Metabolic Signatures of Dual mTOR Inhibition in Diffuse Large B-Cell Lymphoma

Metabolic Signatures of Dual mTOR Inhibition in Diffuse Large B-Cell Lymphoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Metabolic Signatures of Dual mTOR Inhibition in Diffuse Large B-Cell Lymphoma Kavindra Nath, Pradeep Gupta, Shengchun Wang, Shilpa Rao, Mamta Gupta, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8928389/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Diffuse large B-cell lymphoma (DLBCL) is an aggressive and heterogeneous malignancy in which many patients relapse or fail to respond to standard therapies. Constitutive activation of the mechanistic target of rapamycin (mTOR) pathway, involving both mTOR complex 1 (mTORC1) and mTOR complex 2 (mTORC2), promotes tumor growth and metabolic reprogramming. First-generation inhibitors targeting mTORC1 alone have shown limited efficacy, partly due to incomplete pathway suppression and compensatory mTORC2 signaling. We evaluated Torin2, a potent ATP-competitive mTOR kinase inhibitor targeting both complexes, in four DLBCL cell lines with variable sensitivity to mTORC1 inhibition. In responsive models, Torin2 suppressed proliferation, induced apoptosis, impaired cell-cycle progression, and downregulated metabolic and proliferative transcriptional programs. Integrated metabolomic and transcriptomic analyses demonstrated broad inhibition of glycolysis, amino acid metabolism, and phospholipid biosynthesis. Torin2 reduced lactate and alanine levels detectable by noninvasive proton magnetic resonance spectroscopy ( 1 H MRS) in vitro and in mouse xenografts, with metabolic changes paralleling tumor growth inhibition. Modulation of choline-containing metabolites further distinguished sensitive from less responsive tumors. These findings show that dual mTORC1/mTORC2 inhibition disrupts metabolic dependencies critical for DLBCL growth and identify 1 H MRS-detectable metabolites as noninvasive pharmacodynamic biomarkers for response assessment and therapeutic stratification in mTOR targeted lymphoma therapy. Biological sciences/Cancer/Cancer imaging Biological sciences/Cancer/Cancer metabolism Diffuse large B-cell lymphoma (DLBCL). Mechanistic target of rapamycin (mTOR) Proton magnetic resonance spectroscopy (1H MRS) Tricarboxylic or Citric acid cycle (TCA) Signaling inhibition RNA Sequence analysis (RNA-Seq) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Diffuse large B-cell lymphoma (DLBCL) is the most common and aggressive subtype of non-Hodgkin lymphoma and is characterized by marked clinical and molecular heterogeneity [ 1 ]. Although chemoimmunotherapy regimens such as rituximab combined with cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) or R-CHOP-like therapies have improved patient outcomes, approximately one-third of patients develop refractory disease or relapse, underscoring the need for more effective targeted therapies [ 1 – 3 ]. The mechanistic target of rapamycin (mTOR) is a central regulator of cell growth, survival, and metabolic reprogramming, integrating signals from PI3K/AKT and other oncogenic pathways [ 4 – 7 ]. mTOR operates through two multiprotein complexes, mTORC1 and mTORC2 [ 8 ], both of which are frequently dysregulated in lymphoid malignancies, including DLBCL. Persistent activation of mTOR signaling has been linked to increased cell growth, altered metabolism, and therapeutic resistance [ 9 , 10 ]. First-generation mTOR inhibitors (rapalogs) primarily target mTORC1 but incompletely suppress pathway activity and may relieve negative feedback mechanism that results in compensatory AKT activation through mTORC2, thereby limiting their clinical efficacy [ 10 ]. These limitations have prompted the development of second-generation ATP-competitive inhibitors capable of directly targeting mTOR kinase and suppressing signaling through both complexes. Torin2 is a potent and highly selective ATP-competitive inhibitor of mTOR that blocks the activity of both mTORC1 and mTORC2 [ 11 ]. Preclinical studies have demonstrated that Torin2 elicits cytostatic and cytotoxic effects across multiple cancer models by inhibiting phosphorylation of downstream key effectors such as 4EBP1, S6K, and AKT, while increasing sensitivity to chemotherapeutic agents [ 11 , 12 ]. Because mTOR signaling regulates tumor metabolism, its inhibition should produce measurable metabolic changes that may serve as early indicators of treatment response. Proton magnetic resonance spectroscopy ( 1 H MRS) enables noninvasive quantification of metabolites linked to glycolysis, amino acid metabolism, and membrane turnover, providing a strategy to monitor targeted therapy in vivo . Here, Torin2 inhibited DLBCL cell and xenograft growth, disrupted glycolytic and oxidative metabolism, and suppressed growth-associated metabolites and gene expression. 1 H MRS detected reductions in lactate, alanine, and total choline preceding measurable tumor growth inhibition and strongly correlating with therapeutic response. These findings support dual mTORC1/mTORC2 inhibition as an effective approach in DLBCL and identify MRS-detectable metabolic alterations as early, noninvasive biomarkers of response to mTOR-targeted therapy. Materials and Methods mTOR inhibitor Torin2 was purchased from AmBeed (Arlington Heights, IL, USA). For in vitro experiments, Torin2 was dissolved in dimethyl sulfoxide (DMSO) and applied to cells at a final concentration of 250 nM. For in vivo studies, Torin2 was suspended in 2.2 mL of 10% hydroxypropyl-β-cyclodextrin solution and administered orally to mice at a dose of 10 mg/kg once daily. Cell lines and culture conditions The parental WSU-DLCL2 cell line was kindly provided by Dr. Mohammad Al-Katib (Wayne State University, Detroit, MI, USA) and maintained as previously described (Lee et al. PMID 19040203). A derivative of this line, partially resistant to a rapalog Everolimus (WSU-DLCL2PR) and JP cell line were generated in the laboratory of M.A. Wasik with the latter. The TMD8 cell line was obtained from a collaborator, Dr. Y. Yang, PhD, at Fox Chase Cancer Center. The cells were cultured in the 10% FBS RPMI1640 medium, authenticated, and checked periodically to exclude mycoplasma contamination. Xenograft development All animal studies were approved by the University of Pennsylvania Institutional Animal Care and Use Committee (IACUC). Ten million DLBCL cells were subcutaneously injected with Matrigel into male SCID mice obtained from the Stem Cell and Xenograft Core at the University of Pennsylvania. MRS studies were performed on hemispherical tumors of approximately 250 mm³, which provided sufficient size for optimal spectral quality and compatibility with the available radiofrequency coils. Cell count assay DLBCL cells (2 × 10 4 cells/well) were cultured in 96-well plates (Corning, Inc.) for 48 hours with various concentrations of Torin2. The cells were suspended in PBS containing trypan blue and counted microscopically using a hemocytometer. Cell growth assay Cells were plated in 96-well plates (3-5x10 3 cells/well), treated with Torin2 or the drug vehicle, labeled with MTT (Promega) at 5 mg/ml for 4 h, and solubilized with 10% SDS in 0.01 M HCl. The optical density (O.D.) of the culture supernatant, corresponding to the MTT conversion-mediated change in supernatant color, was determined at 570 nm using a Titertek Multiskan reader (Titertek Instruments). Cell cycle assay BrdU/7-AAD staining was performed using the FITC BrdU Flow Kit (BD Pharmingen) according to the manufacturer's protocol. Briefly, the cells were incubated for 1 h with BrdU (10 µM), treated with DNase, exposed to fluorescent anti-BrdU, and stained for total DNA. The cells were assayed using a FACSCan flow cytometer (BD Biosciences). The data were analyzed using FlowJo v10.8.0 software. Apoptotic cell death/DNA fragmentation (TUNEL) assay DLBCL cell lines were treated with Torin2 or drug medium for 72 hours. Cells were collected and fixed for TUNEL labeling (Roche Life Science) and analyzed by flow cytometry (LSR, BD Biosciences). Analysis of glucose metabolism and mitochondrial respiration DLBCL cell lines were analyzed using the Seahorse XFe96 Analyzer (Agilent) following the manufacturer’s protocols. Cells were seeded in 96-well plates at 1.2 × 10⁵ cells/well. For the glycolysis stress test, cells were cultured in medium supplemented with 2.0 mM glutamine, while for the mitochondrial stress test, medium contained 10.0 mM glucose, 1.0 mM sodium pyruvate, and 2.0 mM glutamine. The extracellular acidification rate (ECAR) was measured in glucose-free medium before and after sequential injections of 10.0 mM glucose, 1.0 µM oligomycin, and 50.0 mM 2-deoxy-D-glucose (2-DG). The oxygen consumption rate (OCR) was assessed under basal conditions and following sequential addition of 1.0 µM oligomycin A, 2.0 µM FCCP, and 0.5 µM rotenone/antimycin A. Protein content was quantified by the Bradford assay and used to normalize metabolic parameters. Each experiment was performed with at least six technical replicates, and data were analyzed using XF Wave software (Agilent). Glucose and lactate concentration analysis The amounts of secreted lactate and glucose taken up by each DLBCL cell line were calculated using a YSI Glucose/Lactate Analyzer (YSI 2300 STAT Plus, YSI). DLBCL cells were added to fresh medium at 1–2 million cells/mL, treated for up to 72 h with vehicle or 250 nM Torin2, and incubated for 4–6 h before measurement. RNA sequence analysis (RNA-Seq) RNA integrity was assessed using the Bioanalyzer 2100 system (Agilent Technologies, USA). mRNA was enriched with poly-T oligo-attached magnetic beads and fragmented, followed by first- and second-strand cDNA synthesis. Strand-specific libraries were prepared using dUTP incorporation in the second-strand synthesis, while non-strand-specific libraries were prepared without this modification. After end repair, A-tailing, adapter ligation, size selection, PCR amplification, and purification, library quality was evaluated with Qubit, qPCR, and the Bioanalyzer. Libraries were pooled and sequenced using Illumina sequencing-by-synthesis and paired-end 150 bp reads. Raw fastq files were trimmed to remove 3’ adapters using Cutadapt, and reads shorter than 50 bp were discarded. Trimmed reads were aligned to human genome version GRCh38 using STAR aligner (version 2.7.3a) with default settings, followed by summarization of raw counts for each gene using the featureCounts package and Ensembl gene models. Data analyses were performed in R using Bioconductor’s DESeq2, EdgeR and limma packages. Correlation heatmaps and PCA plots on variance stabilized transformed data were used to visualize overall sample relatedness. Differential expression analysis used the Limma-voom workflow. Low-expressing genes were filtered out using the filterbyExpr function. TMM normalization was applied across the samples. voom transformation to log2 counts per million (CPM) followed by linear model fitting using the lmfit function of limma package was performed, and differential expression was identified by contrasts between fitted Torin2 treated and control conditions at 12hr and 24hr timepoints for each cell population. A false discovery rate (FDR) cut-off of 5% (Benjamini-Hochberg adjusted P-value) was applied to select differentially expressed genes. The fgsea package was used for gene set enrichment analysis against the MSigDB’s Hallmarks gene set collection. Enrichment bar plots were generated using ggplot2. Torin2-affected gene sets were analyzed by Hallmark- and KEGG-based functional enrichment to identify corresponding functional cell pathways and programs. Metabolomic analysis The four DLBCL cell lines were analyzed in triplicates, 15 million cells per sample, by The Wistar Institute Proteomics and Metabolomics shared resource. All samples underwent extraction for polar metabolites using an ice-cold extraction solution comprising 80% MeOH and 20% water. Samples were analyzed by LC-MS/MS on a Q Exactive HF-X mass spectrometer equipped with an HESI II probe in-line with a ThermoScientific Vanquish LC System. The analysis was carried out in a pseudorandomized order. LC separation was performed under HILIC conditions using a ZIC-pHILIC column (150 × 2.1 mm, 5 µM) maintained at 45°C (EMD Millipore). Mobile phase A consisted of 20 mM ammonium carbonate, 0.1% ammonium hydroxide, pH 9.2, while mobile phase B was acetonitrile. Analytical separation was achieved at a flow rate of 0.2 ml/min using the following gradient: 0 min, 85% B; 2 min, 85% B; 17 min, 20% B; 17.1 min, 85% B; and 26 min, 85% B. Samples were analyzed using either full MS scans with polarity switching (for all samples) or full MS/data-dependent MS/MS scans with separate acquisitions for positive and negative polarities (for unlabeled samples in isotope tracing experiments; sample pool, QC in non-tracing experiments). Relevant MS parameters included: sheath gas at 40, auxiliary gas at 10, sweep gas at 2, auxiliary gas heater temperature at 350°C, spray voltage at 3.5/3.2 kV for positive/negative polarities, capillary temperature at 325°C, and S-lens RF at 40. Full MS scans were obtained using a scan range of 65 to 975 m/z, at a resolution of 120,000, with an automated gain control (AGC) target of 1E6 and maximum injection time (IT) of 100 ms. Data-dependent MS/MS was performed on the 10 most abundant ions with a resolution of 15,000, AGC target of 5E4, maximum IT of 50 ms, isolation width of 1.0 m/z, and stepped normalized collision energy of 20, 40, 60. Raw data were processed using Compound Discoverer 3.1 (ThermoFisher Scientific) with separate analyses for positive and negative polarities. Metabolites were identified by matching accurate mass and retention time to standards or by querying MS/MS scans against the mzCloud spectral database (full match, score > 50; mzCloud.org). Pathway impact and enrichment analyses were performed using the MetaboAnalyst 5.0 software program at https://www.metaboanalyst.ca . In vitro Measurement of Intracellular Metabolites by High-Resolution ¹H MRS All four DLBCL cell lines were cultured in T-182 suspension flasks and treated with either vehicle (0.1% DMSO) or Torin2 (250 nmol/L in 0.1% DMSO) for 48 h. Following treatment, 15 × 10⁶ cells were harvested, washed in cold PBS, and stored at − 80°C. Cell pellets were extracted in 80% methanol-water, homogenized, and sonicated (2–3 cycles), followed by centrifugation (16,000 × g, 10 min). Supernatants were transferred to labeled Eppendorf tubes and freeze-dried using the Labconco FreeZone 4.5 lyophilizer (Labconco Co., Kansas City, MO, USA). Lyophilized extracts were resuspended in 600 µL of deuterium oxide (D 2 O) containing 0.2 mmol/L TSP and transferred to a 5 mm NMR tube. The High-resolution ¹H MRS spectra were acquired on a 9.4 T/8.9 cm Varian spectrometer using a PRESAT sequence (45° flip angle, TR = 8.8 s, SW = 6756.8 Hz, 16,384 points, 128 scans). Data were processed with MestReC 6.1, applying a 1 Hz exponential filter, and peak areas were normalized to TSP and proton number for quantification. In Vivo ¹H MRS of Lactate, Alanine, and Choline In vivo MRS studies were carried out on a 9.4 T/31 cm horizontal bore Bruker (Billerica, MA, USA) console. 1 H MRS-detectable biomarkers were examined utilizing a custom single-frequency (1H) slotted tube resonator. 1 H MRS was performed on days 0, 2, and 7 after the oral administration of Torin2 at a dosage of 10 mg/kg, administered daily. This schedule allowed for the monitoring of biomarker changes over time in response to the treatment. WSU-DLCL2, TMD8, and JP tumors were examined in male SCID mice. To detect the lactate and alanine signals, 1 H MRS with Had-Sel-MQC transfer pulse sequence was utilized. The acquisition parameters were set as follows: NP = 1000, TR = 4 s, and NT = 32. Additionally, a localized water signal was acquired using a similar slice without water suppression (TR = 4 sec, NT = 4) to normalize the lactate and alanine signals [ 13 – 15 ]. Total choline (3.2 ppm) was quantified using a stimulated echo acquisition mode (STEAM) pulse sequence with the following parameters: NP = 2048, TR = 3 s, TE = 14 ms, and NT = 128. The in vivo MRS data were processed using NUTS and MestRec postprocessing software packages. To enhance the apparent signal-to-noise ratio of the 1 H MRS data, a 10 Hz exponential filter was applied, followed by baseline correction before plotting and calculating the peak areas. Tumor volume measurement Tumor dimensions were measured with calipers in three orthogonal directions. Volumes were calculated using the ellipsoid formula: V = π(a×b×c)/6, where a, b, and c are the length, width, and depth of the tumor, respectively. Statistical analysis Data in each figure are presented as the mean ± standard error of the mean (SEM). Two-tailed Student’s t-tests assuming equal variance were used to calculate p -values, with α = 0.05 considered statistically significant. Multiple paired t-tests were performed to analyze glycolytic and mitochondrial stress test results using Seahorse assays. Fluxes derived from metabolic network analysis, are reported as fitted flux ± standard deviation (SD) calculated using Monte Carlo simulations. Gene expression p- values were corrected for multiple comparisons using the Benjamini-Hochberg false discovery rate method. Metabolite intensities were normalized to total ion count and protein concentration. Significant differences were defined as fold change ≥ 1.5 and adjusted p < 0.05. Pathway enrichment analysis was performed using MetaboAnalyst 5.0. Results mTOR inhibition suppresses DLBCL growth, cell-cycle progression, and survival Direct mTOR inhibition with Torin2 suppressed proliferation across four DLBCL cell lines, with greatest sensitivity in WSU-DLCL2 and its everolimus-partially resistant derivative WSU-DLCL2PR (IC₅₀ ≈ 0.02 µM), intermediate effects in TMD8 (IC₅₀ ≈ 0.1 µM), and relative resistance in JP cells (IC₅₀ ~2 µM) (Fig. 1 A). Similar results with sapanisertib confirmed on-target activity (Fig. S1 ). Torin2 (250 nM) markedly inhibited G1–S progression and increased the sub-G0/G1 fraction in all lines (Fig. 1 B,C), consistent with cytotoxicity, and induced apoptosis (31–48% across models; Fig. 1 D). mTOR inhibition disrupts glycolysis and mitochondrial respiration Seahorse analysis demonstrated impaired glycolytic function following Torin2, with the strongest effects in WSU-DLCL2 and WSU-DLCL2PR (Fig. 2 A). Oxygen consumption was similarly reduced across all models, indicating suppressed mitochondrial respiration (Fig. 2 B). Consistent with these findings, Torin2 decreased glucose consumption and lactate production proportional to growth inhibition (Fig. 2 C). Global metabolomic suppression following mTOR inhibition LC–MS metabolomics revealed broad reductions in metabolites across nucleotide synthesis, amino acid metabolism (including alanine), the TCA cycle, and glycolysis, including lactate (Fig. 3 ; Figs. S2–4), indicating that mTOR supports DLBCL growth through coordinated regulation of bioenergetics and biosynthesis. Transcriptomic effects of mTOR inhibition RNA-seq demonstrated downregulation of pathways governing cell-cycle progression (E2F targets, G2/M checkpoint), c-MYC signaling, oxidative phosphorylation, fatty acid metabolism, and glycolysis (Fig. 4 ; Figs. S6–8). Suppression of PI3K/AKT-mTOR signaling provided an internal control. KEGG analysis confirmed inhibition of pyrimidine metabolism and TCA pathways, indicating transcriptional regulation of metabolic programs. In vitro ¹H MRS detects metabolic consequences of mTOR inhibition 1 H MRS revealed significant reductions in lactate and alanine across DLBCL cells, correlating with growth inhibition (Fig. 5 ). Phosphocholine decreased in highly sensitive WSU-DLCL2 and WSU-DLCL2PR, remained unchanged in TMD8, and increased in JP cells, suggesting compensatory membrane anabolic activity in less responsive models. In vivo metabolic imaging correlates with tumor response In xenografts, 1 H MRS detected reductions in lactate and alanine following Torin2 treatment, greatest in WSU-DLCL2 and more modest in TMD8 and JP tumors, paralleling tumor growth inhibition (Fig. 6 ). Total choline decreased only in sensitive tumors. These findings identify lactate and alanine as robust metabolic biomarkers of mTOR inhibition, with choline-containing metabolites providing complementary information on tumor metabolic state and therapeutic sensitivity. Discussion Our findings demonstrate that direct mTOR inhibition significantly impacts growth and survival of DLBCL cells, with varying effectiveness across cell lines. Low doses of Torin2 profoundly suppressed growth of WSU-DLCL2 and the rapalog-poorly responsive WSU-DLCL2PR cells, whereas TMD8, and JP cells (the latter in particular), required higher drug concentrations. Inhibition of cell-cycle progression, evidenced by an essentially complete halt of S-phase entry in WSU-DLCL2 and WSU-DLCL2PR cells, near-complete suppression in TMD8, and still substantial inhibition in JP cells, underscores the central role of mTOR role in promoting G 0 /G 1 -to S-phase transition. The concomitant induction of apoptotic cell death further highlights mTOR as a regulator of cell survival, consistent with its established role in anti-apoptotic signaling [ 12 , 16 ]. Notably, the preserved sensitivity of the rapalog-poorly responsive WSU-DLCL2PR cells suggests that ATP-competitive mTOR inhibitors such as Torin2 may overcome limitations associated with rapalogs and provide enhanced therapeutic efficacy in DLBCL. Our combined metabolomic and 1 H MRS analyses indicate that decreases in lactate, alanine, and, to lesser extent, choline-containing metabolites represent metabolic signatures of effective mTOR inhibition. These metabolites play central roles in glycolysis, amino acid metabolism, phospholipid turnover, and biosynthetic processes required for cell proliferation, reinforcing the link between mTOR activity and cell growth and energy production [ 17 ]. LC–MS analysis demonstrated suppression of glycolysis/pyruvate metabolism, amino acid metabolism, and membrane biosynthesis, including metabolites, lactate, alanine, and choline-containing compounds, detectable by 1 H MRS. This concordance between pathway-level metabolomics and noninvasive spectroscopy strengthens the mechanistic basis for metabolic imaging as a pharmacodynamic readout of mTOR inhibition. Transcriptomic profiling further supported these findings, showing coordinated downregulation of genes governing glycolysis, amino acid synthesis, the TCA cycle, and related pathways, consistent with broad metabolic suppression. Concurrent inhibition of E2F targets controlling G1–S progression, G2/M checkpoint regulators, and c-MYC target genes highlights mTOR as a central coordinator of metabolic and proliferative programs in DLBCL. Metabolomic and gene-expression data aligned closely with 1 H MRS results, providing cross-platform validation of treatment-induced metabolic reprogramming. Decreases in lactate and alanine across LC–MS and MRS datasets indicate reduced glycolytic flux and pyruvate-to-lactate conversion, reflecting a shift away from aerobic glycolysis and a hallmark of effective mTOR pathway inhibition. The Torin2-induced decrease in total choline in the most sensitive WSU-DLCL2 and WSU-DLCL2PR cells, suggests reduced membrane phospholipid synthesis. In contrast, the absence of this suppressive effect in the less sensitive TMD8 and JP models may reflect compensatory phospholipid turnover or stress-induced membrane remodeling [ 9 , 18 – 20 ]. Because the total choline signal detected in vivo is typically dominated by phosphocholine [ 21 ], persistence of choline-containing metabolites may indicate continued anabolic activity despite mTOR inhibition. Prior clinical MR spectroscopic imaging studies further support the relevance of phosphomonoester (i.e., phosphocholine plus phosphoethanolamine) metabolism as a marker of therapeutic response [ 22 ]. Elevated phosphomonoester levels have been associated with early treatment failure and shorter progression-free survival in DLBCL. Consistent with this, phosphocholine increased after mTOR inhibition in less responsive tumors, particularly JP cells, suggesting activation of membrane anabolic pathways as a metabolic adaptation to therapy. These findings support phosphocholine-related metabolites as imaging biomarkers capable of distinguishing effective pathway suppression from emerging resistance. Translationally, this work provides a mechanistic basis for using noninvasive 1 H MRS metabolic biomarkers to assess early therapeutic response in DLBCL. Rapid reductions in lactate and alanine, observed within 48 h in vitro and one week in vivo , highlight metabolic imaging as a real-time indicator of treatment efficacy. Moreover, heterogeneity in metabolic and growth responses across models suggests that metabolomic stratification may be essential for clinical application of mTOR inhibitors. While highly sensitive tumors may benefit from mTOR-targeted therapy alone, less responsive disease may require rational combinations, including dual pathway inhibition, metabolic targeting, or integration with immunotherapy. Conclusions Direct mTOR inhibition suppresses DLBCL growth and survival, with strongest effects in WSU-DLCL2 and WSU-DLCL2PR models and more modest responses in TMD8 and JP cells, reflecting disease heterogeneity and the need for individualized strategies. Treatment-associated changes in lactate, alanine, and choline-containing metabolites were consistently detected by 1 H MRS, supporting their utility as noninvasive biomarkers of therapeutic response. Collectively, these findings identify mTOR signaling as a metabolically targetable vulnerability in DLBCL and highlight 1 H MRS-based metabolic imaging as a promising approach for early response assessment and patient stratification. Abbreviations mTOR: Mechanistic target of rapamycin ; MRS: Magnetic resonance spectroscopy; 1H MRS: Proton magnetic resonance spectroscopy; TCA: Tricarboxylic or Citric acid cycle; DLBCL: Diffuse large B-cell lymphoma; RNA-Seq: RNA sequence analysis; LC: Liquid chromatography; MS: Mass spectrometry; EAR: Extracellular acidification rate; 2-DG: 2-deoxy-D-glucose; OCR: Oxygen consumption rate; FCA: Fragmented cumomer analysis; D 2 O: Deuterium oxide; Trimethylsilylpropanoic acid (TSP); Had-Sel-MQC: Hadamard selective multiple quantum coherence; STEAM: Stimulated echo acquisition mode; PER: Proton efflux rate; BCR: B-cell receptor; FDG: F-fluorodeoxyglucose uptake Declarations Competing interests The authors declare that they have no competing interests. Authors’ Contributions Funding This work was supported in part by grants from the National Cancer Institute (R01CA250102, R01CA228457, R01CA268601, and R21CA280523) and funds from the Fox Chase Cancer Center Institute for Cancer Research. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. References Bakhshi TJ, Georgel PT. Genetic and epigenetic determinants of diffuse large B-cell lymphoma. Blood Cancer Journal. 2020;10:123. Susanibar-Adaniya S, Barta SK. 2021 Update on Diffuse large B cell lymphoma: A review of current data and potential applications on risk stratification and management. Am J Hematol. 2021;96:617–629. Qualls D, Armand P, Salles G. The current landscape of frontline large B-cell lymphoma trials. Blood. 2025;145:176–189. Morgensztern D, McLeod HL. PI3K/Akt/mTOR pathway as a target for cancer therapy. Anticancer Drugs. 2005;16:797–803. Inoki K, Li Y, Xu T, Guan KL. Rheb GTPase is a direct target of TSC2 GAP activity and regulates mTOR signaling. Genes Dev. 2003;17:1829–1834. Saxton RA, Sabatini DM. mTOR Signaling in Growth, Metabolism, and Disease. Cell. 2017;168:960–976. Laplante M, Sabatini DM. mTOR signaling in growth control and disease. Cell. 2012;149:274–293. Liu P, Gan W, Chin YR, Ogura K, Guo J, Zhang J, et al. PtdIns(3,4,5)P3-Dependent Activation of the mTORC2 Kinase Complex. Cancer Discov. 2015;5:1194–1209. Feng Y, Chen X, Cassady K, Zou Z, Yang S, Wang Z, et al. The Role of mTOR Inhibitors in Hematologic Disease: From Bench to Bedside. Front Oncol. 2020;10:611690. Lee JS, Vo TT, Fruman DA. Targeting mTOR for the treatment of B cell malignancies. Br J Clin Pharmacol. 2016;82:1213–1228. Liu Q, Xu C, Kirubakaran S, Zhang X, Hur W, Liu Y, et al. Characterization of Torin2, an ATP-competitive inhibitor of mTOR, ATM, and ATR. Cancer Res. 2013;73:2574–2586. Simioni C, Cani A, Martelli AM, Zauli G, Tabellini G, McCubrey J, et al. Activity of the novel mTOR inhibitor Torin-2 in B-precursor acute lymphoblastic leukemia and its therapeutic potential to prevent Akt reactivation. Oncotarget. 2014;5:10034–10047. Pickup S, Lee SC, Mancuso A, Glickson JD. Lactate imaging with Hadamard-encoded slice-selective multiple quantum coherence chemical-shift imaging. Magn Reson Med. 2008;60:299–305. Lee SC, Huang MQ, Nelson DS, Pickup S, Wehrli S, Adegbola O, et al. In vivo MRS markers of response to CHOP chemotherapy in the WSU-DLCL2 human diffuse large B-cell lymphoma xenograft. NMR Biomed. 2008;21:723–733. Nath K, Gupta PK, Basappa J, Wang S, Sen N, Lobello C, et al. Impact of therapeutic inhibition of oncogenic cell signaling tyrosine kinase on cell metabolism: in vivo-detectable metabolic biomarkers of inhibition. J Transl Med. 2024;22:622. Wang C, Wang X, Su Z, Fei H, Liu X, Pan Q. The novel mTOR inhibitor Torin-2 induces autophagy and downregulates the expression of UHRF1 to suppress hepatocarcinoma cell growth. Oncol Rep. 2015;34:1708–1716. Panwar V, Singh A, Bhatt M, Tonk RK, Azizov S, Raza AS, et al. Multifaceted role of mTOR (mammalian target of rapamycin) signaling pathway in human health and disease. Signal Transduct Target Ther. 2023;8:375. Fan H, Wu Y, Yu S, Li X, Wang A, Wang S, et al. Critical role of mTOR in regulating aerobic glycolysis in carcinogenesis (Review). Int J Oncol. 2021;58:9–19. Pusapati RV, Daemen A, Wilson C, Sandoval W, Gao M, Haley B, et al. mTORC1-Dependent Metabolic Reprogramming Underlies Escape from Glycolysis Addiction in Cancer Cells. Cancer Cell. 2016;29:548–562. Krug A, Tosolini M, Madji Hounoum B, Fournié JJ, Geiger R, Pecoraro M, et al. Inhibition of choline metabolism in an angioimmunoblastic T-cell lymphoma preclinical model reveals a new metabolic vulnerability as possible target for treatment. J Exp Clin Cancer Res. 2024;43:43. Iorio E, Podo F, Leach MO, Koutcher J, Blankenberg FG, Norfray JF. A novel roadmap connecting the (1)H-MRS total choline resonance to all hallmarks of cancer following targeted therapy. Eur Radiol Exp. 2021;5:5. Arias-Mendoza F, Payne GS, Zakian K, Stubbs M, O'Connor OA, Mojahed H, et al. Noninvasive phosphorus magnetic resonance spectroscopic imaging predicts outcome to first-line chemotherapy in newly diagnosed patients with diffuse large B-cell lymphoma. Acad Radiol. 2013;20:1122–1129. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files SupplementalFigures1.pptx Metabolic Signatures of Dual mTOR Inhibition in Diffuse Large B-Cell Lymphoma Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8928389","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":595681255,"identity":"2db1b432-725a-42bb-aa47-43c716e63ac8","order_by":0,"name":"Kavindra Nath","email":"data:image/png;base64,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","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":true,"prefix":"","firstName":"Kavindra","middleName":"","lastName":"Nath","suffix":""},{"id":595681256,"identity":"8fa70034-bd3e-42e6-9c31-d3254f29e0c5","order_by":1,"name":"Pradeep Gupta","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Pradeep","middleName":"","lastName":"Gupta","suffix":""},{"id":595681257,"identity":"6fe0b78e-2c72-41f4-b924-56198279167d","order_by":2,"name":"Shengchun Wang","email":"","orcid":"https://orcid.org/0000-0003-2296-1577","institution":"Fox Chase Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Shengchun","middleName":"","lastName":"Wang","suffix":""},{"id":595681258,"identity":"1ca1cdc6-6798-406e-8e5e-6a6831ca2769","order_by":3,"name":"Shilpa Rao","email":"","orcid":"","institution":"Fox Chase Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Shilpa","middleName":"","lastName":"Rao","suffix":""},{"id":595681259,"identity":"3c6f1a0b-f180-450d-a58d-d36ec4b4068f","order_by":4,"name":"Mamta Gupta","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Mamta","middleName":"","lastName":"Gupta","suffix":""},{"id":595681260,"identity":"6e6fc7d6-f660-4df9-9ede-bf790e1a75bc","order_by":5,"name":"Aria Osborne","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Aria","middleName":"","lastName":"Osborne","suffix":""},{"id":595681261,"identity":"a9ea56a1-954f-4fe7-a05f-ba4a5157475d","order_by":6,"name":"David Rushmore","email":"","orcid":"","institution":"Fox Chase Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Rushmore","suffix":""},{"id":595681262,"identity":"c7751526-277f-4743-98ac-3c42dda5061c","order_by":7,"name":"Fernando Arias-Mendoza","email":"","orcid":"https://orcid.org/0000-0002-3709-5088","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Fernando","middleName":"","lastName":"Arias-Mendoza","suffix":""},{"id":595681263,"identity":"def18185-74a8-4973-b61d-e52c40d7bec4","order_by":8,"name":"David Nelson","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Nelson","suffix":""},{"id":595681264,"identity":"f8c425be-23a2-4eb2-ad9a-be17a4e4d1b5","order_by":9,"name":"Johnvesly Basappa","email":"","orcid":"","institution":"Fox Chase Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Johnvesly","middleName":"","lastName":"Basappa","suffix":""},{"id":595681265,"identity":"779a8989-2531-41e0-b6ec-4650c6a31730","order_by":10,"name":"Mariusz Wasik","email":"","orcid":"https://orcid.org/0000-0002-1988-3670","institution":"Fox Chase Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Mariusz","middleName":"","lastName":"Wasik","suffix":""}],"badges":[],"createdAt":"2026-02-20 18:35:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8928389/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8928389/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104412573,"identity":"38391788-2e26-4498-a1b0-02fc6fd853e7","added_by":"auto","created_at":"2026-03-11 13:00:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1273164,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of mTOR inhibitors on growth and metabolism of DLBCL cells. (\u003cstrong\u003eA)\u003c/strong\u003e Results of MTT conversion assay with the depicted four DLBCL-patient derived cell lines after 48-hour (2 day) incubation in vehicle (DMSO) or various concentrations of Torin2 ranging from 0.01 to 20 µM/L. (\u003cstrong\u003eB)\u003c/strong\u003e Cell cycle distribution. (\u003cstrong\u003eC) \u003c/strong\u003eCell cycle phases with compartmentalization (G2/M, S, G0/G1; and sub-G0/G1) detected after 48-hour exposure of the DLBCL cell populations to Torin2 (250nM) or the inhibitor’s (control) medium.\u003cstrong\u003e (D)\u003c/strong\u003e Apoptotic cell death detected by a DNA fragmentation (TUNEL) assay after 72-hour all four DLBCL cell exposure to Torin2 (250nM) vs control medium. WSU-DLCL2PR (for rapamycin poorly responsive).\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-8928389/v1/775ef3bb65622b68db15d9a5.png"},{"id":104415188,"identity":"f2782132-d663-405b-9174-e6874cb53d01","added_by":"auto","created_at":"2026-03-11 13:10:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1619938,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA) \u003c/strong\u003eEffect of mTOR inhibition on glucose metabolism and mitochondrial respiration. The index DLBCL cell lines were exposed for 48 h to 250 nM of mTOR inhibitor Torin2 or the drug vehicle and comprehensively tested for glucose metabolism (upper row) and mitochondrial respiration (lower row) by Seahorse-based examination. The depicted difference between mTOR inhibitor-treated vs. control cells at the various stages of the tests were at least: ***p \u0026lt; 0.0001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B)\u003c/strong\u003e Extracellular glucose consumption and lactate production were the key metabolic pathways affected in four cultured DLBCL cell lines treated with Torin2 (250 nmol/L) for 48 h, as measured using a YSI 2300 biochemical analyzer, compared to untreated controls. All experiments were done in triplicates, and the data are shown as mean ± SEM. **p \u0026lt; 0.001; ***p \u0026lt; 0.0001. WSU-DLCL2PR (PR: poorly responsive to a rapalog everolimus).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8928389/v1/2cdcaea201e4af7a1464b1de.png"},{"id":104413999,"identity":"a96fd4c1-b82a-4023-a932-773ebf6944be","added_by":"auto","created_at":"2026-03-11 13:06:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":696073,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolomic profiling of mTOR inhibition. WSU-DLCL2 cells were exposed for 24 hr to Torin2 vs. its vehicle. The depicted altered metabolic pathways and the related metabolites were identified by LC-MS analysis and metabolome-targeting bioinformatics.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8928389/v1/6909d591824adcee83228de9.png"},{"id":104413998,"identity":"1dfef560-fdb4-429e-9065-2c93337a482d","added_by":"auto","created_at":"2026-03-11 13:06:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1313789,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of mTOR inhibition on gene-expression profile.\u003cstrong\u003e \u003c/strong\u003eKey functional programs and pathways affected by exposure of DLCL2 cells to Torin2 for 12 hr, detected by whole transcriptome RNA sequencing and followed by Hallmark and KEGG pathways analyses. Gray arrows highlight the key metabolic pathways and red arrows the mTORC1 and PI3K/AKT-mTOR signaling, serving as a positive control.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8928389/v1/a8d639a2b98ef89110517cca.png"},{"id":104412296,"identity":"c98a9709-508a-45c4-a8c7-762668416483","added_by":"auto","created_at":"2026-03-11 12:59:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":494913,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e) Representative high-resolution \u003csup\u003e1\u003c/sup\u003eH MRS spectra of WSU-DLCL2 cell extracts with Torin2 treatment (Upper) and without Torin2 treatment (Lower), acquired at 9.4 T using a vertical-bore Varian magnet. (\u003cstrong\u003eB\u003c/strong\u003e) Effect of mTOR inhibition (Torin2 with 250nM/L) on key metabolites lactate, alanine, and total choline, expressed as relative molar ratio per cell across all four DLBCL cell lines, as measured by high-resolution \u003csup\u003e1\u003c/sup\u003eH MRS. Data are presented as mean ± SEM, and statistically significant differences between Torin2-treated and control groups are indicated (*p \u0026lt; 0.05). WSU-DLCL2PR (for rapamycin poorly responsive).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8928389/v1/9d007d6135b97912f1b896e3.png"},{"id":104416320,"identity":"e27d3f88-953e-471b-b87c-4415eb70c1e8","added_by":"auto","created_at":"2026-03-11 13:14:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":885143,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIn vivo\u003c/em\u003e metabolic and volumetric assessment of mTOR inhibition in DLBCL xenografts. Upper panel: Representative \u003csup\u003e1\u003c/sup\u003eH MRS spectra acquired from subcutaneous DLBCL xenografts at 9.4 T using a horizontal-bore Bruker console. Lactate and alanine were measured using the Hadamard Selective Multiple Quantum Coherence (Had-Sel-MQC) sequence, and total choline and was quantified using the Stimulated Echo Acquisition Mode (STEAM) sequence. \u003cstrong\u003e(A)\u003c/strong\u003e \u003cem\u003ein vivo\u003c/em\u003e \u003csup\u003e1\u003c/sup\u003eH MRS detectable biomarkers demonstrate mTOR inhibition-mediated suppression of tumor growth, showing normalized spectral peak areas of lactate and alanine relative to the water signal (Had-Sel-MQC). \u003cstrong\u003e(B)\u003c/strong\u003e Total choline quantified using the STEAM sequence. \u003cstrong\u003e(C)\u003c/strong\u003e Tumor volume measured by calipers at Day 0, Day 2, and Day 7 following Torin2 treatment (10 mg/kg, oral, once daily) in WSU-DLCL2 (n = 5), JP (n = 5), and TMD8 (n = 5) xenografts. Vehicle-treated controls were included for all models. Data are presented as mean ± SEM, with statistically significant differences between Torin2-treated and control groups indicated (*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 and **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8928389/v1/978aec7968701ae1d80170a1.png"},{"id":106093501,"identity":"70948b6e-550d-4f8e-a9c7-41bb26409112","added_by":"auto","created_at":"2026-04-03 11:37:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6815379,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8928389/v1/7d740f41-c8d1-40d7-a476-0a66fd8b2ffa.pdf"},{"id":104411987,"identity":"064f23e7-7752-4583-b388-cf914047b6f2","added_by":"auto","created_at":"2026-03-11 12:58:24","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2477492,"visible":true,"origin":"","legend":"Metabolic Signatures of Dual mTOR Inhibition in Diffuse Large B-Cell Lymphoma","description":"","filename":"SupplementalFigures1.pptx","url":"https://assets-eu.researchsquare.com/files/rs-8928389/v1/c5037d707187252748876a67.pptx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Metabolic Signatures of Dual mTOR Inhibition in Diffuse Large B-Cell Lymphoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiffuse large B-cell lymphoma (DLBCL) is the most common and aggressive subtype of non-Hodgkin lymphoma and is characterized by marked clinical and molecular heterogeneity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although chemoimmunotherapy regimens such as rituximab combined with cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) or R-CHOP-like therapies have improved patient outcomes, approximately one-third of patients develop refractory disease or relapse, underscoring the need for more effective targeted therapies [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The mechanistic target of rapamycin (mTOR) is a central regulator of cell growth, survival, and metabolic reprogramming, integrating signals from PI3K/AKT and other oncogenic pathways [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. mTOR operates through two multiprotein complexes, mTORC1 and mTORC2 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], both of which are frequently dysregulated in lymphoid malignancies, including DLBCL. Persistent activation of mTOR signaling has been linked to increased cell growth, altered metabolism, and therapeutic resistance [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFirst-generation mTOR inhibitors (rapalogs) primarily target mTORC1 but incompletely suppress pathway activity and may relieve negative feedback mechanism that results in compensatory AKT activation through mTORC2, thereby limiting their clinical efficacy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These limitations have prompted the development of second-generation ATP-competitive inhibitors capable of directly targeting mTOR kinase and suppressing signaling through both complexes.\u003c/p\u003e \u003cp\u003eTorin2 is a potent and highly selective ATP-competitive inhibitor of mTOR that blocks the activity of both mTORC1 and mTORC2 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Preclinical studies have demonstrated that Torin2 elicits cytostatic and cytotoxic effects across multiple cancer models by inhibiting phosphorylation of downstream key effectors such as 4EBP1, S6K, and AKT, while increasing sensitivity to chemotherapeutic agents [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBecause mTOR signaling regulates tumor metabolism, its inhibition should produce measurable metabolic changes that may serve as early indicators of treatment response. Proton magnetic resonance spectroscopy (\u003csup\u003e1\u003c/sup\u003eH MRS) enables noninvasive quantification of metabolites linked to glycolysis, amino acid metabolism, and membrane turnover, providing a strategy to monitor targeted therapy \u003cem\u003ein vivo\u003c/em\u003e. Here, Torin2 inhibited DLBCL cell and xenograft growth, disrupted glycolytic and oxidative metabolism, and suppressed growth-associated metabolites and gene expression. \u003csup\u003e1\u003c/sup\u003eH MRS detected reductions in lactate, alanine, and total choline preceding measurable tumor growth inhibition and strongly correlating with therapeutic response. These findings support dual mTORC1/mTORC2 inhibition as an effective approach in DLBCL and identify MRS-detectable metabolic alterations as early, noninvasive biomarkers of response to mTOR-targeted therapy.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003emTOR inhibitor\u003c/h2\u003e \u003cp\u003eTorin2 was purchased from AmBeed (Arlington Heights, IL, USA). For \u003cem\u003ein vitro\u003c/em\u003e experiments, Torin2 was dissolved in dimethyl sulfoxide (DMSO) and applied to cells at a final concentration of 250 nM. For \u003cem\u003ein vivo\u003c/em\u003e studies, Torin2 was suspended in 2.2 mL of 10% hydroxypropyl-β-cyclodextrin solution and administered orally to mice at a dose of 10 mg/kg once daily.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCell lines and culture conditions\u003c/h3\u003e\n\u003cp\u003eThe parental WSU-DLCL2 cell line was kindly provided by Dr. Mohammad Al-Katib (Wayne State University, Detroit, MI, USA) and maintained as previously described (Lee et al. PMID 19040203). A derivative of this line, partially resistant to a rapalog Everolimus (WSU-DLCL2PR) and JP cell line were generated in the laboratory of M.A. Wasik with the latter. The TMD8 cell line was obtained from a collaborator, Dr. Y. Yang, PhD, at Fox Chase Cancer Center. The cells were cultured in the 10% FBS RPMI1640 medium, authenticated, and checked periodically to exclude mycoplasma contamination.\u003c/p\u003e\n\u003ch3\u003eXenograft development\u003c/h3\u003e\n\u003cp\u003e All animal studies were approved by the University of Pennsylvania Institutional Animal Care and Use Committee (IACUC). Ten million DLBCL cells were subcutaneously injected with Matrigel into male SCID mice obtained from the Stem Cell and Xenograft Core at the University of Pennsylvania. MRS studies were performed on hemispherical tumors of approximately 250 mm\u0026sup3;, which provided sufficient size for optimal spectral quality and compatibility with the available radiofrequency coils.\u003c/p\u003e\n\u003ch3\u003eCell count assay\u003c/h3\u003e\n\u003cp\u003eDLBCL cells (2 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells/well) were cultured in 96-well plates (Corning, Inc.) for 48 hours with various concentrations of Torin2. The cells were suspended in PBS containing trypan blue and counted microscopically using a hemocytometer.\u003c/p\u003e\n\u003ch3\u003eCell growth assay\u003c/h3\u003e\n\u003cp\u003eCells were plated in 96-well plates (3-5x10\u003csup\u003e3\u003c/sup\u003e cells/well), treated with Torin2 or the drug vehicle, labeled with MTT (Promega) at 5 mg/ml for 4 h, and solubilized with 10% SDS in 0.01 M HCl. The optical density (O.D.) of the culture supernatant, corresponding to the MTT conversion-mediated change in supernatant color, was determined at 570 nm using a Titertek Multiskan reader (Titertek Instruments).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCell cycle assay\u003c/h2\u003e \u003cp\u003eBrdU/7-AAD staining was performed using the FITC BrdU Flow Kit (BD Pharmingen) according to the manufacturer's protocol. Briefly, the cells were incubated for 1 h with BrdU (10 \u0026micro;M), treated with DNase, exposed to fluorescent anti-BrdU, and stained for total DNA. The cells were assayed using a FACSCan flow cytometer (BD Biosciences). The data were analyzed using FlowJo v10.8.0 software.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eApoptotic cell death/DNA fragmentation (TUNEL) assay\u003c/h3\u003e\n\u003cp\u003eDLBCL cell lines were treated with Torin2 or drug medium for 72 hours. Cells were collected and fixed for TUNEL labeling (Roche Life Science) and analyzed by flow cytometry (LSR, BD Biosciences).\u003c/p\u003e\n\u003ch3\u003eAnalysis of glucose metabolism and mitochondrial respiration\u003c/h3\u003e\n\u003cp\u003eDLBCL cell lines were analyzed using the Seahorse XFe96 Analyzer (Agilent) following the manufacturer\u0026rsquo;s protocols. Cells were seeded in 96-well plates at 1.2 \u0026times; 10⁵ cells/well. For the glycolysis stress test, cells were cultured in medium supplemented with 2.0 mM glutamine, while for the mitochondrial stress test, medium contained 10.0 mM glucose, 1.0 mM sodium pyruvate, and 2.0 mM glutamine. The extracellular acidification rate (ECAR) was measured in glucose-free medium before and after sequential injections of 10.0 mM glucose, 1.0 \u0026micro;M oligomycin, and 50.0 mM 2-deoxy-D-glucose (2-DG). The oxygen consumption rate (OCR) was assessed under basal conditions and following sequential addition of 1.0 \u0026micro;M oligomycin A, 2.0 \u0026micro;M FCCP, and 0.5 \u0026micro;M rotenone/antimycin A. Protein content was quantified by the Bradford assay and used to normalize metabolic parameters. Each experiment was performed with at least six technical replicates, and data were analyzed using XF Wave software (Agilent).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGlucose and lactate concentration analysis\u003c/h2\u003e \u003cp\u003eThe amounts of secreted lactate and glucose taken up by each DLBCL cell line were calculated using a YSI Glucose/Lactate Analyzer (YSI 2300 STAT Plus, YSI). DLBCL cells were added to fresh medium at 1\u0026ndash;2\u0026nbsp;million cells/mL, treated for up to 72 h with vehicle or 250 nM Torin2, and incubated for 4\u0026ndash;6 h before measurement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRNA sequence analysis (RNA-Seq)\u003c/h2\u003e \u003cp\u003eRNA integrity was assessed using the Bioanalyzer 2100 system (Agilent Technologies, USA). mRNA was enriched with poly-T oligo-attached magnetic beads and fragmented, followed by first- and second-strand cDNA synthesis. Strand-specific libraries were prepared using dUTP incorporation in the second-strand synthesis, while non-strand-specific libraries were prepared without this modification. After end repair, A-tailing, adapter ligation, size selection, PCR amplification, and purification, library quality was evaluated with Qubit, qPCR, and the Bioanalyzer. Libraries were pooled and sequenced using Illumina sequencing-by-synthesis and paired-end 150 bp reads. Raw fastq files were trimmed to remove 3\u0026rsquo; adapters using Cutadapt, and reads shorter than 50 bp were discarded. Trimmed reads were aligned to human genome version GRCh38 using STAR aligner (version 2.7.3a) with default settings, followed by summarization of raw counts for each gene using the featureCounts package and Ensembl gene models. Data analyses were performed in R using Bioconductor\u0026rsquo;s DESeq2, EdgeR and limma packages. Correlation heatmaps and PCA plots on variance stabilized transformed data were used to visualize overall sample relatedness. Differential expression analysis used the Limma-voom workflow. Low-expressing genes were filtered out using the filterbyExpr function. TMM normalization was applied across the samples. voom transformation to log2 counts per million (CPM) followed by linear model fitting using the lmfit function of limma package was performed, and differential expression was identified by contrasts between fitted Torin2 treated and control conditions at 12hr and 24hr timepoints for each cell population. A false discovery rate (FDR) cut-off of 5% (Benjamini-Hochberg adjusted P-value) was applied to select differentially expressed genes. The fgsea package was used for gene set enrichment analysis against the MSigDB\u0026rsquo;s Hallmarks gene set collection. Enrichment bar plots were generated using ggplot2. Torin2-affected gene sets were analyzed by Hallmark- and KEGG-based functional enrichment to identify corresponding functional cell pathways and programs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMetabolomic analysis\u003c/h2\u003e \u003cp\u003eThe four DLBCL cell lines were analyzed in triplicates, 15\u0026nbsp;million cells per sample, by The Wistar Institute Proteomics and Metabolomics shared resource. All samples underwent extraction for polar metabolites using an ice-cold extraction solution comprising 80% MeOH and 20% water. Samples were analyzed by LC-MS/MS on a Q Exactive HF-X mass spectrometer equipped with an HESI II probe in-line with a ThermoScientific Vanquish LC System. The analysis was carried out in a pseudorandomized order. LC separation was performed under HILIC conditions using a ZIC-pHILIC column (150 \u0026times; 2.1 mm, 5 \u0026micro;M) maintained at 45\u0026deg;C (EMD Millipore). Mobile phase A consisted of 20 mM ammonium carbonate, 0.1% ammonium hydroxide, pH 9.2, while mobile phase B was acetonitrile. Analytical separation was achieved at a flow rate of 0.2 ml/min using the following gradient: 0 min, 85% B; 2 min, 85% B; 17 min, 20% B; 17.1 min, 85% B; and 26 min, 85% B. Samples were analyzed using either full MS scans with polarity switching (for all samples) or full MS/data-dependent MS/MS scans with separate acquisitions for positive and negative polarities (for unlabeled samples in isotope tracing experiments; sample pool, QC in non-tracing experiments). Relevant MS parameters included: sheath gas at 40, auxiliary gas at 10, sweep gas at 2, auxiliary gas heater temperature at 350\u0026deg;C, spray voltage at 3.5/3.2 kV for positive/negative polarities, capillary temperature at 325\u0026deg;C, and S-lens RF at 40. Full MS scans were obtained using a scan range of 65 to 975 m/z, at a resolution of 120,000, with an automated gain control (AGC) target of 1E6 and maximum injection time (IT) of 100 ms. Data-dependent MS/MS was performed on the 10 most abundant ions with a resolution of 15,000, AGC target of 5E4, maximum IT of 50 ms, isolation width of 1.0 m/z, and stepped normalized collision energy of 20, 40, 60. Raw data were processed using Compound Discoverer 3.1 (ThermoFisher Scientific) with separate analyses for positive and negative polarities. Metabolites were identified by matching accurate mass and retention time to standards or by querying MS/MS scans against the mzCloud spectral database (full match, score\u0026thinsp;\u0026gt;\u0026thinsp;50; mzCloud.org). Pathway impact and enrichment analyses were performed using the MetaboAnalyst 5.0 software program at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca\u003c/span\u003e\u003cspan address=\"https://www.metaboanalyst.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn vitro\u003c/b\u003e \u003cb\u003eMeasurement of Intracellular Metabolites by High-Resolution \u0026sup1;H MRS\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAll four DLBCL cell lines were cultured in T-182 suspension flasks and treated with either vehicle (0.1% DMSO) or Torin2 (250 nmol/L in 0.1% DMSO) for 48 h. Following treatment, 15 \u0026times; 10⁶ cells were harvested, washed in cold PBS, and stored at \u0026minus;\u0026thinsp;80\u0026deg;C. Cell pellets were extracted in 80% methanol-water, homogenized, and sonicated (2\u0026ndash;3 cycles), followed by centrifugation (16,000 \u0026times; g, 10 min). Supernatants were transferred to labeled Eppendorf tubes and freeze-dried using the Labconco FreeZone 4.5 lyophilizer (Labconco Co., Kansas City, MO, USA). Lyophilized extracts were resuspended in 600 \u0026micro;L of deuterium oxide (D\u003csub\u003e2\u003c/sub\u003eO) containing 0.2 mmol/L TSP and transferred to a 5 mm NMR tube. The High-resolution \u0026sup1;H MRS spectra were acquired on a 9.4 T/8.9 cm Varian spectrometer using a PRESAT sequence (45\u0026deg; flip angle, TR\u0026thinsp;=\u0026thinsp;8.8 s, SW\u0026thinsp;=\u0026thinsp;6756.8 Hz, 16,384 points, 128 scans). Data were processed with MestReC 6.1, applying a 1 Hz exponential filter, and peak areas were normalized to TSP and proton number for quantification.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn Vivo\u003c/b\u003e \u003cb\u003e\u0026sup1;H MRS of Lactate, Alanine, and Choline\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eIn vivo\u003c/em\u003e MRS studies were carried out on a 9.4 T/31 cm horizontal bore Bruker (Billerica, MA, USA) console. \u003csup\u003e1\u003c/sup\u003eH MRS-detectable biomarkers were examined utilizing a custom single-frequency (1H) slotted tube resonator. \u003csup\u003e1\u003c/sup\u003eH MRS was performed on days 0, 2, and 7 after the oral administration of Torin2 at a dosage of 10 mg/kg, administered daily. This schedule allowed for the monitoring of biomarker changes over time in response to the treatment. WSU-DLCL2, TMD8, and JP tumors were examined in male SCID mice. To detect the lactate and alanine signals, \u003csup\u003e1\u003c/sup\u003eH MRS with Had-Sel-MQC transfer pulse sequence was utilized. The acquisition parameters were set as follows: NP\u0026thinsp;=\u0026thinsp;1000, TR\u0026thinsp;=\u0026thinsp;4 s, and NT\u0026thinsp;=\u0026thinsp;32. Additionally, a localized water signal was acquired using a similar slice without water suppression (TR\u0026thinsp;=\u0026thinsp;4 sec, NT\u0026thinsp;=\u0026thinsp;4) to normalize the lactate and alanine signals [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Total choline (3.2 ppm) was quantified using a stimulated echo acquisition mode (STEAM) pulse sequence with the following parameters: NP\u0026thinsp;=\u0026thinsp;2048, TR\u0026thinsp;=\u0026thinsp;3 s, TE\u0026thinsp;=\u0026thinsp;14 ms, and NT\u0026thinsp;=\u0026thinsp;128. The \u003cem\u003ein vivo\u003c/em\u003e MRS data were processed using NUTS and MestRec postprocessing software packages. To enhance the apparent signal-to-noise ratio of the \u003csup\u003e1\u003c/sup\u003eH MRS data, a 10 Hz exponential filter was applied, followed by baseline correction before plotting and calculating the peak areas.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTumor volume measurement\u003c/h2\u003e \u003cp\u003eTumor dimensions were measured with calipers in three orthogonal directions. Volumes were calculated using the ellipsoid formula: V\u0026thinsp;=\u0026thinsp;π(a\u0026times;b\u0026times;c)/6, where a, b, and c are the length, width, and depth of the tumor, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData in each figure are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM). Two-tailed Student\u0026rsquo;s \u003cem\u003et-tests\u003c/em\u003e assuming equal variance were used to calculate \u003cem\u003ep\u003c/em\u003e-values, with α\u0026thinsp;=\u0026thinsp;0.05 considered statistically significant. Multiple paired \u003cem\u003et-tests\u003c/em\u003e were performed to analyze glycolytic and mitochondrial stress test results using Seahorse assays. Fluxes derived from metabolic network analysis, are reported as fitted flux\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) calculated using Monte Carlo simulations. Gene expression \u003cem\u003ep-\u003c/em\u003evalues were corrected for multiple comparisons using the Benjamini-Hochberg false discovery rate method. Metabolite intensities were normalized to total ion count and protein concentration. Significant differences were defined as fold change\u0026thinsp;\u0026ge;\u0026thinsp;1.5 and adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Pathway enrichment analysis was performed using MetaboAnalyst 5.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003emTOR inhibition suppresses DLBCL growth, cell-cycle progression, and survival\u003c/h2\u003e \u003cp\u003eDirect mTOR inhibition with Torin2 suppressed proliferation across four DLBCL cell lines, with greatest sensitivity in WSU-DLCL2 and its everolimus-partially resistant derivative WSU-DLCL2PR (IC₅₀ \u0026asymp; 0.02 \u0026micro;M), intermediate effects in TMD8 (IC₅₀ \u0026asymp; 0.1 \u0026micro;M), and relative resistance in JP cells (IC₅₀ ~2 \u0026micro;M) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Similar results with sapanisertib confirmed on-target activity (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Torin2 (250 nM) markedly inhibited G1\u0026ndash;S progression and increased the sub-G0/G1 fraction in all lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB,C), consistent with cytotoxicity, and induced apoptosis (31\u0026ndash;48% across models; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003emTOR inhibition disrupts glycolysis and mitochondrial respiration\u003c/h2\u003e \u003cp\u003eSeahorse analysis demonstrated impaired glycolytic function following Torin2, with the strongest effects in WSU-DLCL2 and WSU-DLCL2PR (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Oxygen consumption was similarly reduced across all models, indicating suppressed mitochondrial respiration (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Consistent with these findings, Torin2 decreased glucose consumption and lactate production proportional to growth inhibition (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eGlobal metabolomic suppression following mTOR inhibition\u003c/h2\u003e \u003cp\u003eLC\u0026ndash;MS metabolomics revealed broad reductions in metabolites across nucleotide synthesis, amino acid metabolism (including alanine), the TCA cycle, and glycolysis, including lactate (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; Figs. S2\u0026ndash;4), indicating that mTOR supports DLBCL growth through coordinated regulation of bioenergetics and biosynthesis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptomic effects of mTOR inhibition\u003c/h2\u003e \u003cp\u003eRNA-seq demonstrated downregulation of pathways governing cell-cycle progression (E2F targets, G2/M checkpoint), c-MYC signaling, oxidative phosphorylation, fatty acid metabolism, and glycolysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Figs. S6\u0026ndash;8). Suppression of PI3K/AKT-mTOR signaling provided an internal control. KEGG analysis confirmed inhibition of pyrimidine metabolism and TCA pathways, indicating transcriptional regulation of metabolic programs.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn vitro\u003c/b\u003e \u003cb\u003e\u0026sup1;H MRS detects metabolic consequences of mTOR inhibition\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003eH MRS revealed significant reductions in lactate and alanine across DLBCL cells, correlating with growth inhibition (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Phosphocholine decreased in highly sensitive WSU-DLCL2 and WSU-DLCL2PR, remained unchanged in TMD8, and increased in JP cells, suggesting compensatory membrane anabolic activity in less responsive models.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn vivo\u003c/b\u003e \u003cb\u003emetabolic imaging correlates with tumor response\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn xenografts, \u003csup\u003e1\u003c/sup\u003eH MRS detected reductions in lactate and alanine following Torin2 treatment, greatest in WSU-DLCL2 and more modest in TMD8 and JP tumors, paralleling tumor growth inhibition (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Total choline decreased only in sensitive tumors. These findings identify lactate and alanine as robust metabolic biomarkers of mTOR inhibition, with choline-containing metabolites providing complementary information on tumor metabolic state and therapeutic sensitivity.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings demonstrate that direct mTOR inhibition significantly impacts growth and survival of DLBCL cells, with varying effectiveness across cell lines. Low doses of Torin2 profoundly suppressed growth of WSU-DLCL2 and the rapalog-poorly responsive WSU-DLCL2PR cells, whereas TMD8, and JP cells (the latter in particular), required higher drug concentrations. Inhibition of cell-cycle progression, evidenced by an essentially complete halt of S-phase entry in WSU-DLCL2 and WSU-DLCL2PR cells, near-complete suppression in TMD8, and still substantial inhibition in JP cells, underscores the central role of mTOR role in promoting G\u003csub\u003e0\u003c/sub\u003e/G\u003csub\u003e1\u003c/sub\u003e-to S-phase transition. The concomitant induction of apoptotic cell death further highlights mTOR as a regulator of cell survival, consistent with its established role in anti-apoptotic signaling [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Notably, the preserved sensitivity of the rapalog-poorly responsive WSU-DLCL2PR cells suggests that ATP-competitive mTOR inhibitors such as Torin2 may overcome limitations associated with rapalogs and provide enhanced therapeutic efficacy in DLBCL.\u003c/p\u003e \u003cp\u003eOur combined metabolomic and \u003csup\u003e1\u003c/sup\u003eH MRS analyses indicate that decreases in lactate, alanine, and, to lesser extent, choline-containing metabolites represent metabolic signatures of effective mTOR inhibition. These metabolites play central roles in glycolysis, amino acid metabolism, phospholipid turnover, and biosynthetic processes required for cell proliferation, reinforcing the link between mTOR activity and cell growth and energy production [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLC\u0026ndash;MS analysis demonstrated suppression of glycolysis/pyruvate metabolism, amino acid metabolism, and membrane biosynthesis, including metabolites, lactate, alanine, and choline-containing compounds, detectable by \u003csup\u003e1\u003c/sup\u003eH MRS. This concordance between pathway-level metabolomics and noninvasive spectroscopy strengthens the mechanistic basis for metabolic imaging as a pharmacodynamic readout of mTOR inhibition.\u003c/p\u003e \u003cp\u003eTranscriptomic profiling further supported these findings, showing coordinated downregulation of genes governing glycolysis, amino acid synthesis, the TCA cycle, and related pathways, consistent with broad metabolic suppression. Concurrent inhibition of E2F targets controlling G1\u0026ndash;S progression, G2/M checkpoint regulators, and c-MYC target genes highlights mTOR as a central coordinator of metabolic and proliferative programs in DLBCL.\u003c/p\u003e \u003cp\u003eMetabolomic and gene-expression data aligned closely with \u003csup\u003e1\u003c/sup\u003eH MRS results, providing cross-platform validation of treatment-induced metabolic reprogramming. Decreases in lactate and alanine across LC\u0026ndash;MS and MRS datasets indicate reduced glycolytic flux and pyruvate-to-lactate conversion, reflecting a shift away from aerobic glycolysis and a hallmark of effective mTOR pathway inhibition.\u003c/p\u003e \u003cp\u003eThe Torin2-induced decrease in total choline in the most sensitive WSU-DLCL2 and WSU-DLCL2PR cells, suggests reduced membrane phospholipid synthesis. In contrast, the absence of this suppressive effect in the less sensitive TMD8 and JP models may reflect compensatory phospholipid turnover or stress-induced membrane remodeling [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Because the total choline signal detected \u003cem\u003ein vivo\u003c/em\u003e is typically dominated by phosphocholine [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], persistence of choline-containing metabolites may indicate continued anabolic activity despite mTOR inhibition.\u003c/p\u003e \u003cp\u003ePrior clinical MR spectroscopic imaging studies further support the relevance of phosphomonoester (i.e., phosphocholine plus phosphoethanolamine) metabolism as a marker of therapeutic response [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Elevated phosphomonoester levels have been associated with early treatment failure and shorter progression-free survival in DLBCL. Consistent with this, phosphocholine increased after mTOR inhibition in less responsive tumors, particularly JP cells, suggesting activation of membrane anabolic pathways as a metabolic adaptation to therapy. These findings support phosphocholine-related metabolites as imaging biomarkers capable of distinguishing effective pathway suppression from emerging resistance.\u003c/p\u003e \u003cp\u003eTranslationally, this work provides a mechanistic basis for using noninvasive \u003csup\u003e1\u003c/sup\u003eH MRS metabolic biomarkers to assess early therapeutic response in DLBCL. Rapid reductions in lactate and alanine, observed within 48 h \u003cem\u003ein vitro\u003c/em\u003e and one week \u003cem\u003ein vivo\u003c/em\u003e, highlight metabolic imaging as a real-time indicator of treatment efficacy. Moreover, heterogeneity in metabolic and growth responses across models suggests that metabolomic stratification may be essential for clinical application of mTOR inhibitors. While highly sensitive tumors may benefit from mTOR-targeted therapy alone, less responsive disease may require rational combinations, including dual pathway inhibition, metabolic targeting, or integration with immunotherapy.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eDirect mTOR inhibition suppresses DLBCL growth and survival, with strongest effects in WSU-DLCL2 and WSU-DLCL2PR models and more modest responses in TMD8 and JP cells, reflecting disease heterogeneity and the need for individualized strategies. Treatment-associated changes in lactate, alanine, and choline-containing metabolites were consistently detected by \u003csup\u003e1\u003c/sup\u003eH MRS, supporting their utility as noninvasive biomarkers of therapeutic response. Collectively, these findings identify mTOR signaling as a metabolically targetable vulnerability in DLBCL and highlight \u003csup\u003e1\u003c/sup\u003eH MRS-based metabolic imaging as a promising approach for early response assessment and patient stratification.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003emTOR: Mechanistic target of rapamycin ; MRS: Magnetic resonance spectroscopy; 1H MRS: Proton magnetic resonance spectroscopy; TCA: Tricarboxylic or Citric acid cycle; DLBCL: Diffuse large B-cell lymphoma; RNA-Seq: RNA sequence analysis; LC: Liquid chromatography; MS: Mass spectrometry; EAR: Extracellular acidification rate; 2-DG: 2-deoxy-D-glucose; OCR: Oxygen consumption rate; FCA: Fragmented cumomer analysis;\u0026nbsp;D\u003csub\u003e2\u003c/sub\u003eO: Deuterium oxide; Trimethylsilylpropanoic acid (TSP); Had-Sel-MQC: Hadamard selective multiple quantum coherence; STEAM: Stimulated echo acquisition mode; PER: Proton efflux rate; BCR: B-cell receptor; FDG: F-fluorodeoxyglucose uptake\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported in part by grants from the National Cancer Institute (R01CA250102, R01CA228457, R01CA268601, and R21CA280523) and funds from the Fox Chase Cancer Center Institute for Cancer Research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBakhshi TJ, Georgel PT. Genetic and epigenetic determinants of diffuse large B-cell lymphoma. Blood Cancer Journal. 2020;10:123.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSusanibar-Adaniya S, Barta SK. 2021 Update on Diffuse large B cell lymphoma: A review of current data and potential applications on risk stratification and management. Am J Hematol. 2021;96:617\u0026ndash;629.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQualls D, Armand P, Salles G. The current landscape of frontline large B-cell lymphoma trials. Blood. 2025;145:176\u0026ndash;189.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorgensztern D, McLeod HL. PI3K/Akt/mTOR pathway as a target for cancer therapy. Anticancer Drugs. 2005;16:797\u0026ndash;803.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInoki K, Li Y, Xu T, Guan KL. Rheb GTPase is a direct target of TSC2 GAP activity and regulates mTOR signaling. Genes Dev. 2003;17:1829\u0026ndash;1834.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaxton RA, Sabatini DM. mTOR Signaling in Growth, Metabolism, and Disease. Cell. 2017;168:960\u0026ndash;976.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaplante M, Sabatini DM. mTOR signaling in growth control and disease. Cell. 2012;149:274\u0026ndash;293.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu P, Gan W, Chin YR, Ogura K, Guo J, Zhang J, \u003cem\u003eet al.\u003c/em\u003e PtdIns(3,4,5)P3-Dependent Activation of the mTORC2 Kinase Complex. Cancer Discov. 2015;5:1194\u0026ndash;1209.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng Y, Chen X, Cassady K, Zou Z, Yang S, Wang Z, \u003cem\u003eet al.\u003c/em\u003e The Role of mTOR Inhibitors in Hematologic Disease: From Bench to Bedside. Front Oncol. 2020;10:611690.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee JS, Vo TT, Fruman DA. Targeting mTOR for the treatment of B cell malignancies. Br J Clin Pharmacol. 2016;82:1213\u0026ndash;1228.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Q, Xu C, Kirubakaran S, Zhang X, Hur W, Liu Y, \u003cem\u003eet al.\u003c/em\u003e Characterization of Torin2, an ATP-competitive inhibitor of mTOR, ATM, and ATR. Cancer Res. 2013;73:2574\u0026ndash;2586.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimioni C, Cani A, Martelli AM, Zauli G, Tabellini G, McCubrey J, \u003cem\u003eet al.\u003c/em\u003e Activity of the novel mTOR inhibitor Torin-2 in B-precursor acute lymphoblastic leukemia and its therapeutic potential to prevent Akt reactivation. Oncotarget. 2014;5:10034\u0026ndash;10047.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePickup S, Lee SC, Mancuso A, Glickson JD. Lactate imaging with Hadamard-encoded slice-selective multiple quantum coherence chemical-shift imaging. Magn Reson Med. 2008;60:299\u0026ndash;305.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee SC, Huang MQ, Nelson DS, Pickup S, Wehrli S, Adegbola O, \u003cem\u003eet al.\u003c/em\u003e In vivo MRS markers of response to CHOP chemotherapy in the WSU-DLCL2 human diffuse large B-cell lymphoma xenograft. NMR Biomed. 2008;21:723\u0026ndash;733.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNath K, Gupta PK, Basappa J, Wang S, Sen N, Lobello C, \u003cem\u003eet al.\u003c/em\u003e Impact of therapeutic inhibition of oncogenic cell signaling tyrosine kinase on cell metabolism: in vivo-detectable metabolic biomarkers of inhibition. J Transl Med. 2024;22:622.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C, Wang X, Su Z, Fei H, Liu X, Pan Q. The novel mTOR inhibitor Torin-2 induces autophagy and downregulates the expression of UHRF1 to suppress hepatocarcinoma cell growth. Oncol Rep. 2015;34:1708\u0026ndash;1716.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanwar V, Singh A, Bhatt M, Tonk RK, Azizov S, Raza AS, \u003cem\u003eet al.\u003c/em\u003e Multifaceted role of mTOR (mammalian target of rapamycin) signaling pathway in human health and disease. Signal Transduct Target Ther. 2023;8:375.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan H, Wu Y, Yu S, Li X, Wang A, Wang S, \u003cem\u003eet al.\u003c/em\u003e Critical role of mTOR in regulating aerobic glycolysis in carcinogenesis (Review). Int J Oncol. 2021;58:9\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePusapati RV, Daemen A, Wilson C, Sandoval W, Gao M, Haley B, \u003cem\u003eet al.\u003c/em\u003e mTORC1-Dependent Metabolic Reprogramming Underlies Escape from Glycolysis Addiction in Cancer Cells. Cancer Cell. 2016;29:548\u0026ndash;562.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrug A, Tosolini M, Madji Hounoum B, Fourni\u0026eacute; JJ, Geiger R, Pecoraro M, \u003cem\u003eet al.\u003c/em\u003e Inhibition of choline metabolism in an angioimmunoblastic T-cell lymphoma preclinical model reveals a new metabolic vulnerability as possible target for treatment. J Exp Clin Cancer Res. 2024;43:43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIorio E, Podo F, Leach MO, Koutcher J, Blankenberg FG, Norfray JF. A novel roadmap connecting the (1)H-MRS total choline resonance to all hallmarks of cancer following targeted therapy. Eur Radiol Exp. 2021;5:5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArias-Mendoza F, Payne GS, Zakian K, Stubbs M, O'Connor OA, Mojahed H, \u003cem\u003eet al.\u003c/em\u003e Noninvasive phosphorus magnetic resonance spectroscopic imaging predicts outcome to first-line chemotherapy in newly diagnosed patients with diffuse large B-cell lymphoma. Acad Radiol. 2013;20:1122\u0026ndash;1129.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diffuse large B-cell lymphoma (DLBCL). Mechanistic target of rapamycin (mTOR), Proton magnetic resonance spectroscopy (1H MRS), Tricarboxylic or Citric acid cycle (TCA), Signaling inhibition, RNA Sequence analysis (RNA-Seq)","lastPublishedDoi":"10.21203/rs.3.rs-8928389/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8928389/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDiffuse large B-cell lymphoma (DLBCL) is an aggressive and heterogeneous malignancy in which many patients relapse or fail to respond to standard therapies. Constitutive activation of the mechanistic target of rapamycin (mTOR) pathway, involving both mTOR complex 1 (mTORC1) and mTOR complex 2 (mTORC2), promotes tumor growth and metabolic reprogramming. First-generation inhibitors targeting mTORC1 alone have shown limited efficacy, partly due to incomplete pathway suppression and compensatory mTORC2 signaling.\u003c/p\u003e \u003cp\u003eWe evaluated Torin2, a potent ATP-competitive mTOR kinase inhibitor targeting both complexes, in four DLBCL cell lines with variable sensitivity to mTORC1 inhibition. In responsive models, Torin2 suppressed proliferation, induced apoptosis, impaired cell-cycle progression, and downregulated metabolic and proliferative transcriptional programs. Integrated metabolomic and transcriptomic analyses demonstrated broad inhibition of glycolysis, amino acid metabolism, and phospholipid biosynthesis.\u003c/p\u003e \u003cp\u003eTorin2 reduced lactate and alanine levels detectable by noninvasive proton magnetic resonance spectroscopy (\u003csup\u003e1\u003c/sup\u003eH MRS) \u003cem\u003ein vitro\u003c/em\u003e and in mouse xenografts, with metabolic changes paralleling tumor growth inhibition. Modulation of choline-containing metabolites further distinguished sensitive from less responsive tumors.\u003c/p\u003e \u003cp\u003eThese findings show that dual mTORC1/mTORC2 inhibition disrupts metabolic dependencies critical for DLBCL growth and identify \u003csup\u003e1\u003c/sup\u003eH MRS-detectable metabolites as noninvasive pharmacodynamic biomarkers for response assessment and therapeutic stratification in mTOR targeted lymphoma therapy.\u003c/p\u003e","manuscriptTitle":"Metabolic Signatures of Dual mTOR Inhibition in Diffuse Large B-Cell Lymphoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 12:03:34","doi":"10.21203/rs.3.rs-8928389/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d809fee8-3bc9-48be-8773-132a74c0d84e","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63383414,"name":"Biological sciences/Cancer/Cancer imaging"},{"id":63383415,"name":"Biological sciences/Cancer/Cancer metabolism"}],"tags":[],"updatedAt":"2026-04-01T15:25:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 12:03:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8928389","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8928389","identity":"rs-8928389","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}