Tumor metabolic adaptation induced by L-asparaginase reveals a vulnerability to PARP1/2 inhibitor in B-cell lymphomas

Domain: chemistry
Published: 2026-02-27
Editorial outcome: major_revision

Abstract
Secondary resistance to the amino-acid–depleting agent L-asparaginase (ASNase) remains poorly understood. Using ASNase-sensitive B-cell lymphoma (BCL) models, we investigate tumor relapse during treatment. Through in vitro and in vivo metabolic profiling, here we show that ASNase triggers a metabolic reprogramming characterized by increased de novo serine biosynthesis driven by phosphoglycerate dehydrogenase (PHGDH). This response mitigates treatment-induced oxidative stress and associated DNA damage, enabling malignant cells to survive. We evidence that ASNase-treated malignant cells exhibit features of replication stress and increase activity of poly(ADP-ribose) polymerase (PARP), revealing a dependence on DNA repair. Combining ASNase with the clinically approved PARP inhibitor Olaparib enhances the antineoplastic effect of each monotherapy in vitro and in vivo. Moreover, this combination shows effectiveness in homologous recombination-proficient colorectal cancer cells, suggesting broader therapeutic potential. Overall, our study identifies tumor metabolic and genomic vulnerabilities induced by ASNase and supports a rational combination strategy using clinically approved drugs.

Introduction
Cancer cells exhibit enhanced metabolism to meet expanding energetic and biosynthetic demands, a vulnerability that prompted several laboratories to develop metabolic inhibitors for clinical use in oncology 1-3. Despite successful evaluations on preclinical models of cancers, only a few of these inhibitors have achieved FDA approval, due in part, to significant toxicities and development of resistance mechanisms 1, 4. The intrinsic mechanisms by which tumor cells adapt to metabolic targeting in vivo, remain poorly understood, thereby limiting the development of additional therapeutic strategies for patients who exhibit resistance to routinely used anti-metabolic agents in the clinic.

L-asparaginase (ASNase) is the only clinically approved drug targeting cancer cell addiction for a particular amino-acid, successfully integrated into multi-agents’ chemotherapy regimens to treat both childhood and adult T-cell and B-cell acute lymphoblastic leukemias (T-ALL, B-ALL) and NK/T-cell lymphomas (NKTCL). By catalyzing the hydrolysis of the non-essential amino acid asparagine in the bloodstream, ASNase triggers starvation and selective apoptosis of asparagine-addicted cancer cells. To date, clinical use of ASNase is restricted to ALL and NKTCL, as these hematological malignancies commonly harbor an epigenetic silencing of the gene encoding the asparagine synthetase that catalyzes the ATP-dependent conversion of L-aspartate (Asp) and L-glutamine (Gln) into L-asparagine (Asn) and L-glutamate (Glu) 5-7. Consequently, most ALL and NKTCL are asparagine-auxotrophic, which makes them highly vulnerable to ASNase treatment. Therefore, asparagine synthetase has long been considered a primary determinant of cancer cells’ sensitivity to ASNase treatment 6. However, recent studies have shown that ASNase treatment also exhibits anti-tumor efficacy in a variety of other malignancies displaying basal heterogenous asparagine synthetase expression 8-12. Moreover, our recent work demonstrated that extracellular asparagine prevents its de novo biosynthesis in B-cell lymphomas, regardless of asparagine synthetase expression levels 9. These findings challenge the conventional view that asparagine synthetase expression alone is a reliable factor predicting tumor sensitivity to ASNase treatment in asparagine synthetase-expressing malignancies 9.

Several mechanisms involved in tumor resurgence during ASNase treatment have been described. Due to its bacterial nature, immunization against ASNase may occur during therapy, leading to neutralization of the enzyme activity. This issue is being addressed through the development of 2nd generation of ASNase formulations with reduced immunogenicity 13-15. Additional mechanisms primarily involve the upregulation and/or activation of asparagine synthetase in malignant cells, driven by activation of multiple distinct signaling pathways that converge to an ATF4 (Activating Transcription Factor 4)-dependent transcriptional program promoting cell growth 10-12. Nutrient release by stromal cells, which fuels leukemic blasts 16, 17 along with specific tumor metabolic adaptations, also account for additional reported mechanisms of resistance to ASNase treatment 18-20. However, none have yet led to clinically viable alternative treatments for patients who fail ASNase therapy.

We recently demonstrated the antitumor efficacy of an ASNase-based anti-metabolic strategy in patients with refractory/relapsed (R/R) diffuse large B-cell lymphoma (DLBCL) 8. Despite complete responses achievement, all patients eventually relapsed during or after completed therapy, suggesting that a subset of malignant cells survived and adapted to circumvent treatment.

Our study aimed to uncover additional cancer cell intrinsic metabolic alterations that contribute to tumor relapse during ASNase treatment, with the intent to reveal unknown targetable vulnerabilities. Using a preclinical mouse model of B-cell lymphomas (BCL), we modeled secondary resistance following an initial antineoplastic response to ASNase treatment both in vitro and in vivo. Through comprehensive metabolomic profiling of BCL, complemented by in-depth analysis of altered metabolic pathways, using the stable isotope tracing in both in vitro and in vivo settings, we evidenced that ASNase treatment induces a dynamic metabolic reprogramming characterized by increased de novo serine biosynthesis in malignant cells. Using pharmacological inhibition, shRNA-mediated silencing, and overexpression of PHGDH—the rate-limiting enzyme of the de novo serine biosynthesis—we demonstrated that PHGDH plays a critical role in mediating the tumor’s adaptive response to ASNase. Mechanistically, PHGDH activity mitigates ASNase-induced oxidative stress and related DNA damage, thereby enabling malignant cells to resume proliferation during treatment. Importantly, ASNase-induced DNA damage enhances reliance of malignant B cells on poly(ADP ribose) polymerase 1/2 (PARP) activity, revealing a therapeutic vulnerability to the clinically approved PARP inhibitor, Olaparib, which impairs DNA repair mechanisms.

Results
ASNase-sensitive B-cell lymphomas undergo metabolic reprogramming during ASNase therapy both in vitro and in vivo.

Previously, we demonstrated that B-cell lymphomas (BCL) relying on oxidative phosphorylation (OxPhos) metabolism for energy production are sensitive to E-coli L-asparaginase (ASNase) therapy, whereas glycolytic-dependent BCL exhibit resistance 8. In the present study, we engrafted wild-type C57BL/6 mice with OxPhos-dependent Eµ-Myc cells (malignant B cells) isolated from two individual transgenic Eµ-MycTg/+ mice (#506 or #688). Seven days later, mice were treated either with Vehicle or ASNase every 48 hours until the lymphoma reached the ethical endpoint (Figures 1a, b). ASNase treatment showed significant anti-tumor efficacy resulting in delayed BCL development (Figures 1a, b and S1a, b). This represents the initial antineoplastic response to treatment. However, despite continued treatment, all mice eventually developed BCL, a result consistently observed across two independent Eµ-Myc clones (#506 or #688) showing equivalent initial sensitivity to ASNase treatment in vivo. This secondary response reflects a phase of therapeutic failure, mimicking tumor relapse following an initial favorable response, as observed in patients with R/R DLBCL treated with innovative ASNase-based anti-metabolic therapy 8.

One mechanism of tumor resurgence during ASNase treatment in clinical settings is the production of anti-ASNase antibodies which neutralize the enzyme and are often associated with allergic episodes in patients. To rule out decreased ASNase activity as the cause of tumor relapse, mice received a final bolus of Vehicle or ASNase at endpoint. Four hours post administration, BCL-bearing mice treated with ASNase exhibited undetectable plasma asparagine concentration and elevated plasma aspartate concentration compared to Vehicle-treated controls, indicating sustained ASNase activity despite disease relapse (Figure S1c).

Regardless of the tumor entity or energetic dependency, therapy-resistant cancer cells frequently enhance mitochondrial energetic functions to support survival 21-24. Consistently, we observed a significant increase in the contribution of OxPhos to ATP production in BCL that progressed during ASNase treatment in vivo, suggesting a metabolic reprogramming to meet elevated ATP demands (Figure S1d).

We next conducted targeted metabolomic analysis in BCL undergoing Vehicle or ASNase treatment in vivo. Principal component analysis (PCA) and Venn diagrams showed that the metabolic profile of BCL treated with ASNase in vivo was distinct from that of Vehicle-treated BCL, irrespective of the Eµ-Myc clone used (Figures 1c-e and S1e). Nineteen (14%) significantly deregulated metabolites were common to both datasets (Eµ-Myc #506 cells- or Eµ-Myc #688 cell-derived BCL) and exhibited similar directional regulation in response to ASNase treatment with 14 metabolites increased and 5 metabolites decreased (Figure 1f and Supplementary Data S1). These shared metabolites likely reflect core metabolic response to ASNase therapy, that are independent of the Eµ-Myc clone origin, suggesting conserved metabolic vulnerabilities in our in vivo model.

Metabolite Set Enrichment Analysis (MSEA) using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway computational tool, revealed deregulated nucleotide and amino acid (AA) metabolism in ASNase-treated BCL (Figure 1g). Among proteinogenic AA, serine, glycine, tyrosine, and asparagine were the only significantly and consistently deregulated AA in ASNase-treated BCL, meeting the fold change threshold (ASNase/Vehicle) >1.25 and raw P-value < 0.05, across both datasets (Figures 1e, S1e, Table S1 and S2). Indeed, BCL treated with ASNase in vivo exhibit a two- to three-fold increase in steady-state serine and glycine levels in ASNase-treated BCL compared to Vehicle-treated BCL (Figures 1h, i).

To confirm that ASNase treatment triggered serine metabolism remodeling in malignant cells, Eµ-Myc cells were cultured in DMEM medium lacking asparagine but containing supraphysiological concentrations of serine and glycine, with (+) or without (-) supplementation of asparagine and ASNase. A concentration of 0.003 IU/ml ASNase was used to avoid hydrolysis of glutamine into L-glutamate (Figure S1f). ASNase treatment significantly increased the death of Eµ-Myc cells in 24 hours (Figures 1j and S1g, h). However, Eµ-Myc cells surviving this nutritional stress resumed proliferation over time (Figures 1k et S1i), a process accompanied by a significant increase in intracellular serine and glycine levels following 24 hours of ASNase treatment (Figures 1l, m and S1j, k). To specifically evaluate metabolic adaptations to asparagine deprivation— since ASNase-mediated release of aspartate and ammonium can each influence the tumor metabolic response —we cultured Eµ-Myc cells in asparagine-free medium. This experimental setup ensured that the observed metabolic changes were directly attributable to asparagine depletion, thereby reinforcing the robustness of our conclusions. Our findings indicate that ASNase treatment induces a metabolic response in BCL, both in vitro and in vivo, characterized by elevated serine and glycine levels in malignant B cells.

Increased de novo serine biosynthesis in ASNase-treated B-cell lymphomas, in vitro and in vivo.

Increased total serine levels in malignant cells may result from either enhanced serine import via active transport or increased de novo serine biosynthesis from the glycolytic intermediate 3-phosphoglycerate and glutamine-derived glutamate through a 3-step enzymatic process catalyzed by phosphoglycerate dehydrogenase (PHGDH), phosphoserine aminotransferase-1 (PSAT1), and phosphoserine phosphatase (PSPH). Eµ-Myc cells proliferation was unaffected by extracellular L-serine/glycine withdrawal, regardless of asparagine availability (Figure S2a). In line with a previous report describing asparagine as an amino acid exchange factor facilitating serine import 25, asparagine-restricted Eµ-Myc cells exhibited significantly reduced extracellular serine uptake, despite an increase in total intracellular serine levels (Figures 2a, 1l and S1j). Moreover, incubation of Eµ-Myc cells in serine/glycine-free medium, still resulted in a marked and significant increase in intracellular serine and glycine abundance in ASNase-treated cells compared to control cells, suggesting activation of the de novo serine and glycine biosynthesis pathway upon treatment (Figures 2b, c). Supporting this hypothesis, asparagine-deprived Eµ-Myc cells upregulated PHGDH, PSAT1, PSPH mRNA and protein expression, along with the expected induction of asparagine synthetase (Figures 2d, e and S2b). The glutamine synthase (GLUL) that has been associated with resistance of sarcoma cells to ASNase 18, 19, was upregulated only in glutamine-deprived, but not in asparagine-deprived Eµ-Myc cells, suggesting a weak contribution of GLUL to secondary resistance to ASNase treatment in malignant B cells. The expression level of the glutamate oxaloacetate transaminase 1 (GOT1), which remained unchanged upon nutritional stress, indicates a selective regulation of specific metabolic enzymes consistent with upregulation of the transcription factor ATF4 (Figures 2d, e and S2c), as already described 26-29.

Given that the expression level of metabolic enzyme does not necessarily reflect its activity9, we assessed the activity of PHGDH—the rate-limiting enzyme in de novo serine biosynthesis—as well as the level of de novo-synthesized serine and glycine by tracing [U-13C]-glucose in Eµ-Myc cells in vitro. We observed a significant increase in PHGDH activity (Figure S2d), along with elevated levels of de novo 13C-labelled serine (M+3) and 13C-labelled glycine (M+2) derived from [U-13C]-glucose, in asparagine-deprived Eµ-Myc cells, even in medium containing supraphysiological concentrations of both amino acids (Figures 2f, g and S2e). These cells also exhibited increased levels of 13C-labelled asparagine (M+2 and M+3), consistent with active asparagine synthetase under conditions of extracellular asparagine withdrawal (Figure S2f). Similarly, using -15N-L-glutamine, we detected increased levels of 15N-labelled serine (M+1) and 15N-labelled asparagine (M+1) (Figures S2e, g, h), further supporting increased de novo serine biosynthesis in malignant B cells exposed to an asparagine-free environment.

In vivo, BCL harvested from ASNase-treated mice also exhibit upregulated PSAT1, PSPH, ASNS and ATF4 expression, though PHGDH expression was not (Figures 2h and S2i). Nevertheless, PHGDH activity was significantly increased in BCL of ASNase-treated mice (Figure 2i). Following several weeks of Vehicle or ASNase treatment, BCL-bearing mice received two consecutive boluses of [U-13C]-glucose at a 20 min interval to quantify de novo-synthesized serine, glycine, and asparagine in tumors (Figure 2j). Equivalent enrichment of fully carbon-labeled glucose (M+6) was detected in the plasma of both Vehicle and ASNase-treated mice bearing BCL (Figure S3a). Consistent with in vitro data, levels of carbon-labelled 13C-labelled serine (M+3), glycine (M+2) and asparagine (M+2) were significantly increased in ASNase-treated BCL, accompanied by elevated total levels of serine, glycine and a corresponding decrease in total levels of asparagine, suggesting increased de novo serine/glycine and asparagine biosynthesis from [U-13C]-Glucose in BCL exposed to ASNase treatment in vivo (Figures 2k and S3b-e). As previously reported in WT mice and in multiple murine cancer models 30, we also confirmed elevated plasma serine concentration in BCL-bearing C57BL/6 mice treated with ASNase (Figure S3f).

Altogether, our results demonstrate that malignant B cells exposed to an asparagine-restricted environment support de novo serine biosynthesis, despite available environmental serine and glycine.

PHGDH supports ASNase-sensitive malignant B cells proliferation during therapy.

We next investigated whether ASNase-sensitive malignant B cells increase de novo serine biosynthesis to facilitate their outgrowth in an asparagine-deprived environment. First, we tested several commercially available PHGDH inhibitors—including three allosteric compounds (NCT-503, CBR-5884 and PKUMDL-WQ2101) and one competitive inhibitor (BI-4916) 31-34 in Eµ-Myc cells to evaluate their effects on PHGDH enzymatic activity and cellular viability. While all compounds exhibited concentration-dependent cytotoxicity in vitro (Figures S4a-d), only BI-4916, a cell-permeable and selective NAD⁺/NADH-competitive inhibitor of PHGDH 34, effectively inhibited PHGDH activity in Eµ-Myc cells (Figure S4e). Based on these results, BI-4916 was selected for further investigations. Notably, our findings are consistent with prior studies demonstrating the superior efficacy of BI-4916 in inhibiting de novo serine biosynthesis in the breast cancer cell line MDA-MB-468, compared to allosteric inhibitors 35.

Treatment with 10 µM of BI-4916 inhibited PHGDH activity by 80% (Figure 3a), significantly reduced total intracellular serine levels (Figure S4f) and induced an average significant 30% increase in Eµ-Myc cell death within 24 hours (Figures 3b and S4d). When combined with ASNase, BI-4916 further enhanced cell death in a caspase-dependent manner within 24 hours, compared to either treatment alone (Figures 3b and S4g). Moreover, ASNase/BI-4916 co-treatment significantly delayed the outgrowth of Eµ-Myc cells, underscoring the contribution of PHGDH activity to malignant B cells’ adaptation during ASNase treatment (Figure 3c). Similar effects were obtained with independent ASNase-sensitive Eµ-Myc cells (Figures S4h, i) and with the NAD+/NADH competitive PHGDH inhibitor BI-4924, that was chemically modified to generate the esterified prodrug form, BI-4916 34 (Figures S4j, k).

Due to the intrinsic instability of BI-4924 and BI-4916, which precludes their use in in vivo studies 34, we sought to confirm that the observed effects were attributable to on-target PHGDH inhibition by stably expressing shRNA targeting the murine Phgdh mRNA in Eµ-Myc cells. Two shRNA constructs (#1 and #2) effectively reduced PHGDH protein levels without altering PSAT1 or PSPH expression levels (Figures 3d, e). Consistent with pharmacological inhibition, silencing of Phgdh sensitized Eµ-Myc cells to ASNase-induced cell death and prevented the expansion of surviving cells in vitro (Figures 3f, g). In vivo, ASNase-treated mice engrafted with Eµ-Myc-shPhgdh #1 cells displayed significantly delayed lymphoma progression compared to those bearing control Eµ-Myc-shSCR cells (Figure 3h), confirming PHGDH as a key mediator of BCL relapse during ASNase therapy. Of note, at endpoint, we confirmed downregulated levels of PHGDH protein in BCL of Eµ-Myc-sh Phgdh #1 cells-bearing mice, regardless of treatments (Figures 3i , j). On the contrary, PHGDH overexpression in Eµ-Myc cells accelerated BCL development upon ASNase treatment (Figures S4l-n), further validating PHGDH’s role in the adaptive resistance mechanisms.

Collectively, our results highlight the critical role of PHGDH activity and de novo serine biosynthesis in the proliferation of malignant B cells exposed to an asparagine-deprived environment.

ASNase treatment induces a reversible tumor metabolic response in vivo.

Prolonged nutritional stress, such as that imposed by weeks of treatments with the clinically used ASNase, raises questions about the persistence of mechanisms driving tumor relapse. To assess the reversibility of this adaptation, Eµ-Myc cells that survived 4 days of ASNase treatment in vitro (1st challenge), were released from therapy (C1 cells). Upon re-exposure to ASNase (2nd challenge), C1 cells displayed an ASNase sensitivity comparable to chemo-naïve parental (WT) Eµ-Myc cells, suggesting that the adaptive response is transient (Figures 4a-c). This result was consistent across other independent ASNase-sensitive Eµ-Myc cells (Figures S5a, b).

The expression levels of PHGDH, PSAT1, PSPH, ASNS and of the transcription factor ATF4, which were initially upregulated within the first 24 hours, remained elevated through 4 days of ASNase treatment (Figures 4d and S5c). Following therapy withdrawal, ASNS and PSPH protein levels returned to baseline within 24 hours, while PSAT1 and PHGDH levels modestly decreased at 72 hours and 96 hours (control condition of the 2nd challenge), respectively, consistent with a time-dependent decreased expression level of ATF4. Upon ASNase re-exposure (2nd challenge) these enzymes were re-induced within 24 hours (Figures 4d and S5c), confirming a dynamic and reversible regulatory process.

In vivo, Eµ-Myc cells isolated from BCL treated with either Vehicle or ASNase (1st in vivo challenge) were re-engrafted into WT C57BL/6 mice and subjected to a second round of ASNase therapy (2nd in vivo challenge). Mice engrafted with ASNase-treated cells still exhibited a significant survival benefit upon re-treatment (Figures 4e-g), supporting a model of flexible, non-permanent metabolic adaptation.

To assess whether the metabolic response to ASNase was reversible in vivo, we performed metabolomic profiling of BCL harvested after the 1st or 2nd ASNase treatment (in vivo challenge). Tumors from Vehicle-treated mice (Vehicle-VBCL and Vehicle-ABCL) exhibited distinct metabolic profiles compared to those from ASNase-treated mice (ASNase-VBCL and ASNase-ABCL), regardless of tumor origin (VBCL or ABCL). Importantly, tumors harvested from Vehicle-treated mice (Vehicle-VBCL and Vehicle-ABCL) showed a highly similar metabolic signature, even though Vehicle-ABCL originate from ABCL cells, i.e., malignant B cells that had already adapted to ASNase treatment, suggesting a transient resistance phenotype (Figure 4h). Among the most significantly deregulated metabolic pathways in ASNase-ABCL compared to Vehicle-ABCL, serine/glycine metabolism was prominent, consistent with previous findings (Figure S5d). ASNase-treated BCLs showed elevated serine and reduced asparagine levels, whereas the opposite pattern was observed in Vehicle-treated BCLs, underscoring dynamic metabolic reprogramming (Figure 4i). In line with in vitro results (Figures 4d and S5c), induction of PSAT1, PSPH, and ASNS expression in BCL following 1st in vivo challenge with ASNase, was transient, as their expression levels decreased in Vehicle-ABCL of Vehicle-treated mice (or was maintained in ASNase-ABCL of ASNase-treated mice) following the 2nd in vivo challenge (Figures 4j and S5e). Collectively, our data highlight the metabolic plasticity of malignant B cells exposed to ASNase therapy.

PHGDH activity limits ASNase-induced oxidative stress in malignant B cells.

Given that amino acid or glucose starvation induces oxidative stress 36, 37, we determined whether ASNase treatment induces oxidative stress in Eµ-Myc cells and how these ASNase-sensitive Eµ-Myc cells adapt. Asparagine-deprived Eµ-Myc cells displayed elevated ROS levels and a higher oxidized to reduced glutathione ratio (GSSG/GSH) (Figures 5a, b and S6a, b). This oxidative stress was accompanied by NRF2 (Nuclear Factor Erythroid 2-Related Factor 2) protein stabilization that in turn activated the transcription of canonical antioxidant response genes, including Glutamate-cysteine ligase catalytic subunit (Gclc), Glutamate-cysteine ligase modifier subunit (Gclm) —the first rate-limiting enzymes of glutathione biosynthesis— as well as the NAD(P)H quinone dehydrogenase 1 (Nqo1) (Figures 5c-e).

Given the induction of Gclc, Gclm and the NRF2-targeted gene slc6a9 (solute carrier family 6 member 9) 38, encoding a glycine transporter (Figure S6c), we verified whether glutathione synthesis, which requires cysteine, glutamate and ultimately glycine, was enhanced in asparagine-deprived Eµ-Myc cells. Using 13C -Glycine, we detected increased levels of glycine-13C -labelled GSH (M+2) derived from 13C -Glycine (Figures S6d, e), indicating active glutathione synthesis in asparagine-deprived Eµ-Myc cells. Of note, glycine appears to be specifically catabolized in glutathione biosynthesis, as evidenced by a significant reduction in the abundance of downstream products of glycine metabolism and diminished 13C-ATP (M+2) levels (Figure S6f).

Since de novo serine biosynthesis also contributes to glycine for antioxidant defense and given the observed increased levels of 13C -serine and 13C -glycine derived from [U-13C]-glucose in ASNase-treated cells (Figures 2f, g), we hypothesized that this pathway supports redox homeostasis. Inhibition of PHGDH with BI-4916 significantly increased ROS levels, indicating that de novo serine biosynthesis plays a critical role in maintaining redox homeostasis in Eµ-Myc cells under basal conditions (Figures 5f, g). Furthermore, cells treated with BI-4916 in Asn-free medium or with BI4916/ASNase combination in Asn-containing medium, exhibit higher ROS levels compared to those observed with each single treatment, supporting the contribution of de novo serine biosynthesis to redox homeostasis in ASNase-treated Eµ-Myc cells (Figures 5f, g). Accordingly, scavenging ROS with N-acetyl-L-cysteine (NAC) prevented cell death induced by BI-4916, either alone or in combination with ASNase (Figure 5h).

To promote an effective antioxidant response, cancer cells must not only meet a high demand for glycine but also cysteine, the rate-limiting precursor of glutathione biosynthesis 39-41. Due to the oxidizing tumor microenvironment, extracellular cysteine is unstable and rapidly converts into cystine. Therefore, most cancer cells rely on cystine transport, which is imported and reduced to cysteine in the cytosol, or on de novo cysteine biosynthesis. Surprisingly, Eµ-Myc cells harvested from 12 individual transgenic Eµ-Myc Tg/+ mice, lacked detectable levels of slc7a11 mRNA, the solute carrier family 7 member 11 (also called xCT), which is the main plasma membrane antiporter mediating extracellular cystine uptake in exchange for glutamate (Figure 5i). slc7a11 mRNA remained undetectable by qPCR in Eµ-Myc cells treated with ASNase for 72 hours in vitro or by RNAseq in BCL exposed to several weeks of ASNase treatment in vivo (Figures 5j and S6g). Other cystine transporters, slc7a9 and slc3a1, were also absent, while slc3a2 (also called CD98), encoding the chaperone for xCT and LATs transporters, was ubiquitously expressed (Figures 5i, j). Analysis of The Cancer Genome Atlas (TCGA) database confirmed that human DLBCL exhibit low slc7a11 mRNA expression (Figure 5k), a finding reinforced by RNAS-seq data from 775 human DLBCL samples 42, which showed that 7.28% expressed little to no detectable levels of slc7a11 mRNA, despite heterogeneous expression of slc3a2 mRNA (Figures S6h, i). Overexpression of the murine SLC7A11 in slc7a11-deficient Eµ-Myc cells, attenuated the death induced by inhibition of PHGDH alone or in combination with ASNase (Figures 5l-n). Collectively, our results highlight the role of PHGDH in mitigating ASNase-induced oxidative stress in malignant B cells.

ASNase-induced ROS revealed a therapeutic vulnerability to the clinically used PARP1/2 inhibitor in ASNase-sensitive malignant B cells in vitro.

We next aimed to broaden our investigation into the possible consequences of perturbed redox homeostasis in ASNase-treated malignant B cells. Given that ROS accumulation can induce single-strand or double-strand DNA breaks, we sought to determine if ASNase-induced oxidative stress triggers DNA damage.

Asparagine-restricted Eµ-Myc cells exhibited increased phosphorylation of histone H2AX at Ser139 (H2AX), along with activation of the ATR-dependent DNA damage response (DDR) pathway (Figures 6a-d). This was evidenced by phosphorylation of ATR at Ser428 and its downstream substrates Chk1 at Ser345 and RPA32 at Ser33, in vitro and in vivo (Figures 6b, c and S7a, b), indicating the presence of DNA damage. In contrast, the ATM-dependent DDR was not activated, as KAP1, a downstream substrate of ATM, was not phosphorylated at Ser824 in Asn-restricted Eµ-Myc cells in vitro (Figures 6b,c). Scavenging ROS with NAC reduced H2AX levels, confirming that oxidative stress contributes to ASNase-induced DNA damage (Figures 6e, f). Conversely, PHGDH inhibition modestly increased H2AX in ASNase-treated cells (Figures 6g, h). Consistently, PHGDH overexpression in Eµ-Myc cells (Figures S4l, m) significantly attenuated H2AX expression levels and activation of the ATR-dependent DDR during ASNase therapy in vivo (Figures 6i, j). This supports a protective role for de novo serine biosynthesis in preserving genome stability.

Following an ATR-dependent DNA damage response, the chromatin-associating enzymes poly (ADP-ribose) polymerase 1 (PARP1) and PARP2 catalyze the synthesis and transfer of ADP-ribose polymer (PARs) from nicotinamide adenine dinucleotide (NAD+) onto their serine, tyrosine, and glutamate residues (auto-PARylation), activating them. In turn, activated PARPs enables the PARylation of target proteins, promoting their recruitment to DNA breaks for effective DNA repair. We aimed to investigate whether PARP was activated in ASNase-treated Eµ-Myc cells, as this could represent a therapeutically exploitable tumor vulnerability using clinically approved PARP1/2 (PARP) inhibitors. We observed increased expression of proteins harboring poly(ADP-ribose) motifs, indicating elevated PARP activity in asparagine-restricted Eµ-Myc cells (Figure 7a). PARP inhibitors, such as Olaparib, compete with NAD+ for the catalytically active site of PARP, thereby preventing the initiation of DNA repair mechanisms. To interfere with DNA repair in ASNase-treated cells, we sought to test the effects of ASNase/Olaparib co-treatment in Eµ-Myc cells, in in vitro and in vivo settings. This therapeutic combination enhanced caspase-dependent cell death in two OxPhos-dependent Eµ-Myc lines (#506 and #688), surpassing the effect of either agent alone (Figures 7b-d). When co-treated with Olaparib (0.3 µM), ASNase-treated Eµ-Myc cells did not resume proliferation in vitro (Figure 7e, f). In vivo, co-treatment with ASNase (every 48 hours) and daily Olaparib significantly delayed weight loss and BCL onset in C57BL/6 mice, compared to monotherapies (Figures 7g-i). ASNase/Olaparib co-treatment antineoplastic effect was confirmed in a human colon cancer cell line grown in 3D (Figures S8a, b).

Overall, these results demonstrate that ASNase-induced oxidative stress creates a vulnerability to the PARP inhibitor Olaparib in malignant B cells, and that combining ASNase with Olaparib holds potential as an effective anti-cancer strategy.

Methods
Mice
C57BL/6 Eµ-Myc transgenic mice (Eµ-MycTg/+) were purchased from the Jackson Laboratory (002728) and are bred and maintained at our local animal facility (C3M, INSERM U 1065, Nice, France, B 06-088-20). Wild-type (WT) C57BL/6JOlaHsd female were purchased from Envigo and housed at our local animal facility. All mice were maintained in specific pathogen-free conditions and kept in the 12h light/night cycles at 20-24°C and 50-55% of humidity. For all mice experiment, food (SAFE R04, Safe Lab) was given ad libitum. All animal care procedures, experiments and maintenance complied with the protocols (n° APAFIS#25228-2020032917261458 v9) approved by the Institutional Animal Care and Use Committee (IACUC of University Nice, Côte d’Azur) and the Ministère de la Recherche et de l’Innovation.

Cell lines
Primary Eµ-Myc cells were isolated from individual male or female Eμ-MycTg/+ mice as previously described and characterized 8, 61. In particular, the OxPhos-dependent Eμ-Myc #506 and Eμ-Myc #688 cells were derived from B-cell lymphoma of Eμ -MycTg/+ females #506 and #688, respectively. These cells were maintained in DMEM-GlutaMAX medium (31966047, Thermo Fisher Scientific) supplemented with 10% of fetal bovine serum (FBS) (F7524, Sigma), 50 µM of 2-Mercaptoethanol (31350010, Thermo Fisher Scientific), 0.37 mM of L-asparagine (A0884, Sigma), 10 mM of HEPES pH 7.4 (15630056, Thermo Fisher Scientific) and 100 U/ml of Penicillin-Streptomycin (15140122, Thermo Fisher Scientific). 293T cells (CRL-1573, ATCC) were maintained in DMEM-GlutaMAX media (31966047, Thermo Fisher Scientific) supplemented with 10% FBS and 100 U/ml of Penicillin-Streptomycin and used to produce VSV-G retroviral vectors. The murine Lewis Lung carcinoma (LLC) cell line (CRL1642, ATCC) was maintained in DMEM-GlutaMAX medium (31966047, Thermo Fisher Scientific) supplemented with 10% FBS and 100 U/ml of Penicillin-Streptomycin. The murine colon carcinoma cell line CT26 (CRL-2638, ATCC) was maintained in RPMI-GlutaMAX medium (61870044, Thermo Fisher Scientific) supplemented with 10% FBS, 100 U/ml of Penicillin-Streptomycin and 1 mM of sodium pyruvate (11360070, Thermo Fisher Scientific). The murine colon adenocarcinoma cell line MC38 (SCC172, Sigma) was maintained in DMEM-F12-GlutamaX medium (31331028, Thermo Fisher Scientific) supplemented with 10% FBS, 1 mM of sodium pyruvate and 100 U/mL of Penicillin-Streptomycin. The human colon carcinoma cell line HCT116 (CVCL_0291) was obtained from ATCC, routinely cultured in McCoy medium (16600082, Thermo Fisher Scientific), supplemented with 10% FBS and 100 U/ml of penicillin/streptomycin. All cell lines used in this study are mycoplasma-free and routinely tested for mycoplasma.

Media for in vitro studies
Asparagine-free medium refers to DMEM high D-glucose (4.5 g/L), no L-glutamine, no pyruvate medium (11960044, Thermo Fisher Scientific), supplemented with 10% FBS (F7524, Sigma), 3 to 50 µM of 2-Mercaptoethanol, 10 mM of HEPES pH 7.4, 100 U/ml of Penicillin-Streptomycin, 1 mM of sodium pyruvate (11360070, Thermo Fisher Scientific), 2 mM of L-glutamine (25030081, Thermo Fisher Scientific). This medium contains supraphysiological concentration of serine and glycine.

Asparagine-containing medium refers to the asparagine-free medium described above, supplemented with L-asparagine (0.37 mM).

Asparagine, serine, glycine-free medium refers to MEM no L-glutamine medium (21090022, Thermo Fisher Scientific), supplemented with 10% FBS (F7524, Sigma) dialyzed (132724, Spectra/Por®3 Dialysis Membrane Standard RC Tubing, MWCO 3.5 kDa), 50 µM of 2-Mercaptoethanol, 10 mM of HEPES pH 7.4, 100 U/ml of Penicillin-Streptomycin, 20 mM of D-glucose (J60067, Thermo Fisher Scientific), 1 mM of sodium pyruvate, 2 mM of L-glutamine, 1% of vitamins cocktail (11120052, Gibco).

Glucose and asparagine-free medium refers to DMEM no L-glutamine, no pyruvate medium (A14430-01, Thermo Fisher Scientific), supplemented with 10% FBS (F7524, Sigma), 50 µM of 2-Mercaptoethanol, 10 mM of HEPES pH 7.4, 100 U/ml of Penicillin-Streptomycin, 1 mM of sodium pyruvate, 2 mM of L-glutamine. This medium contains supraphysiological concentration of serine and glycine.

ASNase formulations
For all in vitro experiments, the native Escherichia-coli L-asparaginase II (ASNase) (Kidrolase®, Jazz Pharmaceutical) was used. For in vivo studies presented in figures 1, 2, 4, S3 and S5, we used Kidrolase® and for in vivo studies in figures 3, 6, 7 and S3, we used the native Escherichia-coli L-asparaginase II recombinant protein (Spectrila®).

In vivo transfer of Eµ-Myc cells and survival experiments
Mouse primary Eµ-Myc #506 or #688 cells were transferred via intravenous (I.V) injection (100.000 live cells per recipient mouse) into syngeneic, nontransgenic, 6-weeks-old C57BL/6JOlaHsd females (Envigo), which are less prone to fighting compared to males. Seven days later, mice were treated every 48 hours via intraperitoneal (I.P) injection with Vehicle (NaCl 0.9%) or 2500 IU/kg of the native (Kidrolase®, Jazz Pharmaceutical) or recombinant (Spectrila®) E-coli L-asparaginase (ASNase) until lymphoma reached the ethical limit. ASNase is diluted in NaCl 0.9%. Tumor onset was assessed by palpation of the inguinal lymph node. The ethical limit (disease endpoint) was defined using a disease-associated clinical score approved by the Institutional Animal Care and Use Committee (IACUC of University Nice, Côte d’Azur) and the Ministère de la Recherche et de l’Innovation (n° APAFIS#25228-2020032917261458 v9). Once inguinal tumor onset was detected, disease-associated clinical scores were recorded daily for each mouse. When the maximal score was reached, Vehicle- and ASNase-treated mice received a final injection of Vehicle or ASNase, respectively and were sacrificed three to four hours later by cervical dislocation. Blood was then collected through cardiac punction, placed in lithium heparinate coated tubes (BD Microtainer), centrifuged 6000g for 2 min to isolate the plasma. The plasma samples were collected, snap-frozen and stored at -80°C for metabolomic analysis. Mice survival defined as survival probability (%) was determined as the time between the I.V injection of Eµ-Myc cells and the time when mice had to be sacrificed for ethical reasons when lymphomas progression reached the endpoint. Tumor-free mice (%) was determined as the time between the I.V injection of Eµ-Myc cells and the time when one inguinal lymphoma was palpable.

For the second in vivo therapeutic challenge, syngeneic, nontransgenic, 6-weeks-old C57BL/6JOlaHsd females (Envigo) were intravenously (I.V) injected with 100.000 live Eµ-Myc cells per recipient mouse. Eµ-Myc cells used were harvested from BCL obtained after the 1st in vivo therapeutic challenge. Seven days later, mice were treated with Vehicle or ASNase at same concentration, frequency, and administration route as mentioned above.

For in vivo co-treatment with ASNase and Olaparib, syngeneic, nontransgenic, 6-weeks-old C57BL/6JOlaHsd females (Envigo) were intravenously injected with 100.000 live Eµ-Myc cells per recipient mouse. Seven days later, mice were treated with either Vehicle or 50 mg/kg of Olaparib (daily I.P) or 2500 IU/kg of Spectrila (I.P every 48 hours) or the combination of both drugs (daily I.P Olaparib 50 mg/kg and Spectrila, 2500 IU/kg, I.P every 48 hours). Olaparib (HY-10162, MCE) was dissolved in DMSO at a concentration of 100 mg/ml and then diluted at 5 mg/ml in 20% sulfobutylether-β-cyclodextrin solution (HY-17031, MCE, diluted in NaCl 0.9%). Vehicle consists in daily I.P injections of a 5% DMSO/95% sulfobutylether-β-cyclodextrin (in 20% in NaCl 0.9%) solution and/or I.P of NaCl 0.9% every 48 hours.

Survival functions were estimated using the Kaplan-Meier method and compared with the log-rank test.

RNA extraction, and Real-time Quantitative PCR
Total RNA was extracted from Eµ-Myc cells using the RNeasy mini kit (74104, Qiagen) according to manufacturer’s instructions. Reverse transcription was performed from 2 µg of RNA using the Omniscript RT kit (205113, Qiagen). Relative mRNA levels were obtained by real-time quantitative PCR (qPCR) on the StepOne™ Real-Time PCR System (Thermo Fisher Scientific) using the TaqMan assay primer set (Thermo Fisher Scientific) for murine Phgdh (Mm01623589_g1), Psat1 (Mm01613328_g1), Psph (Mm01197775_m1), Gls (Mm01257297_m1), Gclc (Mm00802658_m1), Gclm (Mm01324400_m1), Nqo1(Mm01253561_m1), slc7a11 (Mm00442530_m1), slc6a9 (Mm00433662_m1), Asns (Mm01137310_g1), and the TaqMan Universal PCR Master Mix (4304437, Fisher Scientific) according to manufacturer’s instructions. The qPCR was performed from 8 ng of cDNA, except for slc7a11 amplification for which we used 100 ng of cDNA. Relative mRNA levels were normalized to mouse Rplp0 (Mm00725448_s1, Thermo Fisher Scientific) and to the control condition (+ Asn, 0.37 mM).

RNAseq data
RNA sequencing (RNA-seq) alignment files and clinical information from the EGAS00001002606 study 42 were downloaded from the European Genome-phenome Archive. Quantification of genes was performed using featureCounts v1.5.0-p3 based on Ensembl GTF release 75 annotations. RNA-seq data were then normalized using the Bioconductor package DESeq2. Eighteen (out of 775) samples showing no expression of Rplp0 were removed from the analysis.

RNA-seq data on human samples (solid cancers and hematological cancers) were retrieved and downloaded from The Cancer Genome Atlas program (TCGA, https://portal.gdc.cancer.gov/) through the Gene Expression Profiling Interactive Analysis website (http://gepia.cancer-pku.cn). The levels of slc7a11 mRNA were represented in transcript per million (TPM). HNSC, Head and Neck squamous cell carcinoma; LUSC, Lung squamous cell carcinoma; LUAD, Lung adenocarcinoma; LGG, Brain Lower Grade Glioma; GBM, Glioblastoma multiforme; STAD, Stomach adenocarcinoma; SKCM, Skin Cutaneous Melanoma; COAD, colon adenocarcinoma; DLBCL, Diffuse Large B-cell Lymphoma; LAML, Acute Myeloid Leukemias.

The RNA sequencing from Eµ-Myc cells cultivated in vitro and from Eµ-Myc cell-derived BCL treated with Vehicle and ASNase in vivo, was performed on Illumina NextSeq500 at the IPMC functional genomics platform (France). Libraries were generated using Truseq Stranded mRNA kit (Illumina), from 1µg of total RNA extracted from different Eµ-Myc cell lines isolated from 12 individual transgenic Eµ-MycTg/+ mice and from B-cell lymphomas harvested from mice engrafted with Eµ-Myc #506 cells, and treated Vehicle or ASNase for several weeks. Libraries were then quantified with Qubit dsDNA High Sensitivity Assay Kit (Invitrogen) and pooled. 4nM of this pool were loaded on a NextSeq 500 High output Flowcell and sequenced on a NextSeq 500 platform (Illumina) with 2 × 75bp paired-end chemistry. Reads were mapped with STAR 2.4.0a on mouse genome (build mm10). Counting was performed with featureCounts (subread-1.5.0-p3-Linux-x86_64, “—primary -g gene_name -p -s 1 -C” options) using Ensembl GRCm38.83 release. Raw counts were transformed into fpkm (fragments per kilobase of transcript per million mapped reads) values to normalize for both sequencing depth and gene length. Expressions values are shown as log 2 (fpkm).

DNA constructs
To amplify mouse Asns or Phgdh cDNA, total RNA was extracted from Eµ-Myc cells and reverse transcription was performed as mentioned above. The murine Phgdh coding sequence (without stop codon) was amplified by PCR using a recombinant Taq DNA polymerase (10342020, Thermo Fisher Scientific) according to manufacturer’s instructions and with the following primers: forward 5'-GCAATGGCCTTCGCAAATCTGC-3' and reverse 5'-GGAGCCAGCCCAGGCGT-3'. The PCR product was cloned into the pCR™ 2.1-TOPO® TA vector using the TOPO™ TA Cloning™ Kit (K455040, Thermo Fisher Scientific) according to manufacturer’s instructions. The resulting plasmid TOPO-Phgdh was amplified in One Shot TOP10 chemically competent E.Coli (C404003, Thermo Fisher Scientific) with ampicillin (A9518, Sigma) selection, purified (740414.50, Macherey-Nagel) and sequenced to confirm the cloning of the murine wild-type Phgdh coding sequence. Once confirmed, Phgdh coding sequence tagged to V5 was subcloned from the TOPO-Phgdh vector into the retroviral GFP-encoded pMIG vector (9044, Addgene), by PCR with the following primers: forward 5'-GAGAGTCGACATGGCCTTCGCAAATCTGC-3’ and reverse 5'-GGTTAACTCACGTAGAATCGAGACCGAGGAGAGGGTTAGGGATAGGCTTACCGAAGCAGAACTGGAAAGCCTCCAATAC-3’. Then, the amplicon was digested using SalI (R3136S, New England Biolabs) and HpaI (R0105S, New England Biolabs) restriction enzymes. The pMIG vector was linearized using XhoI (R0146S, New England Biolabs) and HpaI restrictions enzymes to generate compatible cohesive ends. Digested pMIG vector was dephosphorylated with Antarctic phosphatase (MO289S, Biolabs). The following double-strand DNA fragment which contains the coding sequence of the murine slc7a11 with BglII and EcoRI restriction site was synthesized (Thermo Fisher Scientific): “GGGGAGATCTATGGTCAGAAAGCCAGTTGTGGCCACCATCTCCAAAGGAGGTTACCTGCAGGGCAATATGAGCGGGAGGCTGCCCTCCATGGGGGACCAAGAGCCACCTGGGCAGGAGAAGGTAGTTCTGAAAAAGAAGATCACTTTGCTGAGGGGGGTCTCCATCATCATCGGCACCGTCATCGGATCAGGCATCTTCATCTCCCCCAAGGGCATACTCCAGAACACGGGCAGCGTGGGCATGTCCCTGGTTTTCTGGTCTGCCTGTGGAGTACTGTCACTTTTTGGAGCCCTGTCCTATGCAGAATTAGGTACAAGCATAAAGAAATCTGGTGGTCATTACACATACATTCTGGAGGTCTTTGGTCCTTTGCTGGCTTTTGTTCGAGTCTGGGTGGAACTGCTCGTAATACGCCCTGGAGCTACTGCTGTGATATCCCTGGCATTTGGACGCTACATCCTGGAACCATTTTTTATTCAATGTGAAATTCCTGAACTTGCAATCAAGCTCGTGACAGCTGTGGGCATCACTGTGGTGATGGTCCTAAATAGCACGAGTGTCAGCTGGAGTGCCCGGATCCAGATTTTCCTAACCTTTTGCAAGCTCACAGCAATTCTGATAATTATAGTCCCTGGAGTTATACAGCTAATTAAAGGGCAAACACATCACTTTAAAGATGCATTTTCAGGAAGAGACACAAGTCTAATGGGGTTGCCCTTGGCTTTTTATTATGGGATGTATGCATATGCTGGCTGGTTTTACCTCAACTTTATTACTGAAGAAGTAGACAACCCTGAAAAAACCATCCCCCTTGCAATCTGCATCTCCATGGCTATCATCACAGTGGGCTACGTACTGACAAACGTGGCCTATTTTACCACCATCAGTGCGGAGGAGCTGCTGCAGTCCAGCGCCGTGGCGGTGACCTTCTCTGAGCGGCTGCTGGGAAAATTCTCATTAGCAGTCCCGATCTTTGTTGCCCTCTCCTGCTTCGGCTCCATGAACGGTGGTGTGTTCGCTGTCTCCAGGTTATTCTACGTCGCATCTCGAGAAGGGCACCTTCCGGAAATCCTCTCTATGATTCATGTCCACAAGTCACACTCCTCTGCCAGCTGTTATTGTTTTGCATCCTCTGACGATGGTGATGCTCTTCTCCGGAGACCTCTATAGTCTTCTAAATTTCCTCAGTTTTGCCAGGTGGCTTTTTATGGGGCTGGCAGTCGCAGGACTGATTTATCTTCGATACAAACGCCCAGATATGCATCGTCCTTTCAAGGTGCCTCTCTTCATCCCGGCACTATTTTCCTTCACCTGCCTCTTCATGGTTGTCCTCTCTCTTTACTCGGACCCATTCAGCACCGGGGTCGGTTTTCTTATCACCTTGACTGGGGTCCCTGCATATTATCTCTTCATTGTATGGGACAAGAAACCCAAGTGGTTCAGACGATTATCAGACAGAATAACCAGAACATTACAGATTATACTAGAAGTTGTACCAGAAGACTCTAAAGAATTATGAGAATTCGGGG “. This sequence was then cloned in the pMIG vector using EcoR1 (RO101S, New England Biolabs) and BglII (RO143, New England Biolabs) restriction enzymes.

The shRNA targeting mouse Phgdh were obtained using the oligonucleotide sequences forward 5’-GATCCCCGTGGAGAAGCAGAACTTGATTCAAGAGATCAAGTTCTGCTTCTCCACTTTTTA-3’, reverse 5’-AGCTTAAAAAGTGGAGAAGCAGAACTTGATCTCTTGAATCAAGTTCTGCTTCTCCACGGG-3 (shPhgdh #1) and forward 5’-GATCCCCGGAGGAGCTGATAGCTGAATTCAAGAGATTCAGCTATCAGCTCCTCCTTTTTA-3’, reverse 5’-AGCTTAAAAAGGAGGAGCTGATAGCTGAATCTCTTGAATTCAGCTATCAGCTCCTCCGGG-3’ (shPhgdh #2). As a control, shRNA targeting firefly luciferase were obtained using the oligonucleotides sequences forward 5’-GCGTTATTTATCGGAGTTG-3’ and reverse 5’-CAACTCCGATAAATAACGC-3’ (shSCR). 3 µg of corresponding forward and reverse oligonucleotides were annealed and cloned into the retroviral GFP-encoded pSUPER vector (VEC-PRT-0006, Oligoengine) linearized using HindIII (RO104S, New England Biolabs) and BglII restrictions enzymes according to the manufacture’s instruction.

Upon gel purification (Monarch DNA Gel Extraction Kit, T10205, New England Biolabs), ligation was performed using the T4 DNA ligase (MO202S, Biolabs) with a ratio vector:insert of 1:4. The generated vectors were amplified in MAX Efficiency Stbl2 competent cells (10268019, Thermo Fisher Scientific) with ampicillin selection, purified and sequenced.

Generation of stable genetically modified Eµ-Myc cells
Self-inactivating retroviruses were generated following transient co-transfection of 293T cells (CRL-1573, ATCC) with 8.6 µg of one of the following vectors, pMIG empty vector (CTL), pMIG-Asns-v5 (ASNS OE), pMIG-Phgdh-v5 (PHGDH OE), or pMIG-slc7a11 (SLC7A11 OE), pSUPER-shSCR or pSUPER-shPhgdh (#equence #1 or #2), 3 µg of envelope plasmid phCMV-VSV-G and 8.6 µg of MLV-Gag-Pol, using the classical calcium phosphate method. After 15 hours, transfected 293T cells were incubated in Opti-MEM reduced serum medium (11058021, Thermo Fisher Scientific) for 32h. Cell supernatant was centrifuged 3000g at 4°C overnight to concentrate retroviral particles prior titration. Eµ-Myc cells were then transduced with the resulting retroviruses using a multiplicity of infection (MOI) of 10. 72 hours following transduction, GFP+ Eµ-Myc cells were sorted (SH800S Cell Sorter, Sony).

Determination of glutamine and glutamate concentrations in medium
The native E-Coli L-asparaginase (ASNase, Kidrolase) was incubated in asparagine-containing medium in the absence of cells, at indicated concentrations and at 37°C, 5% CO2. The concentrations of glutamine and glutamate (mM) in the medium were electro-enzymatically determined overtime, using the YSI 2950 Biochemistry Analyzer (Yellow Springs Instruments).

Western blot analysis
Eµ-Myc cells isolated ex-vivo from inguinal B-cell lymphomas or cultivated in vitro were washed in PBS and lysed in laemmli buffer to extract total proteins. Frozen axillary B-cell lymphomas were homogenized using a stainless-steel tissue grinder (1292, BioSpec Products) and proteins were extracted using the laemmli lysis buffer. After protein quantification (23225, Pierce BCA Protein Assay kit, Thermo Fisher Scientific), 15 to 30 µg of whole-cell protein lysates were separated on 8% to 15% SDS polyacrylamide gels and transferred onto polyvinylidene difluoride membranes (Millipore). Membranes were then blotted with antibodies against ASNS (14681-1-AP, Proteintech, RRID: AB_2060119, dilution 1/2000), PHGDH (14719-1-AP, Proteintech, RRID: AB_2283938, dilution 1/1000), PSAT1 (10501-1-AP, Proteintech, RRID: AB_2172597, dilution 1/1000), PSPH (14513-1-AP, Proteintech, RRID: AB_2171464, dilution 1/1000), NRF2 (12721, Cell Signaling, RRID: AB_2715528, dilution 1/1000), GLUL (11037-2-AP, Proteintech, RRID: AB_2110650, dilution 1/1000), GOT1 (14886-1-AP, Proteintech, RRID: AB_2113630, dilution 1/1000), H2AX (ab81299, Abcam, RRID: AB_1640564, dilution 1/2000), ATF4 (11815, Cell Signaling, RRID: AB_2616025, dilution 1/500) for figures 2h, 4d and S5c, ATF4 (81798-2-RR, Proteintech; RRID: AB_3670506, dilution 1/500) for figures 2d, 5c, d, 7a, S2c and S7a, xCT/SLC7A11 (98051, Cell Signaling, RRID: AB_2800296, dilution 1/1000), Poly/Mono-ADP Ribose (89190, Cell Signaling, dilution 1/1000); Phospho-ATR (Ser 428) (2853, Cell Signaling, RRID: AB_2290281, dilution 1/500), ATR (13934, Cell Signaling, RRID: AB_2798347, dilution 1/1 000), Phospho-Chk1 (Ser345) (2348, Cell Signaling, RRID: AB_331212, dilution 1/1000), Chk1 (2360, Cell Signaling, RRID: AB_2080320, dilution 1/1000), Phospho-RPA32 /RPA2 (Ser33) (10148, Cell Signaling, RRID: AB_3099645, dilution 1/1000), RPA32/RPA2 (52448, Cell Signaling, RRID: AB_2750889, dilution 1/1000), Phospho-KAP1 (Ser824) (A300-767A, Thermo Fisher Scientific, RRID: AB_669740, dilution 1/1000), KAP1 (4123, Cell Signaling, RRID: 2256670, dilution 1/1000); V5 (R96025, Life Technologies, RRID: AB_159313, dilution 1/5000) and ERK2 (sc-1647, Santa Cruz, RRID: AB_627547, dilution 1/2000). Immunoreactive bands were detected with anti-mouse (7076, Cell Signaling Technology, RRID: AB_330924, dilution 1/3000) or anti-rabbit (7074S, Cell Signaling Technology, RRID: AB_2099233, dilution 1/3000) IgG horseradish peroxidase (HRP)-linked antibodies. Immunoblots were visualized (FUJIFILM LAS4000) by chemiluminescence using Pierce ECL Western Blotting substrates (32106, Pierce ECL, Thermo Fisher Scientific). ERK2 was used as a loading control in all immunoblotting experiments. When indicated, quantification of the level of indicated proteins was performed using ImageJ software. Relative quantification was obtained by normalizing data to ERK2 levels and to indicated control conditions. Whole-cell lysate obtained from Eµ-Myc cells treated with 1µg/ml of etoposide (HY-13629, MCE) for 3 hours or stimulated with H2O2 (0.5 mM) for 5 minutes were used as a positive control for H2AX expression and PARylation, respectively.

PHGDH activity
For in vitro samples, live Eµ-Myc cells were lysed for 10 minutes on ice, in buffer A, containing 20 mM of Tris pH 7.4, 5 mM of EDTA pH 8.0, 150 mM of NaCl, 0.5 % of NP40 (UN3500, EUROMEDEX) and 0.01 % of cocktail of protease and phosphatase inhibitors (1861280, Thermo Fisher Scientific). For ex-vivo samples, frozen B-cell lymphomas were homogenized using a stainless-steel tissue grinder (1292, BioSpec Products) and proteins were extracted in buffer A. Then, 10 to 30 µg of proteins were incubated in PHGDH activity buffer containing 51.5 mM of Tris pH 8.0, 10 mM of MgCl2, 0.05 % of BSA (P06-1403500, Panbiotech), 0.01 % of Tween 20 (2001-B, Euromedex), 1.25 mM of L-glutamate (49449, Sigma), 0.3 mM of NAD+ (N1511, Sigma) and 0.1 mM of 3-phosphoglycerate (HY-141S412, MedChemExpress) in a black 96-well plate (655079, Greiner). PHGDH activity was measured on a fluoroscan (Synergy H1, Biotek) at 460 nm after excitation at 355 nm as the increase in fluorescence related to NADH accumulation. PHGH activity is determined as the delta absorbance, representing the speed of NADH production. Data are normalized to milligrams of protein and expressed as mean ± SD of the percentage relative to the control condition (+Asn, in vitro or Vehicle, in vivo).

L-[3H(G)]-Serine uptake
A total of 250.000 live Eµ-Myc cells/ml were seeded in asparagine, serine, glycine-free medium supplemented with 0.4 mM of L-serine (S4311, Sigma), 0.4 mM of L-glycine (G8790, Sigma), and with or without 0.37 mM of L-asparagine (A0884, Sigma) and 0.003 IU/ml of E-coli L-asparaginase (Kidrolase) for 24 hours at 37°C, 5% CO2. The number of live cells was determined by 4’,6-diamidino-2-phenylindole (DAPI, D9642, Sigma) (0.5 µg/ml) staining and flow cytometry analysis (Miltenyi Biotech). Then, 1x106 of live Eμ-Myc cells (DAPI negative) were washed twice with PBS and incubated in a 12-well plate in 1mL of pre-warmed asparagine, serine, glycine-free medium supplemented as described above, with L-serine being replaced by 1 μCi/ml or 0,4 μCi/ml of L-[3H(G)]-Serine (NET248250UC, PerkinElmer), for 15 minutes or 3 hours at 37°C, 5% CO2, respectively. Subsequently, cells were washed twice with pre-chilled PBS and lysed with 120 μl of 0.1N NaOH and mixed with 4 ml of Ultima Gold (6013321, PerkinElmer). Radioactivity (count per minute, cpm) was measured using a β-scintillation counter (Tri-Carb 2810 TR, Perkin Elmer). L-[3H(G)]-serine transport rate was expressed as mean ± SD of cpm/106 cells.

Cell death assay
A total of 250.000 live Eµ-Myc cells/ml were treated with either DMSO (0.075%), or E-Coli L-asparaginase (0.003 IU/ml), or indicated concentration of PHGDH inhibitor BI-4916 (HY-126253, MCE), BI-4924 (HY-126254, MCE), NCT-503 (HY-101966, MCE), CBR-5884 (HY-100012, MCE), PKUMDL-WQ2101 (HY-123269, MCE), or Olaparib (AZD2281, HY-10162, MCE), or the combination of indicated drugs, in asparagine-free medium supplemented or not with 0.37 mM of L-asparagine (A0884, Sigma) at 37°C, 5% CO2 for the indicated time. For quenching ROS, 5 mM of N-acetyl-L-cysteine (A9165, Sigma) was added simultaneously. For inhibition of caspases, 20 µM of the pan-caspase inhibitor qVD-OPh (A1901, APExBIO) was added simultaneously. Following 24 or 48 hours of treatment, cells were labeled with 0.5 µg/ml of DAPI (D9642, Sigma) and immediately analyzed by flow cytometry (Miltenyi Biotec). Data represents the percentage of dead cells (DAPI+) and are expressed as mean ± SD.

Cell proliferation assay
A total of 250.000 live Eµ-Myc cells/ml were treated with either DMSO (0.075%), or 0.003 IU/ml of E-Coli L-asparaginase, or 10 µM of BI-4916 (HY-126253, MCE), or 60 µM of BI-4924 (HY-126254, MCE), or 0.3 µM of Olaparib (AZD2281, HY-10162, MCE), or the combination of indicated drugs, in asparagine-free medium supplemented or not with 0.37 mM of L-asparagine, at 37°C, 5% CO2 for the indicated period. For cell proliferation in asparagine, serine, glycine-free medium, a total of 250.000 live Eµ-Myc cells/ml were seeded in the presence or absence of L-asparagine (0.37 mM), and/or L-serine (0.4 mM)/L-glycine (0.4 mM) at 37°C, 5% CO2 for the indicated period.

Cells were diluted in the medium of the corresponding condition when they reached 1.106 cells/ml. The number of live cells was determined overtime by DAPI (0.5 µg/ml) staining and flow cytometry analysis (Miltenyi Biotec). Data are expressed as the mean number of live cells (DAPI-) ± SD or ± SEM.

Measurement of reactive oxygen species
A total of 250.000 live Eμ-Myc cells per ml were treated for 15 or 20 hours (as indicated in the legends) with either DMSO (0.075%), or 0.003 IU/ml of E-Coli L-asparaginase, or 10 µM of BI-4916 (HY-126253, MCE), or the combination of both drugs, in asparagine-free medium supplemented or not with L-asparagine (0.37 mM) at 37°C, 5% CO2. Cells were then counted by flow cytometry following DAPI staining (0.5 µg/ml) and 1x106 live cells (DAPI negative) were seeded in 2 ml of their respective medium in 12-well plates and treated or not with 10 mM of N-acetyl-L-cysteine (A9165, Sigma) 1-hour prior addition of 2,5 μM of CellROX green probe (C10444, Invitrogen) for additional 30 minutes incubation at 37°C, 5% CO2. Cells were then collected and washed once with PBS, resuspended in 200uL of PBS, labelled with 0.5 µg/ml of DAPI (D9642, Sigma) and immediately analyzed by flow cytometry (Miltenyi Biotec). CellROX green is a cell-permeable and non-fluorescent reagent when in a reduced state. Upon oxidation, CellROX green exhibits a strong fluorogenic signal (excitation: 485 nm; emission: 520 nm) that is localized primarily in the nucleus and in the mitochondria, providing a reliable measure of reactive oxygen species (ROS) in live cells. Data are represented as the Median Fluorescence intensity (MFI) of the probe detected in live (DAPI-) cells, normalized to control condition (+Asn) and expressed as mean ± SD.

3D cell culture of HCT116 cells
HCT116 spheroids were prepared by seeding 3000 cells/well in 96-well ultra-low attachment plates (650970, Greiner Bio-One). After 72 hours, the indicated treatments were applied. Cell viability was assessed by adding propidium iodide (final concentration 0.3 mg/L) to each well at the endpoint. After a 30-minute incubation, brightfield and fluorescence images were captured using a fluorescence microscope. The images were then analyzed using the ImageJ software (National Institutes of Health) by calculating the corrected total cell fluorescence (CTCF) using the formula CTCF = integrated density – (area of fluorescent object × mean fluorescence of background readings). The images were artificially colored and cropped for publication.

In vitro stable isotope tracing
For -15N-L-glutamine tracing experiment, 300.000 live Eµ-Myc cells/ml were incubated in asparagine-free medium supplemented with 2 mM of -15N-L-glutamine (NLM-1016-PK, Cambridge Isotope Laboratories) instead of unlabeled L-glutamine, and with or without 0.37 mM of L-asparagine (A0884, Sigma), at 37°C, 5% CO2, for 24 hours.

For 13C -Glycine tracing experiment, 300.000 live Eµ-Myc cells/ml were incubated in asparagine, serine, glycine-free medium supplemented with 0.4 mM of L-serine (S4311, Sigma), 0.4 mM of 13C -Glycine (CLM-1017, Cambridge Isotope Laboratories), and with or without 0.37 mM of L-asparagine, at 37°C, 5% CO2, for 24 hours.

For [U-13C]-glucose tracing experiment, 300.000 live Eµ-Myc cells/ml were incubated in glucose and asparagine-free medium supplemented with 25 mM of [U-13C]-glucose (CML-1396, Cambridge Isotope Laboratories) and with or without L-asparagine (0.37 mM) and E-Coli L-asparaginase (0.003 IU/ml), at 37°C, 5% CO2, for 24 hours.

At endpoint, Eµ-Myc cells were analyzed by flow cytometry to determine the number of live cells (DAPI-) per mL. Then, 1·106 live Eµ-Myc cells per replicate were collected, washed twice in pre-chilled PBS (300g, 5 min, 15°C centrifugation) and snap-frozen. Samples were stored at -80°C for further LC-MS-based metabolomic analyses. Results have been corrected for the presence of naturally occurring 15N or 13C stable isotopes using Metabolite AutoPlotter 62.

Ex-vivo ATP analysis
ATP was measured using the Cell Titer Glo kit (G7570, Promega). Briefly, 20,000 malignant B cells harvested from freshly isolated B-cell lymphomas were resuspended in 80 µL of DMEM medium supplemented with 10% FBS, 2 mM of L-Glutamine and distributed in a 96 well-plate. Cells were then treated in triplicates for one hour with either control (DMSO), or 10 mg/ml of oligomycin (O4876, Sigma-Aldrich) or 10 mM iodoacetate (I9148, Sigma-Aldrich), or the combination of both metabolic inhibitors to obtain the residual amount of ATP. Short time treatment (1hour) was chosen to avoid cell death upon inhibition of both metabolic pathways. 100 µl of Cell Titer Glo reaction mix was then added in each well for a final volume of 200 µl. Plates were analyzed for luminescence with a Luminoscan (Berthold Technologies). We verified that ATP measurements were in the linear range of the detection. The difference between total ATP production and the ATP produced under oligomycin treatment results in OxPhos ATP contribution. OxPhos ATP is represented by percentage of total ATP produced by the cells.

In vivo stable isotope tracing
When lymphoma development reached the ethical endpoint, mice received two consecutive intraperitoneal discrete boluses injections at 20 min interval of 1 g/kg of [U-13C]-Glucose (CLM-1396, Cambridge Isotope Laboratories) in sterile NaCl 0.9% (100 mg/ml). Blood was collected from the tail-vein 40 min after the first [U-13C]-Glucose bolus and plasma was isolated by centrifugation at 6000g for 2 min and snap-frozen. Mice were immediately sacrificed by cervical dislocation. All B-cell lymphomas were harvested and snap-frozen. All samples collected were stored at -80°C and axillary BCL were later used for LC-MS-based metabolomic, immunoblot and other indicated biochemical analyses. Results have been corrected for the presence of naturally occurring 13C stable isotopes using Metabolite AutoPlotter 62.

Steady state metabolomics in vitro
250.000 live Eµ-Myc cells/ml were treated with indicated drugs for 24 hours, in asparagine-containing medium or in asparagine, serine, glycine-free medium supplemented with or without L-asparagine (0.37 mM), L-serine (0.4 mM) and glycine (0.4 mM) at 37°C, 5% CO2. The number of live cells was determined overtime by DAPI (0.5 µg/ml) staining and flow cytometry analysis (Miltenyi Biotec). Then, 1·106 live cells (DAPI-) per replicate were collected, washed twice in pre-chilled PBS (300g, 5 min, 15°C centrifugation) and snap frozen. Samples were stored at -80°C for further LC-MS-based metabolomic analyses. The abundance of metabolite is represented as peak area. The GSSG/GSH ratio was calculated from total GSH and GSSG levels determined by LC-MS.

Targeted LC/MS metabolites analyses
For in vitro samples, frozen pellets of 1·106 Eµ-Myc cells were used for metabolite extraction. For lymphomas, frozen axillary B-cell lymphomas were homogenized using a stainless-steel tissue grinder (1292, BioSpec Products) and 10 mg of tissue were used for metabolite extraction. Extraction solution was composed of 50% methanol, 30% acetonitrile (ACN) and 20% water. The volume of the extraction solution was adjusted to the cell number or tissue weight (1 ml per 1million cells or 1 ml per 50 mg of tissues, respectively). Plasma was diluted 20 folds with the same extraction solvent. After addition of extraction solution, samples were vortexed for 5 min at 4 °C and centrifuged at 16,000g for 15 min at 4 °C. The supernatants were collected and stored at −80 °C until analysis.

Targeted metabolomic analyses of in vitro experiments were performed using a minimum of 3 biological replicates per condition, with the exact numbers of biological and technical replicates provided in the corresponding figure legends. Targeted metabolomic profiling of B-cell lymphomas grown in vivo included 6 to 12 individual samples per group. Stable isotope tracing in vivo was conducted using 4 independent mice per group. Plasma samples analysis included 20 samples per group. Appropriate controls included blank extraction controls, pooled QC samples, and reference standard.

LC/MS analyses were conducted on a QExactive Plus Orbitrap mass spectrometer equipped with an Ion Max source and a HESI II probe coupled to a Dionex UltiMate 3000 UPLC system (Thermo Fisher Scientific). External mass calibration was performed using a standard calibration mixture every seven days, as recommended by the manufacturer. The 5 µl samples were injected onto a ZIC-pHILIC column (150 mm × 2.1 mm; i.d. 5 µm) with a guard column (20 mm × 2.1 mm; i.d. 5 µm) (Millipore) for LC separation. Buffer A was 20 mM ammonium carbonate, 0.1% ammonium hydroxide (pH 9.2), and buffer B was ACN. The chromatographic gradient was run at a flow rate of 0.200 µl min−1 as follows: 0–20 min, linear gradient from 80% to 20% of buffer B; 20–20.5 min, linear gradient from 20% to 80% of buffer B; 20.5–28 min, 80% buffer B. The mass spectrometer was operated in full scan, polarity switching mode with the spray voltage set to 2.5 kV and the heated capillary held at 320 °C. The sheath gas flow was set to 20 units, the auxiliary gas flow to 5 units and the sweep gas flow to 0 unit. The metabolites were detected across a mass range of 75–1,000 m/z at a resolution of 35,000 (at 200 m/z) with the automatic gain control target at 106 and the maximum injection time at 250 ms. Lock masses were used to ensure mass accuracy below 5 ppm. Data were acquired with Thermo Xcalibur software (Thermo) and processed using Thermo TraceFinder 5.2 software. The peak areas of metabolites and isotopologues were determined, identified by the exact mass of each singly charged ion and by the known retention time on the HPLC column. For absolute quantification, the standard addition method was used 63. For Serine quantification in plasma, d-serine was added to pooled samples, and endogenous levels were subtracted from the spiked standards. Calibration curve was prepared in concentration range of 0.05-1.5 mM in a mixed sample to overcome matrix effects. Importantly, the preparation and processing of the calibration curve samples mirrored that of the experimental samples to ensure consistency.

Software, graphs, and illustrations
ImageJ 2.0 software was used for immunoblots and immunofluorescence relative quantification. All charts and statistics presented in this article were built using GraphPad Prism 9 software (RRID: SCR_002798). Flow cytometry data were analyzed using MacsQuantify 2.13.3 (Miltenyi Biotec). Volcano plots and MSEA analyses from targeted metabolomics were produced with MetaboAnalyst 5.0 (RRID: SCR_015539), and volcano plot colors were edited using Affinity designer 1.6.1 software (RRID: SCR_016952). Data from stable isotope tracing experiments were corrected for the naturally abundance of 15N or 13C stable isotopes using Metabolite AutoPlotter (https://mpietzke.shinyapps.io/AutoPlotter/)62. Mice illustrations are publicly available on https://pixabay.com/fr/vectors/souris-le-noir-animal-rongeur-297524/.

Statistics and reproducibility.
All graphical representations and statistical analyses were performed using GraphPad Prism 9 software and MetaboAnalyst 5.0 software. Data are presented as mean ± SD or ± SEM, as indicated in the figure legends. Statistical differences were assessed using the appropriated tests, including two-sided Student’s t-test, one-way or two-way ANOVA followed by Tukey’s or Dunnett’s multiple comparison tests and log-rank test. A P-value < 0.05 was considered statistically significant. No data were excluded from the analyses. For in vitro experiments, samples sizes were chosen according to the common practice of the described assays and are indicated by n or by the number of data points in the graphs. For survival experiments in mice engrafted with Eµ-Myc cells, our previous studies have shown statistical significance with a minimum of 6 mice/group 9, a number confirmed using the G*power software as sufficient to detect significant biological meaningful differences between two (or more) experimental groups. All in vivo experiments randomly allocated mice into experimental groups and disease endpoints were evaluated in a blinded manner. Metabolites multivariate statistical analysis was performed with the Metaboanalyst 5.0 software (https://www.metaboanalyst.ca/)64, 65. Metabolite relative concentration was normalized by median, and the data was auto-scaled (mean-centered and divided by the standard deviation of each variable) prior to analysis. After scaling the data, we performed principal component analysis. Univariate analysis was performed comparing metabolite levels between groups where metabolite differences were defined by the following fold change threshold 0.8 < Fold change (ASNase/Vehicle) > 1.25 (which corresponds to log transformed threshold -0.3219 < log2 (Fold change (ASNase/Vehicle)) > + 0.3219) and significance as raw P-value < 0.05 from the non-parametric Wilcoxon rank-sum test, assuming unequal variance. Volcano plots representation and Metabolite Set Enrichment Analysis (MSEA) were performed based on statistically significant discriminant metabolites and built using the Metaboanalyst software and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway computational tool. P-values for MSEA were from Fisher exact test 65. For clarity, some graphs display statistics only for the most relevant comparisons due to the large number of groups. For detailed individual p-values, please refer to the Source Data file.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, Anais et al generate two models of Eu-Myc B-cell lymphoma with in vivo acquired resistance to asparaginase, and apply metabolomics to investigate the biology of resistance. They find that resistant cases have increased serine and glycine levels, as well as increased expression of serine biosynthetic enzymes. They present evidence that increased intracellular serine biosynthesis promotes asparaginase resistance, including a particularly nice experiment with PHGDH overexpression in vivo. Overall, this is a nice story built on key strong pieces of evidence, however there are also several problems that need to be addressed, particularly 1) a confusing presentation of precisely how the authors propose that serine biosynthesis interacts with asparaginase resistance, with multiple different models proposed in different parts of the paper, and 2) an over-reliance on a single small molecule to inhibit serine biosynthesis, so most experiments arguing that serine biosynthesis is important could be explained by off-target drug toxicity. Several additional critiques/suggestions are also provided below.\nMajor critiques:\n1. Precisely how the authors propose that serine biosynthesis mediates asparaginase resistance is very difficult to follow throughout this paper, as the authors propose 3 unrelated models in different parts of this paper, none of which are definitively proven: i) that this connection is mediated by glutamate (discussed in the last paragraph of p. 9), ii) by the requirement of asparagine as an exchange factor for import of extracellular serine, and iii) by the requirement for serine for glycine and cysteine biosynthesis, which are both required for glutathione and redox homeostasis.\n1a. With regards to the first glutamate model, precisely what the authors are proposing here is difficult to understand and, if I understand this correctly, it is not a very convincing model. The idea (as I can best decipher) is that the asparaginase-induced increase in ASNS activity produces an excess of glutamate, and this glutamate has to be converted to alpha-ketoglutarate by serine biosynthesis (I presume this is to avoid glutamate toxicity, although this is not stated). The problems I see with this argument are that there are several other reactions that can convert glutamate to other useful molecules independent of serine metabolism (i.e., conversion to alpha-ketoglutarate by GDH, or to glutamine by GLUL), and to my knowledge, glutamate is not very toxic to cells lacking glutamate neuroreceptors. Unless the authors can show that i) glutamate accumulates to significantly higher levels in asparaginase-treated cells with low vs. high serine biosynthesis, and ii) that these glutamate levels are toxic to these cells, I don’t see this argument as compelling and I would recommend removing it from the paper to make the manuscript less confusing.\n1b. The second argument that loss of asparagine as an exchange factor for serine import is conceptually appealing, as it is logical that losing the ability to import serine would make cells dependent on intracellular serine biosynthesis. In principle, I would like to see an experiment knocking down the relevant transporter to test this directly, however my brief reading of the amino acid transport literature leads me to believe the relevant antiporter has not been definitively identified. If that is the case, it is not really possible to suggest a specific experiment here, and so I can’t hold the authors to this standard so I think it would be OK to keep the discussion of this possibility as it is.\n1c. The third model of serine -> glycine and cysteine being necessary to maintain a sufficient pool of glutathione for redox homeostasis is plausible, and that this effect is particularly important in these cells lacking expression of the slc7a11 cysteine transporter also makes sense. I would like to see the authors test two predictions to strengthen this model:\n- Is the cytotoxicity of asparaginase + the PGHDH inhibitor rescued by quenching ROS using NAC?\n- Does restoring expression of the cystine transporter slc7a11 also rescue toxicity?\nAs a general comment, while there may be multiple mechanisms that each explain part of the link between asparaginase and serine, only one needs to be definitively demonstrated in this paper. If there are multiple mechanisms that each partially explain this link, I would much prefer for each of these to have its own dedicated manuscript, and the focus in this manuscript could be on what the authors see as the one that is most important or most definitively supported by their data.\n2. This manuscript relies heavily on the toxicity of a small molecule PGHDH inhibitor to argue that serine biosynthesis is involved in this phenotype in multiple different figures, yet the possibility that the toxicity of this inhibitor is off-target is not ruled out. I have several critiques related to this:\n2a. The authors state that monotherapy with this inhibitor had “minimal effects on Eu-Myc cell viability” (line 231) citing Fig S3A, when this figure and Figure 3A show that this drug is killing 25-33% of cells with a highly significant P value. Monotherapy with this inhibitor is definitely toxic so this statement needs to be rephrased.\n2b. While the authors use what may be the best of the available PGHDH inhibitors, all experimental perturbations have off-target effects, and cell death is perhaps the most common off-target toxicity. It is important to strengthen the argument that the toxicity of this drug is on-target by testing i) whether genetic PGHDH knockdown using two different shRNA hairpins (or CRISPRi) phenocopies the drug effect, and ii) demonstrating that the toxicity of at least one hairpin can be rescued by restoring expression of a cDNA that escapes shRNA targeting.\n3. Fig 1G-H: The increase in serine and glycine induced by asparaginase could reflect asparaginase-induced inhibition of protein synthesis, leading to accumulation of other amino acids that are not being utilized for protein synthesis. Are these two amino acids unique in their increase upon asparaginase therapy, or do other proteinogenic amino acids similarly increase upon asparaginase treatment as in this experiment?\n4. Please include the missing data in Figure 3B and E (days 3-4 for DMSO and day 4 for BI4961), this may be better shown on a log scale. For Figure 3E, the authors need to integrate a graph to see the toxicity by BI4916 in control.\n5. In Figure 5A, H and I, it appears all individual control datapoints were normalized to 1.0, which seems inappropriate – it is fine to normalize the average of the values to 1.0, but it is hard to believe there was undetectable variability in these datapoints given the degree of variability in all other conditions. This needs to be properly normalized, and the P values need to be recalculated.\n6. Include the missing data in Figure 6G and H (Ddy 4 – DMSO)\n7. The method used to assess the contribution of oxphos to ATP production (Fig S1D) is not described in the results or legends, and I did not see it in the methods.\nMinor points:\n1. The colors in Figure 4H are too similar, making it difficult to distinguish between them. It would be better to change the colors for clearer differentiation.\n2. To improve readability of the manuscript, recommend minimizing the use of nonstandard abbreviations (L-Asn, ASNase), at least in the main text. It is not trivial for the average reader to distinguish Asn vs ASNase vs ASNS. Also, unless the authors plan to discuss D amino acids, there is no need to specify that they are using L-amino acids in the main text, the \"L\" can be ommitted for simplicity and this can just be stated in the methods.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_3": "In the manuscript, “L-asparaginase treatment induces a tumor metabolic plasticity and reveals a vulnerability to PARP1/2 inhibitor Olaparib, in B-cell lymphomas” the authors present evidence that Phosphoglycerate dehydrogenase (PHGDH) facilitates survival of malignant B cells during asparaginase treatment. They demonstrate that upregulated de novo serine and asparagine biosynthesis enable redox homeostasis and contribute to asparaginase therapy resistance. Since, asparaginase treatment propels oxidative stress and causes DNA damage authors propose targeting of asparaginase resistant B cells by PARP inhibitor Olaparib.\nThe manuscript is timely and has in most cases adequate experimental conditions and controls. I congratulate the authors for this important work. However, I have a few concerns preventing the publication of current version of this manuscript in Nature Communications.\nKey point:\nThe authors did not demonstrate that inhibition of de novo serine biosynthesis limits proliferation of ASNase-treated B cells in vivo. They state in discussion that BI4916 is unstable and only available inhibitor of PHGDH. There are no data presented to support this claim. Moreover, there are many available PHGDH inhibitors (including NCT-502, NCT-503, CBR-5884, BI-4924… ) on the market. I believe that this is key point of this manuscript and should be validated in vivo.\nMinor points:\nCheck and correct first sentence in introduction (starting at page 3 line 43).\nIt might be appropriate to include basic description of metabolic pathways that are studied later in results part (glycolysis, transsulfonation, serine biosynthesis).\nLabeling of y-axis by OS (%) in Figs 1AB, 3G, 4FG, 6I and by PFS (%) is not appropriate. OS and PFS are not defined in Figure Legends.\nFigure 4D authors state: “The expression of metabolic enzymes PHGDH, PSAT1, PSPH, ASNS and of the transcription factor ATF4, which increased within the first 24 hours and persisted during 4 days of ASNase treatment, returned to baseline levels upon release from therapy (Figure 4D).” At least PHGDH did not returned to baseline.\nMore general on all immunoblots: it would be appropriate to quantify intensity of bands to see statistical significance of claimed changes. It is also not clear how many times were immunoblots replicated and how reproducible are observed changes.\nPlease describe in detail type of DNA damage response that is activated in ASNase-treated cells (including markers phosphoCHK1, phosphoCHK2, phospoRPA32)", "reviewer_4": "The manuscript “L-asparaginase treatment induces a tumor metabolic plasticity and reveals vulnerability to PARP1/2 inhibitor Olaparib, in B-cell lymphoma” by Anais A, et.al, reported that L-asparaginase treatment induced de novo serine biosynthesis in ASNase-resistant BCLs. The manuscript provided comprehensive analysis to prove that enhanced serine biosynthesis contributed to redox homeostasis following ASNase treatment. Thus, the manuscript provided evidence that L-asparaginase combined with a PHGDH inhibitor that blocks a key enzyme in serine biosynthetic pathway could overcome therapeutic resistance in a pre-clinical model of BCLs. Of interest, the manuscript provided biochemical and genetic evidence that this dependency of serine biosynthesis in ASNase-resistant BCLs are independent of the expression of asparagine synthetase (ASNS) or de novo biosynthesis of asparagine. Finally, since ASNase treatment induced ROS and DNA damage, the manuscript reported that combining L-asparaginase with PARP1/2 inhibitor had better therapeutic outcomes.\nThis manuscript is an interesting extension of their previous work (Grima-Reyes, et.al, Sci Adv, 2022), providing an alternate explanation of ASNase-resistance in BCLs. In addition, combining L-asparaginase with serine biosynthetic inhibitors or PARP inhibitors may have applicable effect in cancer patients that are resistant to L-asparaginase treatment. However, I have a major concern of the central concept of the manuscript regarding whether the phenotype they observed is independent of ASNS expression and asparagine biosynthesis. Since L-asparaginase depletes extracellular asparagine, the only way cancer cells can acquire asparagine under this circumstance is through de novo biosynthesis. In other words, enhanced serine biosynthesis can not mitigate the asparagine deficiency unless it affects ASNS expression or metabolic flux toward asparagine de novo biosynthesis. The results presented here suggested that ASNase treatment may render BCLs to rely on de novo serine biosynthesis more than uptake due ATF4-dependent induction of serine biosynthetic enzymes, such as PHGDH, PSAT1, PSPH. However, this dependency of serine biosynthesis is not the reason for ASNase resistance in BCLs. Therefore, to conclude that there is a non-genetic mechanism to drive ASNase resistance independent of ASNS expression, the manuscript should provide further experimental results listed below.\nMajor Concerns:\n1. Since BCLs do not require extracellular serine for their growth (Fig. S2A), the manuscript should provide a measurement of intracellular S/G when cells were cultured with or without S/G in the medium following the ASNase treatment. This experiment will determine what is the major source of S/G in cells following ASNase treatment. If the majority of S/G comes from the medium, it will be hard to interpret the in vivo data as S/G are present in the circulation. This experiment is important, as in cell culture system most intracellular amino acids come from the cell culture medium.\n2. I am impressed about the comprehensive in vivo U13C-glucose tracing experiment to show enhanced contribution of glucose-derived carbon to serine following ASNase treatment. However, the current experiment design did not address the relative contribution of serine between uptake and de novo biosynthesis in the setting of ASNase treatment. I understand that it’s difficult to do this in vivo due to the treatment strategy. However, the manuscript should be able to perform the experiment in vitro first. Indeed, in Fig S2E & S2H, they showed that plasma and BCL serine levels increased following ASNase treatment. This is not sufficient to prove that serine uptake is suppressed but de novo biosynthesis is enhanced in vivo. The results can be interpreted as most intracellular serine came from plasma. This is particularly true when % labeling from glucose is less than 20% (Fig 2I). Therefore, a key experiment is to show that in vitro even in the presence of extracellular S/G, ASNase treatment can significantly increase the % of glucose-derived carbon into serine in cells. This result has to be shown in the context of total cellular serine levels.\n3. I don’t think the rationale to decouple asparagine biosynthesis from serine biosynthesis is strong enough. The fact that ASNS OE only provided partial resistance to ASNase treatment can simply be due to the level of OE of ASNS is not sufficient to generate enough asparagine in a given period of time. If the manuscript intends to argue serine biosynthesis can contribute to ASNase resistance independently of ASNS, they have to use CRISPR to delete Asns gene in BCLs and to show that resistance can still occur in a serine biosynthesis-dependent manner.\n4. I don’t think the argument of non-genetic resistance in Fig 4 is rigorous. Like what they showed in Fig 4D, serine biosynthetic enzymes and ASNS expression went down during “drug holidays” likely due to a suppression of ATF4 activities. Thus, I don’t think the results in Fig 4 provided significant conceptual contribution to the entire manuscript\n5. It is interesting that ASNase treatment induced ROS and DNA damage, which was further exaggerated when PHGDH is inhibited. The manuscript should show that PHGDH OE (cells in Fig. 3F) mitigates the DNA damage responses in ASNase-treated cells (Fig 6), which may further compromise the effect of PARP inhibitors (Fig 6E, 6F).\nMinor Concerns:\n1. Fig 1C & 1D the symbols of vehicle and ASNase are the same\n2. In Fig 2I, the total glucose-derived carbon is less than 20%, with majority of them being m+1 with no clear interpretation. In addition, to prove 6% Asn were labeled with glucose-derived carbon is unnecessary as no direct connection between serine biosynthesis and PDH activity (Fig 2K). If they need to show something, it should be more glutamine-derived carbon into alpha-KG, aspartate and asparagine due to enhance PSAT1 activity.\n3. Fig 3D needs to provide the result from control cells without ASNS OE as well as a blot of ATF4, so readers can evaluate the levels of OE and the degree of amino acid starvation.\n4. For most amino acid measuring, such as the results in Fig 4I, the manuscript should provide some other AAs, including EAAs and NEAAs, to determine whether their observations are specific to serine.\n5. The discussion between Glycine and GSH biosynthesis seems to be distractive. The result in Fig S5 is an evidence of increased GSH biosynthesis likely due to the increased expression of GSH biosynthetic enzymes. Since serine and glycine biosynthesis is connected through one-carbon unit metabolism, if the manuscript does not want to make the point that glycine is derived from serine biosynthetic pathway there is no significant addition to the point they have already made in Fig 5.\n6. Line 476, they should cite both Fig 2A and Fig S1J."}, "author_response": "We would like to sincerely thank all the reviewers for their constructive suggestions. We have\ncarefully addressed all concerns to our best. We believe these revisions have significantly\nstrengthened the work, and we are grateful to the reviewers for their valuable input.\nComments to the authors, our reply (in blue)\nReviewer #1 (Remarks to the Author); expert in L-Asp resistance, metabolism:\nIn this manuscript, Anais et al generate two models of Eu-Myc B-cell lymphoma with in\nvivo acquired resistance to asparaginase, and apply metabolomics to investigate the\nbiology of resistance. They find that resistant cases have increased serine and glycine\nlevels, as well as increased expression of serine biosynthetic enzymes. They present\nevidence that increased intracellular serine biosynthesis promotes asparaginase\nresistance, including a particularly nice experiment with PHGDH overexpression in vivo.\nOverall, this is a nice story built on key strong pieces of evidence, however there are\nalso several problems that need to be addressed, particularly 1) a confusing\npresentation of precisely how the authors propose that serine biosynthesis interacts\nwith asparaginase resistance, with multiple different models proposed in different parts\nof the paper, and 2) an over-reliance on a single small molecule to inhibit serine\nbiosynthesis, so most experiments arguing that serine biosynthesis is important could\nbe explained by off-target drug toxicity. Several additional critiques/suggestions are\nalso provided below.\nWe sincerely thank the reviewer for their positive assessment and for highlighting the strengths\nof our study. We fully agree with the main concerns raised. In response, we have 1) thoroughly\nrestructured the manuscript to clarify the proposed model of serine biosynthesis in\nasparaginase resistance, and 2) confirmed our findings using complementary genetic\napproaches. We believe these revisions have significantly strengthened the clarity and\nrobustness of our conclusions.\nMajor critiques:\n1. Precisely how the authors propose that serine biosynthesis mediates asparaginase\nresistance is very difficult to follow throughout this paper, as the authors propose 3\nunrelated models in different parts of this paper, none of which are definitively proven:\ni) that this connection is mediated by glutamate (discussed in the last paragraph of p.\n9), ii) by the requirement of asparagine as an exchange factor for import of extracellular\nserine, and iii) by the requirement for serine for glycine and cysteine biosynthesis,\nwhich are both required for glutathione and redox homeostasis.\n1a. With regards to the first glutamate model, precisely what the authors are proposing\nhere is difficult to understand and, if I understand this correctly, it is not a very\nconvincing model. The idea (as I can best decipher) is that the asparaginase-\ninduced increase in ASNS activity produces an excess of glutamate, and this glutamate\nhas to be converted to alpha-ketoglutarate by serine biosynthesis (I presume this is to\navoid glutamate toxicity, although this is not stated). The problems I see with this\nargument are that there are several other reactions that can convert glutamate to other\nuseful molecules independent of serine metabolism (i.e., conversion to alpha-ketoglutarate\nby GDH, or to glutamine by GLUL), and to my knowledge, glutamate is not very toxic to\ncells lacking glutamate neuroreceptors. Unless the authors can show that i) glutamate\naccumulates to significantly higher levels in asparaginase-treated cells with low vs.\n1\nhigh serine biosynthesis, and ii) that these glutamate levels are toxic to these cells,\nI don’t see this argument as compelling and I would recommend removing it from the\npaper to make the manuscript less confusing.\nWe sincerely thank Reviewer #1 for the thoughtful and constructive comments, which we have\naddressed with the goal of clarifying and strengthening the mechanistic aspects of our\nmanuscript.\nRegarding point 1a, we fully agree with the reviewer’s concerns about the proposed glutamate\nmodel. In response, we have removed this section from the manuscript to improve clarity.\n1b. The second argument that loss of asparagine as an exchange factor for serine\nimport is conceptually appealing, as it is logical that losing the ability to import serine\nwould make cells dependent on intracellular serine biosynthesis. In principle, I would\nlike to see an experiment knocking down the relevant transporter to test this directly,\nhowever my brief reading of the amino acid transport literature leads me to believe the\nrelevant antiporter has not been definitively identified. If that is the case, it is not really\npossible to suggest a specific experiment here, and so I can’t hold the authors to this\nstandard so I think it would be OK to keep the discussion of this possibility as it is.\nRegarding the reduced serine uptake in asparagine-restricted Eµ-Myc cells (comment 1b), this\nobservation is consistent with previous evidence that asparagine functions as an amino acid\nexchange factor, facilitating the uptake of specific amino acids, such as serine. As suggested,\nwe have retained this point in a simplified and concise form in the Results section (page 7).\n1c. The third model of serine -> glycine and cysteine being necessary to maintain a\nsufficient pool of glutathione for redox homeostasis is plausible, and that this effect is\nparticularly important in these cells lacking expression of the slc7a11 cysteine\ntransporter also makes sense. I would like to see the authors test two predictions to\nstrengthen this model:\n- Is the cytotoxicity of asparaginase + the PGHDH inhibitor rescued by quenching ROS\nusing NAC?\n- Does restoring expression of the cystine transporter slc7a11 also rescue toxicity?\nAs a general comment, while there may be multiple mechanisms that each explain part\nof the link between asparaginase and serine, only one needs to be definitively\ndemonstrated in this paper. If there are multiple mechanisms that each partially explain\nthis link, I would much prefer for each of these to have its own dedicated manuscript,\nand the focus in this manuscript could be on what the authors see as the one that is\nmost important or most definitively supported by their data.\nWe would like to thank the reviewer for this insightful and constructive comment. We performed\nthe suggested experiments.\nFirst, we found that quenching ROS with N-acetyl-L-cysteine (NAC) effectively rescued Eµ-\nMyc cells from death induced by the PHGDH inhibitor (BI-4916), both alone and in combination\nwith L-asparaginase. This supports the role of PHGDH in mediating resistance by sustaining\nredox homeostasis. This finding is now shown in the new figure 5h and discussed on page 12.\nSecond, given that Eµ-Myc cells lack mRNA expression of the cystine transporter slc7a11\n(Figures 5i-j), we generated Eµ-Myc cells stably expressing it. The expression level of\nSLC7A11 protein in stable control and SLC7A11-expressing Eµ-Myc cells is now presented in\nthe new figure 5l. Notably, SLC7A11 expression phenocopied the protective effect of NAC\n2\nagainst cell death induced by inhibition of PHGDH alone or in combination with ASNase,\nreinforcing the role of the antioxidant response in tumor relapse during ASNase treatment. This\nfinding is now presented in the new figure 5m and is discussed on page 13.\nCollectively, these additional data highlight the role of PHGDH in mitigating ASNase-induced\noxidative stress, thereby promoting the survival of malignant B cells.\n2. This manuscript relies heavily on the toxicity of a small molecule PGHDH inhibitor to\nargue that serine biosynthesis is involved in this phenotype in multiple different figures,\nyet the possibility that the toxicity of this inhibitor is off-target is not ruled out. I have\nseveral critiques related to this:\n2a. The authors state that monotherapy with this inhibitor had “minimal effects on Eu-\nMyc cell viability” (line 231) citing Fig S3A, when this figure and Figure 3A show that\nthis drug is killing 25-33% of cells with a highly significant P value. Monotherapy with\nthis inhibitor is definitely toxic so this statement needs to be rephrased.\nWe thank the reviewer for bringing this oversight to our attention and apologize for having\nunderestimated the toxicity induced by the PHGDH inhibitor alone. We have revised the\nstatement on page 9 as follows: “Treatment with 10 µM of BI-4916 inhibited PHGDH activity\nby 80%, significantly reduced total intracellular serine levels (Figures 3a and S4e-f) and\ninduced an average significant 32% increase in Eµ-Myc cell death within 24 hours (Figures 3b\nand S4d-g).”\n2b. While the authors use what may be the best of the available PGHDH inhibitors, all\nexperimental perturbations have off-target effects, and cell death is perhaps the most\ncommon off-target toxicity. It is important to strengthen the argument that the toxicity\nof this drug is on-target by testing i) whether genetic PGHDH knockdown using two\ndifferent shRNA hairpins (or CRISPRi) phenocopies the drug effect, and ii)\ndemonstrating that the toxicity of at least one hairpin can be rescued by restoring\nexpression of a cDNA that escapes shRNA targeting.\nTo further validate that PHGDH has a critical role in the tumor adaptive response during\nASNase treatment in vivo, we intravenously injected control (shSCR) and Phgdh-silenced\n(shPhgdh #1) Eµ-Myc cells into C57BL/6 mice seven days prior several weeks of treatment\nwith Vehicle or ASNase until disease endpoint. Upon ASNase treatment, mice engrafted with\nEµ-Myc-shPhgdh cells displayed a significantly prolonged survival compared to controls,\nindicating that PHGDH expression levels influence tumor response to ASNase therapy. This\nfinding is now presented in the new figure 3j and is discussed on page 10.\nNotably, this result mirrors the accelerated lymphoma progression observed upon ASNase\ntreatment when PHGDH was overexpressed in malignant B cells (updated figure S7f),\ntherefore demonstrating in vivo the specific role of PHGDH on B-cell lymphoma relapse during\nASNase treatment.\nUnfortunately, despite multiple attempts, we were unable to perform the rescue experiments\nas intended. The shRNAs designed to target non-coding regions of Phgdh mRNA (shRNA #C\nand #M) did not efficiently reduce PHGDH protein levels (Figure 1, provided for reviewer only).\nAs a result, we were unable to generate rescue cell lines, since shRNAs targeting coding\nregions would also silence the re-expressed Phgdh mRNA.\n3\n#A #B #C #1 #D #E #F #G\nPHGDH\nERK2\n3. Fig 1G-H: The increase in serine and glycine induced by asparaginase could reflect\nasparaginase-induced inhibition of protein synthesis, leading to accumulation of other\namino acids that are not being utilized for protein synthesis. Are these two amino acids\nunique in their increase upon asparaginase therapy, or do other proteinogenic amino\nacids similarly increase upon asparaginase treatment as in this experiment?\nWe thank the reviewer for this important question, which provides an opportunity to clarify\nwhich specific amino acids are deregulated in B-cell lymphomas (BCL) treated with ASNase\nin vivo. Through targeted metabolomics analysis of BCL derived from two different Eµ-Myc\nclones (#506 and #688), we identified that serine, glycine, tyrosine (up-regulated) and\nasparagine (down-regulated) were the only proteinogenic amino acids (AA) significantly and\nconsistently deregulated in BCL of ASNase-treated mice compared to controls, across both\ndatasets, with a fold change (ASNase/Vehicle) >1.25 and raw P-value < 0.05. This result is\nnow included in volcano plots highlighting significantly deregulated proteinogenic AA (in pink)\nin ASNase-treated BCL (#506-BCL, new figure 1e; #688-BCL, new figure S1e) and in the new\ntable S2 indicating all proteinogenic AA fold change (and related statistics) in BCL treated with\nASNase vs Vehicle in vivo. This is now discussed in the main text on page 6.\nNotably, other proteinogenic AA were significantly deregulated in one dataset with a fold\nchange (ASNase/Vehicle) < 1.25, and/or in a manner not consistent with the second dataset,\nexcepted for threonine. Accordingly, these proteinogenic AA are unlikely to reflect the core\nmetabolic response to ASNase therapy, unlike serine, glycine, tyrosine, and asparagine.\n4. Please include the missing data in Figure 3B and E (days 3-4 for DMSO and day 4 for\nBI4961), this may be better shown on a log scale. For Figure 3E, the authors need to\nintegrate a graph to see the toxicity by BI4916 in control.\nWe have included missing data in the newly updated figures 3c, 3e and S4i (formerly figures\n3B, 3E and S3F).\nRegarding figure 3e, we added data demonstrating that BI-4916 induces a similar toxicity\nwithin 24 hours in both control (CTL) and asparagine synthetase overexpressing (ASNS OE)-\nEµ-Myc cells. These results are now presented in the new figure S4m and discussed in the\nmain text on page 9.\nshPhgdh\n#H #I #J #K #2 #L #M #1\nRCShs\nshPhgdh\n55 kDa\n40 kDa\nFigure 1: Efficacy screening of shRNA targeting murine Phgdh mRNA.\nImmunoblot analysis of PHGDH expression levels in the murine colon carcinoma cell line CT26\ntransduced with pSUPER vector-encoding either the firefly luciferase mRNA (shSCR, control) or shRNA\ntargeting mouse Phgdh mRNA (15 different shPhgdh) shPhgdh #C and #M (in red) target different non-\ncoding regions of Phgdh mRNA. ShPhgdh #1 and #2 (in blue) target different regions of the coding\nsequence and are the most effective shRNA.\n5. In Figure 5A, H and I, it appears all individual control datapoints were normalized to\n1.0, which seems inappropriate – it is fine to normalize the average of the values to 1.0,\nbut it is hard to believe there was undetectable variability in these datapoints given the\ndegree of variability in all other conditions. This needs to be properly normalized, and\nthe P values need to be recalculated.\nWe thank the reviewer for pointing this out. We have reprocessed the data shown in figures\n5a, 5f, 5g (formerly figures 5A, 5H, 5I), applying appropriate normalization and have\nrecalculated the P-values using the appropriated statistical test.\n6. Include the missing data in Figure 6G and H (Ddy 4 – DMSO)\nWe have included the missing data in updated figures 7e-f (formerly figures 6G-H). All P-values\nhave been recalculated accordingly using a 2way Anova followed by a Tukey’s test.\n7. The method used to assess the contribution of oxphos to ATP production (Fig S1D)\nis not described in the results or legends, and I did not see it in the methods.\nWe sincerely apologize for this oversight and have now included a method used to assess the\ncontribution of OxPhos to ATP production in tumors ex-vivo in the Materials and Methods\nsection on page 29.\nMinor points:\n1. The colors in Figure 4H are too similar, making it difficult to distinguish between them.\nIt would be better to change the colors for clearer differentiation.\nTo improve visual clarity and facilitate group distinction, we have adjusted the colors in figure\n4h.\n2. To improve readability of the manuscript, recommend minimizing the use of\nnonstandard abbreviations (L-Asn, ASNase), at least in the main text. It is not trivial for\nthe average reader to distinguish Asn vs ASNase vs ASNS. Also, unless the authors\nplan to discuss D amino acids, there is no need to specify that they are using L-amino\nacids in the main text, the \"L\" can be ommitted for simplicity and this can just be stated\nin the methods.\nWe appreciate the reviewer’s suggestion to improve the readability of the manuscript. In\nresponse, we have replaced the abbreviations ‘L-Asn’ and ‘ASNS’ with ‘asparagine’ and\n‘asparagine synthetase’, respectively, throughout the main text. While we have retained the\nabbreviation ‘ASNase’ due to formatting constraints and for clarity in figures, we have ensured\nits consistent and clear usage. Additionally, as the study focuses exclusively on L-amino-acids\nwe have simplified the nomenclature by omitting the ‘L’ prefix in the main text and figures, as\nrecommended.\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nWe thank reviewer #2 for co-reviewing our manuscript.\nReviewer #3 (Remarks to the Author);\nIn the manuscript, “L-asparaginase treatment induces a tumor metabolic plasticity and\nreveals a vulnerability to PARP1/2 inhibitor Olaparib, in B-cell lymphomas” the authors\npresent evidence that Phosphoglycerate dehydrogenase (PHGDH) facilitates survival of\nmalignant B cells during asparaginase treatment. They demonstrate that upregulated\nde novo serine and asparagine biosynthesis enable redox homeostasis and contribute\nto asparaginase therapy resistance. Since, asparaginase treatment propels oxidative\nstress and causes DNA damage authors propose targeting of asparaginase resistant B\ncells by PARP inhibitor Olaparib.\nThe manuscript is timely and has in most cases adequate experimental conditions and\ncontrols. I congratulate the authors for this important work. However, I have a few\nconcerns preventing the publication of current version of this manuscript in Nature\nCommunications.\nWe sincerely thank the reviewer for their thoughtful and supportive comments regarding our\nstudy. We greatly appreciate the recognition of the significance and timeliness of our work.\nThe concerns raised have been carefully addressed in the revised version of the manuscript,\nand we hope the changes meet the reviewer’s expectations.\nKey point:\nThe authors did not demonstrate that inhibition of de novo serine biosynthesis limits\nproliferation of ASNase-treated B cells in vivo. They state in discussion that BI4916 is\nunstable and only available inhibitor of PHGDH. There are no data presented to support\nthis claim. Moreover, there are many available PHGDH inhibitors (including NCT-502,\nNCT-503, CBR-5884, BI-4924… ) on the market. I believe that this is key point of this\nmanuscript and should be validated in vivo.\nWe thank Reviewer #3 for this important comment. We agree that the BI4916 compound is not\nthe only commercially available PHGDH inhibitor, and we apologize for misleading statement\nin the discussion. We have removed that statement to avoid confusion.\nTo address the reviewer’s concern, we evaluated several commercially available PHGDH\ninhibitors (including NCT-503, CBR-5884, PKUMDL-WQ2101, BI4916, BI4924). Among these,\nonly the NAD+/NADH competitive PHGDH inhibitors, BI4916 and BI4924, effectively inhibited\nPHGDH enzymatic activity in Eµ-Myc cells. In contrast, allosteric PHGDH inhibitors (NCT-503,\nCBR-5884, PKUMDL-WQ2101) showed dose-dependent cytotoxicity without reducing\nPHGDH activity, raising concerns about their specificity, at least in our model. These results\nare now presented in the new figures S4a-b-c-d-e-j and are discussed in the main text on\npages 8 and 9.\nwe lack a pharmacologically suitable PHGDH inhibitor for in vivo validation of our findings in\nthe Eµ-Myc model. Therefore, to robustly address this question, we turned to genetic\napproaches. We generated Eµ-Myc cells stably expressing shRNAs targeting murine Phgdh\nmRNA. We then transplanted control (shSCR) and Phgdh-knock-down (shPhgdh #1) Eµ-Myc\ncells into immunocompetent C57BL/6 mice and treated them with ASNase or Vehicle or ASNase.\nMice\nbearing PHGDH-KD lymphomas showed significantly improved survival upon ASNase\ntreatment compared to controls (new figures 3f-g-h-i-j and discussion on page 10),\nindicating\nthat PHGDH expression is a critical player in B-cell lymphomas adaptive resistance\nmechanisms during ASNase treatment ASNase in vivo.\nMinor points:\nCheck and correct first sentence in introduction (starting at page 3 line 43).\nWe have corrected first sentence in the introduction section, on page 3, as follows: “Cancer\ncells exhibit enhanced metabolism to meet expanding energetic and biosynthetic demands, a\nvulnerability that prompted several laboratories to develop metabolic inhibitors for clinical use\nin oncology”.\nIt might be appropriate to include basic description of metabolic pathways that are\nstudied later in results part (glycolysis, transsulfonation, serine biosynthesis).\nWe thank the reviewer for this helpful suggestion. Due to the addition of substantial new data\nand space constraints in the main text, we have included a schematic overview of these\npathways in figure S2d. We hope this will help readers better follow the mechanistic aspect of\nour study.\nLabeling of y-axis by OS (%) in Figs 1AB, 3G, 4FG, 6I and by PFS (%) is not appropriate.\nOS and PFS are not defined in Figure Legends.\nTo address the reviewer’s concern, we have replaced ‘OS (%)’ with ‘survival probability (%)’ in\nfigures 1a-b, S7f (formerly figure 3G), 4f-g, 7i (formerly figure 6I) and ‘PFS (%)’ with ‘tumor-\nfree (%)’ in figures S1a-b. We hope these revised labels improve clarity and accuracy.\nAdditionally, the terms ‘Survival probability (%)’ and ‘Tumor-free (%)’ are defined in the\nMaterials and Methods section, on page 20.\nFigure 4D authors state: “The expression of metabolic enzymes PHGDH, PSAT1, PSPH,\nASNS and of the transcription factor ATF4, which increased within the first 24 hours\nand persisted during 4 days of ASNase treatment, returned to baseline levels upon\nrelease from therapy (Figure 4D).” At least PHGDH did not returned to baseline.\nWe thank the reviewer for pointing this out. We agree with reviewer’s observation and have\nrevised the original sentence accordingly in the main text (page 10), as follows: “The\nexpression levels of PHGDH, PSAT1, PSPH, ASNS and of the transcription factor ATF4, which\nwere initially upregulated within the first 24 hours, remained elevated through 4 days of\nASNase treatment (Figure 4d). Following therapy withdrawal, ASNS and PSPH protein levels\nreturned to baseline within 48 hours, while PSAT1 and PHGDH levels declined at 72 hours\nand 96 hours (control condition of the 2nd challenge), respectively, consistent with a time-\ndependent decreased expression level of ATF4.”\nto see statistical significance of claimed changes. It is also not clear how many times\nwere immunoblots replicated and how reproducible are observed changes.\nQuantification of immunoblots shown in the new figures 3d, 3f, 5c, 6b-c, 6e, 6g and 6i are now\npresented in the new figures S4l, 3g, 5d, 6d, 6f, 6h, 6j-k, respectively. Each dot corresponds\nto the number of independent experiments performed. This information is now indicated in\neach respective figure legends.\nPlease describe in detail type of DNA damage response that is activated in ASNase-\ntreated cells (including markers phosphoCHK1, phosphoCHK2, phospoRPA32)\nWe thank reviewer #3 for this valuable suggestion which had led to important additions that\nwe believe has significantly strengthened our manuscript. In response, we have included the\nnew figures 6b-c-d, 6i-j-k and S7a-b-c, which characterize the type of DNA damage response\n(DDR) activating in ASNase-treated malignant B cells.\nThese new figures highlight activation of the ATR-dependent DDR pathway in ASNase-treated\nEµ-Myc cells, regardless of the Eµ-Myc clone used. This is evidenced by increased\nphosphorylation of ATR at serine 428 (P-ATR Ser428), and of its downstream targets, Chk1\nat serine 345 (P-Chk1 Ser345) and RPA32 at serine 33 (new figures 6b-c-d). In contrast, we\ndid not detect the phosphorylation of the ATM substrate, KAP1 at serine 824 (P-KAP Ser824),\nsuggesting that the ATM-dependent DDR pathway is not engaged in ASNase-treated cells.\nConsistent with our in vitro observations, we confirmed significantly increased expression\nlevels of gH2AX and P-Chk1 at Ser345 in B-cell lymphomas harvested from Eµ-Myc-cells-\nbearing C67BL/6 mice treated with ASNase in vivo (new figure S7a-b-c).\nMoreover, overexpression of PHGDH in malignant Eµ-Myc cells (figure S7d-e) prevented\ngH2AX and P-Chk1 (Ser345) induction in B-cell lymphomas harvested from ASNase-treated\nmice (new figure 6i-j-k). This finding supports the contribution of PHGDH to tumor relapse\nduring ASNase treatment (updated figure S7f).\nThese new data have been incorporated into the main text on page 13 and are also discussed\nin the revised discussion section on page 16.\nIn addition, we detected elevated poly(ADP)-ribose motifs on proteins in asparagine-deprived\nEµ-Myc cells, indicating activation of PARP. This finding is presented in the new figure 7a and\ndiscussed in the main text on page 14.\nCollectively, our data highlight ATR-dependent DNA damage response in ASNase-treated\nmalignant B cells facing oxidative-stress, suggesting replicative stress. This is consistent with\nobserved activation of PARP and supports the identification of PARP as a determinant of tumor\nrelapse during ASNase treatment.\nmetabolism\nThe manuscript “L-asparaginase treatment induces a tumor metabolic plasticity and\nreveals vulnerability to PARP1/2 inhibitor Olaparib, in B-cell lymphoma” by Anais A,\net.al, reported that L-asparaginase treatment induced de novo serine biosynthesis in\nASNase-resistant BCLs. The manuscript provided comprehensive analysis to prove\nthat enhanced serine biosynthesis contributed to redox homeostasis following ASNase\ntreatment. Thus, the manuscript provided evidence that L-asparaginase combined with\na PHGDH inhibitor that blocks a key enzyme in serine biosynthetic pathway could\novercome therapeutic resistance in a pre-clinical model of BCLs. Of interest, the\nmanuscript provided biochemical and genetic evidence that this dependency of serine\nbiosynthesis in ASNase-resistant BCLs are independent of the expression of\nasparagine synthetase (ASNS) or de novo biosynthesis of asparagine. Finally, since\nASNase treatment induced ROS and DNA damage, the manuscript reported that\ncombining L-asparaginase with PARP1/2 inhibitor had better therapeutic outcomes.\nThis manuscript is an interesting extension of their previous work (Grima-Reyes, et.al,\nSci Adv, 2022), providing an alternate explanation of ASNase-resistance in BCLs. In\naddition, combining L-asparaginase with serine biosynthetic inhibitors or PARP\ninhibitors may have applicable effect in cancer patients that are resistant to L-\nasparaginase treatment. However, I have a major concern of the central concept of the\nmanuscript regarding whether the phenotype they observed is independent of ASNS\nexpression and asparagine biosynthesis. Since L-asparaginase depletes extracellular\nasparagine, the only way cancer cells can acquire asparagine under this circumstance\nis through de novo biosynthesis. In other words, enhanced serine biosynthesis can not\nmitigate the asparagine deficiency unless it affects ASNS expression or metabolic flux\ntoward asparagine de novo biosynthesis. The results presented here suggested that\nASNase treatment may render BCLs to rely on de novo serine biosynthesis more than\nuptake due ATF4-dependent induction of serine biosynthetic enzymes, such as PHGDH,\nPSAT1, PSPH. However, this dependency of serine biosynthesis is not the reason for\nASNase resistance in BCLs. Therefore, to conclude that there is a non-genetic\nmechanism to drive ASNase resistance independent of ASNS expression, the\nmanuscript should provide further experimental results listed below.\nMajor Concerns:\n1. Since BCLs do not require extracellular serine for their growth (Fig. S2A), the\nmanuscript should provide a measurement of intracellular S/G when cells were cultured\nwith or without S/G in the medium following the ASNase treatment. This experiment will\ndetermine what is the major source of S/G in cells following ASNase treatment. If the\nmajority of S/G comes from the medium, it will be hard to interpret the in vivo data as\nS/G are present in the circulation. This experiment is important, as in cell culture system\nmost intracellular amino acids come from the cell culture medium.\nWe appreciate the reviewer’s insightful comment regarding the source of intracellular serine\nfollowing ASNase treatment. As suggested, we performed additional experiments to directly\nassess the relative contribution of extracellular uptake versus de novo biosynthesis to the\nintracellular serine pool. The results are now presented in the new figure 2b and are discussed\nin the main text on page 7.\nSpecifically, we observed that ASNase treatment led to increased intracellular serine levels\nunder full media conditions, in consistence with previous observation in two different Eµ-Myc\n9\nclones (updated figures 1l and S1i). Importantly, this increase was significantly reduced when\ncells were cultured in the absence of extracellular serine and glycine. Nonetheless, ASNase\ntreatment still significantly increase intracellular serine levels even in serine/glycine-free media\n(new figure 2b).\nThese findings suggest that both uptake and de novo biosynthesis contribute to the\nintracellular serine pool, and that de novo serine biosynthesis plays an important role in\nmaintaining intracellular serine levels under ASNase treatment.\n2. I am impressed about the comprehensive in vivo U13C-glucose tracing experiment to\nshow enhanced contribution of glucose-derived carbon to serine following ASNase\ntreatment. However, the current experiment design did not address the relative\ncontribution of serine between uptake and de novo biosynthesis in the setting of\nASNase treatment. I understand that it’s difficult to do this in vivo due to the treatment\nstrategy. However, the manuscript should be able to perform the experiment in vitro\nfirst. Indeed, in Fig S2E & S2H, they showed that plasma and BCL serine levels\nincreased following ASNase treatment. This is not sufficient to prove that serine uptake\nis suppressed but de novo biosynthesis is enhanced in vivo. The results can be\ninterpreted as most intracellular serine came from plasma. This is particularly true when\n% labeling from glucose is less than 20% (Fig 2I). Therefore, a key experiment is to show\nthat in vitro even in the presence of extracellular S/G, ASNase treatment can\nsignificantly increase the % of glucose-derived carbon into serine in cells. This result\nhas to be shown in the context of total cellular serine levels.\nWe thank the reviewer for the thoughtful and constructive feedback. We appreciate the\nrecognition of the value of our in vivo [U-13C]-glucose tracing experiment. We fully agree that\nour in vivo data alone cannot definitively distinguish between increased serine uptake vs\nincreased de novo serine biosynthesis in tumors treated with ASNase in vivo, particularly given\nthe observed elevated levels of serine in B-cell lymphomas and in plasma of ASNase-treated\nanimals (updated figures S3b and S3f). We also acknowledge the reviewer’s important point\nthat a labeling percentage below 20% could be interpreted as a minor contribution of glucose-\nderived serine relative to circulating serine uptake.\nTo directly address this concern, we have performed an additional in vitro [U-13C]-glucose\ntracing experiment in Eµ-Myc cells cultivated in the presence or absence of asparagine and\nASNase, in a medium containing extracellular serine and glycine at supraphysiological\nconcentrations. Our data showed that asparagine-deprived cells significantly increased the\nlevels of carbon-labelled serine (m+3) derived from [U-13C]-glucose, even in the presence of\nextracellular serine and glycine, compared to control conditions. Notably, we also observed\nincreased levels of carbon-labelled glycine (m+2) derived from [U-13C]-glucose, indicating that\na fraction of total glycine is derived from serine biosynthetic pathway.\nThese new data are now presented in the new figures 2e-f and are discussed in the main text\non pages 7 and 8.\nFurthermore, these results are consistent with our initial data presented in updated figures\nS2d-f-g, which show increased levels of 15N-labelled serine (m+1) derived from a-15N-L-\nglutamine in asparagine-deprived Eµ-Myc cells. This metabolic adaptation aligns with a recent\nreport in primary murine germinal center B cells cultivated in asparagine-free medium in the\npresence of a-15N-L-glutamine (PMID: 39671468).\nTaken together, these complementary set of data—alongside evidence of reduced serine\nuptake upon ASNase treatment (updated figure 2a) and increased total serine levels in\nASNase-treated cells cultivated in the absence of extracellular serine/glycine (new figure 2b)—\nstrengthen our mechanistic model. They provide robust evidence that ASNase treatment\ninduces metabolic rewiring and that de novo serine biosynthesis has a significant contribution\nover extracellular serine uptake in this context.\n10\n3. I don’t think the rationale to decouple asparagine biosynthesis from serine\nbiosynthesis is strong enough. The fact that ASNS OE only provided partial resistance\nto ASNase treatment can simply be due to the level of OE of ASNS is not sufficient to\ngenerate enough asparagine in a given period of time. If the manuscript intends to argue\nserine biosynthesis can contribute to ASNase resistance independently of ASNS,\nthey have to use CRISPR to delete Asns gene in BCLs and to show that resistance can\nstill occur in a serine biosynthesis-dependent manner.\nWe fully agree that asparagine synthase is indeed a primary determinant of ASNase sensitivity,\nparticularly in the context of asparagine-auxotrophic hematological cancers (such as ALL and\nNKTCL). In line with this, our previous study (PMID: 35857457) demonstrated that Asns-null\nEµ-Myc cells fail to initiate B-cell lymphoma development in C57BL/6 mice under ASNase\ntreatment. Consequently, Asns-null models are not optimum for the identification of other\nmetabolic deregulation that may occur during ASNase treatment, particularly in ASNase-\nsensitive, asparagine-synthetase expressing malignancies, such as B-cell lymphomas.\nWhile we cannot exclude the possibility that ASNS overexpression (ASNS OE) is insufficient\nto fully generate enough asparagine within 24 hours—potentially accounting for the sustained\nATF4 activation, this hypothesis appears unlikely. Indeed, in ASNS OE cells, the combined\nlevels of V5-tagged ASNS and endogenous ASNS protein exceed those seen in control cells\ntreated with ASNase (Figures 3d and S4l). This suggests that the observed partial resistance\nin ASNS OE cells is not simply due to suboptimal ASNS expression.\nImportantly, PHGDH inhibition significantly reduced the proliferation of ASNase-treated ASNS\nOE cells, indicating that PHGDH also contributes to their outgrowth, along with the expected\ninduction of endogenous asparagine synthetase.\nWe have now discussed this finding and toned down the conclusions on page 9, accordingly.\n4. I am not convinced that the argument of non-genetic resistance in Fig 4 is rigorous.\nLike what they showed in Fig 4D, serine biosynthetic enzymes and ASNS expression\nwent down during “drug holidays” likely due to a suppression of ATF4 activities. Thus,\nI don’t think the results in Fig 4 provided significant conceptual contribution to the\nentire manuscript.\nWe thank the reviewer for raising this important point regarding the nature of resistance\ndiscussed in Figure 4. We agree that our data do not conclusively prove that the resistance\nobserved is strictly non-genetic. Our intention was to convey that the resistance mechanism is\nunlikely to arise from stable genetic alterations such as mutations or translocations that would\nconfer irreversible resistance. Instead, we observed a reversible, adaptive response.\nHowever, considering the reviewer’s feedback and given the constraints on manuscript length,\nwe have opted to remove explicit references to “non-genetic resistance” from the text to avoid\noverstating this interpretation.\n5. It is interesting that ASNase treatment induced ROS and DNA damage, which was\nfurther exaggerated when PHGDH is inhibited. The manuscript should show that\nPHGDH OE (cells in Fig. 3F) mitigates the DNA damage responses in ASNase-\ntreated cells (Fig 6), which may further compromise the effect of PARP inhibitors (Fig 6E, 6F).\nWe thank the reviewer for this insightful suggestion. To address this, we have analyzed the\nexpression level of gH2AX and phosphorylated Chk1 at serine 345 (P-Chk1 Ser345), a\ndownstream target of ATR, in B-cell lymphomas harvested from C57BL/6 mice engrafted with\neither control Eµ-Myc cells or PHGDH overexpressing Eµ-Myc cells, that have been treated\nwith Vehicle or ASNase for several weeks until disease endpoint. Our results show that\nand P-ChK1 (at Ser345) expression levels in tumors in vivo, supporting the role of PHGDH in\nmitigating the DNA damage response following ASNase-treatment.\nThese results are now presented in the new figures 6i-j-k and is now discussed in the main\ntext on page 13.\nMinor Concerns:\n1. Fig 1C & 1D the symbols of vehicle and ASNase are the same.\nThank you for pointing this out. In the revised figures 1c and 1d we have adjusted the symbols\nto clearly distinguish the two experimental groups.\n2. In Fig 2I, the total glucose-derived carbon is less than 20%, with majority of them\nbeing m+1 with no clear interpretation. In addition, to prove 6% Asn were labeled with\nglucose-derived carbon is unnecessary as no direct connection between serine\nbiosynthesis and PDH activity (Fig 2K). If they need to show something, it should be\nmore glutamine-derived carbon into alpha-KG, aspartate and asparagine due to\nenhance PSAT1 activity.\nWe thank the reviewer for this important comment. We agree that 13C -Glutamine would be a\n5\nmore appropriate tracer to demonstrate de novo asparagine biosynthesis and enhanced\nPSAT1 activity in tumors in vivo. However, prolonged ASNase treatment in mice induces a\nsystemic glutamine addiction in multiples tissues, which leads to a rapid compensatory\nincrease in asparagine synthesis and elevated plasma asparagine levels following 13C -\n5\nglutamine administration. This systemic response confounds the interpretation of tumor-\nspecific metabolic rewiring under ASNase treatment in vivo. In contrast, [U-13C]-glucose bolus\ndoes not provoke this systemic effect, which is why we selected it for our in vivo tracing\nexperiments.\nWe acknowledge that glucose is not the optimal substrate to trace asparagine biosynthesis.\nHowever, we did observe increased levels of carbon-labelled asparagine (m+2) derived from\n[U-13C]-glucose in ASNase-treated B-cell lymphomas despite an overall reduction in total\nasparagine levels (updated figures S3d-e). We interpret this as evidence of an internal\ncompensatory response to systemic asparagine depletion, occurring alongside with increased\nlevels of de novo-synthesized serine and glycine.\nFurthermore, in vitro experiments with asparagine-restricted Eµ-Myc cells (updated figure S3e)\nshowed elevated levels of carbon-labelled asparagine (m+2 and m+3) derived from [U-13C]-\nglucose, confirming that glucose can also be used as a substrate for de novo asparagine\nbiosynthesis under asparagine limitation.\nTherefore, in the revised version the updated figure S3e now highlights the abundance of\nglucose-derived asparagine (m+2) in tumors harvested from ASNase-treated versus vehicle-\ntreated mice, strengthening our interpretation of the metabolic adaptation induced by ASNase\ntreatment.\n3. Fig 3D needs to provide the result from control cells without ASNS OE as well as a\nblot of ATF4, so readers can evaluate the levels of OE and the degree of amino acid\nstarvation.\nWe thank the reviewer for his comment. In a revised figure 3d, we have included immunoblots\nfor ATF4, PHGDH, PSAT1, PSPH and ASNS expression levels in control and in asparagine\n12\nsynthetase overexpressing (ASNS OE) Eµ-Myc cells following 24 hours of treatment with or\nwithout ASNase.\nWe have also included in the new figure S4l the relative quantification of expression levels of\nthese proteins, from 4 independent experiments. We hope it will help the reader to evaluate\nthe level of nutritional stress and of serine biosynthesis enzymes we discuss in the main text\non page 9.\n4. For most amino acid measuring, such as the results in Fig 4I, the manuscript should\nprovide some other AAs, including EAAs and NEAAs, to determine whether their\nobservations are specific to serine.\nWe have included the new figures 1e, S1e and a new Table S2, highlighting serine, glycine,\ntyrosine and asparagine as the only proteinogenic amino acids deregulated in B-cell\nlymphomas treated with ASNase in vivo (meeting the criteria of fold change (ASNase/Vehicle)\n>1.25 and raw P-value < 0.05), a finding consistent across both datasets. This notion is now\nindicated in the main text on page 6.\n5. The discussion between Glycine and GSH biosynthesis seems to be distractive. The\nresult in Fig S5 is an evidence of increased GSH biosynthesis likely due to the increased\nexpression of GSH biosynthetic enzymes. Since serine and glycine biosynthesis is\nconnected through one-carbon unit metabolism, if the manuscript does not want to\nmake the point that glycine is derived from serine biosynthetic pathway there is no\nsignificant addition to the point they have already made in Fig 5.\nTo clarify and address the concern, we have added in the revised version, the new figures 2e-\nf showing increased abundance of carbon-labelled 13C -serine and 13C -glycine derived from\n3 2\n[U-13C]-glucose in ASNase-treated cells in vitro. These results indicate that a fraction of total\nglycine is derived from serine biosynthetic pathway. This is now discussed in the main text on\npages 7 and 8.\nIn response to the reviewer’s suggestion, we have also simplified and updated figure S6\n(formerly figure S5) to highlight increased carbon-labelled GSH (m+2) levels derived from 13C -\n2\nglycine in ASNase-treated Eµ-Myc cells, supporting enhanced GSH biosynthesis under these\nconditions.\n6. Line 476, they should cite both Fig 2A and Fig S1J.\nWe thank the reviewer for pointing out this oversight. We have cited the revised figure S1i,\nalong with figure 2a, in the main text on page 7, accordingly.\nWe also identified that the original figure S1j mistakenly represented glycine levels in Eµ-Myc\n#506 cells, but not serine levels in Eµ-Myc #688 cells. This has been corrected. Glycine levels\nin asparagine-deprived Eµ-Myc #506 cells are now presented in the new figure 1m, and serine\nand glycine levels in asparagine-deprived Eµ-Myc #688, are shown in the new figures S1i-j.\nWe sincerely apologize for the confusion.\nvery rapid and efficient evalua6on of our revised submission. We greatly appreciate the careful\nconsidera6on of the addi6onal concerns raised and have read the editorial opinion and\nreviewers’ comments with much interest. We have carefully addressed all points to the best\nof our ability, and we believe that these revisions have significantly strengthened the\nmanuscript. We are grateful to the reviewers for their valuable input, which has allowed us to\nfurther improve the clarity and impact of our work.\nComments to the authors, our reply (in blue)\nReviewer #1 (Remarks to the Author):\nThe authors have addressed the major concerns, and the revised version of the manuscript\nis substan@ally strengthened. I am a bit disappointed not to see a rescue experiment of an\nshRNA targe@ng a protein-coding region of Phgdh (in response to previous cri@que #2a); this\ncan be readily achieved by using a cDNA expression construct with wobble-base\nsubs@tu@ons that alter the sequence targeted by the shRNA without altering the protein\nproduced. However, overall the response is sa@sfactory, and includes quite strong new data\nin support of the authors model.\nWe thank the reviewer for the posi6ve evalua6on of our revised manuscript and for\nacknowledging that the new data strongly support our model. We would like to emphasize\nthat the role of PHGDH in the escape mechanism from L-asparaginase was demonstrated using\nboth pharmacological and gene6c approaches, performed both in vitro and in vivo. We believe\nthat these complementary strategies provide robust and convergent evidence for our\nconclusions.\nWe agree with the reviewer that the proposed rescue experiment would be an elegant\napproach to further reinforce these findings. However, due to technical limita6ons inherent to\nour model, this approach is not actually technically feasible. Indeed, our study u6lizes primary\nmurine malignant B cells, which are inherently difficult to gene6cally modify by any\ntechniques. Such an experiment would require simultaneous gene6c modifica6ons of our cells\n—namely, Phgdh knockdown (KD) followed by re-expression at physiological levels using a\nPhgdh cDNA construct insensi6ve to the knockdown—which is impossible to achieve in this\nsystem. Furthermore, these primary cells cannot be maintained long-term in vitro, as extended\nculture induces a metabolic shiS driven by the ar6ficial condi6ons, thereby limi6ng the\nfeasibility of mul6ple or sequen6al gene6c manipula6ons.\nWhile we regret this limita6on, we believe that the combina6on of independent gene6c and\npharmacological approaches already provides strong and convincing support for the\nimplica6on of PHGDH in the adapta6ve resistance mechanism during ASNase therapy.\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communica6ons ini6a6ve to facilitate training in peer review and to\nprovide appropriate recogni6on for Early Career Researchers who co-review manuscripts.\nWe thank reviewer #2 for co-reviewing our manuscript.\nThe authors have made significant efforts to respond to previous reviewer comments,\nincluding new gene@c approaches and expanded mechanis@c insights into DNA damage\nresponses. However, despite these efforts, I find that the current version s@ll falls short of\nthe quality standards expected for Nature Communica@ons. In par@cular, the central role of\nPHGDH as a therapeu@c target remains insufficiently substan@ated, with overreliance on a\nsingle unstable compound.\nWe thank the reviewer for acknowledging the substan6al addi6onal efforts presented in this\nrevised version, including the new gene6c approaches and the expanded mechanis6c insight\ninto DNA damage responses.\nWe would like to respec\\ully clarify a possible misunderstanding: our study does not posi6on\nPHGDH itself as a therapeu6c target. The lack of clinically applicable, highly specific PHGDH\ninhibitors — along with the well-documented abili6es of malignant cells to bypass amino acid\nanabolism inhibi6on through increased amino acid uptake— limits its direct transla6onal\npoten6al.\nInstead, our study provides mechanis6c evidence that PHGDH contributes to the adap6ve\nresistance mechanism of B-cell lymphomas during L-asparaginase treatment. This conclusion\nis supported by the use of a highly specific PHGDH inhibitor in vitro (which, however, is not\nsuitable for in vivo applica6on, see below), and further reinforced by complementary gene6c\nex6nc6on approaches performed both in vitro and in vivo. We therefore believe that the\nevidence for PHGDH involvement in the escape mechanism is robust and not dependent on a\nsingle compound.\nMost importantly, the central message of our work is that this metabolic adapta6on to ASNase\ntherapy uncovers intrinsic tumor cell vulnerability that can be immediately exploited using the\nclinically approved PARP inhibitor, Olaparib. We demonstrate that L-asparaginase–adapted\nmalignant B cells exhibit DNA damage, ac6vate DNA damage response and enhance PARP\nac6vity, which render them sensi6ve to PARP1/2 inhibi6on. We believe that this finding —\nrepurposing an established class of drugs to target B-cell lymphomas by leveraging treatment-\ninduced metabolic plas6city — represents the true transla6onal significance of our study.\nKey point:\nThe manuscript heavily relies on BI4916 as a PHGDH inhibitor, but the authors acknowledge\nits instability and off-target concerns. The observa@on that other reported PHGDH inhibitors\n(NCT-503, CBR-5884, etc.) show cytotoxicity without reducing PHGDH ac@vity raises\nconcerns about specificity and off-target effects of BI4916 in Eμ-Myc cells. To strengthen the\nrole of PHGDH, the authors should include data using PHGDH-overexpressing cells, ideally\nwith cataly@cally inac@ve mutant to dissect enzyma@c func@on from expression effects.\nWe thank the reviewer for this important comment and the opportunity to clarify our\nra6onale. We respec\\ully disagree with the concern that our conclusions rely on an unstable\nor non-specific compound. BI4916 is, to our knowledge, the most specific and effec6ve PHGDH\ninhibitor currently available for cell-based studies (PMID: 35577770). While its\npharmacokine6c proper6es preclude its use in vivo, it has been reported to be highly specific\nand potent in inhibi6ng PHGDH and the de novo serine biosynthesis in vitro assays.\ncompounds of all major classes of commercially available allosteric PHGDH inhibitors (NCT-\n503, CBR-5884, PKUMDL-WQ2101). While these inhibitors were able to reduce PHGDH ac6vity\nin biochemical assays using protein extracts, their efficacy was consistently inferior to that of\nthe compe66ve inhibitors BI4916 and BI4924 (see Figure 1, for reviewer only), consistent with\nprevious findings (PMID: 35577770). For instance, PKUMDL-WQ2101 and NCT-503 reduced\nPHGDH ac6vity by approximately 50 - 60% at concentra6ons of 1 mM and 100 µM, respec6vely\n—at least 10 to 100-fold higher than the concentra6ons required for BI4916 or BI4924 to\nachieve comparable inhibi6on. Similarly, 10 µM of CBR5884 inhibited ~60% of PHGDH ac6vity\nin biochemical assays, but failed to inhibit PHGDH in intact Eµ-Myc cells, in contrast to BI4916\n(please refer to figures S4a to S4d of the study). More broadly, most allosteric PHGDH\ninhibitors exhibited non-specific cytotoxicity and were unable to suppress PHGDH ac6vity in\nintact Eµ-Myc cells (see previous response to reviewer and figures S4a to S4e of the study),\nthereby rendering them unsuitable for inves6ga6ng the role of PHGDH role in our model. This\nstrongly supports our decision to exclude them from the present study.\nTo overcome these limita6ons — and given that BI4916 is not suitable for in vivo use — we\ncomplemented our inhibitor-based experiments with gene6c knock-down of PHGDH using\nindependent shRNA sequences. This strategy enabled us to circumvent reliance on a single\ninhibitor. Importantly, experiments with shPhgdh-silenced cells were performed both in vitro\nand in vivo, thereby independently valida6ng the role of PHGDH in the adap6ve response to\nL-asparaginase. Together, these complementary pharmacological and gene6c approaches,\nacross mul6ple experimental readouts, provides strong and compelling evidence for the\ninvolvement of PHGDH in secondary resistance of ASNase-treated B-cell lymphomas.\nFinally, we would like to reiterate that our study does not propose PHGDH as a direct\ntherapeu6c target. Rather, our work delineates its mechanis6c role in protec6ng ASNase-\ntreated malignant B cells from oxida6ve stress, DNA damage, and ul6mately cell death. In\ncontrast, ASNase-induced metabolic adapta6on and oxida6ve stress promote DNA damage\nand trigger the DNA damage response, thereby iden6fying PARP as a ra6onal therapeu6c\ntarget to sensi6ze malignant B cells to ASNase-induced cell death. This therapeu6c axis\nrepresents the therapeu6c poten6al of our work.\nQuan@ta@ve analyses of immunoblots are provided only for newly added figures; this should\nbe consistently applied across all figures.\nWe have now performed and included quan6ta6ve analyses with appropriate sta6s6cal\ntreatment for all immunoblots throughout the manuscript to ensuring consistency in data\npresenta6on. The only excep6on is the detec6on of PARylated proteins in figure 7a (the results\nshown are representa6ve of 4 independent biological replicates). Since PARyla6on is a post-\ntraduc6onal modifica6on that affect mul6ple proteins simultaneously, the resul6ng signal\nappears as a heterogeneous smear rather than a discrete band on immunoblots, making\nreliable quan6fica6on unfeasible.\nCombining results from mul@ple cellular clones (e.g., Fig. 6F) as biological replicates without\ndis@nguishing clone iden@ty is inappropriate. If such combining is necessary, clones should\nbe clearly indicated with dis@nct colors or markers. The sta@s@cal treatment should be\nrevised to respect biological replicates versus technical repeats.\nWe thank the reviewer for this sugges6on. We have updated figures 2e, 5d, 6d-f-h and S2c to\ndis6nguish individual clones as recommended. Sta6s6cal analyses were checked accordingly.\nWe would like to clarify that, for all graphical quan6fica6ons of immunoblots, each data point\nrepresents an independent experiment. This informa6on has now been explicitly stated in the\nrespec6ve figure legends.\nDNA Damage Response Characteriza@on:\nThe new data iden@fying ATR-dependent DDR ac@va@on is appreciated and improves\nmechanis@c insight. The authors did not address ac@va@on of CHK2 despite being requested.\nWe thank the reviewer for its posi6ve feedback and for acknowledging that our work provides\nan extensive characteriza6on of the ATR-dependent DDR ac6va6on. We greatly appreciate this\nrecogni6on.\nRegarding Chk2, we tested several commercially available an6bodies targe6ng both the\nphosphorylated form on threonine 68 (T68) and total Chk2 protein. However, none of these\nan6bodies enabled specific detec6on of the murine CHK2 protein, and thus we were unable\nto directly assess its ac6va6on. To nonetheless valuate the poten6al ac6va6on of the ATM-\ndependent DNA damage response, we analyzed the phosphoryla6on of KAP1 on serine 824\n(S824) —a well-established downstream target of CHK2. As shown in figures 6b-c, we did not\nobserve phosphoryla6on of KAP1 (S824) in asparagine-deprived Eµ-Myc cells, sugges6ng that\nthe ATM/Chk2-dependent DNA damage response is likely not ac6vated under these\ncondi6ons.\nData showing that PHGDH overexpression acenuates γH2AX upon ASNase treatment and\nleads to accelerated lymphoma development are compelling. Perhaps, effects of PHGDH\noverexpression upon ASNase treatment (Fig. S7F) would be becer placed in an earlier figure\n(Fig. 3) to reinforce PHGDH’s central role in ASNase treatment. Ideally, compared to\ncataly@cally dead PHGDH mutant to clarify if enzyma@c ac@vity drives these effects.\nDDR\nand γH2AX data following PHGDH overexpression can remain as a mechanis@c endpoint.\nthe descrip6on of PHGDH’s role in the adap6ve resistance mechanism during ASNase\ntreatment, we have relocated supplementary figures S7d-e-f to supplementary figures S4l-m-\nn (now part of supplementary figure 4, related to figure 3), as recommended. The\ncorresponding text has also been updated accordingly in page 10 of the manuscript.\nWhile comparing PHGDH overexpression to a cataly6cally dead mutant is scien6fically\ninteres6ng, such an experiment would require successive gene6c modifica6ons of our cells\n—namely, Phgdh knockdown followed by re-expression of a cataly6cally inac6ve mutant—which\nis technically unfeasible in our system for two main reasons : First, our study relies on primary\nmurine malignant B cells, which are inherently difficult to gene6cally modify by any\ntechniques. Second, these primary cells cannot be maintained long-term in vitro, as extended\nculture leads to a metabolic shiS that confound the biological relevance of the model and\npreclude sequen6al manipula6ons. Establishing such a system would therefore be a lengthy\nand technically challenging endeavor, well beyond the scope of the present study.\nFor these reasons, we focused on DDR signaling and γH2AX levels as mechanis6c endpoints,\nwhich we believe provide robust and sufficiently evidence for PHGDH’s role in the adap6ve\nresponse to L-asparaginase.\nOther:\nThere is a probable missing word “dependent” in “two OxPhos Eu-Myc lines” (line 418) and\nfigure 2J legend.\nWe thank the reviewer for poin6ng this out. The missing word “dependent” has been added\nin the main text in page 14 and in the figure 2J and its corresponding legend in page 35.\nIt would conceptually strengthen the study if the authors showed that ASNase plus\nOlaparib treatment spares non-cancerous cells or remains effec@ve on ASNase-resistant\ncells.\nWe agree with the reviewer that these experiments would be conceptually valuable. However,\nthey would require extensive addi6onal work and are not feasible within the scope of the\ncurrent study. We hope to address these important ques6ons in future inves6ga6ons.\nReviewer #4 (Remarks to the Author):\nI appreciated the author’s effort to address most of my concerns experimentally.\nWhat I like about the manuscript is that they found that asparaginase-treated B-cell\nlymphoma became reliant on serine/glycine synthesis de novo. This is at least par@ally\nbecause serine/glycine and its downstream pathways are cri@cal to mi@gate oxida@ve\ndamage and thus compromise DNA damage response that is induced by asparaginase\ntreatment or asparagine deple@on. As a result, there is a synergy by combining asparaginase\nwith DNA repairing inhibitors (such as the PARP1/2 inhibitor) to treat these tumors.\nHowever, what I am not convinced at all is that this phenotype is independent of the ASNS\nexpression and/or the capacity of the cells to produce asparagine de novo. Indeed, even in\n5\ntheir previous publica@on (PMID: 35857457), I do not think the authors addressed this\nques@on rigorously. It’s well known that the capacity of a cancer cell to be sensi@ve to\nasparaginase (or exogenous asparagine deple@on) in vitro rely on two factors: (1) The\nexpression of ASNS at their basal levels; (2) Their capacity to induce the expression of ASNS\nfollowing asparagine deple@on. This second factor relies also on two addi@onal status:\n(a)\nthe ac@va@on of GCN2/ATF4 pathway; (b) the status of the promoter methyla@on of the\nASNS gene (PMID: 30061420, PMID: 31659118, PMID: 31068703, et.c) The current\nmanuscript and their previous publica@on mainly focus on the basal level expression of\nASNS, and did not address the capacity of these cells to induce the expression of ASNS\ncomprehensively.\nWhat their data suggests is that: Some mouse B-cell lymphoma lines express a rela@vely low\nlevels of ASNS. When they were treated with asparaginase, they will ac@vate the\nGCN2/ATF4/ASNS axis; however, (1) they are unable to express ASNS high enough within a\ngiven period of @me; (2) since ATF4 also regulates serine biosynthe@c enzymes, including\nPHGDH, PSAT1, PSPH, these cells become dependent on serine biosynthesis more than they\nneed at the @me asparagine is replete (Figure 1 & 2) ; (3) Indeed, the fact that ASNS OE cells\n(Figure 3d) can s@ll turn on ATF4/PHGDH/PSAT1/PSPH, although in a compromised way,\nwhen treated with asparaginase, suggests that these cells do not express enough ASNS to\ncompletely mi@gate the asparagine starva@on-induced amino acid stress; (4) Furthermore,\nthe results in Figure 4 suggested that ATF4-dependent induc@on of ASNS is the key to\nmaintain this reversible phenotype, which is well-known in the field of integrated stress\nresponses (ISR) due to the established feedback loops on ATF4 (PMID: 36943285, and many\nother examples). Therefore, their results can be explained simply as a consequence of these\ncells to turn on the ATF4 pathway because these cells cannot express ASNS high enough\nfollowing asparaginase treatment.\nI think the manuscript should avoid experimental design and any indica@on that their\nobserva@on is independently of the expression/induc@on of ASNS or the capacity of the cells\nto produce asparagine (which was not determined rigorously too).\nWe thank the reviewer for the detailed and though\\ul comments, and for acknowledging the\nstrengths of our study. We fully agree with the reviewer that ASNS regula6on, expression and\nac6vity are cri6cal determinants of cellular response to asparaginase therapy. We also\nacknowledge reviewer’s point regarding the poten6al connec6on between the ability of\nmalignant B cells to synthe6zed asparagine de novo during ASNase treatment and the\nmetabolic deregula6ons we iden6fied upon treatment.\nIndeed, we cannot exclude the possibility that ASNS expression and/or ac6vity may be\ninsufficient to restore asparagine to levels that fully suppress ATF4 ac6va6on. In this scenario,\nmalignant cells would remain reliant on de novo serine biosynthesis, consistent with the fact\nthat serine pathway enzymes are well-established ATF4 targets. We agree that a rigorous\ndissec6on of whether ac6va6on of de novo serine biosynthesis pathway depends on ASNS\nexpression or on the amount of asparagine synthe6zed upon ASNase treatment, represents\nan important and conceptually interes6ng ques6on that. However, this lies beyond the scope\nof the present study and will require dedicated future inves6ga6on.\nconfusion. This adjustment does not alter our overall conclusions but clarifies the presenta6on\nof the data. Furthermore, we have revised the text in the Discussion (pages 15-16) to ensure\nthat our observa6ons are not interpreted as evidence of independence from ASNS expression,\ninduc6on, or ac6vity.\nhave read the editorial opinion and reviewers’ comments with much interest. We have\ncarefully addressed all points to the best of our ability.\nComments to the authors, our reply (in blue)\nReviewer #3 (Remarks to the Author):\nI appreciate the authors’ responses. However, I remain unconvinced that the major\nconcerns raised in the prior review round have been addressed. In several cases, the\nauthors provide lengthy justifications but little new experimental data.\nThe authors insist that their study does not position PHGDH as a therapeutic target.\nYet, the manuscript still states that PHGDH is “a clinically relevant target in human B-\ncell lymphomas” (lines 462-463). This should be revised to avoid conflating\nmechanistic contribution with therapeutic tractability.\nWe acknowledge that a recent study using the Eµ-Myc mouse model (PMID:\n38357928) described PHGDH as a “clinically relevant target” in human B-cell\nlymphomas. To address the reviewer’s concern, we have revised the sentence in the\nDiscussion, to highlight the mechanistic role of PHGDH in lymphoma progression\nwithout implying therapeutic potential.\nThe central mechanistic claims still depend heavily on BI4916, despite its instability\nand potential off-target issues. The authors argue that shRNA silencing compensates\nfor this weakness, but this does not resolve the problem. The most direct way to\nseparate enzymatic activity from expression effects would be to include a catalytically\ninactive PHGDH mutant alongside wild-type overexpression — which should be\nfeasible given that overexpression experiments were already performed. Instead, the\nauthors provide only a rationale for why this was “technically unfeasible,” which is not\nconvincing.\nWe respectfully disagree with the concern that our mechanistic conclusions rely on an\nunstable or non-specific compound. BI4916 is, to our knowledge, the most selective\nand potent PHGDH inhibitor currently available for cell-based studies (as attested in\nthis publication : PMID: 35577770). While its pharmacokinetic properties limit its in vivo\napplicability, multiple independent studies have demonstrated its high specificity and\nrobust inhibition of PHGDH activity and de novo serine biosynthesis in vitro.\nTo address any limitations inherent to pharmacological inhibition, we complemented\nthe BI4916 experiments with PHGDH genetic silencing using two independent shRNA\nsequences. The concordant results obtained across pharmacological and genetic\nperturbations, further corroborated by our metabolomic analyses, provide strong\nevidence that PHGDH plays a critical role in secondary resistance in ASNase-treated\nB-cell lymphomas.\nWe fully agree that incorporating a catalytically inactive PHGDH mutant could, in\nprinciple, help to distinguish catalytic from non-catalytic functions. However, there are\nsubstantial technical barriers that currently preclude this approach in our experimental\nsystem. First, all published studies employing catalytically inactive PHGDH variants\nhave been conducted in settings where endogenous PHGDH is absent—either through\ncell lines (e.g., PMIDs 25406093, 25926973, 30017355, 36823188, 37726403,\n11751922). To our knowledge, no study has successfully introduced a catalytically\ninactive PHGDH mutant into cells with intact endogenous PHGDH expression, nor has\nany dominant-negative PHGDH mutant been validated. Identifying a mutation capable\nof exerting dominant-negative activity over endogenous PHGDH would require\nextensive protein-engineering and functional validation and, realistically, constitutes an\nindependent project.\nSecond, the use of primary B-cell lymphoma models imposes additional and significant\nconstraints. These cells are notoriously refractory to genetic manipulation.\nImplementing sequential modifications—knockdown of endogenous PHGDH followed\nby physiological re-expression of a catalytically inactive mutant—has not, to our\nknowledge, been achieved in primary lymphoma B cells. The technical feasibility of\nsuch an approach is therefore extremely low within the scope and timeline of a revision,\nand the outcome uncertain even with substantial optimization efforts.\nWhile we acknowledge the conceptual value of dissecting catalytic versus non-catalytic\ncontributions of PHGDH, we believe that the combination of orthogonal\npharmacological and genetic perturbations presented here provides compelling and\nrigorous evidence for a causal role of PHGDH in adaptive resistance to ASNase\ntherapy. We agree that further exploration of non-canonical PHGDH functions would\nbe scientifically valuable, but given the substantial technical constraints outlined above,\nsuch studies are better suited to a dedicated follow-up investigation rather than\ninclusion in the current revision.\nAlthough the authors claim that all immunoblots were quantified with appropriate\nstatistics, this is not the case. For example:\n• Fig. 2H: Missing quantification/statistics for ASNS and ATF4.\n• Fig. 4D: Numerical basis unclear, no statistics.\n• Fig. 5L: Numerical basis unclear, no statistics.\n• Fig. 6A: Numerical basis unclear, no statistics.\n• Fig. 6B: No quantification/statistics at 6 h and 12 h.\nWe have now quantified all immunoblots presented in the study.\nIn particular, the quantifications of ASNS and ATF4 expression levels associated with\nfigure 2h have been added in figure S2i. In figures 4d, 5l and 6a, numerical values\nhave been removed and the relative quantifications of the protein expression levels is\nnow presented in figure S5c, 5m and 6b, respectively. Finally, the quantification of\nimmunoblots previously shown in the former figure 6b (now figure 6c) is presented in\nfigures 6d.\nThe relocation of PHGDH overexpression data improves the manuscript’s flow, but the\nmechanistic point remains incomplete without comparison to a catalytically inactive\nmutant. Importantly, the authors have already performed and presented\noverexpression of wild-type PHGDH. Given this, it is not justified why parallel\noverexpression of a catalytically inactive mutant could not be done within the same\nsystem. Such an experiment would directly address whether the observed effects\n(γH2AX induction, lymphoma acceleration) depend on PHGDH’s enzymatic activity\nrather than potential non-catalytic functions.\nWe appreciate the reviewer’s continued emphasis on distinguishing catalytic from non-\ncatalytic PHGDH functions. As outlined above, introducing a catalytically inactive\nvalidated in PHGDH-null or PHGDH-depleted backgrounds, and no dominant-negative\nvariant capable of functioning in the presence of endogenous PHGDH has been\ndescribed. Implementing sequential knockdown and re-expression strategies in\nprimary B-cell lymphomas—cells that are extremely resistant to genetic\nmanipulation—have, to our knowledge, never been achieved and would require\nextensive methodological development beyond the scope of a revision. While we\nacknowledge the conceptual value of this experiment, the combined genetic and\npharmacological evidence we provide already robustly supports a requirement for\nPHGDH activity in the observed phenotype.\nThe authors’ central translational claim is that ASNase adaptation creates a\nvulnerability exploitable by PARP inhibition. Yet they do not provide any data on (i)\neffects in non-malignant cells, or (ii) efficacy in ASNase-resistant lymphoma cells.\nSimply stating that these experiments are “beyond the scope” is unsatisfactory.\nIn summary, I cannot support publication of current version of this manuscript in Nature\nCommunications.\nWe thank the reviewer for these important points. Our study was specifically designed\nto investigate secondary (adaptive) resistance to ASNase, which emerges only after\nan initial therapeutic response. This clinical scenario is biologically and mechanistically\ndistinct from primary ASNase resistance, for which several pathways have already\nbeen well characterized (e.g., ASNS promoter methylation, SLC1A3 expression,\nproteasomal salvage mechanisms; PMIDs: 30659025, 31523835, 30991026).\nBecause our mechanistic aim was to delineate how lymphoma cells rewire metabolism\nafter responding to ASNase, models of primary resistance fall outside the conceptual\nframework of the present work.\nSimilarly, the study was not designed to assess PARP inhibitor effects in non-\nmalignant cells. While clearly relevant for future translational development, evaluating\ntherapeutic windows and potential toxicities requires dedicated in vivo and ex vivo\nassays and is beyond what can be meaningfully incorporated within a revision focused\non mechanistic discovery.\nImportantly, our findings provide a strong rationale for such follow-up studies. We\nidentify a specific ASNase-induced metabolic state that creates a selective vulnerability\nto PARP inhibition. We agree that future preclinical work should assess (i) the safety\nprofile of this combination in non-malignant cells, and (ii) its potential impact on primary\nresistant models—each requiring dedicated experimental designs distinct from the\nmechanistic questions addressed here.\n3"}]

Decision letter