Targeting a super-enhancer induced aldehyde dehydrogenase metabolic loop mitigates CDK4/6 inhibitor resistance in estrogen receptor-positive cancers

Domain: biology
Published: 2026-03-27
Editorial outcome: minor_revision

Abstract
CDK4/6 inhibitors (CDK4/6i) have shown striking clinical potential in hormone receptor-positive breast cancers (BC) and endometrial cancers (EC), whereas resistance hinders their clinical utilization. The role of epigenetic alterations in CDK4/6i resistance remains poorly elucidated. Herein, through a comprehensive analysis of transcriptomic and chromatin profiles in 16 EC tissues and cell-based resistance models, we delineate the super-enhancer (SE) landscape and identify aldehyde dehydrogenase 1 family member A1 (ALDH1A1) as a SE-driven gene closely associated with CDK4/6i resistance. ALDH1A1 inhibition increases susceptibility to CDK4/6i in multiple EC and BC models. Mechanistically, we demonstrate that ALDH1A1 promotes vitamin A (vitA) metabolism and induces the accumulation of its downstream metabolite, retinoic acid (RA), in resistant cells. In return, the elevated RA potentiates the interaction between retinoic acid receptor alpha (RARα) and estrogen receptor alpha (ERα) in the nucleus and facilitates RARα/ERα-occupied SE-driven transcriptional activation of ALDH1A1, establishing a positive feedback loop that promotes CDK4/6i resistance. These findings highlight the crucial role of vitA metabolism in the epigenetic transcriptional program associated with CDK4/6i resistance, suggesting that avoiding high vitA intake or inhibiting the ALDH1A1-RA axis may be effective strategies to overcome this challenge.

Introduction
Recently, CDK4/6 inhibitors (CDK4/6i) have shown clinical activity in hormone receptor-positive cancers. CDK4/6i inhibit RB1 phosphorylation and impair the transcriptional activity of E2F, leading to cell cycle arrest at G1/S-phase and inhibition of cell proliferation.(1) In estrogen receptor-positive (ER+) breast cancer (BC), CDK4/6i in combination with hormonal therapy has been demonstrated to have excellent clinical outcomes.(2, 3) In ER+ endometrial cancers (EC), several clinical trials have shown that combining CDK4/6i with hormonal therapy yields dramatic therapeutic benefit for EC patients.(4) Combination of letrozole with CDK4/6i has demonstrated a promising clinical response and improved progression-free survival (PFS) in recurrent ER+ EC.(5-7) Despite these encouraging evidence of CDK4/6i in cancers, there remains an urgent challenge posed by the frequent primary and acquired resistance to CDK4/6i, with limited therapeutic responses.

To date, several candidate mechanisms that confer CDPK4/6i resistance have been identified, including genomic alterations and activation of oncogenic pathways.(8) For instance, DNA sequencing of patients revealed that loss-of-function mutations in RB1, TP53, PTEN and FAT1 mediated resistance to CDK4/6i.(9-13) Amplification of S6K1 or CDK6 has also been reported to drive the occurrence of CDK4/6i resistance.(14, 15) Furthermore, the activation of oncogenic compensatory pathways, such as mTOR, c-Met, STAT3 and FGFR signaling plays a significant role in the development of CDK4/6i resistance.(16-19) However, the low frequency of genomic alterations cannot explain the majority of oncogenic signaling distribution in CDK4/6i resistance. Previous studies suggested that CDK4/6i induces substantial epigenetic remodeling to sustain its anti-tumor effects.(20, 21) In addition, non-mutational epigenetic reprogramming can be recognized as hallmarks in drug resistance.(22-24) Thus, we proposed that the epigenetic remodeling may confer CDK4/6i resistance during the treatment. Notably, super-enhancer (SE) regions, characterized by a high density of transcription factors and extensive clusters of enhancers associated epigenetic modifications, can continuously transcriptional express key oncogenes and induce drug resistance.(25, 26) However, the SE remodeling during the development of CDK4/6i resistance remains unexplored, highlighting an urgent need for a systematic investigation into their underlying mechanisms.

Cellular metabolic reprogramming has been shown to promote tumor progression, chemoresistance, and targeted-therapy resistance.(27, 28) Emerging evidence indicates that alterations in tumor metabolism lead to malignant transformation and drug resistance through epigenetic modifications.(29, 30) As essential nutrient sources, vitamin A (vitA) and its downstream metabolites play critical roles in fetal development and maintenance of visual health.(31) However, high concentrations of vitA or its derivatives, such as retinoic acid (RA), may induce hepatic immune tolerance, tumor metastasis and drug resistance.(32) Increased levels of RA have been associated with aggressive malignant behavior and the progression of cancer stem cells.(33) ALDH1A1, also known as aldehyde dehydrogenase, is a cytosolic enzyme that catalyzes the conversion of retinal to RA in the vitA metabolic process.(34) Previous studies have identified the critical role of ALDH1A1 in chemoresistance and radioresistance.(35, 36) Blockade of ALDH1A1 combined with chemotherapeutic drugs synergistically reduced tumor growth and tumorigenesis.(36) However, the role of epigenetic modifications driven ALDH1A1-vitA metabolism activation in the development of CDK4/6i resistance remains unclear.

In this study, we established a comprehensive repository of transcriptomic and histone profiles, and delineated the altered SEs landscape between sensitive and resistant EC tumors and cells. By integrating RNA-seq and ChIP-seq data, we identified that the SE-associated gene ALDH1A1 was significantly elevated in resistant tumors and cells, and was associated with CDK4/6i resistance. Overexpression of ALDH1A1 promoted vitA metabolism and induced RA accumulation, which in turn stimulated RA-mediated RARα/ERα transcriptional complexes and epigenetically enhanced ALDH1A1 expression levels to establish a positive feedback loop that ultimately promoted the CDK4/6i resistance. Utilizing established EC and BC models, including cell lines, cell-derived xenografts (CDXs), patient-derived organoids (PDOs) and xenografts (PDXs), we showed that inhibition of ALDH1A1 or blockade of vitA/RA-RAR axis can markedly enhance the efficacy of CDK4/6i and provided compelling evidence that supplementation of vitA/RA or enforced expression of ALDH1A1 significantly induced CDK4/6i resistance. Overall, our findings elucidate an oncometabolite-mediated epigenetic transcriptional program associated with CDK4/6i resistance, linking vitA/RA metabolism to ALDH1A1 expression, and provide insights into potential strategies for overcoming CDK4/6i resistance.

Results
The landscape of altered SEs associated with CDK4/6i resistance
To explore whether alterations in the transcriptomic and epigenomic landscapes of tumors are associated with the sensitivity of CDK4/6i, we collected 16 EC tumor samples for generation: i) RNA sequencing (RNA-seq) profiles, ii) H3K27ac chromatin immunoprecipitation sequencing (ChIP-seq) profiles, and iii) establishment of matched PDOs (Fig. 1A). All PDOs were confirmed to be derived from EC patients and shared ER and progesterone receptor (PR) positive in pathological diagnosis (Supplementary Table. S1). After the establishment of the PDOs, we performed histological analyses and showed that the morphology of PDOs was similar to that of their corresponding primary EC tumors (Fig. 1B). We subsequently profiled the sensitivity of 16 PDOs to Abemaciclib. However, these PDOs exhibited great distinction in sensitivity to CDK4/6i Abemaciclib (Fig. 1B-C). Based on the established CDK4/6i sensitivity of these PDOs, we categorized the EC tumors and PDOs into sensitive (n=8) and resistant (n=8) groups according to the median IC50 value (Fig. 1C). In addition, the response to CDK4/6 inhibition in PDOs was confirmed with other CDK4/6 inhibitors (Palbociclib and Ribociclib) (Supplementary Fig. 1A-B). Among the original cohort of 16 EC tumor samples, 15 and 14 met the quality control thresholds and were subsequently scheduled for RNA-seq and H3K27ac ChIP-seq processing followed by downstream bioinformatic analysis, respectively. Principal components analysis (PCA) using gene expression profiles and global H3K27ac intensities, effectively distinguished tumors between sensitive and resistant groups (Fig. 1D), suggesting that genome-wide alterations may represent a critical feature of CDK4/6i resistance. By conducting an integrative analysis of RNA-seq and ChIP-seq data, we identified 1541 up-regulated genes with gained H3K27ac, and 1393 down-regulated genes with lost H3K27ac in resistant tumors (Fig. 1E and F). Consistently, the gained H3K27ac exhibited significantly increased enrichment in resistant tumors compared to their sensitive counterparts, while lost H3K27ac displayed an opposite trend (Fig. 1G). To further investigate the SEs associated with CDK4/6i resistance, we identified that 364 genes with gained SEs using the ROSE algorithm, were up-regulated in resistant tumors (Fig. 1H and Supplementary Fig. 1C). The top ranked SE-associated genes within resistant tumors included several key regulators implicated in oncogenesis or drug resistance, such as SMAD family members (SMAD3, SMAD7), ABCA subfamily transporters (ABCA4, ABCA8) and ERα associated genes (BCL2, PRDM1, RAB31) (Fig. 1H, Supplementary Fig. 1D).(37-40) Gene Set Enrichment Analysis (GSEA) employing Hallmark gene sets showed significant enrichment of oncogenic pathways related to epithelial mesenchymal transition (EMT), KRAS and estrogen response signaling in resistant tumors (Fig. 1I and J). Conversely, the cell cycle-related pathways including E2F targets, G2/M checkpoint regulation and DNA repair pathway were obviously enriched in sensitive tumors, which matched with the clinical effectiveness observed for CDK4/6i (Fig. 1I, Supplementary Fig. 1E). Among these tumors, KRAS mutations were detected in 25% (4/16) of tumors, which showed the same frequency of KRAS mutations in the sensitive groups (2/8, 25%) and the resistant groups (2/8, 25%) (Supplementary Table. S2). These data are in line with the study in breast cancer patients that the KRAS mutation status was not relative to the response of CDK4/6i.(12) Importantly, previous public data of BC cell-based Palbociclib resistance models consistently supported our findings in EC, indicating the transcriptomic similarities between EC and BC following treatment with CDK4/6i (Fig. 1K, Supplementary Fig. 1F-H).(18) Collectively, these results indicate that extensive alterations in SEs redistribution across chromatin and transcriptomic changes may manage the sensitivity to CDK4/6i.

ALDH1A1 is a SE-driven gene up-regulated in CDK4/6i resistance
To further elucidate the underlying molecular mechanisms contributing to CDK4/6i resistance, we evaluated the Abemaciclib sensitivity across cell lines and chose two CDK4/6i-sensitive EC cell lines (HEC-1-A and HEC-1-B) and two BC cell lines (MCF7 and T47D ) to establish acquired resistant models, designated as HEC-1-AR, HEC-1-BR, MCF7R and T47DR, through chronic exposure to incrementally increasing concentrations of Abemaciclib (Fig. 2A, Supplementary Fig. 2A-B). Comparative analyses revealed significantly enhanced CDK4/6i resistance in all resistant cell lines (HEC-1-AR, HEC-1-BR, MCF7R, T47DR) relative to their parental counterparts (HEC-1-AP, HAEC-1-BP, MCF7P and T47DP), as evidenced by both cell viability assays and colony formation assays (Supplementary Fig. 2C-E). Next, we characterized the differences in transcriptomic and histone alterations (H3K27ac and H3K4me1) by integrating RNA-seq and ChIP-seq data from HEC-1-AP and HEC-1-AR cells, and identified a total of 1830 differentially expressed genes (DEGs), of which 984 were up-regulated with gained H3K27ac, while 846 were down-regulated with lost H3K27ac in resistant cells (Fig. 2B-C). Notably, a significant distinction was observed between parental and resistant cells based on the alterations of H3K27ac and H3K4me1 (Supplementary Fig. 2F).

To investigate the critical SE-driven transcriptional program underlying CDK4/6i resistance, we identified 48 upregulated SE-associated genes in resistant cells (Fig. 2D). Notably, the SE profile was activated in resistant cells, compared to parental cells (Fig. 2E). Through an integrated analysis of SE-driven genes from resistant tumors and cells, we found six common genes that gained SE and were overexpressed in the context of CDK4/6i resistance (Fig. 2F). Among them, ALDH1A1 emerged as the most significantly altered gene in resistant tumors (Supplementary Fig. 2G) and cell lines (Supplementary Fig. 2H), a previously reported gene associated with chemoresistance.(35) Furthermore, ChIP-seq profiles showed increased H3K27ac occupancy at the ALDH1A1 gene loci in both resistant cells and tumors, accompanied by elevated H3K4me1 levels at the same locus (Fig. 2G). These findings prompted further investigation into ALDH1A1 and led us to hypothesize that ALDH1A1 may serve as a key SE-driven gene mediating CDK4/6i resistance. Through RT-qPCR verification, we showed remarkably elevated mRNA expression levels of ALDH1A1 in resistant tumors (Fig. 2H). The remarkably high expression of ALDH1A1 in resistant cells was confirmed in both EC and BC cell lines (Fig. 2I). Furthermore, we interrogated gene expression data from the NeoPalAna clinical trial and found ALDH1A1 was significantly up-regulated in breast cancers after Palbociclib treatment (Supplementary Fig. 2I).(41) To determine the differential levels of H3K27ac across the ALDH1A1 regions, we designed four sets of primers (P1-P4) for ChIP-qPCR analysis and found significant enrichment of H3K27ac binding at the ALDH1A1 locus in resistant cells (Fig. 2J). Immunofluorescence assays and Western blot (WB) analysis further supported that ALDH1A1 expression was markedly higher in resistant cells compared to sensitive counterparts (Fig. 2K and L). Moreover, immunohistochemistry (IHC) analysis revealed that high ALDH1A1 expression was statistically more prevalent in resistant tumors than sensitive tumors (Fig. 2M), supporting its potential role in clinical response to CDK4/6i. Consistent with these findings, cells exhibiting high levels of ALDH1A1 showed increase IC50 of Palbociclib according to data from the Genomics of Drug Sensitivity in Cancer (GDSC) database (Fig. 2N).(42) Taken together, these data reveal that ALDH1A1 serves as an up-regulated SE-driven gene across resistant tumors and cells, suggesting a crucial role for ALDH1A1 in mediating CDK4/6i resistance.

Inhibition of ALDH1A1 overcomes CDK4/6i resistance
Although ALDH1A1 has previously been identified as an oncogene implicated in chemoresistance, its functional role in resistance to CDK4/6i remains unexplored. (43, 44) To investigate the potential role of ALDH1A1 in CDK4/6i resistance, we employed siRNA-mediated knockdown of ALDH1A1 in two resistant cell lines, and showed that loss of ALDH1A1 significantly restored Abemaciclib sensitivity, as evidenced by impaired proliferation and colony formation (Fig. 3A, Supplementary Fig. 3A). Using Disulfiram, a selective aldehyde-dehydrogenase (ALDH) inhibitor with clinical applications, we observed a significant combinatorial effect of Disulfiram with CDK4/6i in intrinsic or acquired resistant cells (Fig. 3B, Supplementary Fig. 3B). Previous studies showed CDK4/6i can only induce senescence in sensitive cells but not in resistant cells, while combination treatment with a BRD4 inhibitor elicits a strong induction of apoptosis in RB1-knockout resistant cells.(45) The expression levels of RB1 were also significantly decreased in our resistant cell models (Supplementary Fig. 3C). We obtained similar results that combined CDK4/6i with Disulfiram, but not CDK4/6i alone, induced significant cleaved PARP and caspase-3 in resistant cells, whereas no significant senescence was observed (Figure 3C, Supplementary Fig. 3D). Moreover, ectopically expressing ALDH1A1 conferred resistance to Abemaciclib in two sensitive cells (Fig. 3D), confirming its critical role in mediating CDK4/6i resistance.

The strong association between epigenetic regulation of ALDH1A1 and CDK4/6i resistance suggests that targeting epigenetic pathways may effectively suppress ALDH1A1 expression and overcome this resistance. Thus, we performed a combination drug screen using a library of 199 compounds targeted epigenetic modifiers in acquired-resistant HEC-1-AR cell (Supplementary Fig. 3E, Supplementary Data 1). By comparing cell viability when exposed to Abemaciclib alone versus in combination with epigenetic inhibitors, we identified the BRD4 inhibitor dBET6 as the most potent Abemaciclib sensitizer (Supplementary Fig. 3E and F, Supplementary Data 2). To further investigate the combinatorial effects of BRD4 inhibitors with Abemaciclib, we performed combination index (CI) analysis with dBET6 and three other available BRD4 inhibitors: dBET1, dBET57 and JQ1. The data indicated that all the CI values were less than 1, demonstrating a synergistic effect between BRD4 inhibitors and Abemaciclib (Fig. 3E). These results were further confirmed in cell proliferation assays and colony formation assays in both intrinsic and acquired resistant cells (Fig. 3F and G, Supplementary Fig. 3G). Additionally, combining dBET6 with Abemaciclib also elicited cell apoptosis in resistant cells through significant induction of cleaved PARP and cleaved caspase-3 (Fig. 3H), consistent with previous reports.(45) Given that BRD4 inhibitors sensitize CDK4/6i treatment, we sought to determine whether they could suppress ALDH1A1 expression in resistant cells to mitigate the resistance to CDK4/6i. We evaluated the expression of ALDH1A1 in HEC-1-AR and HEC-1-BR treated with dBET6 and JQ1, and found that pharmacological inhibition of BRD4 effectively repressed ALDH1A1 expression in a dose-dependent manner (Fig. 3I). Consistently, genetic knockdown of BRD4 with siRNA significantly reduced ALDH1A1 expression and enhanced the effects of Abemaciclib on cell proliferation and colony formation in resistant cells (Fig. 3J, Supplementary Fig. 3H-J). To further assess the clinical efficacy of inhibiting ALDH1A1 to overcome CDK4/6i resistance, three intrinsically resistant PDOs were selected for evaluation of synergistic treatment. The results showed that these PDOs exhibited resistance to Abemaciclib single regimen, while the combination of Disulfiram or dBET6 plus Abemaciclib efficiently inhibited PDOs growth (Fig. 3K). Taken together, these results indicate that both genetic and pharmacological inhibition of ALDH1A1, or epigenetic silencing of ALDH1A1 with BRD4 inhibitors, synergistically inhibit cell proliferation and induce cell apoptosis, ultimately overcoming the resistance to CDK4/6i.

ALDH1A1 promotes CDK4/6i resistance via vitA metabolism
As a key aldehyde dehydrogenase, ALDH1A1 orchestrates multiple oncogenic metabolic pathways.(46) Consistently, our pathway enrichment analysis revealed marked upregulation of metabolic processes in CDK4/6i-resistant cells (Supplementary Fig. 4A). Untargeted metabolomic profiling of matched treatment-sensitive and -resistant tumors/cell lines identified retinoic acid (RA), a substrate of ALDH1A1 during the vitA metabolic process (34), as the most differentially abundant metabolite in resistant populations (Fig. 4A and B, Supplementary Fig. 4B). In addition, RA signaling was also significantly enriched in resistant groups (Supplementary Fig. 4C), which suggested that RA could be an important regulator associated with CDK4/6 resistance. Given that vitA is the precursor of RA, which is an essential nutrient for embryonic development and abundant in fetal bovine serum.(47) We proposed that ALDH1A1 confers CDK4/6i resistance by enhancing vitA metabolic processes and RA production. By detecting the vitA levels in cell culture medium from parental and resistant cells, we found that the abundance of vitA in resistant cells was decreased, and this decline occurred even more rapidly than in parental cells over time (Fig. 4C), indicating that resistant cells utilize more vitA than parental cells. To investigate whether vitA or RA could contribute to CDK4/6i resistance, we supplied EC cells with RA or vitA. The results showed that supplying EC cells with vitA or RA significantly attenuated the anti-tumor effect of CDK4/6i (Fig. 4D and E, Supplementary Fig. 4D). Moreover, the effects of RA and vitA in mediating the Abemaciclib resistance became more pronounced as their concentrations increased (Supplementary Fig. 4E-F). In contrast, the absence of vitA exacerbated the cell repression triggered by CDK4/6i, whereas the supplementation of vitA rescued this effect (Fig. 4F and G, Supplementary Fig. 4G). Furthermore, we checked the potential involvement of vitA or RA signaling in the sensitivity of Palbociclib across cell lines from GDSC database, and found that cells with high activation of vitA-metabolic, RA-metabolic, or RA-binding signaling pathways were shown with higher IC50 for Palbociclib (Fig. 4H). The vitA metabolism associated pathways were obviously enriched in EC and BC cells (Fig. 4I). These data indicated that CDK4/6i resistant cells may more rely on vitA metabolism to induce CDK4/6i tolerance.

The oxidation of retinal to RA by ALDH1A1 is an important step during vitA metabolism.(46) We proposed that ALDH1A1 induces the drug resistance of CDK4/6i via ALDH enzyme activity. Firstly, we determined the ALDH enzyme activity in parental and resistant cells, and found it to be elevated in resistant cells (Fig. 4J). Previous reports indicated that K193 of ALDH1A1 was a vital activity site that could form the chemical bond with NADP, which is a key step for ALDH enzyme activity.(48, 49) We next stably overexpressed ALDH1A1 with or without a key catalytic site mutation (K193Q/R) (Fig. 4K). The ALDH enzyme activity was significantly increased in the ALDH1A1 wild-type group, but not in K193Q/R-mutant groups (Fig. 4L). To investigate the role of ALDH1A1 enzyme on sensitivity of CDK4/6i, we ectopically expressed wild-type or K193Q/R-mutant ALDH1A1 in parental cells. The results showed that only wild-type ALDH1A1, rather than the mutant, could confer resistance to Abemaciclib (Fig. 4M), which confirmed the critical role of ALDH enzyme activity in CDK4/6i resistance. Furthermore, we found higher levels of RA in cells overexpressing wild-type ALDH1A1 (Fig. 4N), whereas RA levels decreased significantly when knockdown of ALDH1A1 by siRNAs (Fig. 4O). These results indicate that ALDH1A1 promotes vitA metabolism and induces CDK4/6i resistance, relying on ALDH enzyme activity.

RAR signaling is the down downstream effector of ALDH1A1
To investigate the downstream targets and pathways by which ALDH1A1 induces CDK4/6i resistance, we subsequently performed RNA-seq in two resistant cells (HEC-1-AR and HEC-1-BR) with genetic knockdown of ALDH1A1. The results indicated that, in both cell lines, 284 differentially expressed genes (DEGs) were upregulated and 227 were downregulated (Fig. 5A). Pathway analysis demonstrated that knockdown of ALDH1A1 suppressed the RA signaling, which was consistent with our previous findings that ALDH1A1 promotes RA accumulation to induce CDK4/6i resistance (Fig. 5B and C). Multiple representative RA signaling targets were significantly downregulated following ALDH1A1 knockdown in both HEC-1-AR and HEC-1-BR cells (Fig. 5D, Supplementary Fig. 5A-B). ALDH1A1 catalyzes the oxidation of retinal to RA in the vitA metabolism, which serves as a classical ligand for RARα and elicits activation of RAR signaling.(46) These data suggested that RAR signaling could be a downstream effector of ALDH1A1 in CDK4/6i resistance.

Next, we investigated the crucial role of RAR signaling in CDK4/6i resistance. In the GDSC database, high expressions of RAR-associated signals were correlated with Palbociclib resistance (Fig. 5E).(42) Furthermore, by inquiring RAR signaling in transcriptomic data, we revealed a strong positive correlation between vitA signaling, RA signaling, RAR signaling and ALDH1A1 expression as well as drug resistance (Fig. 5F). These findings imply that RARα may serve as a regulatory element in the development of CDK4/6i resistance. Additionally, functional studies showed that knockdown of RARα with siRNAs or pharmacological inhibition of RAR signaling with inhibitors (AGN and AR7) could overcome resistance to Abemaciclib (Fig. 5G-H and Supplementary Fig. 5C-D). The combinatorial effects were further validated through CI index assays (Fig. 5I). Combining RAR inhibitors with Abemaciclib also elicited a strong induction of cell death (Figure 5J). In addition, we selected three resistant PDOs and treated with AGN or AR7 in combination with Abemaciclib, the results recapitulated the phenotypes observed in resistant PDOs that inhibition of RAR signaling exhibited good synergistic effects with CDK4/6i (Fig. 5K).

To illustrate how ALDH1A1-RARα signaling induces CDK4/6i resistance, we observed that the expression of CCND1 was significantly suppressed following the knockdown of ALDH1A1 (Supplementary Fig.5E), a CDK4/6i resistance-related gene reported previously.(8, 50, 51) Consistent with these findings, data from the TCGA database revealed that RA and RAR signaling pathways were positively correlated with CCND1 expression (Supplementary Fig. 5F). CDK4/6i resistant cells exhibited a higher CCND1 expression level compared to parental cells (Supplementary Fig. 5G). In addition, knockdown of CCND1 effectively overcame the resistance to Abemaciclib, which partially recapitulated the phenotypes associated with inhibition of ALDH1A1-RARα signaling (Supplementary Fig. 5H). Importantly, knockdown of ALDH1A1 and RARA with siRNAs resulted in a significant reduction in CCND1 expression, thereby reinforcing its role as a downstream effector within the ALDH1A1-RARα signaling (Supplementary Fig. 5I-J). Additionally, the overexpression of wild-type ALDH1A1, rather than K193Q/R-mutant ALDH1A1, could induce CCND1 expression (Supplementary Fig. 5K). These findings indicate that CCND1 may serve as a downstream target of ALDH1A1/RAR and be involved in the development of resistance to CDK4/6i. Collectively, these results indicate that ALDH1A1 promotes vitA metabolic process and RA accumulation, which subsequently activates RAR signaling and ultimately contributes to CDK4/6i resistance.

Consistent with the findings in RNA-seq (Fig. 1), CDK4/6i-resistant cells exhibit strong KRAS signaling and EMT features (Supplementary Fig. 5L). Moreover, knockdown of ALDH1A1 suppressed the expression of p-ERK, p-MEK and EMT markers, while overexpression of ALDH1A1 upregulated their expression (Supplementary Fig. 5M). These results indicated that resistance to CDK4/6i induced by RARα-ALDH1A1 axis might partially occur through the regulation of downstream effectors in KRAS and EMT signaling pathways.

RA binds to dimers of RARα and ERα to modulate SE activity and induce an ALDH1A1-RARα/ERα feedback loop
To explore which transcription factors (TF) might modulate the transcriptional activity of gained SEs and ALDH1A1 expression, we performed motif analysis using HOMER software to identify enriched trans-regulators.(52) The data indicated that the top-ranking enriched motifs included Homeobox genes, ABI3VP1 TFs, Zinc-Finger proteins (Zf), Nuclear receptors (NRs) and T-box family members (Supplementary Fig. 6A), which have shown to be associated with chromatin remodeling.(53, 54) Interestingly, based on the enriched motifs, we noticed that RARα could be a potential key regulator associated with SEs activity, which served as a sensor in response to RA that was found in our metabolomics in CDK4/6i resistant cells (Fig. 4, Supplementary Fig. 6B). These data collectively supported the notion that RARα could be a master transcriptional regulator of ALDH1A1.

RARα has been proven to interact with ERα, and their binding sites appear to be co-evolved extensively throughout the human genome.(55, 56) ER and its downstream signaling activation is characterized as key features in ER+ cancers and acts as carcinogenic factors during tumor development and progression.(57) Furthermore, our transcriptomic analysis revealed significant enrichment of ER signaling pathways in both EC and BC resistant tumors and cells. (Fig. 1I, Supplementary Fig. 1H and Supplementary Fig. 6C). Cells with ER response activation showed higher IC50 of Palbociclib in GDSC dataset (Supplementary Fig. 6D). These observations led us to hypothesize that RARα interacted with ERα to enhance transcriptional activity of the complex and modulate SE activity. To test this hypothesis, we separated nuclear and cytoplasmic proteins, and found an obvious increase in nuclear accumulation of ERα and RARα in resistant cells (Fig. 6A). Moreover, co-immunoprecipitation analyses revealed an enhanced interaction between ERα and RARα specifically in resistant cells, when compared with their parental counterparts (Fig. 6A). Consistently, we found that ERα and RARα were mostly co-localized in the cytoplasm in parental cells, but predominantly present in the nucleus in resistant cells in immunofluorescence assays (Fig. 6B). Given the significant physical interaction of RARα and ERα, we next investigated potential targets of RARα/ERα transcriptional complexes in CDK4/6i resistance. We performed ChIP-seq profiling against RARα and ERα, as well as two components of SEs, including BRD4 and RNA polymerase II (RNAPII), in HEC-1-AP and HEC-1-AR cells.(58, 59) We discovered 540 putative targets of RARα and ERα that were upregulated in HEC-1-AR cells, accompanied by increased binding of RARα, H3K27ac, H3K4me1, RNAPII, and BRD4 (Fig. 6C and D). Importantly, ALDH1A1 emerged as a critical downstream target of RARα and ERα (Fig. 6D). In addition, ChIP-seq profiles showed enhanced RARα and ERα co-occupancy at the ALDH1A1 gene loci, accompanied with BRD4 and RNAPII, in resistant cells (Fig. 6E). These results were verified by ChIP-qPCR results demonstrating increased binding of RARα or ERα at ALDH1A1 gene loci in resistant cells (Fig. 6F and G). In agreement with these data, sequential ChIP (Re-ChIP) experiments showed that RARα and ERα bound to the same gene loci at ALDH1A1 in resistant cells (Fig. 6H). Depletion of RARA or ESR1 resulted in significantly decreased occupancy of H3K27ac (Fig. 6I). RARA or ESR1 (encoding ER) depletion with siRNAs, or inhibition of RARα or ER via the small molecular inhibitors, significantly inhibited the expression of ALDH1A1 at both mRNA and protein levels (Fig. 6J and K, Supplementary Fig. 6E-H). These data collectively supported that RARα and ERα serve as a transcriptional complex and regulate the SE activity and ALDH1A1 expression.

Previous and our studies showed that RA binds to RARα to induce the expression of its downstream target genes(46), which prompted us to suppose that elevated RA promoted the interaction of RARα and ERα, thereby promoting SE-driven transcriptional activation of ALDH1A1. We next sought to determine the interaction between RARα and ERα in response to RA stimulation in parental cells, the results showed that RA notably induced RARα nuclear translocation and potentiated the binding of RARα to ERα in the nucleus (Fig. 6L). Furthermore, we treated HEC-1-AP cells with RA and showed that the bindings of RARα, ERα, and H3K27ac were enhanced at ALDH1A1 gene loci (Fig. 6M). RA treatment could significantly increase both mRNA and protein levels of ALDH1A1 and exhibit in a concentration-dependent manner (Fig. 6N, Supplementary Fig. 6I-J). By analyzing the public data from the TCGA database, we revealed that RAR and ER associated pathways including RA metabolism, RAR signaling and ER signaling were positively correlated with ALDH1A1 expression in both EC and BC (Fig. 6O). In addition, we found increased bindings of H3K27ac, RARα and ERα at the ALDH1A1 gene loci in ALDH1A1-overexpressing cells, which could be suppressed by RAR inhibition (Supplementary Fig. 6K). Moreover, we knocked down ALDH1A1 expression and showed that the binding of H3K27ac, RARα and ERα at the ALDH1A1 gene loci was decreased, which could be significantly rescued by RA supplementation (Supplementary Fig. 6L). These results suggest that ALDH1A1-mediated CDK4/6i resistance is dependent on the positive feedback regulation of the RA-RAR signaling axis-driven SEs. Taken together, these results suggest that RA accumulation potentiates the RARα/ERα interaction in the nucleus, promoting transcriptional activation of ALDH1A1 driven by RARα/ERα-bound SE. This ultimately established a vitA-RA-RARα/ERα metabolism positive feedback loop in CDK4/6i resistant tumor cells.

Targeting ALDH1A1 enhanced anti-tumor effects of CDK4/6i in vivo
To further validate the findings obtained from our in vitro experiments, we employed multiple mouse models to conduct a systematic study. First, we examined the impact of a high vitA diet on CDK4/6 inhibitors, the data indicated that vitA did not affect tumor growth, but it significantly mitigated the tumor-suppressive effect of CDK4/6i (Figure 7A). Subsequently, we investigated the role of ALDH1A1 in CDK4/6i resistance in vivo using xenograft tumor growth examination in the presence or absence of Abemaciclib in nude mice. ShRNA targeting ALDH1A1 substantially enhanced the anti-tumor effects of Abemaciclib (Figure 7B and Supplementary Fig. 7A). Similarly, inhibition of ALDH1A1 by Disulfiram showed notable combinatorial effects with Abemaciclib (Figure 7C). Furthermore, co-administration of dBET6 and Abemaciclib significantly elicited marked tumor suppression (Figure 7D). To comprehensively evaluate the clinical value, we selected two PDX models, including one PDX model sensitive to CDK4/6i (PDX-EC1) and another one resistant to CDK4/6i (PDX-EC3), for the subsequent in-depth experiments (Supplementary Fig. 7B). Although the treatment of Abemaciclib or Disulfiram alone moderately attenuated the tumor growth of PDX-EC3, their combination resulted in a robust growth inhibition (Fig. 7E). To further mimic the clinical resistance to CDK4/6i after long-term therapy, we established a drug-resistant PDX model (PDX-EC1R) from an initially sensitive PDX-EC1 through continuous dosing of Abemaciclib to mice (Fig. 7F and Supplementary Fig. 7C). Consistently, pharmacological inhibition of ALDH1A1 by Disulfiram or dBET6 effectively reversed Abemaciclib resistance (Fig. 7G and H). In addition, these therapeutic regimens were well tolerated as demonstrated by a minimal body weight loss in the treatment group (Supplementary Fig. 7D). Importantly, the resistant tumors exhibited remarkably increased RA levels (Fig. 7I). RT-qPCR analysis and immunohistochemical staining confirmed that ALDH1A1 expression levels were significantly increased in acquired resistant PDX tumor tissues (Fig. 7J and K), while a reduction in ALDH1A1 expression was observed following dBET6 treatment (Fig. 7L). Collectively, these data support the notion that high vitA intake could promote CDK4/6i resistance, and patients should avoid diets high in vitA during the course of CDK4/6i treatment. Furthermore, ALDH1A1 represents a critical factor contributing to CDK4/6i resistance, and inhibition of ALDH1A1 may re-sensitize tumors to CDK4/6i in vivo, providing a potential strategy in clinical use.

Methods
Ethical permits
Archived patient samples were obtained from Sixth Affiliated Hospital, Sun Yat-sen University with an approval from the medical ethics committee (IRB, L2024ZSLYEC-150). All participated patients were fully informed, followed by documentation in a written, signed, and dated informed consent form. All animal studies were conducted in compliance with animal protocols approved by the Animal Care and Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-sen University and followed the National of Health Guidelines on the Care and Use of Animals (IACUC-2022112101). The ethical authorization limits tumor size to a maximum size of 1800 mm3, and this limit was not exceeded in any experiment conducted for this study.

Sample Acquisition
Patients diagnosed with EC in this study were recruited from the Sixth Affiliated Hospital, Sun Yat-sen University (Guangzhou, China). The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (IRB, 2024ZSLYEC-150). All patients were chemotherapy-naïve, and tumor tissues were collected from surgical specimens. The pathological features were assessed according to the 2023 International Federation of Gynecology and Obstetrics (FIGO) staging system.

Cell Culture and Treatment
All commercial cell lines were purchased from Meisen Cell Technology Co., Ltd (Zhejiang, China). All human tumor cell lines were maintained in DMEM (HEC-1-A, HEC-1-B, RL95-2, KLE, Ishikawa, MFE-296, AN3 CA and MCF7), 1640 (T47D) or F-12K (SNG-M) supplemented with 10% FBS (MIKX, China, MK1123-500C) and 1% penicillin/streptomycin (Gibco). The cell lines were routinely checked to ensure that they were mycoplasma free using the Mycoalert Detection Kit (Lonza, Cat# LT07-318) and grown in an incubator at 37°C and 5% CO2. Cells used for experiments were between 3 and 30 passages from thawing.

For the colony formation assay, single-cell suspensions were plated at a density of 10,000 cells per well in six-well plates. The medium containing different drugs was refreshed every three days during culture for 9 days. Colonies were fixed with 4% paraformaldehyde, stained with crystal violet (0.5% crystal violet, 20% methanol), and subsequently photographed. The colonies were quantified using ImageJ/Fiji software (https://fiji.sc/).

For the cell viability assay, cells were seeded in triplicate at the optimal density in 96-well plates. After 24-hour incubation, cells were divided into control groups and drug-treated groups. The time-zero control groups refer to the groups in which ATP levels were measured at the beginning of the experiment, after cell attachment (prior to drug treatment). The control groups refer to cells cultured in complete medium containing only the vehicle. The drug-treated groups refer to cells treated with the indicated drugs. For the control groups and drug-treated groups, cells were cultured at 37 ºC and ATP levels were measured after 96 hours. The number of viable cells was measured using the Cell Titer-Glo Luminescent Cell Viability Assay (#G7573, Promega, Madison, WI), according to the manufacturer’s instructions.

For B-27 supplement medium, DMEM was supplemented with 2% B-27 supplement (MIKX, China, SZ3021) or vitA-deficient B-27 supplement (MIKX, China, SZ3020).

Establishment of cell lines with acquired resistance to Abemaciclib
Cells were grown with progressively increasing concentrations of Abemaciclib. Subconfluent cells were treated with Abemaciclib at IC50 doses (based on 96-hour cytotoxicity assays) for two 3-day periods, and surviving cells were maintained for another 4 days. The process was repeated once, and the remaining cells were cultured in the presence of drug for 3 additional passages. Resistant cells were maintained in the presence of Abemaciclib, and the same passage parental cells were treated with the same dose of DMSO.

Compound
The compound library targeting histone regulators was purchased from TargetMol (USA, Boston, MA). Abemaciclib (T2381), Disulfiram (T0054), Puromycin dihydrochloride (T2219), JQ1 (T2110), dBET6 (T5130), dBET1 (T4495), dBET57 (T5440), Retinoic acid (T1051), Estradiol (T1048), AR7 (T3955), Fulvestrant (T2146) and AGN 193109 (TQ0097) were purchased from TargetMol (USA, Boston, MA). Vitamin A (BD156547) was purchased from Bide pharmatech Co., Ltd (Shanghai, China).

Organoid Culture
For organoid culture, tumor tissue was dissociated into small pieces and digested with collagenase II (2 mg/mL) and collagenase IV (2 mg/mL) for 30 min at 37°C. Following digestion, the mixture was filtered through a 70-μm cell strainer to remove large undigested fragments. The isolated cells were plated in ice-cold MAcreGel (#120231, Mingao Biotech, Shenzhen, China) in six-well culture plates. After allowing the MAcreGel drops to solidify for 15 min at 37°C, cells were overlaid with endometrial cancer organoid medium, composed of Ad-DF+++ supplemented with 1× O-27 supplement (#R1210, Mingao Biotech, Shenzhen, China), 100 ng/mL recombinant human Noggin (120-10C, Peprotech), 250 ng/mL recombinant human R-spondin-1 (120-38, Peprotech), 1.25 mM N-acetylcystein (A9165, Sigma-Aldrich), 10 mM nicotinamide (N0636, Sigma-Aldrich), 50 ng/mL human recombinant EGF (AF-100-15, Peprotech), 100 ng/mL human recombinant FGF-10 (100-26, Peprotech), 500 nM A83-01 (No.2939, Tocris), 100 nM SB202190 (S1077, Selleck), 10 nM β-estradiol (HY-B0141, MedChemExpress) and 10 μM Y-27632 (M20999, AbMole). Organoids were routinely passaged by dissociation using TrypLETM Express (Gibco, 12604013) for 10 min at 37°C.

For the cell viability assay, organoids were harvested and dissociated using TrypLETM Express (Gibco, 12604013) for 10 min at 37 ºC. The dissociated PDOs were resuspended in ice-cold MAcreGel (#120231, Mingao Biotech, Shenzhen, China) and seeded into a 96-well plate (Corning). After allowing the MAcreGel drops to solidify for 15 min at 37 ºC, PDOs were overlaid with endometrial cancer organoid medium containing the indicated doses of drugs in triplicate, including Abemaciclib (0, 0.1, 0.5, 1, 2.5, 10, 25, or 50 μM), Palbociclib (0, 0.1, 0.5, 2.5, 5, 12.5, 25, or 50 μM), and Ribociclib (0, 0.1, 0.5, 2.5, 5, 12.5, 25, or 50 μM). DMSO-treated wells corresponding to the drug treatment groups served as the vehicle control (100% viability).

Five days after drug treatment, cell viability was measured using the 3D Cell Viability ATP Assay Kit (#R1101, Mingao Biotech, Shenzhen, China) according to the manufacturer’s instructions on a Spark microplate reader (Tecan). The ATP values were normalized to vehicle control (set as 100%) to calculate the relative viability (%) for each well. Dose-response curves were generated by plotting relative viability against the logarithm of drug concentration. The curves were fitted by nonlinear regression using a sigmoidal dose–response model (variable slope) in GraphPad Prism 9.0. Under this model, the half-maximal inhibitory concentration (IC50) is defined as the drug concentration that reduced cell viability to 50% of the vehicle control value. IC50 values were derived from the fitted dose–response curves. The drug treatments (Abemaciclib, Palbociclib, and Ribociclib) across the 16 EC PDOs were conducted in parallel experimental conditions.

For the organoid sphere assay, organoids were plated in 96-well plates and medium with different drugs was refreshed every three days throughout the duration of the 10-day culture period. The organoids were then closely monitored and photographed. The organoids were quantified using ImageJ/Fiji software (https://fiji.sc/).

Immunoblotting and Immunoprecipitation Analysis
Protein extracts were prepared using RIPA cell lysis buffer (150 mM NaCl, 50 mM Tris-HCl, 0.5% deoxychlorate sodium, 200 mM NaF, 200 mM PMSF, 1.0% NP40, 1 mM EDTA) supplemented with the protease inhibitor cocktail (Roche, Basel, Switzerland). Lysates and pre-stained protein marker (M221, GenStar, Beijing, China) were subjected to SDS-PAGE and transferred to PVDF membrane for immunoblotting analysis. The antibodies used in this study are provided in Supplementary Table. S3. Membranes were exposed using ECL Western Blotting Substrate (MK-S500, MIKX, Shenzhen, China). The blots were quantified using ImageJ/Fiji software (https://fiji.sc/).

The immunoprecipitation analysis was conducted according to the Immunoprecipitation Kit IP/Co-IP (Protein A/G Plus Agarose) protocol (Sangon Biotech, Shanghai, China NO. C600689). 2x107 cells were lysed with the Nuclear and Cytoplasmic Protein Extraction Kit (Beyotime, China, P0027) with PMSF on ice. Cell lysates were precleared and immunoprecipitated with the indicated antibodies and Protein A/G Dynabeads (MedChemExpress (MCE), HY-K0202) overnight at 4 °C. Immunoprecipitates were washed with IP wash buffer, then boiled in SDS sample buffer and analyzed through immunoblotting. The antibodies used in this study are provided in Supplementary Table. S3.

Immunohistochemical and Immunofluorescence Staining
The slides were deparaffinized and rehydrated through graded alcohols prior to antigen retrieval, using an antigen retrieval system (10 mM sodium citrate, 0.05% Tween-20, pH 6.0 for 25 min). Endogenous peroxidase was blocked with 0.3% hydrogen peroxide for 10 min at room temperature. Subsequently, the slides were incubated with the primary antibody overnight at 4°C and stained with diaminobenzidine. The antibodies used in this study are provided in Supplementary Table. S3.

Immunoreactivity for IHC staining was evaluated in accordance with the semi-quantitative method previously described.(87) Each slide was assigned an intensity score from 1 to 4 (1, 2, 3, or 4) by two trained researchers. The percentage of positive tumor cells was calculated as a percentage of the total number of tumor cells, with 25% increments. The scores were determined by combining the intensity score with the proportion of the area stained in four grades (1, 2, 3, or 4). A final score was derived as the average of the two scores from the same representative tumor area.

For immunofluorescence staining, cells were fixed in paraformaldehyde at 4°C for 1 hour. Then, the cells were blocked with 1% BSA for 1 hour at room temperature, and incubated with the primary antibody overnight at 4°C. Secondary antibodies were applied for one hour at room temperature before incubation with DAPI (MIKX; CE378) for 10 min.

RT-qPCR
Total RNA was extracted from cells and tumors using the RNeasy Mini Kit (QIAGEN, Duesseldorf, Germany), and genomic DNA was removed with DNase I (GMP-E127, Novoprotein, Suzhou, China) according to the manufacturer’s protocol. RT-qPCR was conducted using the reverse transcription kit (#AT341-02, Transgen Biotech, Beijing, China) and the quantitative kit (#N30920, Transgen Biotech, Beijing, China) on a Biorad CFX96 Real-time PCR machine. All qPCR experiments were performed in triplicates, and mean values were used to determine mRNA levels. Relative quantification was performed using the comparative CT method with 18S as the reference gene and with the formula 2- ΔΔCT. Error bars indicated the standard deviation. The primer sequences are listed in Supplementary Table S4.

RNA Interference
The siRNA targeting ALDH1A1, BRD4, ESR1, RARA, CCND1, and the nontargeting control were purchased from GenePharma (GenePharma, Shanghai, China). Cells were transfected with siRNA duplexes (100 nM) using Lipofectamine RNAiMAX (Life Technologies, Gaithersburg, MD) following the manufacturer’s instructions. The sequences of the siRNAs are as follows: siNC is UUCUCCGAACGUGUCACGUTT, siALDH1A1#1 is GCTGATTTAATCGAAAGAGAT, siALDH1A1#2 is GCCAAATCATTCCTTGGAATT, siBRD4#1 is CCTGGAGATGACATAGTCTTA, siBRD4#2 is GCCAAATGTCTACACAGTATA, siESR1 #1 is UGGACAGGAACCAGGGAAATT, siESR1#2 is CGAAGAGGGUGCCAGGCUUTT, siRARA#1 is CCCAAGATGCTAATGAAGATT, siRARA#2 is CAGCTTCCAGTTAGTGGATAT, siCCND1#1 is CCACAGATGTGAAGTTCATTT and siCCND1#2 is ACAACTTCCTGTCCTACTACC.

Plasmid Constructs and Inducible Cell lines
The primers of ALDH1A1 with or without mutated K to Q/R at 193 residue of ALDH1A1 were referenced from Cuicui Liu et al.’s study.(48) To establish overexpressed stable cells, human ALDH1A1 was amplified from HEK293T cDNA and cloned into pCDH-EF1-MCS-T2A-Puro (System 331 Bioscience, Palo Alto, CA, USA) vectors for lentiviral infection. In brief, the lentiviral expression plasmid (ALDH1A1 plasmid, K193Q/R-mutant ALDH1A1 plasmid and empty vector), packaging plasmid psPAX.2 (Addgene, Cat# 12260) and VSV-G envelope expressing plasmid pMD2.G (Addgene, Cat# 12259) were co-transfected into HEK293T cells. After 48 hours, the lentiviruses were harvested to infect cancer cells and then selected with puromycin (2 µg/ml) (Thermo Fisher Scientific) for 4 days.

ALDH enzymatic activity
ALDH enzymatic activity was examined using cell lysates, or tumor tissue lysates according to the manufacturer’s instructions (Aldehyde Dehydrogenase (ALDH) Activity Assay Kit, Elabscience, China, E-BC-K565-M). Briefly, 0.9% NaCI buffer was used to solubilize HepG2 cell pellet, tissue pellet (broken by tissue blender), for 5 min at -30°C, and centrifuge at 16,000 × g at 4°C for 10 min. Transfer the supernatant into clean tubes and add all the reagents and cover/seal the plate and incubate for 45 mins at 37°C. Then, the absorbance was measured at OD 450 nm with Thermo Scientific Microplate Reader.

Measurements of retinoic acid (RA)
RA concentrations were measured using cell culture supernatants from parental and resistant cells with the Human Retinoic Acid ELISA Kit (Cusabio, CSB-E16712h, China) according to the manufacturer’s instructions. The supernatant was collected after centrifugation at 10,000 g for 10 min at 4 °C. Equal volumes of samples or standard samples were added into each well of 96-well plates, and then HRP-conjugate was added. Subsequently, the samples were incubated for 60 min at 37 °C and washed with washing buffer, TMB substrate was then added to each well. Then the reaction was terminated with stop solution. Finally, the optical density (OD) at 450 nm was measured using a Thermo Scientific Microplate Reader. The concentration of retinoic acid was calculated based on the standard curve and OD values. All experiments were performed in triplicate.

Measurements of Vitamin A
Vitamin A concentrations were measured using cell culture medium according to the method from previous studies.(88) Take 100 μL of the sample in a 1.5 mL centrifuge tube (operated on ice), add 100 μL of acetonitrile and 200 μL of saturated sodium chloride solution. Subsequently, add 600 μL of n-hexane-ethyl acetate extraction solution (v:v = 1:2), vortex for 10 min. Transfer the supernatant to a new 1.5 mL centrifuge tube. Repeat the extraction with an additional 600 μL of extraction solution. Combine the supernatants and dry the mixture by vacuum concentrator. Reconstitute the residue with 100 μL of methanol (containing 0.1% BHT), vortex for 10 min, and centrifuge again under the same conditions. Finally, the supernatant was taken into the LC injection bottle for UPLC-MS/MS analysis.

Chromatin Immunoprecipitation (ChIP)-qPCR
Cells were cross-linked with 1% formaldehyde and quenched with 0.125 M glycine. The cross-linked cells were lysed in the lysis buffer (50 mM Tris-HCl [pH 8.0], 10 mM EDTA, 1% SDS) and sonicated on ice using a Bioruptor (Glen Mills, Qsonica Q700, USA). The lysates were precleared with protein G Dynabeads (Invitrogen), and 5% precleared lysates were taken to elute, reverse cross-linked, and purify for Input-DNA. Immunoprecipitated with protein G Dynabeads (Invitrogen) and incubated with the antibody-beads complex overnight at 4°C. Immunoprecipitations were eluted, reverse cross-linked, and purified for ChIP-DNA. For ChIP-qPCR analysis, the ChIP-DNA was quantitated using SYBRTM Green PCR master mix (Thermo Fisher Scientific) on a Real-Time PCR machine (Thermo, QuantStudio 7 Flex). The primer sequences are listed in Supplementary Table S4.

Re-ChIP
Cells were cross-linked for 10 minutes at room temperature with 1% formaldehyde and quenched with 0.125 M glycine for 5 minutes. The detailed procedures were performed as previously described.(24) Briefly, the first immunoprecipitation was carried out with antibodies cross-linked to protein G Dynabeads using dimethyl pimelimidate•2 HCl (DMP; Pierce). The second immunoprecipitation was performed following ChIP experiments.

RNA-sequencing
Total RNA was extracted using the RNeasy Mini Kit according to the manufacturer’s protocol (QIAGEN, Duesseldorf, Germany). About 1 μg of total RNA was used as input material, and then Ribo-off rRNA Depletion Kit (Human/Mouse/Rat) (Vazyme) was used to remove both cytoplasmic and mitochondrial rRNA. The cleaved RNA fragments were copied into first strand cDNA using reverse transcriptase and random primers. Then, in the process of second strand cDNA synthesis, RNA templates were removed and a replacement strand. CleanNGS magnetic beads (CleanNA) were then used to purify the double-stranded (ds) cDNA from the reaction mix. These ds cDNA fragments then were subjected to end-repair, phosphorylation and 'A' base addition according to Illumina's library construction protocol. The products were purified and enriched with PCR, and the CleanNGS magnetic beads (CleanNA) were used to clean up the amplified target fragments of 200–300 bp to create the final library. The mRNA libraries were sequenced on a NovaSeq sequencer (IIIumina NovaSeq 6000).

ChIP sequencing
ChIP DNA and input DNA were amplified using the GenomePlex Single Cell Whole Genome Amplification Kit (Sigma-Aldrich). After digestion with BpmI (New England Biolabs), ChIP DNA (30 ng) was used for library construction using NEBNext ChIP-seq Library Prep Master Mix Set for Illumina (New England Biolabs) according to the manufacturer’s recommended protocol. The libraries were sequenced using an Illumina Novaseq 6000.

Metabolomic analysis
For sample preparation, add glass beads into appropriate samples and accurately add 500 μL of a 30% methanol aqueous solution (containing 0.1% formic acid). Samples were installed in the grinder and oscillated at 55 Hz for 1 min, and repeat twice. Centrifuged at 12,000 rpm and filtered through 0.22 μm membrane, and the filtrate was added to the LC-MS bottle. The LC analysis was performed on a Vanquish UHPLC System (Thermo Fisher Scientific, USA). Chromatography was carried out with an ACQUITY UPLC ® HSS T3 column (2.1 × 100 mm, 1.8 µm) (Waters, Milford, MA, USA). Mass spectrometric detection of metabolites was performed on Orbitrap Exploris 120 (Thermo Fisher Scientific, USA) with ESI ion source. Simultaneous MS1 and MS/MS (Full MS ddMS2 mode, data dependent MS/MS) acquisition was used.

Metabolites were identified by automated comparison of ion features, including retention time (RT) and molecular weight (m/z), to the online Massbank (89), LIPID MAPS Structure Database (90), mzCloud (91), KEGG Database (92) and Human Metabolome Database (HMDB).(93) Metabolites were identified using variable importance in projection (VIP) measurements.(94) A VIP score of ≥1 has been argued as an adequate threshold to determine discriminant variables. Metabolites that had a bootstrapped VIP of ≥1 and a false discovery rate-corrected Mann-Whitney U-test (MWU) p value of ≤0.05 were chosen.

Bioinformatics Analyses
For RNA-seq analyses, raw counts were filtered and trimmed using fastp software (version 0.12.5) to obtain clean data with default settings.(95) The clean data were subsequently mapped to the human reference genome (GRCh38, hg38) using STAR aligner (version 2.7.0f), and the transcript abundance was calculated employing RSEM.(96, 97) Differential expression analyses were performed using DEseq2 (version 3.14.0) with a significance threshold of p value ≤ 0.05 in the R statistical environment (version 3.5).(98) Gene Set Enrichment Analysis (GSEA) was performed via GSEA software (version 4.0.3) with default parameters.

For ChIP-seq analyses, the clean data were generated from fastp and mapped against the human reference genome (GRCh38, hg38) using Bowtie2 (version 2.3.2) under default settings.(99) Samples were excluded with low sequencing depth (< 20 million reads) and mapping rates (<80%). Reads with duplicates were removed by rmdup for the subsequent analysis. The alignments were sorted and indexed using samtools (version 1.14).(100) Peaks were identified using MACS2 (version 2.1; p < 0.05) (101) and annotated with ChIPseeker (version 1.14.2). Superenhancer regions were obtained using ROSE with H3K27ac peaks.(102) DNA motif analysis was performed using Homer with default settings.(52) Bigwig files produced by deepTools were visualized in Integrative Genomics Viewer (version 2.4.19).(103, 104) For each ChIP-seq experiment, we included critical input DNA controls: genomic DNA from the same cell/tissue sample (without ChIP enrichment) was sequenced to distinguish specific protein-DNA binding from background genomic noise, and we used it to normalize peak signals in downstream analysis.

Identification of SE Regions
SE regions in our study were defined based on the criteria described in previous studies.(25) Briefly, in patient samples, SE regions were identified using ROSE software (default parameters) with H3K27ac peak regions outside ± 2.5 kb of all TSSs. In cell lines, SEs were called within H3K27ac+/H3K4me1+ peak regions outside ± 2.5 kb of all TSSs. The input-subtracted signal of each SE candidate was generated using bigWigAverageOverBed (sum of reads over covered bases). SE regions were ranked by the average H3K27ac ChIP-seq signal difference between sensitive and resistant and sensitive groups. Gained SEs were those with average differential signals >0, and lost SEs were those with <0.

Mice Experiments
The female athymic BALB/c nude mice (5-8 weeks old) were purchased from GemPharmatech (Nanjing, China) and housed in the Biological Resource Centre. Mice were implanted subcutaneously in the flank with 3x106 HEC-1-A cells in 0.1 ml PBS. When tumors reached 70 mm3, randomization was performed by equally dividing tumor-bearing mice with similar tumor burdens into control and indicated treatment groups: DMSO (oral gavage), Abemaciclib (30 mg/kg, every two days, oral gavage), Disulfiram (200 mg/kg, every two days, oral gavage), dBET6 (10 mg/kg, every two days, i.p.), combination in which each compound was administered at the same dose and schedule as the single agent. Tumor progression was monitored with body weight and tumor size for approximately 4 weeks. Tumors were measured by Vernier caliper and calculated according to the following formula: V = W2 x L/2. Tumors were excised upon reaching 1,000 mm3 at the end of the study. All animal studies were conducted in compliance with animal protocols approved by the Animal Care and Ethics Committee of the Sixth Affiliated Hospital of Sun Yat-sen University and followed the National Health Guidelines on the Care and Use of Animals (IACUC-2022112101).

For constructing patient derived tumor Xenograft, female NOD-SCID mice (5-8 weeks old) were purchased from GemPharmatech (Nanjing, China) and housed in the Biological Resource Centre. Mice were implanted subcutaneously with pieces of fresh tumor from whole-tumor just dividing from EC patients in surgery. The tumors were washed in PBS and transported in DMEM/F12 basic medium. Whole tumors were minced with a sterile scalpel into patches which could pass through a needle bore, and then injected into the mice subcutaneously. The mice were randomly assigned to either the control or experimental group, with similar tumor burden. Tumor growth was monitored until a volume of 1000-1,500 mm³ was reached, at which point the tumors were excised.

For vitamin A dietary study design, 10 kg of vitamin-A-control diet was AIN-93G purified diets which composed of 0.16 g vitamin A palmitate (250,000 Ug-1), 4,657 g cornstarch, 1,400 g vitamin free casein, 1,550 g dextrinized corn starch, 1,000 g sucrose, 400 g soybean oil, 500 g Alphacel non-nutritive bulk, 350 g AIN-93M mineral mix, 108 g L-cystine, 25 g choline bitartrate, 0.080 g T-butyl hydroquinone, 0.3 g niacin,0.16 g D-calcium pantothenate, 0.07 g pyridoxine HCl, 0.06 g thiamine HCl, 0.06 griboflavin,0.02 g folic acid,0.002 g biotin, 0.25 g vitamin B-12 (0.1% Trit), 3 g alphatocopherol powder (250Ug-1), 0.025 g vitamin D3 (400,000Ug-1), 0.008 g phylloquinone and 96 g powder sugar. Vitamin-A-high diet contained 1 g vitamin A palmitate (250,000 Ug-1) per 10 kg of diet based on AIN-93G.

The method of establishment Abemaciclib acquired resistant PDX was referenced from previous studies.(105) We firstly constructed PDXs with female NOD-SCID mice (5-8 weeks old), which were purchased from GemPharmatech (Nanjing, China) and housed in the Biological Resource Centre. When tumors reached 70 mm3, PDXs were treated with Abemaciclib (30 mg/kg, every 2-3 days, oral gavage). Tumor growth was monitored and would be excised upon reaching 1,000 mm3. The excised tumor tissue was minced with a sterile scalpel into patches and implanted subcutaneously for passage into the next generation of PDXs. The process was repeated, and the PDXs were fed in the presence of drug for 3 additional passages. Resistant PDXs were maintained in the presence of Abemaciclib, and the same passage sensitive PDXs were treated with vehicle (saline).

Statistical analysis
The presented data are derived from three independent experiments and analyzed using GraphPad Prism software (GraphPad Software, La Jolla, CA). Statistical significance between individual groups was evaluated using two-tailed independent Student’s t-tests. When examining multiple groups, a one-way or two-way analysis of variance was performed. Statistical significance was set at p-value < 0.05.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this study, Chen and colleagues investigated the effects of abnormal ALDH1A1 expression on CDK4/6i resistance in hormone receptor-positive breast and endometrial cancers. The researchers demonstrate that high ALDH1A1 levels and their downstream effects contribute to CDK4/6i insensitivity.\nWe have some comments:\n1) The methodology used for the sensitivity profiling of abemaciclib against 16 PDOs from EC is unclear. How do other CDK4/6 inhibitors such as Palbociclib and Ribociclib compare? Is the response CDK4- or CDK6-specific?\n2) What dose is considered the threshold for Abemaciclib resistance in vitro? In Supplementary Figure 2, the authors show response curves from a wide variety of endometrial cancer cells. Indicating IC50 values would increase the interpretability of the results.\n3) Quantification and replicates of analyses in Figure 3, as the effects of knockdown, overexpression of ALDH1A1 or its inhibition, and others throughout the manuscript seem to be missing.\nMoreover, in the manuscript, considerable amount of conclusions were drawn from visual data, such as colony formation assays and immunofluorescence / immunohistochemistry. Although they might still convey the main idea, the quality of the visuals is low, making a through revision challenging. It is essential that the manuscript includes either significantly improved visuals or clear, detailed quantitative data to support the findings. As an example, in Figure 2K, although the difference is clear, the cells in the resistance group look rather overexposed, very likely due to image quality. Additionally, immunofluorescence images should include a legend indicating the color assigned to each marker.\n4) The ChIP-seq data and others don’t seem to be available for review. Has it been uploaded to a repository? No quality metrics seem to be provided. What where the controls in these experiments?\n5) In the experiments shown in Figure 4A and Figure 6M, the authors indicate that they treat the cells with 20 μM ATRA. In our experience this is way over the relevant in vitro therapeutic dose, which is conventionally between 0.01-1 µM. This is also valid for Vitamin A treatments - the authors should test if the effects of RA and Vitamin A are dose-dependent, in order to ensure that very high doses are not skewing the responses away from what is physiologically and therapeutically relevant.\n6) In Figure 2J, what do P1, 2, 3, and 4 correspond to? In the legend it was stated that the data was shown as mean ± SD of technical triplicates. If they are biological replicates, it would be more sensible to show the cumulative data as n=4 and perform statistics. Moreover, the graph type that was chosen is not suitable for a t-test.\nIn general, the number of biological replicates seems not to be indicated in all of the experiments, so the authors should provide this information.\n7) Throughout the manuscript, some data were shown as mean ± SD, while some were shown as mean ± SEM (although authors only stated it as ± SEM, which needs additional correction). The data presentation must follow a uniform format to ensure clarity and consistency across the manuscript.\n8) The authors established 4 Abemaciclib resistant cell models with HEC-1-A, HEC-1-B, MCF7 and T47D cells through chronic exposure. Figure 2A indicates that they used increasing doses of Abemaciclib, while DMSO seems to be constant. In our experience DMSO itself might have substantial effect on the cells. Was DMSO amount also increased in parallel? The authors should describe the establishment of their acquired resistance models in more detail.\n9) The authors had 8 EC cells lines at the hand, why did they choose to move forward with HEC-1-A and HEC-1-B cells, since HEC-1-B is the substrain of HEC-1-A cells and therefore very similar? Wouldn’t a more distinct cell model be preferable?\n10) In Figure 1J, the authors show enrichment of epithelial mesenchymal transition (EMT), KRAS and estrogen response signaling in resistant tumors, not surprisingly so, considering KRAS is known to be frequently mutated in Type I endometrial EC, which represents 87.50% of the cohort of the study. Can authors provide information regarding KRAS status of their patient cohort? Could KRAS activating mutations already be an indication of resistance? The same question also stands for the dataset they use for T47D palbociclib resistance model - KRAS is also highly expressed in T47D. Could the palbociclib treatment select the cells with strong KRAS signaling and have increased EMT features? Notably, given that these pathways appear critical to their resistance models, how might the authors relate this to their current findings? Does KRAS activation favor higher ALDH1A1 activity as well? Additionally, as EMT was shown to be a key feature of Abemaciclib resistance, did authors observe any change in the EMT features in their ALDH1A1 overexpression / silencing models?\nThese points should be more clearly integrated into the manuscript to strengthen the overall narrative of the work.\n11) In lines 144-146, authors state that their histological analyses showed morphological similarities between PDOs and their corresponding primary EC tumors. However, PDOs exhibited great distinction in sensitivity to CDK4/6i Abemaciclib. In its current form, the sentence implies that authors tested Abemaciclib sensitivity of tumors before the PDOs were formed and there is a difference after PDO formation. If this was done, the authors should show the data and discuss the potential mechanisms. If not, the sentence should be revised.\n12) For experiments with viral transduction, such as in Figure 3D, comparisons focus on control groups, but the relationship between empty vector controls and parental cells is not addressed.\n13) In Figure 4H, the scatter diagrams show the IC50 of Palbociclib in GDSC database between high and low activation of vitA metabolic, RA signaling, RA metabolic and RA nuclear binding. This is particularly interesting, considering CDK4/6i such as palbociclib are considered candidate drugs to be used in combination for differentiation therapy in cancers such as acute myeloid leukemia and neuroblastoma (PMID: 37734383", "reviewer_338": "Additionally, functional studies showed that genetic deletion of RARα 339 with siRNAs...", "reviewer_technical_replicates": "mplies that some of the experiments were performed only once, with technical replicates. The authors should either reach the number of enough biological replicates before performing statistics or correct the manuscript if it is a typographical error.\nThe list of experiments where only technical replicates were shown;\n-Figure 2I; lines 1148-1150 in the legend: “RT-qPCR validation of ALDH1A1 in parental and resistant cells. Data are shown as mean ± SD of technical triplicates and analyzed by unpaired t-test”\n-Figure 2J; lines 1152-1152 in the legend: Data are presented relative to input, shown as mean ± SD of technical triplicates and analyzed by unpaired t-test.\n-Figure 3K; lines 1188-1192 in the legend: Representative images of PDO67, PDO76 and PDO77 treated with DMSO, Abemaciclib (1 μM), Dis (2 μM), dBET6 (2 μM), or combination after 10 days culture. Scale bar = 200 μm (left). The matching scatter diagram shows data as mean ± SD of technical triplicates, ***p < 0.001, one-way ANOVA with Dunnett’s multiple comparisons test (right).\n-Figure 4B: lines 1196-1199 in the figure legend: Determination of relative RA concentrations between parental cells (HEC-1-AP and HEC-1-BP) and resistant cells (HEC-1-AR and HEC-1-BR). Data are shown as mean ± SD of technical triplicates and analyzed by unpaired t-test.\n-Figure 4C: lines 1199-1202 in the figure legend: Determination of relative vitA concentrations in cell culture medium at the indicated time points. Data are shown as mean ± SD of technical triplicates and analyzed by two-way ANOVA with Dunnett’s multiple comparisons test, ***p < 0.001.\n-Figure 4G; lines 1208-1212 in the figure legend: Resistant cells are treated with the indicated concentrations of Abemaciclib in B-27 supplement medium, vitA-deficient B-27 supplement medium or vitA-deficient B-27 supplement medium with vitA (10 μM). The number of viable cells is measured at 96 hours. Data are shown as mean ± SD of technical triplicates and analyzed by two-way ANOVA with Dunnett’s multiple comparisons test, ***p < 0.001.\n-Figure 4J; lines 1216-1218 in the figure legend: Relative ALDH enzyme activity between parental cells (HEC-1-AP and HEC-1-BP) and resistant cells (HEC-1-AR and HEC-1-BR). Data are shown as mean ± SD of technical triplicates and analyzed by unpaired t-test.\n-Figure 4L; lines 1219-1221 in the figure legend: Relative ALDH enzyme activity of indicated cells. Data are shown as mean ± SD of technical triplicates and analyzed by One-way ANOVA with Dunnett’s multiple comparisons test.\n-Figure 4N; lines 1223-1225 in the figure legend: Determination of relative RA concentrations in indicated cells. Data are shown as mean ± SD of technical triplicates and analyzed by one-way ANOVA with Dunnett’s multiple comparisons test.\n-Figure 4O; Determination of relative RA concentrations in cells transfected with siNC or siALDH1A1. Data are shown as mean ± SD of technical triplicates and analyzed by unpaired t-test. ****p < 0.0001.\n-Figure 5J; lines 1248-1252 in the figure legend; Representative images of PDO77, PDO76 and PDO80 treated with DMSO, Abemaciclib (1 μM), AGN (20 μM), AR7 (20 μM), or combination after 10 days culture (left). Scale bar = 200 μm. The matching scatter diagram shows data as mean ± SD of technical triplicates, one-way ANOVA with Dunnett’s multiple comparisons test (right). **p < 0.01, ***p < 0.001, ****p < 0.0001.\n-Figure 6F-G; lines 1265-1267 in the figure legend; ChIP-qPCR analysis of RARα and ERα occupancy at genomic loci of ALDH1A1 in HEC-1-AP and HEC-1-AR, HEC-1-BP and HEC-1-BR cells. Data are presented relative to input, shown as mean ± SD of technical triplicates and analyzed by unpaired t-test.\n-Figure 6H; lines 1268-1270 in the figure legend; Re-ChIP analysis shows the concurrent presence of both ERα and RARα at genomic loci of ALDH1A1 in HEC-1-AP and HEC-1-AR cells. Data are presented relative to input, showed as mean ± SD of technical triplicates and analyzed by t test.\n-Figure 6I; lines 1270-1273 in the figure legend; ChIP-qPCR analysis of H3K27ac occupancy at genomic loci of ALDH1A1 in HEC-1-AR cells transfected with control siRNA, siRARA or siESR1. Data are shown as mean ± SD of technical triplicates and analyzed by unpaired t-test.\n-Figure 6J; lines 1273-1276 in the figure legend; RT-qPCR validation of RARA and ALDH1A1 in HEC-1-AR and HEC-1-BR cells transfected with control siRNA or RARA siRNA. Data are shown as mean ± SD of technical triplicates and analyzed by unpaired t-test.\n-Figure 6K; lines 1276-1278 in the figure legend; RT-qPCR validation of ESR1 and ALDH1A1 in HEC-1-AR and HEC-1-BR cells transfected with control siRNA or ESR1 siRNA. Data are shown as mean ± SD of technical triplicates and analyzed by unpaired t-test\n-Figure 6M; lines 1281-1284 in the figure legend; ChIP qPCR analysis of H3K27ac, ERα and RARα occupancy at genomic loci of ALDH1A1 in HEC-1-AP cells treated with DMSO and RA (20 μM) for 72 hours. Data are presented relative to input, shown as mean ± SD of technical triplicates and analyzed by unpaired t-test.\n-Figure 6N (up); lines 1284-1287 in the figure legend: RT-qPCR validation of ALDH1A1 in HEC-1-AP and HEC-1-BP cells treated with DMSO or RA (10 μM and 20 μM) (up). Data are shown as mean ± SD of technical triplicates and analyzed by one-way ANOVA with Dunnett’s multiple comparisons test.\n-Figure 7I; lines 1315-1317 in the figure legend: Determination of relative RA concentrations in PDX-EC1P and PDX-EC1R xenograft tumor tissues. Data are shown as mean ± SD of technical triplicates and analyzed by unpaired t-test.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "The current paper addresses potential mechanisms of resistance to CDK4/6i therapy. Such small molecules are currently used in hormone receptor positive breast cancer and have therapeutic benefit. However, resistance remains an issue. Multiple mechanisms of resistance have been broadly described and most reflect non-genomic mechanisms. This paper provides evidence that increased expression of ALDH1A1 increases vitamin leading to increased retinoic acid accumulation. Critically, overexpression of ALDH1A1 was sufficient to drive resistance and it was critical for maintenance of resistance. RA accumulation led to increased RARa and ERa signaling that perpetuated ALDH1A1. This is an interesting model and much of the data was strong. However, there are significant issues that need to be addressed. The most significant of which is how SE are called from the genomic experiments. It is not described and it seems that enhanced H2K27ac is the only marker for SE. This is not fully justified and further it is unclear what or how SE were called; how much of an increase or clustering. Ultimately, while ALDH1A1 is up, it is far from clear that this reflects SE increase and further what drives this mechanism. Additional points follow.\n1. How SE are called from the genomic experiments. It is not described and it seems that enhanced H2K27ac is the only marker for SE.\n2. Through the paper, methods and experiments are not described with sufficient clarity to understand experimental procedures sufficiently to interpret data provided.\n3. Why are the authors observing cell death with CDK4/6i? These are cytostatic drugs that trigger quiescence or ultimately senescence. This is a flaw…if death is observed, it suggests off target effects, given that in vivo death generally reflects recruitment of cytotoxic immune cells.\n4. No description of proliferation assays. Is cell doubling measured? Or is this a metabolic assay used as a surrogate. If the latter, this is not valid given that C/CDK4/6 regulate cell metabolism independently of cell division.\n5. The authors need to measure cell senescence with single drug and combinations.\n6. In figure 4 the authors state that resistant cell “outcompete” parental cells. This experiment as described does not address competition. It simply demonstrates that resistant cells utilize more Vit A than parental.\n7. How were doses of Vit A and RA? Dose response? From Literature? No description.\n8. In figure 5, all conclusions are based on RNA-seq. Transcription does not necessarily reflect translation. The authors need to demonstrate that increases observed by RNA-seq are reflected at the protein level.", "reviewer_4": "The manuscript entitled “Targeting Super-Enhancer-1 Driven ALDH1A1-RARα/ERα Feedback Loop to Mitigate CDK4/6 Inhibitor Resistance via Suppression of Vitamin A Metabolism” by Chen et al. addresses mechanisms of resistance to CDK4/6 inhibitors (CDK4/6i), an important clinical problem. Inhibitors of CDK4/6 activity have shown promise in the clinic and palbociclib, abemaciclib, and ribociclib are FDA-approved for use in patients. However, the therapeutic promise of these agents is dampened by inevitable emergence of resistance. The authors present a compelling study in which they identify a novel mechanism of CDK4/6i resistance involving epigenetic alterations. The study is predominantly performed in endometrial cancer (EC) models, although data from breast cancer cells are also presented. Integration of transcriptomic and chromatin data (RNA-seq and ChIP-seq data) from sensitive and resistant EC organoids and EC cell lines identified ALDH1A1 as a super enhancer-driven gene associated with CDK4/6i resistance. The authors delineate a novel positive feedback axis that promotes CDK4/6i resistance in which increased levels of ALDH1A1 accumulate in resistant cells, promoting vitamin A metabolism, accumulation of retinoic acid, interaction between RARα and ERα in the nucleus, and RARα/ERα-occupied super enhancer-driven transcriptional activation of ALDH1A1. They further determined that ALDH1A1 can be epigenetically silenced with BRD4 inhibitors to mitigate CDK4/6i resistance. Based on their findings, the authors propose that inhibition of this novel axis (e.g., ALDH1A1 inhibition) is a promising approach to re-sensitize tumors to CDK4/6i. The data are generally of good quality and supportive of the conclusions of the authors. While the study should be of interest to the broad readership of Nature Communications, the following issues should be addressed.\nGeneral:\n1) In many cases (e.g., colony formation assays, Western blots), the number of biological replicates performed is not clear. While technical replicates are indicated, the number of times the experiment was carried out with similar results is not specified. This should be corrected throughout the manuscript.\n2) Western blot data should be quantified, especially in experiments where changes are modest.\n3) Information on the characteristics of the 16 endometrial cancer organoids generated should be provided. Were there any trends regarding the response of these organoid lines to CDK4/6i based on EC type, grade, etc?\n4) Experimental details on how CDK4/6i-resistant EC cells and PDXs were generated (e.g., drug concentrations, duration of treatment at each concentration) should be included in the Materials and Methods.\n5) All the experiments were performed using a single CDK4/6i inhibitor (abemaciclib) and results are expected to apply to other clinically relevant CDK4/6i. If possible, confirmation that the pathway is present in tumor cells that are resistant to another FDA-approved CDK4/6i would strengthen the study.\n6) There are many language and grammatical errors throughout the manuscript that need to be addressed.\nSpecific Comments:\n1) Fig. 3I and 3J: Near complete knockdown of BRD4 resulted in modest downregulation of ALDH1A1. Can the authors comment on other potential mediators of ALDH1A1 accumulation in CDK4/6i resistant cells?\n2) Figs. 3K and 5J: A larger scale should be used on the y-axis to clarify the observed effects.\n3) The data in Fig. 4M are not compelling.\n4) Some of the data in Fig. 5I are not convincing (e.g., HEC-1-AR cl-Cas3). Blots of better quality should be provided and quantified.\n5) The graph on the right in Fig. 7A appears to be mislabeled.\n6) In Fig. 7B, there appears to be a robust effect of Abem on HEC-1-AR tumor weight. Can the authors explain this effect in resistant cells?\n7) Supplemental Fig. 5C: Levels of cyclin D1 protein should be shown."}, "author_response": "Response to Reviewer: We appreciate the positive comments from the Reviewer. We believe that we have fully addressed all the concerns in the point-by-point responses below.\nWe have some comments:\n1) The methodology used for the sensitivity profiling of abemaciclib against 16 PDOs from EC is unclear. How do other CDK4/6 inhibitors such as Palbociclib and Ribociclib compare? Is the response CDK4- or CDK6-specific?\nResponse to Reviewer: We appreciate the constructive suggestions from the Reviewer. We have amended the description of methodology used for the sensitivity profiling of CDK4/6 inhibitors in the Method section (Page 23-24): “For the cell viability assay, organoids were harvested and dissociated using TrypLETM Express (Gibco, 12604013) for 10 min at 37 ºC. The dissociated PDOs were resuspended in ice-cold MAcreGel (Mingao Biotech) and seeded into a 96-well plate (Corning). After allowing the MAcreGel drops to solidify for 15 min at 37 ºC, PDOs were overlaid with endometrial cancer organoid medium containing the indicated doses of drugs. Five days after drug treatment, cell viability was measured using the Cell Viability ATP Assay Kit (Mingao Biotech) according to the manufacturer’s instructions on a Spark microplate reader (Tecan).”\nAs for the sensitivity of PDOs to other CDK4/6 inhibitors, we have conducted cell viability assay using other clinically used CDK4/6 inhibitors (e.g., Palbociclib, Ribociclib) to assess the half-maximal inhibitory concentration (IC50) values of 16 PDOs (Response Figure 1A). The results showed that PDOs exhibit comparable sensitivity to both Palbociclib/Ribociclib and Abemaciclib, suggesting that the sensitivity of PDOs to CDK4/6 inhibitors stems from their CDK4/6-specific on-target effect rather than a unique response to a single inhibitor. We have appended Response Figure 1A to Supplemental Figure 1 with the description (Page 6): “In addition, the response to CDK4/6 inhibition in PDOs was confirmed with other CDK4/6 inhibitors (Palbociclib and Ribociclib) (Supplementary Fig. 1A-B).”\nAs the Reviewer mentioned whether the response of CDK4/6i was CDK4- or CDK6- specific, we analyzed the expression levels of CDK4 and CDK6 in the EC tumors from RNA-seq data. The results showed no statistical difference in expression levels of CDK4 or CDK6 between sensitive and resistant groups to CDK4/6i (Response Figure 1B), indicating that the response to CDK4/6i may be independent of CDK4 or CDK6.\nResponse Figure 1\nB\nA\nPalbociclib Ribociclib ns\nI\nC 5 0 (\nM)\nµ\n1\n2\n1\n5\n5\n0\n0\n.\n.\n.\n.\n0\n0\n0\nR\ne\ne\nn\nsi\ns\ns\ni\nt\nt\na\niv\nn\ne\nt\nI\nC 5 0 (\nM)\nµ\n2\n1\n1\n5\n0\n5\n0\n.\n.\n.\n.\n0\n0\n0\nR\ne\ne\nn\nsi\ns\ns\ni\nt\nt\na\niv\nn\ne\nt\nlevel\n(counts)\nD\nK4\n2\n4\n6\n8\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\nlevel\n(counts)\nD\nK6\n1\n2\n3\n4\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\nns\nC C\n0.0\nP D\nO81\nP D\nO58\nP D\nO56\nP D\nO82\nP D\nO53\nP D\nO57\nP D\nO55\nP D\nO78\nP D\nO79\nP D\nO77\nP D\nO65\nP D\nO67\nP D\nO80\nP D\nO83\nP D\nO68\nP D\nO76\n0.0\nP D\nO81\nP D\nO58\nP D\nO56\nP D\nO82\nP D\nO53\nP D\nO57\nP D\nO55\nP D\nO78\nP D\nO79\nP D\nO77\nP D\nO65\nP D\nO67\nP D\nO80\nP D\nO83\nP D\nO68\nP D\nO76\nRelative\nS\n0\nensitive Resistant\nRelative\nS\n0\nensitive Resistant\nResponse Figure 1. (A) Growth-inhibitory effect of 16 PDOs treated with different doses of Palbociclib and Ribociclib. The y-axis shows the IC50 values. (B) The counts of indicated genes in EC tumors from RNA-seq data.\n2) What dose is considered the threshold for Abemaciclib resistance in vitro? In Supplementary Figure 2, the authors show response curves from a wide variety of endometrial cancer cells. Indicating IC50 values would increase the interpretability of the results.\nResponse to Reviewer: We thank the Reviewer for pointing out this issue. We defined the Abemaciclib-resistant cell lines using an IC50 cut-off value of 1.0 μM. We have updated the IC50 values of Abemaciclib for the indicated cells and amended them in the revised version as follows.\nResponse Figure 2\n1. A 2 HEC-1-AIC50=0.S26N5G4-M B\nHEC-1-BIC50=0.M41F5E3-296\n1.2 RL95-2 IC50=0.K61L2E8\ne\nati o n i n d ex 0 0 1 . . . 6 9 2 Pr olif e ati o n i n d ex 0 0 0 . . . 3 6 9 Pr olif e ati o n i n d ex 0 0 0 . . . 3 6 9 H H R A E N E L9 C C 3 5 - - C 1 - 1 2 - - A A B A I K M S s N L N h F E i 3 G E k a - C - 2 M w A 9 a 6 I I I I I C C C C C 5 5 5 5 5 0 0 0 0 0 = = = = = 1 1 1 3 4 . . . . . I0 3 9 7 5 s5 6 3 3 6 h6 2 2 1 1 ikawa 0 0 0 0 1 0 0 0 0 1 . . . . . . . . . . 00 0 3 6 9 2 0 3 6 9 2 ..0011 00 Pr olif er ati o n i n d e x ..11 0 0 0 0 1 . . . . .\n0\n0 3 6 9 2\n.01\n.1\n0\n- - 0 1 1 1 . . 0 0 0 0 1 0 0 0 0 13 0 - - 4 A A . . . . . . . . . . 5 00 0 3 6 9 2 0 3 6 9 20 7\n0\n11\n0\n0\n0 3 6 9 2\n.01\n.\nEE\n1\n1\n1\n0 0 C C =\n0\n= 0 - - 2 1 1 . . 6 9 - - 5 B B 8\n1\n5 4 P R 4\n00\nolif 0.0 Abem(µM) Abem(µM)\nPr\n0.3\n0.0\n0\n1\n.0\n0.1\nAbem\n1\n(uM)\n10 100\n0.01 0.1 1 10 100\nAbem (uM)\n0.0\n0.01 0.1 1 10 100\nAbem (uM)\nResponse Figure 2. (A) EC cell lines were treated with the indicated concentrations of Abemaciclib. The number of viable cells was measured at 96 hours. Data are represented the mean of three independent replicates. The IC50 values of Abemaciclib for EC cell lines after exposure durations of 96 hours are shown. (B) Parental and resistant cells treated with the indicated concentrations of Abemaciclib. The number of viable cells was measured at 96 hours. Data are represented the mean of three independent replicates. The IC50 values of Abemaciclib for HEC-1-AP, HEC-1-AR, HEC-1-BP and HCE-A-BR after exposure durations of 96 hours are shown.\n3) Quantification and replicates of analyses in Figure 3, as the effects of knockdown, overexpression of ALDH1A1 or its inhibition, and others throughout the manuscript seem to be missing. Moreover, in the manuscript, considerable amount of conclusions were drawn from visual data, such as colony formation assays and immunofluorescence / immunohistochemistry. Although they might still convey the main idea, the quality of the visuals is low, making a through revision challenging. It is essential that the manuscript includes either significantly improved visuals or clear, detailed quantitative data to support the findings. As an example, in Figure 2K, although the difference is clear, the cells in the resistance group look rather overexposed, very likely due to image quality. Additionally, immunofluorescence images should include a legend indicating the color assigned to each marker.\nResponse to Reviewer: We thank the Reviewer for pointing out these issues. We have quantified the intensity of all the bands in the Western Blot experiments using ImageJ/Fiji software and labeled the normalized relative gray values in the updated version of the manuscript. In addition, we have performed statistical analysis of all colony formation assays and their corresponding replicate experiments using ImageJ/Fiji software. We have also updated the images to high-resolution to correct those with suboptimal exposure in all image data (Response Figure 3). Furthermore, we have added color legends to immunofluorescence images to indicate the corresponding colors for each marker (Response Figure 4). The updated images and figures are provided in Response Figure 3, and we have amended the previous Figure 3 with this Response Figure 3 in our manuscript.\nIn Method section (Page 22): “The colonies were quantified using ImageJ/Fiji software (https://fiji.sc/).”\nIn Method section (Page 24): “The blots were quantified using ImageJ/Fiji software (https://fiji.sc/).”\nIn Figure Legend section (Page 41): “Representative immunofluorescence (IF) images of HEC-1-AP, HEC-1-AR, HEC-1-BP and HEC-1-BR cells stained with ALDH1A1, scale bar = 10 μm, blue color shows DAPI signal, green color shows ALDH1A1 signal. (left). The intensity of ALDH1A1 in parental and resistant cells were quantified and analyzed by t test. ***p < 0.001. (right).”\nResponse Figure 3\nA B C\nDMSO Dis HEC-1-AR HEC-1-BR\nsiNC siALDH1A1 DMSOAbem Dis Comb Abem Comb Abem - + - + - + - +\nH E C-1- A R\nH E C- 1- B R\nA\nA\nL\nL\nG\nG\nD\nD\nA\nA\nH\nH\nP\nP\n1\nD\nA\nA\nH\nH\n1\n1\ns 1\n1\niN .\n.\n0\n0\n0\nsi\n0\n.\n.\n#\n3\n2\n6 1\nDH\n0\n0\n.\n.\n1\n2\n4 #\n9 2\nAbe m D M S O\nAb e m siNC\nsiA\n#\nL\n#\nA\n2\ncolony ar ea\nRelative colony area\nA\nRelative colony\narea\n0 1 1 0\n0 1 1 0\nb\n0 0 1 1\n0 0 1 1\ne\n. . . .\n. . . .. . . .\n. . . . 5 0 5 0\n5 0 5 00 5 0 5\nm\n0 5 0 5\nC o m b\nH\nH\nM\nE\nE\nIs M\nC\nC\nF\nT\nh E\n-\nC\n-\n4\ni\n1\n1\nk -\n-\n-\nA\n7 w 9\nR\nR\nR\nR\n6 a Relative\ncolony ar ea0 0\n1\n0\n0 0 1 00 0 0 1\n0 0 0\n.\n.\n. . . .. . . .\n. . .\n.\n4 8\n2\n0\n4 8 2 00 4 8 2\n0 4 8\nM\nC\nC\n-1\nF\n-\nR\nR\nM\nHE\nF E\nC\n-\n-\n2\n1\n9\n- B\nIs\nT\nh\n4\n7\na\nD\nw\nR\na C\nc\nc a\nl\nl\ne\nG e s\n-\np - A\nP P\nC a P\nA A\na s\nD\nD e\nR R\ns\ni\nH\nP P\n3 3 1 1\n1 1\n. .\n. .\n0 0\n0 0\n-\n0 0\n0 0\n1 1\n1 1\n. .\n. .\n0 0\n3 2\n-\n5 6\n3 0\n1 1\n1 1\n. .\n. .\n6 1\n2 1\n+\n1 1\n7 8\n2 1\n2 1\n+\n. .\n. .\n9 1\n7 3\n6 9\n3 4\n1 1\n1 1\n. .\n. .\n0 0\n0 0\n-\n0 0\n0 0\n0 0\n0 0\n. .\n. .\n9 9\n9 7\n-\n5 2\n5 2\n1 0\n0 1\n. .\n. .\n3 9\n8\n+\n0\n5 4\n4 4\n1 1\n1 1\n+\n. .\n. .\n6 0\n2 2\n3 6\n1 2\nH E C- 1- B P H E\nC-1- A P\nD\nA\nA\nL\nL G G\nD\nD A A\nH\nH P P\n1\nA\nA H H\n1\n1 1\n.\n.\n0\n0\nt\n0\nr\n0\nl\n1\n.\n.\n0\n9\n3\n6\n.\n.\nL\n1\n4\nA A\nE\nA A V\nV\n1 1\n0 0.1 0\nA\n.2\nbem\n0\n(\n.\nµ\n1 2\nR el ati v e c ol o n y\nar e a\nRelativ e c\nolony ar ea Relative\ncolony\narea\n0 0 1 1\n0 1\n1\n0 0 1 1 0 0\n0 1\n1\n. .\n.\n. . . . .. . . ..\n. .\n.\n5 0\n5\n0 5 0 5 00 5 0 50\n5 0\n5\n0.\nE\n0 0 0 n\nn\nV\n. .0 s\ns\n0 0 . 1 0 0\nn\n*. .0\ns\nA 1 1.2 b 0\nn\n0 *\nA\ne .\ns\n* 0. 2 m 2\nL\n.5 0\nD\n*\n0 *\n(\n*\n. 1µ .\nH\n5\n*\n5. M 0 1 *\n1\n*\n1 *\n*\n. )2\nA\n0 . *\n*\n.00\n1\n2 *\n*\n2 . *\n*\n0 . * 0\nE\nCo m bin ation\nindex\n0 1\n1\n00 0 1\n1\n. .\n.\n.. . .\n.\n5 0\n5\n00 5 0\n5\n0.0\ndBE\nT\n..44\nE\n0\nd\n..\nR\nB\nE\nr\nT\na\n5\nct\n7\n1 i 1 o ..2 n 0 0 1\n1\n2 . . .\n.\n0 5 0\n5\n0 a . f 0 f\nd\ne\nB\nc 00 t\nE\ne ..22\nT\nd\n.4\nC-\n1\n0.\nQ\n.\n-\n0.8 11.0.0\nF G H\nHEC-1-AR HEC-1-BR\nH E C-\n1- B\nR H E C- 1- A R Abe m\nAbe m\nD M S O\nD M\nS O D M S O d B E T 6 d B E T 57 d B E T 1 J Q 1 Relative colo ny ar ea 0 0 1 0\n0\n0\n1\n00 0\n0\n1 0 0 0 1 . . . .\n.\n.\n.\n.. .\n.\n. . . . . 4 8 2 0\n4\n8\n2\n00 4\n8\n2 0 4 8 2 D B E M T S i O A C b o e m m b M Is\nM\nF h E\nC\nik -\nF\nK a 2 w\nR\nE a 6 D M S O Abe m d B E T6 Co m b Relativ e c olony area 0 0 1\n.0\n0 0 1 4 8 2\n0.\n. . .\nK 0\n4 8 2\nLE M F E\nD d\n-\nB\nS\ns\nT\nh\nO\ni\n6\nk a wa\nA C\nM C\nb o\nF\ne m\n7\nm\nR\nb C c\nc\na l\nl\ne\nG\ne\ns d -\np\n-\nA\nP A P\nC\nB a\nP\nA b A\na\ns E e\nD\nR e\ns\nR T m\nH\nP P 6\n3\n3 1 1 1\n1\n. . .\n.\n0 0 0\n0\n- - 0 0 0\n0\n1 1 1\n1\n. . .\n.\n+ 3 0 0\n1\n- 3 7 0\n2\n1 1 1\n1\n. . .\n.\n+ - 2 1 0\n2\n6 4 6\n1\n0 3 0\n3\n+ + . . .\n.\n7 4 8\n7\n3 8 3\n1\n1 1 1\n1\n. . .\n.\n0 0 0\n0\n- - 0 0 0\n0\n1 1 1\n1\n. . .\n.\n1 2 1\n0\n+ - 6 5 4\n4\n1 1 1\n0\n. . .\n.\n+ - 0 4 1\n9\n0 0 9\n1\n0 2 1\n2\n+ + . . .\n.\n8 5 0\n1\n9 7 3\nHEC-1-AR PDO67 Dis Abem+dBET6\ndBET6 Abem+Dis\nALDH1A1 d 0 BE 0 T . 6 5 (µ 1 M) 0 JQ 0 1 . 1 (µM 0 ) .2 DMSO Abem Dis Abem+Dis dBET6Abem+ dBET6 1 0 m ) µ 4 2 10 1 0 0 1 *** ***\n1.00 0.70 0.51 1.00 0.63 0.48\na\n(\nGAPDH\nAr\n0.1\nHEC-1-BR 0.01\ndBET6 (µM) JQ1 (µM) PDO76\nALDH1A1\n0 0.5 1 0 0.1 0.2 DMSO Abem Dis Abem+Dis dBET6Abem+ dBET6\nm\n)2 100\nµ\n1.00 0.77 0.73 1.00 0.81 0.41 04 10\nGAPDH e a ( 1 1 *** ***\nAr\nJ HEC-1-AR HEC-1-BR 0.1\nsiBRD4 siBRD4 PDO77\nsiNC #1 #2 siNC #1 #2 DMSO Abem Dis Abem+Dis dBET6Abem+ dBET6 100\nALD B H R 1 D A 4 1 1\n1\n.0\n.0\n0\n0\n0\n0\n.\n.\n4\n6\n0\n9 0\n0\n.\n.\n5\n2\n4\n5 1\n1\n.0\n.0\n0\n0\n0\n0\n.\n.\n1\n5\n5\n5 0\n0 #\n.\n.\n4\n2\n3\n2 1\ne a\n( 1 0 m ) µ 4 2 10\n1\n*** ***\nGAPDH\nAr\n0.1\nResponse Figure 3. (A) Immunoblot analysis of ALDH1A1 in HEC-1-AR and HEC- 1-BR cells transfected with control siRNA or ALDH1A1 siRNA. Shown are representative blots from three independent experiments. (left). Representative images and quantification of colony formation assay in cells treated with siNC, siALDH1A1, Abemaciclib (0.5 μM), or combination. Data are shown as mean ± SD of three independent experiments and analyzed by One-way ANOVA with Dunnett’s multiple comparisons test. ***p < 0.001. (right). (B) Representative images and quantification of colony formation assay in cells treated with DMSO, Abemaciclib (Abem) (0.5 μM), Disulfiram (Dis), or combination in the indicated cells. Data are shown as mean ± SD of three independent experiments and analyzed by One-way ANOVA with Dunnett’s multiple comparisons test. ***p < 0.001. (C) Immunoblot analysis of PARP, cleaved-PARP, Caspase 3 and cleaved-Caspase 3 in HEC-1-AR and HEC-1-BR cells treated with DMSO, Abemaciclib (0.5 μM), Disulfiram (1 μM for HEC-1-AR and 2 μM for HEC-1-BR), or combination for 72 hours. Shown are representative blots from three independent experiments. (D) Immunoblot analysis of ALDH1A1 expression in HEC- 1-AP/HEC-1-BP cells (control), overexpressing empty vector (EV) or ALDH1A1. Shown are representative blots from three independent experiments. (left). Representative images and quantification of colony formation assay in HEC-1- AP/HEC-1-BP cells overexpressing empty vector (EV) or ALDH1A1, which were treated with Abemaciclib (0.5 μM). Data are shown as mean ± SD of three independent experiments and analyzed by unpaired t-test. ns, not significant, *p < 0.05, **p < 0.01, ***p < 0.001. (right). (E) CI analysis of Abemaciclib and BRD4 inhibitors in resistant cell lines. Data are represented the mean of 3 biological replicates. CI greater than 1 is defined as antagonism; CI less than 1 is defined as synergy. (F) Representative images and quantification of colony formation assay in HEC-1-AR and HEC-1-BR cells treated with DMSO, Abemaciclib (0.5 μM), dBET6 (0.5 μM for HEC-1-AR and 2 μM for HEC-1-BR), dBET57 (2 μM for HEC-1-AR and 10 μM for HEC-1-BR), dBET1 (2 μM for HEC-1-AR and 5 μM for HEC-1-BR), JQ1 (0.2 μM), or combination. Data are shown as mean ± SD of three independent experiments and analyzed by One-way ANOVA with Dunnett’s multiple comparisons test. ***p < 0.001. (G) Representative images and quantification of colony formation assay in KLE, MFE-296 and Ishikawa cells treated with DMSO, Abemaciclib (0.5 μM), dBET6 (0.25 μM for KLE, 0.5 μM for MFE-296 and Ishikawa, 0.1 μM for MCF7R), or combination. Data are shown as mean ± SD of three independent experiments and analyzed by One-way ANOVA with Dunnett’s multiple comparisons test. ***p < 0.001. (H) Immunoblot analysis of PARP, cleaved-PARP, Caspase 3 and cleaved-Caspase 3 in HEC-1-AR and HEC-1-BR cells treated with DMSO, Abemaciclib (0.5 μM), dBET6 (0.5 μM for HEC-1-AR and 2 μM for HEC-1-BR), or combination for 72 hours. Shown are representative blots from three independent experiments. (I) Immunoblot analysis of ALDH1A1 expression in HEC- 1-AR and HEC-1-BR cells treated with BRD4 inhibitors (dBET6 and JQ1) for 72 hours. Shown are representative blots from three independent experiments. (J) Immunoblot analysis of ALDH1A1 expression in HEC-1-AR and HEC-1-BR cells transfected with control siRNA or BRD4 siRNA for 72 hours. Shown are representative blots from three independent experiments. (K) Representative images of PDO67, PDO76 and PDO77 treated with DMSO, Abemaciclib (1 μM), Dis (2 μM), dBET6 (2 μM), or combination after 10 days culture. The experiment was performed in three biological replicates. Scale bar = 200 μm. (left). The matching scatter diagram shows surface area of organoids, and one dot represents one organoid. Data are shown as mean ± SD and analyzed by Kruskal-Wallis with Dunn’s multiple comparisons test. ***p < 0.001. (right). Source data of (A), (C-F), (H-K) are provided in Source Data file.\nResponse Figure 4. (A) Representative immunofluorescence (IF) images of HEC-1- AP, HEC-1-AR, HEC-1-BP and HEC-1-BR cells stained with ALDH1A1, scale bar = 10 μm, blue color shows DAPI signal, green color shows ALDH1A1 signal. (left). The intensity of ALDH1A1 in parental and resistant cells were quantified and analyzed by t test. ***p < 0.001. (right).\n4) The ChIP-seq data and others don’t seem to be available for review. Has it been uploaded to a repository? No quality metrics seem to be provided. What where the controls in these experiments?\nResponse to Reviewer: We thank the Reviewer for pointing out these issues. ChIP-seq data generated in this study have been deposited in the Genome Sequence Archive in the National Genomics Data Center, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human: HRA008718). Following online publication of this study, the data will be accessible via https://ngdc.cncb.ac.cn/gsa-human, which has been described in the Method section. During the peer review stage, we provided a data access link in the nature portfolio Reporting Summary (https://ngdc.cncb.ac.cn/gsa-human/s/udT1vJ7Y).\nFor the quality control (QC) of ChIP-seq data in our study, we ensured sufficient sequencing depth (≥20 million uniquely mapped reads per sample) to capture specific binding events. Samples were excluded with low mapping rates (<80%). Reads with duplicates were removed by rmdup for the subsequent analysis. Significant peaks were called using MACS2 (p < 0.05). For the control groups of each experiment, we included critical input DNA controls: genomic DNA from the same cell/tissue sample (without ChIP enrichment) was sequenced to distinguish specific protein-DNA binding from background genomic noise, and we used it to normalize peak signals in downstream analysis.\nWe have provided detailed information for the quality control and analysis process in the Method section (Page 28):\n“Cells were cross-linked with 1% formaldehyde and quenched with 0.125 M glycine. The cross-linked cells were lysed in the lysis buffer (50 mM Tris-HCl [pH 8.0], 10 mM EDTA, 1% SDS) and sonicated on ice using a Bioruptor (Glen Mills, Qsonica Q700, USA). The lysates were precleared with protein G Dynabeads (Invitrogen), and 5% precleared lysates were taken to elute, reverse cross-linked, and purify for Input-DNA. Immunoprecipitated with protein G Dynabeads (Invitrogen) and incubated with the antibody-beads complex overnight at 4°C. Immunoprecipitations were eluted, reverse cross-linked, and purified for ChIP-DNA.”\nAnd in the Method section (Page 30):\n“For ChIP-seq analyses, the clean data were generated from fastp and mapped against the human reference genome (GRCh38, hg38) using Bowtie2 (version 2.3.2) under default settings.(1) Samples were excluded with low sequencing depth (< 20 million reads) and mapping rates (<80%). Reads with duplicates were removed by rmdup for the subsequent analysis. The alignments were sorted and indexed using samtools (version 1.14).(2) Peaks were identified using MACS2 (version 2.1; p < 0.05) (3) and annotated with ChIPseeker (version 1.14.2). Super enhancer regions were obtained using ROSE with H3K27ac peaks.(4) DNA motif analysis was performed using Homer (homer.salk.edu) with default settings.(5) Bigwig files produced by deepTools were visualized in Integrative Genomics Viewer (version 2.4.19).(6, 7) For each ChIP-seq experiment, we included critical input DNA controls: genomic DNA from the same cell/tissue sample (without ChIP enrichment) was sequenced to distinguish specific protein-DNA binding from background genomic noise, and we used it to normalize peak signals in downstream analysis.”\n5) In the experiments shown in Figure 4A and Figure 6M, the authors indicate that they treat the cells with 20 μM ATRA. In our experience this is way over the relevant in vitro therapeutic dose, which is conventionally between 0.01-1 µM. This is also valid for Vitamin A treatments - the authors should test if the effects of RA and Vitamin A are dose-dependent, in order to ensure that very high doses are not skewing the responses away from what is physiologically and therapeutically relevant.\nResponse to Reviewer: We thank the Reviewer for pointing out this issue. To investigate whether the effects of RA and VitA are dose-dependent, we performed experiments with concentration gradient ranging from 0.1 to 50 μM of RA/VitA to treat the HEC-1-A and HEC-1-B cells. As the drug concentration increased, the expression of ALDH1A1 also increased significantly in a dose-dependent manner (Response Figure 5A-B). We treated the cells with Abemaciclib in combination with increased concentrations of RA or vitA. The results showed that the effects of RA and vitA in mediating the Abemaciclib resistance became more pronounced as their concentrations increased (Response Figure 5C-D). In addition, the cell viability was not impaired by the increasing dosages of RA or vitA (Response Figure 5E-F). Therefore, we used 20 μM RA and 10 μM VitA for further experiments to obtain the optimal results.\nActually, the concentration ranges of RA and VitA in cellular experiments vary depending on cell types, experimental purposes, and mechanisms of action. For example, 20 µM RA was used in MDA-MB-231 cells, where cell viability was not affected.(8) In addition, 2 μM RA was used in transfected HEK-293T cells for luciferase reporter assay (9), 10 μM RA was used in human neuroblastoma SH-SY5Y cells (10), and 10-30 μM RA was used in HL60 and NB4 cells.(11) As for vitA concentrations, Cabezas-Wallscheid N et al. treated hematopoietic stem/progenitor cells with 10 μM VitA. (12) Colucci M et al. treated RWPE-1 and 22RV1 cells with 10 μM VitA. (13) These reports indicated that a wide range of RA/VitA doses were used in different studies.\nWe have amended the description in the Result section (Page 11). “Moreover, the effects of RA and vitA in mediating the Abemaciclib resistance became more pronounced as their concentrations increased (Supplementary Fig. 4E-F).”\nResponse Figure 5. (A-B) RT-qPCR validation of ALDH1A1 in HEC-1-AP and HEC- 1-BP cells treated with DMSO, RA (0.1 μM, 1 μM, 10μM, 20 μM and 50 μM) or vitA (0.1 μM, 1 μM, 10 μM and 20 μM). Data are shown as mean ± SD of and analyzed by one-way ANOVA with Dunnett’s multiple comparisons test. (C-D) HEC-1-AP and HEC-1-BP cells treated with combinations of RA (0 μM, 0.1 μM, 1 μM, 10 μM and 20 μM) and Abemaciclib (0.5 μM), or combinations of vitA (0 μM, 0.1 μM, 1 μM, 10 μM and 20 μM) and Abemaciclib (0.5 μM). The number of viable cells is measured at 96 hours. Data are shown as mean ± SD and analyzed by one-way ANOVA with Dunnett’s multiple comparisons test. (E-F) HEC-1-AP and HEC-1-BP cells treated with RA (0 μM, 0.1 μM, 1 μM, 10 μM and 20 μM) and the number of viable cells is measured at 96 hours. Data are shown as mean ± SD. *p < 0.5, **p < 0.01, ***p < 0.001.\n6) In Figure 2J, what do P1, 2, 3, and 4 correspond to? In the legend it was stated that the data was shown as mean ± SD of technical triplicates. If they are biological replicates, it would be more sensible to show the cumulative data as n=4 and perform statistics. Moreover, the graph type that was chosen is not suitable for a t-test.\nIn general, the number of biological replicates seems not to be indicated in all of the experiments, so the authors should provide this information.\nResponse to Reviewer: We thank the advises from the Reviewer. P1–P4 represent the ChIP primers across the SE regions of ALDH1A1, which was shown in the Supplemental Figure 2I. We have amended the description in the Result section (Page 8-9): “Next, we designed ChIP primers (P1-P4) across the SE regions of ALDH1A1 (Supplementary Fig. 2J), performed ChIP-qPCR analysis and found significant enrichment of H3K27ac binding at the ALDH1A1 locus in resistant cells (Fig. 2J).”\nWe apologized for the mistake in describing \"technical triplicates\" in the original legend. Actually, the experiments in Figure 2J were performed in three independent biological replicates, we performed technical triplicates to ensure detection accuracy during each independent biological replicate. After verifying the consistency of technical triplicates, we calculated the mean value of each independent biological experiment’s technical triplicates as the final data point for that sample. We have corrected the descriptions in the figure legend in the revised version of the manuscript.\nThe updated figures are shown as follows (Response Figure 6A) and the repetitive data were shown in Source Data.\nIn Figure 2J, the analysis was specifically focused on comparing the enrichment levels of H3K27ac binding at the four gene loci P1, P2, P3, and P4 of ALDH1A1 between two independent groups: parental and resistant cells. The use of an independent samples t-test is appropriate.\nAll experiments in the study were performed at least three times from biological replicates or independent experimental replicates. And we have annotated the number of independent samples/experiments for all experiments in the updated versions.\nResponse Figure 6. (A) ChIP-qPCR analysis of H3K27ac occupancy at genomic loci of ALDH1A1 (P1-4) in parental (P) and resistant (R) cells. Data are presented relative to input, shown as mean ± SD of three independent experiments and analyzed by unpaired t-test. **p < 0.01, ***p < 0.001.\n7) Throughout the manuscript, some data were shown as mean ± SD, while some were shown as mean ± SEM (although authors only stated it as ± SEM, which needs additional correction). The data presentation must follow a uniform format to ensure clarity and consistency across the manuscript.\nResponse to Reviewer: We thank the Reviewer for the advises. All the data previously shown as mean ± SEM were re-analyzed and were presented as mean ± SD in the updated version.\n8) The authors established 4 Abemaciclib resistant cell models with HEC-1-A, HEC-1-B, MCF7 and T47D cells through chronic exposure. Figure 2A indicates that they used increasing doses of Abemaciclib, while DMSO seems to be constant. In our experience DMSO itself might have substantial effect on the cells. Was DMSO amount also increased in parallel? The authors should describe the establishment of their acquired resistance models in more detail.\nResponse to Reviewer: We thank the Reviewer for pointing out this issue. The DMSO amount was increased in parallel during the establishment of drug-resistant cells. Since Abemaciclib was dissolved in DMSO, each time the concentration of Abemaciclib was increased, the DMSO level was adjusted synchronously in both parental and resistant cells to ensure consistency between the two groups. Given that DMSO itself exhibits certain cellular toxicity, its final concentration in the cell culture medium was maintained at ≤ 0.1% according to previous studies.(14) Consequently, although the DMSO concentration may increase slightly with rising Abemaciclib concentrations, this increase is essentially negligible.\nWe have amended the description of methodology about constructing acquired resistance models in the Method section (Page 22-23):\n“Establishment of cell lines with acquired resistance to Abemaciclib\nCells were grown with progressively increasing concentrations of Abemaciclib. Subconfluent cells were treated with Abemaciclib at IC50 doses (based on 96-hour cytotoxicity assays) for two 3-day periods, and surviving cells were maintained for another 4 days. The process was repeated once, and the remaining cells were cultured in the presence of drug for 3 additional passages. Resistant cells were maintained in the presence of Abemaciclib, and the same passage parental cells were treated with the same dose of DMSO.”\n9) The authors had 8 EC cells lines at the hand, why did they choose to move forward with HEC-1-A and HEC-1-B cells, since HEC-1-B is the substrain of HEC-1-A cells and therefore very similar? Wouldn’t a more distinct cell model be preferable?\nResponse to Reviewer: We thank the Reviewer for pointing out this issue. HEC-1-A, HEC-1-B, RL95-2, KLE, Ishikawa, MFE-296, AN3 CA, and SNG-M cells are commonly used endometrial cancer cell lines in multiple studies. Among these strains, we identified HEC-1-A and HEC-1-B cells as the most sensitive cells to CDK4/6i in the IC50 profiling (Response Figure 7A). Consequently, these two cell lines were utilized to establish acquired-resistance models. Although HEC-1-B is a substrain of HEC-1-A cells, significant differences exist between them in genomic and protein expression characteristics, and both are extensively used in various studies.(15-17)\nMore importantly, in this study, our findings were further validated in multiple additional models, including breast cancer cell lines T47D and MCF7 (Fig. 2I, Fig. 2L, Fig. 3B, Fig. 5H), CDK4/6i resistant cell lines T47DR, MCF7R, MFE-296 and Ishikawa (Fig. 3B, Fig. 3G, Supplementary Fig. 5B), three cases of PDOs (Fig. 3K, Fig 5J), two cases of PDX models (Fig. 7) and clinical samples. These data collectively support the reliability of the findings and conclusions presented herein.\nResponse Figure 7\nA\n1.2\nHHEECC-1-1-A-A IC50=0.26S54NG-M\nHHEECC-1--1B- BIC50=0.41M53FE-296\nRRLL9955-2- 2IC50=0.6128KLE\n11..22\nx0.9 AANN33 C CA AIC50=1.056Ishikawa\ne\n1.2 n d HEC-1-A SSNNGG-M-M IC50=1.362\ne\nati o n i n d e x 0 0 . . 6 9\nPr\nolif er ati o n i n\nd e\nx\nPr olif e\nati o\nn i\nPr\nolif e ati o n i n\nd e\nx0\n0\n0\n0\n0\n0\n0\n0.\n.\n.\n.\n.\n.\n.\n.9\n6\n3\n3\n6\n3\n6\nR\nA\nE\nN\nL9\nC\n3\n5\n-\nC\n-\n1\n2\n-\nA\nB M\nK\nIsI\nK\nM\ns\nL\nh\nF\nL\nh\nE\nF\ni\nE\nk\nE\ni\nE\nak\n-\nI\nwa\n-92\n5\nwa\n6\n0\n9\naI\n=\nI6\nC\nC\n3\n5\n5\n.7\n0\n0\n3\n=\n=\n1\n4\n1\n.\n.\n5\n9\n6\n3\n1\n2\nolif 0.0\nPr\n0.3\n0.01 0.1\nAbem\n1\n(uM)\n10 100\n00..00\n00..0011 00..11 11 1100 110000\n0.0\nA\nA\nb\nb\ne\ne\nm\nm\n(u(µ M\nM\n)\n)\n0.01 0.1 1 10 100\nAbem (uM)\nResponse Figure 7. (A) EC cell lines were treated with the indicated concentrations of Abemaciclib. The number of viable cells was measured at 96 hours. Data are represented the mean of three independent replicates. The IC50 values of Abemaciclib for EC cell lines after exposure durations of 96 hours are shown.\n10) In Figure 1J, the authors show enrichment of epithelial mesenchymal transition (EMT), KRAS and estrogen response signaling in resistant tumors, not surprisingly so, considering KRAS is known to be frequently mutated in Type I endometrial EC, which represents 87.50% of the cohort of the study. Can authors provide information regarding KRAS status of their patient cohort? Could KRAS activating mutations already be an indication of resistance? The same question also stands for the dataset they use for T47D palbociclib resistance model - KRAS is also highly expressed in T47D. Could the palbociclib treatment select the cells with strong KRAS signaling and have increased EMT features? Notably, given that these pathways appear critical to their resistance models, how might the authors relate this to their current findings? Does KRAS activation favor higher ALDH1A1 activity as well? Additionally, as EMT was shown to be a key feature of Abemaciclib resistance, did authors observe any change in the EMT features in their ALDH1A1 overexpression / silencing models? These points should be more clearly integrated into the manuscript to strengthen the overall narrative of the work.\nResponse to Reviewer: We thank the Reviewer for pointing out these issues. To determine the mutation status of KRAS in our cohort, we have performed Sanger sequencing to detect mutations in KRAS in tumor tissues. Among the 16 EC samples, 4 cases (25%) of them contained KRAS mutations, which was similar to previous studies that about 20% mutation rates of KRAS were present in EC.(18, 19) In addition, the same frequency of KRAS mutations was found in sensitive group (2/8, 25%) and resistant group (2/8, 25%) (Response Figure 8A), which was in line with the study in breast cancer patients that the KRAS mutation status was not relative to the response of CDK4/6i. (20) Based on these findings, KRAS activating mutation served as an indication of CDK4/6i resistance needs to be further investigated.\nTo clarify whether CDK4/6i treatment could select the tumor cells with strong KRAS signaling and increase EMT features, we detected expression of multiple key markers including p-ERK, p-MEK, E-cadherin and Vimentin in parental and resistant cells. The results showed that the resistant cells have stronger KRAS and EMT signaling (Response Figure 8B), which were consistent with the findings from RNA-seq (Fig. 1I-K).\nTo determine whether KRAS activation favors higher ALDH1A1 activity, we treated tumor cells with KRAS inhibitor (BI-2865) and found no significant changes of ALDH1A1 expression (Response Figure 8C). In addition, we found that knockdown of ALDH1A1 suppress expression of p-ERK, p-MEK and EMT features, while overexpression of ALDH1A1 reversed the effect, which were in line with previous reports (Response Figure 8D).(21-23) These results indicated that the resistance to CDK4/6i induced by RARα-ALDH1A1 axis might partially occur through the regulation downstream effectors in KRAS and EMT signaling pathways. Thus, further investigation would be valuable to clarify regulatory network between ALDH1A1 and KRAS/EMT signaling.\nWe appreciate the Review’s comments and agree that these points are important and can strengthen the overall narrative of the work. We have revised the description in the Result section and updated the Supplementary Figures as follows:\nIn Result section (Page 7): “Among these tumors, KRAS mutations were detected in 25% (4/16) of tumors, which showed the same frequency of KRAS mutations in the sensitive groups (2/8, 25%) and the resistant groups (2/8, 25%) (Supplementary Table. S2). These data are in line with the study in breast cancer patients that the KRAS mutation status was not relative to the response of CDK4/6i.(20)”\nIn Result section (Page 14): “Consistent with the findings in RNA-seq (Fig. 1), CDK4/6i-resistant cells exhibit strong KRAS signaling and EMT features (Supplementary Fig. 5L). Moreover, knockdown of ALDH1A1 suppressed the expression of p-ERK, p-MEK and EMT markers, while overexpression of ALDH1A1 upregulated their expression (Supplementary Fig. 5M). These results indicated that resistance to CDK4/6i induced by RARα-ALDH1A1 axis might partially occur through the regulation of downstream effectors in KRAS and EMT signaling pathways.”\nResponse Figure 8\nA B\nHEC-1-A HEC-1-B\nP R P R\np-ERK\n1.00 2.15 1.00 1.52\np-MEK\n1.00 2.61 1.00 2.06\nE-Cadherin\n1.00 0.18 1.00 0.15\nA\nVimentin\n1.00 9.60 1.00 8.37\nGAPDH\nC HEC-1-AR HEC-1-BR\nBI-2865 (µM) BI-2865 (µM)\n0 1 5 0 1 5\nALDH1A1\n1.00 0.99 0.97 1.00 1.09 1.01\nGAPDH\nD\nHEC-1-AP HEC-1-BP\nHE\ns\nC\niA\n-1\nL\n-\nD\nA\nH\nR\n1A1\nHE\ns\nC\niA\n-1\nL\n-\nD\nB\nH\nR\n1A1 H1\nA1\nH1\nA1\nsiNC #1 #2 siNC #1 #2 E V AL D E V AL D\nALDH1A1 ALDH1A1\n1.00 0.17 0.16 1.00 0.13 0.23 1.00 5.58 1.00 3.79\np-ERK p-ERK\n1.00 0.63 0.58 1.00 0.67 0.58 1.00 3.14 1.00 1.42\np-MEK p-MEK\n1.00 0.57 0.28 1.00 0.45 0.52 1.00 2.70 1.00 2.15\nE-Cadherin E-Cadherin\n1.00 3.08 2.74 1.00 2.72 3.27 1.00 0.40 1.00 0.20\nVimentin\nVimentin\n1.00 0.16 0.17 1.00 0.21 0.24 1.00 9.16 1.00 7.17\nGAPDH GAPDH\nResponse Figure 8. (A) KRAS mutation types in EC patients. (B) Immunoblot analysis of p-ERK, p-MEK, E-cadherin and Vimentin in indicated cells. (C) Immunoblot analysis of p-ERK, p-MEK, E-cadherin and Vimentin in HEC-1-AR and HEC-1-BR cells transfected with control siRNA or ALDH1A1 siRNA (left), or in HEC-1-AP and HEC-1-BP cells overexpressing empty vector (EV) or ALDH1A1 (right).\n11) In lines 144-146, authors state that their histological analyses showed morphological similarities between PDOs and their corresponding primary EC tumors. However, PDOs exhibited great distinction in sensitivity to CDK4/6i Abemaciclib. In its current form, the sentence implies that authors tested Abemaciclib sensitivity of tumors before the PDOs were formed and there is a difference after PDO formation. If this was done, the authors should show the data and discuss the potential mechanisms. If not, the sentence should be revised.\nResponse to Reviewer: We appreciate the Reviewer for pointing out the misleading implication in the original statement. We only tested the sensitivity of PDOs to Abemaciclib after their establishment in our study. We have revised the sentence in the updated version as follows.\nIn Result section (Page 6) “After the establishment of the PDOs, we performed histological analyses and showed that the morphology of PDOs was similar to that of their corresponding primary EC tumors (Fig. 1B). We subsequently profiled the sensitivity of 16 PDOs to Abemaciclib. However, these PDOs exhibited great distinction in sensitivity to CDK4/6i Abemaciclib (Fig. 1B-C).”\n12) For experiments with viral transduction, such as in Figure 3D, comparisons focus on control groups, but the relationship between empty vector controls and parental cells is not addressed.\nResponse to Reviewer: We thank the Reviewer for pointing out this issue. To elucidate the relationship between the empty vector control group and parental cells, we assessed the sensitivity of parental cells, as well as parental cells overexpressing EV or ALDH1A1, to Abemaciclib via colony formation assays in both HEC-1-AP and HEC-1-BP cells. The results showed no significant differences in either ALDH1A1 expression or sensitivity to Abemaciclib between parental cells and cells overexpressing EV (Response Figure 9A-C).\nA number of comparable studies in the literature omitted blank controls in their experimental design.(24-26) However, in the interest of experimental rigor and reliability, we fully agree with the reviewer’s recommendation that blank controls are of great importance, and we will ensure greater attention to this critical experimental detail in future studies.\nResponse Figure 9. (A) Immunoblot analysis of ALDH1A1 expression in HEC-1- AP/HEC-1-BP cells (control), overexpressing empty vector (EV) or ALDH1A1. Shown are representative blots from three independent experiments. (B-C) Representative images (B) and quantification (C) of colony formation assay in HEC-1-AP/HEC-1-BP cells (control), overexpressing empty vector (EV) or ALDH1A1, which were treated with Abemaciclib (0.5 μM). Data are shown as mean ± SD of three independent experiments and analyzed by One-way ANOVA with Dunn’s multiple comparisons test. ns, not significant, *p < 0.05, **p < 0.01, ***p < 0.001.\n13) In Figure 4H, the scatter diagrams show the IC50 of Palbociclib in GDSC database between high and low activation of vitA metabolic, RA signaling, RA metabolic and RA nuclear binding. This is particularly interesting, considering CDK4/6i such as palbociclib are considered candidate drugs to be used in combination for differentiation therapy in cancers such as acute myeloid leukemia and neuroblastoma (PMID: 37734383"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have revised their manuscript in response to the reviewers’ suggestions, and several aspects have improved. Nonetheless, some points remain that require clarification or further detail.\nResponse to comment 1 and 2:\nThe methodological description used to determine the IC50 values in Fig. 1C and the Response Fig. 1A requires some further clarification in the Methods section. Based on the graphs, we assume that PDOs were exposed to the drug concentrations indicated on the y-axis, however this should be stated somewhere. In addition, we could not find information on “MAcreGel” and Mingao Biotech.\nWe appreciate the inclusion of the new data on Palbociclib and Ribociclib. It would be important to clarify whether these experiments were performed in parallel with the Abemaciclib treatments. The striking similarity of the responses raises the question whether the PDOs display a general, non-specific sensitivity or reduced survival capability rather than a selective response to the individual CDK4/6 inhibitors. It may be worth testing additional more monospecific CDK inhibitors, since this could help disentangle compound specific from general CDK related effects. Some work already exists that investigates the synergistic effects of ATRA with CDK inhibitors of different specificity, including ryuvidine and more selective CDK4 inhibitors.\nResponse to comment 5:\nRegarding the ATRA concentrations used, we would like to note that pharmacokinetic studies in patients with APL indicate that the clinically applied doses result in peak plasma levels of approximately 1 µM ATRA. In our experience, concentrations as low as 10 nM ATRA can already influence RAR reporter assays. In the manuscript cited by the authors stating: “10–30 µM RA was used in HL60 and NB4 cells (11)”, one can see that although concentrations up to 30 µM were tested, 1 µM already caused the major effects on cell viability.\nWe therefore asked whether a concentration of 20 µM is still specific for ATRA mediated activation and transmitted by retinoic acid receptors or whether such a dose might induce additional, non specific effects, including increased expression of ALDH1A1.\nIs a higher concentration required for RAR reporter activity in this particular cell type, and what would be the mechanistic explanation? How can peak plasma levels of 20 µM ATRA be reached in patients? How do other cell lines respond? Are the observed effects cell type specific? If so, we would suggest reflecting this in the title of the manuscript.\nResponse to comment 8:\nIt may be helpful to rename the primer sets (P1–4) to improve clarity. In addition, ALDH1A1 should be mentioned in the figure.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "The authors have made extensive modifications to address concerns raised and in my opinion have addressed critical issues as needed.", "reviewer_4": "The authors have satisfactorily addressed the concerns raised by this reviewer in the original review."}, "author_response": "Response Letter to Research Article-NCOMMS-25-43591A\nKEY:\nBLACK TEXT: Editor comments\nBLUE TEXT: Author responses\nRED TEXT: Changes made to text in manuscript\n*********************\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nIn this study, Chen and colleagues investigated the effects of abnormal ALDH1A1 expression on CDK4/6i resistance in hormone receptor-positive breast and endometrial cancers. The researchers demonstrate that high ALDH1A1 levels and their downstream effects contribute to CDK4/6i insensitivity.\nResponse to Reviewer: We appreciate the positive comments from the Reviewer. We believe that we have fully addressed all the concerns in the point-by-point responses below.\nWe have some comments:\n1) The methodology used for the sensitivity profiling of abemaciclib against 16 PDOs from EC is unclear. How do other CDK4/6 inhibitors such as Palbociclib and Ribociclib compare? Is the response CDK4- or CDK6-specific?\nResponse to Reviewer: We appreciate the constructive suggestions from the Reviewer. We have amended the description of methodology used for the sensitivity profiling of CDK4/6 inhibitors in the Method section (Page 23-24): “For the cell viability assay, organoids were harvested and dissociated using TrypLETM Express (Gibco, 12604013) for 10 min at 37 ºC. The dissociated PDOs were resuspended in ice-cold MAcreGel (Mingao Biotech) and seeded into a 96-well plate (Corning). After allowing the MAcreGel drops to solidify for 15 min at 37 ºC, PDOs were overlaid with endometrial cancer organoid medium containing the indicated doses of drugs. Five days after drug treatment, cell viability was measured using the Cell Viability ATP Assay Kit (Mingao Biotech) according to the manufacturer’s instructions on a Spark microplate reader (Tecan).”\nAs for the sensitivity of PDOs to other CDK4/6 inhibitors, we have conducted cell viability assay using other clinically used CDK4/6 inhibitors (e.g., Palbociclib, Ribociclib) to assess the half-maximal inhibitory concentration (IC50) values of 16 PDOs (Response Figure 1A). The results showed that PDOs exhibit comparable sensitivity to both Palbociclib/Ribociclib and Abemaciclib, suggesting that the sensitivity of PDOs to CDK4/6 inhibitors stems from their CDK4/6-specific on-target effect rather than a unique response to a single inhibitor. We have appended Response Figure 1A to Supplemental Figure 1 with the description (Page 6): “In addition, the response to CDK4/6 inhibition in PDOs was confirmed with other CDK4/6 inhibitors (Palbociclib and Ribociclib) (Supplementary Fig. 1A-B).”\nAs the Reviewer mentioned whether the response of CDK4/6i was CDK4- or CDK6- specific, we analyzed the expression levels of CDK4 and CDK6 in the EC tumors from RNA-seq data. The results showed no statistical difference in expression levels of CDK4 or CDK6 between sensitive and resistant groups to CDK4/6i (Response Figure 1B), indicating that the response to CDK4/6i may be independent of CDK4 or CDK6.\nResponse Figure 1\nB\nA\nPalbociclib Ribociclib ns\nI\nC 5 0 (\nM)\nµ\n1\n2\n1\n5\n5\n0\n0\n.\n.\n.\n.\n0\n0\n0\nR\ne\ne\nn\nsi\ns\ns\ni\nt\nt\na\niv\nn\ne\nt\nI\nC 5 0 (\nM)\nµ\n2\n1\n1\n5\n0\n5\n0\n.\n.\n.\n.\n0\n0\n0\nR\ne\ne\nn\nsi\ns\ns\ni\nt\nt\na\niv\nn\ne\nt\nlevel\n(counts)\nD\nK4\n2\n4\n6\n8\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\nlevel\n(counts)\nD\nK6\n1\n2\n3\n4\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\n0\nns\nC C\n0.0\nP D\nO81\nP D\nO58\nP D\nO56\nP D\nO82\nP D\nO53\nP D\nO57\nP D\nO55\nP D\nO78\nP D\nO79\nP D\nO77\nP D\nO65\nP D\nO67\nP D\nO80\nP D\nO83\nP D\nO68\nP D\nO76\n0.0\nP D\nO81\nP D\nO58\nP D\nO56\nP D\nO82\nP D\nO53\nP D\nO57\nP D\nO55\nP D\nO78\nP D\nO79\nP D\nO77\nP D\nO65\nP D\nO67\nP D\nO80\nP D\nO83\nP D\nO68\nP D\nO76\nRelative\nS\n0\nensitive Resistant\nRelative\nS\n0\nensitive Resistant\nResponse Figure 1. (A) Growth-inhibitory effect of 16 PDOs treated with different doses of Palbociclib and Ribociclib. The y-axis shows the IC50 values. (B) The counts of indicated genes in EC tumors from RNA-seq data.\n2) What dose is considered the threshold for Abemaciclib resistance in vitro? In Supplementary Figure 2, the authors show response curves from a wide variety of endometrial cancer cells. Indicating IC50 values would increase the interpretability of the results.\nResponse to Reviewer: We thank the Reviewer for pointing out this issue. We defined the Abemaciclib-resistant cell lines using an IC50 cut-off value of 1.0 μM. We have updated the IC50 values of Abemaciclib for the indicated cells and amended them in the revised version as follows.\nResponse Figure 2\n1. A 2 HEC-1-AIC50=0.S26N5G4-M B\nHEC-1-BIC50=0.M41F5E3-296\n1.2 RL95-2 IC50=0.K61L2E8\ne\nati o n i n d ex 0 0 1 . . . 6 9 2 Pr olif e ati o n i n d ex 0 0 0 . . . 3 6 9 Pr olif e ati o n i n d ex 0 0 0 . . . 3 6 9 H H R A E N E L9 C C 3 5 - - C 1 - 1 2 - - A A B A I K M S s N L N h F E i 3 G E k a - C - 2 M w A 9 a 6 I I I I I C C C C C 5 5 5 5 5 0 0 0 0 0 = = = = = 1 1 1 3 4 . . . . . I0 3 9 7 5 s5 6 3 3 6 h6 2 2 1 1 ikawa 0 0 0 0 1 0 0 0 0 1 . . . . . . . . . . 00 0 3 6 9 2 0 3 6 9 2 ..0011 00 Pr olif er ati o n i n d e x ..11 0 0 0 0 1 . . . . .\n0\n0 3 6 9 2\n.01\n.1\n0\n- - 0 1 1 1 . . 0 0 0 0 1 0 0 0 0 13 0 - - 4 A A . . . . . . . . . . 5 00 0 3 6 9 2 0 3 6 9 20 7\n0\n11\n0\n0\n0 3 6 9 2\n.01\n.\nEE\n1\n1\n1\n0 0 C C =\n0\n= 0 - - 2 1 1 . . 6 9 - - 5 B B 8\n1\n5 4 P R 4\n00\nolif 0.0 Abem(µM) Abem(µM)\nPr\n0.3\n0.0\n0\n1\n.0\n0.1\nAbem\n1\n(uM)\n10 100\n0.01 0.1 1 10 100\nAbem (uM)\n0.0\n0.01 0.1 1 10 100\nAbem (uM)\nResponse Figure 2. (A) EC cell lines were treated with the indicated concentrations of Abemaciclib. The number of viable cells was measured at 96 hours. Data are represented the mean of three independent replicates. The IC50 values of Abemaciclib for EC cell lines after exposure durations of 96 hours are shown. (B) Parental and resistant cells treated with the indicated concentrations of Abemaciclib. The number of viable cells was measured at 96 hours. Data are represented the mean of three independent replicates. The IC50 values of Abemaciclib for HEC-1-AP, HEC-1-AR, HEC-1-BP and HCE-A-BR after exposure durations of 96 hours are shown.\n3) Quantification and replicates of analyses in Figure 3, as the effects of knockdown, overexpression of ALDH1A1 or its inhibition, and others throughout the manuscript seem to be missing. Moreover, in the manuscript, considerable amount of conclusions were drawn from visual data, such as colony formation assays and immunofluorescence / immunohistochemistry. Although they might still convey the main idea, the quality of the visuals is low, making a through revision challenging. It is essential that the manuscript includes either significantly improved visuals or clear, detailed quantitative data to support the findings. As an example, in Figure 2K, although the difference is clear, the cells in the resistance group look rather overexposed, very likely due to image quality. Additionally, immunofluorescence images should include a legend indicating the color assigned to each marker.\nResponse to Reviewer: We thank the Reviewer for pointing out these issues. We have quantified the intensity of all the bands in the Western Blot experiments using ImageJ/Fiji software and labeled the normalized relative gray values in the updated version of the manuscript. In addition, we have performed statistical analysis of all colony formation assays and their corresponding replicate experiments using ImageJ/Fiji software. We have also updated the images to high-resolution to correct those with suboptimal exposure in all image data (Response Figure 3). Furthermore, we have added color legends to immunofluorescence images to indicate the corresponding colors for each marker (Response Figure 4). The updated images and figures are provided in Response Figure 3, and we have amended the previous Figure 3 with this Response Figure 3 in our manuscript.\nIn Method section (Page 22): “The colonies were quantified using ImageJ/Fiji software (https://fiji.sc/).”\nIn Method section (Page 24): “The blots were quantified using ImageJ/Fiji software (https://fiji.sc/).”\nIn Figure Legend section (Page 41): “Representative immunofluorescence (IF) images of HEC-1-AP, HEC-1-AR, HEC-1-BP and HEC-1-BR cells stained with ALDH1A1, scale bar = 10 μm, blue color shows DAPI signal, green color shows ALDH1A1 signal. (left). The intensity of ALDH1A1 in parental and resistant cells were quantified and analyzed by t test. ***p < 0.001. (right).”\nResponse Figure 3\nA B C\nDMSO Dis HEC-1-AR HEC-1-BR\nsiNC siALDH1A1 DMSOAbem Dis Comb Abem Comb Abem - + - + - + - +\nH E C-1- A R\nH E C- 1- B R\nA\nA\nL\nL\nG\nG\nD\nD\nA\nA\nH\nH\nP\nP\n1\nD\nA\nA\nH\nH\n1\n1\ns 1\n1\niN .\n.\n0\n0\n0\nsi\n0\n.\n.\n#\n3\n2\n6 1\nDH\n0\n0\n.\n.\n1\n2\n4 #\n9 2\nAbe m D M S O\nAb e m siNC\nsiA\n#\nL\n#\nA\n2\ncolony ar ea\nRelative colony area\nA\nRelative colony\narea\n0 1 1 0\n0 1 1 0\nb\n0 0 1 1\n0 0 1 1\ne\n. . . .\n. . . .. . . .\n. . . . 5 0 5 0\n5 0 5 00 5 0 5\nm\n0 5 0 5\nC o m b\nH\nH\nM\nE\nE\nIs M\nC\nC\nF\nT\nh E\n-\nC\n-\n4\ni\n1\n1\nk -\n-\n-\nA\n7 w 9\nR\nR\nR\nR\n6 a Relative\ncolony ar ea0 0\n1\n0\n0 0 1 00 0 0 1\n0 0 0\n.\n.\n. . . .. . . .\n. . .\n.\n4 8\n2\n0\n4 8 2 00 4 8 2\n0 4 8\nM\nC\nC\n-1\nF\n-\nR\nR\nM\nHE\nF E\nC\n-\n-\n2\n1\n9\n- B\nIs\nT\nh\n4\n7\na\nD\nw\nR\na C\nc\nc a\nl\nl\ne\nG e s\n-\np - A\nP P\nC a P\nA A\na s\nD\nD e\nR R\ns\ni\nH\nP P\n3 3 1 1\n1 1\n. .\n. .\n0 0\n0 0\n-\n0 0\n0 0\n1 1\n1 1\n. .\n. .\n0 0\n3 2\n-\n5 6\n3 0\n1 1\n1 1\n. .\n. .\n6 1\n2 1\n+\n1 1\n7 8\n2 1\n2 1\n+\n. .\n. .\n9 1\n7 3\n6 9\n3 4\n1 1\n1 1\n. .\n. .\n0 0\n0 0\n-\n0 0\n0 0\n0 0\n0 0\n. .\n. .\n9 9\n9 7\n-\n5 2\n5 2\n1 0\n0 1\n. .\n. .\n3 9\n8\n+\n0\n5 4\n4 4\n1 1\n1 1\n+\n. .\n. .\n6 0\n2 2\n3 6\n1 2\nH E C- 1- B P H E\nC-1- A P\nD\nA\nA\nL\nL G G\nD\nD A A\nH\nH P P\n1\nA\nA H H\n1\n1 1\n.\n.\n0\n0\nt\n0\nr\n0\nl\n1\n.\n.\n0\n9\n3\n6\n.\n.\nL\n1\n4\nA A\nE\nA A V\nV\n1 1\n0 0.1 0\nA\n.2\nbem\n0\n(\n.\nµ\n1 2\nR el ati v e c ol o n y\nar e a\nRelativ e c\nolony ar ea Relative\ncolony\narea\n0 0 1 1\n0 1\n1\n0 0 1 1 0 0\n0 1\n1\n. .\n.\n. . . . .. . . ..\n. .\n.\n5 0\n5\n0 5 0 5 00 5 0 50\n5 0\n5\n0.\nE\n0 0 0 n\nn\nV\n. .0 s\ns\n0 0 . 1 0 0\nn\n*. .0\ns\nA 1 1.2 b 0\nn\n0 *\nA\ne .\ns\n* 0. 2 m 2\nL\n.5 0\nD\n*\n0 *\n(\n*\n. 1µ .\nH\n5\n*\n5. M 0 1 *\n1\n*\n1 *\n*\n. )2\nA\n0 . *\n*\n.00\n1\n2 *\n*\n2 . *\n*\n0 . * 0\nE\nCo m bin ation\nindex\n0 1\n1\n00 0 1\n1\n. .\n.\n.. . .\n.\n5 0\n5\n00 5 0\n5\n0.0\ndBE\nT\n..44\nE\n0\nd\n..\nR\nB\nE\nr\nT\na\n5\nct\n7\n1 i 1 o ..2 n 0 0 1\n1\n2 . . .\n.\n0 5 0\n5\n0 a . f 0 f\nd\ne\nB\nc 00 t\nE\ne ..22\nT\nd\n.4\nC-\n1\n0.\nQ\n.\n-\n0.8 11.0.0\nF G H\nHEC-1-AR HEC-1-BR\nH E C-\n1- B\nR H E C- 1- A R Abe m\nAbe m\nD M S O\nD M\nS O D M S O d B E T 6 d B E T 57 d B E T 1 J Q 1 Relative colo ny ar ea 0 0 1 0\n0\n0\n1\n00 0\n0\n1 0 0 0 1 . . . .\n.\n.\n.\n.. .\n.\n. . . . . 4 8 2 0\n4\n8\n2\n00 4\n8\n2 0 4 8 2 D B E M T S i O A C b o e m m b M Is\nM\nF h E\nC\nik -\nF\nK a 2 w\nR\nE a 6 D M S O Abe m d B E T6 Co m b Relativ e c olony area 0 0 1\n.0\n0 0 1 4 8 2\n0.\n. . .\nK 0\n4 8 2\nLE M F E\nD d\n-\nB\nS\ns\nT\nh\nO\ni\n6\nk a wa\nA C\nM C\nb o\nF\ne m\n7\nm\nR\nb C c\nc\na l\nl\ne\nG\ne\ns d -\np\n-\nA\nP A P\nC\nB a\nP\nA b A\na\ns E e\nD\nR e\ns\nR T m\nH\nP P 6\n3\n3 1 1 1\n1\n. . .\n.\n0 0 0\n0\n- - 0 0 0\n0\n1 1 1\n1\n. . .\n.\n+ 3 0 0\n1\n- 3 7 0\n2\n1 1 1\n1\n. . .\n.\n+ - 2 1 0\n2\n6 4 6\n1\n0 3 0\n3\n+ + . . .\n.\n7 4 8\n7\n3 8 3\n1\n1 1 1\n1\n. . .\n.\n0 0 0\n0\n- - 0 0 0\n0\n1 1 1\n1\n. . .\n.\n1 2 1\n0\n+ - 6 5 4\n4\n1 1 1\n0\n. . .\n.\n+ - 0 4 1\n9\n0 0 9\n1\n0 2 1\n2\n+ + . . .\n.\n8 5 0\n1\n9 7 3\nHEC-1-AR PDO67 Dis Abem+dBET6\ndBET6 Abem+Dis\nALDH1A1 d 0 BE 0 T . 6 5 (µ 1 M) 0 JQ 0 1 . 1 (µM 0 ) .2 DMSO Abem Dis Abem+Dis dBET6Abem+ dBET6 1 0 m ) µ 4 2 10 1 0 0 1 *** ***\n1.00 0.70 0.51 1.00 0.63 0.48\na\n(\nGAPDH\nAr\n0.1\nHEC-1-BR 0.01\ndBET6 (µM) JQ1 (µM) PDO76\nALDH1A1\n0 0.5 1 0 0.1 0.2 DMSO Abem Dis Abem+Dis dBET6Abem+ dBET6\nm\n)2 100\nµ\n1.00 0.77 0.73 1.00 0.81 0.41 04 10\nGAPDH e a ( 1 1 *** ***\nAr\nJ HEC-1-AR HEC-1-BR 0.1\nsiBRD4 siBRD4 PDO77\nsiNC #1 #2 siNC #1 #2 DMSO Abem Dis Abem+Dis dBET6Abem+ dBET6 100\nALD B H R 1 D A 4 1 1\n1\n.0\n.0\n0\n0\n0\n0\n.\n.\n4\n6\n0\n9 0\n0\n.\n.\n5\n2\n4\n5 1\n1\n.0\n.0\n0\n0\n0\n0\n.\n.\n1\n5\n5\n5 0\n0 #\n.\n.\n4\n2\n3\n2 1\ne a\n( 1 0 m ) µ 4 2 10\n1\n*** ***\nGAPDH\nAr\n0.1\nResponse Figure 3. (A) Immunoblot analysis of ALDH1A1 in HEC-1-AR and HEC- 1-BR cells transfected with control siRNA or ALDH1A1 siRNA. Shown are representative blots from three independent experiments. (left). Representative images and quantification of colony formation assay in cells treated with siNC, siALDH1A1, Abemaciclib (0.5 μM), or combination. Data are shown as mean ± SD of three independent experiments and analyzed by One-way ANOVA with Dunnett’s multiple comparisons test. ***p < 0.001. (right). (B) Representative images and quantification of colony formation assay in cells treated with DMSO, Abemaciclib (Abem) (0.5 μM), Disulfiram (Dis), or combination in the indicated cells. Data are shown as mean ± SD of three independent experiments and analyzed by One-way ANOVA with Dunnett’s multiple comparisons test. ***p < 0.001. (C) Immunoblot analysis of PARP, cleaved-PARP, Caspase 3 and cleaved-Caspase 3 in HEC-1-AR and HEC-1-BR cells treated with DMSO, Abemaciclib (0.5 μM), Disulfiram (1 μM for HEC-1-AR and 2 μM for HEC-1-BR), or combination for 72 hours. Shown are representative blots from three independent experiments. (D) Immunoblot analysis of ALDH1A1 expression in HEC- 1-AP/HEC-1-BP cells (control), overexpressing empty vector (EV) or ALDH1A1. Shown are representative blots from three independent experiments. (left). Representative images and quantification of colony formation assay in HEC-1- AP/HEC-1-BP cells overexpressing empty vector (EV) or ALDH1A1, which were treated with Abemaciclib (0.5 μM). Data are shown as mean ± SD of three independent experiments and analyzed by unpaired t-test. ns, not significant, *p < 0.05, **p < 0.01, ***p < 0.001. (right). (E) CI analysis of Abemaciclib and BRD4 inhibitors in resistant cell lines. Data are represented the mean of 3 biological replicates. CI greater than 1 is defined as antagonism; CI less than 1 is defined as synergy. (F) Representative images and quantification of colony formation assay in HEC-1-AR and HEC-1-BR cells treated with DMSO, Abemaciclib (0.5 μM), dBET6 (0.5 μM for HEC-1-AR and 2 μM for HEC-1-BR), dBET57 (2 μM for HEC-1-AR and 10 μM for HEC-1-BR), dBET1 (2 μM for HEC-1-AR and 5 μM for HEC-1-BR), JQ1 (0.2 μM), or combination. Data are shown as mean ± SD of three independent experiments and analyzed by One-way ANOVA with Dunnett’s multiple comparisons test. ***p < 0.001. (G) Representative images and quantification of colony formation assay in KLE, MFE-296 and Ishikawa cells treated with DMSO, Abemaciclib (0.5 μM), dBET6 (0.25 μM for KLE, 0.5 μM for MFE-296 and Ishikawa, 0.1 μM for MCF7R), or combination. Data are shown as mean ± SD of three independent experiments and analyzed by One-way ANOVA with Dunnett’s multiple comparisons test. ***p < 0.001. (H) Immunoblot analysis of PARP, cleaved-PARP, Caspase 3 and cleaved-Caspase 3 in HEC-1-AR and HEC-1-BR cells treated with DMSO, Abemaciclib (0.5 μM), dBET6 (0.5 μM for HEC-1-AR and 2 μM for HEC-1-BR), or combination for 72 hours. Shown are representative blots from three independent experiments. (I) Immunoblot analysis of ALDH1A1 expression in HEC- 1-AR and HEC-1-BR cells treated with BRD4 inhibitors (dBET6 and JQ1) for 72 hours. Shown are representative blots from three independent experiments. (J) Immunoblot analysis of ALDH1A1 expression in HEC-1-AR and HEC-1-BR cells transfected with control siRNA or BRD4 siRNA for 72 hours. Shown are representative blots from three independent experiments. (K) Representative images of PDO67, PDO76 and PDO77 treated with DMSO, Abemaciclib (1 μM), Dis (2 μM), dBET6 (2 μM), or combination after 10 days culture. The experiment was performed in three biological replicates. Scale bar = 200 μm. (left). The matching scatter diagram shows surface area of organoids, and one dot represents one organoid. Data are shown as mean ± SD and analyzed by Kruskal-Wallis with Dunn’s multiple comparisons test. ***p < 0.001. (right). Source data of (A), (C-F), (H-K) are provided in Source Data file.\nResponse Figure 4. (A) Representative immunofluorescence (IF) images of HEC-1- AP, HEC-1-AR, HEC-1-BP and HEC-1-BR cells stained with ALDH1A1, scale bar = 10 μm, blue color shows DAPI signal, green color shows ALDH1A1 signal. (left). The intensity of ALDH1A1 in parental and resistant cells were quantified and analyzed by t test. ***p < 0.001. (right).\n4) The ChIP-seq data and others don’t seem to be available for review. Has it been uploaded to a repository? No quality metrics seem to be provided. What where the controls in these experiments?\nResponse to Reviewer: We thank the Reviewer for pointing out these issues. ChIP-seq data generated in this study have been deposited in the Genome Sequence Archive in the National Genomics Data Center, China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA-Human: HRA008718). Following online publication of this study, the data will be accessible via https://ngdc.cncb.ac.cn/gsa-human, which has been described in the Method section. During the peer review stage, we provided a data access link in the nature portfolio Reporting Summary (https://ngdc.cncb.ac.cn/gsa-human/s/udT1vJ7Y).\nFor the quality control (QC) of ChIP-seq data in our study, we ensured sufficient sequencing depth (≥20 million uniquely mapped reads per sample) to capture specific binding events. Samples were excluded with low mapping rates (<80%). Reads with duplicates were removed by rmdup for the subsequent analysis. Significant peaks were called using MACS2 (p < 0.05). For the control groups of each experiment, we included critical input DNA controls: genomic DNA from the same cell/tissue sample (without ChIP enrichment) was sequenced to distinguish specific protein-DNA binding from background genomic noise, and we used it to normalize peak signals in downstream analysis.\nWe have provided detailed information for the quality control and analysis process in the Method section (Page 28):\n“Cells were cross-linked with 1% formaldehyde and quenched with 0.125 M glycine. The cross-linked cells were lysed in the lysis buffer (50 mM Tris-HCl [pH 8.0"}]

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