Plasmablastic lymphoma (PBL) is a rare form of non-Hodgkin lymphoma that is highly aggressive and carries a poor prognosis. Although the standard chemotherapy choice for most diffuse large B-cell lymphomas (DLBCL) is R-CHOP , subtypes of DLBCL such as PBL are less responsive to this treatment regimen. The preferred regimens for PBL include infusional EPOCH , HyperCVAD , or CODOX-M/IVAC . Recent studies have begun to investigate the addition of other agents to these regimens to improve survival. This case report is about a patient with a history of advanced acquired immunodeficiency syndrome (AIDS) with a cluster of differentiation 4 (CD4) count <20 who had CD20 negative plasmablastic lymphoma and was successfully treated with the combination of bortezomib and dose-adjusted EPOCH (V-EPOCH) and intrathecal chemotherapy, achieving complete response with optimal tolerance. To our knowledge, this is the first case to demonstrate a complete response with V-EPOCH for PBL in advanced AIDS with CD4 <20. We aim to highlight the importance of standardizing effective chemotherapeutic approaches to this cancer entity and augment the effectiveness of V-EPOCH therapy in the literature review. Plasmablastic lymphoma (PBL) is a rare form of non-Hodgkin lymphoma that is highly aggressive with no standardized regimens for treatment currently. Although the common chemotherapy choice for most diffuse large B-cell lymphomas (DLBCL) is R-CHOP , subtypes of DLBCL, such as PBL, are less responsive to CHOP  therapy as per National Comprehensive Cancer Network (NCCN) guidelines. The preferred recommended regimens for PBL include infusional EPOCH , HyperCVAD , or CODOX-M/IVAC  -3. RecenThe patient was a 46-year-old African American female with a past medical history of AIDS, gastro-esophageal reflux disease (GERD), obstructive sleep apnea, and mild intermittent asthma who presented with diffuse lymphadenopathy and fever. She was diagnosed with human immunodeficiency virus (HIV) 20 years ago but was not compliant with her antiretroviral therapy (ART) for the past two years and restarted ART three weeks prior to presentation. On presentation, her HIV viral load was 61,918,000 copies/mL and CD4 count was 15 cells/uL (2%) (advanced AIDS). Computed tomography (CT) imaging of thorax, abdomen, and pelvis showed pulmonary nodules and retroperitoneal, iliac, inguinal, mediastinal, hilar, and axillary lymphadenopathy as seen in Figures She was started on treatment with the V-EPOCH regimen with intrathecal chemotherapy. Her ART was changed from Genvoya®  to dolutegravir, lamivudine, and tenofovir to optimize chemotherapy for future cycles and avoid interactions with chemotherapy. Following Cycle 3, imaging findings were consistent with a near-complete response (CR); CT thorax showed that several of her pulmonary nodules had decreased in size and one had completely resolved. CT abdomen and pelvis showed resolution of retroperitoneal and pelvic adenopathy, and CT neck no longer showed the nasopharyngeal mass nor necrotic right lymph node. One month after Cycle 4, CD4 count was at 66 cells/uL and HIV viral load was undetectable. She went on to complete Cycles 5 and 6 with the V-EPOCH regimen. Repeat positron emission tomography (PET) after Cycle 6 showed a complete response with no PET-avid fluorodeoxyglucose (FDG) uptake anywhere.The World Health Organization (WHO) defines PBL as a mature B-cell lymphoma (BCL) with plasma cell antigens that lacks certain B-cell antigens that are commonly found in other BCLs, such as CD19, CD20, and PAX5. PBL was originally described as residing in the oral cavity of HIV positive patients but is now known to also appear in other extranodal sites and in HIV negative patients . CompareDespite the controversial efficacy of bortezomib in patients with DLBCL, recent literature has suggested that bortezomib is particularly beneficial for patients with PBL, significantly improving response and survival rates -13. BortWe present this case to illustrate how V-EPOCH can be safely used in an immunocompromised patient with advanced AIDS and a CD4 count <20 without significant adverse side effects and excellent efficacy. This patient’s complete response following bortezomib with dose-adjusted EPOCH therapy provides hope for patients with PBL and other rarer, more aggressive forms of DLBCL and encourages further long-term research exploring the side effects and long-term survival of V-EPOCH in PBL with severe immunocompromised states in establishing an acceptable and standardized regimen to improve survival rates and quality of life.


Pancreatic β-cell failure is central to the development and progression of type 2 diabetes (T2D). The aggregation of human islet amyloid polypeptide (hIAPP) has been associated with pancreatic islet inflammation and dysfunction in T2D. Alpha1-antitrypsin (AAT) is a circulating protease inhibitor with anti-inflammatory properties. Here, we sought to investigate the potential therapeutic effect of AAT treatment in a mouse model characterized by hIAPP overexpression in pancreatic β-cells.Mice overexpressing hIAPP (hIAPP-Tg) in pancreatic β-cells were used as a model of amyloid-induced β-cell dysfunction. Glucose homeostasis was evaluated by glucose tolerance tests and insulin secretion assays. Apoptosis and amyloid formation was assessed in hIAPP-Tg mouse islets cultured at high glucose levels. Dissociated islet cells were cocultured with macrophages obtained from the peritoneal cavity.MafA and Pdx1, which were downregulated in pancreatic islets from hIAPP-Tg mice. AAT prevented the formation of amyloid deposits and apoptosis in hIAPP-Tg islets cultured at high glucose concentrations. Since islet macrophages mediate hIAPP-induced β-cell dysfunction, we investigated the effect of AAT in cocultures of macrophages and islet cells. AAT prevented hIAPP-induced β-cell apoptosis in these cocultures without reducing the hIAPP-induced secretion of IL-1β by macrophages. Remarkably, AAT protected β-cells against the cytotoxic effects of conditioned medium from hIAPP-treated macrophages. Similarly, AAT also abrogated the cytotoxic effects of exogenous proinflammatory cytokines on pancreatic β-cells.Nontreated hIAPP-Tg mice were glucose intolerant and exhibited impaired insulin secretion. Interestingly, AAT treatment improved glucose tolerance and restored the insulin secretory response to glucose in hIAPP-Tg mice. Moreover, AAT administration normalized the expression of the essential β-cell genes These results demonstrate that treatment with AAT improves glucose homeostasis in mice overexpressing hIAPP and protects pancreatic β-cells from the cytotoxic actions of hIAPP mediated by macrophages. These results support the use of AAT-based therapies to recover pancreatic β-cell function for the treatment of T2D.  •Alpha1-antitrypsin (AAT) ameliorates glucose intolerance in hIAPP transgenic mice.•AAT improves insulin secretion in hIAPP transgenic mice.•AAT prevents apoptosis and amyloid deposition in cultured hIAPP transgenic islets.•AAT protects β-cells from hIAPP-induced cytotoxicity mediated by macrophages.•AAT abrogates the cytotoxic effects of proinflammatory cytokines on β-cells. Pancreatic β-cell failure is central to the development and progression of T2D. Pancreatic islets in T2D are characterized by a deficit in β-cell mass, impaired β-cell function, and the presence of extracellulr amyloid deposits . These aOne of the mechanisms by which hIAPP aggregation is associated with β-cell dysfunction and death is through the induction of islet inflammation . hIAPP aAnti-inflammatory agents represent a promising approach to treating the low-grade inflammation commonly found in T2D subjects. It has been shown that drugs blocking IL-1β such as IL-1 receptor antagonists (IL-1Ra) or anti-IL-1β antibodies can preserve β-cell function in T2D patients ,13 and rin vivo [Alpha1-antitrypsin (AAT) is an abundant circulating serine protease inhibitor that has been proven to be a safe drug and has effectively been used for long-term treatment of pulmonary emphysema in patients with hereditary deficiency of AAT for decades . Besidesin vivo . However2Experimental Animal Model. Heterozygous male transgenic mice with β-cell-specific expression of hIAPP (FVB/N-Tg (Ins2-IAPP)RHFSoel/J) were purchased from The Jackson Laboratory. Nontransgenic wild-type (WT) littermates were used as controls. Mice aged 6 weeks were intraperitoneally injected three times per week with human AAT  until 16 weeks of age. The initial dose of AAT was 2 mg/mouse for the first 2 weeks, and then, the dose was gradually increased by 0.5 mg/mouse with each injection until reaching a maximum dose of 6 mg/mouse. Protocols were approved by the Animal Ethics Committee of the Universitat de Barcelona, and the Principles of Laboratory Animal Care were followed.Intraperitoneal Glucose Tolerance Tests (IPGTTs) and Insulin Tolerance Tests (ITTs). IPGTTs were performed by intraperitoneal injection of d-glucose (2 g/kg body weight) in hIAPP transgenic (hIAPP-Tg) and WT mice unfed for 16 h. Glucose levels in tail vein blood samples were measured with a blood glucometer (Nova Pro) at different time points after glucose injection. Blood samples for insulin determination were collected through the tail vein just before the injection of glucose and 15 min after injection. Plasma insulin levels were measured using an ELISA kit . For ITTs, 0.7 U/Kg human insulin (Novo Nordisk) was intraperitoneally injected and glycemia was measured at different time points.Islet Isolation and Culture. Mouse pancreatic islets were isolated from C57BL/6J male mice and from hIAPP-Tg mice and nontransgenic littermates by collagenase digestion and Histopaque gradient (Sigma–Aldrich) as described elsewhere [lsewhere . Islets lsewhere . The IL-Peritoneal Macrophages. 10 mL of sterile PBS was intraperitoneally injected into C57BL/6J mice and the peritoneal cavity content was aspirated. The aspirates were centrifuged, washed, and seeded into 96-well plate (100.000 cells/well) in RPMI 1640 medium supplemented with 10% FBS (v/v), 2 mM glutamine, 100 U/mL penicillin, and 100 μg/mL streptomycin. After an overnight recovery, peritoneal macrophages (pMФ) were primed with LPS (5 ng/mL) (Sigma–Aldrich) for 3 h. hIAPP (Bachem) was dissolved in water and rapidly added to the medium at a final concentration of 10 μM. The purity of peritoneal macrophage cultures was determined by CD68 immunostaining.Gene Expression Analysis. Total RNA from pancreatic islets was extracted using the miRNeasy Kit  and reverse-transcribed using SuperScript (Invitrogen). Quantitative PCR was performed using SYBR Green (Invitrogen) in a 7900HT Fast Real-Time PCR system (Applied Biosystems). Expression levels were normalized to the expression of an endogenous house-keeping gene (Hprt1). See Immunohistochemistry. Islets and DICs were fixed for 15 min with 10% formalin and then were permeabilized with 0.2% Triton X-100 and blocked with 1% BSA. For in toto immunostaining, islets were permeabilized with 0.5% Triton X-100 (Sigma–Aldrich) and blocked with 10% FBS in PBS. Samples were incubated with polyclonal guinea pig anti-insulin  and rabbit anti–cleaved caspase-3  antibodies, followed by secondary incubation with Alexa Fluor 555 conjugated goat anti–guinea pig IgG  and Donkey anti-Rabbit Alexa Fluor 488 . For double insulin and amyloid (thioflavin S) staining, sections of isolated pancreatic islets embedded in 2% agarose were stained for insulin as described above, followed by incubation in 0.5% thioflavin S (Sigma–Aldrich) solution for 2 min and rinsed twice with 70% ethanol. TUNEL staining was performed in agarose-embedded islets and in DICs using the DeadEnd Fluorometric TUNEL system (Promega). Hoechst 33342 (Sigma–Aldrich) was used to stain nuclei. Images were taken with an epifluorescence microscope  and, for in toto immunostaining, with a Leica TCS SPE confocal microscope. Fluorescence images were obtained using Leica LAS Image Analysis software. ImageJ version 1.49 software  was used to determine the islet area, insulin-positive area, and thioflavin staining.Statistical Analysis. Data are expressed as mean ± SEM. The D'Agostino–Pearson omnibus normality test was carried out to test whether the variables followed a normal distribution. Statistical significance was determined by the unpaired Student's t-test for parametric quantitative variables and the Mann–Whitney U test for quantitative nonparametric variables when comparing two groups. One-way or two-way ANOVA with post hoc Tukey's test was used when comparing more than two groups. A value of P < 0.05 was considered statistically significant.3AAT Treatment Restores Glucose Homeostasis in hIAPP Transgenic Mice. To explore a potential protective effect of AAT in hIAPP transgenic (hIAPP-Tg) mice in vivo, AAT or a vehicle solution was administered intraperitoneally three times per week (2–6 mg/mouse) for 10 weeks in hIAPP-Tg mice and nontransgenic littermates (the latter incapable of forming islet amyloid). AAT treatment did not alter body weight and food intake (data not shown). Pharmacokinetic analysis revealed increased levels of human AAT (hAAT) and antielastase activity in the serum of AAT-treated mice along the treatment period , Mpeg1, and Lyz1 was upregulated in hIAPP-Tg mice islets (Il1b) followed the same pattern rather than the macrophage marker genes (Il1r) were not altered in transgenic mice and were not modified by AAT treatment  levels. This condition accelerates hIAPP aggregation and the formation of extracellular amyloid deposits, providing an experimental model to explore these events. We first analyzed apoptosis in pancreatic islets cultured for 2 days by immunostaining of cleaved caspase-3. We observed an increased percentage of apoptosis in hIAPP-Tg islets  compared to WT islets (0.4 ± 0.1%) and, remarkably, AAT reduced apoptosis in transgenic islet cells by 34.6% , Ccl2 (Ccl2), and Nlrp3 (Nlrp3) or the anti-inflammatory molecules IL1-R antagonist (Ilrn) and IL-10 (Il10) (rphology A. Both Lrphology B. Gene ereatment . Similar0 (Il10) . Neverth0 (Il10) C, which Then, we addressed whether AAT was able to protect β-cells against macrophage-secreted factors. For this purpose, conditioned medium from nontreated macrophages or LPS-primed macrophages treated with hIAPP was added to isolated pancreatic islets for 24 h. In the presence of this conditioned medium, the frequency of islets showing at least one TUNEL-positive β-cell increased from 9.6 ± 3.7% in islets treated with control medium to 29.7 ± 0.9% in islets treated with medium from hIAPP-treated macrophages D. RemarkAAT Prevents Cytokine-Induced Apoptosis and Stress in Pancreatic Islets. Our observation that AAT protects against extracellular factors secreted by hIAPP-treated macrophages prompted us to assess whether AAT was also effective in protecting β-cells from exogenously administered proinflammatory cytokines. Isolated pancreatic islets from wild-type C57BL/6J mice were exposed to a combination of proinflammatory cytokines including IL-1β (1 U/mL), IFN-γ (20 U/mL), and TNFα (20 U/mL) for 24 h. The concentrations of these cytokines were lower than those used in other settings that try to mimic the islet environment in the context of T1D [MafA and Pdx1 in islets exposed to cytokines (Chop and Trib3, as well as the apoptotic gene Fas. AAT significantly reduced the induction of these stress and apoptotic genes by cytokines (t of T1D  as we soytokines C. The efytokines C. These 4β-cell failure is central to the development and progression of T2D, and therapeutic strategies aimed at rescuing pancreatic β-cell function are emerging in T2D treatment. Compelling evidence suggests that aggregation of hIAPP contributes to islet inflammation and dysfunction in T2D ,11,24,29In the present study, we describe a novel potential therapeutic action of AAT for the treatment of T2D. AAT is a circulant serine protease inhibitor that exerts anti-inflammatory and immunomodulatory properties . Most ofex vivo at high glucose. Of note, apoptosis was detected in islets cultured 2 days at high glucose, but not after 7 days of culture, when high amounts of amyloid deposits were detected. This is consistent with in vivo time-course studies illustrating that hIAPP-Tg animals show the highest frequency of β-cell apoptosis at 10 weeks of age, when almost no amyloid is formed, and the frequency decreases at 40 weeks, when islet amyloid reaches the highest levels [The fact that hIAPP-Tg mice exhibit normal insulin sensitivity ,24 sugget levels . Moreovet levels ,32.in vivo and in vitro is able to reduce the formation of amyloid deposits in hIAPP-Tg islets [hIAPP has been shown to drive pancreatic islet β-cell death through the secretion of proinflammatory cytokines by macrophages, particularly IL-1β, causing islet dysfunction and ultimately glucose intolerance ,9,11,33.g islets ,9. In keg islets  and to ig islets ,30,35. Ig islets . AAT didg islets , and AATAAT protected β-cells from secreted factors released by hIAPP-treated macrophages. This was in line with the observation that AAT also protects β-cells from exogenously administered proinflammatory cytokines. An effect of AAT on β-cell survival is supported by studies demonstrating that AAT is internalized and inhibits caspase-3 in MIN6 β-cells  and in lThe IL-1β blockade has been shown to be effective in protecting hIAPP-Tg mice islets . HoweverIn summary, here, we demonstrate that treatment with the protease inhibitor AAT recovers glucose homeostasis in a mouse model overexpressing hIAPP in pancreatic β-cells. Furthermore, AAT exerts a protective effect on pancreatic β-cells against the cytotoxic action of hIAPP and proinflammatory cytokines. These results highlight the potential of AAT administration as a novel therapeutic approach to rescue pancreatic β-cell function for the treatment of T2D.J. R.-C., J. M.-V, M. O., C. C., S. P., G. A.-V., D. D.-C., A. M., R. H., and M. C. contributed to the performance of experiments and data analysis. J. R.-C., A. N., and J.-M. S. wrote the manuscript. A. N. and J.-M. S. designed the study. All authors contributed to the discussion of results, reviewed the manuscript, and approved the final version of this manuscript.


Multiple myeloma (MM) is a hematological malignancy characterized by the clonal expansion of plasma cells in the bone marrow. To date, this disease is still incurable and novel therapeutic approaches are required. Phosphoglycerate dehydrogenase (PHGDH) is the first and rate-limiting enzyme in the de novo serine synthesis pathway, and it has been attributed to bortezomib-resistance in MM.Two different PHGDH inhibitors, CBR5884 and NCT-503, were tested against human myeloma cell lines, primary MM cells from patients, and peripheral blood mononuclear cells isolated from healthy donors. The PHGDH inhibitors were then tested in combination with proteasome inhibitors in different MM cell lines, including proteasome-resistant cell lines. Furthermore, we confirmed the effects of PHGDH inhibition through knocking down PHGDH and the effect of NCT-503 in vivo in the 5T33MM mouse model..All the tested myeloma cell lines expressed PHGDH and were sensitive to doses of NCT-503 that were tolerated by peripheral blood mononuclear cells isolated from healthy donors. Upon testing bortezomib in combination with NCT-503, we noticed a clear synergy in several HMCLs. The sensitivity to bortezomib also increased after PHGDH knockdown, mimicking the effect of NCT-503 treatment. Interestingly, targeting PHGDH reduced the intracellular redox capacity of the cells. Furthermore, combination treatment with NCT-503 and bortezomib exhibited a therapeutic advantage in vivoOur study shows the therapeutic potential of targeting PHGDH in MM, and suggest it as a way to overcome the resistance to proteasome inhibitors. Multiple myeloma (MM) is a hematological malignancy with clonal expansion of plasma cells infiltrating the bone marrow (BM), and it accounts for approximately 10% of all hematological malignancies , 3.Proteasomes are protein complexes that form a ubiquitin-dependent protein degradation system . Since mMetabolic reprogramming is a hallmark of cancer, and metabolic alterations have a vital role in cell survival and development of drug resistance also in multiple myeloma , 7. Inte+-dependent reaction. This pathway is important for the production of serine and glycine, the latter being an important source of one-carbon units for purines, thymidine, and glutathione (GSH) synthesis  and bortezomib-resistant [BR]) and control (CTR) clones of two MM cell lines, INA6 and AMO1, were used. For each of the cell lines the three clones combined will further be referred to as INA6-res and AMO1-res. The PI-resistant clones of INA6 and AMO1 were adapted to tolerate higher doses of PIs through gradually increasing the PI concentrations in their growth media. The CTR clones had been cultivated alongside the PI-resistant clones without any drugs –24.Annexin-V-fluorescein isothiocyanate (FITC) kit  was used to determine cell viability. 50 000 cells were seeded in 96-well plates, treated as indicated overnight, before incubating them with annexin V FITC (0.2 μg/ml in 1× annexin binding buffer) for 1 h on ice. Propidium iodide (1.4 μg/ml) was added 5 min prior to data acquisition using an LSRII flow cytometer . FlowJo Software v10.1 was used to perform the data analysis. Cells negative for both annexin V and propidium iodide staining were considered viable.CellTiter-Glo (CTG)  was used to determine cell proliferation. 10,000 cells were seeded in 96-well plates and treated as indicated, before measurement of cell proliferation according to the manufacturer’s protocol. The readout is Relative Luminesence Unit (RLU) and was determined with a Victor 1420 multilabel counter . All conditions were done in at least duplicates, and all experiments were performed at least three times.+ , respectively, following the manufacturer’s instructions.GSH-Glo Glutathione Assay and NADP/NADPH-Glo Assay (both from Promega) were used to measure the levels of GSH and NADPH/NADP® CA-630 (Sigma-Aldrich), 150 mM NaCl, 50 mM Tris–HCl pH 7.5, 10% glycerol, 50 mM NaF, 1 mM Na3VO4, and a protease‐phosphatase inhibitor mixture . After 30 min on ice, the cell debris was pelleted by centrifugation at 12,000g, 4 °C for 10 min. Protein concentrations were measured using Quick Start™ Bradford 1× Dye Reagent  and iMark™ Microplate Reader (Bio-Rad). Afterward, the samples were diluted to similar concentration and mixed with lithium dodecyl sulfate sample buffer  with 10 mM dithiothreitol, heated for 10 min at 70 °C and separated on 4–12% Bis‐Tris gels with MOPS running buffer (Invitrogen). Proteins were then transferred to a nitrocellulose membrane using the iBlot Dry Blotting System (Invitrogen). Membranes were blocked with 5% BSA in Tris‐buffered saline with 0.05% Tween20 and incubated with antibodies against indicated proteins overnight at 4 °C. Detection was performed with horseradish peroxidase‐conjugated antibodies  and Supersignal West Femto Maximum Sensitivity Substrate . Images were acquired with Odyssey Fc imager  and processed using Image Studio software (LI-COR Biosciences).Cells were washed with ice-cold phosphate-buffered saline (PBS) and lysed in lysis buffer: 1% of IGEPALThe stock of CBR5884 , NCT-503 , bortezomib , and carfilzomib  were prepared in dimethyl sulfoxide (Sigma-Aldrich). Antibodies used were against PHGDH , PSAT1 (#PA5-22124), PSPH (#sc-271421), cleaved PARP (CST #5625), cleaved caspase-3 (CST #9664) and β-Actin (CST #4970). L-Serine , Glycine (Millipore) and Minimum Essential Medium (MEM) (Gibco #21090) were used in serine starvation experiments.5 5T33MM cells and divided into four groups: vehicle, bortezomib, NCT-503, and combination. Starting from day 1, 40 mg/kg NCT-503 treatment was given 6×/week intraperitoneally (IP) for the NCT-503 groups. Starting from day 2, 0.6 mg/kg bortezomib was given twice per week subcutaneously (SC). Injection volumes did not exceed 100 μl. The vehicle is composed of 5% ethanol, 35% PEG, and 60% of an aqueous 30% hydroxypropyl-â-cyclodextrin solution, and saline. At day 18, spleens were harvested and weighed, the percent plasma cells in the BM was determined on May Grunwald-Giemsa stained BM smears from one femur, and the M spike was measured as described [C57BL/KalwRij mice  were housed and treated according to procedures approved by the Ethical Committee for Animal Experiments of the Vrije Universiteit Brussel (project number: 19-281-7). Female mice were injected intravenously (IV) on day 0 with 5 × 10escribed .+ measurements between INA6-KD cells. Unpaired t test was used to calculate the statistical differences of data from the in vivo experiment.GraphPad Prism software version 8.0.0  was used for statistical analysis and generating figures. Non-linear regression analysis was used to fit the dose–response curves and to calculate IC50 values based on the variable slope model. Statistical significance was tested by extra sum-of-squares F test for IC50 values comparison of two groups, and two-way analysis of variance (ANOVA) followed by Bonferroni post-test for comparing GSH and NADPH/NADPTo assess the role of SSP in our MM cell lines, we used Western blot (WB) and investigated the expression of the first three enzymes in this pathway, PHGDH, phosphoserine aminotransferase 1 (PSAT1), and phosphoserine phosphatase (PSPH)  , and bortezomib-resistant (BR). First, the IC50 dose of NCT-503 was determined for each of the resistant cell lines  [D-2HG is an oncometabolite that serves as a competitive inhibitor of αKG-dependent enzymes, such as histone demethylases, and is could promote leukemogenesis [Treating HMCLs with either NCT-503 or CBR5884 reduced proliferation. However, the two inhibitors had different potency on the tested cell lines. All cell lines expressed PHGDH, but the level of expression varied. The expression levels did not correlate with the sensitivity to either of the inhibitors. One possible explanation for this discrepancy is the cells’ capability to import serine. Hence, we checked for the sensitivity of the HMCLs to serine starvation as an indicator of the dependency of these cells on external serine for proliferation. The cell lines’ expression of PHGDH was inversely proportional to their sensitivity to serine starvation. Yet again, we found no direct correlation between the sensitivity to serine starvation and the sensitivity to either of the inhibitors. Another possible contributor to the discrepancy could be the difference in the cellular redox state between these cells. In a recent study, the cellular redox state was found to constrain SSP through regulation of the availability of the co-factor NADactivity . Moreove (D-2HG) . D-2HG iogenesis –37.The two PHGDH inhibitors have different mechanisms of action, hence this may explain the difference between the two in spite of targeting the same protein , 27. NCTUnlike the NCT-503 treatment, the stable knockdown of PHGDH seemed not to have an effect on INA6 survival in the presence of exogenous serine. The latter could be due to the knockdown reflecting a low-dose treatment of NCT-503 that may not affect survival, but enough to drive the synergy with bortezomib. Furthermore, the stable knockdown of PHGDH would become a selective survival pressure favoring the survival of cells able to survive the reduced PHGDH levels.Our results showing synergy between NCT-503 and bortezomib are in line with the previous study attributing reduced bortezomib cytotoxicity to upregulated SSP in MM . Knowing+, and NADPH/NADP+ ratio.In addition to its role in bortezomib resistance in MM, SSP’s was also found to contribute to the resistance against BRAF inhibitors used to treat cancers expressing V600E BRAF mutation, such as melanoma, pancreatic and non-small cell lung cancer cells . Similar. confirmed our results  INA6, KJON1, and IH1. B) OH2, ANBL6, and U266. C) JJN3 and RPMI. The graphs represent three independent experiments with minimum two replicates. Error bars are ± SEM.Additional file 2: Fig. S2. PHGDH expression in proteasome-resistant cell lines. A) and B) PHGDH expression in INA6-res and AMO1-res cell lines, respectively, was assessed via WB.Additional file 3: Fig. S3. Synergy experiments between NCT-503 and BTZ in proteasome-resistant cell lines. CTG was used to assess the synergic effect. A) INA6 res. and B) AMO1 res. cell lines were treated with 1/3 of their corresponding IC50 of either CBR5884 or NCT-503, in combination with either carfilzomib or bortezomib. The graphs represent three independent experiments with minimum two replicates. Error bars are ± SEM.Additional file 4: Fig. S4. Cell viability was not affected by NCT-503 treatment for 1 h. Annexin V/PI assay was used to determine cell viability. HMCLs were treated for 1 h with double of their corresponding IC50 of NCT-503. The graphs represent two independent experiments with minimum two replicates. Error bars are ± SEM.


To fill this gap, we used six recently developed patient-derived IDHmut GSC lines and performed a large-scale drug screening with 147 Food and Drug Administration (FDA)-approved anticancer drugs. GSCs were subjected to the test compounds for 72 h in concentrations ranging from 0.0001 to 1 µM. Cell viability was assessed by CellTiterGlo and the induction of apoptosis by flow cytometry with Annexin V/propidium iodide staining. The initial screen was performed with two IDHmut GSC lines and identified seven drugs  with a substantial antiproliferative activity, as reflected by half maximal inhibitory concentrations (IC50) below 1 µM and maximum inhibitory effects (Emax) below 25%. These findings were validated in an additional four IDHmut GSC lines. The candidate drugs, of which plicamycin and omacetaxine are known to cross the blood brain barrier, were used for subsequent cell death analyses. A significant induction of apoptosis was observed at IC50 values of the respective drugs. In summary, we were able to identify seven FDA-approved drugs that should be further taken into clinical investigations for the treatment of IDHmut gliomas.The discovery of the isocitrate dehydrogenase (IDH) mutation in glioma led to a paradigm shift on how we see glioma biology. Difficulties in cultivating IDH mutant glioma stem cells (IDH Compared to glioblastoma patients, treatment modalities and timing are not as well defined and depend on the patient’s risk stratification for recurrence and malignant transformation [mut glioma, which has hindered the development of innovative therapies. While IDHwt glioma cell models have shown easy expansion and maintenance, difficulties in cultivating IDHmut tumor cells have forced many to introduce the IDH mutation artificially into wildtype tumor cells, not accounting for the natural genetic background of the tumor [mut glioma stem cell (GSC) cultures that could be used to gain further insight into the biology of LGG. These cultures grow as neurospheres, stably produce the oncometabolite (D)-2-hydroxyglutarate (D2HG), and have shown to induce tumor growth in xenograft models [The differentiation of glioma patients into isocitrate dehydrogenase (IDH) mutant and wildtype tumors , growth inhibitory and apoptotic inducing potential were then confirmed for the remaining seven candidate compounds, of which two have already been shown to cross the blood brain barrier (BBB). In this study, we used a drug library consisting of 147 Food and Drug Administration (FDA)-approved drugs, tested pharmacologically-induced growth inhibition and induction of cytotoxic apoptosis in an initial screen with two IDHmut glioma cell lines. Four of six of them were derived from IDHmut gliomas classified as recurrent GBM WHO°IV, one patient harbored an anaplastic oligodendroglioma WHO°III and one patient was diagnosed with an anaplastic astrocytoma WHO°III . In order to increase the accuracy of the half maximal inhibitory concentration (IC50) values, the subsequent validation experiments were performed in a seven-step concentration range: 0.1 nM, 1 nM, 5 nM, 10 nM, 50 nM, 100 nM, and 1000 nM.We used in total six IDH WHO°III . GSC linh et al. . For drumut GSCs, as described by Balss et al. [D2HG levels were measured in the cell pellets of IDHs et al.  In brief® 3D Cell Viability Assay , which measures ATP as an indicator of viability and generates a luminescent readout. The ready-to-use mix was added 72 h after compound inhibition, and luminescence was determined on a Tecan Infinite 200 reader . Cell viability and induction of apoptosis was analyzed using Annexin V‒fluorescein isothiocyanate and propidium iodide staining. After drug treatment, neurospheres were centrifuged at 300 g at 4 °C, washed in 1 ml of ice-cold PBS, and gently dissociated into single cells using accutase. The washing step was then repeated three times. The GSCs were resuspended in 1 ml 1 x Annexin V binding buffer , stained with 2 µl Annexin V antibody , and incubated for 15 min in the dark at 4 °C. Finally, propidium iodide (PI) was added at a concentration of 50 µg/ml. Flow-cytometric measurements were done on a BD FACS-Calibur machine and analyzed with FlowJo X software . Cell proliferation was tested using the CellTiter-Glo50, the area under the curve (AUC), and the Emax. A student’s t-test was applied to test for differences in apoptosis between treated and control GSCs. Significance was considered if p < 0.05. GraphPad Prism 7  was used to calculate dose response curves using normalized nonlinear regression with a sigmoid dose response to obtain the ICmut glioma, we performed a high-throughput screening, utilizing a drug library consisting of 147 drugs approved by the FDA for the treatment of different cancer types. These compounds covered a diverse spectrum of drug classes, mode of actions, and targets (n = 24), alkylating agents , and antimetabolites . The remaining drugs were distributed over 41 subcategories, containing one to seven members and covering a considerable range of anti-neoplastic modes of action known in cancer therapy. Additionally, drugs currently being used in the treatment of IDHmut glioma were included in the screening . As many substances have potent antineoplastic activity in many cancer types, we tested all drugs irrespective of their ability to cross the BBB because many modern drug delivery systems, such as convection enhanced delivery (CED), may overcome this obstacle. Detailed information on all drugs of the screened library can be found in In order to identify novel and effective antineoplastic drugs for the treatment of IDH targets . The thrmut glioma, we used two IDHmut GSC lines for the initial screen derived from an astrocytoma WHO°III (NCH1681) and a recurrent GBM WHO°IV . These have already been extensively used in other studies [To identify promising drugs for the treatment of IDH studies ,22,23,2450 values in the nM range, we tested five concentrations starting from 0.1 nM to 1000 nM. Cell growth was assessed by the CellTiterGlo 3D assay. We analyzed several dose-response parameters, such as the area under the curve (AUC), the half maximal inhibitory concentration (IC50), and the maximum inhibitory effect (Emax) (50 but a high Emax might not be as potent as a drug with a higher IC50 but an Emax at zero (and thus a high AUC). Therefore, we applied a three-step filter process to select drugs for the subsequent validation (To primarily identify drugs that are effective at low concentrations with ICt (Emax) A. Each plidation B.n = 51, 34.7%; NCH1681: n = 44, 29.9%). Since we aimed to identify drugs for the treatment of IDHmut glioma regardless of the WHO grade, only the drugs demonstrating comparable effects in both GSC lines were further considered. After using strict selection criteria of AUC below 400, IC50 below 1 µM, and Emax less than or equal 25%, the following seven candidate drugs remained: daunorubicin, doxorubicin, epirubicin, bortezomib, carfilzomib, plicamycin, and omacetaxine. Interestingly, of the established chemotherapies, PCV, and temozolomide, only vincristine showed growth inhibition in both GSC lines . Furthermore, dose-response testing with a total of seven concentrations, ranging from 0.1 nM to 1000 nM, was refined to improve the accuracy of the IC50 values. Representative dose-response curves of one biological replicate are shown for all seven drugs in 50 values depending on the substance class between the GSC lines was observed. Bortezomib and carfilzomib, both proteasome inhibitors, showed the lowest IC50 values of all validated drugs . Interestingly, they were also the drugs with the broadest IC50 ranges . In addition, in NCH551b, both early and late apoptotic fractions increased, as compared to NCH1681 and NCH612, where mostly the late apoptotic fraction increased. In NCH1681 and NCH612, the apoptotic effect was less pronounced as in NCH551b, reaching statistical significance in 3/4 and 2/4 drugs, respectively. As NCH612 is a slow proliferating oligodendroglial GSC line, one could speculate, if that might be the reason for the differences seen compared to the faster growing cell lines NCH551b and NCH1681. Taken together, we conclude that the candidate drugs not only showed growth inhibition in the CellTiterGlo measurements but were also capable of inducing apoptosis at the IC50 concentrations established in our previous experiments. To further corroborate the effectiveness of the aforementioned drugs, induction of apoptosis was determined by using Annexin V/PI staining. Carfilzomib, doxorubicin, omacetaxine, and plicamycin were tested in NCH551b, NCH1681, and NCH612 GSC lines at the previously determined ICd NCH612 . The thrd NCH612 . (1) Unad NCH612 . NCH551bmut GSC lines and identified seven drugs that effectively reduced GSC growth at very low concentrations. Furthermore, we have demonstrated that four representative drugs were able to induce cell death in three IDHmut GSC lines, including two highly effective candidates, which are known to cross the BBB  [In summary, we screened 147 FDA-approved drugs using six IDHcamycin) ,26.mut GSC lines. Seven drugs, of which two have already shown BBB penetration (omacetaxine and plicamycin), have shown a potent antiproliferative effect in the initial screen, already at low concentrations in the well-characterized tumorigenic GSC lines, NCH551b and NCH1681 [mut GSC lines. Subsequent cell death analysis by Annexin V and PI flow cytometry confirmed effective induction of apoptosis and significantly diminished living cells at the previously determined IC50 values. In summary, our analysis revealed seven promising drug candidates for the treatment of IDHmut glioma, which warrants further preclinical and clinical testing. In this study, we used a library of 147 FDA-approved anticancer drugs to screen for cytotoxic activity in six IDH NCH1681 ,26. Thesmut glioma, most of the patients ultimately end up with a malignantly progressed tumor with very limited treatment options [Despite the less aggressive nature of low-grade IDH options ,2. To da options ,28. PCV  options ,7,8. Bot options . TherefoInterestingly, several of the candidate compounds have also been identified in previous drug screenings, conducted with either glioma cell lines or other tumor cell lines, such as neuroblastoma or renal cell carcinoma  and omacetaxine (aka homoharringtonin), were the only drugs where penetration of the BBB has been reported ,26,36,37nhibitor . Our resnhibitor . Althougnhibitor ,41. Omacnhibitor . In our  benefit . This mamut glioma and one chondrosarcoma cell lines [Bortezomib and carfilzomib are proteasome inhibitors and showed the strongest inhibitory effect at doses below the lowest tested concentration of 0.1 nM . Their antineoplastic effect is based on the inhibition of the proteasome. This leads to an accumulation of ubiquitinated misfolded proteins and subsequently induces apoptosis . Bortezoll lines . mut glioma patients , only vincristine showed growth inhibition in our initial drug screen in both cell lines. Although it did not pass our criterion of Emax ≤ 25%, this finding does support the validity of our preclinical model.Of the existing chemotherapeutic agents already being administered to IDHwt glioma cell lines. This limits the usability of their results for the treatment of IDHmut glioma, since glioma stem cells grown as neurospheres seem to have a distinct drug sensitivity compared to cells grown adherently [mut neurosphere cultured GSC lines and proved a robust inhibition of proliferation for all identified candidate drugs at nanomolar concentrations. Despite known difficulties in establishing IDHmut GSCs, our GSC panel included one astrocytoma WHO°III and one oligodendroglioma WHO°III, in addition to the four rGBM stem cell lines. We do acknowledge that minor differences in drug sensitivity between the GSC cultures exist and that the limited number of GSC lines per tumor subtype does not allow us to draw conclusions as to associations of tumor subtypes to a specific substance class. These differences may be caused by intertumoral heterogeneity, as described in IDHwt glioblastoma [mut glioma is relatively low [mut inhibitors in lymphatic cancers and in preclinical models of IDHmut glioma did not translate into clinical effectiveness for the treatment of lower-grade glioma [mut glioma patients.Since the tested drugs have already successfully received FDA-approval, we believe that our in vitro results may enable faster translation into in vivo studies and subsequent clinical trials. Several projects performing large drug screenings are underway, sharing the same motive . Howeverherently . Furtherherently ,56. Hencblastoma . Personavely low . Only the glioma . Therefomut glioma stem cell lines to screen 147 FDA-approved drugs for antineoplastic and apoptotic activity. We were able to identify seven highly effective compounds demonstrating potent activity in all GSC lines. As new drug delivery systems, such as local applications, are constantly evolving, not only the drugs with a proven BBB penetration (omacetaxine and plicamycin) represent attractive candidates, but also the proteasome inhibitors  and anthracyclines  are worth considering for future preclinical and clinical testing. In this study, we used six unique patient-derived IDH


Cricetulus griseus) tissues  by tandem mass tag (TMT) labeling followed by mass spectrometry, providing a comprehensive hamster tissue and cell line proteomics atlas. Of the 8470 unique proteins identified, high similarity was observed between CHO-S and CHO DG44 and included increases in proteins involved in DNA replication, cell cycle, RNA processing, and chromosome processing. Alternatively, gene ontology and pathway analysis in tissues indicated increased protein intensities related to important tissue functionalities. Proteins enriched in the brain included those involved in acidic amino acid metabolism, Golgi apparatus, and ion and phospholipid transport. The lung showed enrichment in proteins involved in BCAA catabolism, ROS metabolism, vesicle trafficking, and lipid synthesis while the ovary exhibited enrichments in extracellular matrix and adhesion proteins. The heart proteome included vasoconstriction, complement activation, and lipoprotein metabolism enrichments. These detailed comparisons of CHO cell lines and hamster tissues will enhance understanding of the relationship between proteins and tissue function and pinpoint potential pathways of biotechnological relevance for future cell engineering.Chinese hamster ovary (CHO) cells are the predominant production vehicle for biotherapeutics. Quantitative proteomics data were obtained from two CHO cell lines (CHO-S and CHO DG44) and compared with seven Chinese hamster ( Cricetulus griseus dates back to the 1950s when Theodore T. Puck and colleagues established the CHO-K1 cell line1. Today, CHO cell lines generated from the Chinese hamster dominate the biopharmaceutical industry and their products generate billions of dollars in revenue annually3. The success of CHO cell lines for recombinant protein production can be attributed to their high growth rate, ease of genetic modification, and ability to post-translationally modify proteins via glycosylation, including galactosylation and sialylation. The original CHO cell line was adapted and modified by various researchers to create cell lines, such as CHO-DXB11, CHO DG44, and CHO-S5. Thus, there may be genetic differences across CHO parental cell lines, as well as clonal- and process- dependent variations2. These clonal variabilities may potentially lead to differences across transcriptomes and proteomes. Since each and every CHO cell line exhibits significant genomic, transcriptomic, and proteomic signatures, a single CHO-ome is not necessarily directly applicable across different laboratories. In addition to cell line differences, variations in the bioprocess conditions, including media formulations and bioreactor operations, can alter the transcriptome and proteome.The history of CHO cell line establishment from Cricetulus griseus genomes. The draft CHO-K1 genome was established in 20116, and the Chinese hamster genome followed two years later7. In addition, the CHO-DXB11 genome was sequenced to understand the DHFR negative phenotype and cell line drift8. Other efforts have focused on creating bacterial artificial chromosome libraries for CHO-K1 and CHO-DG44 cell lines in order to visualize hamster chromosome re-arrangements9. The recent PICR Chinese hamster genome has utilized real time sequencing with Illumina-based assemblies to generate a more complete CHO genome10.Initial efforts to understand CHO include the sequencing of both the CHO and 11. More recent studies in our lab and others have revealed extensive CHO proteomics databases as well as databases for the secretome and CHO cell lines in exponential and stationary phases13. In addition, different bioprocess conditions have been studied via transcriptomics and proteomics to yield insights into protein production, cell growth, cell death, favorable glycosylation, and optimized media formulations15.Similar efforts have been used to understand mRNA and protein expression in CHO using transcriptomics and proteomics, respectively. Through advancements in sample preparation and mass spectrometry (MS) technology, it is possible to identify and quantify thousands of cellular proteins. Initial CHO proteomic analyses revealed enrichment in protein processing and apoptosis pathways at the proteomic level in the CHO-K1 cell line compared to the human, mouse, and CHO genome and transcriptome16 and mouse17. Hamsters represent another important small animal model in biomedicine for studying diseases and evaluating the effect of potential therapies in pharmaceutical sciences. Indeed, Chinese hamsters have been applied as a small animal model for diabetes, cancer, and the impact of radiation21. More recently, hamster species represent one of the prime small animal models for examining the impact of SARS-CoV-2 infections and potential therapies22. Understanding the proteomics profiles across different tissues will help biomedical scientists to understand how expressed proteins are responsible for tissue-specific functionalities. From a disease perspective, tissue proteomics can enable scientists to appreciate why different diseases and drugs affect various tissues differently. From a biotechnology perspective, specific functionalities present in different tissues can provide insights into ways to improve the capabilities of CHO cell line production hosts by adding capabilities present in certain tissues that may be distinct from current hosts.Proteomics has been used to study the proteome of specific organs in order to elucidate how protein expression changes across different tissues in the hamster, as previously studied for humanTherefore, in this study, we employed comparative proteomics using tandem mass tag labeling (TMT) in order to compare two representative CHO cell lines and seven different tissues from Chinese hamster. The CHO-S and CHO DG44 cell lines were used as model cell lines for comparison to hamster tissue expression patterns. Multiple organs  relevant to pharmaceutical sciences, disease, and biotechnology were used to generate tissue-specific proteomes, providing the most comprehensive and diverse tissue proteome available for hamsters to date. Tissue-tissue and tissue-cell line comparisons suggest functions and pathways with significant differential expression across different cell types, with cell lines tending to upregulate proteins associated with growth and gene expression, while tissue samples exhibit upregulation in tissue-specific functional pathways. Examining proteins in CHO cell lines and tissues will enhance our understanding of why tissue exhibit certain characteristics, how cell lines are adapted for cell culture and protein production, and highlight how CHO cells could potentially be modified to include useful tissue-specific functions in future cell engineering efforts.2 and verified by abdominal puncture, in accordance with all guidelines and regulations of the Johns Hopkins University Animal Care and Use Committee (Approved Protocol HA14A84). Upon harvest, each organ was divided into small pieces, rapidly frozen on dry ice, and subsequently stored at − 80 °C until further analysis.Tissues from various organs  were harvested from female Chinese hamsters (age 5–10 weeks) generously provided by the lab of Dr. George Yerganian . Euthanization was performed by CO2 shaking at 120RPM; viable cell density and viability were determined by hemocytometer and trypan blue staining. For sample collection, approximately 3 × 106 cells were centrifuged, washed with PBS on ice, frozen rapidly on dry ice and stored at −80 °C until analysis. Based on the growth profiles for both cell lines (data not shown), samples collected on day 2 were identified as corresponding to mid-exponential phase and used for further analyses.Two commercial suspension CHO cell lines, CHO-S and CHO DG44, were grown in batch culture and the samples were collected and stored. CHO-S cells were cultured in CD-CHO medium supplemented with 8 mM glutamine , and CHO DG44 cells were cultured in DG44 medium supplemented with 2 mM glutamine . Both cell lines were incubated at 37 °C with 8% CO23. During the FASP method, tetra-butyl ammonium bicarbonate (TEABC) was added after the urea washes to increase protein recoverability from the filters. The samples were finally digested using trypsin/LysC enzyme mix (Promega V507A) at an enzyme to substrate ratio of 1:10, overnight at 37 °C on a shaking platform. After digestion, peptides were cleaned up by C18 cartridges and labeled with TMT reagents. All TMT labeled samples were combined and vacuum centrifuged to dryness removing the entire liquid.Samples for proteomics were thawed on ice and lysed in a solution of 2% sodium dodecyl sulfate (SDS) in 500 µL of cell lysis buffer supplemented with 0.1 mM phenylmethylsulfonyl fluoride (PMSF) and 1 mM ethylenediaminetetraacetic acid (EDTA), pH 7–8. Lysates were sonicated x3 times for 60 s at 20% amplitude followed by a 90 s pause. Protein concentration was measured by a bicinchoninic acid (BCA) protein assay. One hundred micrograms of each sample were reduced in 10 mM tris(2-carboxyethyl) phosphine (TCEP), pH 7–8, at 60 °C for 1 h on a shaking platform. Iodoacetamide was added to alkylate the sample to a final concentration of approximately 17 mM and incubated for 30 min in the dark. Next, samples were passed through 10 kDa filters to reduce the SDS concentration as described in the filter-aided sample preparation (FASP) methodIn order to compare protein expression, samples were labeled in duplicate  using two TMT-10plex labeling kits . Triplicates were used for ovary tissue and CHO-S. Each of the 10 reagents has the same nominal mass and chemical structure, so that for each sample a unique reporter mass (126–131 Da) was used to relate protein expression levels. Specifically, we included technical and biological replicates of CHO-S (two samples in TMT 1 and one sample in TMT 2) to aid in comparisons between and within the TMT experiments. All other samples contained only biological replicates, which were randomly assigned to one of the TMT-10plex kits. The TMT labeling design is provided in Table m/z, data dependent HCD MS/MS analysis of top 10 precursors with minimum signal of 2000, isolation width of 1.9, 30 s dynamic exclusion limit and normalized collision energy of 35. Precursor and the fragment ions were analyzed at 60,000 and 7500 resolutions, respectively.Digested peptides were fractionated using an Agilent 1200 Capillary LC with a 254 nm Agilent variable wavelength detector on a basic reversed phase column  at a flow rate of 250 µL/min. The gradient was set from 100% A to 100% B (A = 10 mM Triethyl Ammonium Bicarbonate (TEAB) and B = 90% acetonitrile, 10 mM TEAB). 84 fractions were collected over 105 min and concatenated into final 24 samples prior to second dimension LC and MS analysis. Tandem mass spectrometry analysis of the peptides was carried out on the LTQ-Orbitrap Velos  MS interfaced to Eksigent  nanoflow liquid chromatography system with an Agilent 1100 autosampler . Peptides were enriched on a 2 cm trap column , fractionated on Magic C18 AQ, 5 µm, 100 Å, 75 µm × 15 cm column  and electrosprayed through a 15 µm emitter . The reversed-phase solvent gradient consisted of solvent A (0.1% formic acid) with increasing levels of solvent B  over a period of 90 min. LTQ Orbitrap Velos parameters included 2.0 kV spray voltage, full MS survey scan range of 350–1800 Cricetulus griseus database (Proteome Discoverer v1.4) for CHO cell lines. Oxidation on methionine, deamidation NQ, and phosphoSTY were set as variable modifications, and carbamidomethyl on cysteine was set as fixed modifications in Mascot search node used in Proteome Discoverer  workflow. Mass tolerances on precursor and fragment masses were 15 ppm and 0.03 Da, respectively.Peptide sequences were identified from isotopically resolved masses in MS and MS/MS spectra extracted with and without deconvolution using Thermo Scientific MS2 processor and Xtract software nodes. Data were searched against all entries in the https://biodbnet-abcc.ncicrf.gov) for functional analysis by gene ontology (GO). For GO annotation, gene symbols were mapped to biological processes, using the GO-CHO platform (https://ebdrup.biosustain.dtu.dk/gocho). All programming for the hypergeometric test were calculated in MATLAB version 2015vB (https://www.mathworks.com/products/matlab) and RStudio (https://www.rstudio.com). Enrichment and depletion p-values were calculated using the hygecdf and hygepdf functions in MATLAB. These values were used to generate heat maps and k-means clustering in Genesis software version 1.7.624. Pathways of interest were plotted and evaluated using IPA software (https://www.ingenuity.com/).Protein identifications were made using Proteome Discoverer software with a high confidence cutoff [< 1% false discovery rate (FDR)]. The protein intensities were evaluated by fold change, using the CHO-S cell line technical replicate in each TMT as the basis. Protein accession numbers were mapped to gene symbols using the biological database network  and ingenuity pathway analysis (IPA) were used for functional analyses of the differentially expressed proteins. GO analysis converts gene symbols to their molecular function, cellular component, and biological processes in order to evaluate the relationship between the molecular activities of gene products, location of activity, and pathways comprising the activity of multiple gene products, respectively27. In another functional analysis, each gene symbol was mapped to the relevant IPA pathways28, suggesting enriched and depleted pathways for comparisons between different tissues and cells. For both GO and IPA, the enrichment and depletion values were determined based on p-values that were calculated via the hypergeometric distribution, with p < 0.05 set for evaluating significance.Following MS identification, the protein accession numbers were determined using the annotated Chinese hamster genomehttps://peptide.nist.gov). Over 6000 proteins were identified in each sample with at least two unique peptides per protein; this corresponded to a total of 8470 unique proteins containing a false discovery rate (FDR) of 1% for protein identification when the TMT samples were combined and the duplicates were removed  comprises tissues with dense connective tissue and capillary systems30. In contrast, the second cluster (lung and kidney) is specialized for transport, with tubular systems and thin insterstitium33. Ovary and brain were clustered separately from the other organs.Protein intensity fold change ratios were initially evaluated through principal component analysis (PCA) of proteins identified in all samples, as shown in Fig. 2 value assessed the linearity of the two replicates. Shown in Fig. 2 value for each cluster, specifically lung (from the lung/kidney cluster) and heart (from the heart/kidney/spleen cluster), plus brain and ovary. For the following analyses, we studied these representatives from each of the clusters; additional data on all proteins is provided in Supplementary Table Next, the two replicates from the same tissue were plotted to examine their consistency. Fitting a curve to the scatter plot and calculating the RTo compare between cell lines and tissues, protein intensity was averaged between replicates and plotted in Fig. When comparing the cell lines to each other, no significant differences were observed between the CHO-S and CHO DG44 cell lines Table A at a p-In comparison to ovary tissue , calcium and integrin binding protein, and serine/threonine protein kinase  were highly upregulated in hamster ovary  which was also significantly upregulated in our hamster heart proteome 48. Thus, the capacity for ROS metabolism may be critical for lung function and adaptation to changes in oxygen levels in different environments.Pathways enriched in the lung include catabolism of branched chain amino acids (BCAAs), metabolism of reactive oxygen species (ROS), trafficking of vesicles, and synthesis of lipids Fig. D. The lu49, accompanied by increases in ribosome activity and translation50. Finally, protein functions enriched in the ovary include development of extracellular matrix, docking of vesicles, and exocytosis (Fig. 31, critical to the fertility process51. Further, malfunctions of the matrix are observed during ovarian cancer52. High levels of extracellular matrix components in Chinese hamster ovaries are in agreement with our previous research examining the hamster ovary tissue proteome using label-free proteomics approaches12.Similarly, in the heart, enriched pathways include metabolism of alpha-amino acid, sorting of protein, and translation Fig. C. Amino For GO functional analysis, gene symbols were annotated for biological processes in order to group proteins with similar biological relevance. The biological process GO category was analyzed to determine enrichment and depletion p-values for the cell-to-tissue and tissue-to-tissue comparisons. The appendix lists the top 10 most enriched biological processes for CHO-S (Supplementary Table 27. In both CHO-S and CHO DG44, the most common biological processes enriched involve DNA and mRNA processing, and metabolism. Some of the most common enriched processes in CHO-S include mRNA processing and splicing, DNA replication and repair, transcription, cell cycle, chromatin modification, and chromosome condensation.Biological processes, representing a biological function involving the gene product, complements the pathway analysis in “27. Ion transport helps to maintain the stability of cerebral function due to the key roles that ions play in currents and synaptic transmission53. Abnormal distributions of ions in the brain can lead to defects in neuronal function including seizures and depression.In comparison, signaling, transport, and adhesion were significantly enriched across the different hamster tissues. Enriched brain biological processes relative to the CHO cell lines include ion transport, axon guidance, synaptic transmission, and metabolic process, among others55. Biological processes that are enriched in the lung as compared to the CHO cell lines include G-protein coupled receptor signaling pathway, innate immune response, vesicle-mediated and transmembrane transport, and signal transduction. Indeed, vesicle transport is an important component of secretory pathway machinery. Unraveling the complexities of lung secretions may yield new insights into ways by which secretion differs in tissues and cell lines. Signaling through G-protein coupled receptors and other pathways is an important proinflammatory response in lungs, which can undergo modifications leading to lung cancers57. Not surprisingly, protection of the lungs using the host innate immune response is critical as this organ is exposed to a variety of pathogens including bacterial, fungal, and viral, during breathing59.Enriched biological processes in the heart compared to the CHO cell lines show functions related to circulation and heart function such as vasoconstriction, sodium-independent organic ion transport, complement activation, blood coagulation, and lipoprotein metabolism. For example, complement activation involves mannose-binding lectin and complement components, C3, C5, and CD59. The complement cascade can be activated during heart disease or failure, especially in cases of myocardial ischemia and reperfusion59, which is consistent with these proteomic results. Cell adhesion is also important to ovary function and follicle maturation through interactions with the extracellular matrix and direct cell–cell contacts. Mutation of the ovarian surface is causative of approximately 90% of malignant ovarian tumors61.Finally, enriched biological processes in the ovary highlight differences between the cells and tissue around the region from which CHO cells were derived. Enriched ovarian biological processes include transmembrane transport, protein transport, vesicle-mediated transport, cell adhesion, and cell death. When vesicle production rate was quantified in the ovary, the turnover indicated high vesicle recycling across the endomembrane systemThe results from the Chinese hamster proteome provide new insights into global protein expression across a wide variety of tissues and multiple cell lines. These differences highlight the role of tissues in executing key organ functions which require a specific metabolic processes, such as transport and communication, in comparison to CHO cells, which are focused on replication and gene expression, characteristics useful for rapid growth and the production of biologics. Because of their relevance to biomedicine and the biotechnology industry, we compared the tissue proteome to CHO cell lines and each other in order to identify functional differences in expression across tissues and cell lines. Specifically, we observed enrichment of many physiological pathways in tissues that were not enriched in cells, such as ion, protein, and vesicle transport, signal transduction, and cell adhesion. Often, these differences correlated with specific tissue functions while the activities in cell lines were often correlated with DNA replication, cell cycle, or RNA processing. Furthermore, some of the proteins with high expression in lung, ovary, or other tissues versus CHO, such as vesicle-mediated and protein transport, provide significant opportunities for CHO cell engineering going forward. In this way, the study expands on our CHO and Chinese hamster tissue knowledge base by virtue of establishing an atlas to differentiate proteins across cells and tissue for this critically important biotechnological and biomedical host species. Indeed, this comparison has enabled us to appreciate the changing proteomic landscape across cells and tissues and furthermore to recognize how the expressed proteins from different cell types can represent signatures for some of their key physiological or biotechnological functions.Supplementary Information 1.Supplementary Information 2.


Chemical-genetic interaction profiling in model organisms has proven powerful in providing insights into compound mechanism of action and gene function. However, identifying chemical-genetic interactions in mammalian systems has been limited to low-throughput or computational methods. Here, we develop Quantitative and Multiplexed Analysis of Phenotype by Sequencing (QMAP-Seq), which leverages next-generation sequencing for pooled high-throughput chemical-genetic profiling. We apply QMAP-Seq to investigate how cellular stress response factors affect therapeutic response in cancer. Using minimal automation, we treat pools of 60 cell types—comprising 12 genetic perturbations in five cell lines—with 1440 compound-dose combinations, generating 86,400 chemical-genetic measurements. QMAP-Seq produces precise and accurate quantitative measures of acute drug response comparable to gold standard assays, but with increased throughput at lower cost. Moreover, QMAP-Seq reveals clinically actionable drug vulnerabilities and functional relationships involving these stress response factors, many of which are activated in cancer. Thus, QMAP-Seq provides a broadly accessible and scalable strategy for chemical-genetic profiling in mammalian cells. Identifying chemical-genetic interactions in mammalian cells is limited to low-throughput or computational methods. Here, the authors present QMAP-Seq, a broadly accessible and scalable approach that uses NGS for pooled high-throughput chemical-genetic profiling in mammalian cells. Likewise, the function of a gene can be inferred by comparing its chemical-genetic interaction profile (the quantitative landscape of the effects of this particular gene on the efficacy of a panel of compounds) to the profiles of genes with known functions6.Chemical–genetic interaction profiling in model organisms, such as yeast, has emerged as a powerful strategy to reveal functional insights into compounds, genes, and cellular processes. In these studies, the mechanism of action of a compound can be deduced by comparing its chemical–genetic interaction profile  to the profiles of compounds with known cellular targets to identify the most similar profiles7 and define mechanisms of drug resistance9. Even focused studies interrogating limited numbers of chemical–genetic interactions can reveal critical insights. For example, one recent study demonstrated that the efficacy of a handful of clinical compounds was unaffected by knockout of their putative targets10, highlighting the power of using chemical–genetic approaches to validate on-target activity of drug candidates. Despite their utility, these studies are low-throughput and thus limited to investigating small numbers of compounds.The development of highly specific and efficient genetic perturbation tools based on CRISPR-Cas9 has enabled similar types of chemical–genetic studies in mammalian systems, albeit at much smaller scales than in model organisms. Most often, chemical–genetic studies in mammalian systems involve genome-scale loss-of-function screens against one compound over the course of several weeks to identify drug targets11, describes cell death resulting from the combination of a genetic variant and a chemical perturbation, where each individual perturbation is viable. By exploiting genetic variants  that differentiate tumor from normal tissue, synthetic lethal interactions provide a therapeutic window for selectively targeting cancer cells. The potential of synthetic lethality is best exemplified by the development and FDA approval of PARP inhibitors for patients with BRCA-mutated ovarian, breast, and prostate cancers13. There is also value in identifying synthetic rescue interactions, where a cytotoxic compound has reduced efficacy in the presence of a particular genetic variant, thus providing insights into drug resistance mechanisms14. The only existing strategies for identifying clinically relevant chemical–genetic interactions for more than a handful of genetic variants and compounds rely entirely on predictive approaches. Some of these predictions are based on genetic or chemical–genetic interactions identified in yeast16, while others, such as the Cancer Cell Line Encyclopedia17, Genomics of Drug Sensitivity in Cancer20, Cancer Therapeutics Response Portal23, and PRISM25, are based on computational methods that correlate genetic and molecular features of human cancer cell lines with drug response. While these correlative approaches have been useful, they are limited by the fact that many features are rare and lack sufficient representation—or are not even present—in current cancer cell line collections, reducing the statistical power to detect significant correlations. Related to this, even correlations involving more common features are confounded by the multitude of additional features that also distinguish each cell line. Thus, these approaches still require direct experimental validation, ideally in a manner that tests individual features along with their corresponding isogenic controls in relevant mammalian models.There is a growing interest in identifying synthetic lethal and synthetic rescue chemical–genetic interactions that can serve as the basis for cancer therapeutic strategies. Chemical–genetic synthetic lethality, a concept rooted in classical genetics31, but are not easily druggable32, we reasoned that QMAP-Seq could be used to identify synthetic lethal chemical–genetic interactions dependent on the activation status of these factors. Furthermore, because the individual branches of the proteostasis network are typically studied in isolation, and the functional relationship within and between the branches remains largely unexplored, we postulated that QMAP-Seq could also be used to provide functional insight into the proteostasis network. We first perform QMAP-Seq with one cell line and demonstrate that it generates precise and accurate quantitative measures of compound efficacy that are concordant with established cell viability assays based on live-cell imaging. We then expand QMAP-Seq to multiple cell lines, which enables the parallel measurement of 86,400 cell viability phenotypes in a single experiment. Altogether, we identify 60 sensitivity interactions and 124 resistance interactions and validate a subset of these interactions individually using an established metabolic-based cell viability assay. This work illustrates the power of systematic, high-throughput chemical–genetic profiling in mammalian systems.Here, to systematically and directly measure the contribution of individual genes to acute drug response, we devise quantitative and multiplexed analysis of phenotype by sequencing (QMAP-Seq). Unlike most chemical–genetic strategies in mammalian systems, QMAP-Seq is characterized by short-term compound treatment, which better recapitulates the timing of most high-throughput drug screening assays and enables testing of thousands of compounds in parallel. As proof-of-concept, we apply QMAP-Seq to the protein homeostasis (proteostasis) network, a critical set of cellular stress response factors that maintain proper protein function from synthesis to folding to degradation. Because individual proteostasis factors are activated to varying degrees across tumors to cope with cancer-associated and drug-induced stressERN1), XBP1, ATF3, ATF4, ATF6), oxidative stress response (NRF2 (NFE2L2), KEAP1), and in autophagy (ATG7). As a control to validate that QMAP-Seq can detect known chemical–genetic interactions, our library also contained an sgRNA targeting SLC35F2, a solute carrier required for cellular uptake of the cytotoxic compound, YM15533. We engineered MDA-MB-231 triple-negative breast cancer cells with these 11 single-gene knockouts and a pool of five non-targeting (NT) sgRNA controls. Because constitutive expression of Cas9 can result in off-target effects34 and cell toxicity35, we designed a system with doxycycline-inducible Cas9, providing temporal control over gene knockout , unfolded protein response . We next amplified the 768 samples, corresponding to distinct compound-dose-replicate combinations, using unique sets of i5 and i7 indexed primers  , demonstrating the high degree of accuracy of QMAP-Seq  Fig. . To comp14) Fig. . As expeSeq Fig. . Taken tBecause the genetic and epigenetic background of a cell line can impact therapeutic response, we next expanded QMAP-Seq to multiple cell lines Fig. . We seleTo ensure that each cell line would be similarly represented in our assay despite different doubling times, we measured the relative abundance of each of the five cell lines grown in heterogeneous pools. We prepared five pools, each containing 20% of one cell line expressing ZsGreen and 20% of each of the other four cell lines expressing dTomato Fig. . We co-cAfter pooling the five cell lines each possessing our panel of proteostasis factor knockouts at the optimized pooling ratio, we performed the QMAP-Seq workflow Fig. . Seven dP = 6.3e−3)      using QMAP-Seq. As expected, the ER+ cell line ZR-75-1 was preferentially sensitive to 4-Hydroxytamoxifen, and the HER2+ cell line SKBR3 was preferentially sensitive to Lapatinib Fig. . In addir = 0.9324)  Fig. . Thus, nP < 0.05) and 124 cell line–gene–drug interactions that conferred compound resistance (AUC difference >25 and P < 0.05) compared to sgNT (see “Methods” for details)  Fig. . To charls) Fig.  with thels) Fig. , suggestls) Fig. .43. To validate these pooled screening hits in traditional arrayed format, we treated MDA-MB-231 cells possessing individual gene knockouts with Carfilzomib for 72 h and measured their intracellular reducing potential as a proxy of cell viability. As we observed using QMAP-Seq, knockout of these proteostasis factors further sensitized MDA-MB-231 cells to Carfilzomib   Fig. . This neNFE2L2), the master transcriptional regulator of the oxidative stress response44, we expected a low degree of correlation between these two factors. Indeed, NFE2L2 and KEAP1 displayed the third lowest correlation. In addition, we observed a high correlation between ERN1 and XBP1, which was expected given that the ER stress sensor, IRE1 (ERN1), activates XBP1 through mRNA splicing45.To model the overall structure of the proteostasis network, we assessed the functional similarity of each proteostasis factor’s chemical–genetic interaction profiles. Specifically, we quantified the Spearman correlation between all gene–gene pairs based on AUC difference across all cell line and compound contexts Fig. . Using t46 and retinal pigment epithelium cells47. Furthermore, the paralogs HSF1 and HSF2 have been reported to function cooperatively51, antagonistically52, and in a context-dependent manner53. Interestingly, HSF1 and HSF2 displayed the second highest correlation, providing evidence in support of a cooperative interaction in breast cancer. The lowest correlation genetic interaction was observed between ATF4 and KEAP1, which is not entirely surprising considering KEAP1 has been reported to negatively regulate ATF4 expression in other cancer models54. Altogether, we conclude that chemical–genetic profiling using QMAP-Seq provides insight into the organization of the proteostasis network in breast cancer cells and has the power to reveal genetic relationships.This approach also revealed previously unknown genetic relationships Fig. . For exa24, an approach that uses microsphere bead technology for multiplexing cell lines, but not compound treatments. Another important feature of QMAP-Seq is the use of genetically engineered isogenic cell line pairs, which enables direct assessment of the contribution of a single genetic perturbation to compound sensitivity. It is worth noting, however, that other pooled assays could in principle profile isogenic cell line pairs, while QMAP-Seq could be performed with pools of non-isogenic cell lines. QMAP-Seq leverages readily available next-generation sequencing, which itself continues to improve in throughput, and is compatible with common sequencing libraries, such as RNA-Seq. As a result, the costs associated with QMAP-Seq are 1/10th the cost of an ATP-based cell viability assay of the same magnitude performed in 384-well format. The compound requirements are also lower, which is particularly useful when profiling scarce compounds or natural products.Here, we introduce QMAP-Seq, a highly multiplexed chemical–genetic profiling strategy that enables systematic phenotyping of dozens of cell lines with defined genetic perturbations across thousands of individual compound treatments. We also present a bioinformatic analysis pipeline that simplifies the processing of thousands of fastq files into over 10,000 dose–response curves. QMAP-Seq has several major advantages over existing methods for the identification of chemical–genetic interactions in mammalian cells. For one, our pooled approach provides substantial gains in throughput, facilitating over 50 times as many cell viability measurements per sample as established arrayed assays. By introducing sample-specific indexes, thousands of compound treatment samples are further pooled and sequenced together in a single next-generation sequencing run. This distinguishes QMAP-Seq from PRISMImportantly, the short-term nature of QMAP-Seq  enables profiling of thousands of compounds, which would be technically challenging with existing genome-scale chemical–genetic methods that require several weeks of passaging and treating cells prior to readout. The short-term format also minimizes potential secondary effects that may arise due to several weeks of exposure to compounds, and thus identifies the most relevant chemical–genetic interactions. Collectively, these attributes of QMAP-Seq enable accurate mapping of biological network structure, as we demonstrated for the proteostasis network.25 or by profiling one cell line at a time. Additionally, factors secreted by one cell line could conceivably alter the compound sensitivity of another cell line in the pool. Although such paracrine-mediated effects would certainly warrant further studies, we -2A-GFP 58, EcoRI and BamHI restriction sites were introduced flanking the future cell line barcode insertion site by amplifying two overlapping fragments with Barcode Frag1 F/R and Barcode Frag2 F/R . Cell line barcode oligos were inserted within the EcoRI and BamHI restriction sites of the modified lentiGuide-Puro vector.For inserting cell line barcodes downstream of the gRNA scaffold and upstream of cPPT/CTS within the lentiGuide-Puro vector 59 and non-targeting (NT) sgRNAs sourced from ref. 58 and ref. 9 were inserted into the cell line barcoded lentiGuide-Puro vectors by resuspending the top and bottom strands of the sgRNA oligos . sgRNA oligos were cloned into the barcoded lentiGuide-Puro vectors by combining 100 ng vector, 2 μL 1:200 diluted oligo duplex, 2 μL 10x FastDigest Buffer, 1 μL 10 mM DTT, 1 μL 10 mM ATP, 1 μL BsmBI, 0.5 μL Quick Ligase, and ddH2O to a final volume of 20 μL and incubating in thermocycler as follows: 37 °C for 5 min, 21 °C for 5 min, repeat for a total of 6 cycles. Ligation reactions were treated with PlasmidSafe exonuclease by combining 11 μL ligation reaction, 1.5 μL 10x PlasmidSafe Buffer, 1.5 μL 10 mM ATP, and 1 μL PlasmidSafe exonuclease and incubating in thermocycler as follows: 37 °C for 30 min, 70 °C for 30 min. Ligation reactions were transformed into Stbl3 cells. All plasmid sequences were verified by Sanger sequencing.Gene-targeting sgRNAs sourced from the Brunello Human CRISPR Knockout Pooled Library293T, ZR-75-1, SKBR3, HCC-38, MDA-MB-231, and BT-20 cells were obtained from ATCC. 293T cells were cultured in DMEM medium  supplemented with 10% Tet System Approved Fetal Bovine Serum  and 1% Penicillin/Streptomycin . To facilitate co-culturing cell lines in mixtures, ZR-75-1, SKBR3, HCC-38, MDA-MB-231, and BT-20 cells were cultured individually or collectively in a common growth medium: RPMI-1640 medium  supplemented with 10% Tet System Approved Fetal Bovine Serum  and 1% Penicillin/Streptomycin . All cell lines were authenticated at the University of Arizona Genetics Core and tested negative for mycoplasma contamination.For engineering Cas9-expressing breast cancer cell lines, virus was produced from pLVX-TetOne Cas9 lentiviral transfer plasmid as specified in the application note for Lipofectamine 3000 Transfection Reagent , but substituting pMD2.G envelope plasmid and psPAX2 packaging plasmid for ViraPower Lentiviral Packaging Mix. ZR-75-1, SKBR3, HCC-38, MDA-MB-231, and BT-20 cells were transduced with virus and induced with either 100 ng/mL doxycycline  or 10 ng/mL doxycycline  for 7 days (refreshing doxycycline every 3 days). Induced cells were gated into three equal-size bins based on GFP brightness and sorted for medium GFP+ cells.For introducing sgRNA libraries, pooled sgRNA plasmid libraries possessing appropriate cell line barcodes were generated by combining equal amounts of 11 gene-targeting sgRNA plasmids and a pool of five NT sgRNA plasmids (sgNT_0-4). Virus was produced with Lipofectamine 3000 Transfection Reagent  as described above and was functionally titered on its respective cell line using serially-diluted virus. ZR-75-1, SKBR3, HCC-38, MDA-MB-231, and BT-20 Cas9 cells were transduced with appropriate sgRNA libraries at MOI = 0.3, maintaining coverage of at least 7500 cells per sgRNA. Transduced cells were selected using 2 μg/mL puromycin  for 3 days.g for 5 min, and stored at −20 °C.For generating cell spike-in standards, virus for five NT sgRNA plasmids (sgNT_5-9) possessing the 293T cell line barcode was produced in an arrayed format. 293T cells were transduced with the viruses individually. Transduced cells were selected using 2 μg/mL puromycin  for 3 days. To pool, cell lines were detached, resuspended in PBS, counted, and pooled at the following ratio: 1x sgNT_5, 3x sgNT_6, 9x sgNT_7, 27x sgNT_8, 81x sgNT_9. Pooled cells were aliquoted, pelleted at 425 × For assessing induction of FLAG-Cas9, MDA-MB-231 pLVX-TetOne Cas9 cells were treated with 0, 0.5, 1, 2, 5, or 10 ng/mL doxycycline  for 48 h. Cells were harvested and lysed in buffer containing 50 mM Tris, pH 7.5, 1 mM EDTA, 150 mM NaCl, 1% Triton X-100, 0.1% SDS. Protein concentration was measured using the BCA Protein Assay Kit . Five micrograms of total protein per lane was electrophoresed and transferred using an iBlot 2 Dry Blotting System (Thermo Fisher Scientific). Membrane was probed with 1:1000 Anti-FLAG primary antibody  followed by 1:10,000 Anti-Mouse IgG-Peroxidase secondary antibody , developed with Immobilon Western Chemiluminescent HRP Substrate , visualized using a ChemiDoc Touch Imaging System (Bio-Rad), and analyzed using Image Lab 5.2.1 (Bio-Rad). Membrane was stripped with ReBlot Plus Mild Antibody Stripping Solution  and reprobed with 1:10,000 Anti-Alpha Tubulin primary antibody  followed by 1:10,000 Anti-Mouse IgG-Peroxidase secondary antibody .For confirming whole population knockout of the proteostasis factors, MDA-MB-231 pLVX-TetOne Cas9 cells transduced with appropriate sgRNAs were treated with 10 ng/mL doxycycline  for 96 h (refreshing doxycycline every 2 days) to induce Cas9 expression prior to harvesting. Western blot analysis was performed as described above using the following antibodies: 1:1000 Anti-HSF1 , 1:1000 Anti-HSF2 , 1:1000 Anti-IRE1 , 1:1000 Anti-XBP1 , 1:1000 Anti-ATF3 , 1:1000 Anti-ATF4 , 1:1000 Anti-ATF6 , 1:1000 Anti-ATG7 , 1:1000 Anti-NRF2 , 1:1000 Anti-KEAP1 , 1:10,000 Anti-Alpha Tubulin , 1:10,000 Anti-Beta Actin . All uncropped blots are provided as a Source data file.ZR-75-1, SKBR3, HCC-38, MDA-MB-231, and BT-20 cells were transduced with pHIV-Luc-ZsGreen  or pUltra-Chili-Luc  and sorted for GFP+ or RFP+ cells, as appropriate.For preparing the five original pools, fluorescently labeled cell lines were counted, pooled, and frozen in liquid nitrogen. Pools were thawed on Day 0 and cultured normally. Six days and 13 days after thawing, the percentages of GFP+ and RFP+ cells were quantified by flow cytometry analysis using a LSRFortessa Cell Analyzer (BD Biosciences).r) of each of the five cell lines, the equation for exponential cell growth was used:xt = percentage at day t, x0 = percentage at day 0, r = growth rate, and t = time [days]. Growth rates (r) were then used to calculate the optimal starting percentage (x0) of each cell line to achieve 20% representation at t = 7 days.For estimating the growth rate . Heat maps were generated using Cluster 3.0 and Java TreeView 1.1.6r4.For preparing cell pools for QMAP-Seq with multiple cell lines (five cell lines × 12 genetic perturbations), individual cell line pools consisting of 12 sgRNAs (11 gene-targeting sgRNAs + 5 pooled NT sgRNAs) were prepared as described in “Cell engineering”. Each of the five individual cell line pools was counted, pooled with the other four cell line pools according to ratios derived from the relative cell abundance competition experiment, and frozen in liquid nitrogen .For confirming the efficacy of the sgRNAs that target SLC35F2, MDA-MB-231 pLVX-TetOne Cas9 cells engineered with either sgNT or one of the four sgRNAs targeting SLC35F2 were induced with 100 ng/mL doxycycline for 6 days (refreshing doxycycline every three days). In total, 1000 cells were seeded in a volume of 50 μL in opaque 384-well plates. The next day, YM155 was added over a nine-point concentration range in quadruplicate using a D300e Digital Dispenser (Tecan). Seventy-two hours later, cell viability was measured using the CellTiter-Glo Luminescent Cell Viability Assay . Luminescence was read using an Infinite M1000 PRO (Tecan) with an integration time of 500 ms.For live-cell imaging, pooled MDA-MB-231 pLVX-TetOne Cas9 cells engineered with 12 sgRNAs were induced with 100 ng/mL doxycycline for six days (refreshing doxycycline every 3 days). In total, 5000 pooled cells were seeded in a volume of 100 μL in 96-well plates. The next day, 89 compounds were added over the indicated four-point concentration range in duplicate from custom compound plates prepared at the High Throughput Analysis Laboratory (Northwestern University). Seventy-two hours later, the percent confluence of the population of cells in each well was measured  using an IncuCyte ZOOM Live-Cell Analysis System GUI version 2015A (Essen BioScience). To calculate relative cell confluence, the percent confluence of a compound-treated well was normalized to the median percent confluence of DMSO-treated wells.For validating QMAP-Seq hits using Resazurin, MDA-MB-231 pLVX-TetOne Cas9 cells engineered with appropriate sgRNAs were induced with 100 ng/mL doxycycline for 6 days (refreshing doxycycline every 3 days). In total, 5000 cells were seeded in a volume of 100 μL in 96-well plates. The next day, Carfilzomib was added over the indicated four-point concentration range in triplicate using a D300e Digital Dispenser (Tecan). Seventy-two hours later, cell viability was measured using the Resazurin Cell Viability Kit . Relative fluorescent units were read  using an Infinite M1000 PRO (Tecan).For all cell viability assays, dose–response curves were fit in GraphPad Prism 8 using the log(inhibitor) vs. response model (three parameters) with the top constrained to 100%.YM155 was obtained from Selleckchem (#S1130). Compounds for QMAP-Seq were obtained from the FDA-Approved Drug Library , the Clinical Compound Library , or from the following vendors: 4-Hydroxytamoxifen was obtained from Sigma-Aldrich (#H7904), and Bortezomib was obtained from Cayman Chemical (#10008822). For validation experiments, Carfilzomib was obtained from a different vendor  than where it was sourced for QMAP-Seq.60. The existence of specific genetic alterations in breast cancer was confirmed using cBioPortal62. Up to two proteostasis-modulating compounds per target were incorporated.Compounds for QMAP-Seq with multiple cell lines spanned six categories: positive controls, chemotherapeutics, targeted therapies (NCI), targeted therapies (OncoKB), diverse compounds (Informer Set), and proteostasis-modulating compounds. Relevant positive controls  were included. For all remaining compound categories, compounds that were not part of the FDA-Approved Drug Library  or the Clinical Compound Library  were filtered out. One compound per compound class was selected from breast cancer chemotherapeutics  in duplicate using one of two dose ranges. The standard dose range encompassed doses from 10 nM to 10 μM, and the low dose range covered doses from 1 nM to 1 μM. Dose ranges were selected by referencing doses previously used for treating cancer cell lines, including the Informer Set63. For QMAP-Seq with multiple cell lines, pooled cell lines (five cell lines × 12 genetic perturbations) were thawed in media containing 100 ng/mL doxycycline . Three days later, cells were expanded and doxycycline was refreshed. Three more days later, 5000 pooled cells were seeded in a volume of 100 μL in 96-well plates using an EL406 Microplate Washer Dispenser (BioTek Instruments), while maintaining doxycycline induction. The next day, 180 compounds were added over a four-point concentration range in duplicate from custom compound plates prepared at the High Throughput Analysis Laboratory (Northwestern University). Briefly, compound plates containing 500 nL of compound at 1000× were resuspended in 250 μL media per well to achieve 2× compound concentration, and 100 μL of 2× compound was distributed to replicate wells using multichannel pipet. Cells were treated for 72 h.A step-by-step protocol describing the QMAP-Seq protocol can be found at Protocol ExchangeCell spike-in standards were thawed at room temperature for 5 min and resuspended in Lysis Buffer , 0.45% IGEPAL CA-630 , 0.45% TWEEN 20 , 10% Proteinase K , 79.1% Nuclease-Free Water ) to achieve a concentration of 36.3 total cell spike-in standards/μL. Cells were homogenized using a 5 mL syringe and a 21G × 1″ needle three times followed by a 27G × ½″ needle three times. Cells were incubated in 60 °C water bath for 1 h, pipetting up and down every 20 min.After 72 h of compound treatment, compound-treated cells were washed with 100 μL PBS. 50 μL Lysis Buffer containing 36.3 total cell spike-in standards/μL (1815 spike-in cells/well) was added using multichannel pipet. Plates were covered with foil adhesive and incubated in 60 °C oven for 1 h. Cell lysates were transferred to PCR plates using multichannel pipet. Proteinase K from Lysis Buffer was inactivated at 95 °C for 15 min using thermocycler.2, 0.6 μL DMSO , 6 μL 1 μM P5 Primer Mix (mix of 0-8nt staggered primers), 6 μL 1 μM P7 Primer, 8 μL Lysate (Proteinase K inactivated again just prior to PCR), 30.16 μL Water, 0.24 μL Platinum Taq DNA Polymerase . To reduce the likelihood of PCR jackpot effects, half of the PCR volume from each reaction was transferred to a second set of PCR plates. PCRs were run in thermocycler as follows: 94 °C for 4 min, , 72 °C for 15 min, 4 °C hold.A 359–367 bp fragment (depending on stagger length) containing the sgRNA and cell line barcode was amplified with a unique set of P5 and P7 primers for each well by combining the following reagents: 6 μL 10x Taq DNA Polymerase Buffer, 1.2 μL 10 mM dNTP Mix, 1.8 μL 50 mM MgClTechnical duplicates were recombined. Library size was verified by running 5 μL of PCR products on E-Gel 96 2% Agarose gels . An equal volume (6 μL) of each PCR product was pooled together. The concentration of the pooled PCR products was measured using the Qubit dsDNA HS Assay Kit . Pooled PCR products were purified using the QIAquick PCR Purification Kit  with sufficient PCR purification columns to avoid exceeding the maximum binding capacity of each column. The concentration of the purified sample was measured using the Qubit dsDNA HS Assay Kit . The purified sample was purified a second time using the Purification Module with Magnetic Beads . The final library concentration was measured using the Qubit dsDNA HS Assay Kit .The sequencing library was diluted to 2.5 nM, combined with PhiX  to achieve 25% PhiX (to increase nucleotide diversity), and denatured. The sequencing library was loaded on a NovaSeq 6000 (Illumina) using a NovaSeq 6000 S1 Reagent Kit, 200 cycles, 1.3B Reads . Single-end sequencing was performed using the following run parameters: Illumina Read 1 Primer: 164 cycles , Illumina Index 1 Primer: 6 cycles (to sequence i7 index), Illumina Index 2 Primer: 6 cycles (to sequence i5 index).Individual samples were demultiplexed based on i5 and i7 index sequences (Supplementary Data Relative cell numbers were plotted as heat maps and dose–response curves. Heat maps were generated using Cluster 3.0 and Java TreeView 1.1.6r4. Dose–response curves were fit in GraphPad Prism 8 using the log(inhibitor) vs. response model (three parameters) with the top constrained to 100%. The area under the curve (AUC) was calculated using the equation: AUC = 0.5(dose 1 cell number) + dose 2 cell number + dose 3 cell number + 0.5(dose 4 cell number).2(cell number) > 0.7) were excluded, which included these cell line-sgRNA pairs: ZR-75-1 sgHSF1 and ZR-75-1 sgKEAP1. Third, compounds with an AUC > 550 for any cell line-sgRNA pair were excluded, which included these compounds: Ruxolitinib and STF083010 . Fourth, noncytotoxic cell line-compound pairs (relative cell number of sgNT with the highest dose ≥ 90%) were excluded, which included 225 cell line-compound pairs. Fifth, cell line-compound pairs without a significant dose-dependent reduction in cell viability (difference in relative cell number of sgNT between lowest and highest dose < 25%) were excluded, which included 366 cell line-compound pairs (union between fourth and fifth exclusion criteria = 380 cell line-compound pairs).Data were filtered using the following exclusion criteria. First, samples with low read counts were excluded, which included these compounds: ABT199, Belinostat, BMS345541, Dexamethasone, Fingolimod, and RacRotigotine. Second, cell line-sgRNA pairs with high variation  compared to in the five cell line experiment (5000 cells ÷ 60 perturbations ≈ 83 cells per perturbation), five times as many cells for each of the cell spike-in standards were added per sample for the one cell line experiment. The data from Plate 8 was excluded from analysis due to a technical problem with the addition of cell spike-in standards for that plate. The sequencing library was loaded on a NextSeq 500 (Illumina) using a NextSeq 500/550 High Output Reagent Kit, 400M Reads .P < 0.05) and that had a large effect size (absolute magnitude of AUC change >60). These interactions were visualized as an unweighted network using a standard force-directed layout in Cytoscape v3.7.2 (cytoscape.org).For constructing a chemical–genetic interaction network, data were filtered as described above. All interactions were then filtered for those considered significant . For correlation analysis of AUCs between live-cell imaging and QMAP-Seq, Pearson r was reported, and statistical significance of Pearson correlation was determined using a two-tailed test (n = 89 compounds). For correlation analysis of AUCs between two independent QMAP-Seq experiments, Pearson r was reported, and statistical significance of Pearson correlation was determined using a two-tailed test (n = 220 compound-sgRNA combinations). For comparing the variation between plates for reads versus cell number, statistical significance was determined using an unpaired, two-tailed F test to compare variances (n = 16 plates).For correlation analysis of replicates, Pearson t test . The chemical–genetic interactions with an Absolute AUC Difference > 25 and P < 0.05 were designated as hits.For identifying the chemical–genetic interactions with the greatest effect, a volcano plot was produced after data filtering as described above. The magnitude was determined by calculating the difference in mean AUC between sgRNA and sgNT for every cell line-compound combination. The statistical significance was determined using an unpaired, two-tailed n = 180 compounds).For determining pathway enrichment, statistical significance was determined using a one-tailed binomial test to compare observed distribution to expected distribution (Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Peer Review FileReporting Summary