Consequently, Annexins exposed on tumour cells upon cell death are expected to induce tolerance towards tumour antigens, inhibiting tumour rejection.Uptake of apoptotic cells induces a tolerogenic phenotype in phagocytes and promotes peripheral tolerance. The highly conserved Annexin core domain, present in all members of the Annexin family, becomes exposed on the apoptotic cell-surface and triggers tolerogenic signalling in phagocytes Expression analysis for all Annexin family members was conducted in cancer cell lines of diverse origins. Presentation of Annexins on the cell surface during apoptosis of cancer cell lines was investigated using surface washes and immunoblotting. Expression data from the GEO database was analysed to compare Annexin levels between malignant and healthy tissue.Six Annexins at least were consistently detected on mRNA and protein level for each investigated cell line. AnxA1, AnxA2 and AnxA5 constituted the major part of total Annexin expression. All expressed Annexins translocated to the cell surface upon apoptosis induction in all cell lines. Human expression data indicate a correlation between immune infiltration and overall Annexin expression in malignant compared to healthy tissue.This study is the first comprehensive analysis of expression, distribution and presentation of Annexins in cancer.The online version contains supplementary material available at 10.1186/s12885-022-10075-8. The human Anx family is composed of 12 members, AnxA1 to AnxA13 (AnxA12 being a pseudogene), with each member consisting of a C-terminal Anx core domain as well as a flexible N-terminus . Anxs tybridging . Upon exbridging –11.Work from our group and others has demonstrated that AnxA1, AnxA5 and AnxA13, as well as the isolated AnxA1 core domain, similar to apoptotic cells (ACs), prevent the activation of dendritic cells (DCs) and macrophages (MΦs) by inhibition of inflammatory cytokine secretion and reduced expression of co-stimulatory surface molecules , 12–14. An important aspect of this tolerogenic mechanism is the implication that Anxs on apoptotic cancer cells, which occur in the steady-state tumour and, increasingly, after chemotherapy, may also induce tolerance towards tumour antigens associated with apoptotic cancer cells. Thus, exposure of Anxs on apoptotic cancer cells may facilitate immune evasion and inhibit tumour immune rejection .Cell death in malignant disease is an essential component of tumour dynamics and most tumours are associated with a substantial loss of cells . Due to As mentioned above, the immunosuppressive activity of Anxs, mediated by the conserved core domain, is likely a redundant feature throughout all Anxs. Therefore, this study aimed to investigate and characterise the expression of all Anxs in various cancer entities and their translocation to the cell surface during apoptosis. In all tissues analysed several Anxs (6-10) were expressed to different extents, and all of the Anxs present in the cytosol were readily externalised upon apoptosis induction. Moreover, data from online databases indicated that Anx expression levels correlated with tumour immune status. Thus, this comprehensive analysis of Anx expression in cancer provides further insight into this novel class of immune suppressive molecules, which may become relevant for future anti-tumour treatment strategies.All cell lines were maintained at 37 °C in Dulbecco’s Modified Eagles Medium (DMEM) or RPMI-1640 (both Sigma-Aldrich) supplemented with 10 % (v/v) heat-inactivated foetal calf serum (FCS). All used cell lines were authenticated by genotyping and all experiments were performed with certified mycoplasma-free cells. Details about used cell lines are given in Supplementary Information Table SPreparation of Monocyte-derived human DCs was conducted as described previously .6/ml or used directly. For the EDTA surface washes, 20 - 30 x 106 neutrophils were taken.For the preparation of neutrophils, blood was separated using Ficoll solution and the pellet was thoroughly resuspended in 50 ml 1 % Dextran/PBS. The solution was incubated 30 min at RT and the supernatant harvested carefully and centrifuged 10 min at 500 x g at 4 °C. In order to lyse erythrocytes in the resulting pellet, it was resuspended for 1 min in 5 ml ice-cold 0.2 % NaCl before adding 5 ml ice-cold 1.6 % NaCl and 30 ml PBS. Finally, the solution was centrifuged for 15 min at 200 x g at 4 °C and the neutrophil pellet resuspended in medium and either cultured for 2 d in 6-well plates at 2 x 102 UV-C. Irradiated cells were incubated at least 16 h before they were used in further assays. For the chemical induction of apoptosis in cancer cell lines, cells were treated with either 50 μM Etoposide  or 100 μM Dimethylfumarate  [2 in H2O). Neutrophils become apoptotic after 2 d of culture and were compared with freshly isolated neutrophils. Cell viability was determined by staining with AnxA5-FITC (Immunotools) and 7AAD (Sigma-Aldrich) according to the manufacturer's instructions and subsequent analysis in flow cytometry.Early apoptotic 624.38 Mel cells were generated by irradiation with 500 mJ/cmAldrich)  or left Apoptotic and control cells were washed twice with cold Anx Binding Buffer, resuspended in ice-cold 20 mM ETDA/PBS and incubated for 3 min on ice to yield the calcium-dependent surface-bound Anxs. After centrifugation, supernatant was used for cytokine suppression assays and immunoblotting.The cytokine suppression assay was conducted as described previously . Enzyme T method. Detailed sequences of the primers used are listed in Supplementary Information Table STotal mRNA was isolated from cells using the RNeasy Maxi Kit (Qiagen) according to the manufacturer’s instructions. Residual DNA was removed through on-column DNAse digestion. AMV reverse transcriptase (New England Biolabs) was used to generate cDNA which was in turn quantified by detection of SYBR Green Power incorporation  using the ABI Prism 7500 sequence detector system (Applied Biosystems). The housekeeping gene GAPDH was used as an internal control. All primers were synthesised by Sigma Aldrich. Reactions were performed in triplicate to account for technical variation. Quantification of relative gene expression levels was accomplished using the ΔΔC3) overnight at 4 °C or for 1 h at RT. The next day, membranes were washed 3 times with TBS-T and incubated for 1 h with a horseradish peroxidase (HRP)-conjugated secondary antibody at RT. Finally, membranes were washed 3 more times with TBS-T, developed using Western Lightning Plus-ECL detection solution (Perkin Elmer) and recorded with the Chemi-Smart 5100 System. If further antibodies were to be applied to the same membrane, the HRP was first inactivated by three incubation steps in 1 M glycine, pH 1.8. The membrane was then neutralised with TBS-T and blocked again before incubation with the next antibody. Details about the primary and secondary antibodies used are listed in Supplementary Information Tables SProteins were separated using SDS-PAGE and then transferred onto a nitrocellulose membrane using a wet blot-transfer system. Membranes were blocked with 5 % milk powder in TBS-T for at least 1 h at RT. For protein detection, membranes were incubated with primary antibodies (diluted in TBS-T/1 % BSA/Nhttps://www.ncbi.nlm.nih.gov/geo/) from data sets listed in Supplementary Information Table SData of Anx mRNA expression in healthy tumour-adjacent and malignant tissue was taken from the Gene Expression Omnibus (GEO) database . Results with p values below 0.05 were considered statistically significant.Results are shown as mean ± standard deviation or as a box plot with the median highlighted. Statistical significance was analysed using a two-sided Student's Apoptotic tumour cells are engulfed and processed by phagocytes within the tumour microenvironment . Our previa RT-qPCR to create an "Anx fingerprint", the individual Anx expression profile of a tissue or cell line [via immunoblot of cellular lysates. As can be seen in the lower panels of Figure As a model system, the expression of all members of the Anx family for 14 different cell lines derived from 6 cancer entities has been examined ell line . As presGiven this stability of expression, we compared tumour cell lines of the same tumour entity. However, the Anx fingerprints of cell lines from one tumour yielded no consistent pattern. On the contrary, major differences in the amount and distribution of Anxs were detected, not only between cell lines derived from different cancer entities but also between cell lines from the same type of cancer, and Anx fingerprints could not be utilised to distinguish cancer entities from each other Figure B.In general, in all tested cell lines, 5 – 8 Anx family members were expressed to a medium or high degree. Most prominently, AnxA1, AnxA2, as well as AnxA5 constituted the major part of the overall Anx expression, with these three accounting for up to 86 % of total Anx expression  supplied 8 complete data sets of expression levels for the Anx family from 7 cancer entities , which are coherently reported to have high immune cell infiltration but show decreased overall Anx expression , 90. ImmTaken together, this study is the first comprehensive analysis of the expression and translocation of all Anx family members in a tumour setting. Further investigations are required, however, to validate a suppressive or tolerogenic influence of the Anx checkpoint system on the anti-tumour immune response. If so, blocking Anx-mediated immunomodulation might pose an attractive target for future therapeutic interventions.On the surface of apoptotic cancer cells, Anxs are expected to induce tolerance towards tumour antigens and inhibit tumour rejection. Tolerogenic signalling of Anxs is mediated by the highly conserved core domain, present in all members of Anx protein family.Therefore, we investigated the expression and externalisation of all proteins of the Anx family in different cancer entities. Expression analysis showed considerable variation in the expression of the different Anx family members between cancer-derived cell lines of diverse origins. However, AnxA1, AnxA2 and AnxA5 consistently constituted the major part of total Anx expression. Furthermore, we demonstrated that all of the expressed Anx family members translocated to the cell surface upon apoptosis induction in different cancer cell lines. Investigation of human expression data from online databases indicated a correlation between immune infiltration of the respective cancer types and overall expression level of Anxs in malignant compared to healthy tissue.The presentation of Anxs on the surface of tumour cells as an immune-evasive mechanism, could pose an attractive target for future therapies. Interference with these tolerogenic signals, especially in cancer entities with a high immune infiltration that also display high Anx expression levels, could maximise cancer immunotherapy and lead to increased cancer rejection.Supplementary information is available at the British Journal of Cancer’s websiteAdditional file 1. Supplementary Information Table S1.Additional file 2. Supplementary Information Table S2.Additional file 3. Supplementary Information Table S3.Additional file 4. Supplementary Information Table S4.Additional file 5. Supplementary Information Table S5.Additional file 6: Supplementary Information Figure S1. Minute changes of Anx mRNA expression profiles after prolonged culture. The mRNA expression levels of all Anx family members (A1 – A13) in all cell lines were detected via RT-qPCR and depicted relative to Annexin A1 (bar diagrams). The mRNA Anx expression profiles remained stable in all tested cell lines over several months of culture. Time period of culture is indicated below arrows. Results represent mean ± s.d. of technical triplicates.Additional file 7: Supplementary Information Figure S2. EDTA-washes of apoptotic cells suppress human DC/Monocyte activation. (A) Human monocyte-derived DCs were incubated with EDTA surface wash for 4 h. After pre-incubation, DCs were stimulated over night with the TLR agonist LPS. TNFα concentrations in the supernatant were analysed by ELISA 16 to 24 h after stimulation. (B) Representative single experiment. DCs were incubated with 50 and 100 μl of EDTA surface wash, respectively, from 2 day-aged apoptotic neutrophils. (C) Summary of independent experiments (n=8) depicting TNFα secretion normalised to stimulation. Results represent the mean ± s.d. measured in triplicates. (D) Human MonoMac6 cells [n=3 independent experiments. nd= not detectable. * p<0.05, ** p<0.01, *** p<0.001, **** p<0.0001 .c6 cells  were preAdditional file 8: Supplementary Information Figure S3. Flow cytometric cell death analysis of cancer cell lines before and after apoptosis induction. The rate of apoptotic cell death in aged human neutrophils and cancer cell lines has been analysed via flow cytometry using a combined 7AAD and AnxA5 staining. In neutrophils, apoptosis occurred physiologically after 2 days of ex vivo culture. Apoptosis in cell lines was induced by treatment with Etoposide, Dimethylfumarate (DMF) or UV-C radiation followed by incubation for 48 h. Untreated live cells served as controls. PE – Phycoerythrin, FITC - Fluorescein isothiocyanate.Additional file 9: Supplementary Information Figure S4. Detection of Anx family members via immunoblot in lysates of the cell lines Huh-7, DU145, PC-3, HCT-116, HCT 15 and MDA-MB-231. Unedited immunoblots of all Anx family members and Tubulin detected in 50 μg total protein per lane. Lane 1: Huh-7, Lane 2: irrelevant control protein, Lane 3: DU145, Lane 4: PC-3, Lane 5: HCT-116, Lane 6: HCT 15, Lane 7: MDA-MB-231. Arrows indicate expected protein position and dotted boxes indicate the cropped part used in the main figure. Molecular weight of the marker indicated in kDa on the left.Additional file 10: Supplementary Information Figure S5. Detection of Anx family members via immunoblot in lysates of the cell lines PANC-1, MIA PaCa-2, A375 and 624.38 Mel. Unedited immunoblots of all Anx family members and Tubulin detected in 50 μg total protein per lane. Lane 1: PANC-1, Lane 2: MIA PaCa-2, Lane 3: empty, Lane 4: A375, Lane 5: 624.38 Mel. Arrows indicate expected protein position and dotted boxes indicate the cropped part used in the main figure. Molecular weight of the marker indicated in kDa on the left.Additional file 11: Supplementary Information Figure S6. Detection of Anx family members via immunoblot in lysates of the cell lines CaCo-2, COLO 205, SW480 and HT-29. Unedited immunoblots of all Anx family members and Tubulin detected in 50 μg total protein per lane. Lane 1: CaCo-2, Lane 2: COLO 205, Lane 3: SW480, Lane 4: HT-29. Arrows indicate expected protein position and dotted boxes indicate the cropped part used in the main figure. Molecular weight of the marker indicated in kDa on the left.Additional file 12: Supplementary Information Figure S7. Detection of Anx family members via immunoblot in EDTA surface washes of the cell lines HCT 15 and PC-3 before and after apoptosis induction. Annexin family members (AnxA1-A13) were detected in the EDTA washes of different cancer cell lines before and after apoptosis induction. Apoptosis in cells was induced by treatment with Dimethylfumarate (DMF) followed by incubation for 48 h. Subsequently, the cell surface was washed with 20 mM EDTA/PBS and induction of apoptosis was confirmed by AnxA5/7AAD staining. Detection of cytosolic Erk1/2 was used as a control for cell membrane integrity in the samples. Untreated live cells served as controls. Lane 1: HCT 15 untreated, Lane 2: HCT 15 DMF-treated, Lane 3: PC-3 untreated, Lane 4: PC-3 DMF-Treated. Arrows indicate expected protein position and dotted boxes indicate the cropped part used in the main figure. Molecular weight of the marker indicated in kDa on the left.Additional file 13: Supplementary Information Figure S8. Detection of Anx family members via immunoblot in EDTA surface washes of the cell line MDA-MB-231 before and after apoptosis induction. Annexin family members (AnxA1-A13) were detected in the EDTA washes of the cell line MDA-MB-231 before and after apoptosis induction. Apoptosis in cells was induced by treatment with Dimethylfumarate (DMF) followed by incubation for 48 h. Subsequently, the cell surface was washed with 20 mM EDTA/PBS and induction of apoptosis was confirmed by AnxA5/7AAD staining. Detection of cytosolic Erk1/2 was used as a control for cell membrane integrity in the samples. Untreated live cells served as controls. Lane 1: MDA-MB-231 untreated, Lane 2 MDA-MB-231 DMF-treated. Arrows indicate expected protein position and dotted boxes indicate the cropped part used in the main figure. Molecular weight of the marker indicated in kDa on the left.Additional file 14: Supplementary Information Figure S9. Detection of Anx family members via immunoblot in EDTA surface washes of the cell lines Huh-7 and 624.38 Mel before and after apoptosis induction. Annexin family members (AnxA1-A13) were detected in the EDTA washes of different cancer cell lines before and after apoptosis induction. Apoptosis in cells was induced by treatment with Etoposide or UV-C radiation followed by incubation for 48 h. Subsequently, the cell surface was washed with 20 mM EDTA/PBS and induction of apoptosis was confirmed by AnxA5/7AAD staining. Detection of cytosolic Erk1/2 was used as a control for cell membrane integrity in the samples. Untreated live cells served as controls. Lane 1: Huh-7 untreated, Lane 2 Huh-7 Etoposide-treated, Lane 3: 624.38 Mel untreated, Lane 4: 624.38 Mel UV-C-treated, Lane 5: 624.38 Mel Etoposide-treated. Arrows indicate expected protein position and dotted boxes indicate the cropped part used in the main figure. Molecular weight of the marker indicated in kDa on the left.


The expression of voltage-dependent Ca2+ (CaV) channels may modulate the growth of hematologic cancers. Profile analysis of Ca2+ channels, with a focus on the Ca2+ release-activated Ca2+ (CRAC) and L-type CaV channels, was performed on RNA sequencing data from lymphoma cell lines and samples derived from patients with diffuse large B cell lymphoma (DLBCL). CaV1.2 expression was found to be elevated in classical Hodgkin lymphoma (CHL) cell lines when compared to other B cell lymphoma cell lines. In contrast, CHL exhibited reduced expression of ORAI2 and STIM2. In our differential expression analysis comparing activated B cell-like DLBCL (ABC-DLBCL) and germinal centre B cell-like DLBCL (GCB-DLBCL) patient samples, ABC-DLBCL revealed stronger expression of CaV1.3, whereas CaV1.1, CaV1.2, and CaV1.4 showed greater expression levels in GCB-DLBCL. Interestingly, no differences in ORAI/STIM expression were noted in the patient samples. As Ca2+ is known to bind to calmodulin, leading to calcineurin activation and the passage of nuclear factor of activated T cells (NFAT) to the cell nucleus, pathways for calcineurin, calmodulin, NFAT, and Ca2+ signaling were also analyzed by gene set enrichment analysis. The NFAT and Ca2+ signaling pathways were found to be upregulated in the CHL cell lines relative to other B cell lymphoma cell lines. Furthermore, the calmodulin and Ca2+ signaling pathways were shown to be downregulated in the ABC-DLBCL patient samples. The findings of this study suggest that L-type CaV channels and Ca2+-related pathways could serve as differentiating components for biologic therapies in targeted lymphoma treatments.Cell surface calcium (Ca The abirelapsed . The Ca2proteins . Orai3 eproteins . Furtherf cancer .2+ channels are known to be positioned at the plasma membrane of the cell  and cell lines representing diverse types of lymphoma. Expanding on the calmodulin/calcineurin/NFAT signaling pathway, several pathways from BioCarta and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were also examined. Here, we present expression profiles relating to Ca2+ channels - the CRAC and CaV channels in particular - and these pathways. This characterization of expression may aid in cultivating a comprehensive understanding of the molecular basis of lymphoma.Considering the involvement of CaRNA sequencing data were generated at the British Columbia Cancer Research Centre (BC Cancer) from 44 lymphoma cell lines representing multiple pathologies. These datasets were generated at multiple time points and were consolidated for analysis in this study .RNA sequencing reads from each cell line were aligned to the hg19 reference using the STAR aligner (v2.5.2a), which also generated per-gene counts with HTSeq.RNA sequencing data from a previously published cohort of DLBCL patients were also analyzed . Patient2+ channel genes between different pathological groups, RNA sequencing counts from all samples were analyzed in R (v3.6.1) using the DESeq2 package (v1.26.0).To compare the expression of the CaDESeqDataSet object, and lowly expressed genes  were removed. The count data were then normalized using a variance-stabilizing transformation (vst function). Pairwise t-tests were used to compare normalized counts of numerous genes, such as Ca2+ channel genes of interest, between each combination of pathologies. For specific comparisons of interest, differential expression was also performed between pathological pairs (DESeq function), and the results for the Ca2+ channel genes , and log2 fold change) were assessed. The same process was performed merging count data from all DLBCL patients and comparing groups by DLBCL subtype.For cell lines, counts from all samples were read into a merged 2+ channel pathways that were potentially enriched in certain pathological comparisons, the differential expression results were used to perform gene set enrichment using the GSEA tool (v4.1.0). The GSEAPreranked module was used, ranking genes by their differential expression log2 fold change and investigating specific pathways of interest , and Ca2+ entry at the plasma membrane in a process known as store-operated Ca2+ entry (SOCE)  is a pore-forming molecule at the cell surface   , primary mediastinal B cell lymphoma (PMBCL), nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL), GCB-DLBCL, ABC-DLBCL, Burkitt’s lymphoma, and mantle cell lymphoma (MCL) . Engagemy (SOCE) . STIM1 a surface . RNA seq= 0.028) . As incr= 0.028) , and cycignaling , the hig= 0.046) . Interes= 0.046) , suggest2+ channels have been shown to be involved in lymphocyte function as well. We have previously provided evidence of voltage-dependent Ca2+ channel expression in the Jurkat cell line and peripheral blood T cells , and the expression of the CaV1.2 channel was significantly greater in CHL . Differential expression also showed that the expression levels of ORAI2  and STIM2  were lower in the CHL cell lines. In order to determine whether pathways related to Ca2+ were affected in CHL, three pathways from BioCarta and one from the KEGG were evaluated. Although no significant enrichment was detected between CHL and other B cell lymphomas for the calcineurin and calmodulin pathways, the NFAT  and KEGG Ca2+ signaling  pathways were found to be significantly upregulated in CHL (The cell lines of the CHL category were of interest for further study due to the relatively high expression of CaV1.2 . Differechannels . Expressd in CHL .V1.3 channel showing relatively elevated expression (V1 channels found no significant differences when comparing PMBCL to other B cell lymphoma cell lines (data not shown). Similarly, differential expression did not show any differences in ORAI/STIM expression (data not shown). In PMBCL, none of the four pathways showed significant upregulation or downregulation according to GSEA (data not shown).Another category of cell lines that underwent more assessment was PMBCL due to the Capression . HoweverV1 channels , CaV1.2 , and CaV1.4 . In contrast, ABC-DLBCL displayed greater expression of CaV1.3 . Differential expression analysis of the patient samples did not indicate any significant differences in ORAI/STIM expression between ABC-DLBCL and GCB-DLBCL (data not shown). For pathways associated with Ca2+, the calcineurin and NFAT pathways were unchanged between GCB-DLBCL and ABC-DLBCL  and KEGG  pathways were both downregulated in ABC-DLBCL  in the context of T helper 2 (TH2) cells in that IL-13 levels drop in response to CaV1.2 knockdown   “anti-apoptosis” protein, there exists at least one binding site for NF-κB  . Within or NF-κB . BCL2, aor NF-κB . Anotheror NF-κB , and theor NF-κB . Future pathways  were dowV channels represent a potential target class in the field of anti-lymphoma therapeutics. By targeting specific CaV1 channels and, by extension, the mechanisms associated with each channel, this could lead to updated strategies to treat lymphoma.To conclude, the L-type Ca


Genomic analyses have revealed mutational footprints associated with DNA maintenance gone awry, or with mutagen exposures. Because cancer therapeutics often target DNA synthesis or repair, we asked if mutational signatures make useful markers of drug sensitivity. We detect mutational signatures in cancer cell line exomes  by adjusting for the confounding germline mutation spectra across ancestries. We identify robust associations between various mutational signatures and drug activity across cancer cell lines; these are as numerous as associations with established genetic markers such as driver gene alterations. Signatures of prior exposures to DNA damaging agents – including chemotherapy – tend to associate with drug resistance, while signatures of deficiencies in DNA repair tend to predict sensitivity towards particular therapeutics. Replication analyses across independent drug and CRISPR genetic screening data sets reveal hundreds of robust associations, which are provided as a resource for drug repurposing guided by mutational signature markers. Mutational signatures can reveal the impact of mutagenic processes in cancer, including exposure to therapy. Here, the authors develop an approach that can accurately predict drug responses in cancer using mutational signatures while simultaneously correcting for germline variants with an ancestry matching procedure. It is paramount to identify predictive markers that are associated with failures of DNA maintenance in cancer cells.Cancer precision medicine draws on the presence of somatically acquired changes in the tumor, which serve as predictive markers of response to drugs and other therapies. Commonly these markers are individual genetic changes, such as driver mutations affecting oncogenes or tumor suppressor genes, or copy-number alterations thereof. Many commonly employed cancer drugs act by interfering with DNA synthesis or maintenance or by damaging DNA. Therefore, the altered capacity of cancer cells to repair and/or replicate DNA is the basis of many classical therapies, such as platinum-based agents, and also recently introduced or upcoming therapies, such as PARP inhibitors or ATR inhibitors  pathway, even in the absence of deleterious genetic variants in the BRCA1/2 genes.However, while DNA repair is often deficient in tumors, many DNA repair genes such as 11 as well as DNA polymerase proofreading13 and base excision repair (BER)16 and nucleotide excision repair (NER)17 failures. Inducing DNA repair deficiencies in cancer cell lines is able to reproduce some of these signatures21. Other types of mutation signatures based on small insertions and deletions (indels)9 and on structural variants22 are also starting to be introduced.In addition to gene expression, mutational signatures–readouts of genome instability–can characterize DNA repair deficiencies. One common type of signature describes relative frequencies of somatic single-nucleotide variants (SNV) across different trinucleotide contexts. Certain mutational signatures were found to be associated with failures in DNA mismatch repair (MMR) and HR pathwaysBRCA1 and BRCA2 genes23, thus identifying HR deficient tumors. The signature is common in ovarian and breast cancers, but genomic analyses have detected it across other cancer types25, suggesting the potential for broad use of drugs that target HR-deficient cells, such as PARP inhibitors. To this end, genomics-based predictors that draw on mutational signatures of HR deficiency have been developed27. We propose that this principle may extend to other types of mutational processes, potentially revealing tumor vulnerabilities.Because mutational signatures describe the state of the DNA repair machinery of a cancer cell, they may be able to serve as a drug sensitivity marker. This is exemplified by a mutational signature associated with pathogenic variants in 29 have identified correlations between the sensitivity to a drug (or to a genetic perturbation), and the genetic, epigenetic, or transcriptomic markers in the cell lines. Encouragingly, genetic markers known to have clinical utility  are also evident in cell line panel data analyses30, suggesting potential for discovery of further useful genomic markers.Human cancer cell line panels provide an experimental model for the diversity in tumor biology that is amenable to scaling-up. Drug screens and genetic screens on large cell line panels32, somatic mutations are still greatly outnumbered by the residual germline variants . We then compared the reconstructed somatic SNV mutation spectrum to the true somatic spectrum, obtained by contrasting tumor exomes with the matched healthy tissue exomes from the same individuals.Ancestry-matching improves over the commonly used strategy, that is simply filtering out known germline variants according to population genomic databases Fig. ; error iWe considered various numbers of population clusters according to the error in reconstruction of the correct somatic trinucleotide spectrum. Selecting three clusters, expectedly, recovers the major ethnicity groups  variants were reported to be increased in Europeans, NCG>NTG mutations in Native Americans and NAC>NCN and TAT>TTT in some East Asians36. These reports, together with our benchmarking using simulations, support the use of ancestry-specific baselines in inferring somatic mutational spectra of unmatched cancer genomes, such as cell line genomes.Encouragingly, comparing the 13 ancestry clusters sorted by self-reported ethnicity , where we extracted those NMF solutions that resembled previously reported tumor mutational signatures9 of single base substitutions (SBS). We tested a number of variations on the data filtering and the mutation extraction methodology  signatures together with a set of indel mutational features. Examining the SBS part of the spectrum, this yielded 30 cell line mutational signatures that very closely match (at a cosine similarity cutoff ≥0.95) the known tumor SBS signatures, and a further 22 cell line signatures that match known SBS tumor signatures . Note that a comparable degree of ambiguity is also observed among some of the known tumor SBS mutational signatures . In some cases, our cell line signatures were similar to more than one previous tumor SBS , meaning they did not closely match one of the signatures from current tumor catalogs , recurrent focal copy number alterations (CNAs at 425 genes30), and DNA methylation data at informative CpG islands (HypMet at 378 genes30). Additionally, we examined gene expression patterns (mRNA levels of 1564 genes that are either represented in the L1000 assay39 or are known drug target genes40), because gene expression can be highly predictive of drug response41, possibly because it reflects differences between various cancer types and subtypes.Genetic and epigenetic alterations in cancer cell lines are often investigated as markers of sensitivity to chemical compounds37. In particular, we used Random Forest (RF) regression applied to the complete set of genetic or epigenetic markers (listed above) in an individual cell line as features .We predicted the sensitivity (log IC50 concentration) of a panel of 930 cell lines (separately for 29 cancer types that had a sufficient number of cell lines available) to a set of 518 drugs from the GDSC databaseres Fig. . In addip < 0.05, by corrected Friedman test followed by the post-hoc Nemenyi test on ranks; Fig. 32, comparing across the set of cell lines that overlap between the publications .Next, we applied a different test that considers the average error in predicting the drug sensitivity profile  could also have been predicted by the two RF models drawing on the individual sets of features – on gene expression only, or on mutational signatures only Fig. . The num45 that might stem from technical reasons or from cancer cell line evolution during culture, we asked if the associations between mutational signatures and drug responses replicate across data sets. To this end, we tested for associations involving various markers, with the additional condition that the associations also replicate in an independent data set. We implemented a randomization-based procedure that tests that the smallest effect size (Cohen’s d statistic) across both datasets is above chance  between the cell lines positive for a marker and those negative for a marker is greater than the pooled standard deviation (of the log IC50) of the two sets of cell lines. These replication tests were performed on binarized mutational signatures i.e. the signature present/absent indicator variables  were rarely inflated for mutational signatures, and in fact commonly exhibited deflation . In total, 290 drug-mutation signature associations were robustly supported across both GDSC and PRISM , observed across diverse tissues and diverse signatures  might be of interest for use as supporting evidence, corroborating other associations (see below).Given that the amount of cell lines available to the replication analysis is reduced and thus statistical power is limiting, particularly for some tissues  .Secondly, we performed an internal replication analysis within GDSC, enforcing that associations must be detected with two or more drugs that share the same molecular target. In total, 228 drugs in the GDSC data could be tested in this “same-target” analysis. Effectively, multiple drugs serve as pseudoreplicates, and additionally this test may help discard associations due to off-target effects, which are more likely to differ between two drugs than their on-target effects. Here, we identified 971 significant associations for mutational signatures, 206 for driver mutations, 288 for copy-number changes, and 762 for promoter DNA methylation (at effect size p < 0.005) threshold, to be potentially suitable for further analyses or follow-up.Some associations overlapped between the same-target and the GDSC-PRISM replication analyses, suggesting more robustness: Supplementary Data 48 that overlapped the GDSC cell lines. We identified many associations (at effect size d > 0.5 and FDR<15%) with conventional markers – oncogenic driver mutations (n = 123), copy number alterations (n = 100), and DNA methylation (n = 86) – that replicated across drug and genetic data  amplification being sensitive to inhibitors of EGFR and ERBB2 (afatinib and 4 other drugs), and also to the ERBB2 gene knockout . Similarly, we recapitulated the known associations between amplifications of a chromosomal segment 7q31 including the MET oncogene, and sensitivity of esophageal/gastric cancer to crizotinib50 and also 3 other MET inhibitors  and to knockouts in CDK4 and CDK6 genes. In contrast, RB1 mutations were associated with resistance, consistent with the biological roles of these genes, as well as prior preclinical studies . The same is observed in ovarian cancer cell lines, again involving AKT2 knockout and the same two inhibitors . This is supported by additional AKT inhibitor drugs: afuresertib (FDR=6%), AKT inhibitor VIII (FDR = 21%), and uprosertib (FDR = 5%) in colon, and MK-2206 (FDR = 9%) in ovary . These genomic associations support that the AKT2 amplifications may bring a selective benefit to ARID1A-mutant tumors. Overall, our analyses solidify the notion that the PI3K/AKT/MTOR signaling inhibition is a vulnerability of ARID1A-mutant cells57, as reported before for individual examples of cell lines sensitized to AKTi drugs upon silencing of ARID1A56, and we further suggest specifically AKT2 as an opportune point of intervention.We highlight a robustly supported synthetic lethality example involving mutations in the ein Fig. . In partNext, we applied this same statistical methodology . In a joint analysis of MSI-prone cancer types , we found links between the MMR SBS signatures that we inferred in the cell line exomes and the sensitivity to WRN knockout. However, levels of statistical support were variable across the MMR signatures  for SBS20, 15, 6, 26/12L, 14, 21, and 44L, respectively; Supplementary Fig. WRN loss  and therefore deficient in the DNA mismatch repair (MMR) pathway, which do not tolerate the loss of the WRN disruption, the MMR signatures as well as other mutation signatures can predict sensitivity to many perturbations, including those that the MSI status nor the other genetic markers would not predict  that replicated across data sets – a higher number than for oncogenic driver mutations, CNAs, and DNA methylation . These associations  broadly matches the number of replicated associations involving common driver mutations such as EGFR or TP53 or KRAS , or known copy number change events such as ERBB2 gain (n = 17 replicated associations in Project SCORE)  Fig. . We also62.We note that in this and further analyses we have, conservatively, stratified colorectal cancer cell lines into MSI and MSS , likely NER failures (SBS8L/4L) and replicative DNA polymerase failures . As tentatively DNA repair-associated signatures, here we additionally considered66 SBS18/36L based on the similarity of the spectrum to the SBS36/18 and because it was found in MUTYH-variant patients67, and additionally SBS33L, SBS54, and SBS-CL1 because they have prominent indel components and were associated with sensitivity to WRN loss .When inspecting the overall balance of sensitivity versus resistance associations, we noted that driver mutations and CNA present a mix of sensitizing and resistance associations. These may be unevenly distributed across genes: see By analogy to this, we also identified two opposing trends in the mutational signatures association tally. Firstly, the signatures associated with DNA repair failures, as listed above, tend to be more often sensitizing , SBS11 , SBS22L , and SBS4/45L . Of note, among these, the oxidative damage SBS18/36L does show some sensitizing associations as well ; see Fig. 71, highlighting the need for identifying predictive biomarkers of response to AKT inhibitors. We considered the possibility that different MMR signatures have varied utility as AKTi markers; indeed, the MMR signature SBS26/12L most commonly associated with sensitivity across different AKTi drugs, with a lower utility of other signatures  proteins, selisistat and tenovin-6, in pancreatic adenocarcinoma, lung squamous cell carcinoma, sarcoma, and lymphoid leukemia , associates with sensitivity to two related classes of drugs . These associations are some representatives among many other examples with a similar degree of confidence  in the ‘golden set’ of associations .An important caveat to our study is that the discovered associations do not necessarily imply a causal relationship: the mutational signature-generating process and the drug phenotype may be only indirectly associated. In other words, the mutational signature might serve as a marker for another alteration (e.g. a driver mutation), which may be the proximal cause of the drug sensitivity or resistance. This is similarly possible with mutational signatures as with other genetic markers (mutations or CNA in cancer genes), and also with gene expression markers. Because these various sets of genomic/transcriptomic features can correlate across tumors, prioritizing the likely causal relations for further follow-up is a challenge that remains to be addressed; larger cell line panels will be helpful, as well as use of isogenic cell panels.45) replication analyses across diverse datasets will be conservatively biased. It bears mentioning that absence of a significant association in our lists does not imply there is not an association of that marker with that drug, but might rather mean that the analysis at current sample sizes may be underpowered to detect the association  is complicated by the diversity of therapeutic regimes: most patients are treated with multiple overlapping sets of drugs and possibly radiotherapy, which makes it challenging to identify effects of individual drugs by retrospective analysis. Additionally, the drug assignment may be confounded by demographics and by cancer stage/grade or subtype, further complicating analysis. Large controlled randomized trials with treatment and control arms, for which the tumor genomic data is also available, would facilitate identifying various types of genomic markers  relevant to drug response and patient survival.81. A further issue that merits attention is timing: a genomic analysis of cell lines31 suggested that the activity of some mutational processes is variable in time. While a genome sequence reflects a record of mutagenic activity in the past, it may or may not reflect current mutagenic activity, which is presumably more relevant for drug sensitivity phenotypes. Occurrence of recently active signatures is difficult to identify from bulk DNA sequencing from cell culture, as the recent mutations may not rise to sufficient allele frequencies to be detected, and may conservatively bias the results of an association analysis such as ours. We note that a related issue could affect also the more established markers i.e. driver mutations or CNAs in cancer genes: given the rapid accumulation of genetic changes in cultured cancer cell lines83, and prevalent epistasis in cancer85, it is plausible that recently occurring, unobserved mutations or CNAs affect the ability to identify drug sensitivity markers from analyses of cell line screening data.With respect to mechanistic insight, future improvements in methodology will refine the mutation signature markers and clarify the underlying mechanisms. For instance, a known limitation of various mutational signature extraction methods – including ours – is the difficulty of discerning the ‘featureless’ signatures such as SBS3, SBS5, and SBS8An interesting observation about mutational signatures associated with drug activity is that some likely result not from DNA repair deficiencies, but instead from exposure to mutagenic agents. Some of these signatures presented many associations in our analyses and were, overall, more commonly associated with drug resistance rather than sensitivity  might result from varied mechanisms that converge onto the same spectrum, some of which are from chemotherapy exposure, while others may be endogenousMSH689. It is conceivable that also the various epigenetic changes resulting from carcinogen exposure might confer similar properties. In other words, even a temporary exposure to a mutagen may prime tumor cells for resisting later drug treatment.Another explanation may be that prior exposure to a carcinogen would select tumor cells with an altered DNA replication/repair state, which continues after the carcinogen is withdrawn, thus generating resistance in cancer cells. Indeed, prolonged exposure to mutagens that are also cytotoxic – as is the case for many cancer therapeutics – is likely to select resistant cells, in some cases via altered DNA replication or repair mechanisms. For instance, therapy of tumors with temozolomide (associated with SBS11) is known to select for cells that are resistant due to a MMR-deficiency via loss-of-function of n = 1072; cancer cell lines from Genomics in Drug Sensitivity in Cancer (GDSC)) from European Genome-phenome Archive (EGA) (ID number: EGAD00001001039) and WES bam files (human reference genome version hg38) of primary tumors (n = 6154) and their matched normal samples (n = 6154) from the TCGA repository at NCI Genomic Data Commons (access via dbGaP accession phs000178). We downloaded samples from the following TCGA cohorts: BLCA, BRCA, COAD, GBM, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, OV, PAAD, READ, STAD, THCA, UCEC. We aligned the human cancer cell line bam files to the human reference genome (version hg38) using the bwa90 software, and sorted and indexed them using the samtools software91. We used the Strelka2 software (version 2.8.4)92 to call single nucleotide variants (SNVs) and small insertions and deletions. We called SNVs and indels for cell lines, primary tumors and normal samples. In samples where Strelka2 was unable to run, a re-alignment was performed using Picard tools (version 2.18.7)93 to convert the bams to FASTQ and, following that, the alignment was performed by executing bwa sampe (version 0.7.16a) with default parameters. The resulting bam files were sorted and indexed using Picard tools. We used SNVs and indels marked as “PASS” in the Strelka2 output. We annotated SNVs and indels with minor allele frequencies (MAF) obtained from the gnomAD database94 (for SNVs and indels that could be found in gnomAD).We obtained WES bam files (human reference genome version hg19) of human cancer cell lines  as a measure of the activity of a compound against a given cell line. If for the same drug activities were available from both GDSC1 and GDSC2 versions, we used GDSC1. We downloaded the information about drugs (the list of drugs’ putative targets and target pathways) from the GDSC website (https://www.cancerrxgene.org/). We manually curated the list to correct inconsistencies 30 cancer driver genes (Muts), regions of recurrent focal copy number alterations (CNAs), hypermethylated informative CpG islands (HypMet), and microsatellite instability status (MSI). We downloaded ANOVA_input.txt files for 18 cancer types and pan-cancer analysis from https://www.cancerrxgene.org/gdsc1000/GDSC1000_WebResources/Pharmacogenomic_interactions.html. We downloaded cancer cell line gene expression data (“sanger1018_brainarray_ensemblgene_rma.txt”) from https://www.cancerrxgene.org/. We selected expression data for 1564 genes corresponding to the L1000 assay39 and to known drug targets40.We obtained the following genetic features of cancer cell lines from the GDSC repository:46 database (secondary-screen-dose-response-curve-parameters.csv). Drugs and cell lines from PRISM and GDSC databases were matched via drug names, obtaining in total 348 cell lines and 178 drugs that overlap between the two databases.The drug response data (IC50) for 1502 drugs was downloaded from the PRISMThe gene-level fitness effects for 16,827 genes in 786 cancer cell lines were downloaded as the Integrated cancer dependency dataset from Wellcome Sanger Institute (release 1) and Broad Institute (19Q3)48 and matched to the GDSC cell lines via cell line names, obtaining in total 517 overlapping cell lines (https://score.depmap.sanger.ac.uk/downloads). from the Project SCORE database95 (iv) we discarded regions with frequent somatic deletions, namely pan-cancer deletions of significant SCNA96 and frequently deleted chromosome arms (deleted in >18% of tumor samples);97 (v) we detected copy number changes of cell lines with CNVkit98 and removed deleted regions (log2 score < −0.3). From the remaining regions of the exome we selected common germline variants across the cell line exomes and the TCGA exomes (MAF>5% in GnomAD).To perform ‘ancestry matching’ of cancer cell lines with TCGA normal samples, we performed Principal Component Analysis (PCA) on the matrix of common germline variants, followed by clustering according to principal components (PC) (see below).To ensure that genomic data is comparable between cell lines and tumors we performed the following steps: (i) we used only SNVs detected in the regions of the exome with well sequencing coverage (≥20 reads in 9 out of 10 randomly selected samples); (ii) to avoid gender bias during analysis, variants on X and Y chromosomes were not used; (iii) only uniquely mappable regions of the genome were used, as defined by the Umap k36 criterion;99 discards outlying samples) on the first 140 principal components derived from the common germline variants. Initially, we considered the first 150 principal components, however, some PCs were attributed to the batch effect, i.e., they separate cell lines from TCGA samples. We removed the top 10 such PCs as determined by feature importance of the random forest classifier (“randomForestSRC” R package) trained to distinguish between TCGA samples and cell lines. We used the remaining 140 PCs as an input to the tclust algorithm. Outlying samples, as determined by tclust, were discarded. We varied the number of clusters from 4 to 20, determining the optimal number of clusters (13) by using simulated cell line exomes ; additionally, we filtered germline variants that appeared >5% of samples in our TCGA data set or cell lines data set which removes germline variants that might be particular to this data set and also suspected sequencing artifacts). Next, from the remaining SNVs, for each cell line and TCGA germline sample, we calculated its trinucleotide mutation spectrum. The mutation spectrum contains 96 components, which are the counts of six possible mutation types  in 16 possible 5′ and 3′ neighboring nucleotide contexts for each mutation type. For each cell line, from the cell line’s 96 trinucleotide spectrum, we subtracted the median 96 trinucleotide frequencies of its TCGA ‘ancestry-matched samples’ . In case the subtraction resulted in a negative count for some of the contexts, we set it to zero.Here we considered SNVs from the regions of the exome as specified before (steps i–v), with the difference that we used SNVs with the sequencing coverage of ≥8 reads in at least 90% samples (9 out of 10 randomly selected samples). We next filtered germline variants from cancer cell lines following common practicesIn addition to the standard 96 trinucleotide spectra, to extract mutational signatures from cell lines (see below) we considered additional features based on small insertions and deletions. We filtered the regions of the exome using the steps (i-iv) as described above. Similarly as for SNVs, we discarded indels found at MAF>0.001% in the gnomAD population database and filtered the ones that appeared in >5% of cell line samples. The remaining indels were classified into 4, 5, 8, 14, or 32 different indel types considering: the length of insertion or deletion , microhomology at deletion sites (considering the lengths of microhomology 1 or 2+), and microsatellites at indel loci (considering repeat sizes of 1 and 2–5+). We benchmarked the cell line mutational signatures (see below) that used different indel types. The most favorable indel types according to that benchmark were the simplest where we differentiate 4 indel types: deletions with microhomology (Del-MH), deletions at microsatellite loci (Del-MS), other deletions (Del-Other), and insertions at any locus (Insertion).We used TCGA exomes to simulate cell line exomes  in order to benchmark our ancestry matching procedure for efficacy in reconstructing the mutation spectrum of cancer cell line exomes. Additionally, we used the benchmark to optimize the number of clusters in the inference of subpopulations. To simulate variant calls from cell line exomes, we performed the variant calling for tumor samples in the same way as for the cell lines, i.e., without matched normal samples. In addition, for every sample we merged SNV calls obtained thus with the somatic SNV calls of tumors, ensuring that the true somatic mutations are a part of a simulated cell line to enable an accurate estimation of errors. In the tumor types that were represented both in the set of cancer cell lines and the set of TCGA tumors, we randomly selected 450 tumor samples taking approximately the same number of samples per tumor type as in cell lines. In the subsequent analysis involving simulated cell line exomes, we removed their corresponding normal samples from the pool of germline samples. We performed the ancestry matching procedure involving simulated cell line exomes in the same way as described above Fig.  and eval34), we performed hierarchical clustering  of the median mutation spectra of TCGA subpopulations  populations  to the baseline method of determining somatic variants in a cell line genome  and the bootstrap self-similarity method Fig. . The booP-values were calculated by Wilcoxon rank-sum test (artifact signatures were excluded from the test).We compared the true number of somatic mutations versus the number obtained with the ancestry-matching and the MAF filtering  to quantify the degree of overfiltering and underfiltering of these two filtering approaches  The agreement with an established set of SBS signatures (PCAWG signatures9) measured as the number of PCAWG signatures that were recapitulated at ≥0.85 cosine similarity cutoff; (b) The similarity of the cell line signature exposure profiles across cancer types to the exposure profiles of PCAWG signatures, measured as cosine similarity between an average exposure per-cancer-type profile of a cell line signature and its matching PCAWG signature (for signatures recapitulated at ≥0.85 cosine similarity); (c) The accuracy of the signatures in predicting drug sensitivity profiles across cancer cell line panels (see below for method).We extracted cancer cell line mutational signatures from 96 component trinucleotide mutation spectra of cancer cell lines obtained with the ancestry matching procedure. In total, we used mutation spectra of 930 cancer cell line exomes . For each bootstrap sample, we varied the number of signatures from 2 to 40. Computations were performed on an NVIDIA GeForce RTX 2080 Ti GPU.From the matrix containing mutation spectra of samples, we generated 300 matrices by bootstrap resampling. One bootstrap sample is obtained by applying the sampling function of the “UPmultinomial” R package to each sample’s spectrum. Next, we applied the NMF algorithm to each of the bootstrap samples  was clustered into k clusters with k-means clustering, varying k from 2 to 30. (ii) We applied the RTOL-filtering (relative tolerance) as proposed by Degasperi et al.38, where (separately for each different number of signatures parameter) NMF runs that diverge for more than 0.1% from the best NMF run are removed, as measured by the root-mean-square deviation of the factorization. (iii) We implemented the ‘hierarchical extraction’ procedure proposed by Alexandrov et al.9 where NMF is repeated iteratively while removing (or down weighting) the well-reconstructed samples (cosine similarity above a specified threshold) from a previous iteration to uncover additional signatures. We allowed a maximum of 3 iterations and tested different cosine similarity thresholds ranging from 0.95 to 0.99. In the case of down weighting, we multiplied the sample’s mutation spectra by 0.05 instead of removing it. (iv) Similarly as in Ghandi et al.42, we performed joint signatures inference of cell line exomes and tumor exomes. To the initial matrix containing mutation spectra of cell lines, we added mutation spectra of 6154 TCGA somatic exomes which were preprocessed in the same way as cell line exomes: the same regions of the exome were used .We implemented the following modifications to the above basic procedure . We compared the individual signatures to the PCAWG signatures searching for the closest matching cell line signature for each PCAWG signature. As a final set of tumor-like cell line signatures, we kept the closest matching cell line signature if its cosine similarity to the best matching PCAWG signature exceeded 0.85. For cell line signatures that used indel features in addition to 96 tri-nucleotide spectra, cosine similarity was calculated only on 96 spectra since the known PCAWG SBS mutational signatures do not have an indel component in their current implementation (v3.2). We considered an additional criterion for matching cell line and tumor signatures where in addition to the mutation-spectrum cosine similarity, a cosine similarity between signature’s average per-cancer-type exposure profile (for cancer types available in both cell line and PCAWG data). The spectrum-cosine and exposure-cosine similarities were combined into a single metric given the weight ‘w’ controlling the relative contribution of each: w×spectrum-cosine + (1–w) × exposure-cosine. We tested different weights: w = 0.5, 0.7, 0.9, and 0.99. For each PCAWG signature, from a set of cell line signatures that match it with spectrum-cosine ≥0.85, we selected the best matching cell line signature as the one with the highest combined metric.From the candidate cancer cell line mutational signatures obtained by the above described methodologies, we searched for the signatures that closely resemble the ones that were previously found in human cancers31 and Alexandrov et al.9. We used the “sigproSS” tool of SigProfilerExtractor framework101 to attribute exposures to the extracted cell line signatures . Additionally, we used a custom script based on regularized regression from “glmnet” R package enforcing different degrees of sparseness. We considered Ridge, Lasso and Elastic Net regression to model cell line’s mutation spectra as a combination of cell line signatures, where the resulting regression coefficients are considered as exposures to signatures. We enforced non-negative coefficients, no interaction term, and used crossvalidation to determine the optimal value of the lambda parameter.We considered using additional regression post-processing for assigning signature exposures to samples, similarly as done by Petljak et al.w = 0.9 as weight on the trinucleotide-cosine and 0.1 on exposure-cosine, for matching our cell line signatures with the known COSMIC tumor signatures. No post-processing of exposures to signatures was performed since it did not yield improvements according to the evaluation criteria, thus, we used the raw NMF scores of the exposure matrix. A notable modification from the signature extraction method presented in Alexandrov et al.33 is that we did not limit to a single value of the “number of signatures” NMF parameter . Instead, inference was run for many values of this parameter, and the final set of mutational signatures consists of solutions from different values for the ‘number of signatures’ parameter.The different procedures for mutational signature extraction we implemented were evaluated according to the above-described criteria (a–c), results are presented in Supplementary Data This procedure yielded 52 signatures , and in our examined territory after having removed common SNP loci. The trinucleotide composition was highly similar  where the agreement with PCAWG signatures was maximized in terms of the number of cluster medoids that resemble PCAWG signatures (at cosine similarity ≥0.85). From such a set of signatures we selected the ones dissimilar from any of PCAWG signatures (cosine similarity <0.8), yielding in total 5 cancer cell line-specific signatures (named SBS-CL). These SBS-CL signatures appear together with real signatures and are robust  therefore they are also likely bona fide mutational signatures that might originate, for instance, from cell-line specific mutational processes.We next searched for cell line-specific signatures, i.e., signatures that commonly appear in cell line data and do not resemble any of the known tumor signatures. To this end, we employed k-means clustering . Each batch of NMF solutions  was clustered into In total, this yielded 57 cancer cell line signatures  ; we iterated this over all drugs. Another possible way to run Random Forest, not employed here, would be to binarize the log IC50 drug response and run the RF algorithm in classification mode rather than regression mode. However, binarizing the drug response implies some loss of information, and moreover, it would necessitate an extra parameter in the analysis (choice of the binarization threshold) so we opted to use continuous drug response values here.We built predictive models of drug response using the Random Forest (RF) algorithm as implemented in the “randomForestSRC” R package. We compared the predictive performance of different predictors: mutations in cancer driver genes (Muts), recurrent copy number alterations (CNAs), DNA hypermethylation (HypMet), gene expression, previously reported cancer cell line mutational signaturesWe built cancer type specific models for each drug separately, considering only models where at least 15 cell lines with drug response were available to train the model. For model validation we used 10-fold cross validation which was repeated five times to get more stable results. We built random forests with 100 trees and a minimal number of samples in terminal nodes set to 2. We assessed the predictive performance of the predictors by the relative root-mean-square-error (RRMSE), i.e., a root-mean-square error divided by the root-mean-square error of the default model. The default model predicts a constant value for all cell lines equal to the average ln IC50 across the training set. RRMSE < 1, therefore, denotes better accuracy better than the one of the (uninformative) default model. We define such models as predictive. Results are presented as an average RRMSE per cancer type across all models built for a cancer type Fig. . Note th102. More specifically, to statistically compare the predictive performance of multiple predictors over multiple datasets we use the corrected Friedman test and the post-hoc Nemenyi test103. Here, a dataset corresponds to a pair of one cancer-type and one drug. Due to the requirements of this test, only the intersection of drugs modeled across all cancer types and predictors were considered . For each drug-cancer-type pair, predictors are ranked according to their RRMSE for that drug-cancer-type pair, where rank 1 corresponds to the best (i.e. the lowest) RRMSE, 2 to second-best, etc. Ranks are then averaged across all drug-cancer-type pairs to obtain the average rankings of predictors. The Nemenyi test performs a pairwise comparison of predictors’ performance based on the absolute difference of the average rankings of the predictors to assess whether there are statistically significant differences among predictors. The test determines the critical difference (CD) for a given significance level α, if the difference between the average ranks of two predictors is greater than CD, the null hypothesis (that the predictors have the same performance) is rejected., i.e., there is a statistically significant difference between the two predictors. The results from the Nemenyi post-hoc test are presented with an average ranks diagram102. The average predictor rankings are depicted on an axis, in such a manner that the best ranking algorithms are at the left-most side of the diagram. The algorithms that do not differ significantly (in performance) for a significance level of 0.05 are connected with a bold line, therefore, predictors that are not connected are statistically significantly different according to the test.To assess the statistical significance of the differences in the predictive performance Fig. , we follWe performed two-way association testing where we searched for robust associations that replicate in two independent datasets , as well as additional 29 cell lines that were reported to be misclassified105. In addition, for each cancer type, we removed outlier cell lines by the total number of mutations , here defined as having the number of mutations >3× interquartile range + upper quartile, or <3 × interquartile range - lower quartile . We used binarized exposures to mutational signatures where, for each signature, values below the 5% of the value of the second-highest exposure score (used in order to avoid single high-score outliers observed in some signatures) of that signature were set to 0, and the rest to 1 , glioblastoma and brain lower grade glioma (denoted as GBM/LGG), and head and neck squamous cell carcinoma and lung squamous cell carcinoma (denoted as HNSC/LUSC). We additionally considered two groups of cell lines obtained by dividing colorectal cell lines according to microsatellite instability status stability labels to warrant such division). Prior to the association search, we removed 21 cell lines that exhibited either sensitivity or resistance nonspecifically towards a large number of drugs  of the two independent datasets i.e. positive Cohen’s d implies sensitivity and negative resistance associations. Here, effect size is the Cohen’s d statistic: difference of mean drug sensitivity (ln IC50) between the cell lines having the feature and those not having it, divided by the pooled standard deviation of the data. To obtain the association’s empirical p-value, we performed a randomization procedure where we calculated the association score 100,000 times for the randomly shuffled features. For the sensitivity test, the formula for the p value is: p-values were adjusted with the Tibshirani-Storey method47.All of the association tests were performed by modeling the drug response (or gene fitness score) to associate it with the status of a feature  searching separately for sensitivity and resistance associations. For each association, we calculated the association score as the minimum (in the case of sensitivity) or maximum (resistance) effect size  changes. Based on this we considered aggregated mis-match mutational signatures where we merged p-values and effect sizes of SBS6, SBS15, and SBS44L (denoted as SBS-MMR1); SBS21 and SBS26/12L (denoted as SBS-MMR2). Similarly, we considered the aggregate of the two APOBEC signatures SBS2 and SBS13 (denoted as SBS-APOBEC). Pooled p-values of aggregated signatures were obtained by Fisher's method, while pooled effect size was obtained by averaging. Pooled p-values were adjusted the same as described above.Recent work suggested that MMR-failure signatures can be grouped into a few broad typesd > 0.5 in both tests .We consider a two-way association as statistically significant if FDR<15% and additionally we imposed an effect size threshold of Cohen’s d > 0.5 in both tests (or d > 1 for the GDSC/GDSC two-way test) and a nominal p < 0.005). From these, an association (between a feature and drug within a certain cancer type) was listed in the silver set if it was confirmed in more than one ‘two-way’ replication tests, or was seen in more than one cancer type. We require that at least one of the supporting associations has FDR<25%. Additionally, we collated a ‘golden set’ of high-priority list of associations involving mutational signatures and cancer functional events which we consider to be suitable for follow up work: we require that an association involving the same drug is supported in at least three cancer types or is replicated in all three ‘two-way’ tests where at least one association has FDR < 25% (995 associations; Supplementary Data We collated a list of 3911 associations (the ‘silver set’; Supplementary Data A list of abbreviations of cancer types used in this study: ALL/CLL, acute/chronic lymphoblastic leukemia; BLCA, bladder urothelial carcinoma; BONE, bone cancer other/not classified further; COREAD, colon adenocarcinoma and rectum adenocarcinoma; COREAD_MSI, microsatellite instable colon and rectum adenocarcinoma; COREAD_MSS, microsatellite stable colon and rectum adenocarcinoma; ESCA, esophageal carcinoma; ESCA_STAD, esophagus carcinoma and stomach adenocarcinoma; EWING, Ewing's sarcoma; GBM, glioblastoma multiforme; GBM_LGG, glioblastoma and brain lower grade glioma; HNSC, head and neck squamous cell carcinoma; HNSC_LUSC, head and neck squamous cell carcinoma and lung squamous cell carcinoma; KIRC, kidney renal clear cell carcinoma; LAML, acute myeloid leukemia; LGG, brain lower grade glioma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; LYMP, lymphoma; MESO, mesothelioma; MM, multiple myeloma; NB, neuroblastoma; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; SARC, sarcoma other/not classified further; SCLC, small cell lung cancer; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; THCA, thyroid carcinoma; UCEC, uterine corpus endometrial carcinoma.Further information on research design is available in the Supplementary InformationSupplementary TablesReporting Summary


Coffea arabica beans that have distinct biological activities. Recent research describes their potential activities, which include anti-inflammatory, anti-diabetic, and anti-cancer properties, among others. The two diterpenes have been shown to have anticancer effects in various in vitro and in vivo cancer models. This review aims to shed light on the recent developments regarding the potential effects of kahweol and cafestol on various cancers. A systematic literature search through Google Scholar and PubMed was performed between February and May 2022 to collect updates about the potential effects of cafestol and kahweol on different cancers in in vitro and in vivo models. The search terms “Kahweol and Cancer” and “Cafestol and Cancer” were used in this literature review as keywords; the findings demonstrated that kahweol and cafestol exhibit diverse effects on different cancers in in vitro and in vivo models, showing pro-apoptotic, cytotoxic, anti-proliferative, and anti-migratory properties. In conclusion, the diterpenes kahweol and cafestol display significant anticancer effects, while remarkably unaffecting normal cells. Our results show that both kahweol and cafestol exert their actions on various cancers via inducing apoptosis and inhibiting cell growth. Additionally, kahweol acts by inhibiting cell migration.Kahweol and cafestol are two diterpenes extracted from  Coffee is one of the most widely consumed beverages in the world. This may be attributed to its desirable stimulatory effect and distinctive taste. Fortunately, coffee’s constituents were found to have protective functions against many diseases, including type 2 diabetes mellitus, liver cirrhosis, and neurodegenerative disorders ,4,5,6. TThe diterpenes kahweol and cafestol are fat-soluble compounds derived from Coffee arabica beans . They arIn unfiltered coffee, such as Turkish coffee or espresso, kahweol and cafestol are found in relatively higher concentrations in comparison to filtered and instant coffee. A study performed on healthy ileostomy volunteers showed that, upon coffee consumption, gastric juices break down nearly 30% of kahweol and cafestol, and 64–70% of cafestol and 70–73% of kahweol are absorbed in the small intestine, with minute amounts of glucuronidated or sulphated conjugate forms being excreted in the urine . As onlyKahweol and cafestol’s role in raising serum lipids is well-documented. The long-term ingestion of unfiltered coffee has been linked to increased plasma levels of triacylglycerol and low-density lipoprotein (LDL) cholesterol in human subjects, and cafestol and kahweol are the primary coffee constituents responsible for this effect, with cafestol exerting a more potent effect than kahweol . Some inEmerging evidence has shown that kahweol and cafestol target several pathways to prevent disease. Their anti-inflammatory effect is prominently displayed by their ability to regulate several inflammatory mediators, such as ICAM1, MCP1, and IL-8, which are involved in cardiovascular disease progression ,17. In aThe chemopreventive function of diterpenes can be seen in their ability to induce phase 2 detoxifying enzymes and anti-oxidant proteins, which prevents early mutagenic events. Their pro-apoptotic characteristic also contributes to their anti-tumorigenic effects by downregulating anti-apoptotic proteins, such as Bcl-2, Bcl-xL, mcl1, cFLIP, decreasing the release of cytochrome c and activating caspase enzymes . In addiThis is the first review focusing on the various effects exerted by kahweol and cafestol on different in vitro and in vivo cancer models and the pathways involved in the eradication of such cancers. The current literature primarily focuses on the general bioactivity and pharmacological effects of these two diterpenes, including their anti-inflammatory, anti-diabetic and anti-osteoclastogenesis activity, with only a cursory description of their anticancer effects . Thus, tKahweol and cafestol exert anticancer effects on several cancer cell lines. The pathways through which they exhibit their properties are discussed in this section. Kahweol and cafestol were tested in vitro against different lung cancer cell lines, and kahweol-induced apoptosis was observed in all cell lines. With regard to mesothelioma, MSTO-211H cells and H28 cells were examined . The apoFinally, non-small cell lung cancer was also tested, specifically, the cell lines NCI-H358 and NCI-H1299 . ApoptosThe oral squamous cancer cell lines HN22 and HSC4 were used to help assess the effects of kahweol in an in vitro environment . SuppresThe in vitro impact of kahweol acetate and cafestol was assessed in the human prostate cancer cell lines PC-3, DU145, and LNCaP . KahweolKahweol acetate and cafestol also exhibited anti-oncogenic activity in vivo. In a xenograft study where human cell lines DU-145 were injected in SCID mouse models, the oral intake of kahweol and cafestol significantly reduced tumour growth.2O2 cytotoxicity in a dose-dependent manner [Kahweol has been shown to exert an anti-tumour effect on breast cancer cell lines. In vitro treatment of MDA-MB231 with kahweol resulted in the inhibition of cell proliferation along with the induction of apoptosis . The levt manner .Kahweol’s effects on the MDA-MB231 cell line can be traced back to the increased levels of phosphorylated Akt (p-Akt) and extracellular-signal-regulated kinase (ERK), activating a signalling pathway that regulates multiple cellular processes such as proliferation and apoptosis . The migKahweol treatment resulted in reduced proliferation and increased apoptosis in the HER2-overexpressing SKBR3 cell line . It alsoColorectal cancer cell lines have also been shown to be susceptible to kahweol. Kahweol induced apoptosis in HCT116 cells in vitro through the overexpression of ATF3, which is mediated by CREB1, ERK1/2, and GSK3β . In addiKahweol’s anti-apoptotic characteristics were also observed in the human colon adenocarcinoma HT-29 cells ,31. CellKahweol’s anti-tumour properties were assessed in several renal carcinoma cell lines in vitro, with a predominant focus on Caki cells. TRAIL, a natural ligand for death receptors found on cancer cells, exerted its apoptotic function on Caki cells more effectively when it was combined with kahweol . Co-treaCafestol was also shown to exhibit its anti-carcinogenic effect in renal Caki cells. Cafestol-induced apoptosis was found to be mediated by the activation of caspase-2 and 3, the upregulation of the pro-apoptotic proteins Bim and Bax, and the downregulation of anti-apoptotic proteins, including c-FLIP, Bcl-2, Mcl-1, and Bcl-xL . The apoThe combined anti-tumour activity of kahweol acetate and cafestol was documented in renal Caki and ACHN cell lines . In thesFinally, cafestol was tested on the Caki cells both in vitro and in vivo using BALB/c-nude mice as the cancer cells recipients . Anti-caThe U937 leukaemia cell line was tested on in vitro to evaluate kahweol’s apoptotic effect, which was found to be mediated by the activation of caspase-3 and the downregulation of Bcl-2 . The AKTOther cell lines such as NB4, K562, HL60, and KG1 were also tested in vitro using cafestol and Ara-c, which is an anti-leukemic agent that was used as a positive control . IncreasKahweol and cafestol were also shown to enhance the activity of the NK cell line KHYG-1 and its cytolytic effect on the NK-sensitive leukaemia cell line K562. The two diterpenes increased granzyme B expression in NK cells, likely through the phosphorylation of ATF-2, c-Jun, and CREB, transcription factors involved in the regulation of granzyme B. The net result was the enhancement of the cytolytic activity of NK cells in leukaemia cell lines .Kahweol acetate was found to attenuate cancer formation, proliferation, and migration by the fibrosarcoma HT-1080 cell line by inhibiting MMP-9, which is usually upregulated by PMA, 12-phorbol 13-myristate acetate, a synthetic compound known for its oncogenic effect . KahweolIn vitro, kahweol exerted apoptotic effects as well as inhibited cell proliferation in the hepatocellular carcinoma cell lines Hep3B, SNU182, and SNU42 . The SrcUpon the addition of cafestol in vitro, the cell lines of SCC25, CAL27 and FaDu were found to undergo apoptosis in a dose-dependent manner. When combined with cisplatin, cafestol displayed an antagonistic effect in all cell lines; moreover, its addition to radiation therapy showed an additive effect in the cell lines SCC25 and CAL27 . Very liCoffea arabica beans and are known for their disease-preventing characteristics. Kahweol and cafestol, two diterpenes, were found to have a striking role in preventing the progression of several types of cancer, as discussed above. This effect is achieved mainly by inducing apoptosis, alongside cytotoxicity and mitochondrial damage, which are mediated by targeting certain downstream molecules and pathways, as summarised in Diterpenes are fat-soluble compounds that can be derived from Taxus, indicated for the treatment of breast, ovarian, and prostate cancers, among others [Anticancer properties can also be exerted by other diterpenes and their derivatives. Taxanes, a well-known group of chemotherapeutic agents, are a class of diterpenes extracted from the plant genus g others . Anotherg others . It possg others . The chemotherapeutic role of both diterpenes is mainly exerted in cancer cell lines, with a possible minimal effect on normal body cells. The anticancer effects of these diterpenes are dose-dependent and time-dependent. According to the current literature, the doses of kahweol and cafestol required to exert their anticancer properties are, dependent on the type of cancer and cell line. For example, kahweol suppressed proliferation and promoted apoptosis in lung adenocarcinoma A549 cells at a dose of 10–40 μM when administered for 24 to 48 h . SimilarCompared to the anticancer doses of kahweol and cafestol, their anti-inflammatory doses are lower. Both diterpenes can inhibit the production of PGE2 and NO, two inflammatory modulators, in lipopolysaccharide (LPS)-activated macrophages in a dose-dependent manner . For insAlongside their potent anti-inflammatory roles, kahweol and cafestol’s anti-oxidant effects are also prominent. The diterpenes exhibit the ability to induce the Keap1/Nrf2/ARE signalling pathway, which is primarily responsible for protection against reactive oxygen species within the liver cells . This acDiterpenes have also been utilised in the treatment of other diseases. Vitamin A, a lipid-soluble diterpene, is a dietary supplement that is essential for human health, as it has functions in vision, early growth and development, as well as higher brain functions such as learning and memory . MoreoveIn conclusion, this review provides sufficient evidence portraying the anticancer role of kahweol and cafestol in vitro and in vivo. Inhibition of proliferation and the induction of apoptosis are the primary mechanisms by which the two diterpenes exert their tumour-suppressive effects. The literature surrounding in vitro studies is impressive, especially in regard to renal cell carcinoma, breast cancer, leukaemia, and colorectal cancer, as the tumour-suppressive effects of kahweol and cafestol were verified in numerous studies. Other cancer cell lines, including mesothelioma, lung adenocarcinoma, non-small cell lung cancer, oral squamous cancer, prostate cancer, hepatocellular carcinoma, fibrosarcoma, and head and neck squamous cell carcinoma, were tested using the two diterpenes in no more than two in vitro studies. Prostate cancer and renal cell carcinoma cells are the only cell lines used in in vivo studies, which confirmed their potential response to kahweol and cafestol, calling for a more extensive clinical investigation. As such, the chemotherapeutic function of these diterpenes should be assessed alongside their pharmacokinetics, including their absorption, bioavailability, metabolism, and elimination, as well as their safety profile via toxicological testing. The two diterpenes have a promising potential to revolutionise the field of cancer treatment.A systematic literature search was conducted in the databases PubMed and Google Scholar between February 2022 and May 2022. The search terms were “Kahweol and Cancer” and “Cafestol and Cancer”. Each term was searched separately on both databases, and the articles whose title or abstract was found to be relevant were filtered based on the inclusion criteria. The researchers independently selected the articles that discussed the effect of kahweol and cafestol on human cancer cell lines in vivo and in vitro and discarded the articles that did not discuss the anticancer effects of the two diterpenes.


The molecular classification of breast cancer (BC) dictates pharmacological treatment. Estrogen receptor α (ERα) expressing tumors are treated with 4OH-tamoxifen or fulvestrant, which inhibits the receptor, or with aromatase inhibitors  that reduce the 17β-estradiol (E2) circulating blood levels. Besides such endocrine therapy (ET) drugs, ERα-positive BCs can be treated with epidermal growth factor receptor (EGF-R) inhibitors  according to HER2 expression. Notwithstanding these anti-BC drugs, novel personalized approaches for BC treatment are required because prolonged administration of those pharmaceutics determines resistant phenotypes, which result in metastatic BC. We have recently reported that the cardiac glycoside (CG)  ouabain could be repurposed for ERα-positive primary and metastatic BC treatment as it induces ERα degradation and kills BC cells. Here, we evaluated if other CGs could represent additional treatment options for ERα-positive BCs and if the Na/K ATPase could be considered a biomarker for ERα-positive BC treatment. The results indicate that the ATP1B3 Na/K ATPase isoform can educate the choice for the personalized treatment of ERα-positive BC with CGs and that CGs could be more efficacious if they are administered in association with gefitinib. Different molecular subtypes of breast cancer (BC) exist and are called clinical surrogates. The molecular classification of BC is necessary to drive the therapeutic approach for this disease. Indeed, most of BCs express the estrogen receptor α (ERα) and display a more favorable prognosis with respect to ERα-negative tumors that are often more aggressive and determine an unfavorable outcome .The ERα expression dictates the clinical treatment of BCs ,2. WomenDespite the proven efficacy of the treatments for ERα-positive BCs, the prolonged administration of women  with these drugs often results in the development of resistant phenotypes and determines a relapse of the disease . UnfortuIn this respect, we have shown that drugs inducing ERα degradation in BC cells through the activation of various intracellular mechanisms could block BC cell proliferation ,10,11,12Because CGs are a class of numerous natural compounds with anti-tumor activity , here wehttps://depmap.org/portal (accessed on 1 July 2022)). This free web-based database contains experimental data regarding the profiling of different parameters including drug sensitivity and other several omics data, such as the expression and protein array data in several cancer cell lines. The sensitivity of all the BC cell lines to all the CGs profiled in the database , K-strophanthin (K-Stro), digitoxin (Digi), and digoxigenin (Digoxig), lanatoside-C (Lana), peruvoside (Peru), digoxin (Digo) and ouabain (OU)) was studied by stratifying the results according to the ERα expression [We initially evaluated the sensitivity in terms of cell proliferation of BC cell lines to CG by using the DepMap portal  with ERα mRNA expression (p = 0.0592).We have previously demonstrated that the OU and Digo anti-proliferative activity is dependent at least in part on their ability to induce ERα degradation . Therefopression . Next, wpression , while nAltogether, the reported observations indicate that ERα-positive BC cell lines are more sensitive to CGs than ERα-negative BC cell lines and that the sensitivity of ERα-positive BC cell lines to OU increases in cells expressing high ERα mRNA levels.Because we observed that in cells modeling LumB BC the sensitivity to OU increases with the ERα mRNA expression, the effects of OU in the BT-474 cell line, which represent a cellular system for triple-positive BC  ,15, wereGrowth curve analyses showed that OU and fulvestrant , used as an internal control, reduce the BT-474 cell proliferation. Interestingly, the effect of OU in preventing BT-474 cell proliferation is higher than that elicited by ICI administration A. Next, 50) of 1150 ± 430 nM. Remarkably, in BT-474 cells, the calculated IC50 of OU for the inhibition of cell proliferation (50) for its ability to reduce receptor levels  and metastatic BC  , the effferation F and ther levels D were 3.r levels G. As expr levels G’. Next,r levels H,H’. As r levels I, Na/K Ar levels L, 26S prr levels M,M’, andr levels O,O’ in MOverall, the data reported here indicate that OU activates the 26S proteasome, prevents cell proliferation, and induces ERα degradation in cells modeling the LumB BC phenotype, thus demonstrating that OU works in LumB ERα-positive BC cell lines as we already reported in two LumA BC cell lines   and furtPrompted by these results, we next reasoned that the expression of Na/K ATPase, which is the pharmacological target of OU, could dictate a different survival rate in women carrying ERα-positive or ERα-negative breast tumors.https://tnmplot.com/analysis/ (accessed on 1 July 2022)) and used the RNAseq data [To tackle this issue and because different Na/K ATPase isoforms are expressed in different human tissues , we initseq data . As showhttps://kmplot.com/analysis/ (accessed on 1 July 2022)) [Next, we sought to understand whether different levels of ATPA1, ATPA2, ATPB1, and ATPB3 mRNA expression could impact the survival of women carrying ERα-negative or ERα-positive BCs to gain insight into the possibility to use OU or possibly other CGs as an additional treatment option for tumor management. Kaplan–Meier curves were retrieved by the Kaplan–Meier Plotter database (y 2022)) . Women wTo test this hypothesis, we inspected the DepMap portal to evaluate the potential correlation between the sensitivity to OU and the mRNA expression of ATP1A1, ATP1A2, ATP1B1, and ATP1B3 in ERα-positive BC cell lines. According to the RFS data, we observed a significant positive linear correlation between the sensitivity to OU and ATP1B3 , but nothttps://www.cbioportal.org/. (accessed on 1 July 2022) database. As a control, we also included the analysis of ATP1B3 as its mRNA expression is unchanged between normal and tumor tissue.Because ATP1A1 and ATP1B1 mRNA expression is upregulated while ATP1A2 expression is down-regulated, we next determined their differential expression in ERα-positive BC and ERα-negative BC by using the Metabric data available in the As shown in We have previously  demonstrTherefore, we finally sought to determine whether OU treatment could synergize with the drugs used in the clinics to treat ERα-positive tumors  and HER2 inhibitors ). For this purpose, we retrieved the sensitivity data from the DepMap database and performed correlation analyses. Significant inverse linear correlation has been evidenced in ERα-positive BC cell lines for the sensitivity to OU and fulvestrant  or OU anOverall, these data suggest that selective co-administration of OU with specific ET drugs in ERα-positive BC could provide possible additional strategies for the management of this kind of disease.Despite the progress in the therapy for breast cancer (BC), this disease remains the most frequent and deadly neoplasm for women . At the Our laboratory has introduced a novel perspective to identify new anti-BC drugs by implementing the degradation of the ERα as a primary target for treatment . Indeed,In this work, through a combination of bioinformatics and molecular biology approaches, we report that ERα-positive BC cell lines are overall more sensitive to the antiproliferative effects of CGs than the ERα-negative cell lines, most likely because these compounds can activate the 26S proteasome and induce the ERα degradation, thus increasing their anti-proliferative activity in ERα-positive BC cell lines. According to this notion, we found that the sensitivity to OU of ERα-positive BC cell lines is linearly correlated with the receptor mRNA expression. Furthermore, such correlation is more evident in cell lines modeling LumB tumors. Interestingly, because LumB BC generally expresses a lower level of ERα than LumA BCs, the present observation supports the concept that the more the receptor is expressed the more OU has an anti-proliferative activity. In turn, we extend our previous findings regarding the effect of OU in ERα-positive BC cells from LumA cell lines  to LumB In the perspective of providing indications for the personalized uses of OU as a function of the molecular phenotypes of BC, we next evaluated the ERα-positive BC progression in relationship with the expression of the Na/K ATPase, which is the pharmacological target for OU and other CGs . Eight NIn this respect, it has to be pointed out that past and present discoveries indicate that OU, in addition to the inhibition of the Na/K ATPase, can induce the hyperactivation of the proteasome, which acts by ERα inducing degradation . These tTherefore, it is critical to understand in what case OU (or possibly other cardiac glycosides) can be administered in women with ERα-positive BC. In turn, present results suggest that the treatment of patients with OU or possibly other CGs could be pursued only after the molecular characterization of breast tumors has evidenced a high expression of ATP1A3 and a low expression of ATP1A1 and ATP1B1. Accordingly, the sensitivity of ERα-positive BC cell lines to OU increases when the cells express high levels of the ATP1B3 mRNA, but not that of the other analyzed Na/K ATPase isoforms.Present and previous results  demonstrAlthough we did not perform a detailed synergistic analysis among ET drugs, HER2 inhibitors, and OU, the data extrapolated by several ERα-positive BC cell lines suggest not only that OU could be co-administered with gefitinib, but also that the inhibition of HER2 activity could synergize with the receptor degradation for the effective reduction in BC cell proliferation, thus implying a molecular circuitry linking HER2 to ERα stability. Notably, an inverse correlation has been observed between fulvestrant and OU as well as letrozole and OU, suggesting these drug combinations could not work for ERα-positive BC treatment. However, these observations further indicate that OU administration alone could be effective when a reduced sensitivity to fulvestrant or letrozole is scored.In conclusion, the data reported here confirm that OU works as an “anti-estrogen-like” drug in all the ERα-positive BC subtypes  and further provide evidence that the use of OU and possibly of other CGs could be indicated for women with ERα-positive tumors expressing high levels of ATP1B3 and low levels of ATP1A1 and ATP1B1. Therefore, we suggest that the ATP1B3 Na/K ATPase isoform could be considered a novel putative biomarker to educate the personalized treatment of ERα-positive BC with OU.BT-474 and MDAMB231 were purchased by ATCC . DMEM (with and without phenol red) and fetal calf serum were purchased from Sigma-Aldrich . Bradford protein assay kit, as well as anti-mouse secondary antibodies, were obtained from Bio-Rad . Antibodies against ERα  and ubiquitin  were obtained from Santa Cruz Biotechnology ; anti-vinculin (mouse) antibody was purchased from Sigma-Aldrich . Chemiluminescence reagent for Western blotting was obtained from BioRad Laboratories . Fulvestrant  was purchased by Tocris (USA). All the other products were from Sigma-Aldrich. Analytical- or reagent-grade products were used without further purification. The identity of the BT-474 and MDAMB231 cell lines was verified by STR analysis .Cells were grown in DMEM with phenol red plus 10% fetal calf serum for 24 h and then treated with the indicated compounds at the indicated doses for the indicated periods. After treatment, cells were lysed in Yoss Yarden (YY) buffer (50 mM Hepes (pH 7.5), 10% glycerol, 150 mM NaCl, 1% Triton X-100, 1 mM EDTA and 1 mM EGTA) plus protease and phosphatase inhibitors. Western blot analysis was performed by loading 20–30 µg of protein on SDS-gels. Gels were run, and the proteins were transferred to nitrocellulose membranes with a Turbo-Blot semidry transfer apparatus from Bio-Rad . Immunoblotting was carried out by incubating the membranes with 5% milk or bovine serum albumin (60 min), followed by incubation overnight (o.n.) with the indicated antibodies. Secondary antibody incubation was continued for an additional 60 min. Bands were detected using a Chemidoc apparatus from Bio-Rad .6 cells were washed twice with PBS, fixed dropwise with ice-cold ethanol (70%), and rehydrated with PBS. DNA staining was performed by incubating cells for 30 min at 37 °C in PBS containing 0.18 mg/mL propidium iodide (PI) and 0.4 mg/mL DNase-free RNase (type 1-A). Samples were acquired with a CytoFlex Flow Cytometer (Beckman Coulter) equipped with 488 nm and 635 nm laser sources. Cell cycle analysis was performed using CytExpert v.2.4 software (Beckman Coulter). Doublet discrimination was performed by an electronic gate on FL2-Area vs. FL2-Height parameters.Growth curves have been performed as previously reported ,12,28. FProteasome activity was measured by employing the Proteasome-Glo™ Assay Kit for chymotrypsin-, trypsin-, and caspase-like activities, purchased from Promega . Briefly, for measurements of proteasome activity in cells, BT-474 cells were seeded in 96-well plates  in the growth medium, for 24 h. Next, cells were treated with different doses of ouabain or equal quantities of vehicle for an additional 24 h. Notably, the proteasome inhibitor Mg-132 (1 µM) was used as an internal control for all assays. After treatment, the 3 activities of the proteasome were measured according to the manufacturer’s instructions, in a Tecan-Spark Elisa reader every other 30 s, for a total period of 30 min.2O. Then, cells were incubated for 10 min at 37 °C in Assay Buffer 2X  in the presence or absence of different doses of ouabain (10−9 to 10−2 M) or equal quantities of vehicle for an additional 24 h. Next, 1 mM ATP was added to the reaction mixture, and the plates were incubated for 24 h at 37 °C in 5% CO2. The following day, the reaction was blocked using 25 µL/well of SDS 5%, and 125 µL/well of colorimetric solution (ammonium molybdate/H2SO4 followed by ascorbic acid) was added, to measure the concentration of free Pi in each sample. The Na/K ATPase activity was derived by subtracting the activity measured in the presence of ouabain from the total activity. Numeric values of absorbance were obtained with a Tecan-Spark Elisa reader, with the wavelength set at 630 nm. All experiments were performed at least in triplicates.Cells were seeded in 96-well plates  in the growth medium for 24 h. After 24 h, the Na/K ATPase assay was performed as previously reported . BrieflyColony assay formation was performed by plating 500 cells per well in 60 mm dishes in triplicate for each condition. Twenty-four hours after plating, cells were washed in PBS, and then the drugs were added to cells in a fresh medium. The medium was changed every 3 days. After 3 weeks, cells were stained with crystal violet. Following extensive washes, stained cells were acquired by computer scanning, and retained crystal violet was quantitated by solubilizing cells with 1% SDS and by reading the corresponding absorbance at Tecan Spark Reader at 575 nm.p values are given in figure captions. Student t-tests and ANOVA were used when appropriate for evaluating statistical differences among samples.Statistical analysis was performed using the InStat version 8 software system . Densitometric analyses were performed using the freeware software Image J by quantifying the band intensity of the protein of interest with respect to the relative loading control band  intensity. The