Author response: Renal medullary carcinomas depend upon SMARCB1 loss and are sensitive to proteasome inhibition (2019)

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Renal medullary carcinoma (RMC) is a rare and deadly kidney cancer in patients of African descent with sickle cell trait. We have developed faithful patient-derived RMC models and using whole-genome sequencing, we identified loss-of-function intronic fusion events in one SMARCB1 allele with concurrent loss of the other allele. Biochemical and functional characterization of these models revealed that RMC requires the loss of SMARCB1 for survival. Through integration of RNAi and CRISPR-Cas9 loss-of-function genetic screens and a small-molecule screen, we found that the ubiquitin-proteasome system (UPS) was essential in RMC. Inhibition of the UPS caused a G2/M arrest due to constitutive accumulation of cyclin B1. These observations extend across cancers that harbor SMARCB1 loss, which also require expression of the E2 ubiquitin-conjugating enzyme, UBE2C. Our studies identify a synthetic lethal relationship between SMARCB1-deficient cancers and reliance on the UPS which provides the foundation for a mechanism-informed clinical trial with proteasome inhibitors. https://doi.org/10.7554/eLife.44161.001 eLife digest Renal medullary carcinoma (RMC for short) is a rare type of kidney cancer that affects teenagers and young adults. These patients are usually of African descent and carry one of the two genetic changes that cause sickle cell anemia. RMC is an aggressive disease without effective treatments and patients survive, on average, for only six to eight months after their diagnosis. Recent genetic studies found that most RMC cells have mutations that prevent them from producing a protein called SMARCB1. SMARCB1 normally acts as a so-called tumor suppressor, preventing cells from becoming cancerous. However, it was not clear whether RMCs always have to lose SMARCB1 if they are to survive and grow. Often, diseases are studied using laboratory-grown cells and tissues that have certain features of the disease. No such models had been created for RMC, which has slowed efforts to understand how the disease develops and find new treatments for it. Hong et al. therefore worked with patients to develop new lines of cells that can be used to study RMC in the laboratory. These RMC cells started dying when they were given copies of the SMARCB1 gene, which supports the theory that RMCs have to lose SMARCB1 in order to grow. Hong et al. then used a set of genetic reagents that can suppress or delete genes that are targeted by drugs, and followed this by testing a range of drugs on the RMC cells. Drugs and genetic reagents that reduced the activity of the proteasome – the structure inside cells that gets rid of old or unwanted proteins – caused the RMC cells to die. These proteasome inhibitor drugs also killed other kinds of cancer cells with SMARCB1 mutations. Proteasome inhibitors are already used to treat different types of cancer. Potentially, a clinical trial could be run to see if they will treat patients whose cancers lack SMARCB1. Further work is also needed to determine the exact link between SMARCB1 and the proteasome. https://doi.org/10.7554/eLife.44161.002 Introduction Renal medullary carcinoma (RMC) was first identified in 1995 and is described as the seventh nephropathy of sickle cell disease (Davis et al., 1995). RMC is a rare cancer that occurs primarily in patients of African descent that carry sickle cell trait and presents during adolescence with symptoms of abdominal pain, hematuria, weight loss and widely metastatic disease. Due to the aggressive behavior of this disease and the small numbers of patients, no standard of care exists. Patients are generally treated with multimodal therapies including nephrectomy, chemotherapy and radiation therapy. Despite this aggressive regimen, the mean overall survival rate is only 6–8 months (Alvarez et al., 2015; Beckermann et al., 2017; Ezekian et al., 2017; Iacovelli et al., 2015). Recent studies have implicated loss of SMARCB1 in RMC (Calderaro et al., 2016; Carlo et al., 2017; Cheng et al., 2008). SMARCB1 is a tumor suppressor that when conditionally inactivated in mice leads to rapid onset of lymphomas or brain tumors (Han et al., 2016; Roberts et al., 2002). Furthermore, SMARCB1 is a core member of the SWI/SNF complex where alterations of one or more members have been identified in up to 20% of all cancers (Helming et al., 2014; Kadoch et al., 2013) including malignant rhabdoid tumors (MRTs) and atypical teratoid rhabdoid tumors (ATRTs). MRTs and ATRTs harbor few somatic genetic alterations other than biallelic loss of SMARCB1 and occur in young children (Chun et al., 2016; Gröbner et al., 2018; Lee et al., 2012; Ma et al., 2018; Torchia et al., 2016). In contrast, RMC patients present as adolescents/young adults, are primarily of African descent and have been found to have fusion events in SMARCB1 and gene mutations in ERG, PDGFRB, MTOR, and ERBB2 (Calderaro et al., 2016; Carlo et al., 2017). Other pathways implicated in this disease included loss of TP53 and VEGF/HIF1A (Swartz et al., 2002). An unresolved question is whether these cancers depend upon loss of SMARCB1. Furthermore, there is an unmet need to identify therapeutic targets to provide better treatments for these patients. Here, we have developed and characterized faithful cell lines of this rare cancer. We demonstrate that RMC depends on loss of SMARCB1 for survival and, through integrated genetic and pharmacologic studies, we uncover the proteasome as a core druggable vulnerability in RMC and other SMARCB1-deficient cancers. Results Derivation and genomic characterization of RMC models From September 2013 until September 2018, three patients who had a diagnosis of renal medullary carcinoma (RMC) were consented to IRB approved protocols (Materials and methods). All patients were of African descent and adolescents. We first attempted to create a patient-derived xenograft from each patient by implanting tissue in the sub-renal capsule or subcutaneously in immunodeficient mice but these samples did not form tumors after 6 months of monitoring. We then attempted to develop cell lines from these patients and generated cell lines from two of the three patients (CLF_PEDS0005 and CLF_PEDS9001) (Materials and methods). For the first patient (CLF_PEDS0005), we obtained the primary tissue from our local institution at the time of the initial nephrectomy. We generated a short-term culture normal kidney cell line, CLF_PEDS0005_N, and a tumor cell line, CLF_PEDS0005_T1 (Figure 1—figure supplement 1a). In addition, we obtained fluid from a thoracentesis performed when the patient relapsed 8 months into therapy. We isolated two cell lines that grew either as an adherent monolayer, CLF_PEDS0005_T2A, or in suspension, CLF_PEDS0005_T2B. Each of these tumor cell lines expressed the epithelial marker, CAM5.2, and lacked expression of SMARCB1 similar to that observed in the primary tumor (Figure 1—figure supplement 1b). For the second patient (CLF_PEDS9001), we partnered with the Rare Cancer Research Foundation and obtained samples through a direct-to-patient portal (www.pattern.org). The primary tumor tissue from the second patient was obtained at the time of the initial nephrectomy. From this sample, we generated the tumor cell line, CLF_PEDS9001_T1. Cell lines were generated from patients who received 4–8 weeks of neoadjuvant chemotherapy prior to their nephrectomy. Sequencing and cytogenetic efforts have identified deletion of one allele of SMARCB1 along with fusion events in the second allele of SMARCB1 in RMC patients (Calderaro et al., 2016; Carlo et al., 2017). We performed WES (CLF_PEDS0005) or whole genome sequencing (WGS; CLF_PEDS9001) on the primary kidney tumor tissues. In both patients, we confirmed the presence of sickle cell trait but also found tumor purity was <20%, which, like prior studies, prevented the identification of the fusion events (Figure 1—figure supplement 1c). This low tumor purity is attributable to the stromal desmoplasia seen in RMC (Swartz et al., 2002). We then performed WES on the normal cell line (CLF_PEDS0005_N) or whole blood (CLF_PEDS9001) and compared it to the primary tumor cell lines (CLF_PEDS0005_T1 and CLF_PEDS9001_T) and metastatic cell lines (CLF_PEDS0005_T2A and CLF_PEDS0005_T2B). We found a low mutation frequency (1–3 mutations/mb; Materials and methods; Figure 1a) in the tumor cell lines similar to that of other pediatric cancers and cell lines such as MRT, ATRT and Ewing sarcoma (Cibulskis et al., 2013; Johann et al., 2016; Wala et al., 2018). We found that only the metastatic cell lines harbored mutations in TP53 and TPR (Materials and methods; Supplementary file 1) (Cibulskis et al., 2013). Using copy number analysis, we confirmed the heterozygous loss of SMARCB1. In agreement with prior studies, we failed to find an identifiable mutation or deletion to account for the loss of the second SMARCB1 allele with WES. Figure 1 with 1 supplement see all Download asset Open asset Patient-derived models of RMC have quiet genomes and are driven by intronic translocations. (a) Copy number analysis of RMC models from WES identifies heterozygous loss of alleles particularly at 22q where SMARCB1 resides or low-level gains in primary and metastatic tumors. Red indicates degree of genomic amplification while blue indicates degree of genomic loss. Rates of mutations per megabase are consistent with patients with RMC or other pediatric cancers such as rhabdoid tumor. (b) Circos plots from WGS to represent structural alterations seen in RMC cell lines. Red indicates a deletion. All deletions are located in the introns. Blue arcs indicate fusions identified with SvABA v0.2.1 (Wala et al., 2018). Blue star indicates a SMARCB1 rearrangement. (c) Read counts from WGS identify single copy deletion of SMARCB1 in CLF_PEDS0005_T1 indicated by a Tumor/Normal ratio of approximately 0.5 where SMARCB1 is located. (d) Second allele of SMARCB1 is lost by a balanced translocation occurring in intron 1 of SMARCB1 and fuses to the C-terminal end of C1orf116 in chromosome one in CLF_PEDS0005_T1. (e) Read counts from WGS identify single copy deletion of SMARCB1 in CLF_PEDS9001_T1. (f) Second allele of SMARCB1 is lost by a balanced translocation occurring in intron 6 of SMARCB1 and fuses to the anti-sense intron 10 of PLEKHA5 in chromosome 12 in CLF_PEDS9001_T1. https://doi.org/10.7554/eLife.44161.003 We used dual-color break apart FISH and found that a fusion event led to loss of the second SMARCB1 allele as seen in prior studies (Materials and methods; Supplementary file 2) (Calderaro et al., 2016; Carlo et al., 2017). We then performed WGS to assess for structural variations that would not be captured by WES to elucidate the breakpoint of the rearrangement in SMARCB1 (Figure 1b; Materials and methods). For CLF_PEDS0005_T1, we found a large deletion between BCR and MYH9 which is predicted to lead to loss of one allele of SMARCB1 (Figure 1c) along with a balanced translocation between intron 1 of SMARCB1 to the intron region following the C-terminal end of C1orf116, yielding a non-functional allele (Figure 1d and Supplementary file 3). For CLF_PEDS9001_T1, we found a large deletion between TTC28 and VPREB that would lead to loss of one allele of SMARCB1 (Figure 1e) along with a balanced translocation that leads to fusion of intron 10 of PLEKHA5 to intron 6 of SMARCB1 (Figure 1f and Supplementary file 4). Both translocations involved inactivation of the C-terminal end of SMARCB1 (Figure 1—figure supplement 1d). We confirmed these findings by Sanger sequencing of the breakpoint, by qRT-PCR and by immunoblotting to demonstrate loss of SMARCB1 expression (Supplementary file 5, Figure 1—figure supplement 1e–f; Materials and methods). We then assessed previously identified breakpoint SMARCB1 sequences with the breakpoints identified in this study and failed to find alignments, fragile sites or other repetitive DNA elements that were shared amongst these sequences (Figure 1—figure supplement 1g). Taken together, we have developed in vitro cell line models from two patients with RMC which faithfully recapitulate known genomics of this disease. Patient-derived models of RMC are similar to SMARCB1 deficient cancers We performed RNA-sequencing and transcriptomic profiling to compare the RMC models to other renal tumors or tumors that harbor loss of SMARCB1. Specifically, we compared the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) RNA-sequencing data from pediatric renal tumors (e.g. Wilms Tumor, Clear Cell Sarcoma of the Kidney, and Malignant Rhabdoid Tumor) or normal kidney tissues with the RMC models using t-distributed stochastic neighbor embedding (tSNE) (Materials and methods). The normal cell line, CLF_PEDS0005_N, clustered with TARGET normal kidney tissues and RMC cell lines from both patients clustered with the TARGET Rhabdoid Tumor samples (Figure 2a). These observations showed that these RMC cell lines share expression patterns with patients with MRTs. Figure 2 with 1 supplement see all Download asset Open asset Patient-derived models of RMC are functionally similar to SMARCB1 deficient cancers. (a) Using tSNE, gene expression by RNA-sequencing from patients with pediatric renal tumors profiled in TARGET such as clear cell sarcoma of the kidney (CCSK), malignant rhabdoid tumor (MRT) or Wilms tumor (WT) was compared with patient-derived models of RMC. RMC cell lines (red) cluster with rhabdoid tumor samples (purple). The normal cell line (orange) (CLF_PEDS0005_N) clusters with other normal kidneys profiled in TARGET. (b) tSNE analysis of gene-expression array data shows RMC cell lines (red) clustering with RMC patients (blue) and these cluster with other MRT (purple) or ATRT (yellow) samples. However, these do not cluster as closely with synovial sarcomas (black). (c) Glycerol gradients (10–30%) followed by SDS-PAGE analysis of rhabdoid tumor cell line G401, as compared to SMARCB1 wild-type cell line HA1E, show ARID1A is seen in higher fractions when SMARCB1 is expressed (left). Gradients were then performed on patient-derived models of RMC with doxycycline-inducible SMARCB1. A similar rightward shift of ARID1A occurs upon re-expression of SMARCB1. These same shifts occur with SMARCA4 (right). These experiments are representative of at least two biological replicates. (d) Fraction 14 of the glycerol gradients shows a modest increase in SWI/SNF complex members, SMARCC1, SMARCC2, SMARCA4 and ARID1A in HA1E, a SMARCB1 wild type cell line. When SMARCB1 is re-expressed in G401 and RMC lines, a similar pattern is seen. Images are representative of 2 biological replicates. (e) Using cell lines with stably transfected and inducible SMARCB1, cell viability was assessed with or without expression of SMARCB1 over 8 days. There is no significant difference in SMARCB1 wild type cell line, HA1E. Re-expression of SMARCB1 leads to significant decreases in cell viability as compared to LacZ control in SMARCB1 deficient cancer cell lines G401, CLF_PEDS9001_T, and CLF_PEDS0005. Error bars are standard deviations based on number of samples in parentheses. (f) CLF_PEDS0005_T2A cell line shows signs of senescence following re-expression of SMARCB1. Images representative of 3 biological replicates. https://doi.org/10.7554/eLife.44161.005 Figure 2—source data 1 Source data for Figure 2e. https://doi.org/10.7554/eLife.44161.006 Download elife-44161-fig2-data1-v2.xlsx To determine if the RMC cell lines clustered separately among other SMARCB1-deficient cancers, we performed gene expression analysis of our RMC models and compared them to publicly available datasets of MRT cell lines, patients with RMC, MRT or ATRT or synovial sarcoma (a cancer driven by the fusion oncoprotein SSX-S18 which displaces SMARCB1; Materials and methods) (Barretina et al., 2012; Calderaro et al., 2016; Han et al., 2016; Johann et al., 2016; Richer et al., 2017). Using tSNE, we found that the RMC cell lines closely mapped to a French cohort of RMC, MRT and ATRT patients (Figure 2b). These observations demonstrated that RMC cell lines and SMARCB1 deficient patients express similar gene expression programs. We then assessed the consequences of re-expressing SMARCB1. Specifically, we generated doxycycline-inducible open reading frame (ORF) vectors harboring SMARCB1 and stably infected our RMC models, G401 (MRT cell line) and HA1E (SMARCB1 wild-type immortalized epithelial kidney cell line) (Hahn et al., 1999). We confirmed that the addition of doxycycline used in our studies did not affect the proliferation of the parental cell lines (Materials and methods and Figure 2—figure supplement 1a–b). We used these inducible cell lines to assess the biochemical stability of the SWI/SNF complex by using 10–30% glycerol gradient sedimentation followed by SDS-PAGE (Figure 2c–d and Figure 2—figure supplement 1c). In HA1E SMARCB1 wild-type cells, the SWI/SNF complex members SMARCB1, ARID1A and SMARCA4 were robustly expressed at higher molecular weights (e.g. fractions 13–16). In G401 SMARCB1 deficient cells, the SWI/SNF complex is smaller and seen at lower molecular weights (e.g. fractions 10–14). Furthermore, expression of SWI/SNF complex members was modestly decreased in G401, consistent with our prior studies (Nakayama et al., 2017; Wang et al., 2017). In our RMC cell lines, we found that the majority of ARID1A and SMARCA4 was observed in fractions 11–13 similar to what we found in the MRT cell line G401. In addition, we found increased expression and a shift of ARID1A and SMARCA4 to larger fractions 13–15 upon re-expression of SMARCB1 in the RMC lines similar to what we observed in the SMARCB1 wild-type HA1E cell line. We concluded that the composition of the SWI/SNF complex is similar between RMC and other SMARCB1 deficient cancers. We then used the inducible cell lines to measure the consequence of SMARCB1 re-expression on the viability of the cells. We also generated cell lines with inducible expression of a LacZ control to compare with re-expression of SMARCB1. In the HA1E SMARCB1 wild type cells, we found no significant difference in viability between induction of SMARCB1 versus induction of LacZ using direct counting of viable cells (Figure 2e). In contrast, induction of SMARCB1 in the MRT G401 SMARCB1 deficient cells decreased the number of viable cells by 41%. Similar to G401, we found that re-expression of SMARCB1 in each of the RMC models led to significant decreases in cell viability (37–62%) (Figure 2e; Figure 2—source data 1). These observations suggest loss of SMARCB1 is required for the proliferation and viability of RMC cells. Since MRT cell lines arrest and senesce when SMARCB1 is re-expressed (Betz et al., 2002), we looked for evidence of senescence by staining for senescence-associated acidic β-galactosidase in the RMC cells when SMARCB1 was re-expressed. Following 7 days of SMARCB1 or LacZ re-expression, we stained the cells for β-galactosidase (Materials and methods). We failed to observe cells expressing β-galactosidase upon expression of LacZ in the RMC cells, but when SMARCB1 was expressed, we found 44.6% (±17%) of the RMC cells stained for β-galactosidase (Figure 2f and Figure 2—figure supplement 1d). These studies showed that re-expression of SMARCB1 in RMC cells may also lead to senescence. We then assessed what genes were differentially expressed upon SMARCB1 re-expression in the RMC and MRT cell lines as another way to assess the similarity between these two cancers. We performed RNA-sequencing on the doxycycline-induced SMARCB1 RMC cell lines and compared them to the uninduced cell lines or doxycycline-induced LacZ cell lines. We then re-analyzed our previously published studies of MRT cells with SMARCB1 re-expression and compared them to our RMC cells (Wang et al., 2017). We found 1719 genes to be significantly different (false discovery rate of <0.25) in the RMC cells and 2735 genes in MRT cells. We identified 527 genes that significantly overlapped between the RMC and MRT cell lines (hypergeometric p-value less than 4.035e-63; Supplementary file 6). We compared this group of genes to the genes differentially expressed between MRT tumor and normal tissues from TARGET (n = 6,311). We identified 257 genes that overlapped with the 527 significantly differentially expressed genes induced by re-expression of SMARCB1 (Supplementary file 6). Using this list of 257 genes, we performed gene ontology (GO)-based Gene Set Enrichment Analysis (GSEA) (Subramanian et al., 2005) and identified significantly enriched genes sets (q-value <0.1), including those related to the cell cycle and the ubiquitin-proteasome system (UPS). We then analyzed the kinetics by which these gene expression changes occur after SMARCB1 was re-expressed. Specifically, we analyzed 5 genes of these 257 genes that are implicated in regulation of the G1/S (RRM2, TOP2A) or G2/M (PLK1, CCNB1, UBE2C) phases of the cell cycle. For PLK1 and CCNB1, we observed a gradual decrease in expression over the course of 120 hr and a more decrease in expression after the first hr and then a modest decrease over the following hr (Figure 2—figure supplement These findings that changes in the following SMARCB1 re-expression in RMC cell lines are similar to other SMARCB1 deficient cancer cell lines. In these observations indicate that the RMC cell lines are functionally similar to those from other SMARCB1 deficient cancers. RNAi and CRISPR-Cas9 loss-of-function screens and small-molecule screens in RMC models identify proteasome as a vulnerability MRT, ATRT and RMC are aggressive and cancers. We performed genetic and and pharmacologic screens to identify druggable targets that would decrease proliferation or survival for these cancers. Specifically, we used the Cancer and on targets that were identified by with gene deletion with genome and by small et al., 2016; et al., 2015). We for in the RNAi screens by using for each We performed these three screens on both metastatic models of RMC, CLF_PEDS0005_T2A and (Figure We the into these two cell lines and the of the after days using sequencing (Materials and methods). We confirmed of known essential genes such as (Figure supplement 1a). We then analyzed the between the and control to to gene with RNAi Gene Enrichment et al., 2008). genes, genes with a in CLF_PEDS0005_T2A and genes in CLF_PEDS0005_T2B. Figure 3 with 1 supplement see all Download asset Open asset genomic screens identify of the proteasome as a vulnerability in RMC. (a) RNAi of genes (red) identifies genes that when caused a significant viability loss in created by CRISPR-Cas9 in genes (blue) identify genes that cause a viability loss. RNAi and CRISPR-Cas9 screens were performed in biological replicates. in with identifies that lead to significant viability loss. genes across these three The same screens were performed with and genes were found to be significantly when by by CRISPR-Cas9 or when treated with a small of genes that overlapped between functional screens from CLF_PEDS0005_T2A and can be into genes the ubiquitin-proteasome cell cycle and (Supplementary file (b) of small-molecule screens between and cell the genes identified in Figure are either in or blue Each is representative of the of two replicates. (c) in from CRISPR-Cas9 screens between and genes in the the proteasome Data is at days following and compared to an time compared to the sarcoma cell line of the proteasome leads to a more viability loss as compared with Each is representative of a of 2 biological replicates. (d) of the normal cell line (CLF_PEDS0005_N) or of the cell line were compared for of viability to the primary tumor cell lines following with for and for Error bars represent standard deviations from two biological replicates. (e) of the normal cell line (CLF_PEDS0005_N) or of the cell line were compared for of viability to the primary tumor cell lines following with Error bars represent standard deviations from two biological replicates. (f) Re-expression of SMARCB1 in G401 leads to a rightward shift in the with compared with uninduced cells. Error bars represent standard deviations from two biological replicates. Re-expression of SMARCB1 in leads to a rightward shift in the with compared with uninduced cells. Error bars represent standard deviations from three biological replicates. Figure data 1 Source data for Figure Download In we the CRISPR-Cas9 to determine the of the CRISPR-Cas9 between 6 and days to identify genes or enriched in this by sequencing (Materials and methods). We confirmed that the of among biological was (Figure supplement When compared to the there was significant of essential genes such as (Figure supplement We used to the to gene and found genes a of and genes a of with a in CLF_PEDS0005_T2A and cell lines, We performed a small-molecule using a of that have known targets in the RMC cells (CLF_PEDS0005_T2A and et al., 2016). This approved in clinical and based on our prior We an the based on an range and 0.5 as the significantly decreased cell viability in CLF_PEDS0005_T2A and significantly decreased cell viability in CLF_PEDS0005_T2B. We then looked for genes or targets of the small that in all three of the CRISPR-Cas9 and small-molecule We identified genes in CLF_PEDS0005_T2A and genes in (Supplementary file of which genes in both screens (Figure the genes were of the ubiquitin-proteasome system (e.g. and of the cell cycle and and genes involved in and To small and targets that affect normal renal we the normal cell line (CLF_PEDS0005_N) with the small-molecule We the for these screens in relationship to the Cancer Cell to the to (Barretina et al., 2012; et al., 2016; et al., 2015). We then compared the of this small-molecule with the RMC cancer cell lines (CLF_PEDS0005_T2A and CLF_PEDS0005_T2B). We found that the tumor cells were differentially to two standard upon with proteasome and when compared to the normal cell line (Figure Figure data 1). These findings suggest that the vulnerability to proteasome may be on loss of SMARCB1. of proteasome as a in SMARCB1 deficient cancers To the of SMARCB1 deficient tumors to the ubiquitin-proteasome we assessed the consequences of proteasome on survival of the primary tumor cell line, CLF_PEDS0005_T1, by of the proteasome with We compared these findings with a of