Hybrid In Vivo Breast Cancer Model Reveals Transcriptomic Insights into Cancer Progression with Age

doi:10.21203/rs.3.rs-7871000/v1

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Hybrid In Vivo Breast Cancer Model Reveals Transcriptomic Insights into Cancer Progression with Age

https://doi.org/10.21203/rs.3.rs-7871000/v1

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Aging is a key risk factor for breast cancer; however, the independent effects of the aged extracellular matrix (ECM) remain understudied. To address this, we developed a novel hybrid in vivo model that enables the independent investigation of age-related ECM influences on breast cancer development and progression. To examine the effects of genes known to be enriched in a cancerous and aged microenvironment, we first seeded normal or stable knockdown cells onto decellularized ECM (dECM) from aged murine mammary glands, and implanted them into young Rag1^−/− mice. We identified LOX as a principal driver of tumor progression, with knockdown reducing invasion and stress-related pathways. To further isolate the independent influence of ECM aging on tumor growth, normal MCF10A cells were seeded atop young or aged matrices and implanted. Aged tumors exhibited significantly greater volume and a larger tumorigenic region when compared to young, with single-cell RNA sequencing revealing enrichment of inflammatory and invasive genes. Together, these findings identify LOX as a driver of tumor progression and a potential therapeutic target and demonstrate that the aged ECM alone is sufficient to promote breast cancer progression.

Breast cancer incidence increases with age, where it also disproportionately impacts older patients who experience worse outcomes.^1–4 Despite advances in screening and treatment, it remains a prevalent and metastatic disease with an ever-increasing incidence rate, primarily attributed to the growing aging population and the associated biological mechanisms that drive changes in aged breast tissue properties.^1,2,5,6

Historically, aging research has focused on genetic and cellular-level changes – such as genomic instability and senescence – many of which overlap with the hallmarks of cancer.⁷ This perspective has provided key insights into how aging influences cancer initiation and progression, leading to breakthroughs in areas such as oncogene activation, tumor suppressor loss, and DNA repair deficiencies in aged tissues.^7,8 Other major changes observed linking aging and cancer have been the extracellular matrix (ECM) and its composition and mechanics, along with immune system dysregulation. The ECM is a dynamic structure that provides mechanical and biochemical cues to cells. As the body ages, ECM naturally stiffens due largely to the crosslinking of collagen. This stiffening of the matrix influences how cancer cells proliferate, invade, and respond to therapy. Additionally, both aging and cancer lead to compositional changes that create an imbalance in the normal environment, thus promoting invasion and metastasis.^{9 10–14} In addition to mechanical and compositional changes, the immune system also becomes dysregulated within both the aged and cancerous environment through a process of immunosenescence.^15,16 In this, some immune cells, such as macrophages, can become upregulated and tumor-associated, where they begin to promote cancer-permissive factors that promote cancer cell invasion.^17–20 Several recent studies linking aging and cancer have shown that the cellular population (epithelial, stromal, and immune cells) shows a shift in proportion and cellular identity, influencing cancer risk and the aged microenvironment.^21,22

While aging is a well-established risk factor and previous studies serve as comprehensive sources, the independent effects of the aged extracellular matrix (ECM) on cancer predisposition, onset, and progression remain understudied.^23,24 While recent studies have looked into cellular population shifts due to aging, they overlook how age-related changes in the ECM directly influence cancer development, and those that do are unable to decouple other systemic aging effects, such as immunosenescence. Addressing this gap requires models that can isolate the ECM’s independent contributions to tumor growth. While most breast cancer age-related models rely on comparisons between young and aged animals, the length of time required to develop an appropriately aged animal that mimics human age is substantial, increasing the financial and material costs.^25–27 Additionally, these studies typically do not decouple the age-related alterations in the ECM from other systemic differences, including cellular senescence and immunosenescence, also known to increase susceptibility to cancer.²⁸ These lead to paradoxical results where studies performed on aged mice have shown varied results, with some showing that with age, the ECM changes can lead to more invasive and prone to distant metastasis²⁹, while others show that older patients have a slower tumor growth and reduced metastasis.³⁰ Understanding these mechanisms, as well as the differences between in vitro and in vivo environments, is critical for identifying new therapeutic targets and evaluating the influence and effectiveness of existing and future treatments.

We have previously shown in vitro that the aging ECM harbors key biochemical, physical, and mechanical cues leading to invasive and cancer-like behavior in both normal and cancerous mammary epithelial cells. These findings highlight the need to examine these ECM driven effects in a more physiological setting.¹³ To address current limitations and better understand the mechanisms of breast cancer progression and metastasis, and to enhance recent studies^13,21,22, we developed a hybridized model that allows for the independent study of the aged ECM and its role in breast cancer development and progression. We first generated stable knockdown MCF10 DCIS.com cell lines of genes known to be enriched within both the aged matrix and breast cancer – LOX, IL1B, and P4HA. In vivo studies showed that LOX and IL1B contributed to a decrease in tumor growth, while P4HA1 knockdown resulted in an increase in tumor volume. When comparing the LOX knockdown group to the control group in vivo, scRNA sequencing results showed that knockdown-suppressed genes are responsible for mostly tumor progression and metastasis. Moreover, LOX knockdown inhibited gene clusters responsible for tumor-promoting immune response, suggesting roles in the immune response. After validating the differential response within our model, we moved to assess the effect that ECM alone influences on cancer growth and development. We find that the aged ECM alone was sufficient to enhance tumor growth within the same-host model. Transcriptomic analysis through single-cell sequencing further showed that the aged ECM upregulates genes related to cancer cell initiation, progression, and metastasis while downregulating genes related to ECM stability, cell signaling, and cancer suppressive immune response.

Knockdown of LOX and IL1B Suppresses Tumor Growth on Aged ECM

To investigate ECM-mediated tumor progression with age, we identified several targets known to literature to be upregulated within the aged environment and related to tumor growth and metastasis: IL1B^31,32, P4HA1^33,34, and LOX^13,35. We first generated and validated stable knockdown cell lines (shIL1B, shLOX, and shP4HA1) from MCF10A DCIS.com cells (Fig. 1A, 1C, 1E). From this, we engineered a novel hybridized mouse model by utilizing decellularized ECM (dECM) derived from aged (24–48 months) murine mammary glands of C57BL/6 mice and implanted them into young Rag1^−/− mice. Before implantation, dECM were seeded with a control (shNT) and one of the knockdown cell lines. The mice were split into two groups with reversed implantation locations to eliminate the influence of environmental variation on tumor growth between the left and right sides of the mammary fat pad (Fig. 1A). This experimental design allowed for a direct comparison of the tumor growth between control and knockdown tumors under controlled conditions. Each line was seeded onto aged dECM and implanted into young Rag1^−/− mice mammary fat pad, with matched shNT controls implanted on the opposing side. After 6 weeks, mice were sacrificed, and tumors were excised for analysis. shLOX tumors were significantly smaller than shNT (n = 9, p = 0.0135). IL1B knockdown resulted in a non-significant decrease in tumor volume compared to control (Fig. 1D). Interestingly, P4HA1 knockdown increased tumor volume compared to control, suggesting a potentially context-dependent role in the aged ECM (Fig. 1F)

LOX Knockdown Suppresses Pro-Metastatic Cell State and Gene Expression

As LOX was found to be the most impactful and consistent on tumor development and growth, we next processed shLOX and their respective shNT tumors for scRNA-sequencing (scRNA-seq) analysis to further study the differences at a transcriptomic level. Before scRNA-seq processing, CD11b⁺ (myeloid cells) and CD11b⁻ (tumor and stromal cells) were separated using magnetic bead enrichment and sequenced separately. scRNA-sequencing was performed on both the tumor (Fig. 2, Table S1) and immune (Fig. S1, Table S2) cells.

Analysis of the tumor cells revealed 4 distinct clusters and their respective marker and significant genes (Fig. 2A-C) – Cluster 0: stressed, Cluster 1: ECM/remodeling, Cluster 2: invasive, Cluster 3: cancer-associated fibroblasts (CAFs). LOX knockdown led to reduced relative proportions of Clusters 0 and 2 and an increase in Cluster 1. Cluster 0 exhibits high expression of LINC01091 and HSD11B1-AS1, both of which are long noncoding RNAs, the latter having known regulatory roles in cancer.^36–40 In addition, high expression of ABCC2 and TMCC3 have been linked to chemoresistance and stress response.^41–43 Cluster 1 expresses markers such as MUC5B, MUC16, and MMP7, indicating that this cluster has ECM remodeling and mucin production roles.^44–47 Cluster 2 expresses several invasion and migration related genes, such as RASA3 and MMP3,^48–50 indicating a pro-metastatic program. In addition, this cluster is marked by immune evasion and hypoxic response genes such as HLA-F-AS1 and HIF1A-AS3, respectively.^51–55 Cluster 3 CAFs are marked by ECM genes such as COL5A1, AEBP1, and FBLN2. Within the shLOX group, the relative frequencies of Clusters 0 and 2 decreased, whereas the relative frequency of Cluster 1 increased (Fig. 2B). This shift suggests that after intracellular LOX knockdown, the transcriptional programs with roles in tumor progression and metastasis are suppressed, whereas those responsible for ECM structure are enhanced.

LOX Knockdown Alters Oncogenic and Hallmark Signaling Pathways

The transcriptional differences between shLOX and shNT were further examined through differential gene expression (DEG) analysis (Table S3). Genes such as HMGA2⁵⁶ and KCNMA1⁵⁷, which have been shown to be linked to various cancer types as biomarkers for cancers initiation and invasion, were significantly downregulated on shLOX tumors (Fig. 2D). Gene Set Enrichment Analysis (GSEA) of the Oncogenic Gene Set (Table S4-S5) showed downregulation of JAK, Wnt, and YAP signaling – mechanosensitive pathways which are often dysregulated in cancerous environments.^58–60 BRCA1⁶¹ and ATM⁶² – DNA repair pathways – were also downregulated. Interestingly, KRAS-related gene-sets were elevated in both shNT and shLOX groups, suggesting that LOX may play a dual role in modulating this pathway in the aged microenvironment (Fig. 2E). Further analysis with the Hallmark Gene Set (Table S6-S7) demonstrated suppression of epithelial-to-mesenchymal-transition (EMT) and inflammatory pathways while showing upregulation of pathways related to cholesterol homeostasis and reactive oxygen species (ROS) (Fig. 2F). Together, these results indicate that the knockdown of LOX is capable, within the aged environment, of suppressing pathways beneficial for tumor proliferation, invasion and stress response, supporting its role as a key effector of matrix-induced tumor progression.

Aged ECM Drives Tumor Growth In Vivo

With observed differential tumor growth between control and knockdown cell lines in our model, we next aimed to isolate the independent contribution of the aged ECM to tumor progression. Rather than assessing with differential cell lines, we utilized decellularized ECM (dECM) derived from young (2–4 months) and aged (24–48 months) murine mammary glands of C57BL/6 mice and implanting them into young Rag1^−/− mice. Before implantation, dECM were seeded with normal MCF10A DCIS.com cells. The mice were split into two groups with reversed implantation locations to eliminate the influence of environmental variation on tumor growth between the left and right sides of the mammary fat pad (Fig. 3A-B). This experimental design allowed for a direct comparison of the tumor growth in young vs. aged environments under controlled conditions. After, tumors on the aged dECM were found to be significantly larger, with an average volume of 327 ± 132 mm³, compared to those on the young dECM, which averaged 162 ± 60 mm³ (p = 0.0032, n = 10) (Fig. 3C-D). Further histological analysis with hematoxylin and eosin (H&E) staining confirmed an approximate 14% average increase in tumorigenic region in tumors from the aged sides (Fig. 3E-F), confirming that the aged ECM independently promotes tumor growth and tissue invasion.

Aged ECM Enriches Invasive and Inflammatory Phenotype

To better understand the underlying transcriptomic mechanisms and cell populations contributing to tumor growth and development between the groups, scRNA-seq was performed on the excised tumors (Fig. 4, Fig. S2, and Table S8). Transcriptomic analysis revealed 12 distinct cell clusters and their defining marker genes (Fig. 4A-C). These clusters exhibited a differential proportional distribution between tumors grown in young vs. aged ECM conditions.

Clusters 0, 1, and 11 were found to be most expanded within the aged matrices and were mostly related to ECM remodeling, cell cycle regulators, and genome regulators, respectively. Cluster 0 showed upregulation of genes such as KRT23^63,64, DNER^65,66, and TSPAN2^67,68, which are typically associated with cell structure and various cellular processes. These genes have been previously implicated as cancer biomarkers where they play a role in cell migration, invasion, downstream gene expression, and metastasis. Cluster 1 displayed strong expression of mitotic and cell cycle regulators, including CEP55^69,70, HMMR^71,72, and CCNA2^73,74, as well as microtubule-associated proteins – such as KIF2C^75,76 and KIF18B^77,78. Together, these indicate a highly proliferative transcriptional state within the aged ECM. Finally, Cluster 11 was enriched in genes relating to DNA replication and cell cycle regulators such as MCM6^79,80, E2F1^81,82, WDR76^83,84, and DTL^85,86. This further suggests that the aged ECM promotes aberrant replication and proliferative signaling, with many of the upregulated genes having been previously linked to cancer-associated cell cycle dysregulation and proliferative biomarkers. Conversely, within the young ECM Clusters 3, 5, and 7 were found to be enriched with roles in ion channel and immune regulation, ECM stability, and tumor suppression genes. Cluster 3 was highly defined by the expression of various ion channel regulators such as KCNMA1^87,88 and TRPV3^89,90, alongside immune regulatory genes including PLCG2^91,92 and C6orf141^93,94, suggesting a role in maintenance of coordinated cellular communication and response and immune-mediated tumor suppression. Cluster 5 exhibited high level of ECM stability and remodeling genes – MMP1⁹⁵, MMP9⁹⁶, MMP10⁹⁷, indicating ECM remodeling within the young ECM. While these MMPs are frequently associated with cancer initiation and invasion^98–101, in the young ECM they marked a state of remodeling redirected toward tumor suppression rather than initiation. Finally, Cluster 7 was enriched in tumor suppressor genes, including CLCA2^102,103, CADM3^104,105, further supporting that the young ECM fosters a microenvironment that limits tumor progression. Complementing the cluster-specific findings, volcano plot analyses of significant genes (Fig. S3) revealed that the aged ECM biases dual-function genes toward tumor-suppressive programs. Notably, DDIT4¹⁰⁶, typically characterized as a biomarker of cancer progression¹⁰⁷ but also documented as a tumor suppressor¹⁰⁸, was upregulated, demonstrating the influence of the aged microenvironment’s role in tumor development. Similarly, the aged ECM showed enrichment of C5orf46¹⁰⁹, a gene correlated with poor prognosis due to its role in driving malignant phenotypes and reshaping the immune landscape toward tumor support¹¹⁰ (Fig. 4D). To further contextualize these changes, we investigated AIF-1 – a gene previously implicated in breast cancer development, progression, and metastasis – which revealed an average 12% expression in tumors gown on aged ECM, compared to 8% in young, within the same host model (Fig. 4E-F). Up-regulation of AIF-1 suggests tumor cells adopt a unique inflammatory signature in the aged microenvironment. Together, the age-respective profiles suggest that the aged ECM promotes a tumor-permissive environment, contrasting the young ECM’s immune regulation and tumor suppression.

This work is particularly critical as the aging ECM and its effects are becoming increasingly acknowledged as a key component in tumor progression. While several recent, high-resolution, single-cell sequencing studies have looked into mammary murine tissues revealing compositional shifts in epithelial, stromal, and immune cell populations in mice in both healthy and the tumor microenvironment with respect to aging^21,22, these studies fail to decouple the independent effect of the ECM as a contributor to tumor growth and development.

Using knockdown and control cell lines implanted within the same host on aged ECM we first find that LOX, a key protein upregulated within the aged environment, emerged as a key regulator of ECM-induced tumor progression. LOX knockdown significantly and consistently reduced tumor volume when compared to non-knockdown control cells within the same host. Further scRNA-seq analysis of the shLOX group on aged matrix revealed decreases in transcriptional programs related to cellular stress and invasion. We see a clear transcriptional shift away from pro-inflammatory and invasive programs and toward ECM structure and organization. Cluster 1, enriched after LOX knockdown, was characterized by genes such as MUC5B, MMP7, and MUC16 – associated with mucin production and matrix regulation – indicating a more structurally engaged and less migratory phenotype. Complementary to these transcriptional alterations, GSEA analysis at the pathway level revealed a downregulation of various mechanotransducive and oncogenic programs. The Hallmark Gene set revealed LOX knockdown significantly reduced pathways related to EMT and inflammation while increasing those related to cholesterol homeostasis and ROS. Additionally, analysis of the Oncogenic Gene Set showed LOX knockdown and its dual role in various pathways, such as KRAS, upregulation of pathways, such as JAK, and downregulation of pathways, such as BRCA1. These results highlight LOX as a potential therapeutic target that inhibits tumor growth by enhancing ECM dynamics and reducing metastatic potential. Additionally, it shows that LOX alone has the potential to facilitate tumor proliferation and development and that its cellular knockdown within the aged environment alone can significantly reduce the tumor volume.

Furthermore, we demonstrate that the aged ECM alone creates a tumor-permissive microenvironment that promotes tumor growth and metastasis. We find that the tumor growth within the aged ECM is significantly greater than that of the young, within the same host. H&E analysis further showed a greater cancerous region within the excised aged tumors when compared to their young counterpart. Transcriptional profiling confirmed that the aged ECM independently promotes an environment conducive to tumor growth and metastasis by enriching clusters associated with cell cycle activation, DNA replication, and chromatin regulation. Moreover, the aged ECM demonstrated enrichment of C5orf46, a gene associated with adverse outcomes by facilitating malignant progression and immune evasion, highlighting the tumor-permissive nature of the aged ECM. Similarly, the aged ECM, with its larger tumor volume and upregulation of known tumor-suppressor and promoter genes, such as DDIT4. The increased tumor size reflects context-dependent reprogramming driven by the aged ECM, which enhances the tumor-promoting activity and demonstrates its capacity to independently influence cancer progression.

In contrast, the young ECM alone is indicated to promote a tumor suppressive microenvironment through the upregulation of ECM-stabilizing and cell-signaling genes. Tumors grown on the young ECM were transcriptionally enriched in ion channel regulators and immune-modulatory genes, which were indicative of a more stable and regulated microenvironment that supports cellular communication and suppresses tumor initiation. These findings indicate that the ECM age is a deterministic factor in shaping a tumor-permissive environment, where the aged ECM can accelerate initiation and metastasis.

The aged ECM tumors exhibited an increase in unique inflammatory signals, seen by upregulation of AIF-1, an inflammatory regulator that is also implicated in breast cancer and linked to EMT and metastasis. This contrasts with the immune-regulator signals found within the young ECM tumors, where the immune enrichment was marked by known, tumor suppressive genes PLCG2 and C6orf141. Together, this work supports recent studies whereby the aging environment enhances tumor initiation and metastasis due to cell cycle and immune changes.²¹

Our hybridized model offers a distinct advantage over traditional aged mice models by isolating the specific contributions of the ECM from system aging effects such as immunosenescence and hormonal changes. Unlike conventional aging models which require long timeframes and lead to paradoxical results, this approach enables direct comparison of aged and young matrices within the same host, substantially reducing variability and improving cost effectiveness. By complementing existing aged mouse models, our system offers platform to dissect how ECM aging alone drives tumor progression and metastasis. While this study provides a framework for the independent study of aged ECM on tumor behavior, several limitations should be considered. Rag1^−/−, while advantageous for isolating ECM-specific effects, precludes full ECM-immune characterizations known to occur during tumor progression. Additionally, while the model enables short-term tumor development studies, it fails to capture the longitudinal remodeling process that accompanies chronic aging. Future efforts integrating this model with an immune-competent model and longitudinal follow-ups could offer a more comprehensive understanding of how matrix aging influences tumor progression in vivo.

These findings provide insight into how ECM aging may shape tumor behavior, offering potential targets for modulating the tumor microenvironment across age-related contexts. Given the increasing aged population, strategies that address the ECM directly may enable improvements toward treatment efficacy and metastatic prevention. These findings are consistent with our previous in vitro studies and recent in vivo studies from aged and young mice models.^13,21,22 By isolating the ECM from senescent cells and mature T and B cells, our model provides evidence that the aged ECM alone can facilitate tumor progression. It enables the study of the ECM alone, allowing for the study of both young and aged environments within the same host system, allowing for better opportunities for comparative studies.

This study demonstrates that the aged ECM alone drives breast cancer growth and progression. Using a novel, hybrid in vivo model, we first find that knockdown of LOX reduced tumor volume and suppressed transcriptomic programs linked to metastasis while enriching genes and pathways for ECM structure. IL1B and P4HA1 showed a more complex or potentially context-dependent role in the aged environment, highlighting a need for further studies. We further show that the aged ECM leads to increased tumor growth and reprograms tumor cell states toward invasive and inflammatory phenotypes. Transcriptomic profiling reveals that the aged ECM drives a tumor-permissive state while the young promotes a suppressive state. These findings underscore the importance of ECM in cancer progression and therapeutic value and support LOX as a promising therapeutic target. Moreover, this model offers a novel platform for investigating ECM-driven diseases beyond cancer, such as fibrosis and other age-related pathologies.

Cell Culturing

GFP tagged MCF10A DCIS.com and all genetic knockdown cells (MCF10A DCIS.com pLKO, MCF10A DCIS.com pLKO-LOX shRNA, MCF10A DCIS.com pLKO-P4HA1 shRNA, and MCF10A DCIS.com pLKO-IL1B shRNA cells) were maintained in DMEM F12 1:1 – DMEM low glucose media in a 1:1 ratio with F12 (Ham’s) media further supplemented with 10% FBS, 1% P/S, and 0.75 µg/mL puromycin.

Mouse Mammary Breast Removal

4th mammary glands were first isolated from young (2–4 months) and aged (24–48 months) C57BL/6 mice in accordance with IACUC guidelines (protocol number: 18-05-4687) with the approval of Notre Dame, which has an approved Assurance of Compliance on file with the National Institutes of Health, Office of Laboratory Animal Welfare. Mice were sacrificed in CO2 chambers, and tissues collected and used immediately, or wrapped in aluminum foil, flash frozen in liquid nitrogen, and stored at − 80°C until use.

Decellularization and Delipidation

To section the tissues in cryostat, tissues were thawed at room temperature (RT), blotted on a tissue paper, embedded in optimum cutting temperature (O.C.T.) compound (Tissue-Tek, Sakura, USA), frozen at − 20°C, and sectioned at 300 µm thickness. Sections were washed with PBS to remove the optimum cutting temperature (O.C.T) compound.

For decellularization, whole tissues or tissue sections were incubated in 0.5% SDS for 4 and 2 days, respectively, at 4°C, with gentle agitation and SDS change every 12 h. For delipidation, isopropanol was used again for 4 and 2 days, respectively, at 4°C. Tissues and sections (final dimensions ≈ 2 mm × 2 mm × 0.3 mm) were washed with PBS and stored at 4°C until use (within 2 weeks).

DNA content of the matrices was measured using the PicoGreen assay as described previously. Briefly, samples (n = 3) were frozen at − 80°C, lyophilized, weighed, incubated in pepsin (Sigma, USA) solution (1 mg mL–1) for 16 h, and the supernatants were assayed in reference to double stranded DNA standards. Results were normalized to dry weights.

Reseeding of Cells

Green Fluorescent Protein (GFP)-tagged MCF10A DCIS.com (Comedo Ducal Carcinoma In-Situ) cells were then seeded atop the prepared dECM, either young or aged. The reseeded dECM was cultured for 12 days to allow for cellular adhesion, proliferation, and interaction with the ECM. To ensure consistency, 10 similarly sized dECM from each group were selected before implantation. Additionally, the matrices were examined under a fluorescent microscope to validate the similar cellular reseeding. Chosen matrices were completely covered by the GFP-tagged cells.

Tumor growth and Isolation

Reseeded tissues were implanted into Rag1^−/− mice and were inserted into a pocket created between the skin and the peritoneal lining near the 4th mammary fat pad. Weekly, the tumor sizes were recorded via a micrometer, and the mice were sacrificed at 4 weeks (first in vivo) or 6 weeks (second in vivo). The tumors were then excised, dimensions measured using a micrometer, and their volumes calculated.

Genetic knockdown

To knockdown the IL1B, LOX, and P4HA1 genes, E. coli bacterial stocks containing pLKO.1-Puro plasmids were purchased with the Mission® shRNAs (Sigma-Aldrich) for:

IL1B:

TRCN0000358600 (IL1B 0)

TRCN0000358601 (IL1B 1)

TRCN0000358661 (IL1B 61)

TRCN0000358662 (IL1B 62)

LOX:

TRCN0000045989 (LOX 89)

TRCN0000045990 (LOX 90)

TRCN0000045991 (LOX 91)

P4HA1:

TRCN0000303933 (P4HA1 33)

TRCN0000303934 (P4HA1 34)

TRCN0000303872 (P4HA1 72)

Bacteria was cultured in broth culture and plasmids were isolated using the PureLink™ HiPure Plasmid Midiprep Kit (Thermo Fisher Scientific, Cat. No: K210004) following the manufacturer’s instructions.¹¹¹ Briefly, columns were equilibrated using a 10 mL equilibration buffer. In the meanwhile, bacteria (all 10 of them) were spun down in a 50 mL tube at 4000g for 10 min and the pellet was resuspended in 4 mL resuspension buffer, followed by addition of 4 mL of lysis buffer (wait for 5 min) and then 4 mL of precipitation buffer. Tubes were capped and mixed by inverting slowly. Tubes were centrifuged at 13,000g for 10 min. Supernatant was collected and loaded into equilibrated columns, the elute was allowed to drain and the column was washed with Wash buffer (2 times). The elute was discarded. Next, the 5 mL of Elution buffer was added into the column and eluted into sterile 15-mL centrifuge tubes. Isopropanol (3.5 mL) was added, and the tubes were mixed thoroughly. The tube was incubated overnight at 4°C, then centrifuged at 13,000g for 30 min at 4°C, and the pellet was collected and resuspended in 3 mL 70% ethanol. The tubes were centrifuged at 13,000g for 5 min at 4°C and supernatant discarded. Pellet was airdried and resuspended in 200 µL TE buffer. Plasmid DNA amounts were quantified using Nanodrop.

For transfection, plasmids with the specified shRNAs (IL1B 00, IL1B 01, IL1B 61, IL1B62; LOX 89, LOX 90, LOX 91; P4HA1 33, P4HA1 34, P4HA1 72), as well as the control pLKO.1-Puro plasmid with no shRNA, were transfected to HEK293T cells using the Lipofectamine™ 3000 Transfection Reagent (Thermo Scientific, Cat. No: L3000008). Briefly, Lipofectamine 3000 reagent (7.5 µL) was added onto 125 µL Opti-MEM reduced serum medium (Gibco) in a 1 mL centrifuge tube, and plasmid DNA (2.5 µg) containing the specified shRNA and P3000 reagent (5 µL) were added onto 125 µL Opti-MEM medium in a separate 1-mL centrifuge tube.¹¹² The solutions were then mixed and incubated for 15 min. Next, the solution (250 µL) was added onto HEK293T cells plated in the well of a 6-well plate (at around 70% confluency) containing 2 mL growth medium (DMEM with 10% FBS). Medium was refreshed after 24 h to remove the plasmid DNA-Lipofectamine in the medium. The medium containing the lentiviral particles (1.5 to 2 mL) was collected at days 2 and 4 after transfection. The medium was added onto MCF10A DCIS.com cells plated in 75-mL culture flasks and containing 10 mL growth medium (DMEM-F12 (1:1) with 10% FBS). This was done with both day 2 and day 4 supernatants collected from HEK cells separately (2 times) after refreshing the DMEM-F12 media on DCIS.com cells. After a week, the DCIS.com cell were treated with growth medium containing 0.75 µg/mL puromycin to select the transduced cells. After selection, cells were replated and used in future experiments.

Western Blot

Knockdown DCIS.com cells were replated in 12-well plates and cultured for 5 days. Next, the cells were washed and lysed using 200 µL RIPA buffer (30 min in 4°C). Protein amounts were quantified using BCA assay, and lysates containing 15 µg of proteins were loaded into SDS-PAGE. Before loading the samples, loading buffer (4x) and β-mercaptoethanol were added onto the samples. Samples were run in electrophoresis and transferred to nitrocellulose membranes as described previously.¹³ For staining, the following antibodies were used:

Beta Actin (Abcam, rb pAb to Beta Actin, Cat No: ab8227), 1:1000 dilution

IL1B antibody (Cell Signaling, IL-1b (D3U3E) Rabbit mAb, Cat. No: 12703S), 1:1000 dilution)

LOX antibody (Abcam, Rabbit Recombinant Anti-LOX antibody [EPR4025], Cat. No: ab174316), 1:400 dilution)

P4HA1 antibody (Abcam, Anti-P4HA1 rabbit antibody, Cat. No: ab244400), 1:1000 dilution)

Secondary Antibody (Abcam, Goat Anti-Rabbit IgG H&L (HRP), Cat No: ab6721), 1:2000 dilution)

The MCF10A DCIS.com cells transduced with IL1B 62, LOX 91, and P4HA1 33 shRNAs were selected, as they yielded the best knockdown efficiency.

scRNA-seq of Models with Control and Knockdown Cells

Instead of the cells being labelled with human HTO, CD11b was used to separate the immune cells (CD11b+) from the tumor cells (CD11b-) (Milenyi Biotec, CD11b MicroBeads UltraPure, Mouse Cat. No: 130-126-725). As the tumor cells were human in origin, the bead separation ensured the enrichment of the tumor cells so that it can be fully aligned to the readout of the human genome. Cells were fixed, barcoded, and subjected to library preparation for next-generation sequencing. Cells were then fixed using the Evercode Cell Fixation v2 kit (Cat. No: ECF2101) following the Parse Fixation Protocol¹¹³. Fixed cells were shipped on dry ice to UT Southwestern Medical Center for library preparation and preserved at − 80 ºC. Next, the fixed cells underwent three rounds of barcoding using the Parse Bioscience Evercode whole transcriptome WT v2 kit with a target recovery of 50,000 cells total, followed by cell lysis, and sublibrary preparation. 8 sublibraries were sequenced on a NovaSeq-X instrument and the raw data was processed using the Parse Biosciences analysis pipeline. Reads obtained from the tumor cell samples were aligned to the human reference genome while the immune (CD11b+) sample reads were aligned to the mouse genome. Downstream processing, RNA normalization, and DEG analysis was performed in RStudio using the Seurat package.

scRNA-seq of Models with Young and Aged dECM

Three sets of tumors (6 young and 6 aged) were selected for single-cell RNA-sequencing (scRNA-seq). To process the tumors for scRNAseq, tumors were minced using blades, and the matrix was disassociated using 5 mL enzyme mix (Milenyi Biotec, Human Tumor Dissociation Kit Cat. No: 130-095-929) for each tumor in a 50 mL tube. Mixtures were incubated at 37°C with continuous rotation for 50 min. Undissolved tissue was removed using cell strainers (pores size: 70 µm) and DMEM media was passed (20 mL) through the strainers. The cell suspension was centrifuged at 300g for 7 min and supernatant was aspirated. The pellet was reconstituted in DMEM media and cell aggregates were removed by straining the cells through 30 µm strainers. Next, dead cell removal kit (Miltenyi, Cat. No: 130-090-101) was used to remove the dead cells. The cells were diluted with DMEM media and tubes were centrifuged. Cells were reconstituted in dead cell removal microbeads (100 µL per 10⁷ cells) and incubated for 15 min at room temperature. Next, 500 µL of buffer was added on to the cells and the cell suspension was poured through equilibrated columns by placing on the magnets on a MACS separator. Effluent (live cell fraction) was collected, and the cells were labelled with human-HTO antibodies and sent to 10x Genomics for analysis.

Funding Statement:

Research reported in this publication was supported by NIH award number 1R01CA275423-01A1.

Competing Interests Statement: The authors have no competing interest to disclose.

Ethics Statement: All animal work was performed under the approved IACUC protocol number 18-05-4687.

Author Contribution

LH: Wrote the main manuscript, prepared figures, designed work; additionally acquired, analyzed, and interpreted data.GB: Designed the work; additionally acquired, analyzed, and interpreted data.JY: Designed work and prepared figures.EA: Analyzed and interpreted data.EH: Designed the work and additionally acquired, analyzed, and interpreted data.SZ: Conceptualized and designed the work. Additionally, analyzed and interpreted the data.PZ: Conceptualized and designed the work.All authors reviewed and edited the manuscript.

Data Availability

Data is provided within the manuscript or supplementary information files. Additional data is available upon request.

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Hybrid In Vivo Breast Cancer Model Reveals Transcriptomic Insights into Cancer Progression with Age

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Version 1

Abstract

Figures

Introduction

Results

Knockdown of LOX and IL1B Suppresses Tumor Growth on Aged ECM

LOX Knockdown Suppresses Pro-Metastatic Cell State and Gene Expression

LOX Knockdown Alters Oncogenic and Hallmark Signaling Pathways

Aged ECM Drives Tumor Growth In Vivo

Aged ECM Enriches Invasive and Inflammatory Phenotype

Discussion

Conclusion

Materials and Methods

Cell Culturing

Mouse Mammary Breast Removal

Decellularization and Delipidation

Reseeding of Cells

Tumor growth and Isolation

Genetic knockdown

IL1B:

LOX:

P4HA1:

Western Blot

scRNA-seq of Models with Control and Knockdown Cells

scRNA-seq of Models with Young and Aged dECM

Declarations

Funding Statement:

Author Contribution

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1