Breast cancer (BCa) has long been a health burden to women across the globe. However, the burden is not equally carried across races. Though the manifestation and behavior of BCa differs among racial groups, the racial representation of models used in preclinical trials and clinical trial participants lacks this heterogeneity. Women of African Ancestry (WAA) are disproportionately afflicted by having an increased risk of developing BCas that are more aggressive in nature, and consequently suffer from poorer outcomes relative to women of European ancestry (WEA). Notwithstanding this, one of the most commonly used tools in studying BCa, cell lines, exhibit a sizeable gap in cell line derivatives of WEA relative to WAA. In this review, we summarize the available BCa cell lines grouped by race by major suppliers, American Type Culture Collection (ATCC) and the European Collection of Authenticated Cell Cultures (ECACC). Next, examined the enrollment of WAA in clinical trials for BCa. Of the cell lines found provided by ATCC and ECACC, those derived from WEA constituted approximately 80% and 94%, respectively. The disparity is mirrored in clinical trial enrollment where, on average, WEA made up more than 70% of participants in trials found where ancestry information was provided. As both experimental models and clinical trial participants primarily consist of WEA, results may have poorer translatability toward other races. This highlights the need for greater racial diversity at the preclinical and clinical levels to more accurately represent the population and strengthen the translatability of results.The online version contains supplementary material available at 10.1186/s13058-022-01551-x. Women of African ancestry (WAA) are 40% more likely to die of BCa than women of European ancestry (WEA) and have the highest death rates due to BCa of all racial groups . Data frWomen of African ancestry are usually diagnosed with BCa at advanced stages and are disproportionally diagnosed with the most aggressive form of BCa , 4. SeveBreast cell lines from the American Type Culture Collection (ATCC) and European Collection of Authenticated Cell Cultures (ECACC) databases were found using the search terms: “breast cell lines,” “breast cells,” and “breast cancer cell lines.” Both cancerous and non-cancerous cell lines were included when compiling the list of available cell lines. The race of the individual from which the cell lines were derived was determined based on information provided by ATCC and ECACC. In cases where race was not specified by these suppliers, ancestry information was obtained from the Swiss Institute of Bioinformatics.Trials included in Additional file With the exception of the antiestrogen effect of IC146474 clinical trial, clinical trials that did not provide the racial background of its participants were excluded. Cases of male breast cancer were not considered.No limitation was placed on the date for studies included in the literature review.The systematic approach utilized over the years to understand and treat cancers includes preclinical models followed by clinical trials. Preclinical models comprise in vitro and in vivo research and, in both instances, the application of 2D models, cell lines that are also used as organoids and 3D cultures are all vital. Taking sociological factors into account, a closer look at the representational BCa models that have been used over the decades to understand, treat, and prevent BCa is crucial. As described above, there is a clear disparity in incidence and mortality rates for WAA with BCa. What is more is that women of WEA have higher incidence rates, yet women of WAA have higher mortality rates and are more prone to developing the more aggressive and least studied form of BCa—triple negative breast cancer (TNBC). This then begs the question, as to how much do socioeconomical factors contribute versus lifestyle and genetic predisposition. As scientists and clinicians, our preclinical models and clinical trials are primarily employed to address and find answers for the latter reasons and so this review aims to take a closer look at the representational models used to understand, treat, and prevent BCas.Cultured cells have proven to be cornerstone catalysts in propelling oncology by acting as disease models. Such models have allowed for the elucidation of pathways responsible for cancer initiation and progression, the study of the behavior of cancers, and the discovery of novel therapeutic agents. Although primary cells represent the most appropriate models for analysis owing to their close resemblance to the source tissue, their limited lifespans in culture present a roadblock to long-term investigation. Cell lines remedy this by circumventing the major barrier to immortality—senescence.Although BCa is of a heterogenous nature, the racial diversity of commercially available cell lines borders on the homogeneous. This is reflected in the catalogue of breast cell lines provided by two major suppliers of cell lines: the ATCC and the ECACC. Of ATCC’s breast cell lines where race could be determined  was examined. The trial, in which tamoxifen was used to treat hormone sensitive BCa, involved 46 patients (races not specified) for a period of more than 3 months . Later, Scientific American reports that although racial minorities constitute 40% of the US population, they are typically vastly outnumbered in clinical trials by participants of European descent, who can constitute up to 80–90% of recruits [The racial disparity in BCa clinical trial enrollment is evident across trials supported by several organizations  is expressed across all BCa subtypes; however, overexpression appears more common in aggressive disease subtypes. Trop-2 is expressed in the majority of TNBCs and may be considered an actionable biomarker. Sacituzumab govitecan-hziy is an antibody–drug conjugate that targets Trop-2 for the selective delivery of SN-38. In 2020, Sacituzumab govitecan  was approved by the FDA for the treatment of patients who have received at least two prior therapies for metastatic disease . The phaIn the IMpassion130 phase 3 clinical trial  of Atezolizumab and Nab-Paclitaxel in advanced TNBC, it was shown that the addition of the immunotherapy drug Atezolizumab to chemotherapy (nab-paclitaxel) prolonged progression-free survival among patients with metastatic TNBC. In this TNBC specific study, the study population was 67.5% WEA (609 patients), with WAA comprising 6.5% (59 patients) . The resP = 0.007. The association was also seen with early-stage post-menopausal BCa with a HR for death of 1.49, 95% CI = 1.28—1.73; P < 0.001 [Although the aforementioned clinical trials did not stratify treatment outcome data by race, a study examined 6676 BCa patients (662 were WAA) enrolled in phase 3 clinical trials carried out by the Southwest Oncology Group (SWOG) between 1974 and 2009. When factors such as stage, therapy, and follow-up were kept equal, WAA had poorer survival relative to WEA. The WAA diagnosed with early-stage premenopausal BCa, regardless of receptor status, had increased mortality with a hazard ratio (HR) for death of 1.41, 95% confidence interval (CI) = 1.10—1.82;  < 0.001 . HoweverThe field of medicine is evolving toward a more personalized approach to patient care, aimed at improving outcomes while reducing treatment-related toxicities. This personalized approach has to begin by systematically addressing commonalities and differences in biomarker expression, at the somatic and germline levels among races taking into account the geographical locale and socio-economic factors among women with BCa. Toward this end, an intentional course at increasing the representation of preclinical models and enrollment in clinical trials is necessary. As such, this needs to be factored in at the levels of funding agencies, IRB, suppliers of in vitro models, and sponsors/investigators of clinical trials. For example, the African Genome Registry (AGR) project a part of the African Caribbean Cancer Consortium (AC3) group, to be undertaken by researchers of the African-Caribbean scNetwork aims to address genetic differences between cancerous and neighboring normal cells of prostate, breast, and cervical cancers from MAA and WAA. This would be done via single cell sequencing of the aforementioned cancers from patients living in the USA, Caribbean, and Africa. Similarly, a current clinical trial project, IRONMAN that is amassing the largest registry of men with advanced PCa has been intentional about increasing the representation of MAA with PCa. Another project similar in nature to IRONMAN under the AGR aims to create a registry of patients of African descent with BCa and PCa of all types and stages from the USA, Caribbean, and Africa. These are being done by strategically partnering with various organizations like the African-Caribbean Cancer Consortium (AC3) and Prostate Cancer Transatlantic Consortium (CaPTC), research consortia that include research groups in the Caribbean and Africa that are high in representation from people of African ancestry. Notwithstanding, tackling BCa wholistically, has to also consider socioeconomic factors such as access to healthcare, dietary trends, and environmental factors, which will maximize the improvement of patient care for women with BCa.For the efficacy of therapeutic agents and the findings from all stages of research to have stronger external validity, consideration must be taken when choosing in vitro models and recruiting patients such that they reflect that population they intend to benefit. It was demonstrated that a clear racial disparity exists among the most commonly used in vitro models, cell lines, where on average those derived from WEA constituted more than 80% of those commercially available from major suppliers. Consequently, if a conscious effort is not made to include the fewer cell lines from racial minorities, those used as disease models skew toward European derivatives. The same can be said for the lack of diversity in clinical trials, especially those geared toward TNBC, which disproportionately affect WAA. At this stage, it becomes even more critical for racial equality among study participants as these results will determine if therapies advance to the final stages to eventually become available as treatment options to the public. Acknowledging and shedding light on the issue at hand is the first step in moving toward more inclusive research that, over time, ensures that clinical outcomes are more favorable toward racial minorities.Additional file 1. Racial diversity of breast cancer clinical trials funded by various organizations N = number of participants.Additional file 2. Cell lines supplied by the ATCC and ECACC disaggregated by race N = number of cell lines.


Somatic embryogenesis could potentially contribute to the maximization of solid polyploid production due to the single cell origin of regenerants. In the present study, a novel procedure for establishing homogeneous tetraploid embryogenic cell lines in Magnolia officinalis has been established. Embryogenic cell aggregate (ECA) about 100–200 μm across, and consisting of dozens of cells, regenerated into a single colony of new ECAs and somatic embryos following colchicine treatment. Histological analysis indicated that the few cells that survived some colchicine regimes still regenerated to form a colony. In some colonies, 100% tetraploid somatic embryos were obtained without mixoploid formation. New granular ECA from single colonies with 100% tetraploid somatic embryos were isolated and cultured individually to proliferate into cell lines. These cell lines were confirmed to be homogeneous tetraploid by flow cytometry. Many tetraploid somatic embryos and plantlets were differentiated from these cell lines and the stability of ploidy level through the somatic embryogenesis process was confirmed by flow cytometry and chromosome counting. The establishment of homogeneous polyploid cell lines, which were presumed to represent individual polyploidization events, might expand the phenotypic variations of the same duplicated genome and create novel breeding opportunities using newly generated polyploid plantlets.The production of synthetic polyploids for plant breeding is compromised by high levels of mixoploids and low numbers of solid polyploid regenerants during  Artificial polyploid induction can be used for the improvement of important plant traits, in support of crop breeding strategies . It has In vitro regeneration systems provide a powerful tool for mitotic ploidy manipulation  of 0, 0.05, 0.1, 0.15, and 0.2% . The culTo estimate the number of ECAs in the cell suspension, aliquots of 1 mL of cell suspension were precipitated by centrifugation, and resuspended in 0.1 mL of M3 medium and pipetted onto a glass slide. Images were taken under an OLYMPUS SZX16 microscope and the number of ECAs were counted with CellSens Dimension software.After plating the colchicine treated ECAs onto a piece of filter paper and cultured on semi-solid M4 medium for 4 weeks, each surviving ECA regenerated into a single colony consisting of somatic embryos and/or new ECAs . The numFor plantlet conversion, the cotyledonary somatic embryos were first transferred to 9 cm Petri dishes containing 20 mL of semi-solid M4 medium for germination. The cultures were maintained at 25°C under a 16 h photoperiod. The germinated somatic embryos were transferred to glass culture vessels containing 100 mL of semi-solid M4 medium and cultured at 25°C under a 16 h photoperiod.To trace the cellular origin of ECAs following colchicine treatment, fluorescein diacetate (FDA) and propidium iodide (PI) were used to label living cells and dead cells, respectively . ECAs weFlow cytometry was used to determine the ploidy level of cell lines, somatic embryos and plantlets . For DNAThe ploidy level of tetraploid plantlets and somatic embryos verified by flow cytometry were further confirmed by chromosome counting to precisely determine the number of chromosomes . Root ti2. For stomatal length and width measurements, three stomata in each image were randomly selected and measured (n = 24).To compare the difference of stomata characteristics between diploid and tetraploid plantlets, nail polish imprints were made from the abaxial surface of leaves. Briefly, the abaxial surface of the leaves were covered with a thin layer of nail polish and allowed to dry. The polish imprints were then carefully lifted off with forceps and placed on a glass slide. The stomata were observed and imaged under LEICA DM5500 B microscope with LAS software. For evaluating the stomatal density, the number of stomata per field of view under the 40× objective was recorded in eight different images. The size of the image was measured to calculate the number of stomata per mmFlow cytometry were used for screening of clones with a high percentage of tetraploid plantlets. To establish tetraploid cell line production, single granular ECAs from the colonies confirmed with a high percentage of tetraploid plantlets were isolated and cultured individually on semi-solid M2 medium for proliferation . These cin vitro plantlets of diploid and tetraploid status were collected, and the biomass was measured.To assess the effects of polyploidization, the size of the somatic embryos and biomass of the plantlets (3 months after somatic embryo germination) regenerated from diploid and tetraploid cell lines were measured and compared. Diploid and tetraploid cell lines were used to produce somatic embryos with the same method. ECAs with a diameter of 200–450 μm were obtained and plated on a piece of filter paper and cultured on semi-solid M4 medium for somatic embryo initiation. Thirty somatic embryos initiated from diploid and tetraploid cell lines were collected 5 weeks after somatic embryo initiation. Images were taken under an OLYMPUS SZX16 microscope. The length and diameter of somatic embryos were calculated with CellSens Dimension software. For plant conversion, germinated somatic embryos were transferred to glass culture vessels containing 100 mL of semi-solid M4 medium and cultured at 25°C under a 16 h photoperiod. Following 7 weeks culture, 12 p < 0.05. The measurement of somatic embryos, in vitro plantlets and stomata characteristics were analyzed with Student’s t-test at p < 0.05.The colchicine treatment experiment was designed with a randomized complete block design, and each treatment contained five replicates. Data were processed and analyzed with SPSS 19.0 software using Duncan’s multiple range tests at M. officinalis were initiated from mature zygotic embryos on semi-solid M1 medium. After initiation, PEMs were subcultured on semi-solid M2 medium for proliferation , yielded 24 reatment . The frereatment . Fifteenreatment . Relativreatment . One plaWhen colchicine treated ECAs were plated onto a piece of filter paper and cultured on semi- solid M4 medium, each surviving ECAs regenerated into a single-ECA-derived colony. Three types of regeneration response could be distinguished in single-ECA-derived colonies . Each sin = 24, 100%) recovered from the treatment of ECAs with 0.2% colchicine for 72 h were determined to be tetraploid , which represented the highest embryogenic potential among other size ranges of tetraploid ECAs. However, this response was still much lower than their diploid counterpart, which produced 217 somatic embryos in 50 ECAs with a diameter of 200–450 μm in the same period. Significant differences in the morphologies of diploid  was established by integrates in vivo bud regeneration via calluses with polyploid induction. At the mature leaf stage, not just octoploids but also tetraploid, diploid and mixoploid plants could be forecasted with a high degree of accuracy by observing the size and shape of shoots and leaves (Fortunella crassifolia) is much larger than the diploid, and the two types could be clearly discriminated by size in a mixed population of diploid and tetraploid cells (In the present study, somatic embryos regenerated from 0.2% colchicine treatment for 72 h could be clearly discriminated from those of control, as they are much bigger in size. This kind of “giant” somatic embryos was also observed in other colchicine regimes. When these somatic embryos were selected based on morphology characteristics and the ploidy levels were analyzed by flow cytometry, all were found to be tetraploid. After the tetraploid cell lines were established and verified, somatic embryos were produced and compared with the diploid lines. Generally, tetraploid somatic embryos were shorter and thicker in appearance than those of diploid lines. These findings suggest that the morphology of somatic embryos may be employed as a potential morphology indicator to predict ploidy level to expedite identification of putative tetraploid regenerates at an early stage before plantlet conversion. Other systems show a similar change in morphological phenotype with polyploidization. The d leaves . A widesid cells .Magnolia officinalis belongs to the Magnoliaceae, which consists of over 300 species in two genera–Magnolia L. and Liriodendron L (Liriodendron tulipifera was published nearly 40 years ago (Liriodendron tulipifera (M. officinalis in the present study, is based on the manipulation of the somatic embryogenesis process following antimitotic agent treatment. Our method has great potential for the creation of new polyploid germplasm for species with established somatic embryogenesis protocols, especially in the magnolia family.endron L . Many maears ago . Since tlipifera . The effM. officinalis demonstrate the potential of establishing polyploid embryogenic cell lines via induction along the somatic embryogenesis pathway. The use of very small ECAs composed of dozens of cells is highlighted. Only a few cells in the ECAs survived colchicine treatment and regenerated into single colonies. However, the dual potential of differentiation and proliferation of the ECAs was then manipulated to produce somatic embryos for ploidy analysis and new ECAs for cell line establishment, respectively. The present study shown that a high frequency of homogeneous polyploid cell lines can be established by proliferation of single new granular ECAs picked up from colonies with a high presence of polyploid somatic embryos. Somatic embryos and new ECAs in the same colonies derived from each colchicine treated ECA share a common origin from a very limited numbers of cells. The ploidy level of somatic embryos can reflect the ploidy level of cells that survived antimitotic agent treatment and regenerate. This increases the likelihood of being able to establish and purify polyploid cell lines. Our results provide insight into the potential of obtaining new polyploid embryogenic cell lines via polyploid induction with a somatic embryogenesis system.Our results with The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.in vitro tissue culture experiments and chromosome set doubling experiments and carried out the phenotypic analysis. YG, JM, and YJ performed the flow cytometry analyses. JC and YG performed the Chromosome counting experiment. LL, WL, and HC revised the manuscript. All authors contributed to the article and approved the submitted version.LL conceived and designed the experiments and wrote the manuscript. YG, JM, and QX performed the The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Breast cancer subtypes are characterized by the expression and activity of estrogen-, progesterone- and HER2-receptors and differ by the treatment as well as patient prognosis. Tumors of the HER2-subtype overexpress this receptor and are successfully targeted with anti-HER2 therapies. We wanted to know if the HER2-receptor and the downstream signaling network act similarly also in the other subtypes and if this network could potentially be a therapeutic target beyond the HER2-positive subtype. To this end, we quantitatively assessed the wiring of signaling events in the individual subtypes to unravel the characteristics of HER-signaling. Our data along with a model-based analysis suggest that major parts of the intracellular signal transduction network are unchanged between the different breast cancer subtypes and that the clinical differences mostly come from the different levels at which these receptors are present in tumor cells as well as from the particular mutations that are present in individual tumors.1 regularization, where the impact of these mutations on pathway activation was uncovered. Finally, we predicted and experimentally validated the proliferation response of cells to drug co-treatments. We developed a unified mathematical model that can describe the ERBB receptor and downstream signaling in response to therapeutic drugs targeting this clinically relevant signaling network in cell line that represent three major subtypes of breast cancer. Our data and model suggest that alterations in this network could render anti-HER therapies relevant beyond the HER2-positive subtype.Targeted therapies have shown striking success in the treatment of cancer over the last years. However, their specific effects on an individual tumor appear to be varying and difficult to predict. Using an integrative modeling approach that combines mechanistic and regression modeling, we gained insights into the response mechanisms of breast cancer cells due to different ligand–drug combinations. The multi-pathway model, capturing ERBB receptor signaling as well as downstream MAPK and PI3K pathways was calibrated on time-resolved data of the luminal breast cancer cell lines MCF7 and T47D across an array of four ligands and five drugs. The same model was then successfully applied to triple negative and HER2-positive breast cancer cell lines, requiring adjustments mostly for the respective receptor compositions within these cell lines. The additional relevance of cell-line-specific mutations in the MAPK and PI3K pathway components was identified via L The development of therapeutic antibodies and small-molecule kinase inhibitors has revolutionized cancer treatment. Many of these therapeutics target receptor tyrosine kinases, which connect extracellular growth factors with intracellular signaling networks and, when deregulated, drive oncogenic mechanisms . AdvanceThe ERBB signaling network is one of the main drivers of cell growth . The ERBERBB2 and ERBB3 are exceptional as the structure of the ERBB2 ligand-binding domain is constitutively in a dimerization-prone conformation rendering the binding of a ligand unnecessary to induce dimerization, while ERBB3 is kinase-impaired . Upon liAmong them, the MAP kinase (MAPK) cascade as well as the phosphoinositide 3-kinase (PI3K) pathway play essential roles in signal processing and propagation ,13,14,15Regulation of the ERBB signaling network is highly complex, varies between individual breast cancer subtypes and has been previously studied using mathematical modeling—a powerful tool to render complex systems intelligible and accessible ,25,26,27Furthermore, the drug-response mechanisms of different breast cancer subtypes have been evaluated before. For example, Schoeberl et al. explored the optimal way to therapeutically inhibit ligand-induced activation of the ERBB–PI3K axis , while IHere, we investigated combinations of several ligands as well as of different therapeutic drugs and their dynamic effects on cell line models of three breast cancer subtypes and their proliferative behavior. Beyond the previously published work, we expanded the analysis by weighting cell-type-specific differences in model parameters and thus the characteristics of the cell lines that propagate in differential activation of the individual signaling branches.To this end, we used data-based mechanistic modeling with ordinary differential equations (ODEs) to understand the response mechanisms to different ligands and drugs in three breast cancer subtypes. A signaling model, including receptors as well as downstream signal transduction was developed and calibrated on time-resolved proteomics data. To link the signaling output to the condition-specific growth response of the cells, we implemented a proliferation model applying a linear regression approach.1 regularization, only two biological specifics that needed to be adjusted to make our model applicable to cell lines of other breast cancer subtypes.A proliferation screen of single treatments provided calibration data for the regression parameters, while we could successfully predict proliferation measurements of various drug co-treatments thereby validating our model. Along these lines, we identified, by LOn the one hand, the receptor expression levels were different in the respective cell lines, and on the other hand, the mutation states in genes that are components of the investigated pathways strongly impacted the wiring of signaling. Still, the core signaling network was not affected by these adjustments thus underlining its general applicability. Our study therefore supports the concept of targeting the ERBB signaling network beyond the HER2-positive subtype of breast cancer.2. All cell lines were authenticated  and negatively tested for mycoplasma contamination before and after completion of the study.The human breast cancer cell lines MCF-7 (HTB-22™), T47D (HTB-133™), MDA-MB-231 (HTB-26™) and SKBR3 (HTB-30™) were obtained from the American Type Culture Collection  and maintained in Dulbecco’s Modified Eagle’s Medium: Nutrient Mixture F-12 , supplemented with 10% FCS , 50 units/mL penicillin and 50 µg/mL streptomycin sulfate  at 37 °C with 5% CODetails on all the antibodies, drugs and ligands used in this study are provided in For time course experiments, 3 × 10The RPPA experiments were performed as previously described ,39. Brie® luminescent assay . Luminiscence was determined using a GloMax® microplate reader .To determine the effects of the different ligands and drug treatments on the viability of cancer cells, 3000 cells of the respective cell lines were seeded in 96-well white plates in DMEM/F12 with 10% FCS . Prior to the addition of ligands, the cells were pre-treated with the corresponding drugs for 1 h. Cells were grown for 6 days, and the effects on cell viability, i.e., the amount of ATP reflecting the number of metabolically active and proliferative cells, was evaluated using CellTiter-Glox denoted the vector of dynamic states, such as c-RAF, MEK, etc., and u represented external perturbations, such as treatment with a ligand or a drug. Initial concentrations and kinetic rates were comprised in The developed model consisted of two parts, a mechanistic ODE model describing the ERBB signaling network and a linear regression model linking cell proliferation to signaling features. The mechanistic signaling model was described by nonlinear ODEs p, including the dynamic model parameters The mechanistic model along with corresponding observations was parameterized by the parameters ormation . The par1 penalty [The mechanistic signaling model was linked to cell proliferation by a regression model. Signaling features, including the area under the curve (AUC), the half-life and the peak height, were derived from the calibrated dynamic model for ppERK, pAKT, pP70S6K and active receptor dimers and used as predictor variables for the linear regression model with the cell proliferation as the response variable. Signaling features with the highest predictive power were selected applying an L penalty , and theeatments .As previous studies reported synergistic effects of drug co-treatments ,48,49,501 prior, and subsequently refitting the model without those fold change parameters was estimated to be compatible with 0. The The identification of cell-line-specific parameters for the cell lines MDA-MB-231 and SKBR3 were performed using a lasso-based regularization approach ,52,53. Tatistics .While kinetic rates constants ERBB downstream activation correlates with the receptor composition. To investigate the impact of various growth factors on the dynamic signaling response of cells derived from different breast cancer subtypes, we studied two cell lines of the luminal A subtype, T47D and MCF7, in addition to the triple-negative MDA-MB-231 and HER2-positive SKBR3 cell lines. The cells were stimulated with four ligands to induce receptor activation and downstream signaling in the absence or presence of four drugs targeting EGFR, ERBB2, or ERBB3. EGF, BTC and TGFa are ligands for EGFR. BTC and NRG1, a ligand for ERBB3, also bind and activate ERBB4, another receptor of the ERBB family a. We assTo experimentally validate this, we inhibited ERBB3 or knocked down ERBB4 and observed that ERBB3 was required for the activation of downstream signaling by NRG1, while ERBB4 was not . We concWe then quantified the dynamic changes in the phosphorylation states and the total protein levels of eight central proteins in the ERBB–MAPK–PI3K signaling network. In total, we experimentally tested 80 conditions, covering four cell lines, which were incubated in an array with four ligands, five drugs and an untreated control c. These In an initial analysis, we wanted to discern cell-line-specific effects focusing on the response to the ligands EGF and NRG1 by assessing their effect on the activation of AKT and ERK proteins as representatives of the PI3K and MAPK pathways, respectively. These two pathways are the two main downstream signaling branches activated upon EGF/NRG1 stimulation. As shown in This indicated cell-line-specific differences in the propagation of the original input signals. To explain the observed differential activation behavior, we first regarded the receptor composition of the four cell lines as described by Niepel et al.  Figure b. The reConsidering the ligand specificity of EGF to EGFR and of NRG1 to ERBB3 (and ERBB4), the differential downstream response might at least partially originate from the distinct ratios of the ERBB-receptors expressed by the cell lines. To test this hypothesis, an activation ratio of NRG1 over EGF was calculated for each protein and cell line, based on the area under the curve (AUC) of the phosphorylation dynamics displayed in Furthermore, there exists a strong dependence of pathway activation on the respective repertoire of receptors. This correlation followed our expectations, as cells with high ERBB3/EGFR ratios, such as MCF7, were more susceptible for NRG1, and conversely MDA-MB-231 was for EGF. Similarly, the horizontal location of pAKT and pERK in the correlation plot reflects the subsequent downstream activation, where AKT is shifted to the right, meaning to an activation via NRG1.The exception is represented by MDA-MB-231, which predominantly expresses EGFR and, in addition, carries an activating mutation in KRAS all favoring signaling via the MAPK pathway. Hence, our findings confirm the known dominant role of ERBB3 in AKT activation and, respectively, EGFR as a main factor in ERK phosphorylation ,56. TakeHaving validated our initial hypothesis, we next wanted to address three main questions: (i) Are there other variables that determine downstream signaling and cellular response on top of the receptor composition? If true, (ii) what other factors are needed to explain the response? Furthermore, (iii), can we establish a unified model of ERBB signaling for the luminal, TNBC and HER2-positive cell-line systems that is able to predict the effect of ligand stimulation and drug treatments for the three breast cancer subtypes?To explore the differential downstream activation of breast cancer subtypes, we established an ODE model describing the ERBB signaling network as displayed in Upon ligand binding, several ERBB homo- and heterodimers form, leading to the trans-activation of receptors and to the induction of downstream signaling . Our modOnce activated, the receptor dimers induce phosphorylation and thereby the activation of the MAPK and PI3K signaling pathways, which ultimately converges in the activation of P70S6 kinase in our experimental system . The matUsing the complete data set, the model was first implemented for the luminal cell lines MCF7 and T47D. The time-resolved total and phosphoproteomics data of EGFR, ERBB2, ERBB3, c-RAF, MEK, ERK, AKT and P70S6K proteins are complemented by the absolute protein abundances taken from Niepel et al.  and usedIn phase I, the inhibition phase, we assessed drug-induced changes independent of any ligand, while in phase II, we addressed the dynamics of the ligand-induced protein activation in unperturbed and in drug-perturbed conditions a. SampleLigands induce a conformational change, dimerization and activation of their respective receptors, whereas our drugs of interest either inhibit receptor dimerization (pertuzumab), impede receptor activation at the level of the extracellular domain  or block ATP-binding in the kinase domain (erlotinib). We implemented this inhibition in the mathematical model by multiplying the dimer formation and activation rate with an inhibition term As shown in Drug effects in combination with this ligand for MCF7 are displayed in Trastuzumab and erlotinib only showed a minor inhibitory effect in combination with this ligand, while cetuximab did not have any. However, this picture turned when cells were stimulated with one of the EGFR ligands. Erlotinib and cetuximab led to a strong reduction of protein phosphorylation, in particular, upon stimulation with EGF or TGFa, while the other drugs had only minor effects . In addi877 and Tyr1221/Tyr1222, of EGFR at Tyr1086 and to a lesser extent also of ERBB3 at Tyr1289  Trastuzumab as well as pertuzumab induced ligand-independent phosphorylation of ERBB2 at Tyr Tyr1289 . This ef416 of the protein kinase SRC. This suggests that these antibodies might trigger phosphorylation of ERBB receptors involving the Src kinase. This drug-induced phosphorylation was stronger in the T47D cell line compared to MCF7 and more pronounced when incubated with trastuzumab as compared to pertuzumab  Lumretuzumab and pertuzumab both led to a strong inhibition of ERBB3 phosphorylation , the slopes and the peak height of states of interest.The selection of states used for the generation of the feature set was made up of pAKT, pERK, pP70S6K and the active dimers, which were previously shown to have predictive power . These f1 regularization. Our analysis showed that the AUC of pAKT combined with the AUC of pP70S6K had the highest predictive power. Thus, these features served as predictor variables to describe the phenotypic readout, i.e., cell proliferation.Cells were treated with the same drug-ligand combinations that had been used to obtain the original data set. Then, a subset of signaling features was selected from the complete feature set, based on their prediction ability as determined by LThe predictive power of all signaling features tested is summarized in We then wanted to test if the resulting proliferation model would correctly predict treatment efficacy. To this end, we generated growth predictions for 24 combination treatments. The model suggested the co-treatment of pertuzumab with erlotinib as the overall most efficient treatment in the luminal A cells b. To valModel predictions and validation data were in good agreement, featuring only a minor increase in the mean estimated error of 0.005, representing 3% of total data spread, compared to the training data set of single treatments c. These We next wanted to assess whether the identified network kinetics are luminal cell-line-specific or if the model could be adapted also to other breast cancer subtypes. To this end, we examined the ligand-induced phosphorylation of MAPK and PI3K pathway proteins under drug influence in TNBC MDA-MB-231 as well as HER2-positive SKBR3 cells.1 regularization as previously described [Time-resolved phosphoproteomics data was generated for these two cell lines applying the same experimental conditions that we previously used with the luminal cell lines, except that we induced signaling in the SKBR3 cells with only EGF and NRG1 and that we used pertuzumab, trastuzumab and lumretuzumab as inhibitors. To identify key functions that are differentially regulated between these cell lines, differences in parameters of the established signal transduction model were analyzed based on Lescribed .1 prior  as well as an activating b-RAF mutation (G464V) . Given t289/Ser296/Ser301.In addition, this specific mutation in b-RAF has previously been reported to trigger feedback inhibition of RAS and c-RAF by ERK, which should result in a decrease in the basal activation ratio of c-RAF ,65. To tThese residues are phosphorylated by ERK and inhibit the interaction between RAS and c-RAF thereby leading to the inactivation of c-RAF as part of a negative feedback loop . MatchinIn this cell line, a strong preference towards PI3K signaling was observed, which was reflected by the low activation rates of c-RAF and MEK as well as an increase in the activation rate of P70S6K by AKT a, comparFinally, we analyzed the cell-line-specific activation of signaling branches. Along these lines, we analyzed the activation fluxes of the P70S6 kinase via either pERK or pAKT and then calculated their respective log10 ratios a. As thePIK3CA present in MCF7 and T47D. However, in these cell lines, the MAPK signaling gained relevance upon stimulation of EGFR, leading to a transient switch towards MAPK-regulated activation of P70S6K. Yet, this impact was short, and the predominant activation via AKT quickly reestablished.A positive score would indicate dominance of the MAPK pathway in the activation of P70S6K over PI3K signaling and vice versa. Signaling via AKT indeed dominated the steady state in the luminal cell lines, which can be explained with activating the hot-spot mutations in This overshoot behavior reflects the transient dynamics of ERK phosphorylation—particularly when the EGFR was activated by EGF. The dynamics of ERK-activation were longer-lasting with the other two EGFR-ligands tested, TGFa and BTC, both leading to a shortly extended activation of P70S6K by ERK. The transient nature of P70S6K activation via EGFR was in contrast to the longer lasting activation via ERBB3 particularly in MCF7, which was clear upon activation with NRG1 see also b.b-RAF.In sharp contrast to the two luminal cell lines, P70S6K was mostly activated by MAPK signaling in MDA-MB-231 cells a. This wAlong the same lines, stimulation with NRG1 did not lead to a sizeable activation of any downstream signaling proteins and thus did not affect the P70S6K activation flux ratio see also . The levThe dominance of PI3K signaling is so strong in SKBR3 that, even with activation of EGFR and downstream MAPK signaling, a balanced ratio of P70S6K activation was not reached for the MAPK and PI3K pathways. ERBB2 is the preferred binding partner of ERBB3, which has six p85 binding sites in its intracellular domain and thus preferentially induces AKT signaling ,67. EvenBased on the experimental data as well as the cell-line-specific parameters we identified in modeling, we propose specific major activation routes for the different cell lines Thus, we established a unified mathematical model for ERBB and downstream MAPK as well as PI3K signaling, which required only a few biologically relevant adaptations to correctly describe the molecular responses of luminal A T47D and MCF7, TNBC MDA-MB-231 and HER2-positive SKBR3 cell lines upon stimulation with several ligands as well as perturbations with drugs targeting EGFR, ERBB2 and ERBB3.The ERBB signaling network plays a crucial role in the regulation of cellular growth, migration and survival, which are all critical phenotypes in health and disease ,9. AlthoEGFR are frequent in relapsed lung adenocarcinoma, BRAF is often mutated in melanoma, and PIK3CA is often mutated in various entities, including luminal breast cancer. Beyond single nucleotide variants, the ERBB2 gene locus is amplified and a diagnostic marker in several entities, including HER2-positive breast cancer. Together, these factors affect tumor physiology. However, the complex interplay of signaling pathways and how these trigger cancer phenotypes in response to different inputs is not fully understood.For example, activating mutations in Here, we established a mathematical model that mechanistically describes the activation dynamics of ERBB proteins and of two prominent downstream pathways, RAF-MEK-ERK and PI3K-AKT. The amounts and phosphorylation states of central components within these pathways were quantified with high temporal resolution to capture dynamic changes in their respective abundance and activities.To perturb and thereby constrain the proliferation signaling of the cell lines in our study, five drugs targeting the ERBB receptor family were analyzed as single agents as well as in combination, always in the presence of different ligands. The drugs specifically targeted the EGFR, ERBB2 and ERBB3 receptor tyrosine kinases, while ligands were selected to either activate EGFR or ERBB3. The ODE model was then calibrated on the experimental data of single drug treatments, initially from the two luminal breast cancer cell lines, MCF7 and T47D.To establish a link between pathway activation and cell proliferation as our phenotypic readout, we combined this mechanism-based signaling model with a linear regression model. Signaling features, such as the area under the curve of single activated proteins, were simulated by our signaling model and served as input for the regression model. The regression model was then calibrated on experimental data, which we collected in a proliferation screen depicting the proliferative response of MCF7 and T47D cells under the respective ligand–drug treatments.The coupling of signaling and proliferation models, both calibrated on single drug treatments, made it possible to reliably predict phenotypic responses also to co-treatments imposed at the receptor level. Thereby, our combined model could be validated. This approach of linking an ODE signaling model with a proliferation regression model enabled us to find a balance between the mechanistic complexity and the comprehensibility of the model.The validity of this implementation is supported by the fact that the applied drugs directly target signaling components, i.e., the growth factor receptors in our study . Thus, wPIK3CA mutation status.Having analyzed two different luminal cell lines, we wanted to know if cell-line-specific differences in the activation of downstream signaling branches needed to be considered in the model. Regarding the initial model calibration on the luminal cell lines MCF7 and T47D, model parameters could indeed be implemented as identical between these two cell lines with the exception of the respective protein abundances as well as the These were estimated separately for each cell line . This isPIK3CA is the most frequently mutated gene in luminal breast cancer and the same hotspot mutations present in MCF7 and T47D have been found in 45% of patient tumors, underlining the clinical relevance of the model systems in our study [Therefore, our data and the model suggested that the H1047R mutation in the kinase domain has a stronger effect than the E545K mutation in the helical domain. This observation is in line with the increased basal activation of pAKT in the T47D cell line. These properties of the two luminal cell lines lead to a dominance of the PI3K-AKT signaling branch also in ERBB-downstream signaling. ur study .1 regularization was applied to analyze whether, and if so, which model parameters were necessary to be adjusted, for this cell line to compensate for cell-line-specific differences in the activation of downstream signaling branches. In this process, both inactive c-RAF as well as increased basal activation of MEK were identified as major distinguishing factors between MDA-MB-231 and the luminal cell lines.In order to assess whether our model was also applicable to TNBC, MDA-MB-231 cells were tested in the same experimental setup as used with the luminal cell lines. LIndeed, the phosphorylation of MEK and ERK was prolonged and elevated even under steady state conditions in MDA-MB-231, which could be due to an activating b-RAF mutation that is present in this cell line . This b-These molecular changes combined with a strong dominance of EGFR in this TNBC cell line, led to a shift between pathway branches from PI3K signaling towards MAPK signaling, standing in sharp contrast to the pathway regulation in the luminal cell lines. Taking these factors into account, our mathematical model then required few further adjustments regarding the receptor dimerization rates to reliably fit the experimental data. Hence, the same model could be applied to luminal and TNBC breast cancer cell lines, which have distinct receptor compositions, mutation states and growth properties and thus going beyond previous modeling approaches .1 regularization identified the basal activation ratio of c-RAF as the most important parameter necessary to be adjusted. However, while the basal MEK phosphorylation was enhanced in the MDA-MB-231 cell line, this parameter was reduced in SKBR3.We then wanted to know whether the model could correctly predict the signaling dynamics also for the HER2-positive subtype of breast cancer. To this end, we employed the SKBR3 cell line as a suitable experimental system. This cell line has a high ratio of ERBB2 over all other members of the ERBB family. LHence, the MAPK signaling pathway seems to be less relevant in SKBR3 as compared to the luminal cell lines MCF7 and T47D, whereas the PI3K pathway strongly dominates signaling downstream of the ERBB receptor level. However, even though the SKBR3 cells appear to be addicted to PI3K signaling, adjustment of our model for the highly biased expression of the signaling components as well as for the other identified cell-line-specific parameters, allowed to precisely describe the experimental data. Hence, our mathematical model is able to comprehensively reflect ERBB signaling towards RAF-MEK-ERK as well as PI3K-AKT signaling in three subtypes of breast cancer.EGFR and other RTKs are flexible in structure, and the binding of growth factors increases their probability of adopting a state, which is favorable for dimerization ,73, tranIn contrast, the presence of an extended tertiary structure is an intrinsic feature of ERBB2, which does not require structural changes during the formation of active heterodimers . However877 and, to a lesser extent, at Tyr1221/Tyr1222 upon incubation with either trastuzumab or pertuzumab, which was more prominent with trastuzumab compared to pertuzumab and in T47D compared to MCF7 cell lines. A similar effect was not observed with cetuximab suggesting that the phosphorylation related to the specific antibodies and/or ERBB2. In HER2-positive SKBR3, Tyr1221/Tyr1222 were found even more strongly phosphorylated while MDA-MB-231 cells appeared to not be affected by any of the therapeutic antibodies. The latter observation might be related to the predominance of EGFR expression in MDA-MB-231.We observed the phosphorylation of ERBB2 at position Tyr877 is phosphorylated by Src [1221 and Tyr1222 are autophosphorylated by ERBB2 [Tyrd by Src  while Ty1086, however, not at Tyr1148. Both these sites are autophosphorylated by EGFR [1086 is also a target of Src kinase [Similar to the phosphorylation of ERBB2, we observed phosphorylation also of EGFR Tyr by EGFR . Howeverc kinase . Hence, Along the lines of activation of ERBB2, phosphorylation of EGFR was not changed by cetuximab in any of the cell lines tested, while we also observed phosphorylation of ERBB3 with trastuzumab and pertuzumab, this was much less compared to ERBB2 or EGFR in all tested lines. This was unexpected as the ERBB3-receptor is even the most strongly expressed member of the ERBB-family in MCF7.The phosphorylation of all kinases was strongest in SKBR3, followed by T47D and MCF7 cells. This order reflects the absolute numbers and receptor-ratios in these cell lines regarding expression of ERBB2. Elevated phosphorylation of EGFR and ERBB2 in response to treatment with trastuzumab has been reported before in ERBB2-overexpressing cell lines . Here, wDokmanovic et al. investigated the impact of trastuzumab at the level of receptor phosphorylation and stability . TrastuzHence, the effects of trastuzumab on receptor stability seem to be cell line-dependent. A reduction in ERBB3 protein levels was observed after several  days of treatment with trastuzumab. Here, we found particularly lumretuzumab to reduce total ERBB3 levels within minutes and even without any ligand.In the time-scale of our measurements, the levels of ERBB3 were independent of drugs other than lumretuzumab, including erlotinib, and were then only and similarly reduced after stimulation with NRG1. Therefore, the dynamics of receptor downregulation seem to depend on the specific antibodies, the receptors and their abundance as well as on the specific growth factors.Different growth factors and downstream pathways are commonly associated with particular phenotypic responses, such as proliferation, migration and survival ,82,83. TPrevious modeling studies have followed a similar idea ,31. HoweThese originate from autocrine or paracrine signaling loops also involving the tumor microenvironment ,85. ThisFurthermore, our implementation contains simplifications in terms of regarded receptors. It has been shown, for example, that the insulin-like growth factor receptor (IGF-R) and the hepatocyte growth factor receptor (cMET) mediate a variety of cellular processes in breast cancer cells as well ,89. It rExploiting our experimental data as well as the mathematical model we were finally out to uncover interactions between ERBB signaling and cell proliferation. Our model implied that co-treatment of luminal cell lines with pertuzumab and erlotinib should have the strongest effect on proliferation. Furthermore, we confirm this in experiments. It should be noted, however, that the observed effects were mild in the tested cell lines.This is in line with the physiology of luminal breast cancer, which is driven by the estrogen receptor much more than by ERBB receptor-signaling. In addition, the uncoupling of PI3K signaling in these cell lines likely supports this phenotype due to the mutated PIK3CA . Still, Endocrine resistance comes also with an upregulation of ERBB-proteins and, according to our model, the receptor composition is likely one major determinant of ERBB and downstream pathway activities on tumor physiology then. Along the same lines, the PIK3CA-inhibitor alpelisib has been approved for treatment of hormone receptor positive, HER2-negative metastatic breast cancer further supporting the impact PI3K-signaling has in the luminal subtypes once endocrine resistance has kicked in  and suppHere, we developed a modeling strategy that combines the molecular ERBB signaling network with the proliferation phenotype. We determined the relevant variables that affect downstream MAPK and PI3K signaling and presented a unified model that enabled us to assess the effect of single and combination treatments and to predict the resulting proliferative behavior in cell lines representing three subtypes of breast cancer. At the same time, the model provided insights into the regulation of the downstream pathway activation and pointed at a shift in activated signaling branches between breast cancer cells of different subtypes.The weighting of cell-type-specific characteristics, which enabled us to analyze this activation of the individual signaling branches and construct one model for all cell lines, represents the major novelty of this work. The complexity of the biological systems was analyzed as well as the responses to drugs and ligands that we could infer, highlight the potential of mathematical modeling for the prediction of signaling readouts and hints at mechanisms in the underlying biology. Our study further supports the concept of anti-HER therapies in HER2-low breast cancer.


The measurement of biological tissues’ dielectric properties plays a crucial role in determining the state of health, and recent studies have reported microwave biosensing to be an innovative method with great potential in this field. Research has been conducted from the tissue level to the cellular level but, to date, cellular adhesion has never been considered. In addition, conventional systems for diagnosing tumor aggressiveness, such as a biopsy, are rather expensive and invasive. Here, we propose a novel microwave approach for biosensing adherent cancer cells with different malignancy degrees. A circular patch resonator was designed adjusting its structure to a standard Petri dish and a network analyzer was employed. Then, the resonator was realized and used to test two groups of different cancer cell lines, based on various tumor types and aggressiveness: low- and high-aggressive osteosarcoma cell lines , and low- and high-aggressive breast cancer cell lines . The experimental results showed that the sensitivity of the sensor was high, in particular when measuring the resonant frequency. Finally, the sensor showed a good ability to distinguish low-metastatic and high-metastatic cells, paving the way to the development of more complex measurement systems for noninvasive tissue diagnosis. Recently, techniques for measuring materials’ dielectric properties have become a pivotal issue due to their impact on scientific research, for example, for industrial, communication, and environmental applications ,2,3,4,5.Microwave sensors offer the possibility of measuring living tissue’s dielectric properties through noninvasive scattering parameters . Changesin vitro conditions, paving the way for in vivo diagnostic applications.Early tumor detection is a critical part of cancer research for reducing the mortality rates of cancers. However, due to the complex and various signaling pathways involved in cancer development, there is currently a lack of knowledge on tumor behavior ,24,25. GIn this context, here, we propose a microwave-based biosensor for the noninvasive detection of cancer cell types with varying degrees of aggressiveness. The resonator was initially designed in the CST Microwave Studio software to properly match the structure of the Petri cell dish surface. This was followed by simulation analyses which were computed to study its response, testing the proposed biosensor in air and with a common basal medium for cellular growth. Finally, the sensor was realized and tested to discriminate between two pairs of adherent cancer cell lines with varying degrees of aggressiveness.®, 60 x 15 mm) dish surface. To this end, we modified a prototype previously realized [L = 100.00 mm) comprising a circular dielectric patch element at the center (r = 20.00 mm). The sensor was designed on a 0.76 mm Rogers RO4835 dielectric layer with a dielectric constant (subε) and a loss tangent (sub (δ)tan) of 3.66 and 0.0031, respectively. The total thickness of the probe was set to h = 0.83 mm. The circular shape of the patch was chosen to guarantee the maximum symmetry below the circular Petri dish surface. It should be noted that the patch radius and the position of the SubMiniature version A (SMA) connector were crucial parameters for the optimal working of the probe, considering their strict relation with the operative resonant frequency. Therefore, we chose a patch radius (r = 20.00 mm) smaller than that of the Petri dish (pr = 25.72 mm), and the coaxial waveguide connector was soldered at the border of the patch for a good impedance. Since we expected to use this sensor in an operating frequency range of 0.001–3.000 GHz, these choices represented the optimal compromise between the resonant frequency and sensor sensitivity within the above reported operating microwave range.In this work, we proposed a circular patch biosensor to detect four cancer cell lines with different origins and varying degrees of aggressiveness in adhesion on a standard Petri | corresponding with the associated resonant frequency (rf). The operating frequency was computed through the following equation [The working principle of the probe consists of the electromagnetic field propagation through the SMA connector, thus, exciting the sensor structure and the material under test (MUT) due to resonance. The reflection coefficient-based method was chosen to evaluate the sensor’s operation by studying the minimum of the scattering parameter |equation :(1)fr=k1k1,1) at 1.84 and the effective radius (er) of the patch at 20.36 mm, and by varying the effective dielectric permittivity (eε), of note, the resulting er is slightly larger than the designed one (r = 20.00 mm) for the influence of the fringing field at the edges. Since the effective dielectric constant eε is strictly related to the radiated material, in simulation analyses we calculated this parameter considering two conditions, one with only the contribution of the probe dielectric substrate in the air (e_εsub = 3.61) and one with the presence of a MUT, namely the Dulbecco’s modified Eagle medium (DMEM) for cell growth (e_εDMEM = 5.04). Based on the literature, the relative permittivity was set at 1.00 and 80.00 for the air and the medium, respectively [By setting the first zero of the derivative Bessel function of order one (ectively . The theairε = 1.00) and with the DMEM (DMEMε = 80.00). The tests were performed in the CST Microwave Studio software and the simulations showed that the sensor produced a different output for different MUT. Considering the operative frequency band of 0.001–3.000 GHz, both curves differed from each other in the minimum of reflection coefficient (11 (f)||Sair = −11.93 dB and |S11DMEM(f)| = −15.35 dB), as well as in resonant frequency (r_DMEMf = 1.916 GHz and r_airf = 2.175 GHz). These preliminary results suggested a possible two-fold sensitivity of the biosensor in terms of signal 11 (f)|min|S and resonant frequency shift. Of note, the simulation results agreed with the theoretical results in terms of resonant frequency with a relative error of 4.2% in air, i.e., without any MUT.®, Taiwan), following the previous design specifications. The SMA waveguide connected the probe with a vector network analyzer MiniVNA-TINY for measuring scattering parameters in the operating frequency range of 0.001–3.000 GHz. The system also comprised a USB serial port, supported by the software VNA/J [re VNA/J . A plexi2. After a series of preliminary experiments, we decided to test all the cell lines 24 h after plating, which was the time of optimal compromise between good cell adhesion and cell confluence. To this end, since the four cell lines have different growth rates, for each experiment, a specific number of cells  was plated at the “time-point 0” in 1.5 mL of DMEM in standard 60 mm Petri dishes in order to achieve a comparable number of cells (7.5 × 105 < x < 9 × 105) at the time of the measurements of the dielectric properties, confirmed by cell count at the end of the test. Representative images of the tested cell lines on the day of the experiments are shown in For this work, low-aggressive osteoblast-like osteosarcoma SaOS-2 cells , high-ag® Research® plus pipette 100–1000 μL), with an uncertainty lower than 0.25% for both systematic and random errors. Eight samples were tested for each of the five conditions (four cell lines and one control), and the experiments were performed on three different days. For each sample, one single measurement was performed with the use of the above-described set-up.To evaluate the sensor capability to distinguish cell types with varying degrees of aggressiveness, we performed cellular experiments involving two pairs of cancer cell lines, namely low-aggressive SaOS-2 and high-aggressive 143B osteosarcoma cells, and low-aggressive MCF7 and high-aggressive MDA-MB-231 breast cancer cell lines. In addition, DMEM was also tested as a control. The medium volume was set equal to 1.5 mL in all the conditions and was prepared by pouring the medium into each Petri dish with successive fillings of 750 μL with a precision pipette  when removing the dishes before each measurement and then still tested on the probe.11 (f)|.|S To increase the resolution of the acquired data as much as possible, we imposed a sampling frequency of 0.5 MHz in the range from 1900.0 to 2600.0 MHz. The reflection coefficient was fitted to a Lorentzian curve (Equation (2)) using a nonlinear least squares method [A is the minimum of the curve; cx is the center at the minimum of the Lorentz curve; and lx and hx are the lower and upper bounds placed at the half-height of the curve, respectively.For each of the eight samples of the five materials under test (MUTs), we acquired and analyzed the reflection coefficient s method :(2)L(x)=11 (f)||S, the correspondent resonant frequency rf, evaluated at cx, the full width at half maximum (FWHM) and the interpolation error in terms of square error. At this point, it should be noted that we decided to employ the FWHM instead of the Q factor [By the fitting, we assessed the minimum of Q factor ,45,46, sQ factor .Data analysis was performed with GraphPad Prism 6.0.11 (f)|min|S, rf, and FWHM; the MANOVA was also weighted by the sum of square residuals (fitting error) obtained by each Lorentzian curve interpolation procedure. The univariate differences in 11 (f)|, frmin|S, and FWHM, respectively, between tumor cell lines and between days were analyzed by two-way analysis of variance (ANOVA) weighted by the fitting error obtained for each fitting procedure, followed by multiple comparisons, adjusted by Bonferroni correction.. The statistical analysis was performed with Stata 17, and the differences were considered to be significant when the p-value was lower than 0.05. For all the measured parameters, the values are expressed as mean ± SD. The coefficient of variation (CV) was computed as the ratio between the SD and the mean value, and expressed as a percentage. The overall differences in the tumor cell lines, consideringalso the day of the experiment as an additional factor on, were assessed by multivariate analysis of variance (MANOVA) based on Wilk’s Lambda test statistics, including all the parameters obtained from the reflection coefficient, namely in vitro for their dielectric properties using the proposed sensor. In the present work, two pairs of cancer cell lines with varying degrees of aggressiveness were tested FWHM (CV = 6.6%) and 11 (f)|min|S (CV = 3.3%), and an extremely low variability in the resonant frequency (CV = 0.02%). In addition, the CV computed for the resonant frequency for the four cell lines resulted almost equal or even lower than that in the DMEM, while the CV computed for the cell lines for the two other parameters resulted higher than in the control medium. p < 0.0001) difference in factors for the joint variables. This outcome indicated that either tumor or days (or both) were significant for at least one of the dependent variables, highlighting that the two factors overall influenced the measurements.p < 0.0001) influence of the day factor on 11 (f)|min|S and FHWM, highlighting that the experimental day affected cell measurements. On the contrary, the frequency variable was significantly (p < 0.0001) affected by the cell line factor but not by the day factor. This in-depth analysis revealed that frequency represents the optimal parameter to investigate differences in cells’ dielectric properties, being also robust to changes in the experimental day. To determine whether the specific dependent variables were significantly influenced by the two factors, cell line and days, we employed the two-way ANOVA test considering each of the three variables of interest separately, and the results are presented in p < 0.0001) difference between the pure DMEM and each of the tested cell lines, showing that the sensor is capable of detecting adherent cells with respect to medium only, with the resonant frequency of the medium containing cancer cells being consistently lower than that of pure DMEM. p < 0.0001) difference between SaOS-2 and 143B cell lines and between MCF-7 and MDA-MB-231 cell lines, showing the potential ability of the sensor to discriminate cell aggressiveness. Interestingly, this was obtained even if the increase in aggressiveness was associated with a different behavior in the two cancer cell types. In particular, high-aggressive 143B osteosarcoma cells showed a decrease in the resonant frequency, while high-aggressive breast cancer cells showed an increase in rf. This outcome highlighted the high sensitivity of the biosensor in terms of frequency. With regard to the difference in aggressiveness within tumors from the same tissue of origin, post hoc tests revealed a highly significant |min|S and FWHM values for all the tested groups. The results also reflect the fact that these two parameters were affected by a higher measurement variance than that reported for the resonant frequency. r)| in correspondence with the associated resonant frequency, and the full width at half maximum (FWHM). It should be noted that the biological samples cannot be considered to be reproducible samples, and the culture medium was the only material that could be employed to test measurement repeatability. The experimental results obtained with a culture medium showed an acceptable variability for all the tested parameters, with coefficient of variation (CV) values always lower than 7%, on average. However, the coefficient of variation value computed for the resonant frequency was about two orders of magnitude lower, highlighting a higher accuracy in measuring this parameter. At the same time, the resonant frequency was more robust to the changes in material under test than the |S11(f)| and FWHM, since its CV values were almost constant for all the tested cell lines. These outcomes are in accordance with the theory underlying the probe design, in which the resonant frequency is the main parameter characterizing the probe response, followed by the FHWM and 11 (f)|.min|S Finally, the resonant frequency was also more robust to potential confounding of external factors, such as the day of the experiment. As a matter of fact, the experimental results showed a significant frequency shift among each of the tested cell lines and the culture medium, and between low-aggressive and high-aggressive osteosarcoma cell lines and between low-aggressive and high-aggressive breast cancer cell lines. Interestingly, a significant frequency shift was also reported between low-aggressive osteosarcoma and high-aggressive breast cancer cell lines and between low-aggressive breast cancer and high-aggressive osteosarcoma cell lines, highlighting the ability of the proposed sensor to discriminate cancer cell line aggressiveness regardless of cancer type. Indeed, even though this result could not be generalized to other malignant cell types, further studies could be conducted to determine whether specific changes occur in highly aggressive cells that directly affect their dielectric properties. In accordance with the accuracy and robustness outcomes, the experimental results showed that the measurements of min|S11(f)| and FWHM did not allow for a precise detection of cancer cell lines, either with respect to the medium, or among the different cells.The aim of this work was to design a new sensor to assess the aggressiveness of cultured cancer cell lines, i.e., when cell–cell interactions and cell–substrate adhesion play a crucial role in their growth . To thisAs previously stated, in this work, we focused on adherent cells, to consider the cell-to-cell and cell-to-substrate interactions, while others have focused on the detection of cells in suspension in their culture medium  even a sIndeed, technologies based on the use of biosensors are rapidly spreading in the field of clinical practice and medical diagnostics thanks to their unique features. In general, biosensors are simple and disposable, usually are cost-efficient and suitable for mass production, and are especially useful to discriminate between a healthy and pathological condition through a noninvasive approach. Many works in the literature have already demonstrated the efficacy and usefulness of biosensors in some human diseases with worldwide incidence and high morbidity and mortality, such as metabolic disorders (primarily diabetes), cardiovascular diseases, and cancer ,53,54,55Our study, although it cannot yet be translated into clinical practice, provides evidence for a future investigation that should be based on primary cells from cancer biopsies. The strength of our work relies on the feasibility to discriminate between low-aggressive and metastatic cancer cells using only the measurement of dielectric cell properties. Indeed, most of the biosensors used in cancer prognosis are able to specifically recognize a cancer biomarker . HoweverIn this study, we proposed an innovative circular biosensor for detecting different adherent cancer cells based on their malignancy degree. First, the probe was designed and realized based on the shape and dimensions of a Petri dish and the operating frequency range of a network analyzer. Then, the sensor was used to evaluate the dielectric properties of four cancer cell lines deriving from different tissues of origin and with varying degrees of aggressiveness, and then compared with a culture medium. The experimental results clearly showed that the resonant frequency was the best parameter to assess differences in the dielectric properties of cell lines, as it turned out to be the most accurate and robust to cell variability and to potential confounding of external factors, such as the day of the experiment.With regard to detecting the cells’ aggressiveness, the proposed sensor could discriminate between the medium alone and each tested cell line, and between low-aggressive versus high-aggressive cell lines, both within the same type of cancer and across the two types.In conclusion, in this work, we suggested a novel resonator to discriminate different cell types, as well as the metastatic grade of cancer cells. This microwave approach offers a potential tool to support conventional diagnostic methods or in the long-term applications, particularly, for early cancer diagnosis.


Recent research has indicated that cuprotosis, or copper induced cell death, is a novel type of cell death that could be utilized as a new weapon for cancer management. However, the characteristics and implications of such signatures in cancers, especially in clear cell renal cell cancer (ccRCC), remain elusive.Expression, methylation, mutation, clinical information, copy number variation, functional implication, and drug sensitivity data at the pan-cancer level were collected from The Cancer Genome Atlas. An unsupervised clustering algorithm was applied to decipher ccRCC heterogeneity. Immune microenvironment construction, immune therapy response, metabolic pattern, and cancer progression signature between subgroups were also investigated.Cuprotosis related genes were specifically downregulated in various cancer tissues compared with normal tissues and were correlated with hypermethylation and copy number variation. Cuprotosis scores were also dysregulated in tumor tissues, and we found that such a signature could positively regulate oxidative phosphorylation and Myc and negatively regulate epithelial mesenchymal translation and myogenesis pathways. CPCS1 (cuprotosis scores high) and CPCS2 (cuprotosis scores low) in ccRCC displayed distinctive clinical profiles and biological characteristics; the CPCS2 subtype had a higher clinical stage and a worse prognosis and might positively regulate cornification and epidermal cell differentiation to fuel cancer progression. CPCS2 also displayed a higher tumor mutation burden and low tumor stemness index, while it led to a low ICI therapy response and dysfunctional tumor immunity state. The genome-copy numbers of CPCS2, including arm- gain and arm- loss, were higher than those of CPCS1. The prognostic model constructed based on subgroup biomarkers exerted satisfactory performance in both the training and validation cohorts. In addition, overexpression of the copper death activator DLAT suppressed the malignant ability, including cell migration and proliferation, of renal cell lines in vitro and in vivo. Finally, activation of cuprotosis in tumors could enhance antitumor immunity through dsDNA-cGAS-STING signaling in ccRCC.The activation of cuprotosis might function as a promising approach among multiple cancers. The cuprotosis related signatures could reshape tumor immunity in the ccRCC microenvironment via cGAS-STING signal, thus activating tumor antigen-presenting process. Upregulation of DLAT expression in ccRCC cell lines could reactivate the copper death pattern and be treated as a suitable target for ccRCC.The online version contains supplementary material available at 10.1186/s13578-022-00948-7. Even equipped with advanced diagnostic and therapeutic approaches, cancer is still the leading disease that accounts for approximately 15% of all deaths worldwide , 3. HoweCopper induced cell death is a newly discovered type of cell death that is different from apoptosis, ferroptosis, pyroptosis and necroptosis. Copper is an important enzymatic cofactor in cellular processes, but even modest intracellular concentrations could be toxic, leading to cell death. It has been recognized that the demand for copper is elevated in tumor cells relative to most other tissues, and represents a metabolic vulnerability that can be exploited by limiting copper availability . GeneticIn this study, we conducted a characterization study of cuproptosis related genes by pan-cancer analysis and stratified ccRCC patients by integrating multiomics data, including prognostic analysis, gene mutation analysis, immune infiltration analysis, and drug sensitivity analysis, and constructed a reliable risk stratification model to predict the prognosis of ccRCC patients. In addition, we identified and confirmed a promising cuproptosis target DLAT, which could function as a new therapeutic target for ccRCC.http://xena.ucsc.edu/) [https://www.ebi.ac.uk/arrayexpress/), and different stage single cell sequence data of ccRCC patients were collected from GEO . Two ccRCC single cell sequence datasets, including GSE159115 and GSE171306, were also adopted. This study also utilized several public cancer datasets, including UALCAN , TIMER (https://cistrome.shinyapps.io/timer/), TIDE (http://tide.dfci.harvard.edu/), and MEXPRESS (https://mexpress.be/) [The workflow of this study is depicted in Additional file sc.edu/) . The extWe collected 10 cuproptosis related genes according to Tsvetkov et al. Additional file https://www.gsea-msigdb.org/gsea/index.jsp) and the ConsensusPathDB (http://cpdb.molgen.mpg.de/) database [R package DEseq2 was used to identify differentially expressed genes (DEGs) between subgroups; thresholds were set at adjusted p < 0.01, and the abstract log Foldchange > 2. After calculating the DEGs, R package ClusterProfiler was used to perform Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GEVA), aiming to explain the biological function and molecular mechanism between CPCS1 and CPCS2. All gmt files used for enrichment analysis were downloaded from the MSigDB (database –15.http://tide.dfci.harvard.edu/) algorithm was used to compare immunotherapy responses between subgroups [We utilized multiple immune cell infiltration algorithms including TIMER, CIBERSORT, QUANTISEQ, MCPCOUNTER, XCELL, and EPIC to calculate cellular components or immune cell enrichment scores in ccRCC tissues –19. In aubgroups .https://cloud.genepattern.org/gp/pages/index.jsf) algorithm based on Euclidean distance [Somatic data were analyzed and visualized via R package Maftools to compare mutational patterns between subgroups . Throughdistance , 24.https://www.cancerrxgene.org/) database. R package pRRophetic was utilized to estimate the half-maximal inhibitory concentration-IC50. In addition, CellMiner (https://discover.nci.nih.gov/cellminer/home.do) and CCLE (https://sites.broadinstitute.org/ccle) databases were also introduced to compare the different sensitivities between ccRCC cell lines [Each patient was assessed for their susceptibility to molecular drugs using the Genomics of Cancer Drug Sensitivity  algorithm was further performed to select crucial signatures. Finally, a risk scoring model was constructed using the best combination of prognostic genes to screen. The JAPAN-ccRCC cohort was used to validate our risk scoring model, and patients in both datasets were divided into high- and low-risk groups based on median risk scores.RT-qPCR and immunohistochemical (IHC) staining were performed to validate DLAT expression in paired tumor and adjacent renal tissue . Primer sequences for RT-qPCR were as follows: primer for DLAT , STING, TBK1, cGAS, IRF2 and GAPDH . Antibodies of DLAT (Cat no: 13426–1-AP), cGAS (Cat no: ab252416) and GAPDH (Cat no: ab8245) were purchased from ProteinTech and Abcam Group. Ltd. The detailed procedure referred to our previous researcher’s protocols –30.6 cells per site). The tumors were dissected and photographed after approximately 4 weeks (n = 5 per group). The tumors were dissected and photographed after approximately 4 weeks (n = 5 per group). Tumor volumes were measured after tumor resection. The detailed procedure of the in vivo experiments is described in our previous study [All the cell lines used in our study, including human and mouse normal renal epithelial cell lines HK-2 and CP-M062 and cancer cell lines , were purchased from the American Type Culture Collection (ATCC). Cell lines were cultured according to the instructions. The DLAT overexpression plasmid was chemically synthesized by Shanghai GeneChem Co., Ltd. 786–0 and OSRC-2 cells were cultured in 6-well culture dishes at 60% density and then infected with DLAT overexpression lentivirus and the negative control (NC) lentivirus. All transfections were supplied with 4ug/ml polybrene  and lasted for 12 h. Screening was conducted with 2ug/ml puromycin  for 3 days to acquire stably transfected cells. QT-PCR and Western blotting were applied to verify the overexpression efficiency of the lentivirus. CCK-8 (Cell Counting Kit-8) was used to detect the difference in cell viability between the negative control and DLAT-overexpression groups. Scratch assays and Transwell migration assays were used to evaluate cell migration ability. A colony-forming experiment was conducted to determine the reproductive ability in vitro. A nude mouse xenograft tumor model assay was applied to investigate the effect of DLAT overexpression in vivo. Nude mice were given xenografts of DLAT overexpression and control 786-O cells  was purchased from BioXcell (Clone RMP1–14). ELISA kits for mouse IL6, TNF-α, IFN-γ, CXCL10 and CXCL11, and cytotoxicity assays based lactate dehydrogenase (LDH) release were purchased form Thermofisher. The cGAMP Activity Assay Kit was obtained from BellBrook. Tumor cell growth was evaluated via IVIS at 12 and 24 days after tumor cell injection. The antibodies for fluorescent dye-conjugated of flow cytometry, including CD45-BV510 and CD8-APC-Cy7, CD3-PE-Cy7, were purchased from Biolegend and eBioscience. The detailed procedure of intracellular staining can be found in each protocol. Flow cytometry analysis was performed with a FACS LSRII or Fortessa X-20  and data were analyzed by FlowJo v.10 software .+ monocytes from health donor’s peripheral venous blood, which were cultured in RPMI-1650  and IL-4(10 ng/ml)), then stimulated by lipopolysaccharide, or LPS(1ug/ml). Before co-culture, ACHN were treated with DMSO or cuprotosis inducer reagents (Elesclomol + Cucl2) at concentration of 2 μmol/L for 3 days. Then DCs were co-cultured with tumor cells pre-treated with DMSO or cuprotosis inducer reagents at a 5:1 ration in the presence of IL-4 (10 ng/ml). After 2 days co-culture, DCs were harvested to perform Western blotting, qPCR, and ELISA to quantify different level of cGAS-STING signal and immune related molecules.The optimal concentration of cuprotosis inducer reagents (Elesclomol + Cucl2) for renal cancer cell lines were identified by LDH release assay results between CP-M062 and RENCA. Dendritic cells, or DCs, were a kind gift from Department of Immunology . In detail, DCs were derived from CD146 cells per mice), respectively. BALB/c mice inoculated with RENCA were observed after 12 days; then those mice were randomized divided into three groups receiving DMSO, cuprotosis inducer reagents at concentration of 2 and 5 μmol/L, and tumor was identified at 24 days by bioluminescent imaging by IVIS Lumina K series III . After the xenografts reached a size of approximately 45 mm3, C57BL/6 mice were randomized into four groups receiving DMSO, anti-PD-1mAB, cuprotosis inducer reagents, and combined therapy (anti-PD-1 + cuprotosis inducer reagents) by intraperitoneal , respectively. After one week, all mice treated with those agents were euthanized and peripheral blood were harvested to quantity the percentage of CD45 + CD8 + T cells among different groups by flow cytometry. All those procedures were approved by the Institutional Animal Care and Use Committee at Navel Medical University.Athymic nude mice  and C57BL/6 mice (5–6-week-old) were obtained from Model Organisms . Renal cancer cells, including ACHN and RENCA treated with DMSO, ICI, or cuprotosis inducer reagents (Elesclomol + Cucl2), were injected into right flank of mice . Differences between subgroups were compared by Kruskal–Wallis test and Wilcoxon test. Differences in clinical characteristics and inhibitor response between subgroups were compared by the chi-square test. Differences in prognosis, including overall survival (OS) and progression-free survival (PFS) were compared by Kaplan–Meier method and Log-rank test. Hazard ratio (HR) differences were calculated by univariate Cox regression and multiple Cox regression analyses. Two-way p-values were taken and P < 0.05 was considered statistically significant.Copper induced cell death has gradually become the focus of attention for cancer research. We first explored the expression pattern of copper induced cell death related genes across cancers. As shown in Fig. After exploring the fundamental reasons for copper induced cell death gene dysregulation, we analyzed the effect of these genes on cancer signaling pathways. GSEA analyses indicated that copper induced cell death genes were mainly positively related to the MYC, MTOR, G2M checkpoint and oxidative phosphorylation signaling pathways, but negatively correlated with epithelial mesenchymal transition (EMT), myogenesis and TNFA—NFκB , similar to ferroptosis in cancer and normal tissues . CPI indAs we found above, copper-induced cell death-related genes were protective factors for ccRCC, which was unique and different from other cancers. Thus, we investigated the characteristics of copper-induced cell death-related genes in ccRCC. The TCGA ccRCC samples were classified into several subtypes using an unsupervised clustering method based on the expression levels of copper-induced cell death-related genes. The optimal classification method was validated, and the PAC method was used to assess the robustness of the analysis. Consequently, the TCGA ccRCC dataset was divided into two subtypes, namely, copper-pattern cancer type 1 (CPCS1) and type 2 (CPCS2) Fig. A–D. AfteSince there were differences in the copper-induced cell death profiles and clinical characteristics between the subgroups, we next investigated the biological function and hallmarks of CPCS1 and CPCS2. GO analysis demonstrated that differentially expressed genes were mainly enriched in cornified envelope, blood microparticle and lipoprotein particle in cellular component; cornification, keratinization, and epidermal cell differentiation in biological process; and serine hydrolase activity, active ion transmembrane transporter activity and anion transmembrane activity in molecular function Fig. A–C. GSEAThe tumor microenvironment (TME) and metabolic pathways were compared between the CPCS1 and CPCS2 subtypes. The CSP2 subtype was significantly activated in exosome secretion, ferroptosis and extracellular vesicle biogenesis and was inhibited in m6A methylation modification Fig. E. The TMImmunotherapy has gradually become the main characteristic of ccRCC treatment in recent years. To define the effect of copper-induced death on immune profiling, we analysed the immune infiltration environment of the two subgroups using GSVA. Immune-related genes of the CPCS2 subtype showed an overall upwards-regulated trend compared with the CPCS1 subtype. The CSP2 subtype expressed higher levels of CCL5, CCL21, CCL26, CXCR4, CXCR5, CXCR10, IL10RB, LAG3, CD276, CD48, CD70 and TCFRSF8  and CPCS2 (copper-induced cell death signature scores low groups). The CSP2 subtype shared a higher tumor mutation burden and low tumor stemness index but led to a low ICI therapy response and tumor immunity dysfunction state. Notably, activation of copper-induced cell death may reshape tumor immunity in the ccRCC microenvironment by facilitating the antigen presentation process. In addition, the prognostic model constructed based on subgroup biomarkers exerted satisfactory performance in both the training and validation cohorts. Finally, DLAT, the core gene of copper-induced cell death, could reactivate the copper death pattern and might be a suitable target for ccRCC therapy.Immunotherapy has been confirmed to be effective in many cancers, including ccRCC. However, therapy resistance diminishes the efficacy of immunotherapy, which can be alleviated by combination therapy, such as targeted therapy and gut microbiota therapy. Among them, copper-induced cell death may collaborate with immunotherapy. Intratumor copper has been shown to influence PD-L1 expression in cancer cells, which suggests that leveraging copper chelation may improve immunotherapy. Wang reported that Cu-triggered hydroxyl radicals and GSH elimination synergized with radiation therapy, remarkably enhancing dendritic cell maturation and promoting antitumour CD8 + T-cell infiltration, thereby potentiating the development of checkpoint blockade immunotherapies against primary and metastatic tumors . Based oIn addition to immunotherapy responses, copper-induced cell death is associated with the dysregulation of many signaling pathways in cancers. Our study indicated that copper-induced cell death-related genes were enriched in RAS-MAPK activation, oxidative phosphorylation accumulation, EMT inhibition and MYC pathways. Consistent with our research, many studies have identified that high copper levels in tumors can regulate kinase activity, inhibit autophagy, and regulate fat metabolism. Pharmacologic copper-chelator treatment using tetrathiomolybdate resulted in decreased tumor burden and increased survival in preclinical murine models of BRAFV600E-driven melanoma , 49. ComFor the root cause, signaling pathway dysregulations often depend on genome mutations. For copper-induced cell death-related genes, we observed that all tested samples had at least one mutation site (729/729). CDKN2A and MTF1 hold the most frequent fractions among mutated genes 54% vs 14%). CDKN2A can form complexes with CDK4 kinase to inhibit cell cycle progression. Mutation or deletion of the CDKN2A gene will relieve the checkpoint function of the cell cycle, and the cells will acquire the ability to proliferate indefinitely. A significant correlation was observed between copper-induced cell death genes and the G2/M checkpoint pathway across cancers. MTF1, a classic metal-sensing transcription factor, promoted myogenesis in response to copper . MTF1 di% vs 14%.Based on multiomics data, our study provided clues for choosing clinical treatment options. The drug sensitivity profiles were investigated between subgroups and their matched cell lines. There were certain differences in the effective targets of CPCS1 and CPCS2 subtypes, which were VEGFR/PDGFR/MET and EGFR/SRC, respectively. In the era of targeted therapy and immunotherapy, the effectiveness of these targets can provide certain clinical guidance for immunotherapy. The CellMiner database provides potential therapeutic targets for the copper-induced death subtype. For instance, although CPCS2 was less sensitive to many molecular inhibitors, the ROCK inhibitor GSK269962A displayed a unique curative effect on copper-induced death desert type (CPCS2). ccRCC is often resistant to chemotherapy. Our data suggested that the combination of paclitaxel and immunotherapy may reactivate the responses of the CPCS2 subtype to paclitaxel. Elesclomol was able to target resistant cancer cells, including cisplatin and proteasome inhibitor resistance. Antiapoptotic inhibitors of the BCL-2 family synergize with tetrathiomolybdate to induce apoptosis in melanoma cells resistant to BRAF and MEK1/2 inhibitors . MoleculFurthermore, the best risk model RCC-CUPT4 was established using Cox regression analysis. The four most relevant genes were MGAM, PTPRB, PAGE2B and RTL1. A high RCC-CUPT4 score indicated poor outcomes of ccRCC. Previous studies have shown that MGAM is closely related to cancer immunotherapy responsiveness in non-small cell lung cancer . Recent Although our study conducted genomics analysis and cell line experiments of copper-induced death, there are certain limitations. Our main findings were confirmed by bioinformatics analysis, which requires further experimental validation. The drug susceptibility of the two subtypes was different, but it also needs to be verified by subsequent animal and clinical experiments. More importantly, the prognostic model may be affected by some confounding factors, and more datasets are needed for revision and refinement.In conclusion, prior to our study, a few reports identified molecular subtypes of ccRCC based on gene expression profiles or mutational signatures , 69–71. Additional file 1: Figure S1. Workflow of this study. Figure S2. Copper-induced cell death-related genes are dysregulated in multiple cancers. (A) The expression of FDX1 in cancer and normal tissues. (B) Mutation frequencies of copper-induced cell death-related genes in multiple cancers. (C) The correlations between overall survival and methylation levels of copper-induced cell death-related genes. Figure S3. The drug sensitivity assessment of several copper-induced cell death-related genes to molecular inhibitors in cancer cell lines. Figure S4. Pathway enrichment analysis of ccRCC subtypes. (A) GSEA pathway analysis of differentially expressed genes between the two subtypes. Heatmap of tumor microenvironment-related pathways (B) and metabolism-related pathway (C) enrichment scores between the two subtypes. Figure S5. Correlation of copper-induced cell death-related gene expression and IC50 of tested drugs obtained from the CellMiner and GDSC databases (A) Drug sensitivity values in the form of normalized AUC using the GDSC renal cancer cell database. (B) Drug sensitivity values in the form of normalized AUC using the CCLE database. Figure S6. Verification of the copper-induced death-related risk model in the JAPAN-KIRC cohort. (A) Risk score analysis of patients in the JAPAN-KIRC cohort. (B) Survival analysis for OS of the two risk signatures in the JAPAN-KIRC cohort. (C) The time-dependent ROC curves for the two risk signatures in the JAPAN-KIRC cohort. Figure S7. The functional verification of DLAT. (A) The expression of DLAT in renal cancer and normal tissues. (B) The expression of DLAT in renal cancer of different stages. (C) Immunohistochemical score of DLAT in cancer and normal kidney tissues. (D) Survival analysis for PFS of DLAT expression. (E) The association between DLAT mutation and immune cell infiltration in ccRCC. (F) The association between DLAT expression and immune signatures across cancers. Figure S8. Different fluorescence and Edu level, and downregulated cuproptosis state in ccRCC malignant cells. (A)Different fluorescence and DdU positive cells (B) between NC and cuproptosis inducer treated groups, respectively.  Umap showing the cuproptosis score at the single-cell level for ccRCC. Figure S9. Impact of cuproptosis in tumor immunity of ccRCC. (A) Correlation of cuproptosis and immune infiltration degree among TCGA-KIRC, JAPAN-KIRC and Cancer cell-KIRC cohorts. (B) Schematic diagram of the activation of tumor immunity in ccRCC through cuproptosis.Additional file 2: Table S1. List of copper-induced cell death-related genes. Table S2. Clinicopathological features of different subtypes in ccRCC.Additional file 3: Table S3. Recurrent amplification and deletion regions in the two subtypes calculated by GISTIC2.0.Additional file 4: Table S4. List of 138 kinds of small-molecule drugs that could be used as potential drugs for ccRCC.