yibin Kang
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Joan Albanell
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Toni Celià-Terrassa
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Contributions

T.C.-T. conceived the study and supervised the project with contribution from J. Albanell. I.P.-N., J. Albanell and T.C.-T. designed the study. I.P.-N. performed all in vitro, in vivo and in silico experiments with the help of C.R., J.A.P., I.S., M.D., A.H.-P., D.D.L., M.S., J.B., R.P. and I.R.R. L.C., S.M. and R.R.G. contributed to IHC analysis. D.C. and D.M. helped with computational analysis. J.C.M. and M.M.A. performed evolution conservation analysis. S.B and J.P. performed single-cell RNA-seq computational analysis. L.M. helped with ChIP–seq analysis. J.M.C., S.S., B.B., L.V., M.K., L.P. and J. Arribas provided clinical samples and assisted with design and advice. Y.K., R.R.G. and J. Arribas provided models and scientific discussions that guided project direction. T.C.-T., I.P.-N. and J. Albanell wrote the paper. All authors discussed the results in the manuscript.

Corresponding authors

Correspondence to Joan Albanell or Toni Celià-Terrassa.

Ethics declarations

Competing interests

IMIM has filed a patent on the findings based on this study, with T.C.-T., J. Albanell, I.P.-N. and C.R. are named as coinventors. J. Albanell has received consulting fees and honoraria from Seagen, Pfizer, AstraZeneca, Lilly, Merck, Roche, Gilead, Novartis and Daiichi-Sankyo, receives royalties from a licensed patent to Biocartis and holds stock options from Inbiomotion. J. Arribas reports grants from Roche, Synthon/Biondys and Molecular Partners; and grants and personal fees from Menarini and Mnemo during the conduct of the study. J. Arribas has a patent for EP 0930183.5 issued, licensed and with royalties paid, a patent for P200801652 issued, licensed and with royalties paid, and a patent for EP20382457.8 pending, licensed and with royalties paid. L.C. reports being an advisory board member and receiving honoraria as speaker’s bureau from Roche. L.P. has received consulting fees and honoraria from Seagen, Pfizer, Astra Zeneca, Merck, Novartis, Bristol Myers Squibb, Genentech, Eisai, Pieris, Immunomedics, Clovis, Syndax, H3Bio, Radius Health, Personalis, Natera and Daiichi; and institutional research funding from Seagen, AstraZeneca, Merck, Pfizer and Bristol Myers Squibb. The remaining authors declare no competing interest.

Peer review

Peer review information

Nature Cancer thanks Justin Balko, Max Wicha and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 CSCs’ ICB resistance in breast cancer.

(a) Growth curves of transplanted 4TO7 IRT and parental tumor cells in Balb/c mice rechallenged with anti-PD-L1 at indicated conditions. n = 10 tumors/condition. (b) ChIP-enrichment analysis (ChEA) of upregulated genes in IRT cells. P-value computed with Fisher exact test. (c) Flow cytometry analysis of CD24^hi/CD29^hi 4TO7 β2m K.O tumors (Extended Data Fig. 8a) with anti-PD-L1 (10 mg/kg) or vehicle every 3 days. n = 3 individual biological replicates, data represents mean ± SEM. (d) Flow cytometry analysis of Pd-l1 in IRT vs IRT-control (I-Ctrl) cells. n = 3 independent biological replicates; data represents relative mean fluorescence intensity (MFI) to isotype ± SEM. (e) Tumorsphere ratio of IRT vs I-Ctrl cells after 3 generations. n = 3 biological independent replicates; data represents mean ± SEM. (f-g) Cell cycle analysis (phases in upper-horizontal bars and % of a representative experiment) and in vitro growth curves of (f) IRT vs I-Ctrl, and (g) 4TO7 CD24^hi/CD29^hi vs CD24^lo/CD29^lo. n = 3 independent biological replicates; data represents mean ± SEM. (h) Time-lapse imaging of CTL assay of AT3 CD24^lo/CD44^hi and CD24^hi/CD44^lo. Tumor cells in red, CD8⁺T cells in blue and cell dead tracker (SYTOX^TM) in green. White arrows indicate killed tumor cells. (i) Flow cytometry analysis of CD69 in CD8⁺ T-cells after CTL. n = 3 individual biological replicates; data represents relative MFI ± SEM. (j) qRT-PCR analysis in SORE6⁺ vs SORE6⁻ Py8119 and AT3 cells. n = 3 individual biological replicates; data represents mean of Log₂ (FC) ± SEM. (k) MFP injection and limiting dilution assay (LDA) of SORE6⁺ and SORE6⁻ Py8119-OVA cells. Table represents serial dilution injections take rate and n indicates the number of MFP injections for each dilution. Tumor-initiating cell (TIC) frequency calculated by ELDA software shown in red. P-value by Pearson’s Chi-squared two-tailed test. (l) Flow cytometry analysis of SORE6⁺ enrichment in Py8119- and AT3-OVA-SORE6 cells after CTL with CD8⁺T cells with/without anti-PD-L1 (1μg/ml) treatment. (m) CTL assay of MDA-MB-231 CD104^hi/CD44^hi and CD104^lo/CD44^hi with PBMCs during 5 days with/without anti-PD-L1. n = 3 individual biological replicates; data represents mean ± SEM in l, m. (n) Flow cytometry analysis of MDA-MB-231 CD104^hi/CD44^hi of digested tumors formed in PBMCs humanized-NSG mice at indicated conditions. (o-q) Analysis of single-cell-RNA-seq¹ of TNBC patients treated with anti-PD1 (n = 29). (o) Uniform Manifold Approximation and Projection (UMAP) and color scale expression score. (p-q) Boxplots defining the interquartile range split by median with whiskers from minima to maxima of (p) LCOR expression and (q) LIU ER⁻ CSCs signature. Not significant differences by Wilcoxon paired test. Scales bars: 100 μm in e and 40 μm in h, l. Exact P-value by one-way ANOVA in a; two-tailed Student’s t-test in e, j, l, m. For c, f, g cells were gated from P3; for i, j, k, l, m, n from P4 and P5 (Extended Data Fig. 9a).

Source data

Extended Data Fig. 2 Breast CSCs express low LCOR and low APM pathway.

(a) RNA-Seq Z-row score analysis of APM gene expression from available transcriptome² of fetal MaSCs (fMaSCs), basal (B), luminal progenitors (LP) and mature luminal (ML) cells. (b, c) qRT-PCR analysis of (b) APM genes in MDA-MB-231 CSCs (CD104^hi/CD44^hi) vs non-CSCs (CD104^lo/CD44^hi) and HMLE stem cells (SC) (CD24^lo/CD44^hi) vs non-SCs (CD24^hi/CD44^lo) and (c) LCOR expression in stem cell-like populations: HMLE CD24^lo/CD44^hi; MDA-MB-231 CD104^hi/CD44^hi, ALDH⁺ and SORE6⁺ CSCs; mouse fMaSCs vs luminal gene expression²; AT3 CD24^lo/CD44^hi, and 4TO7 CD24^hi/CD29^hi. n = 3 independent biological replicates; heat maps represent mean of Log₂ (FC). (d, e) Flow cytometry analysis of (d) β2M and pan-HLA-ABC in HMLE SC vs non-SC; MDA-MB-231 CD104^hi/CD44^hi (CSC) vs CD104^hi/CD44^lo (non-CSC) and ALDH⁺ vs ALDH⁻ and (e) β2m and pan-H2K^d and D^d in 4TO7 CSC vs non-CSC; and IRT vs IRT-control (I-Ctrl) cells. n = 3 independent biological replicates; data represents relative mean fluorescence intensity (MFI) ± SEM. (f) LCOR-GFP knock-in generation scheme and (g) validation by immunofluorescence in MDA-MB-231 cells. DAPI (blue), LCOR-GFP (green). A representative experiment from n = 3 biological replicates g. (h) Flow cytometry analysis of SORE6 reporter in LCOR-GFP cells. Left: scheme of MDA-MB-231 LCOR-GFP knock-in cells with SORE6 reporter system; middle: dot plot and correlation between LCOR (GFP) and SORE6 (mCherry); right: immunofluorescence of LCOR-GFP (green) and SORE6 (red). (i) Flow cytometry isolation and (j) qRT-PCR of OVA^−/low and OVA^hi Py8119 cells with ovalbumin (Py8119-OVA). n = 3 individual biological replicates; data represents mean ± SEM in j. (k) Flow cytometry analysis of Pd-l1⁻ and Pd-l1^hi cell distribution in AT3 CD24/CD44 populations. (l) Tumorsphere ratio between OVA^−/low vs OVA^hi Py8119 (orange) and AT3 (blue) cells, 2,000 cells seeded each passage. n = 3 biological independent replicates; data represents mean ± SEM. (m) MFP injection and limiting dilution assay (LDA) of OVA^−/low and OVA^hi Py8119-OVA cells. Table represents serial dilution injections with the corresponding take rate and n indicates the number of MFP injections for each dilution. Tumor-initiating cell (TIC) frequency calculated by ELDA software shown in red. P-value by Pearson’s Chi-squared test. Scales bars: 40 μm in g, h; and 100 μm in l. Exact P-value by two-tailed Student’s T test in b, c, d, e, j, k. For e, h, i, k cells were gated from P3; P4 or P5 for d, e (Extended Data Fig. 9a).

Source data

Extended Data Fig. 3 LCOR and APM genes analyses.

(a) Heat map of qRT-PCR analysis APM genes levels in LCOR-OE and LCOR-KD transduced HMLE cell lines relative to their respective controls. Data represents mean of 3 technical replicates. (b-d) Flow cytometry analysis of (b) proteasome 26 S activity reporter pQCXIN/ZsGreen³, GFP indicates inactive proteasome (c) β2M, and (d) pan-HLAs levels after IFN-γ at 10 ng/ml or ruxolitinib at 1μM treatment in HMLE LCOR-OE cells and LCOR-KD respect to their controls. n = 3 independent biological replicates; data represents relative mean fluorescence intensity (MFI) ± SEM for c and d; and % or positivity of a representative experiment for b. (e) Flow cytometry analysis of SIINFEKL OVA peptide presented by H2-K1^b in Py8119-OVA cells at the indicated conditions (IFN-γ 10 ng/ml, and ruxolinitib 1μM). n = 3 individual biological replicates; data represents relative mean fluorescence intensity (MFI) to the isotype ± SEM. (f) Heat map of qRT-PCR analysis APM genes levels in LCOR-OE and LCOR-KD transduced Py8119-OVA cell lines relative to their respective controls. Data represents mean of 3 technical replicates. Exact P-value by two-tailed Student’s t-test in c, d, e and f. For b-e cells were gated from P3 (Extended Data Fig. 9a).

Source data

Extended Data Fig. 4 LCOR binding to APM genes, conservation of the HTH domain, and POLR2B recruitment to MHC genes.

(a-c) Characterization pLEX-LCOR-HA, pLEX-LSKAA-HA and pLEX-ΔHTH-HA mutant variants in MDA-MB-231 cells. Determination of ectopic variants by (a) qRT-PCR (b) anti-HA western blot, and (c) anti-HA immunofluorescence. Representative data from a n = 2 for b, c and n = 3 independent biological replicates for a. (d) Generation of endogenous LCOR-HA fused knock-in in MDA-MB-231 cells and anti-HA ChIP qPCR analysis. n = 3 independent biological replicates; data represents mean ± SEM. (e) ATAC-seq peak analysis of fetal MaSCs (fMaSCs), basal (B), luminal progenitors (LP) and mature luminal (ML)² cells of murine APM MHC cluster region located in chromosome 17. n = 1 for each. (f) Conservative analysis of LCOR in vertebrates. Alignment of vertebrates LCOR protein sequences showing high (in grey) or low (in red) conservation. (g) Schematic representation of HTH DNA-binding domain (green) in LCOR homologs beyond vertebrates to procaryotes. (h, i) Flow cytometry analysis of ISRE reporter in MDA-MB-231 cells (h) LCOR-OE vs control cells under control conditions or 24 h treatment with IFNγ (10 ng/mL), Ruxolitinib (0.5 μM) and BX-795 (TBK1i, 0.5 nM); and; or (i) LCOR-KD vs control under control conditions or after 24 h treatment with IFNγ (10 ng/mL). n = 3 independent biological replicates; data represents relative Mean Fluorescence Intensity (MFI) ± SEM. (j, k) Anti-POLR2B ChIP qPCR analysis of LCOR mutants vs Ctrl (j) or LCOR KD vs Ctrl (k) in MDA-MB-231 cells. n = 3 independent biological replicates for both; data represents mean ± SEM. Exact P-value by two-tailed Student’s t-test in a, d, j, k, and one-way ANOVA with Bonferroni post-hoc test in h and I. For, h and i cells were gated from P3 (Extended Data Fig. 9a).

Source data

Extended Data Fig. 5 Immunological effects by LCOR in tumors.

(a-e) Cytotoxic T-cell lymphocyte (CTL) assays at the indicated conditions of effector (E) cells and tumor (T) cells ratios with (a, b) OT-1 CD8⁺T cells in (a) Lcor-OE or (b) Lcor-KD transduced Py8119-OVA cells relative to their controls; or with (c-e) JEDI CD8 + T cells in (c) Lcor-OE; (d) Lcor-OE β2m K.O.and (e) Lcor KD 4TO7-EGFP relative to their control. n = 3 independent biological samples; data represents mean ± SEM in a-e. (f-i) Orthotopic MFP injection of 4TO7 cells in immunocompetent (Balb/c) and immunodeficient (NSG) mice; (f, g) control vs Lcor-OE; and (h, i) control vs Lcor-KD. (f, g) Growth curves and Kaplan-Meier curves showing overall survival of orthotopic MFP transplants Lcor-OE vs control 4TO7 cells in Balb/c NSG mice. (h, i) Growth curves and Kaplan-Meier curves showing overall survival of orthotopic MFP transplants LCOR-KD vs control transduced 4TO7 cells in Balb/c and NSG mice. n = 10 mammay glands for f-i. Data represents mean ± SEM. (j-l) Flow cytometry analysis (Extended Data Fig. 9b) of % tumor-infiltrating immune cells in control and Lcor-OE 4TO7 tumors; (j) immune infiltration (CD45⁺); (k) non-lymphoid immune infiltration (CD45⁺/CD3⁻); (l) macrophages (CD11b⁺/F4/80⁺) and dendritic cell (CD11c⁺/F4/80⁻) tumor infiltration. n = 10 independent biological samples for j; n = 5 for k and l; data represents mean ± SEM. (m) Computational immune signature analysis of infiltration enrichment of CD4⁺, CD8⁺ and Tγδ cells in stratified samples based on LCOR median expression. Boxplots defining 1.5 interquartile range (IQR) with whiskers from minima to maxima. Data represents mean of score ± SEM. Exact P-value by two-tailed Student’s t-test in a-e, m; one-way ANOVA in f, h; and logrank test in g, i.

Source data

Extended Data Fig. 6 LCOR modulation preclinical and clinical ICB response.

(a) Boxplots defining the interquartile range split by median and with whiskers from minima to maxima of LCOR levels in melanoma anti-PD-1 on-treatment biopsies phs001919⁴. (b) GSEA of the melanoma Jerby-Arnon immunoresistance signature⁵ with the IRT transcriptome. (c, d) Correlation of the Jerby-Arnon signature with LCOR by GSEA (c) and GSVA (d) in TNBC METABRIC dataset stratified by LCOR median. (e) qRT-PCR analysis and (f) flow cytometry analysis of Pd-l1 levels (gated from P3, Extended Data Fig. 9a) in transduced AT3 and 4TO7 cells as indicated in the figure. n = 3 technical replicates. Data represents mean of technical replicates ± SEM in e; and a representative experiment in f. (g) Rho correlation of LCOR and PD-L1 expression in TNBC METABRIC dataset. (h) qRT-PCR analysis of Lcor expression of 4TO7 control tumor and the only Lcor-OE tumor which did not respond to anti-PD-L1. n = 3 technical replicates; data represents mean of technical replicates ± SEM. (i) Tumor growth of AT3 cells injected in C57BL/6 J mice and individualized treatment starting at 0.5 ×0.5 cm tumor size, with the indicated conditions. Treatments were administered intraperitoneally at the indicated dose regimes. n = 6 mammary glands for each; data represents mean ± SEM. Right panel: waterfall plot representing tumor volume change percentage (%) from first day of treatment to the respective endpoint. (j) Cytotoxic T-cell lymphocyte (CTL) assays with anti-PD-L1 treatment (1μg/ml) of AT3-OVA cells co-cultured with OT-1 CD8⁺T cells at the indicated effector (E): target (T) ratio. n = 3 independent biological replicates. Data represents ± SEM. (k) Tumor growth of 4TO7 cells orthotopically injected in BALB/c mice and individualized treatment starting at 0.5 ×0.5 cm tumor size, with the indicated conditions. Treatments were administered intraperitoneally at the indicated dose regimes. n = 10 mammary glands for each; data represents mean ± SEM. (l) Overall survival curves of mice referent to Fig. 6i. Exact P-value by two-tailed Student’s t-test in a, e, j; logrank test in l.

Source data

Extended Data Fig. 7 LCOR mRNA therapy in vitro.

(a) Flow cytometry analysis of the CD24^hi/CD29^hi 4TO7 CSC population (Extended Data Fig. 9a) 3 days after treatment with control extracellular vesicles (EVs) or Lcor-EVs. n = 3 independent biological replicates; data represents % of CSCs from representative experiment. (b) RT-qPCR analysis of APM genes in control- or Lcor-EV 4TO7 treated cells. n = 3 independent biological replicates. Data represents mean ± SEM. (c) Cytotoxic T-cell lymphocyte (CTL) assays of control-EVs or Lcor-EVs 4TO7-EGFP treated cells for 72 h co-cultured with OT-1 CD8⁺T cells during 72 h at the indicated conditions of effector (E) cells and tumor (T) cells ratios. n = 3 independent biological replicates; data represents mean ± SEM. Exact P-value by two-tailed Student’s t-test in b, c.

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Extended Data Fig. 8 Validation of knock-outs, ectopic overexpression, knock-downs and raw images of western blots.

(a) Flow cytometry analysis of β2m in Control or β2m knock-out (K.O.) 4TO7 cells. n = 1 independent biological replicate; data represents % of positivity. (b) qRT-PCR analysis of human LCOR KD or mouse Lcor KD (left graph) and overexpression (right graph) in all the models used in this study. n = 5 independent biological replicates; data represents mean ± SEM. Exact P-value two-tailed Student’s t-test in b.

Source data

Extended Data Fig. 9 Flow cytometry gating strategies.

(a) Gating strategy used to define cancer stem cell/stem cell populations (P4) and non-CSC/SC populations (P5) for all the models used in this study. (b) Gating strategy to define each specific immune subset: immune infiltration (P4), non-lymphoid compartment (P5), lymphoid compartment (P6) and the specific populations CD8 + T cells, CD4 + T cells, macrophages (Mϕ) and dendritic cells (DCs) indicated in the figure plots. In general, cells were selected (P1) and gated to exclude doublets (P2) and death cells (P3).

Supplementary information