Abstract

   Tumor suppressor p53 (TP53) is frequently mutated in cancer, often
   resulting not only in loss of its tumor-suppressive function but also
   acquisition of dominant-negative and even oncogenic gain-of-function
   traits. While wild-type p53 levels are tightly regulated, mutants are
   typically stabilized in tumors, which is crucial for their oncogenic
   properties. Here, we systematically profiled the factors that regulate
   protein stability of wild-type and mutant p53 using marker-based
   genome-wide CRISPR screens. Most regulators of wild-type p53 also
   regulate p53 mutants, except for p53 R337H regulators, which are
   largely private to this mutant. Mechanistically, FBXO42 emerged as a
   positive regulator for a subset of p53 mutants, working with CCDC6 to
   control USP28-mediated mutant p53 stabilization. Additionally,
   C16orf72/HAPSTR1 negatively regulates both wild-type p53 and all tested
   mutants. C16orf72/HAPSTR1 is commonly amplified in breast cancer, and
   its overexpression reduces p53 levels in mouse mammary epithelium
   leading to accelerated breast cancer. This study offers a network
   perspective on p53 stability regulation, potentially guiding strategies
   to reinforce wild-type p53 or target mutant p53 in cancer.

   Keywords: Genome-wide CRISPR Screening, Mutant p53, p53 Stability,
   Breast Cancer, Fluorescence-based Stability Reporter

   Subject terms: Cancer; Chromatin, Transcription & Genomics; Proteomics

Synopsis

   graphic file with name 44320_2024_32_Figa_HTML.jpg

   Genome-wide p53 protein stability screens provide a comprehensive
   network view of the processes regulating wild type and mutant p53 and
   uncover potential targets for reinforcing wild-type p53 or targeting
   mutant p53 in cancer.
     * Most proteins regulating wild-type p53 also affect mutant p53,
       except for p53 R337H, which harbors a distinct set of regulators.

     * FBXO42 is a novel positive regulator for a subset of p53 mutants
       and collaborates with CCDC6 in regulating USP28-mediated
       stabilization of mutant p53.
     * Loss of FBXO42 or CCDC6 reduces metastasis of p53 mutant pancreatic
       cancer cells, similar to depletion of mutant p53 itself.
     * C16orf72/HAPSTR1 is a negative regulator affecting the stability of
       both wild-type and mutant p53, depending on its nuclear
       localization and the activity of the E3 ubiquitin ligase HUWE1.
     * C16orf72/HAPSTR1 is a proto-oncogene and its overexpression reduces
       p53 levels and accelerates tumor growth in a p53 wild-type breast
       cancer mouse model.
     __________________________________________________________________

   Genome-wide p53 protein stability screens provide a comprehensive
   network view of the processes regulating wildtype and mutant p53 and
   uncover potential targets for reinforcing wild-type p53 or targeting
   mutant p53 in cancer.

   graphic file with name 44320_2024_32_Figb_HTML.jpg

Introduction

   Approximately half of all tumors harbor mutations in the p53 (TP53)
   gene, making TP53 the most commonly mutated gene in cancer
   (Freed-Pastor and Prives, [69]2012; Oren and Rotter, [70]2010;
   Petitjean et al, [71]2007; Vogelstein et al, [72]2000). The majority of
   these mutations are missense mutations, most of which not only deprive
   p53 of its tumor suppressor activities, but might also function in a
   dominant-negative manner, suppressing canonical p53 functions upon
   oligomerization with wild-type p53 (Chene, [73]1998; Giacomelli et al,
   [74]2018; Liu et al, [75]2010; Malkin, [76]2011; Varley et al,
   [77]1997). Some missense mutations also confer a so-called
   gain-of-function (GOF) phenotype, converting mutant p53 into a
   cancer-promoting protein that renders cancer cells more malignant by
   increasing growth rate, motility, invasion, drug resistance, and
   tumorigenicity, while reducing the apoptotic rate (Blandino et al,
   [78]1999; Dittmer et al, [79]1993; Eliyahu et al, [80]1984; Muller and
   Vousden, [81]2013; Oren and Rotter, [82]2010; Peled et al, [83]1996;
   Vogelstein et al, [84]2000; Weisz et al, [85]2004; Wolf et al,
   [86]1984; Zalcenstein et al, [87]2003). Importantly, compared to tumors
   with wild-type p53 or those lacking p53 altogether, tumors expressing
   GOF mutant p53 are more invasive, metastatic, and proliferative, and
   display increased genome instability and chemoresistance in mouse and
   human (Blandino et al, [88]1999; Bougeard et al, [89]2008; Lang et al,
   [90]2004; Liu et al, [91]2000; Morton et al, [92]2010; Olive et al,
   [93]2004; Oren and Rotter, [94]2010; Zerdoumi et al, [95]2013). Two
   main classes of p53 hotspot mutations have been distinguished — those
   that affect residues directly involved in protein-DNA interaction, such
   as R248 or R273 (“contact mutants”), and those that affect residues
   involved in stabilizing the tertiary structure of the protein, such as
   R175, G245, R249 and R282 (‘conformational’ or ‘structural’ mutants).

   Germline TP53 mutations also exist, often target the same hotspot
   residues, and are the underlying cause of Li-Fraumeni Syndrome, which
   predisposes to a wide spectrum of early-onset cancers. In Brazil, the
   TP53 R337H founder mutation exists at high frequency and represents the
   most common germline TP53 mutation reported to date (Bouaoun et al,
   [96]2016). Interestingly, unlike most hotspot mutations, this mutation
   is not located in the DNA-binding domain of p53 but in the
   oligomerization domain and disrupts p53 oligomerization.

   While wild-type (WT) p53 protein expression is tightly regulated and
   kept at a low under homeostatic conditions, mutant p53 is often
   stabilized and highly overexpressed in tumors, which is thought to be
   required for mutant p53 to exert its oncogenic effects. In fact, strong
   immunohistochemical staining patterns of nuclear p53 still serve as a
   surrogate marker for TP53 mutations in the clinic (Rotter, [97]1983;
   Yemelyanova et al, [98]2011). Interestingly, knock-in mouse models of
   p53 and Li-Fraumeni patients carrying germline p53 GOF mutations highly
   express mutant p53 specifically in tumor cells, but show low or
   undetectable levels of mutant p53 in the surrounding, phenotypically
   normal tissues (Lang et al, [99]2004; Olive et al, [100]2004; Oren and
   Rotter, [101]2010; Terzian et al, [102]2008). This observation
   indicates that mutant p53 is not intrinsically stable and that its
   levels are kept in check in healthy cells, but that this regulation is
   perturbed in cancer (Lang et al, [103]2004; Olive et al, [104]2004;
   Oren and Rotter, [105]2010). The stability of wild-type p53 is
   regulated mainly through MDM2/4-mediated ubiquitination and degradation
   (Eischen and Lozano, [106]2014; Haupt et al, [107]1997; Kubbutat et al,
   [108]1997), but little is known about the factors that regulate mutant
   p53 stability.

   Here, we employed functional genomics and proteomics approaches to
   systematically profile the processes that regulate the stability of
   wild-type, as well as of the most common p53 mutants that collectively
   account for ~50% of all mutant p53 (Bouaoun et al, [109]2016). These
   screens identified 864 genes whose loss either increases or decreases
   the stability of p53 mutants. Mining this dataset, we report that the
   FBXO42-CCDC6-USP28 axis acts as a positive regulator of mutant p53
   stability, and the C16orf72/HAPSTR1-HUWE1-USP7 axis acts as a negative
   regulator of p53 stability.

Results

A fluorescence-based p53 stability reporter system

   To monitor p53 stability at the single-cell level, we generated a
   lentiviral protein stability reporter (Fig. [110]1A) consisting of a
   p53-mClover-P2A-mRFP cassette that permits translation of a mClover-p53
   protein fusion and a red fluorescent RFP protein from the same mRNA
   transcript, similar to previous protein stability reporters (Yen et al,
   [111]2008; Yu et al, [112]2014). The p53-mClover fusion assesses p53
   stability, while mRFP serves as an internal control to monitor the
   expression of the bicistronic transgene (Fig. [113]1A).

Figure 1. CRISPR screen for regulators of wild-type and mutant p53 stability.

   [114]Figure 1
   [115]Open in a new tab

   (A) Reporter design. Schematic of the fluorescence reporter based on a
   bicistronic p53-mClover stability sensor followed by a co-translational
   self-cleaving P2A peptide and mRFP that serves as an internal control.
   Any perturbation that specifically affects p53 stability would result
   in an altered mClover/RFP ratio (#2 horizontal axis), while any
   perturbation that results in overall increased transcription or general
   differences of proteostasis would affect RFP as well as p53-mClover (#1
   diagonal axis). (B) Flow cytometry blots depicting the level of
   wild-type and mutant p53 protein reporters upon Nutlin-3a (10 μM, 24 h)
   treatment to inhibit the interaction between MDM2 and p53 and upon
   Nutlin-3a withdrawal (24 h). Results are reproducible over biological
   triplicates. (C) Flow cytometry blots depicting the levels of wild-type
   and p53 R273H protein reporters upon CRISPR/Cas9-mediated MDM2
   depletion (top), or upon subsequent MDM2 overexpression (bottom),
   measured 7 days post-transduction. Results are reproducible over
   biological triplicates. (D) Schematic of the screening setup and
   analysis. Each clonal reporter line was transduced with a lentiviral
   genome-wide CRISPR knockout library (TKOv3). Infected populations were
   drug-selected and sorted by Fluorescence-Activated Cell Sorting (FACS)
   into mClover-low and mClover-high pools. sgRNA barcodes were amplified
   and their abundance in each pool was determined by next-generation
   sequencing. (E, F) Screen results for wild-type and p53 R273H protein
   reporters. Volcano plots displaying the perturbation effects (log
   2-fold change, LFC) of each gene based on two replicates per screen. To
   compare among different screens (G, H), the perturbation effects of
   each gene were further normalized as normZ scores, considering both the
   LFC and false discovery rate (FDR) values. Hits were defined as having
   |normZ| ≥ 3. On the volcano plots, hits satisfying both FDR < 0.5, and
   LFC ≥ 1 (red) or LFC ≤ −0.8 (blue), are labeled in red for genes whose
   losses lead to increased p53 levels and blue for genes whose losses
   decreased p53. Screens were performed in two technical replicates. (G)
   Unsupervised hierarchy clustering of the wild-type and five screened
   p53 mutants, using the normalized screening results (normZ) of all
   18053 genes. (H) UpSet plot displaying the relationships of hits shared
   amongst each mutant screened. The loss-of-function of an “up” (red) or
   “down” (blue) hit would result in the p53 mutant to destabilize or
   stabilize, respectively. Each column on the plot denotes a set of WT
   and/or p53 mutants, and the histogram above indicates the number of
   genes in this intersecting set; the filled-in cells denote which p53
   (WT or mutants) is a part of this intersection. [116]Source data are
   available online for this figure.

   To benchmark this reporter, we used an hTERT-immortalized retinal
   pigment epithelium-1 (RPE1) cell line that expresses Cas9 and was
   previously used in CRISPR screens (Hart et al, [117]2015). We used an
   RPE1 subclone in which the gene encoding p53 was knocked out by gene
   editing (Noordermeer et al, [118]2018) and generated isogenic lines
   expressing stability reporters containing either wild-type p53 or eight
   of the most common hotspot p53 mutations (R175H, G245S, R248Q, R248W,
   R249S, R273H, R282W, and R337H). As a control, we also generated a cell
   line expressing the mClover-P2A-RFP cassette without p53. Upon
   activation by the MDM2 inhibitor Nutlin-3a, the levels of the WT p53
   reporter were comparable to endogenous p53 levels in parental RPE1
   cells. We also observed upregulation, at the protein level, of the p53
   target gene CDKN1A (encoding p21) upon Nutlin-3a treatment in the
   reporter cell line, albeit to a reduced level compared to the parental
   cell line. The R273H p53 reporter showed increased p53 levels even in
   untreated cells, but did not upregulate p21 after Nutlin-3a addition,
   as expected for p53 hotspot mutations (Appendix Fig. [119]1A).

   We observed that cells transduced with p53 G245S, R248Q, R248W, or
   R273H exhibited bimodal distribution of p53 levels, with one subset
   expressing hardly any p53 and the other subset expressing higher levels
   of p53. This pattern of p53 protein expression was observed with both
   structural (G245S) and contact (R248Q, R248W, R273H) p53 mutants
   (Fig. [120]1B). Such bimodal distribution was also observed in lymphoma
   cell lines expressing endogenous p53 R248Q, R248W or R273H mutants
   (Jethwa et al, [121]2018) and thus likely reflects the intrinsically
   unstable nature of mutant p53 (Terzian et al, [122]2008). In line with
   previous data showing that MDM2 promotes the degradation of wild-type
   and some mutant p53(Haupt et al, [123]1997; Lukashchuk and Vousden,
   [124]2007; Terzian et al, [125]2008), treatment with Nutlin-3a elevated
   the levels of wild-type p53 and the p53 mutants G245S, R248Q, R248W,
   and R273H, while R175H, R249S, R282W and R337H did not respond to
   Nutlin-3a (Fig. [126]1B). Upon removal of Nutlin-3a, levels of
   wild-type and mutant p53 returned to baseline levels, re-establishing
   the uni- or bimodal distribution of the starting population
   (Fig. [127]1B). Similarly, we observed that irradiation led to
   significantly increased levels of p53 mutants (G245S, R248Q, R248W,
   R273H) with the exception of p53 R175H, R249S, R282W, and R337H
   (Appendix Fig. [128]1B), further indicating that several p53 mutants
   and especially the contact p53 mutants are regulated by the same
   machinery that regulates wild-type p53. In line with these data,
   CRISPR/Cas9-mediated ablation of MDM2 elevated the p53^R273H-mClover,
   which could be reversed by ectopically re-expressing MDM2, further
   validating our reporter system (Fig. [129]1C).

Genome-wide CRISPR screens identify regulators of wild-type and mutant p53
stability

   To identify regulators of p53 stability, we performed genome-wide
   pooled CRISPR screens in RPE1-hTERT Cas9 cells expressing either the
   control mClover-P2A-RFP cassette or the stability reporters for p53
   wild-type, R175H, G245S, R248Q, R273H, or R337H. We used the TKOv3
   lentiviral sgRNA library, which contains 70,948 guides (~4 guides/gene)
   targeting 18,053 protein-coding genes and 142 control sgRNAs targeting
   EGFP (N.B. mClover is a monomeric variant of GFP, so it is targeted by
   the GFP guides), LacZ, and luciferase (Hart et al, [130]2017). We
   transduced the reporter lines at 200X coverage, selected for infected
   cells and isolated cell populations expressing low or high p53-mClover
   by flow cytometry (Fig. [131]1D). For reporter lines with a unimodal
   p53 distribution, we sorted cells with the lowest and highest 15% of
   p53-mClover expression, and for lines with a bimodal p53 distribution,
   we isolated the lower and the upper populations. The seven cell lines
   were screened in duplicates at a minimum, and we observed a good
   reproducibility between sgRNA abundance in the replicates
   (r = 0.44–0.71) (Hart et al, [132]2015) (Appendix Fig. [133]1C).

   For gene-level depletion/enrichment, we calculated a normalized z-score
   (NormZ) of the low and high p53-mClover populations, combining multiple
   sgRNAs per gene. Negative NormZ scores represent genes whose
   inactivation leads to decreased p53 levels (i.e., positive regulators
   of p53 stability), whereas positive NormZ scores represent genes whose
   inactivation leads to increased p53 levels (i.e., negative regulators
   of p53 stability). As expected, sgRNAs targeting p53 and mClover were
   the most depleted sgRNAs, while sgRNAs targeting MDM2 or MDM4 were
   among the most enriched sgRNAs in the p53-high populations. Other known
   positive regulators of p53, such as ATM, USP28, TTI1/2, TP53BP1, or
   CHEK2, and negative regulators of p53 stability such as USP7, HUWE1, or
   PPM1D/G also scored in several p53 wild-type or mutant screens, further
   validating the screens (Fig. [134]1E,F; Appendix Fig. [135]2A–D and
   Dataset [136]EV1). Unsupervised clustering of the screens showed that
   p53 wild-type, G245S, R248Q, R273H mutants clustered closely together,
   while p53 R175H and p53 R337H showed distinct profiles (Fig. [137]1G).

   To identify hits, we selected genes with NormZ values +/− 3, a false
   discovery rate (FDR) lower than 0.5 and excluded genes that also
   affected the negative control mClover-P2A-mRFP reporter. These cutoffs
   identified 292 and 548 genes, whose loss led to decreased and increased
   p53 levels, respectively. While most genes regulated the stability of
   wild-type and several p53 mutants, we also identified p53 wild-type-
   and p53 mutant-specific regulators (Fig. [138]1H). For example,
   wild-type p53 levels were specifically sensitive to perturbation of the
   20S and 19S proteasomal subunits, consistent with the high protein
   turn-over of p53. This is in contrast to the levels of some mutant p53
   proteins, such as R273H and R248Q, whose levels were not significantly
   affected upon genetic ablation of proteasome subunits (Appendix
   Fig. [139]2E). In addition, of the 292 hits whose loss caused p53
   destabilization, the vast majority (182 genes) regulated only p53
   R337H. Conversely, of the 548 hits whose loss led to p53 stabilization,
   only a small subset (52 genes) regulated p53 R337H (Fig. [140]1H;
   Appendix Fig. [141]1B). These data suggest that p53 R337H is controlled
   by mechanisms that are different from those modulating wild-type p53
   and all other p53 mutants tested.

FBXO42-CCDC6 axis regulates wild-type and mutant p53 stability

   One of the strongest hits whose loss led to decreased p53 levels (i.e.,
   represents a positive p53 regulator) in the R273H and R248Q screens was
   FBXO42 (NormZ value of −4.05 and −5.65 for R273H and R248Q,
   respectively), coding for F-Box Protein 42, which functions as a
   substrate-recognition subunit of an SCF (SKP1-CUL1-F-box protein)-type
   E3 ubiquitin ligase complex. Analysis of the Dependency Map (DepMap)
   project (Tsherniak et al, [142]2017) indicated that the genetic
   dependency profile of FBXO42 correlated with that of p53 and its
   activators CHEK2, TP53BP1, ATM, and USP28 and was inversely correlated
   with those of negative p53 regulators such as MDM2, MDM4, PPM1G, and
   USP7 (Fig. [143]2A,B), strongly suggesting a functional connection to
   the p53 pathway. The strongest genetic co-dependency of FBXO42, and one
   of the strongest correlations across all genes and cell lines, was with
   CCDC6, which encodes a coiled-coil domain-containing protein
   (Fig. [144]2A,B). CCDC6 shows a similarly strong co-dependency with the
   p53 pathway in DepMap and loss of CCDC6 caused phenotypes similar to
   those associated with the loss of FBXO42 in our screens, i.e.,
   resulting in decreased p53 R273H and R248Q levels (Fig. [145]2C;
   Dataset [146]EV1).

Figure 2. FBXO42-CCDC6 axis regulates mutant p53 protein stability.

   [147]Figure 2
   [148]Open in a new tab

   (A) Heatmap of the essentiality scores of top correlated (positive) and
   anti-correlated (negative) genes with FBXO42 across 789 cancer cell
   lines screened in DepMap (depmap.org, generated using FIREWORK (Amici
   et al, [149]2020). (B) The 50 top genes correlated (blue) and
   anti-correlated (red) with FBXO42, based on coessentiality results from
   CRISPR screens in 789 cancer cell lines (depmap.org). (C) Heatmap
   displaying the screening results (as normZ scores) of selected hits
   across wild-type and five p53 mutants. A positive normZ (red) indicates
   that genetic ablation of a gene leads to increased p53 protein
   stability, and negative normZ (blue) indicates decreased p53 stability.
   (D) Flow cytometry blots depicting wild-type and R273H p53-mClover
   levels upon CRISPR/Cas9-mediated depletion of indicated genes.
   Independent sgRNAs different from the sgRNAs in the screening library
   were used (red) and the effects were compared against control guides
   targeting the AAVS1 safe harbor (blue). Results are reproducible over
   biological triplicates. (E) Representative Western Blot showing
   p53^R273H-mClover protein levels in clonal RPE1 p53 R273H reporter cell
   line upon clonal depletion of CCDC6 or FBXO42, and upon proteasomal
   inhibition (10 µM MG132 for 12 h). GAPDH serves as a loading control.
   Results are reproducible over biological triplicates. (F)
   Representative Western Blot showing endogenous p53 R273H protein levels
   in PANC-1 cells upon depletion of CCDC6 and FBXO42, and upon genotoxic
   stress (IR 0.5 Gy, 24 h). GAPDH serves as a loading control. The bar
   graph depicts the quantification of p53 levels over three independent
   biological replicates (*p < 0.05, two-tailed t-test). Error bar =
   standard error of the mean (S.E.M.). Results are reproducible over
   biological triplicates. (G) Quantification of the metastatic lung
   colonization. The number of lung foci for each mouse injected with
   PANC1-Cas9 cells with the indicated genotype was plotted (n = 5 mice
   for each condition, with a mix of males and females housed in different
   cages). Mantel-Cox (log-ranked) test was used for statistical analysis.
   Error bar = S.E.M. (H) Flow cytometry blots depicting the
   p53^R273H-mClover levels upon depletion of CCDC6 or FBXO42, and upon
   ectopic re-expression of CCDC6 or FBXO42. Results are reproducible over
   biological triplicates. (I) Flow cytometry blots depicting
   p53^R273H-mClover levels upon depletion of FBXO42 and ectopic
   re-expression of ΔFbox FBXO42 (lacking aa 44–93) or ΔKelch FBXO42
   (lacking aa 132–432). Results are reproducible over biological
   triplicates. [150]Source data are available online for this figure.

   We used two independent sgRNAs with good on-target efficacy to
   corroborate the effect of FBXO42 and CCDC6 loss on p53 stability and
   used sgRNAs targeting the AAVS1 locus as control (Appendix
   Fig. [151]2F). We observed significantly reduced R273H, R248Q, and
   R248W p53mClover levels but failed to see significant effects on
   wild-type, R175H, G245S or R337H p53-mClover levels (Fig. [152]2D and
   Appendix Fig. [153]3A), indicating specificity for some p53 mutants.
   The difference in p53 levels was even more apparent upon irradiation
   (Appendix Fig. [154]3B–D) and in single-cell knock-out clones
   (Fig. [155]2E). We also tested whether proteasomal degradation is
   involved in FBXO42/CCDC6-mediated regulation of mutant p53. Inhibition
   of the proteasome by MG132 modestly increased mutant p53 levels in
   FBXO42 and CCDC6 knock-out cells as assessed by WB analysis and flow
   cytometry (Fig. [156]2E; Appendix Fig. [157]3E), which is consistent
   with our genetic results of ablating proteasome subunits (Appendix
   Fig. [158]2E). Interestingly, we found that inhibition of the lysosomal
   degradation pathway using chloroquine resulted in increased levels of
   mutant p53 R273H levels especially in CCDC6 and FBXO42 KO cells
   (Appendix Fig. [159]3F), which is in line with previous data showing
   that mutant but not wild-type p53 is degraded via chaperone-mediated
   autophagy in a lysosome-dependent fashion (Vakifahmetoglu-Norberg et
   al, [160]2013). Of note, genetic ablation of FBXO42 or CCDC6 did not
   alter cell cycle progression, ruling out potential indirect effect on
   p53 levels through the cell cycle (Reyes et al, [161]2018) (Appendix
   Fig. [162]3G). Importantly, genetic ablation of FBXO42 also reduced the
   levels of p53 in the pancreatic cancer cell lines PANC-1 and
   Mia PaCa-2, which are homozygous for p53 R273H and p53 R248W (Redston
   et al, [163]1994), respectively (Fig. [164]2F; Appendix Fig. [165]3H),
   without affecting TP53 mRNA expression, confirming that FBXO42 acts on
   p53 via posttranscriptional regulation (Appendix Fig. [166]3I).

   Next, we set out to assess the functional consequences of reduced
   mutant p53 R273H levels upon knockout of FBXO42 or CCDC6. Since
   accumulated mutant p53 is required for many gain-of-function
   properties, reducing its level may lead to suppression of tumor growth
   and attenuation of invasion and metastasis formation (Alexandrova et
   al, [167]2015; Freed-Pastor and Prives, [168]2012; Oren and Rotter,
   [169]2010). We thus injected PANC-1 cells that were depleted of either
   FBXO42 or CCDC6 as well as control (sgAAVS1) PANC-1 cells into the tail
   vein of nonobese diabetic/severe combined immunodeficiency-gamma (NSG)
   mice and evaluated lung metastasis colonization. Genetic ablation of
   FBXO42 and CCDC6 resulted in a significant reduction of metastatic
   colonization relative to the control, similar to the depletion of TP53
   (R273H) (Fig. [170]2G and Suppl Fig. [171]4A). Of note, loss of FBXO42
   showed the most dramatic reduction, which was likely due to a stronger
   knock-out efficacy compared to depletion of CCDC6 or TP53
   (Fig. [172]3I). Importantly, to test whether these effects were
   specifically mediated by mutant p53 R273H, we repeated this experiment
   using PANC-1 cells devoid of endogenous p53 R273H. Genetic ablation of
   FBXO42 or CCDC6 no longer affected the metastatic colonization of
   PANC-1-Δp53 cells (Fig. [173]2G and Suppl Fig. [174]4A). Collectively,
   these results suggested that the loss of FBXO42 and CCDC6 destabilize
   mutant p53 and attenuate mutant p53-driven metastatic colonization of
   mouse lungs.

Figure 3. Mapping the genetic interaction network of FBXO42-CCDC6 and mutant
p53.

   [175]Figure 3
   [176]Open in a new tab

   (A) Selected proximity interactors of p53 R273H, CCDC6, and FBXO42 as
   mapped by BioID using HEK293-Flp-In T-REx cell lines stably expressing
   each bait, with or without the proteasomal inhibitor MG 132 [5 μM,
   24 h]. The intensity of the shade filling depicts the spectral count of
   each prey, the relative abundance of this prey compared across all
   baits is indicated by the circle size, and the confidence (Bayesian
   false discovery rate, BFDR) is by the intensity of the edge. (B)
   Interaction of p53 R273H and CCDC6 in PANC-1 cells validated using
   immunoprecipitation (IP). Lysates of PANC-1 cells with or without
   depletion of the endogenous p53 R273H protein were immunoprecipitated
   using an CCDC6-specific antibody or an IgG-isotype control, followed by
   Western blot analysis of the endogenous p53. β-actin serves as a
   loading control for lysate input. Results are reproducible over
   biological triplicates. (C) Proximity ligation assay (PLA) between
   endogenous CCDC6 and endogenous p53 R273H in PANC-1 cells using
   tetramethylrhodamine-5-isothiocyanate (TRITC) as a probe (red) and
   counterstained with DAPI (blue) and phalloidin-FITC (green) to
   visualize nuclear DNA and F-actin, respectively. RNAi-mediated
   depletion of the endogenous p53 R273H protein was used as a control to
   show specificity. Results are reproducible over biological triplicates.
   (D) In vitro evidence for a direct and specific interaction between the
   p53-R273H core domain (p53CD-R273H) and FBXO42c. The MBP-tagged Kelch
   domain of FBXO42 (MBP-FBXO42c, aa 105–360) and the core DNA-binding
   domain of p53 R273H (p53-CD-R273H, aa 90–311) were recombinantly
   expressed and purified from the Bl21DE3 E. coli strain. MBP-FBXO42c was
   pre-coupled to amylose resin. Following incubation of MBP-FBXO42c and
   p53-CD-R273H, amylose-resin coupled MBP-FBXO42c captured a fraction of
   p53-CD-R273H, which was found partly in the bound fraction. As a first
   specificity control, Amylose-coupled MBP-FBXO42c was unable to capture
   the unrelated protein GST in the bound fraction. A second specificity
   control was performed to show that p53-CD-R273H did not bind to the
   unrelated protein MBP-p107. In this control, no p53-CD-R273H was found
   in the bound fraction. The input, unbound, and bound fractions were
   resolved on SDS-PAGE and stained with Coomassie blue. Results are
   reproducible over biological triplicates. (E, F) Scatter plots of the
   perturbation effect of each gene as normalized Z-score (normZ), in the
   p53 R273H reporters before and after the loss of CCDC6 or FBXO42.
   Selected genes whose depletion resulted in p53 stabilization and
   destabilization are marked in red and green, respectively. Screens were
   performed in technical duplicates. (G, H) Representative Western blot
   results showing levels of USP28 and p53 in nuclear and cytoplasmic
   fractions of the p53 R273H reporter cell line with clonal depletion of
   FBXO42 or CCDC6, and upon the ectopic re-expression of CCDC6 or FBXO42.
   Histone H3 and GAPDH served as nuclear and cytoplasmic markers,
   respectively. Levels of CCDC6 and FBXO42 were measured from the total
   cell lysates (H). Results are reproducible over biological triplicates.
   (I) Total cellular USP28 and p53 levels in PANC-1 cells upon depletions
   of CCDC6, FBXO42, or USP28. Cell lines harboring AAVS1-g1, TP53-g1,
   FBXO42-g1, and CCDC6-g1 were further used in metastatic lung
   colonization experiment via tail vein injections (Fig. [177]2G;
   Appendix Fig. [178]4A). Results are reproducible over biological
   triplicates. [179]Source data are available online for this figure.

   Given the strong genetic correlation inferred from the DepMap dataset,
   we next set to test for a potential epistatic relationship between
   FBXO42 and CCDC6, using isogenic FBXO42-knockout (ΔFBXO42),
   CCDC6-knockout (ΔCCDC6) and AAVS1-targeted control RPE-1
   p53^R273H-mClover reporter cell lines (Fig. [180]2E). As expected,
   p53^R273H-mClover levels in ΔFBXO42 and ΔCCDC6 could be partially
   rescued by re-expressing FBXO42 or CCDC6, respectively. Interestingly,
   while expression of FBXO42 did not rescue p53^R273H-mClover levels in
   ΔCCDC6 cells, we found that expression of CCDC6 partly rescued mutant
   p53 levels in ΔFBXO42 cells (Fig. [181]2H), suggesting that FBXO42 may
   function upstream of CCDC6.

   Using FBXO42 truncation mutants, we found that both the Kelch and F-Box
   domains are required to promote p53 R273H stabilization, indicating
   that FBXO42’s ability to stabilize p53 R273H is dependent on both its
   substrate binding domain and its incorporation into an SCF complex,
   likely to promote SKP1/CUL1-mediated ubiquitination of an as-yet
   unidentified substrate (Fig. [182]2I; Appendix Fig. [183]4B–D).
   Together, these data indicate that FBXO42 and CCDC6 function as
   positive post-translational regulators of wild-type p53 and several p53
   mutants in a ubiquitination-dependent manner.

Mapping of the FBXO42-CCDC6 and mutant p53 interaction network

   To investigate how FBXO42-CCDC6 regulates p53 stability, we identified
   vicinal proteins by proximity-dependent biotinylation coupled to mass
   spectrometry (BioID) (Kim et al, [184]2016; Lambert et al, [185]2015;
   Roux et al, [186]2013; Roux et al, [187]2012) using inducible
   expression of biotin ligase (BirA*)-tagged FBXO42, CCDC6 or p53 R273H
   in HEK293 Flp-In T-REx cells. Given the function of FBXO42 in the
   ubiquitin-proteasomal system, we performed these BioID experiments in
   the absence or presence of MG132. Consistent with its role as an SCF-E3
   ligase, the top interactors for FBXO42 were CUL1 and SKP1. FBXO42 BioID
   also enriched for CCDC6, p53 itself, and proteins known to regulate
   p53, such as HUWE1 (Fig. [188]3A; Dataset [189]EV2). p53 R273H proximal
   proteins included known p53 interactors such as MDM2, BRCA2, USP28,
   TP53BP1, PPM1G, BLM, ATRX, and PML (Fig. [190]3A). In addition,
   endogenous CCDC6 as well as V5-tagged CCDC6 co-immunoprecipitated
   (co-IP) with endogenous p53 R273H in PANC-1 cells (Fig. [191]3B;
   Appendix Fig. [192]4E). In further support of this interaction,
   proximity ligation assay (PLA) showed that CCDC6 and p53 R273H are
   proximal predominantly in the nucleus (Fig. [193]3C). Using in vitro
   binding assays of recombinant proteins, we detected a direct
   interaction between FBXO42 Kelch domains 1–3 and the core DNA-binding
   domain of p53 R273H (Fig. [194]3D, Appendix Fig. [195]4C,F,G). This
   interaction was specific as the p53 R273H core domain did not bind p107
   Rb, an unrelated protein of similar size (Fig. [196]3D; Appendix
   Fig. [197]4G). In addition, while wild-type p53 interacted with MDM2,
   the p53 R273H core domain did not, as expected given that MDM2
   interacts with the N-terminal activation domain of p53 (Appendix
   Fig. [198]4H). Together, these data show that R273H p53 interacts with
   both its regulators CCDC6 and FBXO42, with a direct interaction
   demonstrated for FBXO42, and indicates a potential formation of a
   higher order complex that regulates p53 stability.

FBXO42-CCDC6 regulates p53 via USP28

   To identify genetic determinants of FBXO42/CCDC6-mediated p53 R273H
   stabilization, we performed genome-wide CRISPR screens using isogenic
   ΔFBXO42, ΔCCDC6 or control RPE1 p53^R273H-mClover-P2A-RFP reporter cell
   lines. This allowed us to systematically map genetic perturbations that
   regulate p53 R273H stability depending on the presence of FBXO42 and
   CCDC6 (Fig. [199]3E,F). Most genes such as MDM2, MDM4, USP7, CSNK1A1,
   ATM, or TTI retained their function in regulating p53 stability in
   ΔFBXO42 or ΔCCDC6 RPE1 cells. As expected for genes in the same pathway
   and based on our previous data, CCDC6 did not score in the ΔFBXO42
   screen (nor did FBXO42) and FBXO42 did not score in the ΔCCDC6 screen
   (nor did CCDC6). Interestingly, we identified one F-box protein-coding
   gene, FBXL18, which gained the ability to reduce p53^R273H-mClover
   levels in ΔFBXO42 or ΔCCDC6 RPE1 cells but did not have an effect when
   lost in parental RPE1 wild-type cells, suggesting that FBXL18 may
   compensate for the loss of FBXO42/CCDC6 (Fig. [200]3E,F). In addition,
   loss of USP28, whose perturbation led to the strongest reduction in
   p53^R273H-mClover levels in wild-type RPE1, had no effect on
   p53^R273H-mClover levels in the ΔFBXO42 screen, and only a weak effect
   on p53^R273H-mClover levels in the ΔCCDC6 screen, indicating that USP28
   may function in the same pathway as FBXO42/CCDC6 (Fig. [201]3E,F). We
   corroborated these genetic interactions and showed that concomitant
   loss of USP28 in ΔFBXO42 or ΔCCDC6 RPE1 cells has no effect on
   p53^R273H-mClover levels (Appendix Fig. [202]5A). Interestingly, loss
   of FBXO42 or CCDC6 resulted in decreased levels of nuclear USP28 as
   shown by nuclear/cytoplasmic fractionation as well as
   immunofluorescence (Fig. [203]3G; Appendix Fig. [204]5B). Importantly,
   ectopic expression of either FBXO42 or CCDC6 not only rescued the USP28
   and R273H p53 levels in ΔFBXO42 or ΔCCDC6 RPE1 cells but also led to
   increased USP28 levels in the parental RPE1 cells, concomitant with a
   slight upregulation of p53 levels (Fig. [205]3H). Genetic ablation of
   FBXO42 or CCDC6 in PANC-1 cells also reduced USP28 protein abundance,
   without affecting USP28 transcript levels (Fig. [206]3I; Appendix
   Fig. [207]5C), indicating that the post-translational regulation of
   USP28 by FBXO42/CCDC6 is not unique to RPE1 cells. Next, we generated
   ΔUSP28 p53^R273H-mClover RPE1 cells and assessed how overexpression of
   FBXO42 and CCDC6 impacted p53 R273H levels (Appendix Fig. [208]5D).
   While expressing USP28 rescued p53 R273H levels, overexpressing FBXO42
   or CCDC6 in ΔUSP28 cells had no effect, indicating that FBXO42/CCDC6
   act via USP28. Moreover, we found that USP28 overexpression rescued
   mutant p53 levels in ΔFBXO42 or ΔCCDC6 RPE1 cells, indicating that
   USP28 is downstream of FBXO42/CCDC6 (Appendix Fig. [209]5D). Together,
   these data indicate that FBXO42/CCDC6 control mutant p53 levels via
   USP28.

Synthetic viability screen maps positive regulators of p53

   To better understand the genes whose loss leads to p53 stabilization
   (i.e., that act as p53 negative regulators), we first performed pathway
   analysis using gProfiler (Fig. [210]4A; Dataset [211]EV3). Gene sets
   associated with cellular response to stress, extracellular stimuli or
   hypoxia, cell cycle, mitosis, stabilization of p53 and p53-dependent
   and -independent DNA Damage Responses were significantly enriched in
   the 547 hits whose inactivation result in higher p53 levels. Enrichment
   was also found for genes involved in regulation of nonsense-mediated
   decay and programmed cell death. Interestingly, metabolism of RNA and
   rRNA modification in the nucleus and cytosol were the most
   significantly enriched categories. This functional group was also
   enriched in various other screens done to identify genes that cause
   genomic instability or modulate responses to ionizing or UV radiation,
   or screens for ATM/ATR substrates (Hurov et al, [212]2010; Matsuoka et
   al, [213]1998; Paulsen et al, [214]2009; Stokes et al, [215]2007),
   indicating that RNA metabolism is tightly interconnected with DNA
   damage responses. Together, this data indicates that the stability of
   mutant p53 is, to a large extent, regulated by the physiological
   machinery that regulates stress-induced wild-type p53 stability.
   However, this data may also indicate that most of the hits whose loss
   leads to p53 stabilization might regulate p53 indirectly by causing
   cellular stress.

Figure 4. Synthetic viability screen maps regulators of p53.

   [216]Figure 4
   [217]Open in a new tab

   (A) Pathway analysis based on the top scoring genes from the protein
   stability reporter screens that resulted in increased wild-type or
   mutant p53 levels using Reactome pathway analysis. Selected Reactome
   pathways are shown. Fisher’s exact test based on the hypergeometric
   distribution was used for pathway enrichment analysis. (B) Synthetic
   viability screen in RPE1 cells. Bayesian Factors (BF) as a measurement
   of essentiality (high values indicate a lethal gene) are shown for all
   protein-coding genes in p53 wild-type (y-axis) versus p53 null (x-axis)
   background. All BFs were computed using the BAGEL2 algorithm (Kim and
   Hart, [218]2021). Screens were performed in technical triplicates. (C)
   Pathway analysis based on the top-scoring genes in the synthetic
   viability screen using Reactome pathway analysis. Selected Reactome
   pathways are shown. Fisher’s exact test based on the hypergeometric
   distribution was used for pathway enrichment analysis. (D) Venn diagram
   depicting the top scoring genes from the synthetic viability screen and
   the top scoring genes from the p53 stability screens whose mutation
   leads to increased p53 levels. The common genes from both screens are
   depicted. (E) Heatmap of the essentiality scores of top correlated
   (positive) and anti-correlated (negative) genes with C16orf72/HAPSTR1
   across 789 cancer cell lines screened in DepMap (depmap.org, generated
   using FIREWORK (Amici et al, [219]2020). (F) The 50 top genes
   correlated (blue) and anti-correlated (red) with C16orf72/HAPSTR1,
   based on coessentiality results from CRISPR screens in 789 cancer cell
   lines (depmap.org). [220]Source data are available online for this
   figure.

   As an alternative approach to identify regulators of p53, we sought to
   exploit the concept of synthetic viability, which describes a genetic
   interaction where the viability of one genetic mutation is determined
   by the presence of a second genetic mutation (O’Neil et al, [221]2017).
   The mutations in MDM2 and TP53 display a synthetic viability
   interaction, as the embryonic and cellular lethality associated with
   the loss of MDM2 is completely rescued in a p53 null background (Montes
   de Oca Luna et al, [222]1995). To identify genes that are synthetic
   viable with p53, we performed genome-wide CRISPR screens in isogenic
   p53-proficient and -deficient RPE1 cell lines. Synthetic viable
   interactions were defined as genes that had high Bayes factor (BF) in
   p53-proficient cells, indicating essentiality, but negative BF values
   in the p53-deficient cells (Dataset [223]EV4). As expected, the known
   negative p53 regulators MDM2, MDM4, USP7, PPM1D (encoding WIP1) all
   scored highly as synthetic viability hits in the screen. In addition,
   pathway analysis of the top 150 scoring genes using PANTHER (Mi et al,
   [224]2017) revealed enrichment of genes involved in the p53 pathway and
   the related Ubiquitin proteasome pathway (Fig. [225]4C;
   Dataset [226]EV5).

   To delineate high-confidence p53 regulators, we searched for hits that
   scored in both the p53 synthetic viability and the p53 reporter
   screens. 17 out of the 53 top-scoring genes in the synthetic viability
   screen also scored in the p53 reporter screens. In addition to the
   well-known p53 regulators (MDM2/4, USP7, PPM1D/G), ZNF574, APEX2, BRAP,
   NONO, TCOF1, OTUD5, UBR5, LSM12, DSCC1, C14orf80, EXOSC1, and
   C16orf72/HAPSTR1 scored prominently in both screening formats
   (Fig. [227]4D; Dataset [228]EV1 and [229]4). In addition to the RPE1
   cell lines, we also performed p53 synthetic viability screens in two
   other human cell lines: A549, a lung cancer cell line, and RKO, a colon
   cancer cell line. While the synthetic viability hits of from the screen
   done in A549 cells exhibited similarity to those found in RPE cells,
   the synthetic viability hits from the screen done in RKO cells were
   distinct (Appendix Fig. [230]6A; Dataset [231]EV4).

   C16orf72/HAPSTR1 (also known as TAPR1) was previously identified in a
   screen for genes that altered sensitivity to telomere attrition and was
   shown to buffer against p53 activation in response to telomere erosion
   (Benslimane et al, [232]2021). Moreover, we recently identified
   C16orf72/HAPSTR1 in a screen that analyzed genetic vulnerabilities to
   ATR inhibition (Hustedt et al, [233]2019). In addition, analysis of
   coessentiality across 789 cancer cell lines from the DepMap project
   showed a striking association between C16orf72/HAPSTR1 and several
   positive and negative p53 regulators (Fig. [234]4E,F) (Amici et al,
   [235]2022; Benslimane et al, [236]2021). Together, these data
   identified C16orf72/HAPSTR1 as a candidate negative regulator of p53
   stability.

C16orf72/HAPSTR1 is a regulator of wild-type and mutant p53 stability

   To validate the genetic interaction between TP53 and C16orf72/HAPSTR1,
   we performed clonogenic survival assays. Loss of C16orf72/HAPSTR1
   resulted in decreased cell viability and relative cellular fitness
   selectively in the TP53^+/+ background but had no impact in the
   p53-deficient isogenic counterpart, indicating synthetic viability
   (Fig. [237]5A). Loss of C16orf72/HAPSTR1 also caused an increase in p53
   levels for wild-type as well as all tested p53 mutants (Fig. [238]5B,C;
   Appendix Fig. [239]6B).

Figure 5. C16orf72/HAPSTR1 is a regulator of wild-type and mutant p53
stability.

   [240]Figure 5
   [241]Open in a new tab

   (A) Clonogenic survival assays validating the synthetic viability
   between C16orf72/HAPSTR1 and p53. Assayed 13 days after plating, the
   surviving fractions of RPE1-hTERT-TP53^+/+ or TP53^−/− cells transduced
   with the indicated sgRNAs were normalized to those depleted with the
   non-targeting control guide. The two-tailed unpaired t-test was used
   for statistical analysis. Error bar = standard error of the mean
   (S.E.M.), n = 3, **p < 0.01, ***p < 0.001. Results are reproducible
   over biological triplicates. (B) Representative Western Blot results
   showing p53 and p21 protein levels in RPE1 cells transduced with the
   indicated sgRNAs. GAPDH serves as a loading control. Results are
   reproducible over biological triplicates. (C) Flow cytometry blots
   depicting the level of wild-type or p53^R273H-mClover protein levels
   upon depletion of C16orf72/HAPSTR1. Results are reproducible over
   biological triplicates. (D) Interactors of C16orf72/HAPSTR1 as mapped
   by AP-MS in HEK293-Flp-In T-REx and U2OS-Flp-In T-REx cells stably
   expressing FLAG-tagged C16orf72/HAPSTR1. Mass spectrometry was
   performed in biological triplicates. (E) Representative Western Blot
   results showing p53 protein levels in parental RPE1 cells in response
   to depletions of C16orf72/HAPSTR1 and other known E3 ligases of p53
   (MDM2, USP7, and HUWE1) using siRNAs. Results are reproducible over
   biological triplicates. (F) Flow cytometry blots depicting the level of
   p53^R273H-mClover protein levels in a clonal p53^R273H reporter RPE1
   cell line depleted of C16orf72 and transduced with either full-length
   C16orf72 or a C16orf72 truncation mutant lacking the NLS or a control
   overexpression construct (HA). Results are reproducible over biological
   triplicates. (G) Flow cytometry blots depicting the level of
   p53^R273H-mClover protein levels in a clonal p53^R273H reporter RPE1
   cell line depleted of C16orf72 and transduced with either full-length
   C16orf72 or a control overexpression construct (HA) with concomitant
   loss of either HUWE1 by transient siRNA knockdown (siHUWE1) or the
   corresponding non-targeting control (siNT) control. Results are
   reproducible over biological triplicates. (H) Co-immunoprecipitation
   (co-IP) showing an interaction of C16orf72/HAPSTR1 and USP7. HEK 293
   cells stably transduced with an inducible FLAG-USP7 or FLAG-empty
   vector control vector were transfected with either an HA-tagged
   C16orf72/HAPSTR1 or an HA-empty control vector. Lysates with or without
   doxycycline-induction were co-immunoprecipitated using a FLAG-specific
   antibody, followed by Western Blot analysis of HA. GAPDH serves as a
   loading control for the lysate input. Results are reproducible over
   biological triplicates. [242]Source data are available online for this
   figure.

   To gain further insight into the functions of C16orf72/HAPSTR1, we
   performed cycloheximide-chase experiments together with Nutlin
   treatments, which showed that C16orf72 regulates p53 in an MDM2
   independent manner (Appendix Fig. [243]6C). In line with this
   observation, overexpression of C16orf72 in cells depleted of MDM2 could
   not rescue R273H p53 levels, again indicating that MDM2 and C16orf72
   function independently (Appendix Fig. [244]6D). To identify how
   C16orf72/HAPSTR1 might regulate p53 levels, we next performed affinity
   purification coupled to mass spectrometry (AP-MS) on FLAG-tagged
   C16orf72/HAPSTR1. This identified HUWE1 as a prominent interactor of
   C16orf72/HAPSTR1 (Fig. [245]5D; Dataset [246]EV6), in line with
   previous findings that found that C16orf72 is required for the nuclear
   localization of HUWE1 (Benslimane et al, [247]2021; Monda et al,
   [248]2023). HUWE1, a known E3 ubiquitin ligase for p53, scored as a hit
   in our marker-based p53 stability screens and showed the strongest
   coessentiality in DepMap (Figs. [249]2C and [250]5E). siRNA-mediated
   knock-down of HUWE1 led to a slight increase in p53 levels compared to
   the loss of other known negative p53 regulators such as MDM2 or USP7
   (Fig. [251]5E). Given that HUWE1 is a common essential gene (Hart et
   al, [252]2015) and HUWE1 knock-down results in rapid apoptosis of p53
   wild-type RPE1 cells, these experiments are difficult and hard to
   interpret. We thus turned our focus to p53 null RPE1 cells expressing
   the mutant p53 R273H reporter and performed rescue experiments using
   wild-type C16orf72 as well as a truncation C16orf72 mutant lacking the
   nuclear localization signal (NLS). These experiments showed that the
   nuclear localization sequence of C16orf72 is important for the
   regulation of p53 (Fig. [253]5F). We further showed that the ability of
   C16orf72 to regulate p53 is dependent on HUWE1 (Fig. [254]5G).

   The second most significant co-essential gene of C16orf72/HAPSTR1 in
   DepMap was USP7 (Fig. [255]4E,F), which also scored as a synthetic
   viable p53 interaction in RPE1 cells, as well as scoring as a strong
   p53 stability regulator in the reported-based CRISPR screens
   (Figs. [256]2C and [257]4B). To test for a functional relationship
   between C16orf72/HAPSTR1 and USP7, we performed co-immunoprecipitation
   experiments and found that FLAG-tagged full-length USP7 interacts with
   HA-tagged C16orf72/HAPSTR1 (Fig. [258]5H). Together, these data
   indicates that C16orf72/HAPSTR1 might regulate p53 via USP7.

C16orf72/HAPSTR1 functions as an oncogene and regulates p53 stability in the
mammary gland

   The USP7 gene lies directly adjacent to C16orf72/HAPSTR1 and both genes
   are commonly co-amplified in up to 7.6% of cancers as well as being
   co-gained in up to 53% of cases (p < 0.001; Appendix Fig. [259]7A,B).
   Invasive breast cancer showed the highest level of amplification and
   gains of the USP7/C16orf72/HAPSTR1 locus in up to 55% of tumors
   (p < 0.001; Fig. [260]6A), suggesting that these two co-amplified
   negative p53 regulators might cooperate in modulating p53 levels in
   cancer.

Figure 6. C16orf72/HAPSTR1 functions as an oncogene and regulates p53
stability in the mammary gland.

   [261]Figure 6
   [262]Open in a new tab

   (A) cBioPortal OncoPrint displaying a trend toward mutual exclusivity
   between genetic ablation of TP53 and C16orf72/HAPSTR1 amplification,
   and co-amplification between USP7 and C16orf72/HAPSTR1, among breast
   cancer patients. (B) In vivo cell competition assay in the mouse
   mammary glands. LSL-Cas9-EGFP (Trp53^+/+) or the LSL-Cas9-EGFP;
   Trp53^flox/flox (Trp53^−/−) mice were intraductally injected with a
   mixture of control lentiviral particles expressing Cre and BFP as well
   as an sgRNA targeting the Tigre safe harbor, and experimental
   lentiviral particles expressing Cre and RFP as well as an sgRNA
   targeting Tigre, C16orf72/HAPSTR1, or Mdm2. The number of surviving
   cells that had been depleted of each gene was counted 12 days post
   injection and normalized to the number of cells depleted of Tigre in
   the same gland. This ratio was further normalized to the ratio of
   sgTigre:sgTigre in the LSL-Cas9-EGFP; Trp53^flox/flox mouse. Two
   two-tailed unpaired t-test was used for statistical analysis. Error bar
   = standard error of the mean (S.E.M.), n = 3 glands, *p < 0.05. (C)
   Flow cytometry plot depicting the p53^R273H-mClover levels in RPE1
   reporter cells upon overexpression of C16orf72/HAPSTR1. Results are
   reproducible over biological triplicates. (D) Western blot analysis of
   wild-type p53 levels in human MCF10A mammary epithelial cells
   overexpressing C16orf72/HAPSTR1, assayed after treatment with
   Doxorubicin [2 μg/mL] for 6 h. Results are reproducible over biological
   triplicates. (E) Kaplan-Meier plots depicting the tumor-free survivals
   of tumor-prone LSL-Pi3k^H1047R mice (left) and LSL-Pi3k^H1047R;
   Trp53^flox/flox mice (right) that were intraductally injected with
   lentiviral particles expressing Cre as well as C16orf72/HAPSTR1, USP7,
   Mdm2, or control (mRuby) (n = 5 for each condition; n.s. p > 0.25,
   log-rank test was used for statistical analysis). (F)
   Immunohistochemistry staining of p53 and GFP in mouse mammary
   hyperplasia and tumor from mice LSL-Pi3k^H1047R; LSL-EGFP intraductally
   injected with lentiviral particles expressing Cre as well as
   C16orf72/HAPSTR1 or control. Stage-matched lesions from
   LV-C16orf72/HAPSTR1-Cre or LV-Ruby-Cre transduced LSL-Pi3k^H1047R
   glands were stained for p53 and GFP in consecutive sections and
   counterstained by Hematoxylin. GFP serves as a lineage tracer to
   identify transduced cells. Scale bar = 100 μm. [263]Source data are
   available online for this figure.

   To test the role of C16orf72/HAPSTR1 in vivo, we first conducted
   multicolor competition assays in the mammary glands of LSL-Cas9-GFP
   mice. Intraductal injection of lentiviral particles expressing Cre,
   RFP, and a sgRNA cassette targeting Mdm2 or C16orf72/HAPSTR1 led to a
   drastic reduction of mammary epithelial cells of LSL-Cas9-GFP mice when
   compared to a control lentivirus expressing Cre, BFP and an sgRNA
   targeting the inert Tigre locus. The reduced cell viability was
   dependent on p53, as conditional p53 knock-out mice (Trp53^fl/fl;
   LSL-Cas9-GFP) did not show this phenotype (Fig. [264]6B).

   Conversely, overexpression of C16orf72/HAPSTR1 led to a significant
   reduction in p53 R273H levels in RPE1 cells (Fig. [265]6C) and levels
   of wild-type p53 expression in human Pik3ca^H1047R-mutant mammary
   epithelial MCF10A cells (Fig. [266]6D). This led us to ask whether
   overexpression of C16orf72/HAPSTR1 can decrease the latency of
   Pik3ca^H1047R-driven mammary tumors. Of note, we observed that loss of
   p53 cooperates with Pik3ca^H1047R to accelerate mammary tumor
   initiation (Appendix Fig. [267]7C) (Adams et al, [268]2011; Langille E,
   [269]2022). Similar to loss of p53 or overexpression of Mdm2,
   intraductal injection of lentiviral particles expressing Cre and
   C16orf72/HAPSTR1 or USP7 significantly reduced the latency of
   Pik3ca^H1047R-driven mammary tumor development, which was strictly
   dependent on the presence of p53 within those mice (Fig. [270]6E).
   Importantly, C16orf72/HAPSTR1 overexpressing tumors and hyperplastic
   mammary epithelium showed a drastic reduction in p53 levels compared to
   Ruby control lesions (Fig. [271]6F). Together, these data show that
   C16orf72/HAPSTR1 overexpression leads to decreased p53 protein levels
   in vitro and in vivo and leads to accelerated tumor formation.

Discussion

   Over 22 million cancer patients today carry TP53 mutations, the
   majority of which are missense mutations, often resulting in elevated
   expression of mutant p53 proteins (Petitjean et al, [272]2007).
   Lowering mutant p53 expression can reduce tumor growth and metastasis
   and trigger tumor regression (Alexandrova et al, [273]2015; Bossi et
   al, [274]2006; Hui et al, [275]2006), suggesting that tumors become
   addicted to mutant p53. Targeting factors that regulate mutant p53
   stability or reactivate wild-type p53 function might therefore
   constitute a viable therapeutic strategy.

   We conducted genome-wide CRISPR screens in isogenic RPE1 cells
   expressing protein stability reporters for wild-type p53 and five of
   the most common p53 mutants. Unsupervised hierarchical clustering
   showed that most hotspot mutant p53 proteins (R175H, G245S, R248Q,
   R273H) are regulated by a common molecular network, which is, to a
   certain degree, distinct from wild-type p53. Interestingly, the
   Brazilian founder mutation R337H, which is located in the
   oligomerization domain and is the only mutation in our study that
   resides outside of the DNA-binding domain, behaved drastically
   differently to the other hotspot mutations and wild-type p53. As such,
   it will be interesting to further elucidate the molecular underpinnings
   of why some mutations behave like wild-type p53, while others do not;
   furthermore, mutant-specific regulators such as the transfer RNA (tRNA)
   synthetases genes, whose loss specifically leads to the destabilization
   of the R337H p53 mutant, are worth following up. Together, these data
   provide a comprehensive map of genes that regulate wild type and mutant
   p53 protein stability and might have implications for the development
   of agents that target mutant p53 in cancer therapy.

   We next characterized two mutant p53 regulators, FBXO42 and CCDC6, and
   provided strong genetic evidence that FXBO42, in conjunction with
   CCDC6, are novel regulators of certain p53 mutants. FBXO42 was
   previously reported as a negative regulator of wild-type p53 and shown
   to bind a phosphodegron on p53, leading to its proteasomal degradation
   in U2OS cells (Sun et al, [276]2009). The COP9 signalosome-associated
   kinase was found to mediate the phosphorylation of p53’s core
   DNA-binding domain, which was required to allow FBXO42 binding and
   ubiquitination (Sun et al, [277]2011). We found no significant effect
   of FBXO42 on wild-type p53 or R175H, G245S or R337H p53, but genetic
   ablation of FBXO42 caused destabilization of p53 R273H, R248Q, and
   R248W. This is consistent with the co-dependencies of FBXO42 and CCDC6
   in DepMap, which clearly align them with positive p53 regulators such
   as ATM, CHEK2, TP53BP1 and USP28, while they are anti-correlated with
   negative p53 regulators such as MDM2/4, TTC1 or PPM1G/D. Interestingly,
   other previously reported regulators of mutant p53 stability, such as
   TRRAP, BAG2, or BAG5, did not score as hits in our screen, suggesting
   that context may be important for p53 regulation (Jethwa et al,
   [278]2018; Yue et al, [279]2016; Yue et al, [280]2015; Zhao et al,
   [281]2015).

   Similarly, we found that CCDC6 also regulates the same p53 mutants as
   FBXO42 and CCDC6 and FBXO42 were recently shown to interact genetically
   (Garofano et al, [282]2021; Shimada et al, [283]2021). We corroborated
   this interaction data and showed that they also interact physically. In
   addition, we found an interaction between CCDC6 and p53 (by PLA and IP)
   and a direct interaction between FBXO42’s Kelch domains and p53 R273H.
   Epistasis experiments showed that FBXO42 may function upstream of CCDC6
   in regulating p53. In addition, we also found that another well-known
   p53 regulator, BRCA2, interacts with mutant p53 and also surfaced as an
   FBXO42 vicinal protein and a potential FBXO42 target (Fig. [284]3A).
   However, it is still unclear whether these interactions are required to
   regulate p53 protein levels.

   Using further genetic screening in FBXO42- and CCDC6-knockout p53 R273H
   reporter query lines and additional epistasis experiments, we uncovered
   that FBXO42/CCDC6 and USP28 interact genetically. USP28 was originally
   implicated as a protective deubiquitinating enzyme counteracting the
   proteasomal degradation of p53, TP53BP1, CHEK2, and additional proteins
   (Cuella-Martin et al, [285]2016; Lambrus et al, [286]2016; Wang et al,
   [287]2018; Zhang et al, [288]2006). USP28 regulates wild-type p53 via
   TP53BP1-dependent and -independent mechanisms. Concordantly, our data
   shows that USP28 and TP53BP1 are strong positive regulators of
   wild-type p53. However, while USP28 was also a strong hit in the mutant
   p53 R273H screen, TP53BP1 was not, indicating that the effects observed
   upon loss of USP28 on p53 R273H are independent of TP53BP1. In
   addition, we found that genetic ablation of FBXO42 or CCDC6 leads to a
   significant reduction of nuclear USP28 levels, adding further
   biochemical support to the genetic interaction data.
   Vakifahmetoglu-Norberg et al presented evidence that mutant p53 is
   resistant to proteasomal degradation due to an inability of mutant p53
   to be ubiquitinated, which favors lysosomal degradation of mutant p53
   (Vakifahmetoglu-Norberg et al, [289]2013), and it is conceivable that
   FBXO42/CCDC6/USP28 might be involved in this phenomenon. While the
   exact molecular mechanisms are currently unclear, we provide strong
   evidence that FBXO42/CCDC6 is required for USP28-mediated regulation of
   p53 R273H stability, suggesting that interfering with this regulatory
   circuit could present an avenue to prevent or reduce mutant p53
   accumulation in tumors.

   USP28 also regulates other important proteins, such as MYC, by
   counteracting FBXW7-mediated proteasomal degradation, as well as cJun,
   Notch1, LSD1, HIF-1a, and MDC1 (Wang et al, [290]2018). It will be
   interesting to elucidate whether FBXO42 and CCDC6 also impinge on those
   cellular pathways or whether there is some selectivity.

   Our data also shows that there are many genes whose loss results in
   increased p53 levels, which presumably is rooted in the fact that any
   gene loss which causes cellular stress will probably indirectly lead to
   p53 stabilization. Therefore, we cross-referenced our p53 stability
   screen with a synthetic viability screen, which revealed a key role of
   C16orf72/HAPSTR1 as a negative regulator of wild-type and mutant p53.
   The role of C16orf72/HAPSTR1 in a p53 regulatory mechanism is further
   supported by the mutual exclusivity of TP53 genetic alteration and
   C16orf72/HAPSTR1 amplification/gains, which is observed in up to 55% of
   breast cancer genomes. However, the direct implication of the role of
   C16orf72/HAPSTR1 amplification in tumor development is complicated by
   the fact that the USP7 gene is located adjacent to the C16orf72/HAPSTR1
   locus, resulting in concurrent amplification of USP7 and
   C16orf72/HAPSTR1. In addition, recurrent de novo copy number
   amplifications encompassing USP7 and C16orf72/HAPSTR1 are also seen in
   autism spectrum disorder (Sanders et al, [291]2011), and the role of
   p53 and the DNA damage response pathway in neurodegenerative diseases
   and autism is increasingly recognized (Chang et al, [292]2012; Wong et
   al, [293]2016), further indicating a potentially interesting
   connection.

   By generating autochthonous breast cancer mouse models, we could show
   that overexpression of C16orf72/HAPSTR1 or USP7 independently
   accelerates Pik3ca^H1047R-driven mammary tumor formation.
   Mechanistically, we show that C16orf72/HAPSTR1 interacts with USP7 and
   HUWE1, which are bone fide regulators of p53, potentially hinting at
   how C16orf72/HAPSTR1 may regulate p53 stability. Our findings are also
   in line with a recent report showing that C16orf72/HAPSTR1 is crucial
   for mediating telomere attrition-induced p53-dependent apoptosis and
   regulating the effects of ATR inhibition (Benslimane et al, [294]2021;
   Hustedt et al, [295]2019).

   In summary, our study provides a rich resource to mine for candidate
   regulators of wild-type and mutant p53 stability. Moreover, it can
   serve as a template to reveal regulators of any protein of interest on
   a genome-wide level.

Methods

Materials availability

   DNA constructs and other research reagents generated by the authors
   will be distributed upon request to other academic research
   investigators under a Material Transfer Agreement. In addition, DNA
   constructs will also be deposited to Addgene.

Cell lines

   RPE1-hTERT (ATCC CRL-4000), PANC-1 (ATCC CRL-1469), and MDA-MB-468
   (ATCC HTB-132) cell lines were maintained in Dulbecco’s modified
   Eagle’s medium (DMEM; Wisent Inc.), supplemented with 10% (vol/vol)
   fetal bovine serum (Wisent Inc.), 100 U/ml of penicillin, and 100 mg/ml
   of streptomycin (Wisent Inc.) at 37 °C under 5% CO[2].

   To generate RPE1-based clonal stability reporters,
   RPE1-hTERT-Cas9-TP53-KO cells (Zimmermann et al, [296]2018) were
   transduced with recombinant pLKO.1-based lentiviruses carrying the
   p53mClover-P2A-mRFP1 cassette at 0.1 MOI, and single clones selected.
   The pLKO.1-based p53mClover-P2A-mRFP1 cassette carrying control (no
   p53), wild-type, or hotspot mutant p53s was generated by restriction
   enzyme cloning, where all hotspot mutants were first generated using
   site-directed mutagenesis in the pDONR223 entry clone vector and then
   sub-cloned into the cassette.

   To further generate clonal reporters depleted of hit genes, the
   RPE1-based reporter was transfected with CRISPR ribonucleoprotein (RNP)
   complexes containing Cas9 and guides against the gene-of-interests.
   Clonal lines were selected and verified for homozygous deletions by
   both PCR-based Inference of CRISPR Edits (ICE) and Western blotting.

   To generate other cell lines stably expressing Cas9, each line was
   transduced with lentiviruses expressing lentiCas9-Blast (Addgene
   #52962) and selected by Blasticidin to generate either single or clonal
   lines as specified.

CRISPR and FACS-based screens

   Stability reporters [150 million (M) cells total] cultured on 15 cm
   dishes (3 M/plate) were first transduced with the lentivirus-based
   Toronto Knockout v3 library (TKOv3) (Hart et al, [297]2017) at a low
   MOI (~0.30). They were then puromycin-selected [17 μg/mL] for two days
   starting at 24th hour-post-transduction (hpt). At 48 hpt, cells were
   trypsinized and replated back to the same plates while maintaining
   puromycin-selection. At 72 hpt (time point T0), the remaining cells
   were pooled together, counted to confirm MOI, and divided into two
   technical replicates (referred to as p53.1 and p53.2 in figures) for
   subculturing; 30 M cells were further collected to confirm the library
   gRNA abundance at T0. The sub-cultured replicates were further
   maintained in puromycin-free media and passaged every three days (T3
   and T6).

   10 dpt (T7), all cells were harvested by trypsinization and subjected
   to live sorting by Fluorescence-Activated Cell Sorting (FACS).
   Harvested cells were resuspended in FACS sorting buffer (Hanks Balanced
   Salt Solution, 25 mM HEPES pH 7.0, 2 mM EDTA, 1% Fetal Bovine Serum) at
   a concentration of 5 M cells per mL and were filtered by 40-μm nylon
   mesh to eliminate large aggregates. All cells were then live-sorted
   (MoFlo Astrios EQ cell sorter, Beckman Coulter) based on both the
   mClover and mRFP1 signals, where only populations with medium-mRFP1
   signals were collected to eliminate populations with large
   gene-expression changes. For mutants displaying bimodal-mClover
   populations, all cells from the mClover-low and mClover-high
   populations were each collected; for uni-modal mutants, cells with
   mClover intensity within the highest and lowest 15% populations were
   each collected (Fig. [298]1D). Both technical duplicates were
   independently sorted and collectively maintained the 200× coverage.

   Upon sorting, gDNA from cell pellets was isolated using Wizard Genomic
   DNA Purification Kit (Promega, Cat# A1120); genome-integrated sgRNA
   sequences were then amplified by PCR using Q5 Mastermix Next Ultra II
   (New England Biolabs, Cat# M5044L), with primers v2.1-F1-5’
   GAGGGCCTATTTCCCATGATTC 3’ and v2.1-R1-5’ GTTGCGAAAAAGAACGTTCACGG 3’,
   followed by a second round of PCR reaction containing i5 and i7
   multiplexing barcodes. Final PCR products were gel-purified and
   sequenced on Illumina NextSeq500 systems to determine sgRNA
   representation in each sample. The abundance of each guide (guides
   count) was then analyzed by MAGeCK count function with default settings
   (Li et al, [299]2014).

   To generate the log-fold-change (LFC) rank of each gene, the guides
   counts from both the p53-low and p53-high populations were processed
   and compared with the MAGeCK test function with default settings (Li et
   al, [300]2014), and the LFC (p53-high vs p53-low) and padj values were
   computed.

   To generate the normZ scores for normalizing the LFCs across all
   screens, the guides counts from both the p53-low and p53-high
   populations were then analyzed by the DrugZ program with default
   settings (Colic et al, [301]2019). The normZ and padj values were
   computed. In this study, a hit was defined as having a |normZ| > 3 and
   padj < 0.5.

   All flowcytometry was performed on Fortessa X20 (BD), and data was
   analyzed using the FlowJo software (BD).

Plasmids, transfection, and transduction

   To generate the lentiviral plasmids for transducing the reporter lines,
   the reporter plasmid was first built upon the pLKO.1 - TRC cloning
   vector (Addgene #10878), by substituting the Puromycin resistance gene
   with the p53mClover2-P2A-mRFP1 or mClover2-P2A-mRFP1 (empty control)
   cassette. All hotspot mutants were first generated using site-directed
   mutagenesis in the pDONR223 entry clone vector and then sub-cloned into
   the cassette by substituting the relevant regions of WTp53. All
   constructs were confirmed by DNA sequencing. To generate V5-tagged
   lentiviral overexpression constructs, each open reading frame (ORF) was
   first generated as an entry clone into the pDONR223 vector using the BP
   recombination reaction, and then was cloned into the pLEX_306
   expression vector (Addgene #41391) using the LR recombination reaction,
   following manufacturer’s instruction (Invitrogen). Lastly,
   HA-C16orf72/HAPSTR1 was generated in the pCDNA3.1-HA backbone, and the
   FLAG-USP7 was a generous gift from Lori Frappier (University of
   Toronto).

   To generate the entry clone for each of the p53 mutants, the coding
   sequence of WT p53 ([302]NM_000546.6:143-1324) was first cloned into
   the pDONR223 vector, retaining Start codon (ATG) and removing the Stop
   codon (TGA), using the BP recombination reaction (Invitrogen). Each
   mutant was then generated using PCR-based site-directed mutagenesis to
   generate p53^mutant-pDONR223, with specific codon and primers detailed
   in Dataset [303]EV7. All constructs were sequence verified by Sanger
   sequencing covering the entire ORF.

   For in vivo overexpression assays, the pLEX_306-ORF-Cre backbone was
   first generated by modifying the pLEX_306 by substituting the Puromycin
   resistance cassette with NLS-iCre. ORFs were then introduced by
   pDONR223-based entry clone similar to the V5-tagged constructs above.
   For in vivo competition assays, pLKO-H2BXFP-P2A-NLSiCre was first
   generated by replacing the NLSiCre with the H2BXFP-P2A-NLSiCre cassette
   in the pLKO-Cre stuffer v4 (Addgene #158032) backbone; the XFP used was
   mRFP1 or TagBFP. The guides targeting each desired gene were then
   cloned into the vector, and the sequences were confirmed.

   For transfections, cells were grown in 100-mm (for immunoprecipitation)
   dishes to about 70% confluence and transfected with Lipofectamine 3000
   (Invitrogen) according to the manufacturer’s protocols.

   For all lentiviral transduction for in vitro (cultured cells)
   experiments, lentiviral particles were first produced in the 293 TN
   cells (Systembio LV900A-1), by co-transfecting the lentiviral plasmid
   with helper plasmids pMD2.G and psPAX2. Supernatant was then harvested
   at 48 h post-transfection, and used to transduce cells at an MOI of
   ~0.1 to 0.3.

   For all in vivo lentiviral transductions, lentiviral particles were
   first produced similar to those for in vitro experiment, and followed
   by concentration of the viruses. In brief, the produced supernatant was
   first filtered through a Stericup-HV PVDF 0.45 μm filter, and then
   concentrated by ~1000 fold by ultracentrifugation (Beckman Coulter).
   The titer was quantified by FACS of LSL-tdTomato mouse embryonic
   fibroblasts (MEFs).

   sgRNA sequences for validation experiments from screen hits are
   detailed in Dataset [304]EV7.

Analysis of genome editing efficiency

   PANC-1 Cas9 cells (for human guides) or LSL-Cas9-EGFP MEFs (for mouse
   guides) were cultured and transduced with sgRNAs-expressing lentivirus
   that carries either Puro (for PANC-1) or NLSiCre (for MEFs). carrying
   each guide. For PANC-1 cells, transduced cells were first selected
   under Puromycin (5.0 μg/mL) for 48 h, and then cultured in complete
   media with passaging every three days until harvesting on day 10
   post-transduction. For MEFs, cells were live sorted for GFP expression
   and expanded further until harvesting on day 20 post-transduction. At
   the time of harvesting, the cells were collected by trypsinisation and
   genomic DNA was extracted using DNeasy Blood & Tissue Kit (Qiagen). The
   genomic region centered at the sgRNA cutting site, along with >250 bps
   flanking it on each side, was PCR amplified, for both cells transduced
   with guides targeting the desired genes or the control [sgAAVS1 (human)
   or sgTigre (mouse)]; they were then subject to Sanger sequencing. The
   editing efficiency was then determined by analyzing the sequencing
   chromatograms with the web-based Interference of CRISPR Edits (ICE)
   tool,
   [305]https://www.synthego.com/products/bioinformatics/crispr-analysis.
   Primers for ICE analysis are detailed in Dataset [306]EV7.

Irradiation and chemical perturbations

   RPE1 reporters were treated with ionizing radiation (IR) with indicated
   dose using a Faxitron X-ray cabinet (Faxitron, Tucson, AZ, USA).
   Chemical perturbation was performed by adding either Nutlin-3a (10 μM,
   Sigma SML0580) or an equal volume of DMSO to the complete media. For
   MG132 treatment of the reporters, either MG 132 (10 µM, Sigma) or DMSO
   was added to the complete media for indicated times.

BioID and affinity purification mass spectrometry (AP-MS)

   BioID group (p53 R273H, CCDC6 and FBXO42). BioID was carried out,
   essentially as previously described (Coyaud et al, [307]2015; Hesketh
   et al, [308]2017; Roux et al, [309]2012). In brief, 293 Flp-In T-REx
   (Invitrogen) cells inducibly expressing C-terminally-tagged full-length
   human p53R273H, CCDC6, and FBXO42 or controls (GFP in lieu of ORF, and
   vector control alone) were first generated, and inducible expression
   tested by immunoblotting. Sub-confluent (60%) cells (10 × 15 cm plates)
   were incubated for 24 h in complete media supplemented with 1 μg/ml
   tetracycline (Sigma) prior to incubation with 50 μM biotin (BioShop,
   Burlington, ON, Canada), and either 5 μm MG132 (5 plates; calpain
   inhibitor IV, Z-Leu-Leu-Leu-CHO; American Peptide Company, Sunnyvale,
   CA) or DMSO (5 plates) for 24 h. Cells were then collected by first
   washing twice with PBS and then pelleted (500 × g, 5 min), and dried
   pellets were finally snap-frozen.

    Affinity purification (BioID group). Cell pellets corresponding to
   each 15 cm plate were incubated at 4 °C in 1:10 (w/v) modified-RIPA
   buffer (50 mM Tris-HCl pH 7.5, 150 mM NaCl, 1% Triton X-100, 1.5 mM
   MgCl[2], 1 mM EGTA, 0.1%SDS, Sigma protease inhibitors P8340 1:500, and
   0.5% Sodium deoxycholate), and 1 μL of benzonase (250U) was added to
   each sample, for 30 min on a rotator. The lysates were then sonicated
   three times on ice at 35% amplitude. These lysates were then
   centrifuged and supernatant proceeded for streptavidin-Sepharose beads
   (GE Cat# 17-5113-01) affinity purification, which was performed at 4 °C
   on a rotator for 3 h, followed by washing the beads once with 2% SDS
   buffer, twice with modified-RIPA buffer and lastly once in TAP lysis
   buffer (50 mM HEPES-KOH pH 8.0, 100 mM KCl, 2 mM EDTA, 0.1% NP-40).
   Finally, the beads were washed in the ABC buffer (50 mM ammonium
   bicarbonate, pH 8.3), and proteins were digested on beads with
   TPCK-trypsin (Promega, Madison, WI, 16 h at 37 °C). The supernatant
   containing the tryptic peptides was collected and lyophilized. Peptides
   were resuspended in 0.1% formic acid and analyzed by mass spectrometry.

   AP-MS group (C16orf72/HAPSTR1). Similarly, 293 Flp-In T-REx
   (Invitrogen) cells inducibly expressing N-terminally-tagged full-length
   human C16orf72/HAPSTR1 or GFP-control were generated and inducible
   expression tested by immunoblotting. Sub-confluent (60%) cells (2 ×
   15 cm plates) were incubated for 24 h in complete media supplemented
   with 1 μg/ml tetracycline (Sigma) prior to harvesting, similar to the
   BioID group.

    Affinity purification (AP-MS group). Cell pellets corresponding to 2 ×
   15 cm plate were incubated at 4 °C in 1:4 (w/v) FLAG-IP lysis buffer
   (50 mM HEPES pH 8.0, 100 mM KCl, 2 mM EDTA, 0.1% NP40, 10% glycerol,
   1 mM PMSF, 1 mM DTT, and Sigma protease inhibitors P8340 1:500).
   Resuspended lysates were first frozen and thawed twice (dry ice and
   37 °C water bath) for 5–10 min each. They were then sonicated for 20 s
   at 35% amplitude. 200 units of benzonase were then added to the sample
   and incubated at 4 °C on a rotator for 20 min, and centrifuged to
   collect the supernatant (16,000 × g, 20 min, 4 °C). The supernatants
   were then incubated with magnetic anti-FLAG M2 beads (Sigma, # M8823)
   at 4 °C on a rotator for 2 h, at a ratio of 25 μL 50% slurry beads for
   each IP of 2 × 15 cm plates. Beads were then pelleted by centrifugation
   (500 × g, 5 min), followed by three washes with 1 mL FLAG-IP lysis
   buffer and two washes with washing buffer (20 mL Tris-HCl pH 8.0 and
   2 mM CaCl[2]) using a magnetic stand. The samples were then processed
   similarly to the BioID group for on-beads digest and mass spectrometry
   analysis.

   Mass spectrometry and data analysis. Mass spectrometry analysis was
   carried out as previously described (Hesketh et al, [310]2017) using
   the TripleTOF 6600 system (SCIEX). In brief, samples were loaded to
   fused silica capillary columns pre-loaded with C18 reversed phase
   material. Ionised peptides were emitted by nanoelectrospray ion source
   followed by a nano-HPLC system, and analyzed using Data Independent
   Acquisition (DIA) methods.

   Interactor classification. Mass spectrometry data were filtered for a
   minimum unique spectral count of 2. For the AP-MS group, results from
   biological triplicates purifications of the C16orf72/HAPSTR1 and
   control were further filtered via significance analysis of interactome
   (SAINT) (Teo et al, [311]2016), which uses a probability model to
   assign a confidence score to each interaction by comparing the spectral
   counts in the sample and control across replicates, filtered at
   ProteinProphet p value > 0.95 and SAINT BFDR score ≤0.01.

Synthetic lethal screens

   These screens were carried out following previously optimized protocols
   (Hart et al, [312]2015). In brief, isogenic pairs of RPE1-hTERT-Cas9
   cells with TP53^+/+ and TP53^−/− backgrounds were each infected with
   the TKOv3 pooled gRNA library at an MOI of ~0.3. Similar to the
   stability screen, the infected pools were then selected with puromycin
   (17 μg/ml, 48 h) and maintained in culture for 18 days after Day 0,
   with passaging every three days. The genomic DNA from the first and the
   last time point was extracted, and the incorporated gRNA sequences were
   amplified via 2-step polymerase chain reaction (PCR). The amplification
   products from the first and the last time point were subjected to
   Illumina sequencing in order to analyze the fold change of gRNA
   sequence in the cell population.

   Data was analyzed with BAGEL2 (Kim and Hart, [313]2021), where positive
   Bayes factors (BF) values identify genes that are likely essential for
   proliferation. Hits are defined as concurrently fulfilling BF > 15 for
   TP53^+/+ and BF < 5 for TP53^−/−.

Fluorescence microscopy

   Cells were cultured on glass coverslips in 6-well plates for 48 h
   (reaching 70% confluency) before fixation with 3.2% paraformaldehyde
   for 20 min, followed by permeabilization (0.25% v/v Triton X-100 in 1×
   Phosphate Buffer Saline (PBS) and blocking (1% w/v BSA, 1% w/v gelatin,
   0.25% v/v goat serum, 0.25% v/v donkey serum, 0.25% v/v Triton X-100 in
   PBS, in 1× PBS). Samples were then stained for respective antibodies
   diluted in the blocking buffer for 1 h at room temperature, followed by
   washing and staining with secondary antibodies. Coverslips were lastly
   counterstained with DAPI (4′,6-diamidino-2-phenylindole) and mounted on
   slides using DABCO (Sigma). Cells were then viewed using a Nikon
   Ti2-E/A1R-Multiphoton microscope equipped with DS-Qi2 camera (Nikon).

   For quantification, laser power and gain for each channel and antibody
   combination were set using secondary-only control and confirmation with
   primary positive control and applied to all images. Images were
   analyzed using Cellprofiler (Lamprecht et al, [314]2007) with default
   settings to quantify the cytoplasmic to nuclear intensity ratios. The
   following antibodies were used for IF: p53 (DO-7 FITC conjugate, BD
   biosciences), USP28 (Bethyl A300-898A), and F-Actin (phalloidin-FITC,
   Sigma P5282).

Histology and immunohistochemistry

   Tumors or gland tissues were harvested from each endpoint mouse, and
   placed in 4% PFA for 48 h. They were then placed into 70% ethanol and
   stored at 4 °C until ready for standard embedding and sectioning
   procedure. For staining, the dissected serial section slides were
   heated at 60 °C for 15 min, then dewaxed and rehydrated. Slides for
   immunohistochemistry were treated with 3% hydrogen peroxide in PBS for
   15 min to deactivate endogenous peroxidases. Slides were then washed in
   PBS followed by microwave antigen retrieval using Na citrate pH 6.0.
   Following the primary antibody (diluted in Na citrate solution)
   incubation at room temperature for 45 min, anti-rabbit secondary
   antibodies (Vector Labs BA-1000, 1:500 diluted in 0.2% v/v Triton X-100
   in PBS) were applied for 35 min at room temperature. This was further
   followed by an ABC kit (Vector Labs PK-4100) treatment for 25 min, and
   finally, a DAB Reagent (Vector Labs SK-4100) treatment for 4 min at
   room temperature. Slides were lastly counterstained for 8 min in Harris
   Hematoxylin, dehydrated, and mounted in a xylene-based mounting medium.
   Stained sections were digitized at 40x using a Hamamatsu Nanozoomer
   Scanner (2.0-HT).

   The following primary antibodies were used in this study: anti-p53
   rabbit polyclonal antibody (Abcam ab241566, POE316A), anti-GFP rabbit
   polyclonal antibody (Abcam ab290). Antibodies are detailed in
   Dataset [315]EV7.

RNA isolation, cDNA synthesis, and Quantitative Real-Time PCR analysis

   Total RNAs from PANC-1 cells were first collected using TRIzol
   (Ambion), treated with ezDNase (Invitrogen), and reverse transcribed
   into cDNA using SuperScript IV VILO (Invitrogen). Real-time
   quantitative PCR (qRT-PCR) reactions were performed on an CFX384
   (Biorad) in 384-well plates containing 12.5 ng cDNA, 150 nM of each
   primer, and 5 µl PowerUp SYBR Green Master Mix (Applied Biosystems) in
   a 10 µL total volume. All primers were designed to span exon junctions
   using Primer3Plus and were melting-curve validated. Relative mRNA
   levels from experimental triplicates were calculated using the
   comparative Ct method normalized to PPIB mRNA.

   With the normalized mRNA levels of targeted genes in the sgAAVS1 sample
   arbitrarily set as 1.00, the normalized mRNAs in other samples were
   expressed as a ratio relative to that of sgAAVS1. Statistical
   significance was determined at p = 0.05. Primers for qPCR are detailed
   in Dataset [316]EV7.

Immunoprecipitation and Western blotting

   Cell extraction and immunoprecipitation were performed as previously
   described (Lu et al, [317]2014). In brief, whole-cell extracts were
   prepared by lysing cells in buffer X (50 mM Tris pH 8.5, 250 mM NaCl,
   1 mM EDTA, 1% NP-40, protease inhibitor minitablet (Roche)) and
   quantified using Bradford assay (Bio-Rad). Equal amounts of protein
   (lysate or immunoprecipitation samples) were separated by SDS-PAGE and
   transferred to 0.45 μm Polyvinylidene fluoride (PVDF) membranes
   (Immobilon-P, EMD Millipore). For affinity purification of endogenous
   or epitope-tagged binding proteins, the lysates were incubated with
   anti-CCDC6 mouse monoclonal antibody (Santa Cruz Q-23) or mouse IgG
   (Abcam, ab124055), anti-V5 mouse monoclonal antibody (Roche R960-25),
   or anti-FLAG mouse monoclonal antibody (Sigma M2), followed by protein
   G beads (Sigma).

   For Western blotting, the following antibodies were used for Western
   blot: p53 HRP-conjugated antibody (R & D Systems, HAF1355), p53 rabbit
   polyclonal antibody (Santa Cruz FL-393), CCDC6 (Santa Cruz Q-23),
   FBXO42 (Santa Cruz FL-6), GAPDH HRP-conjugated antibody (BioLegend
   W17079A), TP53BP1 (BD Biosciences 612523 Clone 19), USP28 (Bethyl
   A300-898A), USP7 (Bethyl A300-034A), HUWE1 (Abcam ab70161), V5 (Roche
   R960-25), FLAG (Sigma M2 F7425), HA (Covance HA.11 MMS-101R), and
   C16orf72/HAPSTR1 (rabbit polyclonal, in house). Membranes were
   incubated with primary antibodies followed by appropriate horseradish
   peroxidase-coupled secondary antibodies (anti-rabbit or anti-mouse from
   Jackson ImmunoResearch or TrueBlot anti-mouse from eBioscience).
   Western Lightning Plus enhanced chemiluminescence substrate
   (PerkinElmer) was used to visualize proteins on ChemiDoc MP Imager with
   Image Lab 4.1 software (Bio-Rad) or autoradiography film. Antibodies
   are detailed in Dataset [318]EV7.

Nuclear cytoplasmic fractionation

   The fractionation was performed using NE-PER Nuclear and Cytoplasmic
   Extraction Reagents (ThermoFisher) as per the manufacturer’s
   instructions. 10 μg of each fraction was separated by SDS-PAGE and
   transferred to 0.45 μm Polyvinylidene fluoride (PVDF) membranes
   (Immobilon-P, EMD Millipore).

Clonogenic survival assays

   RPE1 cells depleted of each gene using each indicated guide were seeded
   in 6-well plates (250 cells per well) and cultured using complete
   media. After 14 days, colonies were stained with crystal violet
   solution (0.4% w/v crystal violet, 20% methanol), scanned and manually
   counted. Relative survival was calculated by arbitrarily setting the
   number of colonies in the Scrambled control as 100%. Experiments were
   performed in biological triplicates, and the error bar represents the
   standard error of the mean.

In situ proximity ligation assay

   PLA was performed as previously described (Mukherjee et al, [319]2022).
   In brief, cells were first fixed with 4% PFA for 15 min and
   permeabilized with 0.1% v/v Triton for 5 min. PLA was performed using
   the DuoLink In Situ PLA Detection Kit (DUO92101, Sigma). Imaging was
   performed using an LSM 800 (Zeiss) confocal microscope with 40/60×
   objective oil immersion.

Mice

   Animal husbandry, ethical handling of mice and all animal work were
   carried out according to guidelines approved by the Canadian Council on
   Animal Care and under protocols approved by the Centre for
   Phenogenomics Animal Care Committee (18-0272H).

In vivo competition and overexpression

   The parental animals used in this study were Rosa26-LSL-Pik3caH1047R/+
   [Gt(ROSA)26Sortm1(Pik3ca*H1047R)Egan in a pure FVBN background, a
   generous gift of Sean Egan, The Hospital for Sick Children/SickKids],
   Rosa26-LSL-Cas9-GFP (Jackson laboratories #026175, in C57/Bl6
   background), and p53 flox/flox (B6;129S4-Trp53tm5Tyj/J, Jackson
   laboratories #008361 in Bl6 background]. Homozygous [p53 flox/flox;
   Rosa26-LSL-Cas9 GFP], [LSL-Pik3caH1047R], and [p53 flox/flox;
   LSL-Pik3caH1047R] were each generated by crossing the respective
   parental lines.

   Mice were intraductorally injected with lentiviral particles containing
   either (1) sgRNAs (competition assay) in the #3 or 4 mammary glands
   with triplicates spread across a minimum of two mice (i.e., no more
   than two glands per mouse containing the same virus), or (2) ORFs
   (tumor-free survival assay) in both #3 and #4 glands (a total of four
   per mouse) with five mice per ORF.

   Tumor-free survival. Mice were monitored weekly (initial three weeks)
   and then twice weekly for tumor formation by palpation, and the first
   appearance of tumors in any gland of the mouse was noted. Mice were
   harvested when tumors associated with any one gland had reached the
   tumor burden threshold as defined by the animal ethics guidelines.

   Competition. Mice were intraductorally injected with a mixture of
   control lentiviral particles expressing Cre and BFP as well as an sgRNA
   targeting the Tigre safe harbor, and experimental lentiviral particles
   expressing Cre and RFP as well as an sgRNA targeting Tigre, C16orf72,
   or Mdm2. The number of surviving cells that had been depleted of each
   gene was counted 12 days post-injection, and normalized to the number
   of cells depleted of Tigre in the same gland. This ratio was further
   normalized to the ratio of sgTigre:sgTigre in the LSL-Cas9-EGFP;
   Trp53^flox/flox mouse.

   Mammary gland isolation and flow cytometry for lineage tracing. For
   competition assays, individual mammary glands were harvested and
   digested according to Stemcell Technologies gentle
   collagenase/hyaluronidase protocol. In brief, glands were first
   digested overnight (~16 h) with gentle agitation at 37 °C in 250 μL
   Gentle Collagenase (Stemcell Technologies #07919) diluted in 2.25 mL of
   complete Basal Epicult media formulated according to manufacture
   instructions (Epicult Basal Medium Stemcell Technologies #05610, 10%
   Proliferation Supplement, 5% v/v FBS, 1% v/v Penicillin-Streptomycin,
   10 ng/mL EGF, 10 ng/mL bFGF, 0.0004% v/v heparin). The digested glands
   were then treated with ammonium chloride and triturated for 2 min in
   pre-warmed trypsin, followed by dispase. Cells were stained with CD45,
   CD31, Ter119, CD49f, and EPCAM for luminal and basal cell
   identification, and Sytox Red for dead cell exclusion. The following
   antibodies were used for flowcytometry experiment: APC conjugated
   antiCD45 (Clone 30 F11, BioLgend), APC conjugated antiCD31 (Clone
   MEC133, BioLegend), APC anti-mouse Ter119 (Clone TER-119, BioLegend),
   PECy7 anti-human/mouse CD49f (Clone GoH3, BioLegend), APCVio770 mouse
   anti-CD326 EpCAM (Clone caa7-9G8, Miltenyi).

Xenografting

   PANC-1-Cas9 and PANC-1-∆ p53-Cas9 cells that were transduced with
   sgRNA-GFP (12 days post-transduction) were tail vein injected to
   NOD/SCID mice in replicates of five mice per experimental condition
   (AAVS1, CCDC6, FBXO42, and TP53). In brief, the cell suspension was
   prepared to a concentration of 2.5 × 10^5 cells in 50 μL of PBS
   solution per mouse, and kept on ice with occasional agitation. They
   were then injected into the tail vein of eight-week-old recipient
   NOD-SCID mice (NOD.Cg-Prkdc^scid Il2rg^tm1Wjl/SzJ) under anesthesia
   with isoflurane. Mice were monitored weekly (first three weeks), twice
   weekly (weeks four to six), and daily (week seven) for welfare. The
   metastatic colonization was observed at seven weeks post-tail vein
   injection. Mice were sacrificed, and their lungs were harvested and
   imaged under the fluorescence microscope for metastatic colonization in
   the lungs, as indicated by the green fluorescence signal and quantified
   by the spot count. The lungs were preserved by fixing with PFA.

Statistics and reproducibility

   Unless specified, independent biological replicates were performed, and
   group comparisons were made as indicated in the legends. P < 0.05 was
   considered significant. Statistical analysis was performed using
   GraphPad Prism 7. Unless specified, quantitative data are expressed as
   the mean ± SE. Differences between groups were calculated by two-tailed
   Student’s t-test, Wilcoxon Rank-Sum test (normal distribution
   correction), or Log-rank test using Prism 7.

Recombinant expression and purification of prey and bait proteins for pull
down assays

   Protein purification was performed as previously described (Xiao et al,
   [320]2020). All protein constructs [preys: p53-CD-R273H (aa 90-311),
   p53-FL-WT, and MBP-tagged baits: MBP-FBXO42c (aa 105-360), MBP-p107,
   and MBP-MDM2] were cloned in the pETM41 vector. p53 constructs were
   transformed in BL21DE3 E. coli cells and grown in 2TY medium
   supplemented with kanamycin at 200 rpm and 37 °C. Cultures were
   supplemented with 100 µM ZnSO[4] and were induced with 0.8 mM IPTG upon
   reaching an OD[600] = 0.6. Following induction, cultures were grown ON
   at 200 rpm and 17 °C. Bacterial pellets were harvested and pellets
   lysed and passed over an Amylose column (NEB) with Buffer A: Tris-HCl
   50 mM pH 7.5, PMSF 2 mM, NaCl 0.4 M, beta-mercaptoethanol 2 mM and
   eluted with Buffer B containing 20 mM Maltose. Elution peaks were
   collected and dialyzed overnight, at 4 °C in pre-IMAC Buffer (25 mM
   Tris-HCl pH 7.5, 0.2 M NaCl). The sample was removed from dialysis and
   stored cut with TEV protease to remove MBP 3 h at room temperature
   (Conditions: 1:5 m:m/1 mg TEV: 5 mg protein) followed by IMAC (Buffer
   A: Tris-25 mM HCl pH 7.6, 0.5 M NaCl, 1 mM PMSF; Buffer B: Buffer
   A + 0.5 M Imidazole pH 7.6). Flow fractions containing p53 constructs
   were collected and dialyzed ON in S75 Buffer (20 mM phosphate pH 7.0,
   2 mM beta-mercaptoethanol, 200 mM NaCl, 2 mM PMSF). The sample was
   removed from dialysis and concentrated to 2.88 mg/ml (115.2 μM). Purity
   was assessed by SDS-PAGE and conformational homogeneity was assessed by
   Size Exclusion Chromatography. MBP-FBXO42c, MBP-MDM2, and MBP-p107 were
   similarly expressed as described above (without the addition of
   ZnSO[4]) and 15 ml pellets were lysed, sonicated and coupled to Amylose
   resin.

Size exclusion chromatography

   An analytical Superdex 75 column (Cytiva) volume 21.23 ml was
   equilibrated in 20 mM sodium phosphate buffer (Biopack), 2 mM DTT
   (Sigma), 200 mM NaCl at pH 7.00. Runs were performed at a flow rate of
   0.5 ml/min. 150 μL of marker or p53 mixture was prepared, brought to
   volume with buffer, and 100 μL loop injections were performed. The
   p53-CD-R273H stock was used at a concentration of 2.88 mg/ml
   (115.2 µM). Markers used were: Acetone 2% (Vo+Vi), Blue dextran 1 mg/ml
   (Vo), Immunoglobulin 2.5 mg/ml, Transferrin 1 mg/ml, BSA 5 mg/ml, and
   Trypsin inhibitor 3 mg/ml.

Supplementary information

   [321]Appendix^ (10.6MB, pdf)
   [322]Dataset EV1^ (5.9MB, xlsx)
   [323]Dataset EV2^ (909.2KB, xlsx)
   [324]Dataset EV3^ (49.5KB, xlsx)
   [325]Dataset EV4^ (2.7MB, xlsx)
   [326]Dataset EV5^ (20.9KB, xlsx)
   [327]Dataset EV6^ (582KB, xlsx)
   [328]Dataset EV7^ (19.4KB, xlsx)
   [329]Dataset EV8^ (397.8KB, xlsx)
   [330]Source data Fig. 1^ (39.7MB, zip)
   [331]Source data Fig. 2^ (203.3MB, zip)
   [332]Source data Fig. 3^ (38.4MB, zip)
   [333]Source data Fig. 4^ (2.9MB, zip)
   [334]Source data Fig. 5^ (252MB, zip)
   [335]Source data Fig. 6^ (8.2MB, zip)
   [336]Peer Review File^ (5.7MB, pdf)

Acknowledgements