Mutant ribosomal protein RPS15 drives B cell malignancy through oxidative stress and genomic instability

Domain: biology
Published: 2026-03-30
Editorial outcome: major_revision

Abstract
Ribosomal protein mutations are increasingly associated with cancer risk and thought to perturb ribosome function. At the same time, they reportedly activate p53, a critical anti-cancer barrier. To determine how these mutations overcome this protective block to enable tumorigenesis, we generate an in vivo model of the hotspot ribosomal protein RPS15-S138F mutation identified as a putative driver of chronic lymphocytic leukemia. Under pre-leukemic conditions, this mutation induces ribosome biogenesis defects and altered translation resulting in oxidative stress, DNA damage and induction of a p53-dependent response that promote initial cellular hypo-proliferation. However, a subset of aged mice with mutated Rps15 eventually develop B-cell leukemia (37% penetrance), which exhibits increased Myc activity with strong pro-survival and proliferation signatures. Mutant RPS15 thus induces both hypo- and hyper-proliferative signals, initially weighted towards cell cycle arrest; and that through translational rewiring, oxidative stress, DNA damage response defects and genomic instability set the stage for the acquisition of additional driving mutations, such as TP53 deletion, that can overcome this cell cycle block to trigger tumorigenesis.

Introduction
Mutations in ribosomal proteins resulting in ribosomopathies are associated with an increased risk for cancer in humans (1). Recent large-scale cancer sequencing studies have identified ribosomal protein mutations across solid and hematologic malignancies, further implicating them as possible cancer drivers (2–7). In chronic lymphocytic leukemia (CLL), recurrent hotspot mutations have been identified in the ribosomal protein gene RPS15 (~5% of untreated patients) (8,9). These mutations are often clonal (8) and are further enriched (~20%) in relapsed CLL following chemoimmunotherapy (10) and targeted therapy (11), suggesting a yet unproven role in early transformation and disease progression. Collectively, these observations have fueled growing interest in the mechanisms by which altered ribosomal proteins may promote oncogenesis.

The human ribosome, comprised of ~6,000 ribosomal RNA (rRNA) bases and 80 ribosomal proteins assembled into small (40S) and large (60S) subunits, is constructed in a complex and energetically costly process within both the nucleus and cytoplasm (12). The ribosome assembly process begins in the nucleolus with the transcription and modification of rRNA followed by export of pre-40S and pre-60S particles to the cytoplasm for assembly of mature 40S and 60S subunits into 80S ribosomes. Accumulating evidence suggests that ribosome heterogeneity, including diversity in ribosomal proteins, ribosomal mutations, and ribosomal RNA allele usage and modifications can impact ribosome function and protein translation, thereby contributing to cellular rewiring that ultimately promotes tumor progression (13–18). On the other hand, changes in ribosomal protein expression or function often have deleterious impact on the cell (19,20). Accordingly, they activate tumor suppressors (e.g. TP53) that respond to alterations in ribosome function by inhibiting ribosome production, rRNA synthesis and cell cycling, hence promoting cellular quiescence (21–23). Indeed, several ribosomal proteins, including RPS15, directly interact with MDM2, which ubiquitinates p53 and thus modulates its activity (24–30). In ribosomopathies, this characteristic transition from a hypo-proliferative to a hyper-proliferative state with elevated cancer risk is a well-recognized paradox, so-called ‘Dameshek’s riddle’ (31,32).

Multiple previous studies have sought to address the mechanisms underlying such paradoxical transitions in the context of different ribosomopathies, as well as their link with oncogenic predisposition and tumor development. Focusing on modeling of the RPL10-R98S T-ALL mutation in Ba/F3 cells and in patient-derived xenotransplantation models, a previous study demonstrated that the accumulation of oxidative stress in RPL10-R98S cells resulted in their impaired proliferation, with BCL-2 overexpression in these cells allowing them to tolerate high oxidative stress levels (16). In a further study, patients with T-ALL harboring RPL10-R98S and other ribosomal gene mutations were shown to display increased mutation burden, an enrichment of NOTCH1 mutations, and a mutation signature characterized by C:G>A:T transversions (18). Further modeling on Ba/F3 and murine lymphoid cells demonstrated mitigation of RPL10 R98S-associated oxidative stress and cellular hypo-proliferation by NOTCH1 mutation (8,16–18).

Elevated oxidative stress levels associated with ribosomal gene mutations have also been reported in lymphoid cells from patients with Diamond-Blackfan anemia (33). In the context of RPS15 mutations in CLL, translational alterations with resultant changes in the global cellular proteome have been demonstrated within in vitro models (10,34,35). Additionally, reanalysis by Sulima et al of our previously published genomics dataset from the German CLL Study Group (GCLL)-CLL8 trial showed higher mutation burden across CLL tumors harboring ribosomal gene alterations, reminiscent of findings in T-ALL (8,18). However, no in vivo animal model of RPS15-mutant CLL have thus far been developed, and the role of RPS15 defects as an oncogenic driver remains hitherto unproven. Moreover, the in vivo phenotypic and functional consequences of RPS15 mutations in CLL are unknown, and the association between oxidative stress and translational defects has not been definitively established.

To determine whether RPS15 mutation can drive B-cell oncogenesis, either singularly or in combination, and to define the mechanisms by which it might do so, we engineered a mouse model recapitulating a representative hotspot RPS15 mutation, RPS15-S138F. We demonstrate that Rps15-S138F leads to CLL development in elderly mice, with earlier onset and more aggressive disease when co-mutated with Trp53. We also uncover an array of translational defects induced by RPS15 mutation and delineate the mechanisms by which these defects drive tumor progression. Collectively, our results reveal, in vivo, how RPS15 mutations pivot cells from an initial hypo-proliferative pre-malignant phase to a subsequent hyper-proliferative malignant state and support a mechanistic model by which RPS15 mutation generates ‘oncogenic ribosomes’.

Results
RPS15 mutation co-occurs with TP53 mutation in CLL and is associated with poorer OS
To evaluate the co-occurrence of RPS15 mutation (mut-RPS15) with other common CLL drivers, we identified CLL samples bearing mut-RPS15 from two independent cohorts. One was the ‘CLL-map’ (36), in which 26 of 1,063 CLL specimens, profiled by either whole-exome or whole-genome sequencing (WES, WGS), were identified as bearing mut-RPS15 (allelic frequency [AF] >3%; Supp Data 1). The other was the ‘French cohort’, wherein 19 of 277 prospectively collected independent CLL samples characterized by targeted next-generation sequencing of a panel of putative CLL drivers (e.g., RPS15, NOTCH1, FBXW7, POT1, Methods) were identified as harboring mut-RPS15 (AF >1%) (Fig 1a, Supp Fig 1a, Supp Table 1). RPS15 mutations from both patient cohorts occurred exclusively within exon 4 in a region spanning amino acids 129-145 in the C-terminal region of the protein (Fig 1b). This localized pattern was consistent with previous data, and mutations in this region have been shown to have effects on ribosome assembly and maturation in yeast (37), as well as on translation in HEK293T (34) and primary CLL cells (35). Twenty-two RPS15 mutations from these 45 (49%) cases across the two cohorts were clonal (AF >35%), suggesting a possible role for mut-RPS15 in early CLL development. These cases almost uniformly (43 of 45 [96%]) displayed unmutated IGHV status (‘U-CLL’).

Of the U-CLL cases, as has been also reported by others (8,10,38), the most common co-occurring mutation was alteration in TP53 (Supp Fig 1b), either as deletion in chromosome 17 (del[17p]), TP53 mutation, or both (in 13 of 43 [30.2%], compared to 16.4% of U-CLL without mut-RPS15, OR=2, p=0.075). RPS15-mutated samples also harbored mutations in other genes associated with DNA damage response and maintenance of genome stability (i.e., ATM, POT1 and DDX3X) (39), though none of these associations were statistically significant. The CLL-map cohort further provided an opportunity to evaluate how RPS15 mutation might link with a broader array of alterations beyond a limited targeted panel. Mut-RPS15 (located on human chromosome 19) was associated with copy number alterations (CNAs) in chromosome 6q, including arm-level del(6q) (n=6) and focal del(6q25.3) (n=7) (Supp Fig 1b), as compared to wild-type RPS15 U-CLL cases (50% vs 16% [74 of 463], OR 5.2, p=0.0001). Arm-level del(6q) has been previously detected in CLL and is associated with adverse prognosis in diverse lymphoid malignancies (40). The deleted region of del(6q25.3) encodes EZR (a known oncogene in solid tumors, reported to play a role in the activation of BCR-mediated signaling in B-cell lymphomas) (41,42), C6orf99 (an autophagy-related long non-coding RNA associated with breast cancer survival) (43), OSTCP1 (a transmembrane glycoprotein that constitutes a key hub of oncogenic signaling and associated with progressive disease and poor survival) (44), and MIR3918 (a BCL2 inhibitor implicated in numerous cancers) (45,46).

RPS15 mutations in CLL have been previously associated with inferior prognosis (8,10). In our dataset, where we stratified samples by IGHV mutation status, U-CLLs with either sole RPS15 mutation, sole TP53 alteration, and both RPS15 mutation and TP53 alteration (including TP53 mutation, del(17p), and del(17p13.1)) did not have a difference in OS, but altogether had shorter overall survival (OS) compared to cases without RPS15 or TP53 mutation (p=0.0045, comparison of all four groups, log-rank test, Supp Fig 1c). U-CLLs with wildtype TP53 and RPS15 mutations only trended to a shorter overall survival (OS) compared to cases with wild-type RPS15 (p=0.096) but did not reach statistical significance.

Rps15-S138F mutation drives B-cell transformation in vivo
To directly interrogate the effects of RPS15 mutation on B-cell function, we engineered a mouse model with conditional expression of Rps15-S138F, which is among the most commonly occurring of RPS15 mutations, using the Cd19-Cre/loxP system (Fig 1c, Supp Fig 1d, Methods). By intercrossing Cd19-Cre and Rps15-loxP mice (Fig 1d), we generated cohorts of wild-type (WT) (Cd19-Cre/Rps15+/+, ‘Rps15WT’), heterozygous (Cd19-Cre/Rps15fl/+, ‘Rps15Het’ or Het), and homozygous (Cd19-Cre/Rps15fl/fl, ‘Rps15Hom’ or Hom) mutant (MT) mice. Further, as TP53 heterozygous deletions often co-occur with RPS15 mutation, we intercrossed Cd19-Cre/Rps15MT mice with Trp53 heterozygously-deleted mice (Trp53del) to generate Trp53del/Rps15MT co-mutant murine cohorts (Methods). Targeted PCR confirmed the presence of the floxed allele and Cre-mediated activation of the mutant allele exclusively in CD19+ splenic B cells (Fig 1e). Moreover, we confirmed stable expression of the mutant Rps15 allele by quantitative targeted RNA sequencing of splenocytes (Fig 1f) from both heterozygous (~50% of transcripts) and homozygous (~80% of transcripts) young mice (3-6 months of age) and was expressed at similar levels in older mice (>12 months of age) (Supp Fig 1e). We noted no significant differences in RPS15 protein levels across genotypes in young pre-leukemic mice (Supp Fig 1f).

To determine whether Rps15 mutation alone is sufficient to drive B-cell leukemogenesis, we monitored the peripheral blood of three cohorts of mice (Rps15WT, Rps15Het, Rps15Hom, n=30 each) bi-monthly via flow cytometry for the appearance of B220+CD5+ CLL-like cells for up to 24 months of age (gating strategy in Supp Fig 2a-b). No circulating CLL-like cells were detected in any Rps15WT mice, yet 11 of 30 (37%) Rps15Het mice and 3 of 30 (10%) Rps15Hom mice developed CLL-like disease between the ages of 18-24 months (p=0.03; Fig 2a-b), confirmed by CD5/PAX5 immunohistochemical staining of tissue sections (Fig 2c, Supp Fig 2c).

Given the frequent co-occurrence of TP53 heterozygous deletion in RPS15-mutant CLL, we also monitored three additional cohorts of mice with heterozygous Trp53 deletion (Rps15WT/Trp53del [hereafter Trp53del], Rps15Het/Trp53del and Rps15Hom/Trp53del [hereafter combined, Rps15MT/Trp53del], n=30 each). As expected, given that TP53 is a known CLL driver, we observed circulating CLL-like disease in 7 of 30 (23%) Trp53del mice (Fig 2a). Unlike the single-mutant cohort, where heterozygous Rps15 mutant mice predominantly developed B-cell leukemia reminiscent of human CLL, this was seen in only 3 of 30 (10%) Trp53del/Rps15Het mice; instead, 8 of 30 (27%) Trp53del/Rps15Hom developed CLL-like disease (p=0.18).

We additionally observed four cases of diffuse large B-cell lymphoma (DLBCL) unique to the Rps15MT/Trp53del cohorts, as determined by massive splenomegaly (Fig 2d) and characteristic histological and immunohistochemical features including presence of large B cells that formed confluent sheaths, MYC/IRF4 positivity, BCL6 negativity and high Ki-67 proliferation index (Supp Fig 2c-e). This finding is of particular relevance given growing reports of RPS15 mutations (including S138F) identified in human Richter’s Syndrome (RS) characterized by high-grade transformation of CLL to DLBCL (47–49), and stands in stark contrast to prior assumptions that RPS15 is a passenger mutation present in TP53-mutant cells, rather than an independent driver of RS. In all cases, mice with CLL or DLBCL-like disease, whether arising from Rps15-mutant mice (both RpsHet and RpsHom) or from compound mutant mice (with Trp53del), exhibited extensive tumor infiltration of the spleen and peritoneal cavity. For CLL mice, bone marrow involvement was minimal, but was greater for RS/DLBCL mice (Supp Fig 2f).

Consistent with our patient data, next-generation sequencing analysis of BCRs revealed that all murine CLL-like tumors harbored unmutated IGHV (Supp Data 2). Furthermore, we identified increased marginal zone B cells (B220+ CD21high CD23-) in Rps15Hom mice compared to Rps15WT mice (p=0.005; Supp Fig 3a-c) with relative reduction of follicular B cells (B220+ CD21- CD23high, p=0.05) and B1a cells (B220+ CD5high, p<0.0001) and no change in B1b (B220+ CD5low) or transitional B cells (Supp Fig 3c-d). These data indicate that the CLL-like disease likely emerged from marginal zone B cells rather than follicular, B1a, B1b or transitional B cells.

Transcriptional profiling of leukemic and pre-leukemic Rps15-mutant B cells reveals distinct hyper-proliferative and hypo-proliferative signatures
To elucidate the molecular events contributing to Rps15-mutant leukemogenesis, we performed transcriptome analysis of CLL-like (B220+ CD5+) cells isolated through fluorescence-activated cell sorting (FACS) of splenocytes from Rps15Het mice (n=3) and from normal B cells obtained from spleens of age-matched Rps15WT mice (n=3) (all 24 months of age). Principal component (Supp Fig 3e) and differential gene expression analysis (Fig 2e-left, Supp Table 2) revealed clear differences in gene expression between CLL-like and non-CLL B cells, with 2,106 upregulated and 1,972 downregulated genes in CLL-like cells (log FC >0.3, q<0.05). Gene set enrichment analysis (GSEA) revealed marked upregulation of the Myc transcriptional program in Rps15Het CLL-like cells (Fig 2e-right), as well as upregulation of cell cycle, DNA repair, and B cell receptor signaling programs (Supp Fig 3f). Upregulation of Myc target genes could also be recapitulated in RPS15-S138F mutant vs WT isogenic HG3 cell lines with and without TP53 knockout, and in primary samples from 3 treatment-naïve CLL patients harboring clonal RPS15 mutations, compared to 3 RPS15 WT CLL samples of similar genetic background (Fig 2f, Supp Fig 3g). Reactome pathway analysis of these differentially expressed signatures across all three contexts (i.e., murine and cell line models, and primary CLL samples) revealed a common enrichment in translational machinery, such as mRNA splicing and processing, rRNA processing and modification, snRNP assembly, and N-glycosylation of protein (Supp Fig 3h, normalized enrichment score>1, nominal p-value <0.05).

To determine whether similar transcriptional signatures could be observed in the setting of pre-leukemia, we performed RNA sequencing (RNA-seq) analysis of splenic B cells from 3-6 month old Rps15WT, Rps15Het and Rps15Hom mice (n=3 per genotype). We identified a total of 59 and 432 significantly upregulated and 192 and 325 significantly downregulated genes in Rps15Het vs Rps15WT mice and Rps15Hom vs Rps15WT mice, respectively (Fig 3a, Supp Data 3; log FC > 0.3, q < 0.05). While GSEA of differentially expressed genes in Rps15Het vs Rps15WT B cells from 3-6 month old mice exhibited downregulation of Myc target genes (Supp Fig 4a, Supp Data 4), these genes were found to be upregulated in Rps15Het vs Rps15WT non-CLL B cells in aged mice (24 month old) (Supp Fig 4b-c, Supp Table 2, Supp Data 5). Comparison of Rps15Hom vs Rps15WT pre-leukemic B cells (3 month old) also revealed a marked enrichment of Myc target genes (Fig 3b), similar to Rps15Het CLL-like tumor cells (Fig 2e-f). Concomitantly, B cells from 3-6 month old pre-leukemic Rps15Het and Rps15Hom mice displayed downregulation of genes involved in DNA replication, cell cycle regulation, mitosis, DNA repair as well as cellular metabolism and turnover (Fig 3b, Supp Fig 4a). The latter suggests that proportionately fewer Rps15Het and Rps15Hom pre-leukemic B cells were actively cycling compared to their Rps15WT counterpart. Consistent with this finding, we observed a reduced proportion of B cells in the peripheral blood of aged pre-leukemic Rps15Hom vs Rps15WT mice (p<0.0001, Fig 3c), accompanied by lower ex vivo proliferative capacity in both Rps15Het and Rps15Hom B cells compared to Rps15WT B cells upon mitogenic stimulation with lipopolysaccharide (LPS) (p=0.0007, Fig 3d, Supp Fig 4d). Thus, while Rps15-mutant CLL cells displayed exclusively hyper-proliferative transcriptional signatures, hypo-proliferative signatures co-existed with and predominated over hyper-proliferative signatures in pre-leukemic Rps15-mutant B cells.

Rps15-mutant pre-leukemic B cells accumulate oxidative stress and DNA damage
To reconcile the paradoxical finding of B-cell hypo-proliferation in 3-6 month old Rps15-mutant mice with their leukemic potential, we focused our functional analysis on splenic B cells from these pre-leukemic mice. Informed by the downregulation of cell cycle genes from our RNA-seq analysis (Fig 3b; Supp Fig 4a), we performed cell cycle profiling which showed increased G1 cell cycle checkpoint activity in pre-leukemic Rps15Het and Rps15Hom versus Rps15WT B splenocytes, particularly at 24 hours following induction of proliferation (p=0.003 and p=0.013 respectively, Fig 3e, Supp Fig 4e). In Rps15Hom B cells, we additionally observed increased early and late apoptotic activity compared to Rps15WT cells (p=0.0002 and p=0.02 respectively 48 hours following LPS treatment, Fig 3f).

We hypothesized that the suppression of proliferation and potentiation of apoptosis in pre-leukemic Rps15Hom B cells despite Myc upregulation could arise from increased cellular stress in these cells resulting from Rps15 mutation. Consistent with the notion that enhanced oxidative stress is present in Rps15Hom compared Rps15WT B cells, transcriptomic analysis revealed a marked increase in expression of reactive oxygen species (ROS) genes in B splenocytes from pre-leukemic Rps15Hom compared to Rps15WT mice (NES 1.5, GSEA FDR-adjusted two-sided empirical p<0.05) (Supp Data 4). Further, functional evidence of increased oxidative stress in both quiescent and proliferating Rps15Hom B splenocytes (p<0.0001 and p=0.0045 respectively compared to Rps15WT), as well as in quiescent Rps15Het B splenocytes from older (11 month old) pre-leukemic mice (p=0.026 compared to Rps15WT), was confirmed by experimental quantification of mitochondrial ROS (Fig 3g; Supp Fig 4f; Supp Fig 4g). Accordingly, Rps15Hom B cells exhibited hypersensitivity to further potentiation of oxidative stress with parthenolide (EC50 3.5 μM vs 1.2 μM for Rps15Hom vs Rps15WT with 48 hours of treatment, p=0.0009, Fig 3h), a compound selected specifically for its known potent pro-oxidant effect on CLL cells (50–52).

We reasoned that elevated oxidative stress in Rps15-mutant B cells, if unresolved, could lead to deleterious accumulation of DNA damage. Accordingly, we observed an increased accumulation of γH2AX, a marker of DNA damage, in both quiescent and proliferating Rps15Hom B cells (p=0.0149 and p=0.016 respectively compared to Rps15WT), as well as in quiescent Rps15Het B splenocytes from older (11 month old) pre-leukemic mice (p=0.0082 compared to Rps15WT) (Fig 3i, Supp Fig 5a; Supp Fig 5b). To confirm whether DNA damage occurred as a direct result of oxidative stress, we undertook WGS of 4 CLLs arising from Rps15Het mice. Analysis of these genomes revealed profound enrichment for A•T>C•G and C•G>T•A transversions (p<0.0001) that are pathognomonic of peroxide-induced DNA damage (53,54), when compared against matched T-cell normal samples, with no other significantly enriched mutational signatures (see Methods and Supp Data 6) (Fig 3j-k).

p53-mediated response to oxidative stress and DNA damage underlies the hypo-proliferative phenotype of Rps15-mutant pre-leukemic B cells
To determine whether the hypo-proliferative phenotype of Rps15-mutant pre-leukemic B cells is directly linked to oxidative stress, we evaluated the effect of oxidative stress reversal on B cell proliferation. In Rps15Hom pre-leukemic B cells induced to cycle with LPS/IL-4, we observed a dose-dependent reduction in mitochondrial ROS when these cells were concomitantly treated with N-acetylcysteine (NAC), a known antioxidant (Fig 4a). Treatment with 10 mM NAC, which suppressed ROS in Rps15Hom B cells by 52%-73%, resulted in higher ex vivo proliferative capacity upon mitogenic LPS stimulation, consistent with a partial reversal of their hypo-proliferative phenotype (p=0.03, Fig 4b). In contrast, NAC treatment exerted minimal effect on the proliferative capacity of Rps15WT B cells.

We hypothesized that the phenotype of decreased proliferation and increased apoptosis characteristic of Rps15Hom cells in the pre-leukemic setting could be attributed to a p53-mediated response to oxidative stress and DNA damage in actively replicating cells. Ribosomal proteins are known to stabilize p53 by inhibiting the p53-targeting E3 ubiquitin ligase MDM2 (24–30); consistent with this, we observed lower levels of p53 expression in cycling (LPS-stimulated) Rps15Hom compared to Rps15WT B cells (Fig 4c, p=0.0149). Nevertheless, substantial induction of p53 and p21 (CDKN1A) was evident in Rps15Hom cells upon LPS stimulation, the latter a p53-dependent cyclin-dependent kinase inhibitor that functions as a critical mediator of G1 checkpoint activity (55–57). Indeed, we observed comparable p21 levels in cycling Rps15Hom vs Rps15WT cells. This was accompanied by suppressed cyclin A induction in Rps15Hom compared to Rps15WT B cells (p=0.0015) as further evidence of reduced proliferation in Rps15Hom cells (Fig 4c). Collectively, these results indicate that the p53-p21 pathway remained active in cycling Rps15Hom cells despite Rps15 mutation.

To further delineate the role of p53 in the context of RPS15 mutation, we functionally interrogated the double-mutant Rps15Hom/Trp53del mice. Consistent with a partial loss of Trp53, Rps15Hom/Trp53del pre-leukemic B cells displayed reduced p21 induction in response to LPS stimulation compared to Rps15Hom B cells (p=0.0055, Fig 4d). Accordingly, Rps15Hom/Trp53del B cells exhibited reduced G1 cell cycle checkpoint activity compared to Rps15Hom B cells (p=0.0265, Fig 4e). This resulted in higher ex vivo proliferative capacity in Rps15Hom/Trp53del vs Rps15Hom B cells (p=0.0059, Fig 4f). Indeed, the proliferative potential of Rps15Hom/Trp53del cells was comparable to Rps15WT cells consistent with a reversal of the hypo-proliferative phenotype associated with Rps15 mutation. Corroborating our ex vivo data, aged Rps15Hom/Trp53del pre-leukemic mice showed higher proportion of B cells in the peripheral blood compared to their Rps15Hom counterparts (p=0.03, Fig 3c). Moreover, consistent with the role of p53 as a critical protective barrier against genomic insults, pre-leukemic Rps15Hom/Trp53del B cells displayed potentiation of DNA damage (p=0.009, Fig 4g) and oxidative stress (p=0.0015, Fig 4h) to levels beyond those seen in Rps15Hom B cells. Thus, notwithstanding lower p53 expression in RPS15-mutant cells, these findings highlight the functional importance of residual p53 activity in the context of RPS15 mutation that likely underlies the selective pressure for its subsequent loss through TP53 mutation and/or deletion.

Defective DNA damage response in Rps15-mutant pre-leukemic B cells may precipitate genomic instability and transition towards a hyper-proliferative state
Given the hypo-proliferative effects of Rps15 mutation, we sought to better characterize the mechanisms present at the pre-leukemic stage that might eventually tilt the balance towards a hyper-proliferative or malignant state. Since heightened oxidative stress and DNA damage accumulation are key phenotypic features of pre-leukemic Rps15Hom B cells, we reasoned that these cells were hyper-dependent on the DNA damage response. Conversely, defective DNA damage response would precipitate genomic instability and confer susceptibility to the acquisition of additional genomic events such as TP53 loss that could drive cells towards hyper-proliferation. We therefore asked whether pre-leukemic Rps15Hom B cells could effectively respond to further genomic insults.

To address this, we assessed cellular response to hydroxyurea (HU) which induces replication stress, and ionizing radiation (IR) which induces DNA double-strand breaks (DSBs). In both Rps15WT and Rps15Hom B splenocytes, we observed G1- and G2/M-phase cell cycle arrest in response to HU and IR respectively, but proportionately fewer Rps15Hom cells underwent G2/M arrest in response to IR compared to Rps15WT cells (p=0.044 at 6 h after IR, Fig 5a, Supp Fig 5c-d). Moreover, while Chk1 response to HU was preserved in Rps15Hom cells, Chk2 response to IR was diminished (Supp Fig 5e). Given that DSBs frequently arise from ROS-induced DNA damage and represent the most deleterious of DNA lesions (58–62), their effective resolution is critical for the prevention of genomic instability. We therefore examined the integrity of the ATM-Chk2 pathway which is essential for high-fidelity repair and resolution of DSBs. In Rps15Hom B cells, we observed diminished ATM phosphorylation, diminished phosphorylation of downstream ATM targets including SMC1 and Chk2, and decreased p53 induction following IR compared to Rps15WT cells (p=0.049, p=0.027 and p=0.027, respectively for p-SMC1, p-Chk2 and p53, Fig 5c). This was particularly pronounced in one of the Rps15Hom mice (Replicate 3) where abrogation of ATM-Chk2 signaling was evident with compensatory Chk1 activation in response to IR. Taken together, these findings support the notion that DNA damage response is aberrant in Rps15Hom cells.

We also considered the contribution of oncogenic drivers that could support the transition of Rps15-mutant B cells to a hyper-proliferative state by conferring additional survival and proliferative signals, allowing these cells to tolerate further increase in oxidative stress and DNA damage upon the loss of p53-dependent cell cycle regulation and apoptotic response. In addition to Myc pathway upregulation evident from our RNA-seq data, we found increased Zap70 expression in Rps15Hom vs Rps15WT cells, at both the transcriptional (Fig 3b) and protein level (Supp Fig 5f). In contrast, BCR signaling responses were more equivocal, with increased phosphorylation of Erk (p=0.047), a late BCR signaling protein that modulates Myc (63,64), but not proximal BCR signaling proteins such as Syk, Akt and Plcγ1 in Rps15Het or Rps15Hom compared to Rps15WT B splenocytes in response to anti-IgM stimulation (Supp Fig 5g). This suggests potential involvement of recently described Zap70-mediated growth-promoting mechanisms that are not necessarily dependent upon proximal BCR signaling activity (65,66).

Rps15-mutant pre-leukemic B cells and murine CLL-like tumors display heightened genomic instability
To establish whether oxidative stress, DNA damage and defective DNA damage response collectively result in heightened genomic instability in Rps15-mutant pre-leukemic B cells, we compared the frequency of appearance of micronuclei in Rps15WT and pre-leukemic Rps15Hom splenic B cells following 72-hour ex vivo mitogenic stimulation with LPS. Micronuclei results from the sequestration of DNA within aberrant extranuclear structures and is a recognized hallmark of chromosomal instability (67,68). We discovered higher proportions of B cells harboring micronuclei in older (11 month old) pre-leukemic Rps15Hom and Rps15Het pre-leukemic mice compared to Rps15WT mice (25% for Rps15Hom and 13% for Rps15Het vs 4% for Rps15WT, p<0.0001; Fig 5c), indicating heightened genomic instability in these cells.

Furthermore, to determine the burden of additional genomic alterations as a surrogate marker for genomic instability in CLL-like tumors arising from Rps15-mutant mice, we undertook WGS of malignant cells isolated from Rps15Het (n=4) or Trp53del mice (n=2) harboring CLL-like disease, as well as from Rps15Het/Trp53del mice with CLL-like (n=1) or RS-like tumors (n=1). Across all 8 mice, we observed the acquisition of multiple structural variants that reflects genomic instability, including recurrent amplifications of chromosomes 15 (8 of 8 mice) and 17 (4 of 8 mice) encompassing Myc and Mapk, respectively (Fig 5 d, Supp Fig 6a). In both Rps15- and Trp53-mutant CLL-like tumors, we observed amplifications of chromosome 7 that shares homology with human chromosome 19, and these lesions mirror chromosome 19 amplifications or trisomies that have been reported as putative drivers in human CLL (8). Unique to Trp53-mutant CLL-like tumors were amplifications of chromosome 2 and deletions of chromosome 12 involving the CLL drivers Samhd1 and Dnmt3a, respectively (69). Consistent with their heightened chromosomal instability, Rps15Het/Trp53del CLL-like tumors exhibited even more extensive copy number alterations, and uniquely harbored focal deletions of chromosome 4 (Jak1), amplifications of chromosome 10 (Cdk2 and Cdk4) and a focal amplification on chromosome 14. CDK2 and CDK4 are localized to human chromosome 12 and duplicated in trisomy 12, the latter being among the most frequent lesions in RS alongside MYC and MAPK amplification. In line with the copy number increase noted in chromosomes 7, 15, and 17 of Rps15Het mice, most of the genes on these chromosomes were upregulated in CLL cells compared with normal B cells (Fig 5e, Supp Fig 6b). The double mutant RS mouse had unique arm-level deletions at chromosomes 2, 4, and 12 relative to the double mutant CLL mouse, conferring deletions of Jak1 and Dnmt3a (Fig 5d).

We further identified numerous non-synonymous coding somatic single-nucleotide variants (sSNVs) across all 8 mice whose mutations were predicted to have functional impact with deleterious SIFT classification (score<0.05) (Fig 5f). Of note, mutations in genes with high SIFT scores included putative CLL drivers such as Kmt2c (70), Kmt2d (71), Mga (72,73) and Fbxw7 (74,75), each known to play key roles in DNA damage response in other cancers. MGA and FBXW7 are also known negative regulators of the Myc pathway (76–78), and deleterious mutations in these genes could potentially contribute to overwhelming any existing checks and balances on already upregulated Myc activity in Rps15 mutated cells. Further, these genes were present at high allelic frequency within their respective murine hosts (Supp Fig 6c), suggesting that they play a key role in contributing to Rps15-mutant leukemogenesis, possibly as second hits. Finally, several sSNVs were unique to the DLCBL-like cells, including Crebbp, a gene recurrently mutated in human RS (79,80).

RPS15 mutations impair ribosome biogenesis
To uncover the relationship between ribosomal alterations and the distinct phenotypic properties of RPS15-mutant B cells, we turned our focus to investigating ribosomal defects secondary to RPS15 mutation. Specifically, to directly determine if RPS15 mutation generated changes in intrinsic ribosome function that could impact the stability of RPS15 itself as well as ribosome synthesis and assembly, and that could in turn instigate the cell cycle dysregulation and genomic instability that we observed, we used a doxycycline-inducible transposon system to stably introduce the S138-RPS15 mutation as Flag- and HA-tagged proteins into the HG3 CLL cell line (which harbors del(13q) but has intact TP53). Further, we used the CRISPR-Cas9 editing system to generate TP53 knock-out (hereafter referred to as TP53 deleted) HG3 cell lines expressing the recurrent RPS15 mutations (Fig 6a, Methods). To evaluate the consistency of the effects we observed, we also generated lines to model 3 other common RPS15 mutations (G134R, H137Y, and S139F).

Recent reports in HEK293T and MEC1 (a TP53-mutated CLL-like cell line) suggest that RPS15 mutations alter the stability of the mutant protein via ubiquitin-dependent proteasomal degradation (34). In our genetically engineered HG3 cell lines, we similarly observed reduced levels of RPS15 protein, but not mRNA, in two of the 4 RPS15 mutated cell lines (RPS15-S138F and RPS15-S139F) relative to the wild-type RPS15 line (Fig 6b, Supp Fig 7a). Given the central role of p53 in cellular stress responses (22), we asked whether TP53 deletion might result in RPS15 protein stabilization. We noted that TP53 deletion resulted in increased overall expression of transgene mRNA (Supp Fig 7b-c). Yet, while we observed that TP53 deletion rescued RPS15 protein expression, it did not rescue mRNA expression of RPS15 mutants (Fig 6c, Supp Fig 7b-d).

RPS15 has several essential functions in the cell, including entering the nucleolus to facilitate maturation of ribosomal RNA precursors, and mediating the final stages of ribosome maturation (namely, of the small 40S subunit) and assembly in the cytoplasm. Consistent with prior studies in HEK293T (34), our HG3 CLL-like line analyses indicated that RPS15 mutant protein, like RPS15 WT protein, is distributed mainly in the cytoplasm and nucleolus, as determined by immunofluorescence (Supp Fig 7e). Thus, the RPS15 mutations we modeled do not impede nuclear import and export of RPS15.

To evaluate whether RPS15 mutation impacts ribosome maturation, we performed polysome tracing of the HG3 mutant lines. Analysis of both empty vector and wild-type overexpression showed a classical profile of ribosomes and polysomes (Fig 6d-left), with similarly sized small peaks representing the 40S (small) and 60S (large) ribosomal subunits, a large peak representing the 80S mature assembled ribosome (monosome), and subsequent peaks representing polysomes. However, all four RPS15 mutant lines resulted in increased amounts of free 60S subunits and reduced amounts of mature 80S ribosomes (Fig 6d-right, Supp Fig 7f), indicating a likely deficit in 40S maturation that results in accumulation of excess 60S and reduced mature ribosome assembly.

Since studies in yeast have demonstrated a similar phenotype upon RPS15 depletion (37,81–83), we examined if mutant RPS15 was stable enough to co-localize with and incorporate into mature ribosomes. By using sucrose gradient centrifugation to separate free protein and pre-ribosomal particles from mature ribosomes and polysomes, and immunoblotting of individual fraction lysates, we observed that Flag-HA-tagged RPS15 mutants were present in the small subunit and mature ribosome fractions (Supp Fig 7g). The ability of RPS15 mutants to incorporate into the ribosome, and the localization of RPS15 mutants specifically to the C-terminus tail which engages the ribosome decoding center (84–86), all underscore a likely direct impact on ribosome function.

Finally, we evaluated the impact of RPS15 mutants on CLL cell growth. Consistent with our in vivo studies, we confirmed that the RPS15-mutant HG3 cells proliferated more slowly than empty vector and wild-type overexpressing cells, as measured by live-cell imaging (Fig 6e, Supp Fig 7h-top, Methods). By contrast, TP53-deleted cells exhibited more rapid growth across all RPS15 mutants (Fig 6f) and was most apparent in the S138F mutant line (Fig 6g-h, Supp Fig 7h-bottom). Since direct interaction between ZAP70 and the ribosome has been described (65), we asked whether mutant RPS15, given its disruptive role in ribosome synthesis, might impact ZAP70 and ribosome interactions. Through immunoblotting of ZAP70 levels across the small and large ribosomal subunits, mature ribosomes, and polysomes isolated from HG3 lysates, we indeed observed ZAP70 in ribosomal fractions, and further that RPS15 mutation did not ablate this interaction (Fig 6i, Supp Fig 7i).

RPS15-mutant ribosomes exhibit altered translation via changes in translation efficiency, ribosome stalling and stop codon read-through that result in cellular phenotype alterations
In CLL, RPS15 mutations occur in the C-terminal tail of RPS15, a region purported to interact with the A- and P-site tRNAs during translation (84,85). To determine whether RPS15-S138F impacts translation efficiency (TE), we performed ribosome profiling of Rps15WT, Rps15Het, and Rps15Hom pre-leukemic B cells from 3-6 month old mice. By comparing levels of ribosome-associated mRNA (ribosome footprints) against the total mRNA levels present for each gene, we identified 342 genes with differential TE between Rps15Hom vs Rps15WT cells (222 increased, 120 decreased; log FC>1.5, p<0.05 with DESeq2 FDR-adjusted two-sided Wald tests; Fig 7a, Supp Data 7). By GSEA, these were predominantly involved in DNA replication, cell cycle checkpoints, RNA processing, ribosome synthesis and translation, metabolism, and cell signaling (GSEA FDR-adjusted two-sided empirical p<0.05, NES>1.5; Fig 7b). A subset of genes with differential TE were confirmed by manual inspection via the Integrated Genomics Viewer (IGV) (87) (Fig 7c; Supp Fig 8a-b).

We examined potential mechanisms through which differential TE could contribute to the distinct phenotype of Rps15-mutant cells. We prioritized a representative set of the most significantly enriched targets by differential TE analysis for which we also confirmed altered expression by RNAseq (Supp Data 8-last tab) and through proteomic analysis of samples of matched genotype (Methods, Supp Data 9), which we then selected for validation by western blotting. Among proteins with confirmed downregulation in Rps15Hom cells secondary to reduced TE was Gpx1, a glutathione peroxidase that functions as a key cellular antioxidant by scavenging hydrogen peroxide (Fig 7a, c-d; Supp Fig 8b) (88); decreased Gpx1 expression may therefore potentiate oxidative stress and DNA damage in Rps15MT cells. Also significantly downregulated in Rps15Hom cells as a consequence of translational buffering was Otud4, a de-ubiquitinase that promotes the base-excision repair of alkylation-induced DNA adducts (Fig 7a, c-d; Supp Fig 8b) (89). Other DNA repair genes with reduced TE in Rps15Hom cells included Smarca2, a chromatin remodeler critical for non-homologous end joining and homology-directed repair of DSBs (90–94), Fance, a component of the Fanconi anemia complex (95,96), and Tlk1, a mediator of DSB repair (97,98), which could potentially result in their decreased protein expression thereby exacerbating DNA damage and genomic instability. In contrast, important genes regulating cell cycle progression (Ptp4a2 (99), Gtpbp4 (100), Tbrg1 (101)) and translation initiation (Eif1ad) had increased TE in Rps15Hom cells, with resultant protein upregulation of Ptp4a2 and Eif1ad confirmed by western blotting (Fig 7a, c-d; Supp Fig 8a-c); we speculate that these likely shape altered cellular response to DNA damage and govern their transition from a hypoproliferative to a hyperproliferative state.

Notably, we observed marked ribosome accumulation at stop codons in Rps15Hom B cells, suggestive of ribosome stalling (Fig 7e). This was evident in Rps15Het and even more so in Rps15Hom B cells, compared to Rps15WT (Wilcoxon two-side test p<0.0005, 90 and 667 differential stalling genes in Het and Hom samples, respectively; DESeq2 FDR-adjusted two-sided Wald test p<0.25) (Fig 7e-inset). Ribosome stalling at termination elements can precede stop codon read-through (102), and indeed we identified increases in ribosome occupancy of regions 3-28 nucleotides downstream of the stop codon in Rps15Hom vs wild-type cells (p=0.0014; Fig 7f). Notable mRNA transcripts exhibiting stop codon read-through included CD79B, a signaling component of the B-cell antigen receptor, as well as endoplasmic reticulum components integral to appropriate protein translation such as ERGIC2 and SURF4 (Fig 7g, Supp Fig 8d). 76 of 200 (38%) genes with stop codon read-through in Rps15Hom samples were found to co-exhibit ribosome stalling (Fig 7h), including translation-mediating genes RPS18, EIF3I, and NUDT21 as well as genes implicated in cell cycle regulation and DNA repair.

Since mRNA sequences immediately upstream of the stop codon can impact rates of ribosome stalling and stop codon read-through (103,104), we evaluated whether mutated RPS15 was associated with differential protein codon usage on the 27-nucleotide region upstream of the stop codon. Through chi-squared testing, we uncovered localized enrichment of four codons (TAC [tyrosine], AAG [lysine], AAC [asparagine], ATG [methionine]) and depletion of four codons (GGA [glycine], CCC [proline], TGC and TGT [cysteine]) in relation to the Rps15 mutation (Benjamini Hochberg, p-adj <0.05; Supp Fig 8e, Supp Data 10). Notably, while stop-codon read-through can occur via probabilistic molecular errors, most often involving the stop codon UGA (105), we did not observe significant enrichment of this codon in genes with increased stop codon read-through. Instead, codons with Levenshtein distances of ≤1 from TAG, TAA, and ATG were exclusively enriched at that the 5th trinucleotide upstream of the stop codon while codons with Levenshtein distances of ≤1 from GAT, GGG were exclusively depleted (|log2 FC| > 1.5) (Fig 7i, Supp Data 10). These data suggest that Rps15-mutant ribosomes preferentially stabilize interactions between stop codons and tRNAs with anticodons that are semi-complementary to stop codons, which compete with the binding of translation termination factors. Since tRNAs bound to the 5th trinucleotide are expelled from the ribosomal cleft when ribosomes translocate to the stop codon (106), the localization of enrichments to the 5th trinucleotide further suggest that stalling/read-through events are more likely to occur if semi-complementary tRNAs are in the immediate vicinity of the 5’ edge of the ribosome. While transient binding of semi-complementary aa-tRNAs to stop codons could explain Rps15-mutant ribosomal stalling, tRNAs which drive ribosomal translocation through stop codons are expected to be associated with elongation factors (107,108). Consistent with the idea that association with elongation factor is a distinct rate-limiting step for readthrough but not stalling, we observed that ribosomal stalling at the stop codon occurred at a greater rate than stop codon read through in mutant Rps15 mice (Figure 7e). Together, these findings support the notion that stop codon stalling/readthrough depends on channeling of recently expelled, semi-complementary tRNAs back to ribosomes paused at stop codons (109–112).

Finally, to determine whether RPS15 mutation also influences mRNA translation in leukemic B cells, and whether TP53 deletion impacts this relationship, we performed ribosome profiling concurrently in RPS15-S138F mutant and WT HG3 cells with and without TP53 (TP53WT and TP53KO, respectively). We identified 3,308 genes with differential TE between RPS15 mutant vs wild-type cells, of which 1,754 genes had increased and 1,554 genes had decreased TE (log FC >0.5, p<0.1). This dysregulation was even more marked in TP53KO cells, where we identified 3,380 genes with differential TE between RPS15 mutant vs wild-type cells, of which 1,820 genes had increased and 1,560 genes had decreased TE. GSEA analysis of differentially translated genes revealed a strong enrichment of TP53 target genes (Fig 7j-left), consistent with activation of DNA damage and stress response pathways in RPS15-mutant cells. TP53KO cells, however, exhibited a marked increase in TE of Myc target genes and components of the ribosomal machinery (Fig 7j-right, Fig 7k). These findings, together with our studies in murine pre-leukemic models, suggest that RPS15 mutation sets the stage for oncogenesis through Myc signaling dysregulation, and that subsequent loss-of-function mutations in DNA damage response and cell cycle regulator genes such as TP53 may then enable further Myc dysregulation and clonal expansion.

Methods
RESOURCE AVAILABILITY
Materials Availability
The Cd19-Cre/Rps15-S138F flox mouse strain will be available at the Jackson Laboratory Repository with the JAX Stock no. 037632 (http://jaxmice.jax.org/query).

EXPERIMENTAL MODEL AND SUBJECT DETAILS
Our research complies with all relevant ethical regulations. All patient samples were collected in accordance with the principles of the Declaration of Helsinki and with the approval of the Institutional Review Boards of Avicenne Hospital and DFCI. Patients provided samples after their informed consent, and de-identified primary human CLL samples derived from peripheral blood mononuclear cells were used.

All animals were housed at DFCI and procedures were completed in accordance with the Guidelines for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committees at DFCI. A dark-light cycle of 12 hours of light followed by 12 hours of dark were used. Animals were kept at 30-70% relative humidity. A stable ambient temperature was maintained of 68-79°F. Environmental enrichment was provided including nesting materials

Patient samples
Primary CLL samples were obtained from Avicenne Hospital, Assistance Publique-Hôpitaux de Paris, Bobigy, France and from the CLL-map project (including patients from the German, Spanish, NHLBI, UCSD, MD Anderson and Dana-Farber Cancer Institute [DFCI] cohorts; Supp Data 1) (36,116). Samples from the CLL-map project were collected as previously described (36). For samples from Avicenne Hospital, informed consent was obtained from all subjects and specimens were collected in accordance with the principles of the Declaration of Helsinki and with the approval of the Institutional Review Boards of Avicenne Hospital and DFCI. Tumor samples were collected upon diagnosis and prior to therapy. Fresh CLL cells were isolated from peripheral blood via Ficoll and flow cytometry analysis confirmed tumor purity of >80% in all cases. All blood samples underwent IGHV sequencing for the assessment of IGHV mutational status, conventional cytogenetics, fluorescence in-situ hybridization (FISH) analysis of trisomy 12, 11q, 13q and 17p deletions, and amplicon-based next generation sequencing of CLL-related mutated genes (TP53, NOTCH1, SF3B1, FBXW7, RPS15, POT1, MYD88, BIRC3, ATM and XPO1).

Mouse Models
Rps15-S138F floxed mice (C57BL/6 background) were generated (Biocytogen, MA) by flanking endogenous WT exons 3, 4 and a stop codon with Lox-P sites followed by exons 3 and 4 with the RPS15-S138F point mutation knocked-in to exon 4. In presence of the recombinase flippase (Flp), the neomycin/lacZ cassette, flanked by flippase recognition target (Frt) sites, was flipped out. Subsequently, in the presence of Cre recombinase, the sequence between the LoxP sites is removed, allowing for the expression of the Rps15 mutant allele. Trp53del mice (B6.129P2-Trp53tm1Brn/J) were purchased from the Jackson Laboratory.

To obtain B cell-specific expression, Rps15-S138F floxed mice and Trp53 heterozygously deleted floxed mice were crossed with Cd19-Cre/Cre mice (JAX stock #006785) to generate Cd19-Cre/+ Rps15-S138F fl/+ (RPS15Het) and Cd19-Cre/+ Trp53-del fl/+ (Trp53het) mutant mice, respectively. These heterozygous mutant mice were further crossed to generate homozygous mice (Cd19-Cre/+ Rps15-S138F fl/fl or Cd19-Cre/+ Trp53-del fl/fl, respectively).

To generate Rps15MT /Trp53del mice, Cd19-Cre/+ Trp53-del fl/fl and C57BL/6 mice (JAX stock #632) were crossed to generate Cd19-Cre/+ Trp53-del fl/+ Rps15 +/+ mice, and Cd19-Cre/+ Rps15-S138F fl/fl and Cd19-Cre/+ Trp53-del fl/fl were then further crossed to generate Cd19 Cre/+ Trp53 fl/+ Rps15-S138F fl/+ and Cd19 Cre/+ Trp53fl/+ Rps15-S138F fl/fl (RPS15 WT, heterozygous and homozygous) mice.

In all cases, genotypes were confirmed by PCR of tail gDNA using a set of primers targeting the floxed allele:
Rps15 Forward: 5’-GCAAGCGCTCTAGATTGTGACCTC
Rps15 Reverse: 5’-CTCTAAAATGAATCCACCAGCTTCCAC
Trp53 Forward: 5’-GGTTAAACCCAGCTTGACCA
Trp53 Reverse: 5’-GGA GGC AGA GAC AGT TGG AG
Activation of the floxed allele by Cre recombinase was confirmed by PCR on gDNA from purified B cells and non-B cell fractions using a set of primers that amplifies the activated allele:
Cre-activated Forward: 5’-GCAAGCGCTCTAGATTGTGACCTC
Cre-activated Reverse: 5’-GGAGACTGAGGCAGATCTGTCGAC
This reaction amplifies the target activated allele at 142 bp. Presence of the activated allele was only detected in the purified B cell fraction. The following mice will be available from The Jackson Laboratory: Cd19-Cre/Cre Rps15-fl/fl (JAX #037632).

METHOD DETAILS
Clinical outcome modeling
Overall survival was calculated as the time from the date of the sequenced sample to the date of death and censored at the date last known alive. FFS was calculated for treatment-naive patients as the time from the date of the sequenced sample to the date of first treatment (‘natural progression’), progression (if the patient was sampled at the time of enrollment on a clinical trial) or death, and censored at the last known event-free date. Univariate and multivariable Cox regression models were constructed for each subset of data. Final models were selected using the glmnet function for regularized Cox regression using an elastic net penalty within the Coxnet package in R. Tenfold cross-validation using the cv.glmnet function with a partial-likelihood deviance metric to minimize λ was performed and the minimum CV-error model was used. The alpha was set to 1 corresponding to a Lasso penalty. The maximum iterations (maxit) parameter was set to 1,000. Features identified as having non-zero coefficient values using elastic net and selected in the final model were then included in a Cox regression model to obtain the hazard ratios. These hazard ratios estimate the magnitude of effect, but P values and confidence intervals are not readily interpretable in the elastic net model and are therefore not reported. The Breslow approximation was used for handling ties in survival time.

Pyrosequencing
Relative transcript expression of the RPS15 mutant allele compared to the wild-type was quantified by cDNA pyrosequencing as previously described (115). Biotinylated amplicons were generated via cDNA synthesis and PCR amplification of transcripts surrounding the site of RPS15-S138F human and mouse mutation. Immobilized biotinylated single-stranded DNA fragments were isolated per the manufacturer’s protocol (Pyromark Gold Q96, Qiagen) and sequenced using an automated pyrosequencing instrument (PSQ96, Qiagen). The following primer sequences were used for amplification and pyrosequencing of the human and mouse cDNA, respectively:
Human forward PCR primer: 5’-ACAAACCCGTGAAGCATGG
Human reverse PCR primer: 5’Biotin-TACTTCAGGGGGATGAATCTGG
Human pyrosequencing probe: 5’-ATAGGGGCAAECACACT
Mouse forward PCR primer: 5’-CCTACAAACCCGTGAAGCA
Mouse reverse PCR primer: 5’Biotin-TTGA GGGGGATGAATCGG
Mouse pyrosequencing probe: 5’-TCGGTGCCACCCACT
Quantitative analysis was performed using Pyrosequencing software from Qiagen.

Monitoring of disease in peripheral blood
Mice were monitored monthly from 3 to 24 months of age. Peripheral blood (100 µL) was collected from mice via submandibular bleeds into EDTA-coated tubes. Erythrocyte lysis was performed by incubating blood samples in 1 mL of ACK buffer for 5 min and subsequently washed with PBS supplemented with 2% FCS and 2 mM EDTA. Cells were then stained with a cocktail of antibodies targeting CD5 (anti-CD5-FITC, Biolegend #100606), B220 (anti-B220-PacBlue, Biolegend #103227), CD11b (anti-CD11b-APC, Biolegend #101225), CD3 (anti-CD3-APC-Cy7, Biolegend #100222) and IgKappa (anti-IgKappa-PE, Biolegend #409506) for 30 mins at 4°C. Cells were then washed and analyzed using flow cytometry. Gating strategy for disease monitoring was identical to that which we reported previously (48) (Supp Fig 3a in that work). Body weight and body scoring were monitored weekly, and if body weight was reduced by 10%, body weight was monitored daily. Body weight loss equal to 15% and body scoring equal to 2 were considered endpoints for euthanasia. All animals, including mice that did not present signs of disease or morbidity, were euthanized by 24 months.

Immunohistochemistry and pathology
Murine tissues were fixed in 10% buffered neutral formalin overnight, after which bone marrow tissue was decalcified in RapidCal Immuno Buffer (Statlab) for 2 hours as previously described (69). Fixed tissue was then paraffin-embedded and sectioned into 4 µm for immunohistochemistry (IHC) staining. Hematoxylin and eosin (H&E), CD5/PAX5 (CD5=1:300 dilution from Sino Biological; PAX5=1:100 dilution from Cell Signaling Technology AB_2798001), Ki67 (1:800, Cell Signaling Technology AB_2620142), BCL6 (1:1000, Sigma), MYC (1:1000, Abcam RRID: AB_731658) and IRF4 (1:1000, Cell Signaling Technology AB_62834) staining was performed using standard procedures. Spleen, bone marrow and liver from diseased mice were evaluated by a board-certified pathologist and analysis and classification of leukemia, lymphoma and DLBCL was done following published criteria (124). CLL cases were characterized by expansion of small PAX5/CD5 double-positive cells with relatively low proliferation rates (Ki-67low), predominantly adjacent to T-cell areas in the spleen and small focal infiltrates in other organs, resembling human CLL. DLBCL/RS cases revealed effacement of spleen, bone marrow and liver architecture by diffuse proliferation of large PAX5/CLD5med/low/BCL6neg/IRF4+ cells with high proliferation rates (Ki-67high) resembling human DLBCL and RS. Human tissue samples were processed according to CLIA approved protocols from the Brigham and Women’s Hospital pathology department.

BCR-IGHV sequencing
Genomic DNA was isolated from peripheral blood of diseased mice using the QIAGEN DNeasy Blood & Tissue Kit (Qiagen) and analyzed using the mouse-IGH immunoSEQ Assay (Adaptive Biotechnologies). B cell receptor repertoire sequencing was performed using the immunoSEQ Analyzer 3.0 and analyzed using the IMGT/Vquest software to define V, D, J and CDR3 sequences. Mutation status was defined as unmutated when ≥98% homology to germline was detected.

B cell purification
Mice were euthanized in a CO chamber and spleens or femurs were collected and mechanically dissociated to form a single cell suspension. Erythrocyte lysis was performed using ACK buffer and B cells were negatively selected using the MACS Mouse B cell Isolation Kit (Miltenyi Biotec, #130-090-862). B cell purity (>90%) was confirmed post-sorting on a FACS Canto I flow cytometer.

Ex vivo culture of murine primary B cells, drug treatment and BCR stimulation
Ex vivo induction of B-cell proliferation was undertaken using LPS as the mitogenic stimulus, in the presence of IL-4. Purified B cells were seeded at a density of 0.5-0.8 × 106/mL and cultured in RPMI/10% FBS containing LPS (50μg/mL; Sigma, #L6511), murine IL-4 (10ng/mL; R&D Systems, #404ML010) and β-mercaptoethanol (1:500,000; ThermoFischer Scientific, #21985023) prior to downstream functional studies. Ex vivo B-cell cultures were carried out in the presence or absence of the anti-oxidant N-acetylcysteine (NAC; 10 mM; Sigma, #A7250), with NAC being replenished every 24 hours during the 72-hour culture period. For the assessment of sensitivity to parthenolide, B cells were pre-stimulated for 1-2 days with LPS/IL-4 prior to treatment with doubling dilutions of parthenolide (Selleck # S234 1) for a further 1-2 days. For the evaluation of DNA damage response, B cells were pre-stimulated with LPS/IL-4 for 2 days prior to treatment with IR (6 Gy) or HU (1 mM; Sigma, #H86 27) and incubated for 1, 6, 12 or 24 hours, after which the cells were harvested for analysis. For BCR signaling studies, purified splenic B cells (5 × 106) were stimulated with 10 μg/mL goat anti-mouse F(ab’)2 IgM antibody (Southern Biotech, Birmingham, AL, Cat #1023-T01) for 5 or 15 min at 37°C prior to analysis.

Western Blot
Western immunoblot analyses were performed to evaluate the protein expression levels of RPS15 and RPL5, to analyze DNA damage response and BCR signaling, and to validate findings from the Ribo-seq analysis. For all assays, a minimum of 5 × 106 cells were collected, washed with PBS and lysed in RIPA buffer (Sigma #R0278) supplemented with protease and phosphatase inhibitors (Sigma #11836153001 and Sigma #4906845001) for 30 min at 4°C. Cell lysate was clarified by centrifugation at 10,000xg for 5 min. Protein concentration was determined using the Pierce bicinchoninic acid assay (Life Technologies #23225) and 40 µg of protein were loaded per lane on a 4-12% Bis-Tris or 3-8% Tris-Acetate gel (Life Technologies #NP0323 and #EA03785). Proteins were transferred onto polyvinylidene fluoride membranes (Life Technologies IB24002) using an iBlot 2 at 20V for 6-12 minutes and subsequently incubated in 5% BSA, 1X TBST (Teknova #T9511) prior to probing with one of the following primary antibodies: RPS15 (1:1000 dilution; #PIPA562977, Thermo Fisher Scientific), RPL5 (1:1000; #14568S, Cell Signaling Technology), ATM (1:1000; #2873, clone D2E2, Cell Signaling Technology), p-ATM Ser1981 (1:250; #AF1655, R&D), SMC1 (1:1000; #4802, Cell Signaling Technology), p-SMC1 Ser957 (1:1000; #4805, clone 5D11G5, Cell Signaling Technology), Chk2 (1:1000; #sc-5278, clone A-12, Santa Cruz), p-Chk2 Thr68 (1:500; #2661, Cell Signaling Technology), Chk1 (1:1000; #sc-8408, clone G-4, Santa Cruz), p-Chk1 Ser345 (1:500; #2341, Cell Signaling Technology), γH2AX Ser139 (1:1000; #05-636, clone JBW301, Millipore; and 1:1000; #2577, Cell Signaling Technology), p53 (1:1000; #9282 and #2524 [clone 1C12], Cell Signaling Technology), p21 (1:500; #64016, Cell Signaling Technology), cyclin A (1:200, #sc-239, clone BF683, Santa Cruz), AKT (1:1000, #4298, clone 40D4, Cell Signaling Technology), p-AKT Ser473 (1:1000, #4060, clone D9E, Cell Signaling Technology), ERK1/2 (1:1000, #4695, clone 137F5, Cell Signaling Technology), p-ERK1/2 Tyr202/204 (1:1000, #4370, clone D13.4.4E, Cell Signaling Technology), p-SYK Tyr525/526 (1:1000, #2711, Cell Signaling Technology), SYK (1:1000, #13198, clone D3Z1E, Cell Signaling Technology), p-PLCγ1 Tyr783 (1:1000, #2821, Cell Signaling Technology), PLCγ1 (1:1000, #5690, clone D9H10, Cell Signaling Technology), GPX1 (1:200; #AF3798, R&D), EIF1AD (1:500, #20528-1, Proteintech), OTUD4 (1:500; #25070-1, Proteintech), PTP4A2 (1:500, #PA5120349, ThermoFisher Scientific), actin (1:2000; #5125, clone 13E5, Cell Signaling Technology), HSP90 (1:1000; #79641, clone C45G5, Cell Signaling Technology) and GAPDH (1:1000; #2118, clone 14C10, Cell Signaling Technology). Membranes were washed in TBST prior to incubation with a 1:2000 or 1:10,000 dilution of goat anti-rabbit HRP-conjugated IgG antibody (Fisher Scientific 12348MI or Cell Signaling Technology, #7074), a 1:1000 dilution of horse anti-mouse HRP-conjugated IgG antibody (Cell Signaling Technology, #7076), or a 1:1000 dilution of rabbit anti-goat HRP-conjugated IgG antibody (R&D, #HAF017) for 1 hour at room temperature. Membranes were visualized using SuperSignal West Pico Chemiluminescent Substrate (Life Technologies 34580) and quantified using the BioRad ChemiDoc imaging system. Densitometric quantification of protein bands was carried out using the ImageJ software.

Proliferation Assays
Ex vivo proliferation of murine splenic B cells was assessed using the CellTrace Violet Cell Proliferation Kit (ThermoFisher Scientific, #C34557). B cells were resuspended in PBS at 0.8 × 106/mL and incubated at room temperature for 20 min with CellTrace Violet at a final concentration of 10 μM, after which an excess of RPMI/10% FBS was added to stop the reaction. Cells were subsequently cultured in RPMI/10% FBS with LPS/IL-4 for 3 days, harvested and acquired on a LSR Fortessa flow cytometer. Proliferation in the HG3 cell line was measured by plating HG3 cells on 0.01% poly-ornithine coated plates (Sigma Aldrich #P4957). Cells were imaged every 2 hours for the length of time indicated using an IncuCyte S3 Live-Cell Analysis System (Essen Bioscience). Percent confluence was determined by dividing red fluorescence area by total phase cell area, and assay was conducted in triplicates.

Measurement of apoptosis and cell viability
Apoptotic murine B cells were identified by flow cytometry using the Annexin V Apoptosis Detection Kit with PI (Biolegend, #640914). Cells were washed twice with PBS/0.5%BSA, resuspended in Annexin V binding buffer at a concentration of 5 × 106/mL, stained with FITC-conjugated Annexin V antibody (1:20 dilution) and PI (1:10 dilution) for 15 min at room temperature, and acquired on a LSR Fortessa flow cytometer. Viable cells (Annexin V– PI–) were distinguished from early apoptotic (Annexin V+ PI–) and late apoptotic (Annexin V+ PI+) cells. Assessment of ex vivo B cell sensitivity to parthenolide was carried out by flow cytometry using the Zombie Violet fixable viability kit (Biolegend, #423114). Cells were washed in PBS and incubated with the Zombie Violet dye (1:1000 dilution in PBS) at 5 × 106 cells/mL for 15 min at room temperature, and acquired on a LSR Fortessa flow cytometer. The surviving fraction corresponding to each drug dose was calculated. The dose-response data was modeled by non-linear regression and the half maximal effective drug concentration (EC50) was determined using GraphPad Prism v10.0.3.

Cell cycle analysis
Harvested B cells were fixed and permeabilized in ice-cold 80% ethanol and stored overnight at −20°C. Cells were subsequently washed in PBS and resuspended in PI (20 µg/mL; Sigma, #P4864) and RNase A (1 mg/mL; Invitrogen, #12091021), incubated in darkness at room temperature for 15 min prior to acquisition on a LSR Fortessa flow cytometer. Following the exclusion of doublets and cellular debris, the events were displayed on a PI histogram for analysis. PI binds to cellular DNA and therefore measurement of its level reflects the status of a cell within the cell cycle (i.e. G0/G1 phase: 2N; S phase: 2N-4N; G2/M phase: 4N). A minimum of 100,000 events were analyzed, and experiments were performed in triplicates.

Mitochondrial ROS assay
Quantification of mitochondrial ROS was carried out by flow cytometry using the MitoSOX Red mitochondrial superoxide indicator (Invitrogen, #M36008). Harvested B cells were washed in PBS and resuspended at 5 × 106 cells/mL in pre-warmed MitoSOX staining solution (1 µM MitoSOX Red in PBS), incubated for 30 min at 37°C in darkness, and acquired on a LSR Fortessa flow cytometer. All experiments were conducted in triplicate and incorporate both a DMSO control and a positive control. Positive control B cells were pre-treated with antimycin A (50 µg/mL; Sigma, #A8674) for 2 hours at 37°C prior to analysis with MitoSOX Red.

B cell development
Proportions of murine B cell sub-populations at different development stages were determined by flow cytometric analysis of cell surface markers. Cells were extracted from bone marrow, spleen and the peritoneal cavity and processed as described above (B cell purification). Following dissociation and erythrocyte lysis, cells were washed with PBS supplemented with 2% FCS and 2 mM EDTA and incubated with the following cocktails of antibodies (all purchased from BioLegend) at 1:1000 dilution for 15 minutes at 4oC.
Cocktail for splenocyte suspension: CD21-BV421 (Clone 7E9, Cat #123421), B220-BV510 (Clone RA3-6B2, Cat #103247), CD93-BV650 (Clone AA4.1, Cat #136517), CD23-BV786 (Clone B3B34, Cat #563988), CD38-Alexa647 (Clone HIT2, Cat #303514), CD24-Alexa700 (Clone M1/69, Cat #101835), CD95-PE (Clone DX2, Cat #305607), IgD-APC.Cy7 (Clone 11-26c.2a, Cat #405715), and anti-IgM-PE.Cy7 (Clone MHM-88, Cat #314531; for marginal zone B cells, follicular B cells and transitional B cells quantification)
Cocktail for bone marrow suspension: CD43-PE (Clone S11, Cat# 143205), CD23-BV786 (Clone B3B34, Cat #563988), CD24-Alexa700 (Clone M1/69, Cat #101835), CD93-BV650 (Clone AA4.1, Cat #136517), B220-BV421 (Clone RA3-6B2, Cat #103239), CD11b-PerCP.C5.5 (Clone M1/70, Cat #101227), TER-119-PerCP.C5.5 (Clone TER-119, Cat #116227), CD3e PerCP.C5.5 (Clone KT3.1.1, Cat #155615), Ly6G/Ly6C PerCP.C5.5 (Clone RB6-8C5, Cat #108427), IgD-APC.Cy7 (Clone 11-26.c.2a, Cat #405715) and IgM-PE.Cy7 (Clone MHM-88, Cat #314531; for pro-B cells, pre-B cells, immature, transitional and mature B cells quantification)
Cocktail for peritoneal cavity suspension: CD5-APC (Clone 53-7.3, Cat #100625), B220-BV421 (Clone RA3-6B2, Cat #103239), CD43-PE (Clone S11, Cat #143205), IgM-PE.Cy7 (Clone RMM-1, Cat #406513) and CD23-BV786 (Clone B3B34, Cat #563988; for B1a cells quantification).

Culture of cell lines
HG3 cells (purchased from DSMZ #ACC 765) were cultured in RPMI 1640 (Gibco #11875-093) with 15% fetal bovine serum (FBS) (Sigma-Aldrich ), 1% GlutaMAX (Gibco, cat# 35050-061) and 1% penicillin-streptomycin (Life Technologies). Cells were incubated at 37 °C, 5% CO2 and passaged every 48 hours. All subsets of the HG3 cell line were also cultured under these conditions. Cells were routinely tested for mycoplasma per the manufacturer’s instructions (VenorGeM Mycoplasma Detection Kit; Sigma-Aldrich #MP0025).

Generation of mutant human CLL cell lines
Sleeping Beauty transposon plasmids carrying one of four RPS15 CLL mutations (G134R or c.400 G>C; H137Y or c.409C>T; S138F or c.413 C>T; and S139F or c.416 C>T) or the RPS15 WT gene were assembled by introducing gBlocks® (IDT) containing a Flag and HA tag upstream of the RPS15 WT or mutant coding sequence (codon optimized using the IDT Codon Optimization Tool), with SfiI overhangs on each end. Cloning was performed by digesting both the Sleeping Beauty inducible transposon plasmid pSBtet-GP (Addgene plasmid #60495; contains GFP and puromycin selection markers) (125) and each gBlock with SfiI (#R0123S, New England Biolabs). To generate RPS15 mutant HG3 cell lines, HG3 cells were electroporated with 15 µg of pCMV(CAT)T7-SB100 (transposase, Addgene #34879) and 15 µg of each of the mutant transposon vectors. Successfully transposed cells were purified twice by FACS post-nucleofection.

To generate cells compatible with the CRISPR-Cas9 system, lentivirus was generated by co-transfection of LentiCas9-Blast (Addgene #52962) (126) along with pMD2.G (Addgene #12259) and psPAX2 (Addgene #12260) into HEK 293T using Lipofectamine 2000 (Life Technologies). Supernatant containing lentiviral particles was collected at 48 and 72 hours and concentrated using Lenti-X (Clontech). HG3 cells were transduced with fresh lentivirus in the presence of 8 g/mL polybrene and selected by blasticidine (20 μg/ml) for two weeks and Cas9 activity was tested using a previously reported system (127). We generated TP53 knockout HG3 cell lines as previously described (128). sgRNAs were designed using the online CRISPR design tool (http://crispr.mit.edu/) to target TP53. The selection of the sgRNAs was based on choosing those of highest efficiency to target the gene of interest and with the lowest predicted off-targets effects. The sgRNA targeting TP53 (5’-CCATTGTTCAATATCGTCCG-3’) was cloned into pLKO5.sgRNA.EFS.tRFP (Addgene_#57823). Cloning was carried out as previously described (128) and lentivirus was generated as described above. Cas9-expressing HG3 cells were transduced with the above TP53-targeting construct and RFP+ cells were flow-sorted as single cells into 96-well plates. To confirm CRISPR editing of the TP53 loci, gDNA was extracted using the QIAampDNA Micro Kit (Qiagen) following the manufacturer’s instructions. PCR was performed using primers flanking the target sites for the sgRNAs (Forward: AGACCTGTGGGAAGCGAAAA; Reverse: GACAGGAAGCCAAAGGGTGA). For screening the loss-of-function mutations, PCR products were purified using High Pure PCR Product Purification Kit (Roche) and resulting indels at the expected locations were confirmed by Sanger sequencing. The efficiency of the sgRNAs was assessed by Tracking of Indels by Decomposition (TIDE) software (https://tide-calculator.nki.nl; Netherlands Cancer Institute). Western blots were performed to confirm loss of p53 expression as described above (Western Blot).

Quantitative PCR
Total gDNA or RNA was extracted from HG3 cells using the DNeasy Blood & Tissue Kit (QIAGEN) and the RNA purification kit (QIAGEN), respectively. To make cDNA, 2 µg of RNA was reverse-transcribed into cDNA using the SuperscriptII reverse transcription kit (Life Technologies). mRNA or gDNA templates were quantified using the TaqMan Gene Expression Assay probes (Thermo Fisher): RPS15 (Hs01358643_g1), GAPDH (4333764T), and a customized TaqMan probe specific to the codon-optimized RPS15 transgene (RPS15_PCW, Assay ID #APT2ADZ). Quantitative real-time PCR was performed on a QuantStudio 6 Flex Real-Time PCR System (Thermo Fisher). Target gene expression was normalized to the mean Ct values of a housekeeping gene (GAPDH). Number of transgene integrations was determined by normalizing to endogenous RPS15.

Polysome Profiling
Cells were lysed in 500 uL polysome lysis buffer (25 mM HEPES pH 7.5, 5 mM MgCl2, 0.1M KCl, 2 mM DTT, 1% Triton-X, 0.1 mg/mL cycloheximide) and incubated on ice for 5 minutes. The lysate was centrifuged at 13,000 RPM at 4°C for 10 minutes to pellet nuclei. The supernatant was then loaded on top of a 12 mL 10-50% sucrose gradient (25 mM HEPES pH 7.5, 5 mM MgCl2, 0.1M KCl, 2 mM DTT, 0.1 mg/mL cycloheximide) and spun in an ultracentrifuge at 35,000 RPM at 4°C for 2 hours. Gradients were fractionated into 13 samples and the RNA absorbance throughout the gradient was monitored with a BioComp 153 gradient station ip (BioComp Instruments, Fredericton, New Brunswick), using a FC-2 Triax flow cell with software v1.53A (BioComp), and fractionated with a Gilson FC203B fraction collector.

Sucrose Gradients
Cell pellets were lysed in 200 µL lysis buffer (50 mM Hepes pH 7.5, 100 mM KOac, 5 mM Mg(OAc)2, 0.02% digitonin, 1 mM DTT, and 1X cOmplete protease inhibitor cocktail (Roche 1187358)) for 10 minutes on ice and clarified by centrifugation at 21,130g for 10 minutes at 4°C. Lysate was normalized to 6 mg/mL based on A280 readings prior to loading on a 200 µL 10-50% sucrose gradient. Gradients were spun in a TLS55 rotor (Beckman Coulter) at 259,000g for 30 min at 4°C with the slowest acceleration and deceleration settings. An input lysate sample and 11 fractions collected from the top of the gradient were directly analyzed by SDS-PAGE and immunoblotting.

Immunofluorescence microscopy
For the quantification of micronuclei, murine B cells were seeded at 106 cells/mL in 100 μl PBS onto multispot glass slides that had been pre-coated with poly-L lysine (#P4832, Sigma). Cells were fixed with ice-cold 4% paraformaldehyde for 5 min and nuclear extraction was performed with 10 mM piperazine-N,N′-bis(2-ethanesulfonic acid), 300 mM sucrose, 20 mM sodium chloride, 3 mM magnesium chloride and 0.5% Triton X-100. Following washing in PBS, slides were mounted in Vectashield mounting medium with 4',6-diamidino-2-phenylindole (DAPI; # H-1200-10, Vector Laboratories) and micronuclei were visualized and enumerated using an ECHO Resolve R4 fluorescence microscope and the ECHO Pro imaging software (Echo Laboratories).
For immunofluorescence analysis of HG3 cells, the cells were cyto-spun on glass slides at a density of 1x106 cells/mL in 200 µL PBS + 10% FCS, fixed in 4% paraformaldehyde at room temperature for 15 min, washed 3 times with PBS + 0.01% TWEEN-20, permeabilized for 5 min in PBS + 0.1% Triton X-100, and blocked for 1 hour in PBS + 0.1% TWEEN-20 supplemented with 5% FCS. Cells were incubated overnight at 4°C with the primary rabbit FLAG antibody (#14793, Cell Signaling Technology). Slides were subsequently washed 3 times with PBS + 0.1% TWEEN-20, incubated with a secondary anti-rabbit antibody conjugated to Alexa 488 (#ab150077, Abcam) for 1 hour at room temperature and washed 3 times. Nuclei were counterstained with DAPI, and coverslips were mounted using Prolong Gold antifade reagent (#P36934, Life Technologies). Slides were visualized on a Leica SPE confocal microscope. Five fields of view were obtained per condition, and ImageJ software was used for image analysis.

Generation of Ribo-seq Libraries
We obtained ribosome-protected footprints and matching mRNA sequencing data for murine pre-leukemic B cells (3-6 months of age) and HG3 overexpression cell lines (30 µg RNA per sample, 3 biological replicates per condition). RNA-seq libraries were prepared and sequenced as described above. Ribo-seq libraries were generated as previously described (129), with several adaptations. Linkers were designed with 12 random nucleotides forming the UMI rather than 5. RNA unprotected by ribosomes was degraded using a 1:20 dilution of RNaseI and treated for 15 minutes at room temperature. Ribosomes were isolated using a size-exclusion column as described in the Illumina TruSeq Ribo Profile Kit protocol. Ribosome profiling libraries were sequenced with a single-end 50 bp run on a NextSeq500 (HG3 cells) and with a single-end 75 bp run on a NovaSeq S1 platform (murine B cells).

Ribo-seq Analysis
Adapter (AGATCGGAAGAGCACACGTCTGAA) was removed using fastx_clipper from FASTX-Toolkit. UMIs were removed using umi_tools. Ribosomal RNA and tRNA were removed using Bowtie version 1.0.05. For human samples, the remaining reads were aligned to the genome (hg19 / GRCh37) and transcriptome using STAR (v2.5.3a6) (--alignIntronMin 20 --alignIntronMax 100000 --outFilterMismatchNmax 1 --outFilterType BySJout --outFilterMismatchNoverLmax 0.04 --twopassMode Basic). A combination of GENCODE v26lift37 transcriptome annotation was then combined with transcripts annotated as tstatus “unannotated” from MiTranscriptome annotation. To determine the RPF library quality, trinucleotide codon periodicity was plotted using RibORF readDist script8 against annotated protein-coding ORFs (GENCODE v26lift37). Ribo-seq reads were quantified at ORF resolution using a custom script rpfTPM available at https://github.com/TamaraO/Riboseq-tools. Differential translation analysis was performed using DESeq2 and deltaTE (130).

For murine samples, the remaining reads were aligned to the genome (mm10 / GRCm38) and transcriptome using STAR (v2.7.0a) ( --alignIntronMin 20 --alignIntronMax 100000 --outFilterMismatchNmax 1 --outFilterType BySJout --outFilterMismatchNoverLmax 0.04 --twopassMode Basic). GENCODE vM24 was parsed for protein-coding regions and the locations of associated stop codons. Trinucleotide codon periodicity was plotted using RibORF readDist script8 against annotated protein-coding ORFs. Ribo-seq reads were further aligned to padded (± 20 nucleotides) canonical protein-coding ORFs using STAR (v2.7.0a) (-alignIntronMin 2 --alignIntronMax 1 --outFilterMismatchNmax ---scoreDelOpen -10000 --scoreInsOpen -10000 1 --outFilterType BySJout --outFilterMismatchNoverLmax 0.04 --twopassMode Basic) and quantified at ORF resolution using RSEM (v1.2.31) for compatibility with RNAseq analyses. To determine stop codon readthrough and ribosomal stalling levels, RPFs ending ± 27 nucleotides around the stop codon were counted using bedtools (v2.29.0). Differential translation efficiency analysis was performed using DESeq2 and the deltaTE.50 protocol. In brief, we use the DESeq2 GLM framework to estimate and test for the significance of the coefficient of the condition:seqtype interaction term, where the coefficient is equivalent to the change in translational efficiency across conditions. To address consistent 3’ bias in the RNAseq data, a second DTE analysis was conducted considering only RPF and RNAseq read aligning to the 3’-most exons of each gene. Differential ribosomal stalling and stop codon read through analyses were similarly conducted with DESeq2. To control for readthrough and stalling due to probabilistic molecular errors, GLM model designs were updated to include an interaction term to account for the level of ribosomal occupancy upstream of the stop codon. Given the discovery of widespread readthrough and stalling in preliminary analyses of the models, a custom set of 961 negative control genes (1 > RPF terminating at or past the stop codon across samples) was used instead of the default controls to address the assumption in DESeq2 models that genewise estimates of dispersion are largely unchanged across samples.

Selection of DTE Genes for Experimental Validation
Genes were selected for experimental validation based on whether the gene displayed significant evidence of differential translation across 3 analyses: differential translation efficiency (DTE), DTE at the 3’ exon, and differential protein expression (based on proteomics data) analysis.
To derive an appropriate padj threshold for individual comparative analyses, p-values were confirmed to be uncorrelated analyses and such that each analysis could be treated as an independent test for differential translation . Thus, a threshold of padj < 0.368 was enforced for each comparative analysis to achieve an overall false discovery rate (FDR) of (0.368)3 = 0.05 in the final joint analysis. Genes designated as differentially translated between conditions were also required to have log2 fold changes (log2FC) of the same sign (either ‘+’ or ‘-’) across all 3 metrics. Of these 29 genes identified, 6 were selected for validation by western blot based on more stringent criteria (DESeq2 Wald test FDR-adjusted p < 0.25 across all 3 analyses, |log2FC | > 0.5 in DTE analyses).

Generation of Mouse and Primary Human RNA-sequencing Libraries
Murine B cells (1x106) were isolated and purified from the spleens of 3-6 month old Rps15 wild-type, heterozygous and homozygous mutant mice (as described above), or from 20-24 month old non-leukemia mice. B220+ CD5+ CLL cells were FACS sorted from human primary CLL samples (gating strategy identical to that which we previously reported (116). Post-isolation, cells were washed in cold PBS and total RNA was extracted using a RNeasy kit (Qiagen). Total RNA was quantified using the Quant-iT RiboGreen RNA Assay Kit and normalized to 5 ng/µL. 2 μL of ERCC controls (using a 1:1000 dilution) were spiked into each sample. For cDNA library preparation, 200 ng per sample was used as input into an automated variant of the Illumina TruSeq Stranded mRNA Sample Preparation Kit (which preserves strand orientation of the RNA transcript). Oligo dT beads were used to enrich mRNA from the total RNA sample, followed by heat fragmentation and cDNA synthesis. The resultant 400 bp cDNA underwent dual-indexed library preparation, including ‘A’ base addition, adapter ligation using P7 adapters, and PCR enrichment using P5 adapters. After enrichment, libraries were quantified using Quant-iT PicoGreen (1:200 dilution). Samples were subsequently normalized to 5 ng/μL, pooled and quantified using the KAPA Library Quantification Kit for Illumina Sequencing Platforms. The entire process was performed in a 96-well format and all pipetting is executed by either Agilent Bravo or Hamilton Starlet. Samples underwent single-end sequencing for 75 cycles using an Illumina NextSeq platform.

RNA-sequencing Analysis
Patient Samples
RNA-sequencing reads were aligned to the human reference genome GrCH38 using STAR (v2.4.0.1) (131) and TPM (Transcripts Per Kilobase Million) values were used to measure gene expression. Differentially expressed genes were determined using DESeq2. Pathway analysis was performed using the MetaCore software (Clarivate Analytics) or GSEA (132).

Human Cell Lines
RNA-seq reads were aligned with STAR (version 2.5.3a) with the following parameters: --runThreadN 4 --genomeDir /cga/wu/riboseq/reference/STAR_index_RPS15_mCherry --limitBAMsortRAM 30000000000 --outSAMtype BAM SortedByCoordinate --outFilterType BySJout --alignIntronMin 20 --alignIntronMax 100000 --quantMode TranscriptomeSAM --twopassMode Basic. A custom genome was built by adding RPS15 transgene sequences (mutant and wild-type) to the FASTA file of the human genome (hg19). For the transcriptome annotation, a combination of GENCODE v26lift37 transcriptome annotation was combined with transcripts annotated as tstatus “unannotated” from MiTranscriptome annotation (133). RSEM (v1.3) (63) was used to quantify transcription against the same transcriptome annotation as above. DESeq2 was used to perform differential gene expression analysis.

Mouse Samples
RNAseq reads were aligned with STAR as described above, except against mouse genome GRCm38 and GENCODE annotation of the transcriptome vM24. Quantification was performed with RSEM. Differential gene expression analysis was performed with DESeq2.
RNA-seq libraries of aged, non-leukemic Rps15 heterozygous mice showed significant Clontech SMART CDS Primer II A adapter content. To address this issue, we explicitly trimmed and filtered out reads with cutadapt using the following parameters: -b GCTAATCATTGCAAGCAGTGGTATCAACGCAGAGTACATT -b AAGCAGTGGTATCAACGCAGAGTACATT -b GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG -m 40. Following filtering, the libraries displayed no apparent QC issues; we thus proceeded with analysis as described above.

Mass Spectroscopy of Protein Samples
Murine B cells (30 million cells) were isolated and purified from the spleens of 3-6 month old Rps15 wild-type, heterozygous and homozygous mutant mice (n=3 mice per genotype). Total protein content was first quantified with PierceTM micro-BCA assay. Samples were then reduced with DTT and alkylated with iodoacetamide. Proteins were precipitated with a methanol/chloroform solution. Protein pellets were resuspended in 200 mM EPPS, pH 8.0. Protein digestion was performed sequentially using LysC (1:50) and Trypsin (1:100) based on protease to protein ratio. Peptides from each sample were then labelled with a unique tandem mass tag (10 in total) to enable sample multiplexing. To verify successful TMT-labelling, 2 L of each labelled-sample was set aside for checking the relative abundances of TMTs. The single multiplexed sample was then flowed through a basic pH reverse phase fractionation column while gradually increasing buffer ratio over time (Buffer A: 5% ACN, 50 mM Ammonium Bicarbonate pH 8.0m; Buffer B: Buffer B: 90% ACN, Ammonium Bicarbonate pH 8.0). A total of 24 fractions (100 L, 37-second windows) were collected from the flow thru. Proteome data were collected on an Orbitrap Eclipse mass spectrometer (ThermoFisher Scientific) coupled to a Proxeon EASY-nLC 1000 LC pump (ThermoFisher Scientific).
Fractionated peptides were separated using a 120 min gradient at 500 nL/min on a 35 cm column (i.d. 100 μm, Accucore, 2.6 μm, 150 Å) packed in-house. MS1 data were collected in the Orbitrap (120,000 resolution; maximum injection time 50 ms; AGC 4 × 10 5 ). Charge states between 2 and 5 were required for MS2 analysis, and a 120 s dynamic exclusion window was used. Top 10 MS2 scans were performed in the ion trap with CID fragmentation (isolation window 0.5 Da; Rapid; NCE 35%; maximum injection time 50 ms; AGC 2 × 10 4 ). An on-line real-time search algorithm (Orbiter) was used to trigger MS3 scans for quantification. [PMID: 32126768] MS3 scans were collected in the Orbitrap using a resolution of 50,000, NCE of 45%, maximum injection time of 150 ms, and AGC of 1.5 × 105. The close out was set at two peptides per protein per fraction [PMID: 32126768].

Proteomic Analysis
MS2 spectra were analyzed with Proteome Discoverer (v2.5, Thermo Fisher Scientific) using the SEQUEST algorithm against an Uniprot composite database derived from Mouse proteome containing its reversed complement and known contaminants (Peptide Mass Tolerance=20 ppm, Fragment Ion Tolerance=1, Max Internal Cleavage Site=2, Max differential/Sites=4, Differential Modification=Methionine Oxidation). Peptide spectral matches were filtered to a 1% false discovery rate (FDR) using the target-decoy strategy combined with linear discriminant analysis. For reporter ion quantification, we ran SEQUEST with the following parameters: mass tolerance = 0.003, ms3 = 1, peak_picking = max, num_isotopes = 2, ms2_isolation_width = 0.5, ms3_isolation_width = 1.2. Thus, proteins were quantified only from peptides with a summed SN threshold of >100 and MS2 isolation specificity of 0.5.
A matrix of summed intensities representing the abundance of each protein across samples was analyzed with DESeq2 to identify differentially expressed proteins. 6582 of 9544 protein-coding genes from the Ribo-seq dataset were also captured in the proteomics dataset (pre-filtering). Since stringent filtering of proteomics results resulted in the loss of a substantive portion of the dataset, we did not use this proteomics data for systematic corroboration of differentially translated genes identified by Ribo-seq analysis, but rather used it to enable prioritization of targets for validation Western blotting experiments, focusing on the most significant DTE genes across analyses.

Generation of Mouse Whole Genome Sequencing Libraries
Genome sequencing was performed on flow-cytometry-sorted tumor cells from the spleens of four Rps15Het mice, two Trp53del mice, and two Rps15Het/Trp53del diseased mice as well as paired T cells as germline control. One microgram of genomic DNA was used for library preparation. Samples underwent acoustic shearing using a Covaris focused ultrasonicator targeting 385-bp fragments, after which size selection was performed using SPRI cleanup. DNA libraries were constructed using the KAPA Library Prep Kit (KAPA Biosystems) using palindromic forked adapters with 8 base unique index sequences (IDT) embedded within the adapter. Libraries were subsequently quantified using PCR with adapter-specific probes (KAPA Biosystems), normalized to 2.2 nmol/L, pooled into 24 plexes, and loaded onto a NovaSeq 6000 S4 flowcell. Cluster amplification and sequencing were performed via sequencing-by-synthesis kits to produce 151-bp paired-end reads.

Whole Genome Sequencing Analysis
Illumina sequencing data were aligned to the GRCm38 reference sequence with bwa version 0.7.15 (134(134)). Aligned reads were then deduplicated and base quality scores were recalibrated with GATK version 4.1.8.1. Somatic SNVs and InDels were detected by the intersection of two callers: Mutect2 (GATK) and Strelka2 ref version 2.9.3. Candidate InDels were first called by Manta version 1.5.0 before being filtered with Strelka2. Functional impacts of mutations were inferred by and annotated with SIFT4. Somatic mutation signatures were generated as previously described (127). The number of instances of each base-base transitions were counted with respect to their trinucleotide contexts and overrepresented transitions were identified with FDR-corrected hypergeometric tests (q-value < 0.05). The rank of the transition matrix (number of signatures) and overrepresentation of specific transitions were validated with de novo mutational signature analysis using mutSignatures R package (135). Copy ratios over chromosomal segments were first computed with a GATK-based pipeline and recurrent somatic copy number alterations were subsequently identified with GISTIC version 2.0.23 (q-value < 0.25). CNV plots were generated using average sample/normal B-cell copy ratio within a running window of 0.1 M base pairs (136,137).

Statistics and Reproducibility
Statistical analysis was performed using GraphPad Prism v10.0.3 unless otherwise specified in the Methods. For comparison of 3 or more groups, P-values were calculated using one-way ANOVA followed by Tukey’s multiple comparison test. For significantly different standard deviations, p-values were calculated using the Mann Whitney test. Two-group comparisons were performed using two-sample Student t-tests as detailed in each respective figure legend. Survival analyses were conducted using the log-rank test with Bonferroni correction for multiple comparisons. For all of the above, the significance threshold was set at P≤0.05. No data was excluded from these analyses and the investigators were not blinded to allocation during experiments and outcome assessment. No statistical method was used to predetermine sample size for the individual mouse cohorts.

For differential expression analyses comparing cells of a single lineage from mice under a single experimental condition to those of syngeneic, age-matched controls (paired-sample experiment), 3 samples per condition is sufficient to achieve power ≥ 0.8 according to simulation studies and thus a sample size of 3 per condition was selected for all differential expression analyses (138). Genomic characterization studies with paired tumor-normal samples with tumors sequenced to a depth greater than 50x and to a comparable depth for the matched normal sample are sufficiently sensitive (sensitivity > 0.8) to detect somatic mutations with VAF > 0.10 (139). Thus, a tumor sequencing depth of 60x and a matched normal sequencing depth of 30x was selected for whole genome sequencing analyses.

All experimental replicates represent biological replicates. Unless otherwise stated in the figure legend, 3 biological replicates of each genotype were used. In all mouse experiments detailed in this manuscript, cohorts were balanced by sex. Mice were either 3-6 months of age or >12 months of age in the experiments detailed above, as noted in the respective text or figure legends.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this work, the authors have analyzed the co-occurrence of the previously described RPS15 mutations in CLL with other CLL associated mutations in patient samples. As previously reported, a tendency towards more TP53 mutations is seen in RPS15 mutant CLL. To study these RPS15 mutations in more detail, the authors established a conditional RPS15-S138F mouse model with specific expression of the mutation in B-cells (CD19-CRE driven). This mouse develops long latency CLL and was crossed to Trp53 KO mice to delete one Trp53 allele and accelerate tumorigenesis. Furthermore, they generated in vitro human CLL cell line models to study 4 of the most recurrent RPS15 point mutants. The generated models are characterized by polysome profiling and proliferation analysis (CLL cell lines), Ribo-seq (pre-leukemic B-cells of mouse model and RPS138-S138F CLL cell lines), western blot analyses, blood and histopathological analysis (mouse model) and copy number and mutation analysis (mouse CLL tumors), with the main aim to better understand the paradoxical transition from hypo- to hyperproliferation as observed in ribosomopathies in general. A tremendous amount of work is presented in this manuscript and the mouse model that has been generated is of great value for the field. It is fantastic that it will be made available to the community via Jackson labs.\nThe characterization of the generated models nicely confirm and analyze in detail the previously proposed model of translational dysregulation by somatic and congenital RP mutations, the oxidative stress causing hypoproliferation and acquisition of a second hit mutation to transition to hyperproliferation. So this concept is in fact not novel in the field of ribosomal protein mutation driven disease and is just a confirmation in the context of RPS15 mutations. This is not necessarily a problem, provided that the people that initially proposed this model and that have previously shown this for other RP mutations are properly acknowledged. In this regard, it is worth mentioning that Kampen et al. already showed that the RPL10 R98S mutation induces extensive oxidative stress causing the proliferation defect, which is rescued by adding anti-oxidants to the cells (Kampen et al., Leukemia, 2019, PMID: 29930300). Sulima et al. showed the same mechanism for hypo- to hyperproliferation as proposed here, mainly in the context of the RPL10 R98S mutation in ALL, utilizing cell models, cells derived from a transgenic mouse model, and data from leukemia patient samples. NOTCH1 was identified as the critical secondary mutation for RPL10 R98S in T-ALL, and they utilized CLL patient data that support that the model they postulated also applies to CLL, with a higher mutation load in RPS15 mutant CLL patients as compare to RPS15 WT CLLs and they also proposed TP53 mutations as secondary hit (Sulima et al., Cancer Research, 2019, PMID: 30482776; also nicely reviewed and illustrated in Fig. 4 of Kampen et al., NAR, 2020, PMID: 31350888 as well as in Fig. 3 of Sulima et al., Cells, 2019, PMID: 30862070). These findings are now nicely confirmed in this study. It is not correct that no reference at all is made to these previous publications and that there is no section in the introduction and/or discussion covering the fact that the proposed model is in fact not novel. In fact the discussion line 511-514 literally says that the transition from hypo- to hyperproliferation driven by RP mutations is an outstanding question in the field, ignoring all work that was already published on this topic.\nOne question that remains after reading the manuscript is whether TP53 loss is sufficient to cause transition to hyperproliferation. The data in figure 2C show that, at least a heterozygous p53 loss, is not sufficient to transition to hyperproliferation, and also Figure 6A indicates that not all mice with a heterozygous TP53 loss develop malignancy. It would have been nice to test if homozygous loss can cause the transition, as the authors show that most patients have bi-allelic p53 loss as the authors show in Fig 1A. I understand that this is a lot of work and a long experiment to do in the mouse tumor cohort, but an in vitro experiment on cultured cells would have been nice. Furthermore, the authors do not experimentally show that the hypoproliferation is really caused by ox stress and DNA damage as others have done in literature by e.g. rescuing the phenotype with anti-oxidants or by showing less oxidative stress and DNA damage once the cells are converted to the hyperproliferative state by the secondary mutations. Since this is rather a confirmatory study of a previously proposed model, and since the title and abstract really focus on this transition from hypo- to hyperproliferation, one would expect these types of experiments.\nAt several occasions, the section titles in the manuscript are also making claims that rather reflect hypotheses as opposed to solid conclusions that are supported by the data that are shown in the manuscript. Concrete examples of this are explained in the major concerns section below.\nMajor concerns:\n• As explained above, it is essential to put the proposed model for transition of hypo- to hyperproliferation in perspective to the relevant literature and to cite literature accordingly. Also testing in vitro if homozygous TP53 loss is sufficient to cause hyperproliferation would be an added value.\n• At several occasions, the manuscript section titles are making claims that are not supported by the data shown in the manuscript:\no In the abstract (lines 47-48), subtitle (line 285) and line 291, protein instability is claimed. First, this is a very broad description that may interpreted by the reader as general instability of all proteins in the cell, whereas the authors in fact only mean RPS15 protein stability. But second, and most importantly, the claim is not supported by the data. From the 4 mutants that were tested, this phenotype is only seen for S138F and maybe also for S139F but not for G134R and H137Y in HG3 cells. It would have been nice to support the claim by truly monitoring protein stability (i.e. western blot upon cycloheximide treatment). What makes the claim even more problematic is that this phenotype for S138F is not confirmed in the cells of the mouse model. As the data from the mouse cells reflect data from expression of an untagged protein under control of the endogenous promoter, the data from the mouse cells seem much more physiologically relevant and there no effects are seen.\no Line 177: it is postulated that a p53-mediated response to oxidative stress and DNA damage underlies the hypo-proliferative phenotype. First, the authors show less p53 activation in the mutant. Would one not expect more p53 activation if p53 response is causing the growth defect as stated in the title of this section? Furthermore, the authors do not experimentally show that the hypoproliferation is really caused by oxidative stress and DNA damage. This is needed if you want to make such statements as in line 177. In case the authors do not show it here, they should at least discuss the work of other people that have shown this.\n• Line 227-228: the title suggests that MYC and ZAP70 deregulation drive B-cell proliferation, but in fact the authors show that there is already in the pre-leukemic hypoprliferative cells MYC and ZAP70 activation and overall the data in the paper rather seem to support that complete p53 loss may promote the transition to B-cell proliferation. Also in line 466, they suggest myc as driver of oncogenesis. This is not in line with the data that are shown.\nLines 134-141: It is unclear to me what the exact conclusion of this paragraph is or what point the authors want to make. Furthermore, it is unclear which patients are compared to get to the p=0.0045. Is this when comparing RPS15-mut/TP53-alt versus RPS15-WT/TP53 alt patients?\nFigure 5C: it is hard to see what should be seen in this figure because of the overlay of Wt and mutant. Please show them separately.\nMinor points:\n- Lines 83-85: the cited references indeed link TP53 to the RP mutation associated cell cycle defect, but I am not sure if PTEN and RB1 also have been implicated. If so, the appropriate references should be provided. Furthermore, it is not cellular senescence but rather impaired proliferation and cell cycling that has been described as consequence of RP mutations, as the authors also see here in the cells from their mouse model. Also line 611 mentions senescence, which should be replaced by a proliferation or cell cycle defect.\n- Line 116: It should be 28.9% instead of 30.2% (13/45 = 28.9%)\n- Line 119: NOTCH1 is not a typical DNA damage response gene.\n- Line 171-175: The results description of this paragraph might be improved to better describe the results presented. While it is true that the Myc target gene term is differentially enriched in RPS15hom or RPS15het mutants when compared to WT, other terms with a similar enrichment as DNA REPAIR reactome pathway and cell cycle checkpoints are not mentioned.\n- Line 213-215: I am not sure that the claim about replicative stress is very solid. First, it is difficult to correctly appreciate the signal of ChK1 and p-Chk1 on the western blot presented in Suppl. Fig3A after 4 days of LPS although this is the western blot for which quantification is shown. Second if only the baseline level of P-Chk1/Chk1 was to be analysed, then it would have been nice to show a positive control of replicative stress where the ratio is increased. Lastly, on the three replicates quantified for Rps15hom, one seems to be a clear outlier. Without this outlier, the message would have been different with a decreased ratio for the Rps15hom.\n- Line 219-220: most of the refs cited are not on Rps15 but on other RPs that regulate p53.\n- Line 278: pValue is reported as .0539 instead of 0.0539\n- Line 306: I would not use the term extra-ribosomal function to refer to the ribosome biogenesis function of RPS15, as this in the end is also a function that has to do with the ribosome.\n- Lines 333-342: this paragraph comes a bit out of nowhere, I do not really see the relevance in the story and do not understand the point the authors want to make.\n- Figure 4: why is RPS15 is the 60S fractions and not in the 40S fraction? Since it is a constituent of the 40S, this is weird.\n- Figure S1c: which groups are compared to get the p-value in the figure?\n- Figure 2b: there is a reporting mistake: NES of “Hallmark Myc target\"\n- In the data supplement, also a proteomics dataset is mentioned and shown, but this seems not described in the manuscript.\n(Remarks on code availability)\nI have not installed or run the code myself, but a computational biology postdoc in the lab had a look at the code (also no installation) and confirmed me that there are enough instructions and information available to use the code for people that are interested.", "reviewer_2": "This manuscript builds off prior knowledge that the ribosomal protein, RPS15, has C-terminal domain hotspot mutations occurring in patients with CLL and that these mutations are enriched after relapse on CIT and targeted therapies. Additionally, these mutations co-occur with Tp53 alterations.\nThis group developed a mouse model of B cell specific knockin of the RPS15 (S138F) mutation and evaluates changes in gene expression, proliferation, ROS levels, DNA damage, BCR signaling, translatome, Zap70 interaction, and somatic alterations with some of these things evaluated also in the context of co-occuring heterozygous p53 knockout.\nOverall the story is interesting but incomplete, many details are missing (e.g. statistical tests used, biological vs technical replicates, n=?, etc) and the use of the RPS15 (S138F) mutant model flips between using heterozygous and homozygous mice frequently without providing rationale. Additionally, the manuscript is largely descriptive and proposes connections between arms of the study but doesn’t do so mechanistically (eg. Iink ROS and DNA damage but no direct evaluation of ROS mediated DNA damage, proposes Zap70 interaction and BCR signaling may be impacted by RPS15 mutations but doesn’t convincingly show this, etc). Many statements are also not supported by the data.\nSpecific comments:\n• Fig1B/line 152: states the S138F is the most common RPS15 mutation but figure shows T136 residue mutations are more common\n• Fig1C: Flp-Frt recombination is shown in figure but not described in methods\n• Supp 1C: OS was not significantly different among CLL with both RPS15 mutation and TP53 alteration and CLL with sole RPS15 mutation or sole TP53 alteration. Not sure what is the rationale to make a double mutant model other that these mutations are shown to co-occur. What is the survival in the different cohort of mice? These data are missing.\n• Fig 2A is unnecessary as there is no data displayed. Move to supplement.\n• Fig2B/line175: DNA Repair enrichment plot shows insignificant enrichment\n• Fig2B/line 179: text refers to both het and homo mice and cites 2B but 2B is only of homo mice\n• Supp Fig2A: Add FDR/p.adj to enrichment plots as in 2B\n• Fig 2C: How this data was obtained isn’t stated. Is this percent B cells relative to all live cells or CD45+ cells?\n• Supp 2B: technical or biological replicates? Please state\n• Line 194: refers to these mice as CLL mice prior to showing leukemic development\n• Fig 2G: missing # of mice, n=?\n• Fig 2H: 72hr induction stated in text and 48hrs on legend. If DMSO and Antimycin A conditions are just to show that the assay works, I’d move to supplement.\n• Lines 203-4: What data has shown dependence on BCR signaling to this point?\n• Fig 2I: Does parthenolide show pro-oxidant activity in the RPS mutant B cells & reduced expression of NF-kB gene targets?\n• Fig 3A: Should connect the ROS levels directly to DNA damage (eg. Oxidative DNA damage (8-oxo-dG) measurements, etc)\n• What is the link between RPS15 mutations and increased ROS? Do you see increased ROS in the in vitro models as well?\n• Figure 3A and B. Data for het mice are missing.\n• Line244-5: Citation(s) missing. The following data relies on the connection between ROS & IR inducing DSBs\n• Line 255-6: The manuscript to this point hasn’t shown RPS15 mutants as hyperproliferative.\n• Line 261: cite ERK regulation of Myc\n• Fig 3E: Are NF-kB target genes increased?\n• Line 269: change RPS15 mutant to RPS15 homo\n• Line 271-2: Reference to malignant transformation in RPS15 mutant mice but this has yet to be shown in the manuscript. Did the author confirmed the transplantability of the CLL cells this would demonstrate that CD19-restricted deletion of RPS15 reliably results in CLL, confirming this lesion as a CLL driving event.\n• Line 272-3: Show the data\n• Lane 338: “Rps15 is known to stabilize p53 by inhibiting the p53-targeting E3 ubiquitin ligase Mdm2; consistent with this, we observed lower levels of p53 expression in Rps15Hom compared to Rps15WT cells (Fig 3b)”. Not sure I agree that the level of p53 are reduced in the homo cells. Quantification is only shown for IR vs untreated ratios for each genotype. Also suppression of cyclin A is only seen in one replicate.\n• Fig 3G: Is this a percentage of live splenocytes, CD45+, or CD19+ cells? What age are these mice, since the previous statement says no observed differences in 3-6m old mice bone marrow?\n• Lane 269: “Indeed, we were able to validate increased ZAP70 protein expression in Rps15MT vs Rps15WT cells (Figure 3f)”. ZAP 70 is only increased in the homo and not het so the statement is incorrect as it refers to Rps15MT\n• Supp Fig 3F: what are these percentages of?\n• Fig 4I: Legend doesn’t indicate what the fraction numbers correspond to and what “T” is. Are there loading controls that can be used for this assay?\n• Fig 5B: Legend colors don’t match the figure.\n• Fig 5C: These genes appear randomly selected. Why are genes selected below the significance cutoff in the volcano plot? Otud4 trace appears to show increased translation in homozygous mice but shows decreased TE in 5A, though below the significance cutoff?\n• Line363/Ex. Data Table 2: Where is the data for Cybr4? The TE decrease isn’t highlighted in 5A and no protein level WB evaluation is shown.\n• Line 383: Change RPS15 mutant to RPS15 homo. There wasn’t any difference between het and WT.\n• Figure 6A. the authors claim that 7 out of 30 of the p53del mice developed CLL. How was this established? Palanichamy et al (scientific reports 2023) showed that CD19-P53CKO mice developed marginal zone expansion in the spleen, followed by marginal zone lymphoma, some of which progressed to diffuse large B-cell lymphomas. Although they did not specifically study heterozygous loss of P53 their model showed 50% of cases developed splenomegaly. This is very different from what reported in the current study and should be discussed.\n• Line 420-22: Should include in text/legends that large B cells that form confluent sheaths were identified. Do the RS cells show expression of B cell specific markers, ABC-DLBCL phenotypic markers as is common in RS or do they maintain a MZ B cell phenotype, as IRF4 positivity doesn’t rule this out.\n• Also Line 420: “We observed four cases of DLBCL unique to the Rps15MT/Trp53del cohort, as determined by enlarged spleens, lack of circulating disease, and BCL6 negativity/high Ki-67 proliferation by immunohistochemical staining (Supp Fig 6b-c)” Supp Fig 6B shows 78% of tumor cells in the blood of RS mice vs 12% in the RPS15Mut. What am I missing here? Why do the author state a lack of circulating disease. Also what does RPS15Mut refers to Heme or Homo?\n• Line 429: “In all cases, mice with CLL or DLBCL-like disease exhibited enlarged spleens (Fig 6e), with high infiltration of the spleen and peritoneal cavity but only minimal bone marrow involvement (Supp Fig 6d)” Same as above, the RS mice showed more BM involvement, where the label switched?\n• Fig 6C: gates are drawn quite differently between genotypes within the same tissues\n• Figure 6A, B, C it would be great to separate and label het vs homo RPS15 mice. Right now they are all just labeled as RPS15Mut\n• Are RPS15 mutations and the S138F mutation enriched in human RS tumors?\n• Supp Fig6B: Representative flow plots again show large differences in how disease gates are drawn between genotypes\n• Fig 6E: Scale bar unannotated for the pictures. I assume the different colored data points are based on Fig 6A. If 30 mice were monitored, why only few data points are shown? Do all mice in box & whiskers plot have disease? If so, please indicate this.\n• Supp Fig 6D: Unclear how % organ involvement is measured and calculated. Mutant p53 model used but not mentioned?\n• Line 436: indicate markers for CLL-like cells (B220+CD5+?)\n• Fig6/Supp Fig6: Are double transgenic mice homo or het for the RPS15 mutation in all histology, flow, spleen measurements, images, and % organ involvement?\n• Line 436: sorted based on what markers?\n• Fig 7A: Shows enrichment for myc target genes in splenic RPS15-het CLL cells vs WT B cells (24months of age) but in Supp 2a shows RPS15-het vs WT B cells (3-6months of age) with negative enrichment for Myc targets. Is this RPS15-het driven or age/sex driven? Do non-CLL B cells in 24 month old mice also show enrichment of myc target genes? Log2FC cutoff is low despite no validation.\n• Fig 7B: Include supportive volcano plots for this data to better communicate what comparisons have been made. It is unclear if the “murine” column is from the data shown in 7A.\n• Supp Fig7C: Lacking validation the adjusted pvalue should be used.\n• Fig 7C: unclear if mice are heterozygous or homozygous for the RP215 mutation. High pval cutoff without validation. Mean expression should be substituted with a p val.\n• Fig 7C-D: How many mice are used? age/sex matched? Where are the cells derived from and are the sorted for leukemic cells?\n• Line 471: show long latency, incidence over time\n• Supp Fig7F: Are these mutations specific to leukemic B cells or to B cells in general? In the double transgenic mice which mutations are contributed from the RS mouse vs the CLL mouse?\n• “Also consistent with RPS15-mutated human CLL, which is exclusively heterozygous, mice with heterozygous, not homozygous, Rps15 mutation developed CLL-like disease” This is not supported by the data. Indeed 10% Rps15homo mice develop CLL and ~ 33% of the Rps15homoP53del develop CLL/RS\n(Remarks on code availability)", "reviewer_3": "In this manuscript Gutierrez et al. identify a link between mutations in ribosomal protein RPS15 and CLL, and then model these mutations in cellular and animal models to explore the molecular determinants of this connection. Although the RPS15-CLL relationship is not novel, the mechanistic insights provided by the present manuscript represent a substantial advance in the field and the study is overall well conducted. However, in our opinion, further experimentation would be required to fully validate the proposed model, especially regarding the link with genome instability.\n- We do not fully understand the CLL-like phenotype of the mice and how it fits with the proposed model. It seems strange that Rps15-het animals show the greatest incidence of CLL-like disease, even above Trp53-mut single and the double mutant. As a matter of fact, one could conclude that p53 loss suppresses the increased CLL phenotype of the Rps15-het mouse. This is difficult to explain and we do not see how it fits with the model\n- The connection with DNA damage and genome instability is, in our opinion, the strongest contribution of the manuscript, and therefore needs to be established unambiguously. The biggest problem that we see is that Rps15-mutant CLLs do not seem to harbour more overall genome instability, as one would expect from the model. This may be due to the clonal selection, but it is important to establish that the Rps15 mutation/s do indeed lead to increased genome instability. I suggest to directly mesure genome instability (micronuclei, chromosomal aberrations and/or copy-number variation) in the Rps15-modeled animals and/or cell lines.\n(Remarks on code availability)"}, "author_response": "Response We thank the reviewers for the opportunity to clarify our data and improve upon clarity and\ntransparency of our revisions. We acknowledge that Reviewer 1 had asked for individual traces in\nQ4 of the original review and we have now attached the individual traces, as requested at the end\nof this document. Below we also respond to this reviewer’s additional critiques:\n**Regarding Reviewer 1’s interpretation that the traces for Otud4 (Figure 7C) are contradictory to\nthe TE estimates shown in Figure 7A: We emphasize that while the unprocessed, individual traces\nare helpful for transparency, they can be difficult to interpret because the binned coverages are not\nnormalized and they include all genome-aligned reads. In contrast, translational efficiency is\ncomputed only with normalized counts of transcriptome-aligned reads. While we concur that the\nnumber of footprints aligning to Otud4 is indeed higher in the mutant setting, compared to wildtype,\nwe point out that these counts need to be normalized to library size and the level of transcription,\nwhich are also higher in the mutant setting compared to the wildtype context.\nFor these reasons, we highlight that our primary method for testing for significant differences in\ntranslational efficiency between genotypes is through the DESeq2 GLM framework, where we\nspecify an interaction term coefficient that is equivalent to changes in translational efficiency as\npreviously described (“Statistical Analysis” section,\nhttps://currentprotocols.onlinelibrary.wiley.com/doi/10.1002/cpmb.108). To improve the clarity of\nour rationale and approach, we have expanded our Methods section for Ribo-seq analysis such that\nthese details are now highlighted, as follows:\nDifferential translation efficiency analysis was performed using the deltaTE.50 protocol. In brief, we use the DESeq2\nGLM framework to estimate and test for the significance of the coefficient of the condition:seqtype interaction term,\nwhere the coefficient is equivalent to the change in translational efficiency.\n**Regarding mapping patterns shown\nin Supplementary Figures 8b and 7c:\nThere are several notable reasons why\nwe observe some alignment of reads to\n“intronic” regions: (i) In the case of\nOtud4, the intron has a poly-A tail\n(green block on the annotated at the\nbottom right, Fig Q1-1). Likewise,\nmany of the Smc1a intronic regions\nwith high coverage also have A-rich\nregions (Fig Q1-2) or poly-A regions.\nAs result, these introns were captured\nby RNA-seq poly-A selection, and so\nthese alignments represent an\nunavoidable limitation of poly-A\nselection\nFig Q1-1. IGV screenshot of binned (50 nt) alignments of RPFs (traces 1-3, 7-9 from\n(https://pmc.ncbi.nlm.nih.gov/articles/\nthe top) and RNA fragments (traces 4-6, 10-12 from the top) over Otud4 intronic\nPMC1781347/). region. Homozygous RPS15 mutant sample traces are colored in red, wildtype\nRPS15 sample traces in grey/black.\n(ii) Reviewer 1 highlights that there\nappears to be coverage of a non-exonic\nregion of Smc1a by both RPFs and\nRNA fragments. This not an artifact of\npoor mapping but rather an\nunannotated open reading frame\n(nuORF) beginning with a non-\ncanonical start codon (CUG) at\nposition 152,043,001 (IGV screenshot\nin Fig Q1-3-left). In support of this\nconclusion, our group previously\nidentified thousands of nuORFs with\nRibo-seq whose translated products are\ndetectable by HLA class I\nimmunoprecipitation + tandem mass\nspec, including those expressed in B- .\nFig Q1-2 IGV screenshot of binned (50 nt) alignments of RPFs (traces 1-3, 7-9\ncells and CLL cells from the top) and RNA fragments (traces 4-6, 10-12 from the top) over Smc1a\nintronic region. Homozygous RPS15 mutant sample traces are colored in red,\nwildtype RPS15 sample traces in grey/black.\n(https://pmc.ncbi.nlm.nih.gov/articles/PMC10198624). This set of nuORFs includes those from\nnon-coding/intronic regions of Smc1a (Excel table shown Figure Q1-3-right).\n.\nFig Q1-3 IGV screenshot of binned (50 nt) alignments of RPFs (traces 1-3, 7-9 from the top) and RNA fragments (traces 4-6, 10-12 from the\ntop) over Smc1a nuORF region. Homozygous RPS15 mutant sample traces are colored in red, wildtype RPS15 sample traces in grey/black.\n(iii) It may also be important to consider that RefSeq does not annotate all exons for every gene,\nespecially for the mouse genome (https://pmc.ncbi.nlm.nih.gov/articles/PMC4502323/).\nConsidering that we filtered the genome-alignment files to include only reads that mapped uniquely\nto a single region of the genome, it is more likely that the reads aligning to regions annotated as\nnon-exonic represent either introns with poly-As or are unannotated exons/nuORFs, as observed\nabove.\nImportantly, while we used genome-aligned reads for plotting individual traces, only reads which\naligned explicitly to the basic transcriptome were used in testing for differential TE, and\ntherefore capture of reads from poly-adenylated intronic regions, nuORFs, or unannotated exons\nshould not impact our analysis.\n**Regarding Supplementary Figure 8b: This panel was generated to directly respond to the\nrequest for confirmation of the genes already identified as having significant differential\ntranslational efficiency by the statistical testing framework. We agree with Reviewer 1 that it is\ninappropriate to compute translational efficiencies at the nucleotide level, given differing lengths\nof footprints versus RNA reads. Consistent with this line of reasoning, we computed in\nSupplementary Figure 8b the translational efficiencies as the ratio of binned counts of ribosomal\nprotected fragments (RPFs) versus binned counts of RNA-seq reads over windows of 50 nucleotides\nin length. Furthermore, we restricted these calculations to only the exonic protein-coding regions,\nwhere both RPFs and RNA-seq reads are expected to align. Under these conditions, we contend\nthat computing binned coverage ratios is appropriate for verifying the results of testing. We have\nnow updated Supplementary Figure 8b legend to include these important clarifying details.\n**Regarding overall quality of Ribo-seq/RNA-seq libraries + bioinformatic approach: We point out\nthat the shapes of the distributions of the binned coverage ratios for each gene shown in\nSupplementary Figure 8b are similar between sample groups. These similarities should occur only\nif the data from each sample group is comparable, especially regarding aggregate data quality.\nOf relevance to this question, to obtain our sequencing libraries, we performed several optimization\nexperiments for B cell isolation and total RNA extraction for Ribo-seq profiling, ultimately\nselecting the samples (bolded) with best RIN for further analysis (Bioanalyzer traces of\nrepresentative traces shown below the table, as well as examples of the traces themselves):\nSample Name RIN Sample Processing Group Sequencing Name\nWT RNA 1:10 8.70 polysomes\nHET RNA 1:10 9 polysomes\nHOM RNA 1:10 9.30 polysomes\nmouse WT 1 1:10 8.20 1 WT1_AGGCAATG_GAACGAAG\nRNA\nmouse Het 1 1:10 RNA N/A 1 Het1_GTCATCGT_TTGAGCTC\nmouse Hom 1 1:10 6.60 1 Hom1_CAACTCCA_GCCTATGT\nRNA\nWT 2 mouse RNA 1:10 5.70 2\nHet 2 mouse RNA 1:10 8.70 2 Het2_TTACCGAC_AGTACACG\nHom 2 mouse RNA N/A 2\n1:10\nMouse WT 3 RNA 9.00 3 WT3_TCACCTAG_ACCTAGAC\n1:10\nMouse Het 3 RNA 9.40 3 Het3_ATGCCTAG_ACTGCTTG\n1:10\nMouse Hom 3 RNA 8.10 3 Hom3_AAGTCCTC_GTACCACA\n1:10\nWT R4 RNA 1:10 7.10 4 WT4_CATACGGA_TACGACGT\nHom R4 RNA 1:10 2.60 4\nHom R5 RNA 1:10 4.20 4\nHom R6 RNA 1:10 N/A 5 Hom4_GTCGATTG_TAGTGGTG\nTo limit batch effects, only one sample from each experimental group was included per set of\nsamples processed together. When we realized there was 3’ bias in our samples, we checked that\nthis bias was not restricted to a single experimental group. Indeed, as shown in the gene body\ncoverage figures (Fig Q1-4), there is only one sample (green lines) with 3’ bias in each experimental\ngroup (https://rseqc.sourceforge.net/#genebody-coverage-py):\nHet1_GTCATCGT_TTGAGCTC_Aligned.sortedByCoord.out\nHet3_ATGCCTAG_ACTGCTTG_Aligned.sortedByCoord.out\nHet2_TTACCGAC_AGTACACG_Aligned.sortedByCoord.out\n0 20 40 60 80 100\nGene body percentile (5'−>3')\nThus, 3’ bias is not widespread within each experimental group and, furthermore, the three\nexperimental groups are comparable, such that batch effects will not confound effects from RPS15\ngenotypes.\nWe and others (https://pmc.ncbi.nlm.nih.gov/articles/PMC3954844/ ) contend that restricting\nanalysis to only reads aligning to the 3’ end of transcripts is a valid approach to limiting the impact\nof 3’ bias on DGE analyses. Furthermore, the function of the 3’ exon-restricted DTE analysis is not\nto replace the original analysis, but rather to identify genes with evidence for DTE by multiple tests.\negarevoC\n0.1\n8.0\n6.0\n4.0\n2.0\n0.0\nHom1_CAACTCCA_GCCTATGT_Aligned.sortedByCoord.out\nHom4_GTCGATTG_TAGTGGTG_Aligned.sortedByCoord.out\nHom3_AAGTCCTC_GTACCACA_Aligned.sortedByCoord.out\n0 20 40 60 80 100\nGene body percentile (5'−>3')\negarevoC\n0.1\n8.0\n6.0\n4.0\n2.0\n0.0\nWT4_CATACGGA_TACGACGT_Aligned.sortedByCoord.out\nWT1_AGGCAATG_GAACGAAG_Aligned.sortedByCoord.out\nWT3_TCACCTAG_ACCTAGAC_Aligned.sortedByCoord.out\n0 20 40 60 80 100\nGene body percentile (5'−>3')\negarevoC\n0.1\n8.0\n6.0\n4.0\n2.0\n0.0\n.\nFig Q1-4 Gene body coverage graphs plotting mean normalized coverage across length-normalized transcripts. Coverage is first binned by\nlength percentiles for each transcript, divided by the total coverage over the transcript length, and then the mean normalized coverage across\nall transcripts is plotted.\nWe acknowledge that, as Reviewer 1 states, we are using a limited number of 3’ end Ribo-seq reads\nto assess DTE. However, the modeling assumptions of the DESeq2 framework (i.e. that read counts\nfollow a negative binomial distribution) still hold in this limited case. False positives can and were\nminimized to an acceptable level by filtering out transcripts with low counts, performing Benjamini-\nHochberg FDR adjustments, and by considering results that were concordant with the original, un-\nrestricted analysis.\n**Regarding comments on the DTE analysis by Reviewer 1: Here, we reconcile suggestions by\nReviewer 1 and our interpretation of the results, based on simulations, mathematical proofs, and\nreasoning by the authors of deltaTE (PMID 31763789) and RiboDiff (PMID 27634950). deltaTE\nand RiboDiff are widely used computational methods for detecting genes undergoing translational\nregulation from joint Ribo-seq and RNA-seq profiling experiments.\nWe first assert that the number of RPFs, as measured by Ribo-seq, cannot be interpreted as being\nindependent of the distribution of transcript counts. This idea is affirmed directly by the authors of\ndeltaTE and RiboDiff:\n“However, reliably identifying differences in translational regulation is complicated by the fact that the mRNA\nabundance of the transcript directly affects the probability of ribosome occupancy.”\n“In sequencing-based ribosome footprinting, the RF read count is naturally confounded by mRNA abundance.”\nThis is the fundamental assertion of TE analysis and why it would be inappropriate to consider only\ndifferences in RPF distributions as measured by Ribo-seq as indicative of translational regulation.\nWithout comparing ribosomal occupancy to transcript abundance, one is likely to conclude that\n“forwarded” genes (genes identified as both differentially translated and transcribed by independent\nanalyses of the Ribo-seq and RNA-seq data) are under translational regulation when in fact they are\nsolely under transcriptional regulation. Consequently, finding a significant change in TE for a given\ngene across conditions is equivalent to inferring that that gene is under translational regulation.\nAgain, this idea is directly supported by the authors of deltaTE:\n“TE is essentially the number of ribosomes per gene, normalized to transcript abundance. Genes with changes in TE\nbetween conditions are considered to undergo translational regulation [differential translation efficiency genes\n(DTEGs)].”\nGiven all of this, to estimate TE and test for significant differential TE in our dataset, we used the\nDESeq2 protocol recommended by the authors of deltaTE that leverages the equivalence between\nthe interaction coefficient for condition:seqtype variable and TE. As stated by the authors, this\nsingle test is sufficient on its own for identifying genes with differential TE and estimating the\nchange in TE across conditions. Thus, a volcano plot (as we present in Figure 7a) which shows\ngenes stratified by FDR-adjusted p-values and log2 fold change in TE as estimated by this\nprotocol should be sufficient for showing which genes are differential translational efficiency\ngenes (DTEGs).\nAll this said, we agree with\nReviewer 1 that RNA-seq\nversus Ribo-seq FC plots\nallow for additional\ninference into the types of\nregulation that produce\ndifferences in translational\nefficiency. As described in\nthe deltaTE framework,\ngenes can be assigned to\ndistinct regulation classes,\nthereby stratifying the\nFig Q1-5 . Figures 1E and 2C from the deltaTE paper (PMID# 31763789), showing how genes DTEGs by the results of\nundergoing differential translational regulation (DTEG) and/or differential transcriptional\nindependent differential\nregulation (DTG) are stratified by independent tests for differences in RPF count distributions\n(Ribo-seq log2 FC) and differences in RNA count distributions (RNA-seq log2 FC). expression analyses of the\nRibo-seq and RNA-seq\ndata (Fig Q1-5). “Buffered” genes are those whose RNA count distributions are significantly\ndifferent between experimental conditions, but whose RPF count distributions are similar compared\nto the RNA count distributions. Hence, there is\nbuffering of changes in transcriptional regulation by\ntranslational regulation. “Exclusive” genes are\nthose whose RNA count distributions are similar\nbetween experimental conditions, but RPF count\ndistributions are significantly different. Hence, only\ntranslational regulation is inferred to be different\nbetween conditions. “Intensified” genes are those\nwhose RNA count distributions are significantly\ndifferent, and whose RPF count distributions are\nsignificantly different even after controlling for\ndifferences in RNA count distributions. Hence,\nchanges in transcriptional regulation are inferred to\nbe intensified by additional changes in translational\nregulation. Therefore, all genes are assigned the\nregulation classes “buffered”, “intensified”, or\n“exclusive” if and only if they are DTEGs and are\nconsidered to undergo translational regulation.\nFig Q1-6. Genes plotted by log2 fold change in RPF counts vs\nImportantly, this implies that “buffered” genes are log2 fold change in RNA fragment counts between the RPS15\nhomozygous mutant and wildtype samples. Genes are colored\nnot artifacts.\nby their assigned regulation class, which is determined from\nindependent tests for significant differences in RPF count\ndistributions and/or RNA fragment count distributions.\nFor the sake of completeness, we are now adding\nthe RNA-seq versus Ribo-seq FC plot to our manuscript as recommended by reviewer, as a new\nsupplementary figure (see Fig Q1-6), using this framework. Since we have shown above that are\n“buffered” genes (including Otud4), are DTEGs, we contend that they should be inferred to undergo\ntranslational regulation dependent on RPS15 genotype.\nWe point out that that the changes we detected in translated product levels correspond with changes\nat the protein level. In agreement with Reviewer 1, our proteomics data allowed us to infer that\npost-translational processes further decrease Otud4 protein expression after translational buffering.\nQ2 Additional conclusions not supported by data.\nThe conclusion on RPS15 protein stability (see also rebuttal Reviewer 1 Q2a): in the manuscript\nand in the rebuttal, the authors are contradicting themselves. In the rebuttal answer to Q2a and in\nthe abstract of the article (line 48), they first say that there is RPS15 protein instability in the pre-\nleukemic condition (which is in the mice), but further on in the rebuttal and in lines 169-170, they\nliterally say ‘We noted no significant differences in RPS15 protein levels across genotypes’, and\nthe data in Figure S1f indeed show no difference in the pre-leukemic mice. So please remove this\nincorrect claim from the abstract.\nAlso in the title of line 411, the authors claim that RSP15 mutations impair stability. This is a\nvery generalized claim based on the findings in HG3 cells, where they analyze 4 different\nmutations, but only see clearly reduced levels for one of the 4 tested mutations. What makes\nit even more problematic is that the mutants that are expressed in HG3 are tagged,\noverexpressed proteins, much less reliable then the mouse model where they did a knock-in\nat the endogenous locus. So why making this repetitive statement on RPS15 protein stability\nwhich is not supported by consistent data. The claim can easily be removed, and it will not\nremove any impact of the conclusions in this paper.\nThe conclusion on RPS15 mutation association and survival (rebuttal Reviewer 1 Q3 and\nReviewer 2 Q11): In the title on line 112, the authors state that RPS15 mutations are associated\nwith poorer OS, but this is not show in the paper. In the rebuttal (rebuttal Q3 and Q11), the\nauthors now clarified the statistics of the survival analysis in Figure S1C that should support\nthis survival conclusion. They now clarify that the significant p-value comes from comparing\nRPS15-mut/TP53-alt + RPS15-mut/TP53-WT + RPS15-WT/TP53-alt cases versus RPS15-\nWT/TP53-WT cases (also in main text lines 148-153). This is not correct, if you want to analyze\nthe effect of RPS15 mutations on survival you should compare RPS15-mut versus RPS15-\nWT cases and not a group consisting of RPS15-mut + RPS15-wt versus RPS15-wt.\nIn fact, in the discussion on line 682-683, the authors say themselves that RPS15 by itself is\nnot resulting in worse survival, so again the authors are contradicting themselves, and\nconclusions at different places in the paper are opposite.\nResponse We thank the Reviewer for pointing out aspects of the text that can be clarified, and hence\nstrengthen the paper overall.\nFirst, we agree that the results of our in vivo mouse model are far more definitive than our cell line\ndata, for all the caveats that were mentioned by the Reviewer. However, we want to clarify a number\nof points: (i) while RPS15 mutation occurs as a hotspot mutation in CLL, we and others have\nreported that several distinct mutations can be found within the hotspot region. It follows that a\nreasonable question to ask is whether each of these mutations possess equivalent biochemical\neffects; this has served as the rationale underlying our modeling of 4 mutations in the HG3 cell\nlines. Previous work (Bretones et al Blood 2018) has likewise evaluated this question, selecting 8\nmutations to model in a non-CLL (HEK293T) and an aggressive CLL cell line (MEC1)). Our results\nin the HG3 cell line (which better models indolent CLL, with expression of the quintessential CLL\ncytogenetic lesion del(13q)) are consistent with these previously published work. (ii) Our mouse\nmodel represents only one of the 4 mutations examined by the HG3 cell lines—the one that\ndemonstrated greater evidence of degradation of the RPS15 protein (also found by Bretones et al).\nThus, lines 169-170 are not at all contradictory, as these refer to the evaluation of the sole lesion\ninvestigated by the mouse model, in the preleukemic setting. In contrast, the HG3 lines are already\ncancer cell lines. As we explained by the initial response to R1 Q2a: “We would also like to point\nout that the S138F phenotype of instability was observed in leukemic HG3 cells; the absence of a\nsimilar finding in preleukemic murine B cells should not be viewed as problematic, but rather an\nadded point towards the time course over which RPS15 mutation may affect cellular surveillance\nand homeostasis mechanisms over time and disease course.”\nFor greater clarity, we have adjusted the phrase “We noted no significant differences in RPS15\nprotein levels across genotypes” to be more specific as to the age and model:\n“We noted no significant differences in RPS15 protein levels across genotypes in young pre-leukemic mice.”\nWe are choosing to continue to include our cell line data in the manuscript given that it is likely to\nbe of interest to the general community studying RPS15 mutations, as similar findings of different\nRPS15 mutations affecting RPS15 mutant protein stability differently have also been reported in a\nnon-CLL context by Bretones et al 2018 (as cited in the paper and mentioned in our response to\nreviewers, see below).\n“We would like to note that, as shown in Bretones et al. Blood 2017 (which we have cited\nas Ref 33), there are varying degrees of protein stability across RPS15 mutation and that\nour findings largely mirror their findings, though with the added benefit of being in the\nCLL context.”\nWe would also like to point out that while these are tagged and overexpressed proteins, the\nphenotype we describe was seen consistently across replicates, cannot be explained by the number\nof gDNA integrated copies, and match the tagged and overexpressed model established by Bretones\net al.\nRegarding the text related to Supplemental Figure 1c, we agree with the Reviewer that analysis of\nthe effect of RPS15 mutations on survival is more appropriately compared between RPS15-mut\nversus RPS15-WT cases and not a group consisting of RPS15-mut + RPS15-wt versus RPS15-wt.\nWhen we perform such a comparison, there is only a trend for presence of RPS15 mutation to be\nassociated with shorter OS compared to wild-type RPS15 (p=0.096). We have thus revised the text\nas follows (changes in red):\nRPS15 mutations in CLL have been previously associated with inferior prognosis (8,10). In our dataset,\nwhere we stratified samples by IGHV mutation status, U-CLLs with either sole RPS15 mutation,\nsole TP53 alteration, and both RPS15 mutation and TP53 alteration (including TP53 mutation, del(17p),\nand del(17p13.1)) did not have a difference in OS, but altogether had shorter overall survival (OS) compared to\ncases without RPS15 and without TP53 mutation (p=0.0045, comparison of all four groups, log-rank test, Supp Fig 1c). Indeed, U-CLLs\nwith wildtype TP53 and with RPS15 mutations only trended to a shorter overall survival (OS) compared to\ncases with wild-type RPS15 (p=0.096).\"\nQ3 Issue of putting the data in the right context of important prior work by others.\nSee also answer rebuttal Reviewer 1, Q1 : Three references and 1 sentence have been added\non lines 90-92 ‘Previous in vitro studies, focusing particularly on RPL10 mutations in T-ALL,\nhave linked oxidative stress with cellular hypo-proliferation which could be mitigated by\nsecondary genetic alterations (16–18)). However, direct in vivo evidence for this is lacking,…’\nWhereas this is a nice addition, this statement is not complete and not correct. As we indicated\nbefore, previous work also addressed mutation burden, mutational signatures and mutational\nco-occurence in CLL and T-ALL human leukemia samples with RPS15 and RPL10 mutations\nrespectively (https://pubmed.ncbi.nlm.nih.gov/30482776/) . In this prior work, higher mutation\nload in RP mutated as compared to RP wild type T-ALL and CLL samples was shown, and in\nexome data from RPL10-mutated T-ALL patients the oxidative stress mutation signature was\nshown to be strongly overrepresented as compared to RP wild type T-ALL patients.\nFurthermore, the elevated oxidative stress levels associated with RP mutations have been\ndemonstrated in lymphoid cells from RP mutated DBA patients\n(https://pubmed.ncbi.nlm.nih.gov/33348919/) These are strong data from patient samples, the\nbest in vivo system available. So it would be more fair to say that this study is in line with results\nfrom prior work and it is for sure not correct that no in vivo data are available. Also important:\nseveral of the key experiments in this manuscript are on in vitro cultured LPS stimulated cells,\nso not all is in vivo based also in this study.\nAlso in the discussion, the main attention goes to describing the own observations, but the\nfindings should be compared to and put in perspective to what has already been shown. In this\nregard, the nice Rpl11 mouse model that has been described by the group of Manuel Serrano\n(https://pubmed.ncbi.nlm.nih.gov/26489471/) should for sure also be discussed. In this mouse\nmodel, a nice transition from hypo- to hyperproliferation is also shown and the dysregulation\nof TP53 and MYC is also reported in that paper. Again, the authors here are not mentioning\nthis prior work which is very relevant in the context of this study. The article still gives the\nimpression that the authors are ignoring important related prior work.\nResponse We thank the reviewer for highlighting the importance of placing our current work in the context of\nprior work, as well as providing specific suggestions in this regard. We have incorporated these\nsuggestions in their entirety, as follows:\nIn the Introduction section, we have added a new paragraph to specifically highlight several\nprevious studies on RPL10 mutations in T-ALL (Kampen, 2019; Sulima, 2019) and RP-mutated\nDBA (Kapralova, 2020). Specifically, we have added sentences that acknowledge previous work\non linking mutation burden, mutational signatures and mutational co-occurrence to RPL10\nmutations in T-ALL. In the context of CLL, we have highlighted the reanalysis by Sulima et al of\nour previously published genomics study on the GCLL-CLL8 cohort that demonstrated higher\nmutation burden in CLL samples harboring ribosomal gene mutations.\nPage 3-4, line 92-113 (new text in red):\n“Multiple previous studies have sought to address the mechanisms underlying such paradoxical transitions in the\ncontext of different ribosomopathies, as well as their link with oncogenic predisposition and tumor development.\nFocusing on modeling of the RPL10-R98S T-ALL mutation in Ba/F3 cells and in patient-derived\nxenotransplantation models, a previous study demonstrated that the accumulation of oxidative stress in RPL10-\nR98S cells resulted in their impaired proliferation, with BCL-2 overexpression in these cells allowing them to\ntolerate high oxidative stress levels (16). In a further study, patients with T-ALL harboring RPL10-R98S and other\nribosomal gene mutations were shown to display increased mutation burden, an enrichment of NOTCH1 mutations,\nand a mutation signature characterized by C:G>A:T transversions (18). Further modeling on Ba/F3 and murine\nlymphoid cells demonstrated mitigation of RPL10 R98S-associated oxidative stress and cellular hypo-proliferation\nby NOTCH1 mutation (8,16–18). Elevated oxidative stress levels associated with ribosomal gene mutations have\nalso been reported in lymphoid cells from patients with Diamond-Blackfan anemia (33). In the context of RPS15\nmutations in CLL, translational alterations with resultant changes in the global cellular proteome have been\ndemonstrated within in vitro models (10,34,35). Additionally, reanalysis by Sulima et al of our previously\npublished genomics dataset from the German CLL Study Group (GCLL)-CLL8 trial showed higher mutation\nburden across CLL tumors harboring ribosomal gene alterations, reminiscent of findings in T-ALL (8, 18).\nHowever, no in vivo animal model of RPS15-mutant CLL have thus far been developed, and the role of RPS15\ndefects as an oncogenic driver remains hitherto unproven. Moreover, the in vivo phenotypic and functional\nconsequences of RPS15 mutations in CLL are unknown, and the association between oxidative stress and\ntranslational defects has not been definitively established.”\nIn the Discussion section, we have added additional discussion of our data in relation to previous\nstudies, especially previous murine models of other ribosomopathies. This includes the Rpl11 mouse\nmodel of Diamond Blackfan anemia that the reviewer highlighted (Morgado-Palacin, 2015), as well\nas murine models of Rps14 haploinsufficiency in the context of 5q- syndrome (Barlow, 2010;\nSchneider, 2016).\nPage 22-23, line 649-668 (new text in red):\n“Our leukemic mouse models thus underscore that while this balance is initially weighted towards\nhypoproliferation, mediated in part through a p53-dependent induction of cell cycle arrest, this is ultimately\ninsufficient to prevent the accumulation of DNA damage and genomic instability. The latter sets the stage for\nsubsequent genetic hits that eventually reverse the quiescent cellular phenotype and tilt the cell towards\nhyperproliferation, as exemplified by the upregulated MYC and E2F targets and other cell cycle genes observed\nin murine and human leukemic cells, and Myc amplification in murine CLL (69,115,116). Our findings\ncorroborate previous studies of RPL10-mutated T-ALL and RPL11 mutations Diamond-Blackfan anemia that\nlinked cellular hypoproliferation with oxidative stress and DNA damage (16,18,33,117).”\nPage 23-24, line 690-714 (new text in red):\n“Given this, we leveraged our double-mutant cell line and mouse models to explore the relationship between\nRPS15 and TP53 co-mutation. Loss of TP53 in our RPS15 mutant cell lines enabled unchecked propagation of\nRPS15 mutant phenotypes, including increased Myc signaling and cell proliferation. This was corroborated by\nour data from Rps15-mutant pre-leukemic mice that showed a complete reversal of the hypo-proliferative\nphenotype upon the concomitant loss of Trp53, accompanied by further potentiation in DNA damage and\ngenomic instability (Figure 4f), thus underlining the important role of residual p53 activity in countering the\neffects of mut-RPS15-induced DNA damage and accounting for the selection pressure for its subsequent loss\nthrough TP53 deletion and/or mutation. Reminiscent of our findings, a previous murine model recapitulating 5q-\nsyndrome showed Rps14 haploinsufficiency to cause p53-induced bone marrow progenitor cell apoptosis that\nresulted in macrocytic anemia (120). Intercrossing of these mice with p53-deficient mice completely rescued the\nprogenitor cell defect, thus accounting for the frequent co-occurrence and adverse impact of TP53 defects in\nmyelodysplasia with isolated 5q- (121). Likewise, Rpl11 haploinsufficiency in a murine model of Diamond-\nBlackfan anemia was associated with increased basal Myc levels and decreased p53 induction (117), which we\nalso observed in Rps15-mutant pre-leukemic B cells (Figure 3b; Figure 4c). However, our murine models\ndemonstrate that decreased p53 induction does not translate to a complete abolition of p53 activity. On the\ncontrary, Rps15-mutant B cells were highly dependent on such activity (e.g. p21-mediated G1/S cell cycle\ninduction) for the amelioration of oxidative DNA damage essential for the maintenance of genome integrity\n(Figure 4d-h), and such p53 activity was also observed in the context of Rps14 haploinsufficiency where p21\nremained active (122). Indeed, recent mechanistic studies highlight the intricate crosstalk between Myc and p53,\nwhere Myc overexpression induces p53-dependent apoptosis, and as a consequence, Myc-associated tumors\noften require disruption of the apoptosis pathway to promote cell proliferation, thereby conferring an\nevolutionary advantage to TP53 loss (123).”\nQ4 Key data should be presented in a way that is clear to the reader.\nReviewer 2, Q2, Q17: in the rebuttal, the authors now pointed our attention to the legoplot that\nis shown in Figure 3J. This is an important piece of data for this paper, but this plot is very hard\nto interpret, and it is e.g. not clear from this plot how different the results are in the RPS15\nmutant genomes versus the control samples that were analyzed. Why not use a much simpler\nand more commonly used representation such as employed for instance in this paper\n(https://aacrjournals.org/cancerres/article/79/2/320/638274/Ribosomal-Lesions-Promote-\nOncogenic; Figure 2A)?\nResponse We are always striving to present data in the most accessible fashion, and of course would aim to\nensure that key data is presented in the clearest way possible. In Figure 3J, we present the data as\nlegoplots, which include information about the substitution type and the trinucleotide context, to be\nconsistent with the extensively reported schemes from other groups\n(https://www.nature.com/articles/s41586-020-1943-3) and because the trinucleotide context is\nimportant for inferring the mechanism by which the substitution arose\n(https://www.pnas.org/doi/10.1073/pnas.1909021116 , https://www.nature.com/articles/s41588-\n024-01945-x).\nThat said, to improve the clarity of visualization of the difference in mutational signatures between\nthe RPS15 mutant and control samples, we have also re-performed the signature deconvolution and\nreplotted the signatures in the format suggested by Reviewer 1. Notably, we have now provided\nadditional controls: B-cells from CLL mouse models generated with either the minimal deleted\nregion (MDR) and TP53 -/-. These plots (Fig Q4-1) are now included as new Figure 3k.\nAs we can see in both deconvolved\nmutational signatures, and as shown before\nwith the legoplot, not only are A>C\ntransversions (last column on the right)\nsignificantly enriched (hypergeometric p-\nvalue (de novo mouse signature 1) < 0.001,\nhypergeometric p-value (de novo mouse\nsignature 2) < 0.001) but they are unique to\nthe RPS15 heterozygous mutant CLL mouse\nmodels. Here, while G>A transversions\n(third column from the left) are enriched in\nRPS15 heterozygous mutant CLL mouse\n.\nFig Q4-1 Two independent, de novo mutational signatures extracted with\nmodels, they also appear to be enriched in mutSignatures from the variant calls of MDR, RPS15 +/-, and TP53 -/-\nthe other CLL mouse models. leukemic cells vs normal T-cells. Silhouette analysis (not shown) produced\nnegative silhouette scores (poor clustering) when the parameter for the\nnumber of signatures to extract was set to a value greater than two,\nsuggesting there are no more than two mutational processes delineating\nthese models.\nMinor concerns:\nQ1 Figure 4d: the labeling of the western blot should be corrected; the genotypes that are\nindicated above are not correct.\nResponse We acknowledge and apologize for this error. This is now rectified.\nQ2 Lines 225-229: I do not see shared enrichment terms in suppl. Figure 3h as stated in the text.\nResponse We appreciate the point that the language in the manuscript can be more specific. We have\nadjusted the original text as below to specify the pathway names to which we are referring:\n“Reactome pathway analysis of these differentially expressed signatures across all three contexts (i.e., murine\nand cell line models, and primary CLL samples) revealed a common enrichment in translational machinery,\nsuch as mRNA splicing and processing (see “Processing of capped pre-mRNA”, “mRNA splicing”\n“Deadenylation of mRNA”, “Translation”, “Cap-dependent Translation Initiation”, “Ribosome scanning and\nstart codon recognition”, and “Regulation of mRNA stability”), and rRNA processing and modification (see\n“rRNA Modification in the Nucleus and Cytosol”, “rRNA Processing”) (Supp Fig 3h, normalized enrichment\nscore>1, nominal p-value <0.05).”\nWe removed references to snRNP assembly and N-glycosylation of protein as these were not\nincluded as enrichment terms in updated versions of the analysis.\nQ3 Methods: the procedure for RNA-seq is not described.\nResponse We apologize for this omission and have now added the RNA-seq procedure to the Methods section.\nQ4 Fig 3b, enrichment plot for hallmark DNA repair is not convincing. Same applies to\nzMYC targets enrichment plot in Figure S4a.\nResponse We appreciate the reviewer’s point about this enrichment plot and agree that this is a function of\nthe higher FDR, which we have included in the plot for transparency. However, we still prefer to\nshow this enrichment plot for the sake of completeness as it is a part of the discovery process that\nled us to perform the DNA damage functional experiments included later in the figure, which\nestablish DNA damage and repair as an important mechanism in this model.\nWe argue the same point with the Myc enrichment plot obtained from young murine samples in\nFigure S4a (a function of the higher p-adj value), given that this pathway is of great interest as a\npredisposing factor given the more convincing signal seen in older diseased mice.\nQ5 Reviewer 2, Q27 (related to Figure 4C): I agree with reviewer 2 that the claimed effects\non p53, cyclin A, p21 are only visible in the LPS/IL4 stimulated condition, this is not\nclear from the text. I assume that the quantification that is shown below the blot is from\nthe stimulated condition, this again is not clear from the figure legend and should be\nproperly reported.\nResponse We thank the reviewer for raising this important point. Indeed, one would not expect to see\ninduction of p53, cyclin A and/or p21 in murine B cells ex vivo in the absence of LPS/IL-4,\nregardless of their Rps15 genotype, as these cells become entirely quiescent subsequent to\nharvesting from murine spleens. Such cellular quiescence can be demonstrated by the arrest of these\ncells at G0 prior to exposure to LPS/IL-4 (Supp Fig 4e). In non-cycling cells that are not actively\nreplicating, one would expect to see minimal cyclin A and p21 induction, consistent with our\nfindings in Figure 4c. It is specifically in proliferating cells characterized by active DNA replication\nthat the p53-p21 pathway is active, and through the induction of proliferation ex vivo in these B\ncells by the mitogenic stimulus LPS, we sought to recapitulate their proliferative state in vivo. Thus,\ndifferences (or a lack thereof) in cyclin A, p53 and p21 levels across Rps15 genotypes are relevant\nonly in the proliferative (replicating) state which was achieved through mitogenic stimulation with\nLPS.\nWe appreciate that this has not been made completely explicit in the text. We have now revised the\ntext to improve clarity for the reader (Page 11, line 320-332 (new text in red)):\n“We hypothesized that the phenotype of decreased proliferation and increased apoptosis characteristic of\nRps15Hom cells in the pre-leukemic setting could be attributed to a p53-mediated response to oxidative stress\nand DNA damage in actively replicating cells. Ribosomal proteins are known to stabilize p53 by inhibiting the\np53-targeting E3 ubiquitin ligase MDM2 (24–30); consistent with this, we observed lower levels of p53\nexpression in cycling (LPS-stimulated) Rps15Hom compared to Rps15WT B cells (Fig 4c, p=0.0149).\nNevertheless, substantial induction of p53 and p21 (CDKN1A) was evident in Rps15Hom cells upon LPS\nstimulation, the latter a p53-dependent cyclin-dependent kinase inhibitor that functions as a critical mediator\nof G1 checkpoint activity (54–56). Indeed, we observed comparable p21 levels in cycling Rps15Hom vs Rps15WT\ncells. This was accompanied by suppressed cyclin A induction in Rps15Hom compared to Rps15WT B cells\n(p=0.0015) as further evidence of reduced proliferation in Rps15Hom cells (Fig 4c). Collectively, these results\nindicate that the p53-p21 pathway remained active in cycling Rps15Hom cells despite Rps15 mutation.”\nWe have also revised the figure legend for Fig 4c accordingly:\n“c, Western blot analysis of purified B cells from Rps15WT and Rps15Hom mice (3 mice per genotype, 3-6 months\nof age), with and without ex vivo mitogenic stimulation with LPS/IL-4 (48 hours), comparing the magnitude of\np53, p21 and cyclin A induction between the Rps15 genotypes in cycling B cells. Densitometric analysis of\nrepresentative western blots shows decreased cyclin A and p53 but comparable p21 induction in cycling (LPS\nstimulated) B cells from Rps15Hom versus Rps15WT mice. Data shown represent mean ± SEM. Statistical\nanalysis was carried out using unpaired two-tail t-test.”\nREVIEWER #2:\nThe authors have thoroughly addressed all of my previous concerns and have done an excellent job revising the\nmanuscript. I have no further comments.\nREVIEWER #3:\nI appreciate the work of the authors in addressing previous comments. However, I still hold concerns\nestablishing a clear link Rps15mut-DNA damage/genome instability-CLL.\nAccumulation of DNA damage/genomic instability, including the new experiment of micronuclei analysis, is\nonly shown for Rps15Hom. As a matter of fact, there is no effect on the response to damage (cell cycle\nchanges) in Het conditions. Authors can therefore still not link genome instability (observed in Rps15Hom)\nwith increased CLL (observed in Rps15Het).\nThe analysis of tumor copy-number alterations and mutations could help in this regard but the authors need to\nshow clear quantitative and significant differences between Trp53del and Trp53del Rps15Het. In this sense, the\nfocus on driver events may provide a skewed view that may mask the real effects, so an analysis of\npassenger events may be more sensitive to the potential effect of Rps15Het.\nMajor concerns:\nQ1 Accumulation of DNA damage/genomic instability, including the new experiment of micronuclei\nanalysis, is only shown for Rps15Hom. As a matter of fact, there is no effect on the response to\ndamage (cell cycle changes) in Het conditions. Authors can therefore still not link genome\ninstability (observed in Rps15Hom) with increased CLL (observed in Rps15Het).\nWe thank the reviewer for acknowledging our effort in addressing previous comments. We agree\nthat the data presented previously did not go far enough to resolve the apparent discrepancy between\nthe phenotype of increased oxidative stress, DNA damage and genomic instability observed\npredominantly in younger pre-malignant Rps15Hom mice (6 months of age) and the development of\nCLL-like tumors in aged Rps15Het mice and of human CLL in the RPS15Het context. Indeed, we did\nnot observe a significant increase in micronuclei in B splenocytes from 6-month-old Rps15Het mice\ncompared to Rps15WT mice of similar age (Figure 5c-top). We hypothesize that the increase in\noxidative stress, DNA damage and genomic instability in younger 6-month-old Rps15Het mice might\nnot be of sufficient magnitude to be captured by conventional functional assays that we have\nemployed, and that such a phenotype could become more apparent as oxidative stress, DNA damage\nand genomic instability accumulate with age.\nTo this end, we have repeated our assessment of intrinsic oxidative stress (mitochondrial\nreactive oxygen species (ROS)), DNA damage (γH2AX) and genomic instability (micronuclei)\nResponse\nin B splenocytes of older 11-month-old pre-malignant mice. In these mice we witnessed\nsignificantly higher mitochondrial ROS (Supp Figure 4g, and also shown below), γH2AX\n(Supp Figure 5b, also shown below) and micronuclei levels (Figure 5c-bottom, also shown\nbelow) in Rps15Het B splenocytes compared to their Rps15WT counterparts, although they\nremained lower compared to Rps15Hom B splenocytes. Furthermore, although we have\ndemonstrated impaired ATM-Chk2 signaling associated with curtailed G2/M checkpoint response\nto DNA double-strand breaks exclusively within the Rps15Hom genotype (Figure 5a-b), the latter\nrepresents only one of many possible mechanisms through which intrinsic genomic instability\nsecondary to oxidative stress and DNA damage could be potentiated. Our new data in older mice\nshowing increased mitochondrial ROS and γH2AX levels reflect heightened unresolved oxidative\nstress and unrepaired DNA damage in Rps15Het pre-malignant B cells, which translated into greater\ngenomic instability (micronuclei). We believe that notwithstanding the nature of other contributing\nDNA damage response defects that may be involved, our new data provide definitive evidence for\nhigher unresolved DNA damage and genomic instability in Rps15Het pre-malignant B cells\ncompared to their Rps15WT counterparts, thus linking these features with increased frequency of\nmalignant transformation as observed in the Rps15Het mice.\nWe have added additional text to accompany our new data as follows:\n(page 10, line 283 to 296 – Main Text)\n“We hypothesized that the suppression of proliferation and potentiation\nof apoptosis in pre-leukemic Rps15Hom B cells despite Myc upregulation\ncould arise from increased cellular stress in these cells resulting from\nRps15 mutation. Consistent with the notion that enhanced oxidative\nstress is present in Rps15Hom compared Rps15WT B cells, transcriptomic\nanalysis revealed a marked increase in expression of reactive oxygen\nspecies (ROS) genes in B splenocytes from pre-leukemic Rps15Hom\ncompared to Rps15WT mice (NES 1.5, p<0.05) (Supp Table 6). Further,\nfunctional evidence of increased oxidative stress in both quiescent and\nproliferating Rps15Hom B splenocytes (p<0.0001 and p=0.0045\nrespectively compared to Rps15WT), as well as in quiescent Rps15Het B\nsplenocytes from older (11 month old) pre-leukemic mice (p=0.026\ncompared to Rps15WT), was confirmed by experimental quantification of\nmitochondrial ROS; Fig 3g, Supp Fig 4f, Supp Fig 4g). Accordingly, Rps15Hom B cells exhibited hypersensitivity\nto further potentiation of oxidative stress with parthenolide (EC 3.5 μM vs 1.2 μM for Rps15Hom vs Rps15WT\n50\nwith 48 hours of treatment, p=0.0009, Fig 3h), a compound selected specifically for its known potent pro-oxidant\neffect on CLL cells (50–52).”\n(page 43, line 1212 to 1217 – Supp Fig 4g legend)\ng, Left: Representative histograms showing mitochondrial ROS level, assessed by the MitoSOX assay, in B cells\nfrom Rps15WT, Rps15Het and Rps15Hom mice (3 mice per genotype, 11 months of age) at baseline and 72 hours\nafter ex vivo LPS/IL-4 stimulation. Right: Mitochondrial ROS levels were higher in B cells from Rps15Het and\nRps15Hom mice compared to Rps15WT mice as indicated by higher MitoSOX MFI. Data shown represent mean ±\nSEM. Statistical analysis was carried out using one-way ANOVA with Tukey’s post-hoc multiple comparison\ntest. See also Figure 3g.\n(page 10, line 298 to 308 – Main Text)\nWe reasoned that elevated oxidative stress in Rps15-mutant B cells, if unresolved, could lead to deleterious\naccumulation of DNA damage. Accordingly, we observed an increased accumulation of γH2AX, a marker of\nDNA damage, in both quiescent and proliferating Rps15Hom B cells (p=0.0149 and p=0.016 respectively compared\nto Rps15WT), as well as in quiescent Rps15Het B splenocytes from older (11 month old) pre-leukemic mice\n(p=0.0082 compared to Rps15WT) (Fig 3i, Supp Fig 5a; Supp Fig 5b). To confirm whether DNA damage\noccurred as a direct result of oxidative\nstress, we undertook WGS of 4 CLLs\narising from Rps15Het mice. Analysis of\nthese genomes revealed profound\nenrichment for A•T>C•G and C•G>T•A\ntransversions (p<0.0001) that are\npathognomonic of peroxide-induced\nDNA damage (53,54), when compared\nagainst matched T-cell normal samples,\nwith no other significantly enriched\nmutational signatures (see Methods and\nSupp Table 7) (Fig 3j).\n(page 43, line 1227 to 1232 – Supp Fig 5b legend)\nb, Western blot analysis of freshly isolated, quiescent B cells from Rps15WT, Rps15Het and Rps15Hom mice (3 mice\nper genotype, 3-6 months of age [left panel] or 11 months of age [right panel]) showing increased DNA damage\naccumulation in Rps15Het and Rps15Hom compared to Rps15WT B cells obtained from 11 month-old mice as\nindicated by higher γH2AX expression. Data shown represent mean ± SEM. Statistical analysis was carried out\nusing one-way ANOVA with Tukey’s post-hoc multiple comparison test. See also Figure 3i.\n(page 12-13, line 399 to 407 – Main Text)\nTo establish whether oxidative stress, DNA damage and defective DNA damage response collectively result in\nheightened genomic instability in Rps15-mutant pre-leukemic B cells, we compared the frequency of appearance\nof micronuclei in Rps15WT and pre-leukemic\nRps15Hom splenic B cells following 72-hour\nex vivo mitogenic stimulation with LPS.\nMicronuclei results from the sequestration\nof DNA within aberrant extranuclear\nstructures and is a recognized hallmark of\nchromosomal instability (67,68). We\ndiscovered higher proportions of B cells\nharboring micronuclei in older (11 month\nold) pre-leukemic Rps15Hom and Rps15Het\npre-leukemic mice compared to Rps15WT\nmice (25% for Rps15Hom and 13% for\nRps15Het vs 4% for Rps15WT, p<0.0001; Fig\n5c), indicating heightened genomic\ninstability in these cells.\n(page 34-35, line 991 to 998 – Fig 5c legend)\nc, Left: Representative fluorescence microscopy images of DAPI-stained nuclei from Rps15WT, Rps15Het and\nRps15Hom splenic B cells (3 mice per genotype, 3-6 months of age [upper panel] or 11 months of age [lower\npanel]) cultured ex vivo for 72 hours with LPS/IL-4. Micronuclei are indicated by red arrows. Right: B cells\nfrom 11 month-old Rps15Hom and Rps15Hom mice (n=3) displayed higher frequency of micronuclei compared to\nB cells from 11 month-old Rps15WT mice (n=3). Data shown represent mean ± SEM. A minimum of 1000 B\ncells were evaluated per mice. Statistical analysis was carried out using one-way ANOVA with Tukey’s post-\nhoc multiple comparison test.\nWe appreciate that greater oxidative stress, DNA damage and genomic instability in B cells from\npre-malignant Rps15Hom mice may be difficult to reconcile with a lower frequency of CLL\nprogression compared with their Rps15Het counterparts. In contrast to moderately increased\noxidative stress, DNA damage and chromosomal instability as seen in B cells from older Rps15Het\nmice, we argue that excessive levels of such cellular stress and DNA damage in B cells from aged\nRps15Hom mice may not necessarily be the most conducive to malignant progression; rather, the\nlatter when reaching catastrophically high levels could precipitate tumor cell death and clonal\nattrition, thereby constituting a selective disadvantage for tumor growth. Indeed, we saw increased\napoptosis in Rps15Hom B cells from pre-malignant mice (Figure 3f). Although not a subject of our\ncurrent investigation, mitotic catastrophe and ferroptosis could additionally accompany\ncatastrophic levels of oxidative stress, DNA damage and genomic instability (Ref 118-119,\nappended below). In relation to this, we would like to highlight our view presented in the existing\ndiscussion section of our manuscript text (line 679 to 690) which we have now expanded further\n(new text in red):\n“We speculate that, given that wild-type Rps15 can stabilize p53 through the Mdm2-p53 regulatory axis, which is\nkey to ribosome surveillance and cell cycle regulation (30), complete absence of the Rps15 WT allele in the\nhomozygous setting may result in a less effective p53-mediated response to cellular stress and dysfunction.\nMoreover, excessive cellular stress and DNA damage is incompatible with cell survival and may result in more\nrapid cellular demise in Rps15Hom cells through apoptosis, ferroptosis and/or mitotic catastrophe (118,119).\nIndeed, we found increased apoptosis in Rps15Hom B cells from pre-malignant mice compared to their Rps15Het\nand Rps15WT counterparts (Figure 3f). Ultimately, reduced cell viability results in a smaller pool of pre-\nmalignant cells and hence constituting a selective disadvantage with fewer opportunities for cells to accumulate\nthe mutations necessary to transform – and would support why heterozygous, and not homozygous, RPS15\nmutations are predominantly observed in CLL patients and in our mouse model.”\nWe hope that our new data together with the discussion provided above satisfactorily resolve any\nlingering concerns relating to the relationship between DNA damage, genomic instability and\nCLL/tumor development.\nRef (118-119):\n1. Vitale, I., Galluzzi, L., Castedo, M. et al. Mitotic catastrophe: a mechanism for avoiding\ngenomic instability. Nat Rev Mol Cell Biol 12, 385–392 (2011).\nhttps://doi.org/10.1038/nrm3115\n2. Gujar V, Li H, Paull TT, Neumann CA, Weyemi U. Unraveling the nexus: Genomic\ninstability and metabolism in cancer. Cell Rep. 2025 Apr 22;44(4):115540. doi:\n10.1016/j.celrep.2025.115540.\nREVIEWER#1\nI thank the authors for further clarification, textual adaptations and additional data\nshown to address the remaining questions.\nI only have three remaining remarks related to Q1 and Q2 as indicated below.\nQ1 (questions related to Ribo-seq interpretation)\nI truly appreciate the addition of Supplementary Figure panel 8C, clearly showing that\nOtud4 is a buffered gene with a mixed transcriptional/ translational regulation profile. It\nwould be nice to also highlight this buffered (and hence mixed\ntranscriptional/translational regulation) in the main text, which now still gives the\nimpression that there is only translational regulation ongoing. Just a textual change to\nmake this more clear would be more transparent and clear to the reader.\nWe appreciate this suggestion and have adjusted the text to highlight this example of\ntranslational buffering as below:\n“Also significantly downregulated in Rps15Hom cells as a consequence of translational buffering\nwas Otud4, a de-ubiquitinase that promotes the base-excision repair of alkylation-induced DNA\nadducts (Fig 7a, c-d; Supp Fig 8b) (89).”\nQ2\nStatement on effect of RPS15 mutations on protein stability: Thanks for the clarification,\nbut the abstract still contains the statement: ‘Under pre-leukemic conditions, this\nmutation induced instability of the RPS15 protein’. This statement in the abstract is not\nsupported by the data in the preleukemic mice shown in the paper (as the authors say\nthemselves). Please remove this wrong statement from the abstract, this is now\nconfusing to the reader.\nWe have removed this statement from the abstract, which now reads as below:\n“Under pre-leukemic conditions, this mutation induces ribosome biogenesis defects and altered\ntranslation resulting in oxidative stress, DNA damage and induction of a p53-dependent\nresponse that promote initial cellular hypo-proliferation.”\nStatement on effects of RPS15 on survival: line 127 still claims that RPS15 is\nassociated with poorer OS. Please adapt, this is confusing the reader.\nWe have updated the text to clarify that while there is a trend towards poorer OS in\ncases with RPS15 mutation alone, this did not reach statistical significance in our\ncohort. We have included this text below for ease of viewing:\n“RPS15 mutations in CLL have been previously associated with inferior prognosis\n(8,10). In our dataset, where we stratified samples by IGHV mutation status, U-CLLs\nwith either sole RPS15 mutation, sole TP53 alteration, and both RPS15 mutation and\nTP53 alteration (including TP53 mutation, del(17p), and del(17p13.1)) did not have a\ndifference in OS, but altogether had shorter overall survival (OS) compared to cases\nwithout RPS15 or TP53 mutation (p=0.0045, comparison of all four groups, log-rank\ntest, Supp Fig 1c). U-CLLs with wildtype TP53 and RPS15 mutations only trended to a\nshorter overall survival (OS) compared to cases with wild-type RPS15 (p=0.096) but did\nnot reach statistical significance.”\nREVIEWER#3\nThe new experiments presented show the spontaneous occurrence of DNA damage in\nHet mice, therefore allowing to propose this as a potential cause of the increased\ntumorigenesis. In what relates to my area of expertise, I am therefore satisfied, and\nwould recommend publication, pending that the concerns of the other reviewers are\nalso addressed.\nWe appreciate this reviewer’s comments and believe that the current manuscript text\nreflects these conclusions. No further edits are deemed necessary at this time."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I thank the authors for further clarification, textual adaptations and additional data shown to address the remaining questions.\nI only have three remaining remarks related to Q1 and Q2 as indicated below.\nQ1 (questions related to Ribo-seq interpretation)\nI truly appreciate the addition of Supplementary Figure panel 8C, clearly showing that Otud4 is a buffered gene with a mixed transcriptional/ translational regulation profile. It would be nice to also highlight this buffered (and hence mixed transcriptional/translational regulation) in the main text, which now still gives the impression that there is only translational regulation ongoing. Just a textual change to make this more clear would be more transparent and clear to the reader.\nQ2\nStatement on effect of RPS15 mutations on protein stability: Thanks for the clarification, but the abstract still contains the statement: ‘Under pre-leukemic conditions, this mutation induced instability of the RPS15 protein’. This statement in the abstract is not supported by the data in the preleukemic mice shown in the paper (as the authors say themselves). Please remove this wrong statement from the abstract, this is now confusing to the reader.\nStatement on effects of RPS15 on survival: line 127 still claims that RPS15 is associated with poorer OS. Please adapt, this is confusing the reader.\n(Remarks on code availability)\nI have not reviewed the code and reproduced the results, as this is not my expertise.", "reviewer_3": "The new experiments presented show the spontaneous occurrence of DNA damage in Het mice, therefore allowing to propose this as a potential cause of the increased tumorigenesis. In what relates to my area of expertise, I am therefore satisfied, and would recommend publication, pending that the concerns of the other reviewers are also addressed.\n(Remarks on code availability)"}, "author_response": "We appreciate this reviewer’s comments and believe that the current manuscript text reflects these conclusions. No further edits are deemed necessary at this time."}]

Decision letter