H-pyrazolopyridine and pyrazolopyridine embelin derivatives was synthesized through a multicomponent reaction from natural embelin, 3-substituted-5-aminopyrazoles and aldehydes. The synthesized compounds were evaluated against three hematologic tumor cell lines, HEL (acute erythroid leukemia), K-562 (chronic myeloid leukemia) and HL-60 (acute myeloid leukemia), and five breast cancer cell lines . The primate non-malignant kidney Vero cell line was used as the control of cytotoxicity. From the obtained results, some structure–activity relationships were outlined. Furthermore, in silico prediction of physicochemical properties and ADME parameters were determined for the derivatives with the best antiproliferative values.A set of new dihydro-1 H-pyrazolopyridines and pyrazolopyridines have raised great interest due to the biological activities that they exhibit. A) with cytotoxic activity against triple-negative breast cancer MDA-MB-231 cell line  pyridines and pyrazolopyridines are found in the literature. Of special interest are those based on multicomponent reactions (MCRs), such as those involving the condensation of 1,3-dicarbonyl compounds with aldehydes and aminopyrazole derivatives [b]pyridines] from isatins, pyrazol-5-amines and β-ketonitriles pyridine and pyrazolopyridine embelin analogues and evaluate their potential as antiproliferative agents against eight different human hematological , and breast cancer cell lines .With these antecedents and because of our interest in the preparation of bioactive derivatives from the natural benzoquinone embelin ,16,17,181) has a masked 1,3-dicarbonyl moiety, the condensation of (1) with 4-nitrobenzaldehyde (2a) and 3-phenyl-5-aminopyrazole (3a) was carried out in order to obtain the desired dihydro-1H-pyrazolopyridine derivatives, using EtOH as solvent at room temperature. Ethylendiamine diacetate  was also used as an effective organocatalyst for the initial Knoevenagel condensation. Under these reaction conditions the product 4a was obtained in 33% yield. The structure of 4a was determined and corroborated by 1D and 2D NMR experiments  and electron-donating groups (4l–4o) good yields (73–98%) were obtained. The use of heteroaromatic aldehydes led to the corresponding products (4p–4s) in moderate yields (42–63%). This methodology is also tolerant to aliphatic aldehydes since the corresponding dihydro-1H-pyrazolopyridine adducts (4t–4w) were obtained.As we can observe, different yields were obtained depending on the type of aldehyde used in the Knoevenagel condensation. Thus, with aromatic aldehydes substituted with both electron-withdrawing (4) is shown in 2) and embelin (1) results in the formation of a methylene quinone reactive intermediate (A), which is trapped by the 3-phenyl-5-aminopyrazole (3). The reaction takes place via Michael addition to the methylene quinone system by the nucleophilic carbon in the α position to the amino group of the 3-phenyl-5-aminopyrazole to give the intermediate (B) which after a proton transfer gives the dihydropyridine system by intramolecular cyclization and dehydration.A plausible mechanism for the formation of these embelin derivatives , K-562 (chronic myeloid leukemia) and HL-60 (acute myeloid leukemia), and five breast cancer cell lines . The primate non-malignant kidney Vero cell line was used as control of cytotoxicity. The results obtained are shown in 50 values between 0.7 and 7.5 μM.None of the compounds evaluated showed significant cytotoxic activity in the triple negative breast cancer cell lines MDA-MB-231 and HS-578T. Several of the compounds showed good cytotoxic activity against the rest of cell lines evaluated with IC4a, with a 4-NO2 group, exhibited the best cytotoxic activity in HL60 with an IC50 of 0.70 ± 0.14 μM. This compound also showed good activity values in HEL (1.05 ± 0.35 μM) and K562 (1.25 ± 0.35 μM). Derivatives 4c, 4d, 4e and 4g, with substituents 4-Cl, 4-Br, 4-F and 4-CF3, respectively, also presented good values of cytotoxic activity in the three leukemia cell lines with IC50 values between 0.90 and 3.30 μM. The best IC50 value in acute erythroid leukemia (HEL) was achieved with compound 4g (1.00 ± 0.42 μM), whereas in chronic myeloid leukemia cell line (K-562), the derivative 4k (4-CN-Ph) showed the best IC50 value (0.92 ± 0.32 μM).In general terms, the best results were obtained in the leukemia cell lines compared to the breast cancer cell lines. Concerning the leukemia cell lines, compound para position of the phenyl ring at the 1,4-dihydropyridine ring, is highlighted. For the derivatives 4f, and 4h, having a -F and a -NO2 group, at the meta position a loss of cytotoxic activity was detected compared to the corresponding para-substituted derivatives (4e and 4a). Interestingly, the replacement of these electron-withdrawing groups at the para position of the phenyl ring by others such as the -COOH (4i), or -CO2CH3 (4j) also leads to a loss of activity.According to these first results, the importance of substituents in 2, -3F-4MeO, 3-4-MeO, 3,4-methylenedioxy  leads to worse values of IC50 in the leukemia cell lines, compared with those of 4b (-Ph). A clear loss of activity when heteroaromatic rings were attached at the dihydropyridin nucleus (4p–4s) was also observed, while aliphatic substituents  leads to higher values of IC50 with the exception of the derivative 4t, with a cyclohexyl group, which keeps a good cytotoxic activity (1.30 ± 0.28 μM) against HEL.On the other hand, the presence of electron-donor groups at the phenyl ring such as -NMe4a (4-NO2) showed the best cytotoxic activity against SKBR3 (1.30 ± 0.28 μM). In the MCF7 cell line, only the derivatives 4a (4-NO2) and 4u (-CH2CH3) were active, with IC50 values of 3.25 ± 1.91 and 4.75 ± 1.20 μM, respectively. Finally, several of the derivatives evaluated in BT549 cell line  presented IC50 values around 3.00 μM.With respect to the breast cancer cell lines , derivative 4c, 4d, 4g, 4t) and breast cancer  cell lines. Taking advantage of the versatility of this multicomponent reaction, and given the good results of cytotoxic activity obtained for some of the dihydro-1H-pyrazolopyridine derivatives previously synthesized, we decided to carry out modifications in the quinone and aminopyrazole components to evaluate the effect of these structural variations on the cytotoxic activity.All previously synthesized nitrogenated embelin derivatives were also evaluated in the Vero cell line of primate kidney, to determine its cytotoxicity in a non-tumor cell line and to study its viability for carrying out future in vivo tests. Interestingly, inhibition of cell viability induced by several compounds were 8- to 20-fold lower in Vero cells compared to hematological  and the presence of fluorinated groups in biologically active molecules, such as the trifluoromethyl group, is of great interest pyridine derivatives through corresponding MCRs using embelin (1) and 4-(trifluoromethyl)benzaldehyde (2g). The results obtained are shown in The aminopyrazoles (14a–14f). Furthermore, as we can see in 14g (82%) having a 2-furyl group, or aliphatic groups such as 5-methyl-3-aminopyrazole .The corresponding derivatives were obtained in good yields, from 76 to 94%, regardless of the nature of the substituent at the pyrazole ring , 14d (3-F-Ph), 14e (4-MeO-Ph) and 14g (2-furyl) presented a significant improvement in their cytotoxic activity with values of IC50 from 2.01 ± 1.12 µM to 2.40 ± 0.28 µM. All of them also showed an IC50 > 10 µM in MDA-MB-231 and HS-578BT cell lines.Some of the new derivatives showed good cytotoxic activities, especially in breast cancer cell lines. The derivative 14d (3-F-Ph) with an IC50 of 1.05 ± 0.64 μM in HL60 cell line and 0.95 ± 0.4 μM in K562 cell line. Derivatives 14e (4-MeO-Ph) and 14g (2-furyl), as in some of the breast cancer cell lines, also have good cytotoxicity values in HL60 with IC50 of 1.10 ± 0.14 and 1.10 ± 0.04 μM, respectively.Concerning the hematological tumor cell lines, we can observe an improvement in the cytotoxic activity for the derivative 4g was treated with DDQ, the corresponding pyrazolopyridin derivative (15) was obtained in 82% yield .Since other embelin derivatives have shown inhibitory activity against the human protein kinase CK2 ,18, we tUnderstanding the physicochemical properties and the pharmacokinetic profile  of molecules is an essential step to avoid failures in the selection of lead molecules during the different stages of development and discovery of new drugs. Therefore, it is essential to design chemical leads with acceptable ADME and good drug-like properties. However, about the drug-likeness of natural product derivatives, it is important to mention that many derivatives of them do not comply with drug-like rules during the lead optimization process. A lot of these compounds are found outside the drug-like space because they usually have a higher molecular weight and can be more complex than drug-like. Although the greatest value of the use of the privileged structures present in natural products is that they were optimized during the course of evolution and also, they can explore parts of chemical space that synthetic drug-like compounds do not essentially cover.H-pyrazolopyridine embelin derivatives with the best antiproliferative values . This study was performed using the Qikprop module of Schrödinger software pyridine embelin derivatives were efficiently synthesized from a modular approach that includes Knoevenagel condensation/Michael addition/ intramolecular cyclization and dehydratation. The influence on the antiproliferative activity of structural variations was investigated. Thus, for the eight tumoral cell lines tested  the presence of the free hydroxyl group at the benzoquinone nucleus, and the dihydropyridine ring were key. The best results were obtained for a C-11 long chain. Regarding the nature of the substituents at the dihydropyran ring, phenyl substituents with halogens, or -NO2 or -CF3 groups at position 4 yielded good IC50 values, while the modifications carried out in the pyrazol moiety revealed that 4-F-Ph, 3-F-Ph, 4-OCH3-Ph and 2-furyl resulted be the best substituents for the antiproliferative activity. Furthermore, the QikProp module of Schrödinger software was used as a computational method for analyzing the pharmacokinetic descriptors of the compounds with the best antiproliferative activities . The findings from this study are paving the way for further investigations concerning to obtain more selective and active compounds.A new family of dihydro-1


Despite continuous improvements in multimodal therapeutic strategies, esophageal carcinoma maintains a high mortality rate. Metastases are a major life-limiting component; however, very little is known about why some tumors have high metastatic potential and others not. In this study, we investigated thermogenic activity and adhesion strength of primary tumor cells and corresponding metastatic cell lines derived from two patients with metastatic adenocarcinoma of the esophagus. We hypothesized that the increased metastatic potential of the metastatic cell lines correlates with higher thermogenic activity and decreased adhesion strength. Our data show that patient-derived metastatic esophageal tumor cells have a higher thermogenic profile as well as a decreased adhesion strength compared to their corresponding primary tumor cells. Using two paired esophageal carcinoma cell lines of primary tumor and lymph nodes makes the data unique. Both higher specific thermogenesis profile and decreased adhesion strength are associated with a higher metastatic potential. They are in congruence with the clinical patient presentation. Understanding these functional, biophysical properties of patient derived esophageal carcinoma cell lines will enable us to gain further insight into the mechanisms of metastatic potential of primary tumors and metastases. Microcalorimetric evaluation will furthermore allow for rapid assessment of new treatment options for primary tumor and metastases aimed at decreasing the metastatic potential. Many solid tumors are characterized by a good prognosis in early stages if a complete resection can be achieved but by a tremendously bad prognosis in metastatic stages. Metastases usually present the limitation of therapy and are associated with a high mortality rate. One of the cancer types in which this is highly pronounced is esophageal carcinoma, which shows an increasing incidence of adenocarcinoma in the western population over the past decades . MortaliAn emerging alternative is the analysis of biophysical markers of tumor cells. The focus here lies on physical aspects of the cells themselves, such as cell deformability, cell membrane water transport, or adherence of cells to, for example, the extracellular matrix (ECM) ,4,5,6. MA further biophysical approach of tumor cell analysis is the investigation of the bioenergetic capability of tumor cells. This can, for example, be determined by measuring tumor thermogenesis, which in turn is linked with almost all cellular events in the cells’ energy turnover . In thisIn the current study, we used two paired stable esophageal adenocarcinoma cell lines generated from primary tumors and lymph nodes of the same patients. Comparing cell lines derived from the same patient makes our data unique even though the number of patients is low, as matched primary tumor and lymph node cell lines are rare. Furthermore, we investigated the thermogenic activity as well as the adhesiveness of primary cells and their corresponding metastatic cell lines in combination the first time. We evaluated any correlations between metastatic potential of the metastatic cell lines and higher thermogenic activity or decreased cell adhesion strength, respectively.2.Cell lines PT1590, LN1590, PT6216, LN6216c and LN6216gc were acquired from primary tumor and lymph nodes of patients with esophageal adenocarcinoma at the University Medical Center Hamburg-Eppendorf as described previously ,14,15. Ct-test or analysis of variance with a post-hoc test was used. p values less than 0.05 were defined as significant. The error bars in all bar plots represent one standard deviation.For microcalorimetric measurements, a 48-channel isothermal microcalorimeter  was used as previously described . Tumor c2SO4 (concentrated): H2O2 (30%) (v/v)) for 30 min. Cover glasses were washed in ultrapure water and incubated for 1 h with shaking in 2% v/v (3-Glycidoxypropyl) trimethoxysilane  in a solution of 88% v/v ethanol and 10% v/v water. Subsequently, the surfaces were washed in ethanol and incubated at 80 °C for 30 min. Silanized cover glasses were then functionalized with 1 mg/mL Collagen type I  in 100 mM HEPES pH 8 for 1 h, washed with ultrapure water and dried with nitrogen. Then, the surface was blocked with 5% heat-inactivated BSA in PBS for 30 min and washed with ultrapure water. Esophageal carcinoma cells were allowed to attach for 4 h at 37 °C. Before spinning, the cell suspension was removed and replaced with PBS; then, the cover glasses were mounted on the spinning disk device and immersed in PBS at room temperature. The spinning routine consisted of a 20 s acceleration ramp, 5 min steady spinning at the indicated rotations per minute value, and 20 s deceleration. The hydrodynamic forces at any point on the surface of the cover glass vary linearly with radial distance and can be described by the following equation:The spinning disk apparatus was built in-house based on previously described designs ,18. The 50) [τ is the applied shear stress, r is the radial position relative to the center of the cover glass, ρ is the spinning buffer density, µ is the fluid dynamic viscosity, and ω is the rotational speed. After spinning, the cover glasses were imaged at 10× magnification on an Olympus IX81 microscope ). A custom Python-based image analysis script was used to segment the cells from the background by applying a gray-value threshold. The script returns the radius of the cover glass and a list containing the area, XY coordinates and distance to the center of the cover glass, for each cell. The fraction of adherent cells (f), at different positions on the disk, was calculated by normalizing the density of cells at each section of the disk with the density of cells at the center of the disk, where the shear forces are close to zero. Detachment profiles (f vs. τ) for three cell replicates per line were plotted and fitted with a global sigmoid probabilistic model to extract the value of the shear stress at which 50% of the cells remain adherent (τ50) : (2)f=1/50 is used as a measure of mean adhesion strength for comparison of the different cell lines and is reported with 95% confidence interval determined by the fit.The τ1, M0, G3 in 1997 [PT1590 cell line was established from a patient with an apparently localized adenocarcinoma of the esophagus pT1, pN in 1997 . The pat in 1997 . After c in 1997 ,20. ThesThe human cell lines PT6216, LN6216c and LN6216gc were isolated from primary tumor and lymph node metastases of a patient with adenocarcinoma of the esophagus . After iWith respect to the location of the primary tumor at the distal esophagus, the closest lymph node from the tumor was LN6216c, and LN6216gc was further away.Isothermal microcalorimetry was performed to compare the heat production of cells from the primary tumor to that of tumor cells generated from lymph nodes from both patients separately. Cell line LN1590 showed an increased heat flow over 24 h as well as a much higher overall heat production compared with cell line PT1590 B,C. The 50 parameter is the shear stress at which 50% of the cells are detached from the disk. τ50 for PT6216 was 143.28 ± 6.01 dyn/cm2, while it was 87.72 ± 15.04 dyn/cm2 for LN6216gc and 57.72 ± 4.26 dyn/cm2 for LN6216c. The assay revealed that, after 4 h of incubation, both LN6216gc and LN6216c cells are less adhesive compared with PT6216 cells, which results in a greater readiness to detach . These same unfavorable conditions are also known to promote metastasis.The factors contributing to tumor cell adhesion are heterogeneous and complex. Several adhesion factors have been proposed to play a role in metastatic processes. For example, it has been shown that with a decrease of adhesion strength tumor cells display a decreased focal adhesion turnover . MoreoveOur own investigations in migrating neuroblastoma cells have revealed an increased expression of the water channel AQP1 that furthers migration by promoting water in- and efflux through the tumor cell membrane and facilitating restructuring of the cytoskeleton . On the Our future investigation will focus on the role of AQP1-facilitated membrane permeability and plasticity and possible role in tumor cell thermogenesis for the migratory process.In health, cell migration is a highly orchestrated process that is required for embryonic development. It is vital for tissue repair and regeneration ,39. In c


The ToGA trial has demonstrated, in HER2-expressing patients, that unresectable and advanced gastric cancer, chemotherapy and trastuzumab in combination increase overall survival, even if it is still unclear why after one year the same patients are non-responsive to trastuzumab treatment. Here, we have demonstrated that in HER2-positive gastric cancer cell lines, the addition of duligotuzumab, targeting HER3 receptor, or ipatasertib, targeting AKT protein, enhances the antitumor effect of trastuzumab in vitro through a full inhibition of the membrane signals, on HER2 and HER3, and of downstream signaling, including AKT, and MAPK pathways. Hence, this study suggests a novel and biomarker-driven therapeutic strategy supporting further evaluation of the anti-tumor efficacy of these combinations in HER2 human gastric cancer patients.The anti-HER2 monoclonal antibody trastuzumab is a key drug for the treatment of HER2-positive gastric cancer (GC); however, its activity is often limited by the onset of resistance and mechanisms of resistance are still poorly understood. Several targeted agents showed synergistic activity by concomitant use with trastuzumab in vitro and are under clinical investigation. The aim of this study was to assess the antitumor activity of duligotuzumab, an anti HER3/EGFR antibody or ipatasertib, an AKT inhibitor, combined with trastuzumab in a panel of HER2-positive human gastric cancer cells (GCC), and the efficacy of such combinations in HER2-resistant cells. We have assessed the efficacy of duligotuzumab or ipatasertib and trastuzumab in combination, analyzing proliferation, migration and apoptosis and downstream intracellular signaling in vitro on human HER2-positive GCC  and in negative HER2 GCC (MKN28). We observed a reduction of proliferation, migration and apoptotic rate in HER2-positive OE33, OE19 and N87 cell lines with the combination of duligotuzumab or ipatasertib plus trastuzumab. In particular, in OE33 and OE19 cell lines, the same combined treatment inhibited the activation of proteins downstream of HER2, HER3, AKT and MAPK pathways. Targeting both HER2 and HER3, or HER2 and AKT, results in an improved antitumor effect on HER2-positive GCC. Gastric cancer is the fourth most common malignant neoplasm and the second leading cause of cancer-related death in Western countries . AlthougSignificant progresses in the comprehension of pathogenesis of gastric cancer and a better knowledge of its biological heterogeneity have led to the identification of new targets and to the development of new molecular drugs .Trastuzumab, a recombinant humanized monoclonal antibody that targets the extracellular domain IV of HER2, is the first and only molecular target drug approved for the treatment of advanced HER2-positive gastric cancer ,6,7.The ToGA trial has shown that trastuzumab plus conventional chemotherapy has demonstrated a significant clinical benefit compared to chemotherapy alone in terms of response rate, progression-free survival (PFS) and overall survival (OS), and has therefore become the new standard of treatment for metastatic HER2-positive gastric cancer .However, some evidence indicates that the efficacy of trastuzumab plus chemotherapy may actually be less than expected on the basis of the results of the ToGA trial in actual clinical practice .Further clinical trials conducted with other anti-HER2 agents such as lapatinib, TDM-1 and pertuzumab, failed to show a survival advantage, compared to standard first- or second-line treatment ,11,12.Several preclinical studies have investigated the mechanisms of resistance to trastuzumab, in order to identify new molecular targets in HER2-positive gastric cancer patients .In particular, activation of the PI3K/AKT signaling pathway and enhanced signaling from HER family receptors, including overexpression of HER3, are involved in mediating trastuzumab resistance in HER2-positive GC cells ,17,18,19Thus, selective PI3K- or AKT-inhibitors and anti-HER3 antibodies represent a novel option to overcome resistance to anti-HER2 therapy and improve gastric cancer prognosis. To date, a number of drug candidates targeting HER3 and PI3K/AKT pathways are under clinical evaluation for the treatment of gastric cancer ,21,22.Ipatasertib (GDC-0068) is an oral, investigational small molecule ATP-competitive with the potential to inhibit of all three isoforms of AKT .In preclinical models of gastric cancer cells, ipatasertib acts synergistically with fluorouracil (5-FU) and platinum chemotherapy. A clinical trial aiming to evaluate the efficacy of ipatasertib in combination with FOLfox6 in (NCT01896531) HER2-negative metastatic gastric cancer patients is ongoing .Duligotuzumab  is a novel humanized IgG1 antibody that is directed at HER3 and EGFR, which is under investigation in phase I and II trials for metastatic colon rectal cancer and squamous cell carcinoma of the head and neck ,26.The aim of our study is to investigate the efficacy of duligotuzumab or ipatasertib in combination with trastuzumab by evaluating in vitro the antiproliferative, pro-apoptotic and anti-migration effects in HER2-positive gastric cancer cells.The antiproliferative effects of duligotuzumab or ipatasertib as single agents or in combination with trastuzumab was evaluated in a panel of Her2 positive or negative human gastric cancer cell lines with different biologic profiles.MKN28 gastric cells are human gastric adenocarcinoma-derived cell lines and were selected as a HER2-negative model. Among HER2-positive human gastric cancer cell lines, we chose three cancer cell lines: NCI–N87, 0E19 and OE33. Cell proliferation was measured with the 3--2,5 diphenyltetrazolium bromide (MTT) assay. As anti-HER2 agent, we selected trastuzumab, currently used for the treatment of metastatic gastric HER2-positive cancer patients. Different doses of ipatasertib, duligotuzumab, trastuzumab alone and the combination of trastuzumab with duligotuzumab or ipatasertib were tested.From the dose response curve results, using doses from 0.01 to 25 µM for each drug, IC50 values ranged from ≈0.1 to ≈0.5 µM for duligotuzumab and ipatasertib, whereas the IC50 values for trastuzumab ranged from ≈0.5 to ≈1 µM  The sameCombined treatment of duligotuzumab or ipatasertib and trastuzumab resulted in an important antiproliferative effect as compared to single agents all of HER2 gastric cancer cells A–F.The synergistic effect of the two drugs is evident with dose of 0.5 µM and therefore this dose was chosen for the subsequent experiments. The experiments were conducted on the two cell lines that showed higher response: OE19 and OE33 cells. For this reason, cells were treated with duligotuzumab, ipatasertib alone or in combination with trastuzumab observing that cells that manage to retain the ability to migrate to and form colonies with the single agent reach 70–80%, while the combination of two drugs, duligotuzumab plus trastuzumab and trastuzumab plus ipatasertib, drastically reduces these cell abilities under 40% of the initial population .Apoptosis analysis was performed after 72 h treatment with duligotuzumab or ipatasertib and trastuzumab combination in NCI–N87, 0E19, OE33 human gastric cancer cell lines. As shown in Western blot analyses were performed to evaluate the effect on intracellular signaling pathways on protein extracts from OE33 and OE19 cell lines after 48 h treatment with duligotuzumab or ipatasertib and trastuzumab, at IC50 doses from cell growth inhibition tests, as single agents or in combination .First, the expression of specific proteins in extracts of each cell line was verified, in particular the presence of all the markers involved in the signaling mechanism of PI3K/AKT and of HER2 and HER3 receptors, in both total and phosphorylated isoform. Then, Western blot analysis was performed following treatments to investigate the variation of these markers with single agents and combinations A.We found that single agents, duligotuzumab, ipatasertib and trastuzumab, have no effect on the expression of AKT and MAPK proteins, while the combination of two drugs, duligotuzumab plus trastuzumab and trastuzumab plus ipatasertib, resulted in a strong reduction in phosphor-AKT and phosphor-MAPK, leaving the expression of the total forms of AKT and MAPK unchanged both in KATOIII and in OE19 cell lines, thus demonstrating a downstream effect on the PI3K/AKT pathway. A similar “switch-off” effect was also observed in the expression of HER2 and HER3 receptors, which were reduced by the combination of the two drugs in both cell lines, though most significantly in the OE19 cell line B,C. The The phase III ToGA trial proved the superiority of trastuzumab, plus standard chemotherapy in patients with HER2-positive metastatic gastric cancer compared to chemotherapy alone in terms of OS, PFS and response rates. However, the objective response rate to trastuzumab combined with chemotherapy failed to reach 50% in a real-practice setting, with a median PFS of 6.7 months .The subsequent randomized phase III trials evaluating other anti-HER2 agents such as lapatinib or TDM-1 in this setting of patients failed to show any clinical benefit compared to chemotherapy alone ,11,12.Finally, the addition of pertuzumab to chemotherapy plus trastuzumab was also not associated with a survival advantage compared to trastuzumab plus chemotherapy .Based on these data, there is a compelling need to explore resistance mechanisms associated with anti-HER2 therapy and to establish rational strategies to improve trastuzumab efficacy in HER2-positive GC patients ,26,27,28In a study designed to explore potential causes of failure of anti-HER2 agents in HER2-positive GE adenocarcinomas, Kim et al. observed that the majority of HER2-amplified tumors harbored secondary oncogenic alterations involving genes related to cell-cycle regulation such as PI3K pathway mutations and receptor tyrosine kinase  amplifications. Several studies have demonstrated that somatic mutations in PI3KCA/AKT and in HER3 are present respectively in 4–25% and 16–59% of patients with gastric carcinoma ,30.This evidence suggests that additional oncogenic alterations may represent the key molecular features responsible for the lack of benefit derived from HER2 inhibition in patients with HER2-positive GC .In the present work, we developed a preclinical model using human HER2-positive gastric cancer cells in order to assess the activity of trastuzumab in combination with the AKT-inhibitor ipatasertib or the anti-HER3 antibody duligotuzumab. We reported evidence supporting the improved anti-proliferative in vitro effect of the combination vs. single agent treatment in multiple human gastric cancer cell lines. In particular, we demonstrated that the combination of duligotuzumab or ipatasertib, plus trastuzumab, induced a stronger inhibition of cell proliferation compared to single treatment alone by using the 3--2,5 diphenyltetrazolium bromide (MTT) assay in HER-2 positive, but not in HER2-negative cell lines.The synergistic effect of trastuzumab combined with different anti-HER3 antibodies, such as 1A 5 -3D4, and AKT inhibitors such as MK-2206, has been confirmed in several HER2-positive GC cell lines ,33,34.Our work showed that combination treatments resulted in reduced migration and invasion of gastric cancer cells in vitro, with an increased percentage of apoptotic cells in all cell lines tested by flow cytometric analysis. For instance, in OE33 and OE19 cells, the apoptotic rate increased from 35% to 60% when duligotuzumab or ipatasertib were combined with trastuzumab.Additionally, immunoblotting analysis showed that while the expression of the total forms of AKT and MAPK were unchanged in OE33 and OE19 cell lines, a strong reduction in phosphor-AKT and phosphor-MAPK levels was observed only with the combination of duligotuzumab or ipatasertib plus trastuzumab, thus demonstrating an improved inhibitory effect on the PI3K/AKT pathway. A similar “switch-off” effect was also observed by assessing expression levels of HER2 and HER3 receptors. A more marked effect associated with combination treatment was obtained in the OE19 cell line, probably because of the higher HER2 expression level of these cells, which translates in a stronger HER2 activity of this model. Taken together, these results suggest that a complete inhibition of HER3, AKT and MAPK signaling pathways is needed for an optimal antitumor effect of trastuzumab.This study suffers from multiple limitations, including the limited number of HER2-amplified gastric cancer cell lines, which makes our finding difficult for the generalization of our findings. Furthermore, our study did not evaluate simultaneously the effect of these combinations on trastuzumab-resistant HER2-positive gastric cancer cell lines, although experiments in this setting are ongoing.Finally, our findings demonstrate that inhibition of other pathways may enhance trastuzumab sensitivity in HER2-amplified gastric cancer cells, suggesting the potential for combinational strategies in this setting.2 and cultured with Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 20% fetal bovine serum  and 1% antibiotics/antimycotics  and Roswell Park Memorial Institute  medium added with 10% FBS and 1% antibiotics/antimycotics, OE33/OE19 and NCI-N87/MNK28, respectively.The human gastric cancer OE33, NCI-N87 and MNK28 cell lines were provided by American Type Culture Collection  and OE19 by Sigma Aldrich . Cells were maintained in stable humid conditions at 37 °C with 5% COSTR profiling (Promega) was used to confirm the identity of all cell lines prior to performing experiments, and was repeated after the majority of the experiments were performed.Each cell line contains genomic mutations in one or more of the following genes according to the Sanger COSMIC database: CDKN2A, TP53, SMAD4, CDH1, CTNNB1, KRAS, MLH1 and PIK3CA/AKT (8–11%). The OE19 cell line contains a 100-fold amplification of the ERBB2 gene, and highly expresses its messenger ribonucleic acid. OE33, has an ERBB2 amplification of a lesser copy number. The NCI -87 cell line expresses levels of c-myc and c-erb-B 2 RNA that is comparable to other cell lines. Trastuzumab, duligotuzumab and ipatasertib, provided by Roche Genentech, were dissolved in sterile dimethylsulfoxide (DMSO) while working concentrations were prepared in culture medium just before each experiment.50.Cancer cells were seeded in 96-multiwell plates  and treated for 72 h with an increasing range of concentrations of each drug to determine the ICThe proliferation rate was measured with the MTT assay, as previously described. The results showed the median of three separate experiments, each performed in quadruplicate. Synergism was calculated with ComboSyn software, ComboSyn Inc., Paramus, NK 07652, USA.The assay was performed in 6-well tissue culture dishes where cells were distributed at 300 cells per well and treated with duligotuzumab, trastuzumab and ipatasertib. All conditions were performed in triplicate using untreated cells as reference control. Cells were cultivated for 7 days, at which point they were fixed with 4% paraphormaldeid, stained with crystal violet and colonies counted using the GelCount  .The in vitro invasive ability of cancer cells was analyzed by using 24-well Transwell chambers  according to the manufacturer’s protocol . BrieflyApoptosis assay was performed by flow cytometry according to the manufacturer’s instruction . Early apoptotic cells , necrotic/late apoptotic cells (double positive), as well as living cells (double negative) were detected by FACS Calibur flow cytometer and then analyzed by Cell Quest software . Argon laser excitation wavelength was 488 nm, whereas emission data were acquired at wavelength 530 nm (FL-1 channel) for FITC and 670 nm (FL-3 channel) for propidium iodide.Following treatment, cancer cells were lysed with RIPA buffer (0.1% sodium dodecylsulfate (SDS), 0.5% deoxycholate, 1% Nonidet, 100 mmol/L NaCl, 10 mmol/L Tris–HCl (pH 7.4), 0.5 mmol/L dithiotritol, and 0.5% phenylmethyl sulfonyl fluoride, protease inhibitor cocktail  and isolated by centrifugation at 14,000 rpm for 10 min at 4 °C. After Bradford assay quantification , protein lysates were subjected to SDS-PAGE and Western blot analysis.Primary antibodies for western blot analysis against p-EGFR (Tyr1068), EGFR, p-MAPK44/42 (Thr202/Tyr204), MAPK44/42, p-AKT (Ser473), AKT, pHER2 (Tyr1221/1222), HER2, pHER3 (Tyr1289) and HER3 were obtained from Cell Signaling Technology; monoclonal anti-α-tubulin antibody (T8203) from Sigma Chemical Co. St. Louis, MO, USA. The following secondary antibodies from Bio-Rad were used: goat anti-rabbit IgG and rabbit anti-mouse IgG. Immunocomplexes were detected with the enhanced chemiluminescence kit ECL plus  using the ChemiDoc (Bio-Rad). Each experiment was done in triplicate.P values represent 2-sided tests of statistical significance effects.Results are expressed as means ± s.d. from three independent experiments. Differences between groups were assessed by one-way analysis of variance (ANOVA). For all analyses To date, trastuzumab remains the only biologic target agent approved for the treatment of HER2-positive metastatic gastric cancer. In the current study we have demonstrated that in HER2-positive gastric cancer cell lines the addition of duligotuzumab, targeting HER3 receptor, or ipatasertib, targeting AKT protein, enhances the antitumor effect of trastuzumab in vitro through a fully inhibition of the membrane signals, on HER2 and HER3, and of downstream signaling, including AKT, and MAPK pathways. These results suggest that targeting both HER2 and HER3 or HER2 and AKT provide a biological rationale for further evaluation of the anti-tumor efficacy of these combinations in HER2 human gastric cancer patients.


ATP23, RAD51C and RPA3 independently associated with poor prognosis in glioblastoma patients. Finally, we observed a substantial upregulation of DDR genes after treatment with TMZ and/or RT, particularly in RT-treated glioblastoma cells, peaking within 24 h after treatment. Our results confirm the potential influence of DDR genes in patient outcome. The observation of DDR genes in response to TMZ and RT gives insight into the global response of DDR pathways after adjuvant treatment in glioblastoma, which may have utility in determining DDR targets for inhibition.Glioblastoma is a highly aggressive, invasive and treatment-resistant tumour. The DNA damage response (DDR) provides tumour cells with enhanced ability to activate cell cycle arrest and repair treatment-induced DNA damage. We studied the expression of DDR, its relationship with standard treatment response and patient survival, and its activation after treatment. The transcriptomic profile of DDR pathways was characterised within a cohort of isocitrate dehydrogenase (IDH) wild-type glioblastoma from The Cancer Genome Atlas (TCGA) and 12 patient-derived glioblastoma cell lines. The relationship between DDR expression and patient survival and cell line response to temozolomide (TMZ) or radiation therapy (RT) was assessed. Finally, the expression of 84 DDR genes was examined in glioblastoma cells treated with TMZ and/or RT. Although distinct DDR cluster groups were apparent in the TCGA cohort and cell lines, no significant differences in OS and treatment response were observed. At the gene level, the high expression of  Glioblastoma is the most common and aggressive type of primary malignant brain tumour in adults. The diagnosis of glioblastoma, updated in the WHO Classification of Tumors of the Central Nervous System 2021, involves a combination of histological (microvascular proliferation or necrosis) and molecular characteristics, including the criteria of having the isocitrate dehydrogenase (IDH) wild-type gene .Evidence suggests that DNA damage response (DDR) pathways respond to treatment-induced damage and aid in tumour survival, leading to treatment resistance in glioblastoma . The basWe investigated the transcriptomic expression of DDR genes and pathways in a TCGA cohort of IDH wild-type glioblastoma patients and its relation to overall patient survival. We identified DDR profiles of 12 patient-derived glioblastoma cell lines and compared TMZ and RT sensitivity of cells stratified on the degree of DDR pathway expression. Lastly, these cell lines were exposed to a clinically relevant dose of RT and/or TMZ to investigate the extent and timing of DDR genes and pathways responding to treatment-induced damage.This study was approved by the Human Research Ethics Committee of the University of Newcastle (H-2020-0389).z-scores for data visualisation. RNAseq by expected maximisation (RSEM) data were collated for 84 DDR genes (p < 0.05), using clinical covariables that were determined to be significant through univariate Cox regression  values were collated from 140 IDH wild-type glioblastoma patients within the TCGA database . FPKM vaDR genes . Patientgression .® -coated tissue culture flasks in StemPro® NSC SFM  containing 100 I.U./mL penicillin and 100 µg/mL streptomycin , and incubated at 37 °C in 5% CO2/95% humified air. Cells were passaged using StemPro® Accutase® solution  for detachment of adherent cells. TMZ was purchased from Sigma-Aldrich , aliquoted in dimethyl sulfoxide (DMSO) (100 mM) and stored between 2 and 8 °C. RT was delivered using a medical linear accelerator (LINAC) at GenesisCare, Gateshead NSW  or RS-2000 Small Animal Irradiator .Twelve patient-derived glioblastoma cell lines were kindly provided by the QIMR Berghofer Medical Research Institute . The cell lines are fully characterised with publicly available molecular and patient data, published by Stringer et al. . Cells whttps://www.qimrberghofer.edu.au/commercial-collaborations/partner-with-us/q-cell/ accessed on 10 May 2021). RNA extraction methods and RNA sequencing analysis from the cell lines are described in Stringer et al. [RNA sequencing data from all 12 glioblastoma cell lines were obtained from the publicly available QMIR database  was added to each well and incubated for 3 h. The medium/MTT solution was aspirated and DMSO (120 µL) added into each well. Each plate was shaken using an IKA® MS 3 basic shaker  at 600 rpm for 2 min. Absorbance was read at 570 nM using the SPECTROstar®Nano microplate reader .To assess cell viability, 96-well plates were coated with Matrigel under ice-cold conditions prior to plating with cells. Adherent glioblastoma cell lines were passaged and seeded at 4000 Cells/well in 96-well plates overnight, before treatment with a clinically relevant dose of TMZ (35 µM)  or RT (2t-test.Glioblastoma cell lines were stratified into two cluster groups (C1 and C2) based on hierarchical clustering of ssGSEA scores in each DDR pathway. Cell lines were also assigned into “high” or “low” expression groups according to each DDR pathway based on a median split of ssGSEA scores. Differences in TMZ or RT cell viability between cluster groups or “high” vs. “low” DDR expression groups was assessed through an unpaired student’s ®Gene Expression Custom Array Card . Samples were run as biological triplicates using the QuantStudio™ 7 Pro Real-Time PCR System . The geometric means of housekeeping genes  were seeded at 300,000 cells/well in 6-well Matrigel-coated plates overnight and treated with a clinically relevant dose of RT (2Gy), followed by TMZ (35 µM) one hour later. Cells were harvested at 2, 24 and 48 h after TMZ treatment and extracted for RNA using the AllPrep DNA/RNA/Protein Mini Kit . RNA was converted to cDNA using the High-Capacity cDNA Reverse Transcription Kit . In accordance with supplier instructions, gene expression of 84 DDR genes  was examng genes  were usep-values < 0.05 were considered significant.The statistical analyses including the Mann–Whitney test, the Kruskal–Wallis test and Dunnett’s multiple comparison test were conducted in GraphPad Prism 7. Unsupervised hierarchical clustering (Ward’s method) of ssGSEA scores was performed in R using the ‘stats’ package and visualised using the ‘ComplexHeatmap’ package. Survival analyses using the log-rank test and Cox regression were performed in R using the ‘survival’ package. MGMT methylated patients when comparing cluster C1 through to C3 (C1 = 34.2% (13/48), C2 = 41.5% (17/41), C3 = 53.3% (16/30)). A similar trend occurred for the proportion of TP53 alterations (C1 = 13% (7/54), C2 = 20.8% (11/53), C3 = 33.3% (11/33)). Although distinct DDR gene profiles were apparent, no survival difference was found across clusters  genes (p = 0.154). To determine whether cell line DDR gene expression clusters had similarities to the TCGA gene expression clusters, hierarchical clustering was performed on combined ssGSEA scores of TCGA samples and cell lines. All cell lines from C1 clustered within a combined cluster resembling TC1, while all cell lines belonging to C2 did not associate with any TCGA DDR cluster  genes A. NHEJ, 4) genes A, was no cluster .MGMT methylation status is a clinical biomarker of TMZ sensitivity [MGMT methylated cell lines had a significantly reduced cell viability than MGMT unmethylated cell lines (p = 0.004) in response to TMZ . Whilst distinct differences in DDR gene expression were observed between clusters C1 and C2, no significant differences in cell viability were observed in response to TMZ (p = 0.32) or RT (p = 0.097)  and/or RT (2Gy) was assessed . Across sitivity . As expee to TMZ . Three c= 0.097) B,C.p = 0.032), while cells with high MMR gene expression were more sensitive to TMZ (p = 0.0039) (p = 0.0038) whilst there was no significant difference in other DDR pathways  D. Interepathways E. CollecMGMT statuses, and responses to TMZ or RT treatment  treated with a clinically relevant dose of TMZ and/or RT at 2, 24 and 48 h post-treatment. These cell lines represented to various degrees different cluster groups, reatment . Within When observing the frequency and distribution of DEGs across all cell lines, several trends appeared. Differences in the frequency of DEGs were apparent between treatments across time points. On average, TMZ induced the lowest frequency of DEGs, whereas RT induced the most robust response causing the highest frequency of DEGs across each time point B,C. The The frequency of DEGs belonging to specific DDR pathways was also distinct for each treatment between time points. Although variability in response was observed between cell lines , trends NEIL3 and CCNO (BER), XRCC2, RAD54L and ATM (HR), DDB2 (NER), and MSH2 (MMR) were among the most differentially expressed and upregulated  and appeared in cells treated with TMZ or TMZ + RT  complex involved in HR and DSB repair and has been implicated in glioblastoma OS outcome [Firstly, we identified three distinct clusters (TC1–3) of TCGA glioblastoma patients based on the overall expression of DDR pathway genes using ssGSEA. Despite the clusters displaying low, moderate, and high DDR gene expression, respectively, no significant OS differences were observed between clusters. A study by Meng et al.  found lostresses . Furtherpatients , its higse to RT . RAD51C ed in HR , while RMGMT methylation status. In regard to these discrepancies between the TCGA data and the baseline data from the 12 patient-derived glioblastoma cell lines, Stringer et al. showed in their original publication of the cell line data that 7 of the 12 cell lines maintained a molecular signature equivalent to that from the original tumour tissue [Stringer et al.  describer tissue . As suchr tissue . HoweverWhen examining individual pathway expression in the cell lines, we showed that genes in DDR pathways are associated with response to TMZ or RT. Cells with a higher expression of NER genes were more resistant to TMZ, while high expression of MMR genes conferred TMZ sensitivity. Previous studies have suggested similar trends with respect to NER components ,25, whilATM), ataxia-telangiectasia and rad3-related (ATR), and DNA-dependent protein kinase (DNA-PK) [6-meG adducts caused by TMZ form DNA breaks only after several replication cycles have occurred [A limitation to this analysis is the bulk transcriptomic lens of tumours prior to any treatment, which does not adequately reflect changes in DDR expression from standard treatment. Thus, studying DDR across a pre- and post-treatment time course may reveal greater insight. In this regard, one of the important aspects of the work presented here is the time course analysis of DDR gene expression response, showing that several genes and pathways are upregulated in response to standard treatment, especially RT. These data may inform the feasibility of targeting DDR components to enhance treatment response in glioblastoma patients. One such approach is being explored through the development of small-molecule inhibitors of DDR proteins including poly (ADP-ribose) polymerase (PARP), Wee1, checkpoint kinase 1 and 2 (Chk1 and Chk2), ataxia-telangiectasia mutated ((DNA-PK) ,30. Acrooccurred ,32, in coccurred . The comNEIL3, XRCC2, CCNO, RAD54L, ATM, DDB2, and MSH2, all of which have been linked to treatment resistance in glioma or solid tumours such as colorectal cancer [RPA3 and RAD51C rarely changed although being associated with OS outcomes in the TCGA cohort, while ATP23, was either upregulated or downregulated depending on the cell line. This suggests that baseline expression and associated patient outcome may not entirely capture the direct role such genes play in DDR when examined across time, and further investigation is needed. This study also shows the upregulation of several NER genes in response to TMZ and RT, which is an underreported DDR pathway in glioblastoma. ATR, a global sensor of DNA damage, has been implicated in NER activation [Across the four cell lines, several DDR genes were frequently observed as differentially expressed. The most notable and frequent included l cancer ,36,37,38tivation  and is ttivation ,41,42. TOverall, this study reveals an influence of DDR genes and subsequent pathways in glioblastoma cellular responses to treatment. Specific clusters of DDR expression failed to show significant differences in patient survival outcome or cell line response to TMZ or RT. However, our analysis revealed that the high expression of three DDR genes associated with poorer overall patient survival, while expression of MMR, NER and HR influenced sensitivity to TMZ or RT in glioblastoma cell lines. Our results suggest that the DDR is primarily upregulated within a 24 h period after treatment of TMZ and/or RT, with distinct trends of DDR activation apparent between treatments. Such data give insight into the changes in DDR gene expression in response to standard treatment and the potential for targeting commonly upregulated DDR components to produce radio- or chemo-sensitising agents.


MEF2D-HNRNPUL1 fusion high-risk subtype, for which this drug activates pro-apoptotic ERK signaling associated with molecular mediators of pre-B cell negative selection. Our data is the foundation for the interactive online Functional Omics Resource of ALL (FORALL) with navigable proteomics, transcriptomics, and drug sensitivity profiles at https://proteomics.se/forall.Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Although standard-of-care chemotherapeutics are sufficient for most ALL cases, there are subsets of patients with poor response who relapse in disease. The biology underlying differences between subtypes and their response to therapy has only partially been explained by genetic and transcriptomic profiling. Here, we perform comprehensive multi-omic analyses of 49 readily available childhood ALL cell lines, using proteomics, transcriptomics, and pharmacoproteomic characterization. We connect the molecular phenotypes with drug responses to 528 oncology drugs, identifying drug correlations as well as lineage-dependent correlations. We also identify the diacylglycerol-analog bryostatin-1 as a therapeutic candidate in the  Childhood acute lymphoblastic leukemia is characterised by a range of genetic aberrations. Here, the authors use multi-omics profiling of ALL cell lines to connect molecular phenotypes and drug responses to provide an interactive resource of drug sensitivity. Additionally, ~80% of survivors of childhood ALL will experience a post-treatment life-threatening medical event by age 45, an effect hypothesized to result from the severity and duration of ALL treatment protocols5. Targeted therapy protocols have demonstrated the potential to reduce the likelihood of post-treatment health complications, improve outcomes, and address resistance to chemotherapy. These targeted approaches have been successfully implemented clinically in protocols giving tyrosine kinase inhibitors to Philadelphia chromosome-positive ALL patients (NCT03911128)6. Recently, targeted treatment strategies have expanded to include immunotherapeutic agents such as antibody and chimeric antigen receptor (CAR) -T-cell therapies7. However, the adoption of these agents is limited by challenges in production, identifying suitable antigens, and the clinical emergence of adverse events9. Consequently, there is an immediate need for novel therapeutic modalities for the treatment of high-risk patients and patients who relapse.Acute lymphoblastic leukemia (ALL) accounts for ~30% of all cancers in children, making it the most common childhood cancer. Despite the clinical success of broad chemotherapy protocols and allogeneic hematopoietic stem cell transplantation (HSCT)11, somatic mutations can only partially explain the underlying biology and phenotype, and approximately 25% of childhood ALL patients lack a detectable driving mutation12. Although genetic characterizations have improved risk stratification and hope of new molecular targets for therapy, the primary challenge of identifying novel and effective treatments for ALL is achieving more reliable phenotypic stratification that can improve therapeutic response13.Therefore, bridging the gap in therapeutic options for ALL will likely require the development of both pharmacologic and cellular therapies; and this process will depend on the deeper characterization of both the biomarkers and biology of nonresponsive subtypes. While the genetic landscape of childhood ALL has been extensively studied by the implementation of next-generation genomic, transcriptomic, and epigenetic sequencing tools14, the proteome represents an ideal framework for understanding cellular phenotypes. Recent studies have demonstrated a poor correlation between the proteomic and the genetic phenotypes of cancers17, including childhood ALL18. Studies of tumors and human-derived cell lines22 demonstrated the importance of proteomic characterization, however, our recent characterization of ETV6-RUNX1 and hyperdiploid ALL18 and a study on PDX models23 are the only in-depth studies that have used proteomics methods to understand the biology of childhood ALL.Considering that post-transcriptional and post-translational mechanisms have diverse impacts on the protein levels for each cellular component in ways shaped by cellular fitness requirementsMEF2D-HNRNPUL1 rearranged high-risk subgroup. We also provide a user-friendly web application for exploration of the proteomic, transcriptomic, and drug sensitivity data described in this study, available at https://proteomics.se/forall.In this work, we provide a proteomics-guided analysis of 49 childhood ALL cell lines, generate a comprehensive resource of biomarkers and drug sensitivities, and identify a potential therapeutic vulnerability to target the 24 for all cell lines and available replicates , which correctly indicated B-cell-receptor or T-cell-receptor signaling pathways for each respective lineage  of proteome profiles separated four major lineage-linked groups Fig. . Cell liTCF3-PBX1 fusion26, and ABL1 for BCR-ABL1, NUP214-ABL1, and SPFQ-ABL1 fusions , excluding the two EBV samples. We performed Spearman pairwise correlation for all mRNA-protein pairs (n = 8981), and the median correlation coefficient was 0.55  than at the transcript level (P = 1.4e-75)  Fig. , which w75) Fig. . In lineGINS2 mutation (P19S)35, protein-mRNA differences for GINS-complex members were significantly larger than differences for corresponding protein-mRNA pairs in other cell lines  suggested seven distinct clusters. Hierarchical clustering including all samples and proteins stratified the cell lines into consensus leukemic clusters (CLC)  along with several cell lines with a previously unspecified cytogenetic background. CLC3 contained 16 out of 22 T-ALL cell lines, again with various cytogenetic backgrounds and characterized by G2M checkpoint hallmark, higher spliceosome, and higher E2F target activity when compared to the other six T-ALL cell lines that clustered in CLC7 (Supplementary Data Consensus clustering of the proteome using high variance proteins (LC) Fig. . Althoug38, we performed DE analysis on the transcriptomics data between CLC3 and CLC7 followed by GSEA analysis, which failed to recapitulate the upregulation of the spliceosome  0.61, adjusted P value, q = 1.0)  Fig. . AlteredKMT2A-rearranged subtypes (KMT2A-AFF1 and KMT2A-MLLT1) were classified into RNA cluster 4 and one into RNA cluster 1, whereas the proteomics clustering divided these into three different proteomic clusters , demonstrating that the proteomics analysis delivers an additional angle to reveal phenotypic differences, which may be clinically meaningful. We performed DEqMS analysis between the two KMT2A-rearranged subtypes and identified p53 among the top upregulated proteins in KMT2A-AFF1 cell lines  were classified into the same RNA cluster (cluster 3) but clustered into different proteomic clusters (B-CLC2 and B-CLC5).To gain a better resolution on relative differences between the two different lineages, each lineage was clustered separately. This identified five clusters in the B-lineage (B-CLC) and seven clusters in the T-lineage (T-CLC) cell lines Fig. . Additiones Fig.  suggesti42. To evaluate the lineage states of our cell lines, we used established cellular markers of B-cell and T-cell developmental stages44 to immunotype the BCP-ALL and T-ALL cell lines  is also associated with pathogenic patient risk stratification46. B-CLC5 contained the cell lines most enriched for this protein . Additionally, B-CLC5 was characterized by the abundance of the adhesion molecule CEACAM1, which induces a tolerogenic immune environment through interactions with TIM347, suggesting a common retained affinity for oncogenic immunomodulation48.Within the B-cell state assignments, some cytogenetic subtypes appeared to be confined to one B-cell state, while others occupied multiple cell states and proteomic clusters  against a panel of 528 FDA-approved and investigational oncology drugs at five concentrations. The selective drug sensitivity scores (sDSS) were obtained by normalizing drug sensitivity against drug response of normal bone marrow (BM)49 Fig. .Fig. 4DrKMT2A-AFF1 cell lines (ALL-PO and SEM) with a suspected deregulated p53 pathway which could be due to mutations in TP53 or other dysregulation contributed by the gene fusion50.Broad-spectrum cytotoxic drugs, e.g., conventional chemotherapy drugs demonstrated high activity in a majority of the tested cell lines Fig. . Numerou52, and screening of patient cells ex vivo after diagnosis has been shown to predict response or resistance to these therapeutics53. Most of the cell lines demonstrated substantial sensitivity to dexamethasone, prednisolone, and methylprednisolone, except for 12 cell lines, among them the REH and JURKAT cell lines  protein abundance   are components of standard of care combination chemotherapy protocols for childhood ALL-8) Fig. , consistR ≥ 0.71, P ≤ 2.64e-12) and FLT3 , respectively   while anticorrelating with the target of MDM2 degradation, p53 (R ≤ −0.55)  for each drug, we ranked the drug sensitivity-protein abundance Pearson correlations of each drug and its putative drug target to examine the relationship between target abundance and drug response Fig. . Among tely Fig. . Additio04) Fig. . MDM2 in55) Fig. .58. Tacrolimus does not target this pathway by direct binding, but rather its activity requires the formation of a complex with FKBP1A, a cis-trans prolyl isomerase belonging to the immunophilin protein family59. Despite the crucial role of FKBP1A in this mechanism of action (MoA), we found that the abundance of FKBP1A negatively correlated with sensitivity to tacrolimus . However, another member of the cis-trans prolyl isomerase family, FKBP10, strongly correlated with tacrolimus sensitivity   for twelve ALL cell lines. This compound is widely used clinically as an immunosuppressant due to its ability to inhibit calcineurin-mediated dephosphorylation of NFAT07) Fig. . FKBP10 62. Reverse-phase protein arrays (RPPA)21 using ~230 proteins and data-independent acquisition using MS (~3000 proteins)22 have also been used for protein drug correlations in cancer cell lines, including seven and two ALL cell lines, respectively. These studies and additional meta-analyses63 identified that protein levels had stronger correlations with drug activity than corresponding mRNA levels. To investigate whether these results are confirmed in our childhood ALL cell line panel, we performed pairwise drug sensitivity correlation analysis with our transcriptomics and proteomics data, using drugs with an sDSS of ≥8 in at least two cell lines (n = 281) for the analysis. Cumulatively, relative protein abundances correlated to sDSS significantly higher (P < 1.45e-20) than protein-coding mRNA levels  and RCH-ACV , we monitored HDAC1 engagement using the cellular thermal shift assay (CETSA)67. This demonstrated that tacedinaline treatment thermostabilized HDAC1 to a similar extent (~4 °C) in both sensitive and resistant cells, and that tacedinaline has equivalent target engagement across a concentration range  for two cell lines at minimum, a matrix of Pearson correlation values for proteins correlated to drug sensitivity was used as input for dimensionality reduction using Uniform Manifold Approximation and Projection (UMAP)65. Fig. . We note65. Fig. . For theors Fig. . To studnge Fig. . These oR = 0.87, P = 3.2e-14) and VCP/p97 inhibitor NMS-873   Fig. . These d72. In our dataset, the majority of cell lines were very sensitive to broad BCL2 family member inhibitors   and BCL2L1 , respectively and not exclusively with lineage , a cytokine receptor used as a marker of NOTCH1-dependent developing thymocytes75. However, in BCP-ALL cell lines, IL9R was not correlated with sensitivity , and sensitivity was significantly negatively correlated with PDPK1 protein abundance . Additionally, no PDPK1 interacting proteins from the STRING network database76 correlated with drug activity in BCP-ALL  following OSU-03012 treatment and suggested that HSPs were targets of OSU-0301278. Our dataset identified a significant correlation with HSP90 , but not in T-ALL cell lines . This further suggests that OSU-03012 sensitivity is linked to dependence on chaperonins in BCP-ALL cell lines. Other ongoing clinical, preclinical, and investigational applications of OSU-03012 have also identified the importance of observed changes in chaperone functionality and autophagy induced by drug treatment for several disease indications80.The competitive PDPK1 inhibitor and orphan drug OSU-03012 (AR-12) was another targeted drug with lineage specificity Fig. , where oALL Fig. , and no 81 applied to the sDSS-protein abundance correlations for the BCP-ALL or T-ALL cell lines , but not in T-ALL cell lines . This suggests that the interaction between TXNCD9 and the CCT-complex is more likely to occur in BCP-ALLs, where TXNDC9 abundance correlates with chemosensitivity rather than chemoresistance.To further quantify differences linked to lineage, we used differential correlation analysis (DCA)Together, these differential correlations provide insight into the biology of our cohort which is orthogonal to multi-omics profiling and functionally characterizes important proteins in the context of pharmacologic targeting. Our results demonstrate that integrated proteomics and drug sensitivity analysis is an especially well-suited approach to identify drug-target specificities and to decipher their MoA in ALL. Additionally, our heterogeneous panel of childhood ALL cell lines with varied phenotypes enables a window into the biology of drug response and suggests previously undescribed lineage differences which could have implications in understanding the mechanisms of important therapeutic agents.MEF2D-HNRNPUL1 fusion. MEF2D is a member of the myocyte-specific enhancer factor 2 (MEF2) family of transcription factors, which have key roles in a variety of malignancies83 and in B-cell development84. In the context of childhood ALL, MEF2D has been found fused to at least six different fusion partners86. The MEF2D-rearranged cytogenetic subtype is associated with relatively poor clinical outcomes88, and is also found in adult-onset ALL.Across our analysis of cytogenetic subtype, proteomic clustering, drug sensitivity, and cellular state, we observed striking similarities among a group of cell lines distinguished genetically by a MEF2D-rearrangements are associated with increased HDAC9 levels and MEF2D transcriptional activity. HDAC9 is a class II histone deacetylase that regulates the activity of MEF2 transcription factors89. Two studies demonstrated the sensitivity of xenografted and primary culture samples carrying another MEF2D fusion, MEF2D-BLC9 to broad class I HDAC inhibitors88.MEF2D-rearranged leukemias display a distinct gene expression signature86, which showed substantial overlap and agreement with differentially abundant proteins in the MEF2D-HNRNPUL1 cell lines  derived from an adult patient90. DEqMS analysis identified MEF2C and HDAC9 among the top differentially abundant proteins in comparison to the other BCP-ALL cell lines . Bryostatin-1 is a diacylglycerol (DAG) analog and protein kinase C (PKC) activator92 . By assessing proteomic markers with high Pearson correlations to sDSS, we observed a strong correlation between bryostatin-1 toxicity and markers upregulated in the MEF2D-HNRNPUL1 cell lines , calcium- and DAG-regulated guanine nucleotide exchange factor, which has been identified as a direct mediator of sensitivity to negative selection in pre-B cells93. Negative selection occurs at specific states of B-cell development, and it influences response to BCR stimulus by triggering programmed cell death after sustained, strong BCR signaling. In addition to limiting self-reactive mature B-cells, this mechanism has also been shown to limit the survival of dysfunctional pre-B cells95. In cells that are not vulnerable to negative selection, BCR stimulus can also contribute tonic survival signals, and therefore leukemias often demonstrate alterations in B-cell developmental progression that favor survival96. As cells that have expressed the pre-B-cell receptor at the early pre-B stage, our MEF2D fusion cell lines are at a stage in development that could render them vulnerable to negative selection94. In support of an underlying manipulation of this negative selection pathway, we observed that MEF2D fusion cells had an enriched abundance of the DAG degrading enzyme DGKH . PMA also conferred potent toxicity in MEF2D-HNRNPUL1 cell lines , suggesting that the on-target effects of bryostatin-1 are replicated by PMA. To further confirm that ERK signaling was a mediator of toxicity, we blocked the ERK pathway (phosphorylation) by using the MEK inhibitors tramatenib, UO126, selumetinib, and the ERK inhibitor ERK 11e. In cells treated with 100 nM bryostatin-1 or 25 nM PMA, concurrent dosing with 1 uM of MEK or ERK inhibitors significantly improved viable cell counts for all tested inhibitors . Our data is amenable to methods of analysis that allow subtype-specific multi-omics phenotyping, and which can be applied to obtain specific mechanisms of drug sensitivity in childhood ALL that are relevant for precision medicine.We here report an in-depth multi-omics layered analysis of 49 readily available childhood ALL cell lines, quantifying more than 12,000 proteins and 19,000 protein-coding transcripts as well as sensitivity to 528 oncology and investigational drugs. This represents an in-depth proteomic analysis of childhood ALL cell lines covering numerous cytogenetic subtypes, which complements our previous proteogenomic analysisGINS2 mutation in the REH cell line which we linked to the degradation of the entire GINS complex. Additionally, we perform in-depth characterization of drug–protein correlations, which we used to identify unexpected indications of secondary targets in the sensitivity mechanism for respiratory complex targeting drugs and for the widely used immunosuppressant tacrolimus. This analysis also identified an unexpected drug activity distinction for the HDAC inhibitor tacedinaline, which was specifically less effective in TCF3-PBX1 fusion cell lines relative to other inhibitors of its class, and which was independent of engagement to its putative target HDAC1. Further, we examined the biological impact of cellular lineage by examining lineage-specific drug–protein correlations and drug–drug correlations. The PDPK1 inhibitor OSU-03012 had distinct proteins correlated to toxicity for BCP- and T- lineage cell lines indicating alternative mechanisms of action in each lineage. Further, we performed differential correlation analysis, which indicated TXNDC9 in lineage-dependent roles that had opposing correlations to chemosensitivity, and which could be further explored to understand the response to therapy in childhood ALL and lineage-linked biological differences. These analytical approaches are well suited to identify additional findings and to improve the characterization of biological differences and drug response mechanisms.We illustrate the potential of this dataset by demonstrating correlation analysis to detect post-transcriptional regulation, which can identify specific processes in individual samples, as in the case of the 19 by adding in-depth proteomics profiling of additional 42 childhood ALL cell lines. Overall, our integrative data recapitulated poor mRNA–protein correlations, further highlighting the importance of the addition of proteomic analysis in studying childhood ALL. Although our data is limited to established cell lines which do not cover all known subtypes of childhood ALL, we hope that future studies can cover additional subtypes and close this gap.Genomic profiling has been the major tool utilized for the characterization of childhood ALL. However, protein levels have been demonstrated to have a more direct impact on cellular phenotypes, and our resource and analysis thus provides an improved insight into the biology of leukemia. Our resource complements previous proteomics studies of cancer cell linesMEF2D-HNRNPUL1 cell lines. MEF2D fusions represent ~3.6% of childhood ALL cases, and patients experience 5-year event-free survival of 71%, identifying this patient subset as high-risk of relapse or disease progression in current therapy protocols85. Our observations demonstrate that bryostatin-1 activates the proapoptotic PKC/RASGRP1/ERK signaling pathway in early pre-B cells, the progeny stage at which MEF2D-HNRNPUL1 cell lines are arrested. Bryostatin-1 has successfully completed several phase I and II clinical trials101, and this safety and pharmacology profile makes it an excellent candidate for drug repurposing. Due to limitations of in-vitro cell line models, bryostatin-1 and other drugs represented in the study should be further explored in preclinical models and patient samples that better represent relevant microenvironments and cytokine signaling before possible adaptation into the clinic. Additionally, our identification of negative selection vulnerability in MEF2D-HNRNPUL1 fusion cases represents a biologically therapeutic approach, and as a result of its orthogonal mechanism, it could be especially useful in cases of drug resistance and relapse or pursued in combination therapy. Our results indicate that this drug sensitivity results from on-target DAG analog activity, supported by replicating selective sensitivity using another drug of this class and by rescue experiments. The broad and biologically interconnected landscape of DAG binding proteins should be further characterized to understand the precise mechanism of bryostatin-1 in future studies. Further, we recommend consideration of other orthogonal mechanisms of immune regulation, which were identified to protect against negative selection in pre-B leukemias from other subtypes97 and could be relevant in potential resistance mechanisms.Notably, we identified a DAG analog and proposed PKC activator, bryostatin-1, that demonstrated a phenotype- and subtype-specific efficiency in the https://proteomics.se/forall/) of this childhood ALL cell line panel provide a rich resource for exploration and hypothesis generation.Collectively our proteomics, transcriptomics, pharmacoproteomics analysis, and data portal (l-Ala-l-Gln dipeptide)  supplemented with either 10 or 20% fetal bovine serum , 20 mM HEPES (Gibco/Life Technologies), 1 mM sodium pyruvate (Sigma-Aldrich), 1x MEM non-essential amino acids (Sigma-Aldrich), and 1x Penicillin-Streptomycin (Sigma-Aldrich) was preferably used. For a few cell lines that were not growing well in RPMI, we instead used Iscove’s Modified Dulbecco’s Medium  supplemented with 20% FBS or Nutrient Mixture F-10 Ham supplemented with 10% FBS. Detailed information on the provider of the cell lines and growth media can be found in Supplementary Data 2 to a cell density of ~1–2 million cells/mL. Cells were harvested at 500 × g for 3 min and washed twice with Hank’s Balanced Salt Solution . Aliquots of five million cells were saved for proteomics and transcriptomic analysis. Supernatants were tested negative for mycoplasma by MycoAlert Mycoplasma detection kit (Lonza). All cell lines were authenticated by (short tandem repeat) STR profiling .The 49 childhood ALL cell lines and two EBV-transformed B-cell lines used in this study were obtained from Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH , from Children’s Oncology Group Childhood Cancer Repository , from American Type Culture Collection (ATCC), Japanese Collection of Research Bioresources Cell Bank (JCRB), European Collection of Authenticated Cell Cultures  and Banca Biologica e Cell Factory . Roswell Park Memorial Institute (RPMI) 1640 containing 2 mM stable glutamine . Cell debris were removed by centrifugation at 14,000 × g for 15 min. The total protein amount was estimated (Bio-Rad DC). For filter-aided sample preparation (FASP), 250 µg of protein sample was mixed with 1 mM DTT, 8 M urea, and 25 mM HEPES pH 7.6 in a centrifugation-filtering unit with a 10-kDa cut-off . The samples were then centrifuged for 15 min, 14,000 × g, followed by another addition of the 8 M urea buffer and centrifugation. Proteins were alkylated by 25 mM IAA, in 8 M urea, 25 mM HEPES pH 7.6 for 10 min, centrifuged, followed by two more additions and centrifugations with 4 M urea, 25 mM HEPES pH 7.6. Protein samples were digested on the filter, first by incubation the samples with Lys-C ) overnight at 37 °C and an enzyme:protein ratio of 1:50. In the second digestion step trypsin (Thermo Scientific) was added in 50 mM HEPES at an enzyme:protein ratio of 1:100 and incubated for another 8 h at 37 °C. The addition of trypsin is repeated for a final overnight incubation. After digestion, the filter units were centrifuged for 15 min, 14,000 × g, followed by another centrifugation with 50 µL MilliQ water. Peptides were collected and the peptide concentration was determined. The quality of digest was checked for every sample by LC-MS/MS analysis. Peptides were then dried in a speedvac. About 100 µg of peptides were resuspended in 100 mM TEAB pH 8.5 and labeled with isobaric TMT10-tags (Thermo Scientific) according to manufacturer’s instructions, but for 3 h. Labeling efficiency was determined by LC-MS/MS before mixing the samples. In total ten TMT10 sets were prepared, whereas some cell lines were analysed in duplicates to assess the influence of the biological variance. Each TMT set contained a pool for posterior dataset normalization that was composed of lysates of different cell lines and digested together with the individual samples. An overview of the sets and the pool composition is given in table Supplementary Data Samples were prepared using a modified version of the spin filter-aided sample preparation (FASP) protocol103. Sample pools were subjected to peptide IEF-IPG (isoelectric focusing by immobilized pH gradient) in the pI range 3–10 and 3.7–4.9. Dried peptide samples were dissolved in 250 µL rehydration solution containing 8 M urea, and allowed to adsorb to the gel bridge strip by swelling overnight. The 24 cm linear-gradient IPG strips  were incubated overnight in an 8 M rehydration solution containing 1% IPG pharmalyte pH 3–10 or 2.5–5, respectively . After focusing, the peptides were passively eluted into 72 contiguous fractions with MilliQ water/ 35% acetonitrile (CAN)/ 35% ACN + 0.1% formic acid (FA) using an in-house constructed IPG extractor robotics  into a 96-well plate , which were then dried in a SpeedVac. The resulting fractions were dried and kept at −20 °C.The prefractionation method was applied as previously described in ref. −1, the curved gradient went from 2% B up to 40% B in each fraction as shown in Supplementary Data Online LC-MS was performed using a Dionex UltiMate™ 3000 RSLCnano System coupled to a Q-Exactive-HF mass spectrometer (Thermo Fisher Scientific). Each fraction was subjected to MS analysis. Samples were trapped on a C18 guard-desalting column , and separated on a 50 cm long C18 column . The nano capillary solvent A was 95% water, 5% DMSO, 0.1% formic acid; and solvent B was 5% water, 5% DMSO, 95% acetonitrile, 0.1% formic acid. At a constant flow of 0.25 μl minFTMS master scans with 60,000 resolution (and mass range 300–1500 m/z) were followed by data-dependent MS/MS  on the top five ions using higher-energy collision dissociation (HCD) at 30% normalized collision energy. Precursors were isolated with a 2 m/z window. Automatic gain control (AGC) targets were 1E6 for MS1 and 1E5 for MS2. Maximum injection times were 100 ms for MS1 and 100 ms for MS2. Dynamic exclusion was set to 30 s duration. Precursors with unassigned charge state or charge state 1 were excluded. An underfill ratio of 1% was used.49 was performed using ALL cell lines cultured as previously described, with media conditions as noted in Supplementary Data ® was used to plate the drugs (labcyte). Following incubation for 72 h at 37 °C and 5% CO2, cell viability (ATP levels) were measured using CellTiter Glo (Promega). Data was collected on an Ensight (Perkin Elmer) system. Data on each plate were normalized to a plate-specific negative control (vehicle) and a positive control . Quality control and selective drug-sensitivity score (sDSS) calculation104 and data analysis was performed using Breeze (breeze.fimm.fi)105. The sDSS are a modified area under the curve-based metric for assessing drug sensitivities. The drug sensitivity to TMP269 was carried out at six different concentrations ranging from 0.0001 to 10 µM at tenfold dilution using DMSO in the same 384-well format as the DSRT described above apart from the data analysis.Drug sensitivity and resistance testing6 cells/mL for each cell line, DMSO was added as a vehicle to control samples and incubated for 3 h at 37 °C and 5% CO2. Cell suspensions were then centrifuged at 300×g for 5 min, the supernatant was discarded and the cells were washed twice with Hank’s Balanced Salt Solution . Pelleted cells were resuspended in HBSS and 75 µL cell suspension were aliquoted to 0.2 mL tubes. Samples were then heated in a temperature range of 37–70 °C in a Veriti Thermal Cycler (Applied Biosystems/Thermo Fisher Scientific) for 3 min, followed by 3 min cooling at room temperature and immediate snap-freezing in liquid nitrogen. The cells were then lysed by three repeated freeze-thawing and centrifuged at 21,000×g for 30 min at 4°C. The cleared supernatants were transferred to new tubes, denatured in LDS sample buffer (Thermo Fisher Scientific), and analyzed by western blotting.RCH-ACV and KOPN-8 cells were cultured as previously described, tacedinaline was added to final concentrations of 20 µM to 10 mL suspensions of 1.0 × 106 cells/mL respectively and incubated at 37 °C and 5% CO2 for 3 h. Two replica experiments for each cell line were performed. Cells were then heated at a constant temperature of 53 °C for 3 min in a Veriti Thermal Cycler (Applied Biosystems/Thermo Fisher Scientific) followed by 3 min cooling at RT and immediate snap-freezing in liquid nitrogen. The cells were then lysed by three repeated freeze-thawing and centrifuged at 21,000×g for 30 min at 4 °C. The cleared supernatants were transferred to new tubes, denatured in LDS sample buffer (Thermo Fisher Scientific), and analyzed by western blotting.RCH-ACV and KOPN-8 cells were cultured as previously described, tacedinaline was added to final concentrations of 100, 25, 6.25, 1.56, 0.39, 0.098, 0.024 µM and DMSO in 100 µL aliquots of 1.0 × 10Cells were lysed in Cell Signaling Technologies lysis buffer supplemented with protease and phosphatase inhibitors . Protein concentrations were determined using Bio-Rad DC assay (Bio-Rad). Proteins were denatured in LDS sample buffer (Thermo Fisher Scientific), resolved by SDS-PAGE using NuPAGE™ 4 to 12%, Bis-Tris Gel  and NuPAGE MES SDS Running Buffer , and transferred to Nitrocellulose membranes . SeeBlue™ Plus2 Pre-stained Protein Standard was used as protein ladder . Afterward, the membranes were blocked with 5% nonfat dry milk in TBST (Thermo Fisher Scientific) and incubated with primary antibodies for the appropriate target. Following overnight primary incubation at 4 °C, blots were rinsed using TBST and incubated with the appropriate horseradish peroxidase (HRP)-conjugated secondary antibodies  for rabbit primary ab used at a dilution of 1:5000). All antibody incubations were diluted in 5% nonfat dry milk in TBST. Protein bands were developed with Clarity ECL Substrate Chemiluminescent HRP substrate (Bio-Rad) in an iBright CL1000 Imaging System . Bands were quantified using the ImageJ software version 1.5Oi and iBright Analysis Software version 4.0.1 (Thermo Fisher Scientific). HDAC1 , Phospho-ERK1/2 , ERK1/2 , b-actin  antibodies were used for Western blot to detect corresponding targets.2) in a 96-well sterile tissue culture plate (Corning). All drug treatments and DMSO controls were brought to the same relative DMSO volume of 1:200. Following treatment, non-viable cells were stained using 1:500 Zombie Aqua Live Dead stain (Thermo Fisher), diluted in PBS (Invitrogen), and added directly to the plated cells (1:2 volume). Cell staining was performed for 1.5 h on ice, and during staining and all subsequent steps, cells were protected from light using aluminum foil. Viable cell counts were obtained using a BD Biosciences LSRFortessa flow cytometer, and cells were collected in equal volumes per well using the high throughput sampler (HTS) plate reader. Gating  supplemented with 10% fetal bovine serum , 20 mM HEPES (Gibco/Life Technologies), 1 mM sodium pyruvate (Sigma-Aldrich), 1x MEM non-essential amino acids (Sigma-Aldrich), and 1x Penicillin-Streptomycin (Sigma-Aldrich). All cell lines were diluted to a plating density of 500k cells/mL. Cells were treated with soluble compounds at the stated concentrations for 72 h in standard tissue culture incubation conditions 107 and Percolator (v2.08)108, where search results from eight subsequent fractions were grouped for Percolator target/decoy analysis. All searches were done against the human protein subset of Ensembl 99 in the Galaxy platform109. MSGF + settings included precursor mass tolerance of 10 ppm, fully-tryptic peptides, maximum peptide length of 50 amino acids, and a maximum charge of 6. Fixed modifications were TMT-10plex on lysines and peptide N-termini, and carbamidomethylation on cysteine residues, a variable modification was used for oxidation on methionine residues. Quantification of TMT-10plex reporter ions was done using OpenMS project’s IsobaricAnalyzer (v2.0)110. PSMs found at 1% FDR  were used to infer gene identities.Orbitrap raw MS/MS files were converted to mzML format using msConvert from the ProteoWizard tool suiteProtein quantification by TMT-10plex reporter ions was calculated using TMT PSM ratios to the entire sample set  and normalized to the sample median. The median PSM TMT reporter ratio from peptides unique to a gene symbol was used for quantification. Protein FDR were calculated using the picked-FDR method using gene symbols as protein groups and limited to 1% FDR. The eight technical replicates of the SEM cell lines were combined by taking the protein-wise median levels.https://github.com/NationalGenomicsInfrastructure/ngi_pipeline). The reads were preprocessed for an adapter and quality-based trimming using cutadapt111 and then mapped to the human reference genome GRCh38 (gencode.v31.p12 primary assembly) using STAR aligner112 with enabled chimeric reads detection. All samples showed a good percentage of uniquely mapped reads (average 91%). The mapped reads were summarized/quantified at the gene level using featureCounts113 using gencode.v31 comprehensive gene annotation. The combined counts of all samples were filtered and adjusted for technical variation due to the sequencing batches using the ComBat-seq method implemented in the sva R package114. The adjusted genes counts were normalized using the edgeR package115 using the Trimmed Mean of M-values (TMM) method116. Transcriptomic data for a total of 417 leukemia samples including 18 cases with MEF2D-rearrangements from pediatric patients were obtained from The European Genome-phenome Archive (EGA) (Dataset ID: EGAD00001002704 and EGAD00001002692) after Data Access Agreement (DAC) approval from St. Jude Children’s Research Hospital - Washington University Pediatric Cancer Genome Project Steering Committee. Two additional RNA-seq samples of MEF2D-HNRNPUL1 subtype were obtained from the Shanghai Institute of Hematology (SIH) and were added to the St. Jude cohort and processed accordingly using the same RNA-seq analysis pipeline. The reads from bam files were re-aligned to gencode.v31.p12 human genome then the processing steps and annotation files were used identical to the cell lines RNA-seq data processing steps. STAR mapping statistics summary of 419 clinical patient samples are presented in Supplementary Data Total RNAs were extracted from aliquots harvested at the same time point as proteomics samples using the RNeasy Mini Kit (Qiagen), following manufacturer’s instructions using the option of adding β-mercaptoethanol in the lysis step and using DNAase. RNA concentration and quality were determined using Qubit and RNA Assay kit (Thermo Fisher Scientific) and Bioanalyzer with RNA Nano Chips (Agilent Technologies). RNA libraries were prepared with TruSeq Stranded total RNA RiboZero Kit (Illumina) for ribosomal depletion at the sequencing facilities. Sequencing of the libraries (Paired-end 2 × 150 bp) were performed in three different batches by the NovaSeq6000 S2 platform (Illumina) at the National Genomics Infrastructure (NGI) in Stockholm and SNP&SEQ Technology Platform in Uppsala. Basic quality control of the sequencing data and reads was performed by the facilities using a standard quality control pipeline   using Ensembl database v102 of human genome GRCh38. FusionCatcher was run using default parameters where three aligners  are utilized in combination for detection of potential fusion gene pairs. Genes which showed too many fusion partners (above 99% quantile of a number of partners) were filtered out. A matrix of the (counts per million) CPM of all of the uniquely spanned reads for detected fusions in the cell lines using FusionCatcher are presented in Supplementary Data FusionCatcher v1.33115. FeatureCounts was used to assemble a raw counts matrix, and edgeR used this counts matrix to perform differential expression analysis based on the negative binomial distribution.RNA-based differential expression analyses were performed using edgeR (v.3.32.1)30 were identified while considering variation between the cell lines by calculating a modified “quantile” standard deviation for each protein, ignoring the lowest and highest values for each protein. Then, the distribution of the modified standard deviation was modeled using a mixture of Gaussian distributions and we used an expectation-maximization method (EM) to estimate the different mixture components using the package mixtools, version 1.2.0. The EM process converged in a two distribution solution, which we assumed to represent the highly variable and the unmodulated proteins. Using this model, we estimated the number of highly variable proteins and selected a modified standard deviation threshold, which optimized the number of highly variable minus unmodulated proteins. As the EM process inevitably produces slightly different thresholds every time it is executed, we performed ten iterations and rounded the mean of the iterations to the lower 0.5 in order to have a reproducible solution.Highly variable proteinsWe submitted three different datasets to the clustering procedure: the full panel of cell lines, B cells  only, and T cells (T-ALL) only. For each of these datasets, we identified the set of highly variable proteins as detailed above. We then marked samples that exhibited no Pearson correlation of 0.5 or greater to any other sample using all proteins with valid values only. Sample replicates were excluded. In order to identify the optimal number of clusters, we ran the consensus cluster algorithm using the R package ConsensusClusterPlus, version 1.52.0, applying the following parameters: pFeature =  0.8, pItem = 0.8, reps = 2000, clusterAlg = hc. We filtered the dataset for highly variable proteins and non-outlier samples and clustered with different distance measures  and linkages . The number of clusters was determined by the elbow method and the delta area of the cumulative distribution function  of only protein-coding RNAs. Highly variable protein-coding RNAs were utilized to find an optimal number of clusters using consensus clustering in a similar approach followed for protein data. RNA and protein clusters were compared using the Sankey plot implemented in the ggalluvial R package (version 0.12.3). Uncertainty of RNA clusters were assessed by determining approximate bootstrap probabilities using pvclust R package (version 2.2-0).25. When comparing samples within a specific ALL lineage , we only selected samples belonging to the same lineage. For each specific comparison, we first stratified the samples to be compared into two different groups. The remaining samples were passed in as a combined third group (“other”). Each of the three groups needed to have at least one valid quantification value for a respective protein. In parallel, for each protein, we calculated the sum of quantified PSMs per TMT set. Only sets contributing to the actual DEqMS analysis were taken into account. Then we computed the minimum PSM count across all of these sets while ignoring zero and NA values. If a protein had no quantified PSM, it was excluded from the analysis. This final PSM count per protein was then used to build the empirical Bayes statistics. The significance cut-off was set to an adjusted P value of 0.01 and fold changes to log2(1.5)-fold difference in abundance.Differential abundance of the proteomics data was performed using DEqMS, version 1.6.0n = 8981) between the transcriptomic and proteomic data using 64 matched samples. Correlation between the mRNA and protein abundance values for each of these genes was determined using Spearman’s ρ correlation method. Correlation analysis between sDSS and protein abundance of all quantified proteins  for the 43 cell lines with DSRT were also performed using Pearson Rank Correlation. We used the cor.test function in R which uses t-distribution statistics to calculate the P value of the correlation.Correlation between mRNA and protein levels for each gene was performed using overlapping genes (https://www.gsea-msigdb.org/gsea/index.jsp) of gene lists from DEqMS, edgeR, and drug sensitivity correlation analysis were performed using the GSEA v4.1.0 software against priori-defined gene sets available at available from Molecular Signatures DataBase (MolSigDB). The priori-defined hallmark, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Pathway Interaction Database gene sets were used. Weighted enrichment statistics was used for gene set sizes between 15 and 500, running with 1000 permutations.Gene set enrichment analysis  as calculated above. We excluded proteins of the spliceosome, Nop56p-associated pre-rRNA complex, ribosome, and proteasome in order to avoid bias of large complexes. Subcellular localization (neighborhood level) were downloaded from ref. 117. For miRNA analysis, we downloaded miRNA–mRNA interaction data from118. The initial dataset (hsa_MTI.xlsx file) consists of 502,652 interactions that were filtered for “Functional MTI” support corresponding to 8157 unique interactions. These were further condensed to CORUM complex gene targets, allowing 3500 interactions between 572 miRNAs and 835 gene symbol-centric transcripts for downstream analysis. Degradation profiles were extracted from supplementary table 119. Welch’s t-test and ANOVA were used for two- and multi-level comparisons, respectively, after converting correlations to z-scores. For the identification of significant mRNA-protein deviations, we devised a three-step approach:RNA and protein data were preprocessed by averaging replicate cell lines and filtering to include genes with more than 1 TPM and proteins with non-NA values in at least half of the samples.Stability scores for each gene symbol were derived as standardized residuals after regressing protein log2 ratios to RNA TPM values using locally estimated scatter plot smoothing (loess) function to account for lineage-specific transcript expression.Significant stability was called for absolute standardized residuals greater than 3.To investigate protein complex regulation, we used mRNA–protein correlations of all cell lines for CORUM complexes32 (Complete Dataset).Significant cases were annotated for mutations : DepMap 21Q3 Public. figshare. Dataset. 10.6084/m9.figshare.15160110.v2) and CORUM complex membershipn = 336 drugs, n = 9786 proteins). A scaled, centered matrix was generated and used as input for a PCA using the Seurat package in R (https://cran.r-project.org/web/packages/Seurat/index.html). This PCA calculation was used to generate an elbow plot and a jack straw plot, also using functions in the Seurat package. Assessed using the elbow plot and jack straw plot to identify PCs with significant contribution to variance in the dataset, the number of principal components used as input for UMAP initiation was chosen to be 27. Using the uwot package (https://cran.r-project.org/web/packages/uwot/index.html), the Pearson correlation values were input for calculation of UMAP embedding for two dimensions, performed using the following key parameters: n_neighbors = 25, local_connectivity = 1, spread = 3.5, min_dist = 0.05, metric = “euclidean”, pca = 27, init = “normlaplacian”, nn_method = “annoy”. The 2D UMAP was added to the Seurat package as a dimensional reduction and plotted as a ggplot2 object (https://cran.r-project.org/web/packages/ggplot2/index.html) using the DimPlot function in Seurat.Using the Pearson correlation coefficient results calculated as described above, a 2D matrix of values was assembled representing Pearson correlations to gene symbol-centric protein quantification values for each drug. Drugs were limited to only the screen results meeting a minimum sDSS threshold of 8 for at least one cell line (81 was performed using the DGCA package (version 1.0.2), which was applied to Pearson correlations calculated between the sDSS for the drugs and proteins in BCP-ALL (B_Lineage), or in T-ALL (T_Lineage). The differential correlation design detected differentially correlated drug–protein pairs by lineage. No imputation was performed and NA and zero values were excluded. Permutation testing for P values was performed for n = 10 permutations. The classification was performed to characterize differential correlations by their directions and significance in each group, and all results regardless of classification were analyzed and ranked by z-score and significance.Differential correlation analysishttps://cran.r-project.org/web/packages/ggplot2/index.html), and ggpubr (https://cran.r-project.org/web/packages/ggpubr/index.html). P values were obtained from an unpaired t-test between groups performed using the stat_compare_means function in ggpubr. Significance was validated in un-normalized raw counts data. Criteria for exclusion of acquired data was established prior to experimental data acquisition and analysis, and all analyzed wells were obtained from experiments where event counts over time remained constant throughout flow cytometry acquisition and where DMSO-treated controls from the same cell line culture and plate had more than 100 quantified viable cells per HTS collection, to ensure technical robustness of flow cytometry acquisition and cell line preparation.All quantification was performed using the BD FacsDiva software (BD Biosciences). For each experiment, mean DMSO-treated counts were obtained across technical replicates , and this mean value was used to normalize counts to a ratio of viable treated cell count divided by viable mean DMSO cell count. Normalization operations, statistical tests, and data plotting were performed in R using the packages ggplot2  were calculated with the R functions cor or cor.test which uses t-distribution statistics to calculate the P value of the correlation. Linear models built with the R function lm. Two-sided, unpaired t-tests were performed using t.test and two-sided Welch’s t-test using t.test was performed for pairwise comparisons unless otherwise specified. For the multiple group comparisons analysis of variance (ANOVA) test was performed using the function anova. Figure panels were created using base R graphics and ggplot2 (v.3.3.3) using ComplexHeatmap (v.2.2.0) R packages and Graphpad Prism8.No statistical method was used to predetermine sample sizes. No data were excluded from the analyses. The experiments were randomized based on the date of cell lines obtained and cultured as well as cytogenetic type and subtype. Investigator blinding to conditions and outcome assessment was not applicable. No data were excluded from analysis and all data met the quality control standards as described above. All of the statistical analyses were conducted using R (v.3.6.2 or higher), the drug sensitivity statistics of TMP269 which was conducted using Graphpad Prism 8, and the statistical analysis of the flow cytometry was conducted with Microsoft Excel T.TEST function using a two-sided and homoscedastic calculation. Correlations and associated Further information on research design is available in the Supplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Supplementary Data 14Supplementary Data 15Supplementary Data 16Reporting Summary