Research Paper Volume 13, Issue 3 pp 3290—3312
Suppression of p16 alleviates the senescence-associated secretory phenotype
- 1 Department of Pharmacology and Chemical Biology, UPMC Hillman Cancer Center, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
- 2 Biomedical Sciences Graduate Program, Penn State College of Medicine, Hershey, PA 17033, USA
Received: August 20, 2020 Accepted: January 21, 2021 Published: February 6, 2021https://doi.org/10.18632/aging.202640
How to Cite
Copyright: © 2021 Buj et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Oncogene-induced senescence (OIS) is characterized by increased expression of the cell cycle inhibitor p16, leading to a hallmark cell cycle arrest. Suppression of p16 in this context drives proliferation, senescence bypass, and contributes to tumorigenesis. OIS cells are also characterized by the expression and secretion of a widely variable group of factors collectively termed the senescence-associated secretory phenotype (SASP). The SASP can be both beneficial and detrimental and affects the microenvironment in a highly context-dependent manner. The relationship between p16 suppression and the SASP remains unclear. Here, we show that knockdown of p16 decreases expression of the SASP factors and pro-inflammatory cytokines IL6 and CXCL8 in multiple models, including OIS and DNA damage-induced senescence. Notably, this is uncoupled from the senescence-associated cell cycle arrest. Moreover, low p16 expression in both cancer cell lines and patient samples correspond to decreased SASP gene expression, suggesting this is a universal effect of loss of p16 expression. Together, our data suggest that p16 regulates SASP gene expression, which has implications for understanding how p16 modulates both the senescent and tumor microenvironment.
Senescence is considered a state of stable cell cycle arrest that can occur due to a variety of stimuli . Oncogene-induced senescence (OIS) occurs upon activation of an oncogene such as HRAS or BRAF in normal cells [2, 3]. One of the hallmarks of senescent cells is upregulation of the cell cycle inhibitor CDKN2A (encoding for p16), which restrains cell cycle progression and cellular proliferation [4–6]. Canonically, elevated p16 represses hyperphosphorylation of the retinoblastoma protein (RB), which inhibits E2F transcription factor-mediated expression of proliferative genes . Loss of p16 is a common event in human cancer that has been linked to senescence bypass, increased proliferation, and malignant transformation though both canonical and non-canonical (RB-independent) pathways [8–12].
The acquisition of a senescence-associated secretory phenotype (SASP) is also characteristic of senescent cells . The SASP is composed of a variety of soluble signaling factors including pro-inflammatory cytokines, chemokines, and growth factors, as well as proteases, insoluble extracellular matrix proteins and non-protein components that are transcriptionally and translationally upregulated and secreted into the surrounding microenvironment by senescent cells [14–18]. Due to the impact that SASP can exert on cellular physiology, this program is tightly regulated at multiple levels. At the transcriptional level, several transcription factors (NF-κB, C/EBP-β) and upstream regulators (p38 MAPK, GATA4, p53, and ATM) have been described to either positively or negatively regulate SASP gene expression [16, 19–25]. The SASP is also regulated at both the epigenetic [26–32] and translational level [17, 33]. Recent publications suggest that the initiation of SASP gene transcription during OIS is likely due to loss of lamin B1 (LMNB1) and nuclear integrity [34, 35], leading to the accumulation of cytoplasmic chromatin fragments (CCFs) [36, 37]. CCFs activate the cytosolic DNA sensor cyclic guanosine monophosphate (GMP)-adenosine monophosphate (AMP) synthase (cGAS) that catalyzes the synthesis of the second messenger cyclic GMP-AMP (cGAMP) to bind and activate stimulator of interferon genes (STING), leading to NF-κB activation and cytokine transcription [36, 38, 39]. Therefore, changes in LMNB1 expression are tightly linked to SASP gene transcription.
It is well documented that the SASP modifies the cellular microenvironment and alters neighboring cells, exerting a pleiotropic effect that is not fully understood . On one hand, SASP factors contribute to wound-healing [41–43], normal development [44, 45], and have tumor suppressive effects through the recruitment of different immune cells to clear premalignant cells, a process termed senescence surveillance [46–48]. On the other hand, SASP factors can be pro-tumorigenic by sustaining proliferation, invasion, metastasis, and chemoresistance [49–53]. This paradoxical double role of SASP factors is highly dependent on both genetic background and SASP composition, which is known to be both variable and dynamic . Different genetic backgrounds, cellular contexts, and/or senescence inducers allow for different SASP programs that can promote or inhibit tumorigenesis [55–57]. Interestingly, different SASP programs can also induce senescence in neighboring cells in a paracrine manner that in turn express a particular SASP program . Thus, the final beneficial or detrimental net effect of the SASP is governed by multiple mechanisms that are not yet fully understood . Characterizing whether different genetic backgrounds lead to different SASP programs may be critical to develop efficient and personalized regimens for cancer patients. As ~50% of all human tumors have low p16 expression , understanding its role in regulating the SASP has implications for a large subset of patients.
Here, we investigated the effect of p16 suppression on SASP gene expression. We found that knockdown of p16 leads to decreased IL6 and CXCL8 (encoding IL8) SASP gene expression in both HRASG12V and BRAFV600E models of OIS. This was not due to increased LMNB1 expression or loss of senescence markers, indicating that these changes were not due to inhibition of upstream signaling or a simple artifact due to reduced senescence. We confirmed these results in p16-wildtype melanoma cells upon knockdown of p16 and in DNA damage-induced senescence. Moreover, using publicly-available data, we found that low CDKN2A expression in patient tumors is associated with a decrease in specific SASP programs. Together, our results suggest that p16 may have a role in transcriptionally regulating SASP factors, which has implications for understanding how loss of p16 affects the senescent and tumor microenvironment.
Knockdown of p16 abrogates oncogene-induced IL6 and CXCL8 expression
Upregulation of both p16 and SASP factors are characteristic of OIS cells . A previous study found that overexpression of p16 induces senescence without upregulation of the SASP . However, it is unknown whether p16 upregulation is necessary for SASP gene expression in the context of OIS. In order to better understand the effects of p16 expression on the SASP, we assessed the expression of the most extensively characterized interleukins upregulated in senescence, IL6 and CXCL8 [13, 16, 61]. Knockdown of p16 with BRAFV600E or HRASG12V overexpression (Supplementary Figure 1A) decreased IL6 and CXCL8 expression and suppressed senescence-associated β-galactosidase (SA-β-gal) activity and the cell cycle arrest in IMR90 fibroblasts (Figure 1A–1J), a classical model of OIS [5, 62]. Note that abrogation of p16 expression using these conditions occurs prior to the induction of senescence that typically occurs 2-3 days after oncogene activation [5, 63–65]. A second shRNA targeting p16 confirmed the results, suggesting these observations are not due to off-target effects (Supplementary Figure 1B–1F). Similar results were observed in normal skin fibroblasts Hs 895.Sk (Supplementary Figure 1G, 1H), suggesting this is not a cell line-specific phenomenon. Additionally, knockdown of p16 in the BRAFV600E-induced senescence model decreased the expression of other SASP factors including growth factors, proteases, and ligands (Supplementary Figure 1I), suggesting that this is a broader phenomenon not limited to IL6 and CXCL8. Together, these data suggest that knockdown of p16 may regulate the expression of multiple SASP factors upon oncogene activation.
Figure 1. Knockdown of p16 decreases IL6 and CXCL8 expression in oncogene-induced senescent cells. IMR90s expressing either BRAFV600E or HRASG12V alone or in combination with a shRNA targeting p16 (shp16 hairpin #1). An empty pBabe retroviral vector and a shRNA targeting GFP lentiviral vector were used as controls. See Supplementary Figure 1A for an experimental timeline. (A) Immunoblot of BRAF and p16. Vinculin was used a loading control. (B) Representative images of senescence-associated β-galactosidase (β-GAL) staining and colony formation (CF). (C) Quantification of β-GAL in (B). (D) Quantification of CF in (B). (E) Immunoblot of RAS and p16. β-actin was used as loading control. (F) Representative images of β-GAL staining and colony formation (CF). (G) Quantification of β-GAL in (F). (H) Quantification of CF in (F). (I, J) IL6 and CXCL8 mRNA expression (fold change relative to control mean). Expression of target genes was normalized against multiple reference genes. Data normalized against MRPL9 are shown. n=3/group and mean±SD. 1 out of 3 experiments is shown. *p<0.05.
Knockdown of p16 prior to the induction of BRAFV600E/HRASG12V-mediated senescence bypasses the senescence-associated cell cycle arrest (Figure 1A–1H and Supplementary Figure 1B–1E) . Therefore, it is possible that the SASP expression is low because the cells never undergo OIS. To investigate whether the observed decrease in IL6 and CXCL8 is a direct effect of p16 suppression and not simply a consequence of senescence bypass, we knocked down p16 at two time points after oncogene expression (Figure 2A and Supplementary Figure 2A–2C). Suppression of p16 at day 8 and 10 after oncogene expression did not bypass senescence as observed using multiple markers of senescence (Figure 2B–2D and Supplementary Figure 2D–2G). Consistent with observations using knockdown of p16 prior to senescence induction (Figure 1), IL6 and CXCL8 expression were both decreased when p16 was knocked down at later time points (Figure 2E and Supplementary Figure 2H). All together, these data suggest that the observed effects of p16 expression on IL6 and CXCL8 are uncoupled from senescence bypass.
Figure 2. Knockdown of p16 at later timepoints decreases IL6 and CXCL8 expression without bypassing oncogene-induced senescence. IMR90s expressing BRAFV600E alone or in combination with shRNAs targeting p16 (shp16 hairpin #1 and #2). An empty pBabe retroviral vector and a shRNA targeting GFP lentiviral vector were used as controls. See Supplementary Figure 2A for an experimental timeline. (A) Immunoblot of BRAF and p16. β-actin was used as loading control. (B) Representative images of senescence-associated β-galactosidase (β-GAL) staining and colony formation (CF). (C) Quantification of β-GAL in (B). (D) Quantification of CF in (B). (E) IL6 and CXCL8 mRNA expression (fold change relative to control mean). Expression of target genes was normalized against multiple reference genes. Data normalized against PMSC4 are shown. n=3/group and mean±SD. 1 out of 3 experiments is shown. *p<0.05. ns = not significant.
Knockdown of p16 in tumor cells decreases IL6 and CXCL8 expression
Next, we aimed to investigate whether suppression of p16 leads to decreased expression of IL6 and CXCL8 in tumor cells. Towards this goal, we knocked down p16 in three melanoma cell lines with wildtype p16 (Figure 3A). Consistent with our data in fibroblasts (Figures 1, 2), knockdown of p16 in the melanoma cells also decreased IL6 and CXCL8 (Figure 3B–3D). Notably, this was not a consequence of a reduced burden of spontaneous senescent cells in p16 knockdown cells (Supplementary Figure 3A, 3B). Finally, expression of IL6 and CXCL8 was also significantly reduced by stable knockdown of p16 in melanoma cells induced to senesce using etoposide (Figure 3E–3H). Etoposide induced senescence to a similar extent in both p16 wildtype controls and p16 knockdown cells, suggesting the decrease in expression was not linked to decreased senescence (Figure 3E–3G). Altogether, these data suggest that p16 may directly or indirectly regulate the transcription of both IL6 and CXCL8 and support the hypothesis that this is not a consequence of p16 suppression-mediated senescence bypass since suppression of p16 in both senescent and proliferating melanoma cells abrogates IL6 and CXCL8.
Figure 3. Knockdown of p16 in melanoma cells decreases IL6 and CXCL8 expression. The melanoma cell lines SKMel28, RPMI-7951, and Hs 600T expressing wildtype p16 were infected with lentivirus expressing a shRNA targeting p16 (shp16 hairpin #1). An shRNA targeting GFP lentiviral vector was used as control. (A) Immunoblot of p16. Vinculin was used as loading control. (B–D) mRNA expression of IL6 and CXCL8 (fold change relative to control mean) in SKMel28 (B), RPMI-7951 (C), and Hs 600T (D) melanoma cells. Expression of target genes was normalized against multiple reference genes. Data normalized against MRPL9 are shown. n=3/group and mean±SD. 1 out of 3 experiments is shown. (E–H) p16 was stably knocked down in SKMel28 melanoma cells with a shRNA (shp16 hairpin #1). An shRNA targeting GFP lentiviral vector was used as control. Cells were treated with 1μM etoposide for 6 days. (E) Representative images of senescence-associated β-galactosidase (β-GAL) staining and colony formation (CF). (F) Quantification of β-GAL in (E). (G) Quantification of CF in (E). (H) IL6 and CXCL8 mRNA expression (fold change relative to control mean). Expression of target genes was normalized against multiple reference genes. Data normalized against PMSC4 are shown. n=3/group and mean±SD. 1 out of 2 experiments is shown. *p<0.05.
Low CDKN2A in patient tumors correlates with low SASP expression
It has been widely demonstrated that suppression of p16 leads to increased proliferation, tumorigenesis, and metastasis in vitro and in vivo, and loss of p16 expression is considered a poor prognostic maker [66–69]. To further understand the relationship between loss of p16 and decreased expression of the SASP, we used TCGA data from primary tumors of skin cutaneous melanoma (SKCM, n=103), pancreatic adenocarcinoma (PAAD, n=178), colorectal adenocarcinoma (COADREAD, n=622), mesothelioma (MESO, n=87), bladder urothelial carcinoma (BLCA, n=407) and glioblastoma multiforme (GBM, n=153), six tumor types where loss of p16 is frequently observed and has clinical implications [70–77]. Patients were classified according to their CDKN2A status (p16-low or p16-high, see Methods for details) (Table 1), and differential expression analysis was performed independently for each tumor type. Note that there were no significant differences in the number of normal and tumor cells between p16-high and p16-low tumors (Supplementary Figure 4A). Most of the SASP factors profiled in a published database of RAS-induced senescence (including soluble factors and exosomes, 232 total unique genes) (Supplementary Table 1) , were significantly downregulated in p16-low tumors (Supplementary Figure 4A and Supplementary Table 2). Gene Set Enrichment Analysis (GSEA) also showed a decrease in ‘Senescence Associated Secretory Phenotype, SASP’ (Figure 4B) in p16-low tumors. Interestingly, 4 out of 6 tumors (PAAD, COADREAD, MESO, and BLCA) showed a decrease in pathways related to inflammation and the immune system such as ‘Antigen Processing and Presentation’ and ‘Cytosolic DNA Sensing Pathway’ in p16-low tumors (Supplementary Figure 4B). These data demonstrate that p16 status correlates with expression of SASP factors in human tumor samples. Importantly, to rule out the possibility of less senescent tumor cells in the p16-low patient samples, we used GSEA to cross-compare p16-low vs. p16-high expression profiles with a previously published senescence expression signature . Among the 6 tumor types, only MESO showed a significant negative normalized enrichment score (NES) (Supplementary Table 3). This suggests that in 5 of the 6 tumor types, there was not a significant decrease in the number of intratumoral-senescent cells. Thus, the observed decrease in SASP expression signatures is likely not due to less intratumoral senescent cells. To unravel whether this observation is due to a decrease of immune cell infiltration in p16-low vs. p16-high tumors, we compared the number of infiltrating lymphocytes, monocytes, and neutrophils seen on OCT-embedded tissue slides reported by TCGA in 5 out of 6 tumors (note that there is no available data for GBM). No significant differences were observed between p16-low and p16-high tumors (Supplementary Figure 4C). These data at least in part suggest that changes in SASP expression are not due to differences in intratumoral senescent cells or differential infiltration of immune cells that could potentially bias our analysis.
Table 1. Statistics of TCGA data sets.
|CDKN2A expression (Log2RSEM) mean||7.08||6.73||6.31||6.25||7.56||6.61|
|CDKN2A expression (Log2RSEM) standard deviation (SD)||2.76||2.32||1.74||2.26||3.61||3.15|
|CDKN2A expression (Log2RSEM) median||7.75||6.35||6.22||6.29||8.22||5.11|
|CDKN2A expression (Log2RSEM) first quartile (Q1)||5.15||4.93||5.20||4.59||4.16||4.07|
|CDKN2A expression (Log2RSEM) third quartile (Q3)||9.02||8.38||7.30||7.68||10.82||10.40|
|No. p16-low cases (CDKN2A expression ≤ Q1)||103||45||156||22||102||38|
|No. p16-high cases (CDKN2A expression ≥ Q3)||103||45||156||22||102||38|
Figure 4. Tumors with low CDKN2A expression have decreased expression of SASP. (A) Percentage of SASP genes significantly upregulated and downregulated in CDKN2A-low (i.e., p16-low) expressing tumors when compared to CDKN2A-high (i.e., p16-high) expressing tumors. (B) Negatively enriched SASP term among the six studied tumor types in Gene Set Enrichment Analysis (GSEA) between CDKN2A-low and CDKN2A-high expressing tumors. SKCM (skin cutaneous melanoma), PAAD (pancreatic adenocarcinoma), COADREAD (colorectal adenocarcinoma), MESO (mesothelioma), BLCA (bladder urothelial carcinoma), GBM (glioblastoma multiforme), NES (negative enrichment score). (C) Correlation between CDKN2A and LMNB1 expression for each tumor type. Data are shown as Log2 of RSEM. Coefficient of correlation (r) and p-value were calculated using Pearson’s correlation.
Finally, since we observed a downregulation of the “Cytosolic DNA Sensing Pathway” signature, we correlated the expression level of CDKN2A and LMNB1 in the tumors. We did not observe a strong correlation between CDKN2A and LMNB1 expression (Figure 4C), suggesting that p16 regulation of this pathway and the SASP in tumors may not be directly through modulation of LMNB1. However, we cannot rule out the possibility that other pathways may be altered to affect the cytosolic DNA sensing pathway. All together with our in vitro data, these data in human tumor samples demonstrate a universal, positive correlation between p16 expression and SASP gene expression.
Increased expression of p16 and the SASP are characteristics of OIS; however, the relationship between them is not well understood. While increased p16 expression has a clear role in sustaining the characteristic cell cycle arrest of OIS cells [8, 79], the SASP appears to be a consequence of the DNA damage response induced by OIS and is not necessary for the proliferative arrest [24, 80, 81]. Indeed, this is evident by the observation that p16-mediated senescence induction, a stimulus that a priori only restrains cell cycle progression, does not induce SASP expression . Here we found that knockdown p16 decreases gene expression of two of the most well characterized SASP factors IL6 and CXCL8 [13, 16, 61], in a manner that is uncoupled from senescence bypass (Figures 1, 2). Knockdown of p16 or low expression of CDKN2A in patient tumors was also associated with lower expression of additional SASP factors. Together, this suggests that p16 expression is not sufficient but is necessary to induce expression of the SASP.
One important question that remains is how does p16 loss mechanistically affect SASP gene expression? p16 is a critical cell cycle regulator, and its suppression both enhances proliferation and allows for senescence bypass [8–10, 12, 82, 83]. Decreased LMNB1 expression plays an important role in the establishment of the SASP . Recent publications demonstrate that decreased expression of LMNB1 and the consequential decrease of nuclear integrity leads to the accumulation of cytoplasmic chromatin fragments (CCFs) that in turn activate the cGAS-STING signaling pathway to drive the SASP [36–38, 85, 86]. Importantly, we found that knockdown of p16 decreased SASP gene expression, which was not a consequence of increased LMNB1 expression (Figure 2). These data suggest that the observed changes in SASP gene expression are not due to downregulation of cGAS-STING signaling, which is directly affected by LMNB1 and nuclear integrity . Notably, we found that four out of the six tumor types analyzed here have decreased cytosolic DNA sensing pathway signaling in p16-low vs. p16-high tumors (Supplementary Figure 4B); however, no correlation was found between CDKN2A and LMNB1 mRNA expression (Figure 4C), suggesting that additional mechanisms are at play in tumors with low p16 expression. It is important to note that besides loss of nuclear integrity, there are other phenomena (mitochondrial DNA leakage, upregulation of LINE-1, or increased expression of the endonuclease MUS81) that may contribute to an impaired cytosolic DNA sensing pathway (reviewed in ). Loss of CDKN2A is often due to deletion or hypermethylation of the locus . Interestingly, previous work has suggested that melanomas with low MTAP have decreased cGAS-STING signaling , and MTAP is often co-deleted/silenced with CDKN2A . Therefore, multiple mechanisms may exist in tumors with loss of this locus to suppress SASP gene expression and/or modulate the tumor microenvironment. Additionally, p16 can negatively regulate TP53 (encoding for p53) at the transcriptional level and also at the protein level by increasing Mdm2-dependent degradation of p53 [90, 91]. As p53 is a negative regulator of the SASP , it is possible that the observed decrease in SASP expression upon p16 suppression is due to negative regulation of p53. Future studies are needed to determine the exact mechanism by which p16 suppression decreases SASP gene expression. Moreover, determining whether these transcription changes in SASP gene expression are a direct or indirect effect of low p16 expression will be of great importance.
We and others have shown that in addition to its canonical role regulating cell cycle progression though the RB pathway, p16 has non-canonical activities that regulate other important aspects of cellular physiology such as nucleotide metabolism, reactive oxygen species, and miRNAs among others [93, 94]. In this regard, some studies have found that pharmacological inhibition of canonical downstream targets of p16, namely CDK4/6, leads to an induction of SASP factors, recruitment of antitumor immune cells, and senescence [95–98], suggesting that p16-loss-mediated regulation of SASP expression and the tumor immune microenvironment may be due to non-canonical (RB-independent) mechanisms. On the contrary, other authors suggest that inhibition of CDK4/6 alone does not induce a SASP and immunologic responses . Additional studies are needed to delineate the exact mechanism whereby suppression of p16 decreases SASP and verify that this occurs in an RB-independent, non-canonical pathway.
Our data suggest that multiple tumor types with low CDKN2A (i.e., p16-low) expression have decreased SASP factor expression (Figure 4). A potential caveat of our study is that the SASP factor list we have used for our analysis is broad, and some factors are related to secretion in exosomes . Use of a broad, validated list of SASP factors, while imperfect, allowed us to analyze the TCGA data in a more unbiased fashion versus manually curating a list. Additionally, we demonstrated that knockdown of p16 in BRAFV600E-induced senescence in vitro corresponds with a decrease in different SASP factors including inflammatory factors, growth factors, metalloproteinases, and ligands (Figure 1I, 1J and Supplementary Figure 1I). Nonetheless, our study shows that low CDKN2A expression correlates with downregulation of a distinctive SASP profile depending on the tissue of origin (Figure 4A and Supplementary Table 2). This observation is consistent with previous studies suggesting that the SASP composition is temporally dynamic and context- and senescence inducer-dependent [54, 55, 100]. Characterization of the different SASP profiles and their unique dynamics will be critical not only to assess the senescent cell burden, but also to develop specific and personalized senescence- and SASP-targeted therapies. Moreover, it will be important to determine whether these tumor-specific SASP signatures alter the clinical course of each tumor type or response to therapy. This study is focused on the transcriptional expression regulation of the SASP upon loss of p16; however, additional studies investigating whether these findings impact the translation and secretion of the SASP, especially within exosomes, are warranted. Since suppression of p16 can lead to senescence bypass and promote tumorigenesis [8–10, 12, 82, 83], obtaining profiles of the SASP factors related to this process may help treat the ~50% of all human tumors with low p16 expression .
It is well-established that the SASP has pleiotropic, context-dependent effects that both promote tumor progression, but also enhance anti-tumor immunity (reviewed in ). For example, IL6 promotes chronic inflammation and tumorigenesis . However, recent studies suggest that IL6 can also enhance anti-tumor immunity by resculpting T cell-mediated immune responses . Likewise, other SASP factors, such as IL1a, IL1b, and TNF have this dual role where they can both promote inflammation and tumorigenesis or impair malignant transformation of benign nevi . Using data from TCGA, we found decreased ‘Antigen Processing and Presentation’ signaling [i.e., the ability of antigen presenting cells to present antigens on major histocompatibility complexes (MHCs) to T-cells] in some patients with low CDKN2A expression (Supplementary Figure 4B). Consistent with our observation, it has been shown that OIS primary human melanocytes upregulate the MHC class II apparatus to induce T-cell proliferation and that melanoma patients that sustain this feature have a favorable disease outcome . Additionally, suppression of p16 activity has been associated with immune deserts, immune escape, and low cytolytic activity in melanoma and pancreatic adenocarcinoma [105–107]. Thus, it is possible that in the context of certain tumor types such as those studied here, the decreased expression of SASP factors observed upon p16 knockdown or in CDKN2A-low patients may contribute to abrogation of senescence surveillance by immune cells [48, 108], thereby promoting tumorigenesis. Interestingly, a recent publication shows that depletion of p16 in tumor cells abrogates the cancer immune response and promotes immune checkpoint blockade resistance . Here, we did not observe a difference in immune cell infiltration between p16-low and p16-high patient samples (Supplementary Figure 4C), which may suggest that the activity of the immune cells is altered. Future experiments will determine whether suppression of p16 leads to decreased immune surveillance and the mechanism whereby this occurs.
Although loss of p16 is one of the most common events in cancer (~50% of all human cancers), there are currently no approved targeted therapies . Additionally, we and others have shown that suppression of p16 has roles outside the cell cycle that would not be affected with current therapies undergoing clinical trials targeting CDK4/6 . Therefore, finding downstream targetable pathways may be beneficial for this large subset of patients. For instance, we previously showed that inhibition of nucleotide metabolism through suppression of mTORC1 or the pentose phosphate pathway enzyme Ribose 5-Phosphate Isomerase A (RPIA) induces senescence specifically in p16-low cancers . Here we found that suppression of p16 leads to decreased SASP expression. Therefore, induction of senescence in p16-low cancers may be a valuable strategy to inhibit the cell cycle while not activating the potential deleterious effects of the SASP.
In summary, we found that suppression of p16 decreases expression of multiple SASP genes, which cannot be explained by inhibition of senescence. We found that this phenomenon also occurs in p16-wildtype tumor cells upon p16 knockdown, and there is a decrease in the SASP gene signature in multiple tumor types that are associated with low p16 expression. Understanding whether p16 regulates SASP expression is critical to understand the complex relationship between cellular senescence, the immune system, and the cell cycle, three key players in cancer regulation.
Materials and Methods
Normal diploid IMR90 human fibroblasts were obtained from ATCC (CCL-186) and cultured according to the ATCC protocol in DMEM (Corning, cat#10-017-CV) supplemented with 5% FBS (VWR, cat#97068-085), L-glutamine (Corning, cat#25-015-CI), non-essential amino acids (Corning, cat#25-025-CI), sodium pyruvate (Corning, cat#25-000-CI), and sodium bicarbonate (Corning, cat#25-035-CI). Cells were cultured under physiological oxygen conditions (2% O2) and 5% CO2. Normal skin fibroblasts derived from a melanoma patient Hs 895.Sk were obtained from ATCC (CRL-7636) and cultured in DMEM (Corning, cat#10-013-CV) supplemented with 10% FBS (VWR, cat#97068-085). Experiments were performed on IMR90 between population doubling #25-35 and in Hs 895.Sk between population doubling #4-10. Melanoma cell lines SKMel28, Hs 600.T, and RPMI-7951, were obtained from ATCC (HTB-72, CRL-7368, and HTB-66, respectively). SKMel28 and Hs 600.T as well as the lentiviral and retroviral packaging cells (293FT and Phoenix, respectively) were cultured in DMEM (Corning, cat#10-013-CV) supplemented with 10% FBS, while RPMI-7951 were cultured in EMEM (ATCC, cat#30-2003) supplemented with 10% FBS. Hs 895.Sk and cancer cell lines were cultured under atmospheric oxygen (~20%) and 5% CO2. All cell lines were cultured in MycoZap (Lonza, cat#VZA-2032) and were tested for mycoplasma every two months as described in . All tumor cell lines express wildtype CDKN2A according to TCGA [111, 112].
Lentiviral and retroviral packaging and infection
pBabe BRAFV600E (Addgene cat#15269), pBabe HRASG12V (Addgene cat#9051), and pBabe empty control (Addgene cat#1764) vectors were packaged into retroviral particles using the BBS/calcium chloride method as previously described in . pLKO.1-shp16 #1 (TRCN0000010482), pLKO.1-shp16 #2 (TRCN0000039751), and pLKO.1-shGFP control (Addgene, cat#30323) vectors were packaged using the ViraPower Kit (Invitrogen, cat# K497500). Experimental timelines for IMR90 and Hs 895.Sk are delineated in Supplementary Figure 1A and Supplementary Figure 2A. Briefly, cells were infected with pBabe empty vector control, pBabe BRAFV600E, or pBabe HRASG12V retroviral particles, and 24 hours later cells were infected with a second round of corresponding retroviral particles. Cells were infected with pLKO.1-shp16 or pLKO.1-shGFP when indicated in Supplementary Figure 1A and Supplementary Figure 2A. As noted in Figure Legends, empty vector pBabe and/or pLKO.1-shGFP were used as controls for all experiments to limit possible effects from expression of viral vectors. Cells were selected with 1μg/mL puromycin for single infections or 3μg/mL for double infections until the end of the experimental procedure.
SKMel28 melanoma cells (p16-wildtype) with stable p16 knockdown (using shp16 hairpin #1) or control (shGFP) were treated with either DMSO or 1μM etoposide (Cayman Chemical, cat#12092) for 6 days (drug replacement every 2 days).
Total RNA was extracted from cells with Trizol (Ambion, cat#15596018) and DNase treated, cleaned, and concentrated using Zymo columns (Zymo Research, cat#R1013) following the manufacturer’s instructions. Optical density values for RNA were measured using NanoDrop One (Thermo Scientific) to confirm an A260 and A280 ratios above 1.9. Relative expression of target genes was analyzed using the QuantStudio 3 Real-Time PCR System (Thermo Fisher Scientific) with clear 96-well plates (Greiner Bio-One, cat#652240). Primers were designed using the Integrated DNA Technologies (IDT) web tool (Supplementary Table 4). A total of 50ng of RNA was used for One-Step qPCR (Quanta BioSciences, cat# 95089-200) following the manufacturer’s instructions in a final volume of 10μL. Conditions for amplification were: 10 min at 48° C, 5 min at 95° C, 40 cycles of 10 sec at 95° C and 7 sec at 62° C. The assay ended with a melting curve program: 15 sec at 95° C, 1 min at 70° C, then ramping to 95° C while continuously monitoring fluorescence. Each sample was assessed in triplicate. Relative quantification was determined to multiple reference genes (MRPL19, PSMC4, and PUM1) to ensure reproducibility using the delta-delta CT method.
Cell lysates were collected in 1X sample buffer (2% SDS, 10% glycerol, 0.01% bromophenol blue, 62.5mM Tris, pH=6.8, 0.1M DTT) and boiled (10 min, 95° C). Protein concentration was determined using Bradford assay (Bio-Rad, cat#5000006). Proteins were resolved using SDS-PAGE gels and transferred to nitrocellulose membranes (GE Healthcare Life Sciences, cat#10600001) as previously described . Antibodies used include: anti-BRAF (Santa Cruz Biotechnology, cat#sc-5284, 1:1000), anti-RAS (BD Sciences, cat#610001, 1:1000), anti-p16 (Abcam, cat#ab108349, 1:1000), anti-p21 (Abcam cat#ab109199, 1:1000), anti-cyclin A2 (Abcam cat#ab181591, 1:2000), anti-vinculin (Sigma-Aldrich cat#V9131, 1:1000), β-Actin (Sigma-Aldrich, cat#A1978, 1:10000), anti-mouse HRP (Cell Signaling Technology, cat#cst7076, 1:10,000), and anti-rabbit HRP (Cell Signaling Technology, cat#cst7074, 1:5000).
Senescence and proliferation assays
SA-β-Gal staining was performed as previously described . Cells were fixed in 2% formaldehyde/0.2% glutaraldehyde in PBS (5 min) and stained (40 mM Na2HPO4, 150 mM NaCl, 2 mM MgCl2, 5mMK3Fe(CN)6, 5 mM K4Fe(CN)6, and 1 mg/ml X-gal) overnight at 37° C in a non-CO2 incubator. Images were acquired at room temperature using an inverted microscope (Nikon Eclipse Ts2) with a 20X/0.40 objective (Nikon LWD) equipped with a camera (Nikon DS-Fi3). Each sample was assessed in triplicate and at least 100 cells per well were counted (>300 cells per experiment). For colony formation, an equal number of cells were seeded in 6-well plates (for IMR90s) and 12-well plates (for SKMel28) and cultured for an additional 1-2 weeks. Colony formation was visualized by fixing cells in 1% paraformaldehyde (5 min) and staining with 0.05% crystal violet (20 min). Wells were de-stained in 500mL 10% acetic acid (10 min). Absorbance (590nm) was measured using a spectrophotometer (Spectra Max 190). Each sample was assessed in triplicate.
Differential expression analysis
Preprocessed and processed RNA-Seq data from primary tumors of skin cutaneous melanoma (SKCM), pancreatic adenocarcinoma (PAAD), colorectal adenocarcinoma (COADREAD), mesothelioma (MESO), bladder urothelial carcinoma (BLCA), and glioblastoma multiforme (GBM) TCGA data sets were downloaded from BROAD GDAC Firehose on June 22, 2020 (SKCM, COADREAD, and PAAD) and October 6, 2020 (MESO, BLCA, GBM) [Broad Institute TCGA Genome Data Analysis Center (2016): Firehose 2016_01_28 run. Broad Institute of MIT and Harvard. doi:10.7908/C11G0KM9)]. Processed rnaseqv2 files containing normalized RSEM expression values for each gene in each patient were used to determine the first and third quartile of CDKN2A expression for each tumor type separately (Table 1). Quartile values were used to classify patients into p16-low (CDKN2A expression ≤ Q1) and p16-high (CDKN2A expression ≥ Q3) groups. Differential expression analysis between p16-low and p16-high patients for each tumor type was performed using the preprocessed raw-counts files in R-CRAN (R-3.6.3) and the DESeq2 package.
Gene set enrichment analysis (GSEA)
Genes were ranked according to the fold-change and p-value obtained in the differential expression analysis between p16-low vs. p16-high as follows: -log10(p-value) x sign (log2 fold change). Pre-ranked files were built for each tumor type separately in R-CRAN (R-3.6.3) and used to run pre-ranked GSEA (javaGSEA desktop application) for KEGG and Reactome under predefined parameters (1000 permutations, weighted enrichment statistic, excluding sets larger than 500 and smaller than 15 and using meandiv normalization mode, there were no repeated genes thus collapse mode was not used). Following GSEA documentation indications, terms were considered significant when the FDR adjusted p-value (q-value) was <0.25 (http://software.broadinstitute.org/gsea/index.jsp). Gene sets for “Casella et al. senescence upregulated genes” (50 genes) and “Casella et al. senescence downregulated genes” (18 genes) were built in GMX files from publicly-available expression senescence signatures  and used to run GSEA using previously described pre-ranked files and parameters.
GraphPad Prism version 7.0 was used to perform statistical analysis. The level of significance between two groups was assessed with unpaired t test. For data sets with more than two groups, one-way ANOVA followed by Tukey’s post hoc test was applied. P-values< 0.05 were considered significant. Pearson correlation test in GraphPad Prism version 7.0 was used to assess the correlation between LMNB1 and CDKN2A. The percentages of normal and tumor cells as well as tumor infiltrating lymphocytes, monocytes, and neutrophils for TCGA tumors were obtained from the biospecimen file at BROAD GDAC Firehose on October 6, 2020.
R.B. and K.M.A. designed the experiments; R.B., K.E.L., and M.A.A. conducted the experiments; R.B. performed data analysis; R.B. and K.M.A. wrote the manuscript. All authors contributed to editing the manuscript.
We thank Drs. Erika Dahl, Chi-Wei Chen, and Naveen Kumar Tangudu for critical reading and editing of this manuscript. We also thank Dr. Gary Nolan from Stanford University for providing with Phoenix packaging cells.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
This work was supported by grants from the National Institutes of Health (F31CA250366 to K.E.L. and R37CA240625 and R00CA194309 to K.M.A.) and a Penn State Cancer Institute Postdoctoral Fellowship (R.B.).
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