PI3KCA, TP53, BRAF and MUC16. GDF15 suppression by BET inhibition is particularly effective in cancer cells with low activity of the PI3K/Akt axis and high extracellular concentrations of pantothenate. Our findings provide insights that the risk for GDF15 overexpression and concomitant cachexia can be reduced by a personalized selection of anticancer drugs and patients for precision medicine.GDF15 has recently emerged as a key driver of the development of various disease conditions including cancer cachexia. Not only the tumor itself but also adverse effects of chemotherapy have been reported to contribute to increased GDF15. Although regulation of GDF15 transcription by BET domain has recently been reported, the molecular mechanisms of GDF15 gene regulation by drugs are still unknown, leaving uncertainty about the safe and effective therapeutic strategies targeting GDF15. We screened various cardiotoxic drugs and BET inhibitors for their effects on GDF15 regulation in human cardiomyocytes and cancer cell lines and analyzed in-house and public gene signature databases. We found that DNA damaging drugs induce GDF15 in cardiomyocytes more strongly than drugs with other modes of action. In cancer cells, GDF15 induction varied depending on drug- and cell type-specific gene signatures including mutations in  It is a cytokine that mediates the body response to various stress factors such as oxidative stress, tissue injury and drug toxicity. When binding to GDNF-family receptor α-like (GFRAL), localized mainly in the brain stem, it mediates anorexia, alterations in overall metabolism and energy homeostasis and activation of other endocrine stress responses. In health, these mechanisms alert the body to exo- and endogenous toxins by mitigating the exposure and preventing further organ damage through metabolic reprogramming. In disease such as cancer, GDF15 levels can be elevated up to 100-fold, thereby causing pathological conditions4.GDF15 has been found to be a key driver of cachexia syndrome, significant body weight loss that often occurs in chronic diseases including cancer or in case of drug-induced organ injury6. Drug-induced cardiotoxicity is a serious problem often associated with chemotherapy7. Anticancer drugs used in chemotherapy can cause cellular stress and death leading to drastic GDF15 induction in both cancer and host cells10 22 were reported to bind directly to the GDF15 promoter and function as transcription activators. GSK3β23, NFkB24 and p38 MAPK22 were identified to indirectly activate GDF15 expression through transcription activators  proteins, more particularly bromodomain-containing protein 4 (BRD4)31. Currently, treatment of cachexia is predominantly focused on non-specific approaches such as to increase appetite or muscle mass3. However, the identified causal relations of cachexia with chemotherapy32, tumor-secreted factors6, specific molecular pathways and key molecules1 suggest that cachexia is instead a discrete and targetable condition. Specific antibodies targeting GDF15 or its receptor GFRAL have been developed recently to be tested in clinical studies33. These approaches could alleviate the direct effects of GDF15 but do not tackle the underlying transcriptional dysregulation. Although GDF15 overexpression has been identified as an etiological key driver of cachexia, little efforts have been made yet to characterize the molecular basis of GDF15 induction and suppression by drugs. A tool to predict whether a patient will react to a particular anticancer drug with GDF15 overexpression could help reduce risks for chemotherapy-driven development of cachexia. Furthermore, the prevention of GDF15 transcription, e.g., by epigenetic intervention, has the potential to effectively counteract cachexia. This study was therefore designed to understand the molecular basis of drug-specific and cell type-specific regulation of GDF15 gene induction and suppression by screening cardiotoxic drugs in human cardiomyocytes and analyzing in-house and public gene signatures database of cancer cell lines.Cachexia has been considered as an epiphenomenal and unpreventable complication with severe cancer progressions and anticancer treatment. Patients who suffer from cachexia are less responsive to anticancer treatment because of their overall weaknessAs many anticancer drugs are cardiotoxic and GDF15 is a well-known biomarker for heart failure, we employed a human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) model to investigate the effect of drug-induced cardiotoxicity on GDF15 expression in host tissues. To adequately replicate cardiotoxicity using this model, we selected drugs whose cardiotoxic effect in vivo is based on cytotoxicity to cardiomyocytes. Cell viability screening of 105 known cardiotoxic drugs identified 20 drugs which exerted direct cytotoxicity Fig. . Media cTaken together, we found that induction of GDF15 in hiPSC-CMs by cardio- and cytotoxic drugs varies greatly depending on the drug’s mode of action.34. We used linear regression to quantify the cell line’s drug-specific responsiveness regarding GDF15 induction and categorized the cell lines as “responsive” or “resistant”. Comparing the number of responsive cell lines per drug, we found the ratio of responsive cell lines ranged from 4% for sirolimus to 71% for sorafenib , we generated a network comprising key proteins involved in known GDF15 inducing pathways and added reported direct and indirect interactions. We complemented the resulting network with the identified characteristic expression and activity patterns of the four cell line clusters described above. Based on the software-included interactions, we predicted the cluster-specific resulting activities of different GDF15 induction pathways Fig. C.The identified differences in mutations and gene expressions between cell line clusters indicated that cell-line-specific parameters could correlate with the individual extent of GDF15 induction by certain drugs. By differential analysis and statistical testing of gene expression data from individual cell lines with remarkable GDF15 induction behaviour, we identified additional potentially correlating genes. Together with the previously found cluster-typical expression patterns  of GDF15 with BRD4 induction in 50.0% and a negative correlation (Pearson correlation coefficient <  − 0.4) in 18.3% of NCI-60 cell lines. Among the different tissue types 75% of renal and 83% of CNS cancer cell lines showed a positive correlation of GDF15 and BRD4 induction. In a comparison of GDF15 and BRD4 induction between all GDF15 inducing drugs, we found a significant positive correlation for lapatinib, erlotinib and sorafenib  and no significant correlation for other GDF15 inducing drugs.It has recently been reported that basal GDF15 expression depends on BRD4 in pancreatic and melanoma cancer cell lines5. Whilst a personalized selection of chemotherapeutic agents has the potential to mitigate chemotherapy-driven GDF15 overexpression in cancer cells, it leaves basal overexpression of GDF15 unaffected. Through the data analysis described above, we found a correlation between BRD4 and GDF15 expression in 50% of NCI-60, indicating that GDF15 expression might depend on BRD4 in those cell lines. To test this further, we analyzed our own gene expression data of 21 lung and colorectal cancer cell lines treated with the BET inhibitor BI 89499926. It turned out that in 9 of the 21 treated cell lines, GDF15 expression was significantly reduced through BET inhibition . The more GDF15 expression was reduced, the more GDF11 expression tended to get increased upon BET inhibition in the respective cell lines  of each BET inhibitor for BRD2, BRD3 and BRD4, whenever available. GDF15 fold-change thereby meant the ratio of GDF15 expression induced by doxorubicin only and doxorubicin combined with BET inhibitor normalized to cell viability. The strongest negative relation was observed between IC50(BRD4) and GDF15 fold-change  and Wnt-C59  in CDM-3  and metabolic selection with lactate, hiPSC-CMs were plated at 3 × 104 cells/well on 96-well plates or 1 × 106 cells/well on 6-well plates and cultured in CDM3-M medium  supplemented with 500 µg/ml recombinant human albumin , 4 mM sodium L-lactate , 1 mM sodium pyruvate (Sigma Aldrich P5280), 213 µg/ml L-ascorbic acid 2-phosphate , 10 mM D-galactose , 200 ng/ml triiodo-L-thyronine , 20 µg/ml Insulin  and 1 × chemically defined lipid concentrate ) containing 20% FCS  for 24 h before switching to CDM3-M without FCS.hiPSC-CMs were prepared according to the method described in ref.57 and DNA sequencing58 was performed as described previously.Cancer cell lines were authenticated by short tandem repeat analysis performed according to the manufacturer’s instructions and treated with 10 nM, 35 nM or 100 nM BI 894999 BET inhibitor for 4 h or 24 h  library was prepared by serial dilution of 10 mM stocks in DMSO to 9 different concentrations ranging from 0.001 to 10 mM, which were further diluted into assay plates to final concentrations ranging from 0.001 to 10 µM. BI894987 (Boehringer Ingelheim), BI 894999 (Boehringer Ingelheim), CPI203 , GSK1210151A , I-BET762 , JQ1 , OTX015 , PFI-1  and Doxorubicin  were dissolved in DMSO to make 30 mM or 10 mM stocks which were serially diluted in DMSO and assay media to concentrations as indicated. The final concentration of DMSO for hiPSC-CMs assay was 0.1%. To investigate the influence of BET inhibitor treatment on doxorubicin induced GDF15 mRNA expression, hiPSC-CMs were pre-treated with 20 nM or 200 nM doxorubicin for 48 h and kept in medium without compound for 24 h before 24 h treatment with either 100 nM BI 894999 or 500 nM OTX015. We screened the BET inhibitors JQ1, I-BET762, PFI-1, I-BET151, CPI203, OTX015 and BI 894987 for their effect on doxorubicin-induced GDF15 secretion in hiPSC-CMs by simultaneous treatment with 100 nM doxorubicin and 0.1 to 1000 nM of each BET inhibitor. Cancer cell lines were treated with 10 nM, 35 nM or 100 nM of BI 894999 for 4 h or 24 h  and incubated for 2 h at 37 °C. The fluorescence of resorufin generated by the cells was measured on a BioTek Synergy HTX microplate reader equipped with an excitation filter 530/25 nm and an emission filter 590/35 nm. After correction for background medium fluorescence and averaging of biological replicates, we calculated cell viability relative to untreated control for each compound concentration.For GDF15 mRNA quantification, total RNA was isolated from the cells using an RNeasy kit  to synthesize cDNA using a Taqman Reverse Transcription kit . Quantitative real-time PCR analysis was performed using QuantiNova SYBR Green PCR Kit  on a CFX96 Real-Time System (Bio-Rad) under the following amplification conditions: 95 °C for 2 min followed by 40 cycles of 95 °C for 5 s and 60 °C for 10 s. The primer sequences are GDF15-F: 5′-GAGCTGGGAAGATTCGAACA-3′, GDF15-R: 5′-AGAGATACGCAGGTGCAGGT-3′, ACTB-F: 5′-GTCTTCCCCTCCATCGTG-3′ and ACTB-R: 5′-AGGGTGAGGATGCCTCTCTT-3′. PCR data were analyzed using a modified relative expression software tool (REST) and the expression level of GDF15 gene was normalized to that of β-actin. For secreted GDF15 protein quantification, cell culture medium was collected after drug treatment and stored at − 20 °C until ELISA was performed using Human GDF-15 ELISA Kit  according to the manufacturer’s instruction. Resulting GDF15 concentrations were normalized to non-treated control and respective relative cell viability.We obtained basal gene expression values and mutational status of all cancer cell lines from our in-house cancer cell line database. Gene expression data of NCI-60 after compound treatment, basal protein expression, drug sensitivity data, gene dependency data, metabolite expression and compound specifications such as IC50 values and modes of action were collected from different studies and data bases, as described in the Each NCI-60 cell line was assigned to an array containing 15 drug-specific linear regression slopes of GDF15 mean fold-change. We calculated the Pearson correlation coefficient for every cell line pair to build a 60 × 60 correlation matrix. For agglomerative hierarchical clustering, we applied a “Weighted Pair Group Method with Arithmetic Mean” (WPGMA) algorithm to the resulting correlation matrix. The identified clusters represent groups of cell lines that are closely positioned to each other in this 15-dimensional data set and therefore have more similarities regarding GDF15 expression upon treatment with the 15 drugs compared to cell lines of other clusters.59 was used for differential analysis. We searched for significant differences between the four identified NCI-60 cell line clusters in terms of protein expression, gene signature and hallmark gene set scores and mutations. Besides in-house gene expression and DNA sequencing data, we extracted protein expression values from a public data set60. We applied a modified t-test to identify proteins that differed significantly in their expression level between the different cell line clusters. We included proteins with p-values from 0.001 to 0.05 among which are involved in pathways upstream of GDF15 regulation or reported targets of any of the tested drugs. All the proteins with p-values below 0.001 were included regardless of their involvement in GDF15 regulation or drug mechanisms. Differences in mutation prevalence were quantified using Fisher’s exact test and included whenever the p-value was below 0.05 and the respective gene is involved in GDF15 regulation or is a known target of a tested drug. We additionally deduced specific protein and pathway activities from gene and protein expression data using reported gene signatures and hallmark gene set scores. Detailed references are described in the 34. We compared basal GDF15 gene expression and correlation coefficients of GDF15 and BRD4 induction upon treatment between cell lines of different clusters using standard two-sided t-test. Differences in metabolite concentration between cancer cell lines with strong and weak responsiveness to BET inhibition regarding GDF15 and GDF11 , we generated a network consisting of 44 proteins involved in pathways upstream of GDF15. Depending on the studied cell line cluster, this network was supplemented with respective characteristic expression patterns. We connected the entered proteins (Supplementary Table Supplementary Information.


Activating mutations of Ras genes are often observed in cancer. The protein products of the three Ras genes are almost identical. However, for reasons that remain unclear, KRAS is far more frequently mutated than the other Ras isoforms in cancer and RASopathies. We have quantified HRAS, NRAS, KRAS4A and KRAS4B protein abundance across a large panel of cell lines and healthy tissues. We observe consistent patterns of KRAS > NRAS»HRAS protein expression in cells that correlate with the rank order of Ras mutation frequencies in cancer. Our data provide support for the model of a sweet-spot of Ras dosage mediating isoform-specific contributions to cancer and development. We suggest that in most cases, being the most abundant Ras isoform correlates with occupying the sweet-spot and that HRAS and NRAS expression is usually insufficient to promote oncogenesis when mutated. However, our results challenge the notion that rare codons mechanistically underpin the predominance of KRAS mutant cancers. Finally, direct measurement of mutant versus wildtype KRAS protein abundance revealed a frequent imbalance that may suggest additional non-gene duplication mechanisms for optimizing oncogenic Ras dosage. Ras genes are mutated in ~20% of all human cancer cases . There aAt the most fundamental level it must relate to the opportunity and capacity of each Ras isoform to interact with and activate key effector pathways. This is currently best expressed in the Ras “sweet-spot” model that suggests that Ras dosage  will be a major factor in influencing the availability of individual Ras family members to engage cancer pathways . This moRas dosage is known to be important for KRAS-mediated progression of pancreatic and breast cancer and KRAS and NRAS contributions to myeloid malignancies –12. The Importantly, HRAS, KRAS and NRAS protein abundance was never formally measured in these studies to see whether they conformed with the predicted influence of rare codons. Moreover, rather than exhibiting limited expression, KRAS is actually far more frequently amplified in tumors than the other isoforms . KRAS mRIn order to address this, we have quantified Ras protein abundance across a large panel of cell lines and healthy tissues. Whilst our insights do not agree with the predicted influence of rare codons on KRAS protein expression versus other isoforms, we do observe consistent patterns of Ras isoform expression suggesting that relative dosage is an important feature of their biology and disease association. We also observe an imbalance in the abundance of proteins expressed from mutant versus wild type alleles in some cell lines that suggests the existence of additional mechanisms for achieving disease-associated amplification. These datasets and the patterns observed have broad applications in experimental design, network analysis and understanding the contributions of Ras isoforms to normal and disease-associated biology.The patterns of Ras mutations in cancer are suggested to be influenced by rare codon-mediated differences in protein expression. This was established in mice where the KRAS gene is enriched in rare codons versus HRAS . The codTo formally measure Ras dosage, we employed a mass-spectrometry based protein standard absolute quantitation (PSAQ) approach to determine Ras isoform protein copy number per cell Fig.  25, 28,,28, 29. PSAQ was applied to 78 commonly used mouse and human cell lines. All data are derived from three independently processed cell samples where a common Ras standard was spiked into all samples to allow direct comparison between all cell lines. Total Ras abundance derived from H + N + KA + KB peptide measurements ranges over an order of magnitude from ~50,000 to 550,000 proteins per cell Fig. . RelativQuantitation of Ras isoform protein abundance reveal a relative rank order of KRAS4B > NRAS»HRAS > KRAS4A. KRAS4B is the dominant Ras isoform in 52/78 cell lines, whilst KRAS gene products totaled together represent the most abundant Ras in 64/78 of cell lines Fig. . The uppKRAS4A averages only ~22.5% of total KRAS abundance Fig. . This isThe consistent patterns of Ras isoform abundance across a large panel of normal and cancer cell lines derived from two species are compelling. However, it is formally possible that Ras levels may have been influenced by disease of origin, cell derivation and/or culture conditions. To address this, we profiled a panel of tissues freshly derived from three healthy adult CD1 mice. We observed a 4-fold range in total Ras abundance across the tissues Fig. . Our preMany of the cell lines that we profiled harbored KRAS mutations . When normalized to cell size this narrowed to a 2-3-fold difference in the highest versus lowest values of total Ras abundance for a given cell size. Total Ras abundance for an averaged sized cell was ~200,000 copies per cell. This would place Ras in the top 20% of all proteins based on global estimates of mammalian cell proteome copy number , 30, 31.Across cell lines and normal tissues, we observed a consistent pattern of KRAS being the most abundant isoform. This corroborates and significantly extends previous studies that also identified KRAS to be the most abundant Ras isoform in a small panel of cell lines , 25, 32.Intriguingly, the rank order of Ras isoform abundance differed between cell lines and mouse tissues. In all mouse tissues except spleen, HRAS was more abundant than NRAS. In contrast, in all cell lines apart from MCF10a, DLD1 and LU165 cells, NRAS was more abundant than HRAS. The same batches of Ras standards were used for cell and tissue analysis. HT29 cells were included in all PSAQ sample runs and retained the consistent pattern of NRAS > HRAS seen in all previous runs; therefore, we are confident that the mouse tissue data represent a true reflection of Ras abundance in these samples. The switch in abundance may represent general species differences although we note that our cell panel included eight mouse lines that all displayed the same trends as their human counterparts. Alternatively, it may represent adaptive changes between normal versus the disease associated states that our cells were derived from. Further investigation of normal human tissues and relevant mouse models before and after disease initiation are required to test this.The patterns of Ras isoform expression that we observe potentially inform our understanding of why KRAS is more frequently mutated in cancer. Our results challenge the central tenet of the rare codon model that predicted that KRAS expression would be limited relative to the other Ras isoforms , 13, 16 Whilst our data mean that we do not now think that rare codons explain why KRAS is more often mutated in cancer, the rare codon experiments and work by others have conclusively demonstrated that Ras dosage has a significant influence on Ras oncogenic potential –12, 16. The observation in cancer databases of preferential amplification of KRAS versus HRAS or NRAS might seem counterintuitive if it is already the most abundant isoform. If KRAS is already closer to the sweet-spot such that mutation is all that is needed to reach the right level of signaling, then this might suggest that HRAS and NRAS mutations might also be associated with amplifications to allow them to reach the sweet-spot. This would rely on a double genetic perturbation that makes it less likely to be observed. Furthermore, the extent to which these independent mechanisms enhance Ras signaling hasn’t been properly quantified. However, the fact that KRAS is typically targeted suggest that their influence may be similar so that either signaling enhancement mechanism will work for KRAS but will typically be insufficient for the other isoforms in most contexts.Importantly, we are not suggesting that only the most abundant Ras isoform in any given context is going to be the one occupying the sweet-spot when mutated. The position of the sweet-spot will vary depending on cell state, network topology and microenvironmental influences Fig. . In contRas dosage is likely to be more nuanced than measurements of total abundance and it will be interesting to understand if total versus compartment-specific Ras are the critical determinants of the Ras sweet-spot. The type and amount of mutant Ras are also likely to be influential. Mass spectrometry currently represents the only method for directly measuring mutant Ras protein abundance . Our anaOur mutant Ras data provide evidence of fine tuning of cancer-relevant Ras dosage. The dynamic modulation of optimal total and mutant Ras dosage has been elegantly demonstrated in the characterization of allelic imbalances in mutant versus wild type KRAS during tumor outgrowth and in response to therapy in a mouse model . The incRas dosage is also likely to explain Ras isoform-specific contributions to development. KRAS4B is the only essential isoform, deletion results in embryonic lethality as a result of heart defects and cardiovascular problems –41. GeneWhilst our work has provided the first detailed quantitation of patterns of Ras protein abundance, we recognize that there are some important further experiments before we can properly understand the relative contributions of this parameter to total Ras signaling in healthy and diseased states. These include accurately quantifying the impact of different Ras mutations on GTP loading and effector engagement and the extent to which different mutants favor occupation of a signaling competent state. We also need to better understand the context that Ras operates in, at least in part by quantifying relative protein abundance and mutation state of all nodes within the Ras signaling network in relevant human cells and tissues.In summary, we have generated a comprehensive atlas of Ras isoform protein abundance in tissues and commonly used cell lines. Accurately quantifying Ras protein abundance will help parameterize models of signaling networks and inform cell-based Ras studies. Our data reveal new features of Ras biology and challenge and refine models explaining the pattern of Ras mutations in cancer and isoform-specific Ras contributions to development.6 cells were washed twice with ice cold PBS by centrifugation, snap frozen using liquid nitrogen and stored at −80 °C. Pellets were thawed on ice and lysed in NP40 lysis buffer ). Lysate protein concentration was determined using Pierce bicinchoninic acid (BCA) assay. n = 3 independently prepared cell pellets were used for PSAQ analysis.Cell lines were obtained from sources indicated in Supplementary Table °C. Tissue pieces of ~30–50 mg were crushed with a mini pestle chilled on dry ice before lysis in RIPA buffer ). Samples were sonicated on ice and lysate cleared by centrifugation at 17,000 x g. Protein concentration was determined using Pierce BCA assay. Tissues from n = 3 adult mice were used for PSAQ analysis. Sample sizes are based on convention and the high technical reproducibility observed in preliminary experiments, all source data are provided in Supplementary Table Mouse tissues were harvested from adult male B6/129 S mice, snap frozen in liquid nitrogen and stored at −80 13C6-15N2 [ + 8 Da]), L-arginine-U-13C6-15N4 [ + 10 Da])-labelled His-Ras proteins were prepared exactly as described [His-Ras-encoding pTrcHisA plasmids were generated by subcloning Ras coding sequences from constructs kindly provided by Dominic Esposito (NCI Ras Initiative), or available from previous work  These Hiescribed . Ras pro°C, alkylated in 55 mM iodoacetamide for 30 mins at room temperature, quenched with 10 mM DTT for 5 min at room temperature, then digested with 5 ng / μl Trypsin Gold (Promega) in 9% Acetonitrile and 40 mM Ambic, overnight at 37 °C. Trypsin was quenched using Formic acid and extracted peptides dried using a SpeedVac. Peptides were subjected to C18 desalting using an Agilent 1260 Infinity LC system equipped with an MRP-C18 Hi-recovery column  before SpeedVac drying.20 μg of cell lysate or 40 μg of mouse lysate containing spike-ins of the relevant heavy Ras standard were fractionated using SDS PAGE. The region of the gel containing endogenous and His-Ras standards was excised and dissected into ~1 mm gel cubes. These were reduced using 10 mM DTT in 100 mM Ammonium bicarbonate (Ambic) for 1 h at 56 via a Dionex U3000 nano-LC system (Thermo) mounted with a NanoAcquity 5 µm, 180 µm x 20 mm C18 trap and 1.7 µm, 75 µm X 100 mm analytical column (Waters) maintained at 40 °C. A gradient of 2−50% acetonitrile/0.1% formic acid (v/v) over 45 min was applied to the columns at a flow rate of 300 nL/min. The NanoSpray III source of the mass spectrometer was fitted with a 10 μm inner diameter PicoTip emitter (New Objective). The mass spectrometer was operated in positive ion mode using Analyst TF1.6 software (Sciex) and the MIDAS approach (MRM-initiated detection and sequencing) was used to quantify and confirm the identity of the analytes of interest. The optimized transitions are shown in Supplementary Table Desalted samples reconstituted in 0.1% formic acid were delivered into a QTRAP 6500 (Sciex) Area under curve was extracted for at least 3 combined transitions in Analyst software (Sciex), and further analysis performed in Excel. Briefly, ratios of Light:Heavy for each peptide were used to determine the quantity of peptide present per lane in moles, before conversion into copies per cell using the number of cells counted per μg lysate produced. Three repeats of each experiment were performed, and for each condition a mean was calculated from at least 2 values for inclusion in the final data. For KRas4B up to 6 values were obtained as this was analysed in both “wildtype” and “mutant” heavy spike ins. The same analysis was performed for mouse tissue lysates, although copies per ng total protein were calculated. For mouse tissues, KRas4A and 4B proved to be less consistent than NRas and HRas, so a combined value for KRas copies per ng total protein was calculated by subtracting copies of NRas and HRas from the pan-Ras copy number.Supplementary legendsSupplementary Figure 1Supplementary Figure 2Supplementary Table 1


Alloreactive donor-derived T-cells play a pivotal role in alloimmune responses after allogeneic hematopoietic stem cell transplantation ; both in the relapse-preventing Graft-versus-Leukemia (GvL) effect and the potentially lethal complication Graft-versus-Host-Disease (GvHD). The balance between GvL and GvHD can be shifted by removing T-cells via T-cell depletion (TCD) to reduce the risk of GvHD, and by introducing additional donor T-cells (donor lymphocyte infusions [DLI]) to boost the GvL effect. However, the association between T-cell kinetics and the occurrence of allo-immunological events has not been clearly demonstrated yet. Therefore, we investigated the complex associations between the T-cell kinetics and alloimmune responses in a cohort of 166 acute leukemia patients receiving alemtuzumab-based TCD alloSCT. Of these patients, 62 with an anticipated high risk of relapse were scheduled to receive a prophylactic DLI at 3 months after transplant. In this setting, we applied joint modelling which allowed us to better capture the complex interplay between DLI, T-cell kinetics, GvHD and relapse than traditional statistical methods. We demonstrate that DLI can induce detectable T-cell expansion, leading to an increase in total, CD4+ and CD8+ T-cell counts starting at 3 months after alloSCT. CD4+ T-cells showed the strongest association with the development of alloimmune responses: higher CD4 counts increased the risk of GvHD  and decreased the risk of relapse . Similar models showed that natural killer cells recovered rapidly after alloSCT and were associated with a lower risk of relapse . The results of this study advocate the use of joint models to further study immune cell kinetics in different settings. These TTo reduce the risk of severe GvHD, donor T-cell depletion (TCD) can be applied, although this will decrease the GvL effect . In ordede novo T-cell generation from infused hematopoietic stem cells starting at least 6 months after alloSCT, homeostatic proliferation of T-cells present in the patient or graft, T-cell expansion during infections and expansion of alloreactive T-cells responsible for GvL and GvHD. Especially cytomegalovirus (CMV) reactivations are common during the first 3 months after alloSCT and strongly affect the kinetics of both T-cells and NK cells after alloSCT (could have been measured if kept under follow-up). Likewise, DLI and the use of posttransplant prophylactic immunosuppression are known to affect the risks of relapse and GvHD, but may also affect the T-cell kinetics . All patients received an alemtuzumab-based TCD alloSCT after nonmyeloablative conditioning without any posttransplant prophylactic immunosuppression. Patients with an anticipated high risk of relapse were scheduled to receive an early low-dose DLI prophylactically at 3 months after alloSCT, while prophylactic DLI administration for the other patients started at 6 months. In this unique setting we investigated the impact of the early low-dose DLI on the T-cell and NK cell kinetics during the first 6 months after transplant and the association between these kinetics and the development of clinical events.22.1This retrospective study included all adult patients with acute myeloid leukemia, acute lymphoblastic leukemia or MDS in complete morphologic remission after intensive induction therapy who received their first alloSCT from a 9 or 10 out of 10 HLA matched donor using nonmyeloablative conditioning and alemtuzumab-based TCD  between 2.22 orally or 30 mg/m2 intravenously) and busulfan (2 days 4x0.8 mg/kg intravenously), or the FLAMSA regimen: fludarabine (5 days 30 mg/m2 intravenously), cytarabine (4 days 2000 mg/m2 intravenously), amsacrine (4 days 100 mg/m2 intravenously) and busulfan (4 days 4x0.8 mg/kg intravenously). In both regimens, TCD was performed by adding 20 mg alemtuzumab  to the graft before infusion and by administering 15 mg alemtuzumab intravenously on days -4 and -3. Patients with an unrelated donor (UD) received rabbit-derived anti-thymocyte globulin  additionally on day -2 (until April 2010 2mg/kg and thereafter 1mg/kg). None of the patients received posttransplant GvHD prophylaxis.As conditioning regimen patients received either fludarabine (6 days 50 mg/m6 or 1.5x106 T-cells/kg for patients with a related donor (RD) or an UD, respectively. Early low-dose DLIs given at 3 months after alloSCT contained 0.3x106 or 0.15x106 T-cells/kg for patients with a RD or an UD, respectively. Since May 2010, all patients without any relapse and without GvHD requiring systemic immunosuppressive treatment at 6 months after alloSCT prophylactically  were planned to receive the standard DLI. Patients who were considered to have a high risk of relapse based on the disease characteristics or MRD status at time of alloSCT or who received the FLAMSA regimen were also scheduled to receive the early low-dose DLI prophylactically at 3 months after alloSCT. All patients, including those transplanted before May 2010, could receive pre-emptive DLIs in case of MC or MRD positivity, starting from 3 months after alloSCT. Additionally, as part of several clinical trials, patients could receive modified T-cell products prophylactically or virus-specific T-cell infusions to treat severe viral infections.The dose of unmodified pre-emptive and prophylactic DLIs was based on donor type and timing after alloSCT. Standard DLIs given at 6 months after alloSCT contained 3x102.36 cells/L.CMV serostatus was assessed in all patients and donors before alloSCT. After transplant CMV was monitored routinely by PCR on peripheral blood samples in all patients. Absolute numbers of circulating total (CD3+), CD4+CD8- and CD4-CD8+ T-cells, B cells and NK cells were measured routinely at predefined timepoints on anticoagulated fresh venous blood by flow cytometry with bead calibration . Samples were measured either on a FACSCalibur using anti-CD3-APC, anti-CD4-FITC, anti-CD8-PE, and anti-CD45-PerCP or with anti-CD3-FITC, anti-CD16-PE, anti-CD19-APC, anti-CD45-PerCP, and anti-CD56-PE, or on a FACSCanto using anti-CD3-APC, anti-CD4-PB, anti-CD8-FITC, anti-CD16-PE, anti-CD19-PE Cy7, anti-CD45-PerCP, and anti-CD56-PE . The lower detection limit was 0.5x102.49/l) in the absence of relapse or ongoing myelotoxic medication.Relapse was defined as the recurrence of at least 5% blasts on cytomorphologic bone marrow examination or at least 1% blasts in peripheral blood . We defined clinically significant GvHD as the start of therapeutic systemic immunosuppression for GvHD . We defiFor this study we analyzed the T-cell and NK cell kinetics and events during the first 6 months after alloSCT, during which the early immunological recovery and most CMV reactivations take place. Furthermore, during this period the impact of the early low-dose DLI can be assessed, as the standard DLI is given to all eligible patients around 6 months after alloSCT. As part of the analyses assessing the net impact of the early low-dose DLI on the T-cell and NK cell kinetics and clinical events, patients receiving a standard DLI or modified T-cell product as part of a clinical trial were censored at 7 days after this infusion. We considered this to be non-informative censoring, since these interventions were prophylactic and not driven by the clinical course of the patient. For the T-cell kinetics we considered the circulating cell counts of the total (CD3+) T-cell population and the two major T-cell subpopulations: the CD4+CD8- and the CD4-CD8+ T-cells.2.5Probabilities of overall survival (OS) and relapse-free survival (RFS) after alloSCT with associated 95% confidence intervals (95%-CI) were calculated by the Kaplan-Meier method. The cumulative incidences of clinically significant GvHD and relapse from time of alloSCT were estimated by means of the Aalen-Johansen method, treating other failure (as described in the previous section) as a third competing risk.To study the complex interplay between the immune cell kinetics, DLI and clinically relevant endpoints (GvHD and relapse), two joint models were developed; model I starting at time of alloSCT and model II at time of the early low-dose DLI.Shared-parameter joint models consist of two components: a longitudinal submodel, and a time-to-event submodel . The forBelow follows a concise description of the joint models developed for the present application. Detailed explanation of the statistical models and the underlying rationale can be found in the 2.5.1To investigate the effect of early low-dose DLI on the kinetics of the T-cell and NK cell counts after TCD alloSCT, we performed an intention-to-treat (ITT) analysis with a baseline group distinguishing between those scheduled for early low-dose DLI because of a high anticipated risk of relapse (henceforth ‘high risk’ group) and those who were not (‘non-high risk’ group). We chose this approach instead of a per-protocol analysis since we could not properly define a control group of patients who did not receive early DLI but could have been candidates as we did not know for each patient who was not scheduled for early DLI whether he/she would have been able to receive it.To investigate whether the current slope (i.e. rate of increase or decrease of counts at a given moment) of the T-cell counts was associated with the development of GvHD, we also extended the models by adding the current slope of the log counts in addition to the current value to the time-to-event submodel .2.5.2To further investigate the T-cell kinetics after the early low-dose DLI, we constructed a joint model including only the patients who actually received the early low-dose DLI without any prior event of interest  . As shown in To investigate whether administration of the early low-dose DLI increased the numbers of circulating T-cells during the first 6 months after alloSCT, we performed an ITT analysis using model I (see Methods) to compare the 62 high risk patients who were scheduled for early low-dose DLI with the 104 non-high risk patients who were not. All patients had at least 2 T-cell measurements with a median of 6 measurements per patient (interquartile range: 5-8). Although patients showed very different T-cell kinetics over time Figure 23.2.2To investigate whether the T-cell counts increased after the early low-dose DLI as the ITT-analysis suggested, we used model II including only the 42 patients who actually received this DLI without any prior event and modelled the kinetics during the first 3 months after DLI. One of the 42 patients did not have any T-cell measurement during this period and was excluded. Baseline characteristics of the 41 included patients are described in 3.33.3.1To study the association between the T-cell kinetics and the development of GvHD or relapse after TCD alloSCT and DLI, we added disease risk and donor type as time-fixed covariates alongside the time-dependent T-cell counts in the cause-specific submodels  of model I. As shown in We hypothesized that not only the current value but also the slope of the T-cell counts would be associated with the development of an alloimmune response. To investigate this, we extended the time-to-event submodel of model I by additionally including the current slope of the T-cell counts as a covariate for all endpoints. However, we observed no association between the slope of any of the T-cell subsets and the development of GvHD . We therefore retained the simpler version of model I with only the current value.3.3.2To investigate whether higher T-cell counts were associated with a lower risk of relapse, we examined the risk factors for relapse in the time-to-event submodel of model I. Despite the ATG, patients with an UD had a significantly lower risk of relapse than patients with a RD (HRs ranging between 0.2 (95%-CI 0.1-0.5) and 0.3 (95%-CI 0.1-0.8), Of the 36 patients who experienced other failures, 6 died, 8 developed graft failure, 18 required systemic immunosuppression for a non-GvHD indication (of whom 9 received rituximab for EBV) and 4 received a virus-specific T-cell infusion for a severe viral infection. Only in the CD8 model a trend was observed for a higher risk of other failure in patients with an UD receiving an ATG-containing conditioning regimen . Higher CD4+ T-cell counts significantly lowered the hazard of the composite endpoint other failure .3.3.3To investigate whether the T-cell kinetics were also associated with the development of alloimmune responses in the postDLI setting, we used the time-to-event submodel of model II starting from early low-dose DLI with GvHD and non-GvHD events as competing events. We observed no significant association between the current values and the very heterogenous composite endpoint of relapse and other failure Figure 63.46 NK cells/l for almost all patients within 2 months, before the time of administration of the early low-dose DLI. As shown in both CD4 and NK counts as time-dependent covariates. In this model, CD4 counts were significantly associated with the development of GvHD  while the HR for the NK cell counts was 1.07 , supporting that the CD4+ T-cells were the important drivers for the development of GvHD.To investigate the NK cell kinetics and their association with GvHD and relapse, we returned to model I starting at alloSCT. As shown in 4In this study we investigated the interplay between immune cell kinetics and alloimmune responses after both TCD alloSCT and subsequent DLI using joint modelling. In the ITT analysis we observed significantly more GvHD in the high risk patients intended to receive an early low-dose DLI and an increase in T-cell counts starting at 3 months after alloSCT in high risk patients with an UD receiving an ATG-containing conditioning regimen. The ITT allocation was solely based on the disease characteristics of the patients. Since all patients were in complete remission at time of alloSCT, the TCD strategy was similar between the disease risk groups, and all GvHD in the high risk group only occurred after DLI, the only plausible explanation for both the higher risk of GvHD and the associated T-cell expansion is the administration of the early low-dose DLI. We also observed significant associations between the CD4 counts and alloimmune responses after TCD alloSCT and DLI: an increase in CD4+ T-cells was associated with a higher risk of GvHD and at the same time a lower risk of relapse suggesting establishment of a GvL effect. Interestingly, we only observed DLI-induced T-cell expansion in patients transplanted using an UD. This likely reflects an alloimmune response as GvHD was mainly seen in patients with an UD after receiving a DLI, and the T-cell counts after DLI were associated with the development of GvHD. The alloreactive T-cell expansion may have been more easily detectable in patients with an UD compared to RD because of the deeper lymphopenia at time of DLI due to the long-lasting immunosuppressive effect of ATG that patients with an UD received . In addiOur results suggest a DLI-induced T-cell expansion measurable in total numbers of the major T-cell subsets where others did not observe a significant effect of DLI on the T-cell kinetics –21. ThisThere are several limitations to our study. The total CD3, CD4 and CD8 counts are crude measures for potentially alloreactive T-cells, as only donor-derived T-cells can induce GvHD and GvL and the counts are not informative about the subpopulations, activation status or kinetics of specific T-cell clones. Thus, if we had measured the chimerism status and clonality, we might have expected to find stronger associations between the T-cell kinetics and the clinical events. Moreover, our ITT approach attenuated the observed effects of DLI on the T-cell kinetics and clinical endpoints as not all high risk patients received the early low-dose DLI and most patients who did receive this DLI did not receive it at exactly the same time after transplant. Therefore, we constructed model II starting from early low-dose DLI to see whether similar associations were observed. Joint modelling requires substantial numbers of both clinical events and longitudinal measurements to estimate associations with sufficient accuracy. Despite our comparatively larger sample size, the modest numbers of clinical events limited both the accurate estimation of association parameters (between T-cell counts and the endpoints), as well as the inclusion of additional risk factors for each endpoint. This was especially noticeable in our models focusing on the subset of the patients actually receiving an early low dose DLI. Due to the limited number of events, we used suboptimal composite endpoints such as ‘other failure’ and ‘relapse and other failure’, which hampered estimation of the association between the T-cell kinetics and these endpoints.jointly as part of a multivariate joint model, which will account for the correlation between each subset, but may be complicated to fit and will require larger sample sizes. In our study, we were not able to present such a multivariate joint model because of both sample size and software limitations. Nevertheless, results from the exploratory time-dependent cause-specific Cox model for GvHD with both the CD4 and NK counts hint at the importance of modelling immune subsets jointly.Further studies are necessary to assess the clinical implications of the findings from the present work. Aside from validation of our findings, larger studies must be performed to investigate the predictive utility of the T-cell and NK cell counts. While these counts are crude measures, they are often measured standardly and therefore attractive biomarkers for predicting alloimmune responses in patients receiving alloSCT and/or DLI. Further investigation of the immune cell kinetics in other alloSCT settings is needed to see whether similar associations between the T-cell and NK cell kinetics and alloimmune responses can be observed when using joint modelling. For instance, the recent machine learning analysis by McCurdy et al. also suggested important roles of CD4+ T-cells in the development of acute GvHD and of NK cells in the development of relapse after alloSCT with posttransplant cyclophosphamide . For DLIGenerally speaking, further characterization of the circulating T-cell subsets, differentiation and metabolic fitness could provide valuable additional insight in future studies on T-cell kinetics , 41.In summary, joint modelling allowed us to capture the associations between DLI, T-cell and NK cell counts, GvHD and relapse in a very complex clinical setting, even with modest numbers of patients and events. NK cells recover early after alloSCT and may have a protective effect against relapse. We demonstrate that DLI can induce detectable T-cell expansion and observe that the CD4+ T-cells show the strongest association with the development of alloimmune responses. Higher CD4 counts increase the risk of GvHD and decrease the risk of relapse.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Medical Ethical Committee of Leiden University Medical Center. The patients/participants provided their written informed consent to participate in this study.EK, EB, LW, CH, HP and JF designed the study. EK and EB collected and prepared the data, and performed the data analyses under supervision of LW, CH, HP and JF. All authors helped interpreting the data. EK and EB wrote the manuscript with contributions from LW, CH, HP and JF. PvdB, PvB, EM, JT, TS, DL, HV, JF and CH provided the patients care. All authors critically reviewed and approved the manuscript.


Adaptive divergence leading to speciation is the major evolutionary process generating diversity in life forms. The most commonly observed form of speciation is allopatric speciation which requires that gene flow be prevented between populations by physical or temporal barriers, as they adapt to their respective environments. Eventually, these adaptive responses lead to accumulation of mutations in different lines. The increased genetic distance between the lines is known to lead to the populations becoming reproductively isolated. A widely accepted theory is that speciation simply occurs as a by-product of adaptive response of the populations. Several examples of allopatric speciation from ecology and laboratory exist. However, we know little about the nature (pre- or post-zygotic) of barriers that arise first in this process. Understanding the first barriers that arise between populations is key to understanding how the process of speciation initiates. In recent years, fungi have been used as model organisms to answer questions related to the evolution of reproductive isolation. Here, we show rapid evolution of pre-zygotic barriers between allopatric yeast populations. We further demonstrate that these pre-zygotic barriers arise due to altered mating kinetics of the evolved population. Moreover, our non-adaptive evolution experiments with yeast under limited selection pressure also show rapid emergence of reproductive isolation. Overall, our results show that evolution of pre-zygotic reproductive barriers can occur as a result of natural selection or drift.Saccharomyces cerevisiae. These defects arise because of the altered kinetics of mating of the participating groups. Thus, our work provides an understanding of how adaptive changes can lead to diversification among microbial populations.A population diversifies into two or more species—such a process is known as speciation. In sexually reproducing microorganisms, which barriers arise first—pre-mating or post-mating? In this work, we quantify the relative strengths of these barriers and demonstrate that pre-mating barriers arise first in allopatrically evolving populations of yeast,  Origin of Species explained natural selection as a mechanism for populations to adapt to prevailing environmental conditions, we do not yet understand the fundamental evolutionary forces leading to reproductive isolation. According to one idea, reproductive isolation, leading to speciation, is thought to evolve incidentally as a by-product of adaptation of populations to diverging selection pressures in allopatry  were evolved in glucose- and galactose-limited growth media for 600 generations, respectively. Three MATa and MATα lines were evolved in glucose, and three MATa and MATα lines were evolved in galactose—thus, making a total of 12 allopatric populations in the experiment . To study evolution in the absence of selection, 44 independent lines of haploids (22 lines of MATa and 22 lines of MATα) were also evolved in a mutation accumulation experiment for 70 transfers (~1500 generations). We demonstrate that metabolic specialization in the adaptive evolution experiment and the action of drift alone in the non-adaptive experiment leads to rapid evolution of pre-zygotic barriers, in the form of reduced mating efficiency as a result of altered mating kinetics, where the mating efficiency of different strains as a function of time varies quantitatively from each other. We demonstrate that, in specific environmental contexts, acquisition of a few SNPs only can establish significant barriers to mating. Overall, we demonstrate that evolution of reproductive barriers can start with pre-zygotic barriers among populations in allopatry.In this work, we ask the following question: which barriers arise first between populations in allopatry, evolving under selection and drift? To answer this question, we perform adaptive and non-adaptive evolutionary experiments with the yeast During allopatric evolution, an increase in fitness was observed due to a decrease in the lag phase duration and an increase in the growth rate  are as shown in Reproductive barriers between populations can be quantified via studying three processes in yeast: change in mating efficiency, mitotic growth of the hybrid, and meiotic efficiency of the hybrid. The pairwise mating efficiencies between all evolved haploid lines exhibit a marked decrease, as compared with those of the ancestor ; Fig. S2a was switched to α. A similar mating type switch was done for all haploid lines. We then quantified the mating efficiency of each evolved haploid with itself (carrying the opposite mating type). Each of the evolved haploids mates with their mating type switched counterpart with considerably higher efficiency, as compared with the average mating efficiency of the evolved haploid with all other evolved haploid lines . Seven hybrids, all from the glu1a and glu2a lines, could not be created. Thereafter, we characterized the growth kinetics of the 42 hybrid lines in glucose and galactose environments.From the 12 evolved haploid lines and the ancestor The hybrids were separated into five groups. Those resulting from mating between (i) two haploids evolved in glucose (glu-glu), (ii) haploids evolved in galactose , (iii) a glucose- and a galactose-evolved haploid , (iv) a glucose-evolved haploid with an ancestral haploid (glu-anc), and (v) a galactose-evolved haploid with an ancestral haploid . The performance of all five groups of hybrids was compared with that of the ancestral diploid.In glucose, the hybrids in the glu-glu, glu-anc, and the glu-gal group all exhibited a decrease in the growth rate, compared with the ancestral diploid . The fitOverall, our data suggest that, in allopatry, pre-zygotic barriers arise significantly faster as compared with post-zygotic barriers. In the framework of our evolution experiment, adaptation has two components: (i) decrease in lag phase duration and (ii) increase in growth rate. No correlation exists between these values of components of fitness and that of pre- or post-zygotic barriers to gene flow.To identify the genetic basis of adaptation and reproductive isolation between the evolved haploid lines, we sequenced the genomes of the 12 haploid lines, after adaptation for 600 generations , and compared with the ancestral sequence. The sequencing results show evidence of convergent evolution. Two glucose-evolved lines have mutations in MNS1, an ER membrane protein responsible for α-mannosidase activity (a stop codon and a frameshift mutation). MNS1 mutants exhibit a significantly longer life span in yeast  and in oa and their counterparts α (obtained by switching their mating type). As shown in While these similarities in genic targets exist, considerable difference in the mutational targets are also present between the different evolved lines (Table S3). These results lead us to hypothesize that altered mating kinetics in the evolved lines lead to establishment of first reproductive barriers. To test this possibility, we performed mating experiments where kinetics of mating between evolved haploids a and α each in a mutation accumulation (MA) experiment. The 44 lines (a1-a22 and α1-α22) were propagated for 70 transfers . Since a severe bottleneck is imposed at every transfer, the role of selection is largely absent and drift dictates evolutionary trajectory. MA experiments with microbial systems demonstrate that, under constant propagation under these conditions leads to a reduction in the fitness of MA lines , among the 44 lines evolved. There is no correlation between the adaptive response of the MA lines and the extent of change in the mating efficiency with the ancestral haploid. These results clearly demonstrate that reproductive isolation can, in addition to resulting from adaptation, also result as a by-product of drift .While the identity of mutations accumulated in the MA experiment is not yet known, genetic convergence in these lines is unlikely to have happened. MA experiments lead to evolution largely under the action of drift with minimal selection. As a result, the mutational targets’ identity is not related to adaptation in the environment of propagation. This leads to the observation that, in an MA experiment, fitness decreases with the number of propagations , 47.Speciation is the fundamental process that generates diversity of life forms on Earth. The most widely accepted explanation for speciation is that it occurs as a fallout of adaptation to diverging selection. Evolution of reproductive barriers among individuals in a population is a classic long-standing interest among biologists trying to understand the process of speciation. However, which barriers arise first as groups begin to diverge in allopatry?Our data suggest that upon starting with isogenic populations, adaptation in allopatry leads to rapid evolution of pre-zygotic barriers in yeast. In addition, drift alone can also lead to establishment of pre-zygotic barriers. Mating kinetics in yeast are intricately controlled . In factReproductive barriers could arise in several ways. Pre-zygotic barriers have been known to arise rapidly, in response to divergent selection , 54, altSaccharomyces cerevisiae strain used in this work is a derivative of ScPJB644 (MATa MEL1 ade1 ile trp1-HIII ura3-52)  and ScAM05 (MATa TRP1). The ancestral diploid was obtained by mating the two, and the resulting strain is referred to as ScAM06 (MATa TRP1/MAT α URA3). All strains were grown in YPD  at 30°C and shaking at 250 rpm in 25 × 100 mm test tubes unless specified otherwise. The freezer stocks of all strains are stored at −80°C in 25% (vol/vol) glycerol.URA3 was amplified from the plasmid p426GPD  using thThe GAL4c GAL80s-1 strains used in this work are described in , 68.The ancestral populations for the evolution experiment were started from the freezer stocks streaked on YPD plates. Single colonies of each ScAM04 and ScAM05 were inoculated in 5-mL YPD for 36 hours at 30°C and shaking at 250 rpm. These cultures were used as inoculum for the evolution experiment. Three replicate lines of each haploid strains were started in 0.2% (wt/vol) glucose or galactose in standard synthetic complete medium (SCM: 0.671% YNB with nitrogen base and 0.05% complete amino acid mixture). Thus, in total, we maintain 12 populations, three of each kind.Adaptive evolution experiments were performed by serial dilutions every 24 hours in SCM with the appropriate carbon source. Every 24 hours, growing cultures were diluted 1:100 in fresh SCM medium with appropriate carbon source yielding ~6–7 generations every 24 hours. Intermediate generations were cryopreserved in 25% (vol/vol) glycerol every 200 generations.ScAM04 was spread on YPD plates for single colonies. Prior to spreading, a small area was identified and marked on the plate. The colony inside (or closest to) the marked area, after 48 hours of growth at 30°C, was suspended in 2-mL PBS buffer. A fraction of this volume was spread on a fresh YPD plate for single colonies, and the process is repeated for 70 transfers. A total of 22 independent lines of ScAM04 were maintained in this experiment. Similarly, 22 independent lines for ScAM05 were propagated for the MA experiment.α haploids) from freezer stocks were revived in 5-mL YPD and incubated for 48 hours and then transferred 1:100 to a glycerol-lactate medium , 0.671% YNB with nitrogen base, and 0.05% complete amino acid mixture) and incubated for 48 hours at 30°C and shaking at 250 rpm. Two-mL SCM with appropriate carbon source  were inoculated for a final OD600 of 0.01. 150One hundred fifty µL of the cultures was then transferred to a clear flat-bottom 96-well plate (Costar) in triplicates and incubated at 30°C in an automated microplate reader (Tecan Infinite M200 Pro) until they reached a stationary phase. A gas-permeable Breathe-Easy (Sigma-Aldrich) sealing membrane was used to seal the 96-well plates. OD600 measurements were taken every 1 hour with 15 minutes of orbital shaking at 5 mm amplitude before the readings. Growth rates were calculated by plotting log (OD) from the exponential phase of growth against time. The slope of the straight-line fit was determined as the growth rate of the strain. The point of intersection of this straight line, with the straight line with the equation log , was taken as the duration of the lag phase.Evolved lines and ancestor  and incubated at 30°C for 24 hours and re-patched again on GNA plates for another 24 hours. Small lumps of cells were then patched onto sporulation medium plates containing 1% potassium acetate and 2% agar and incubated at 25°C for 5 days and 30°C for 3 days. Sporulated/un-sporulated cells were directly counted under ×400 magnification using a microscope to determine the sporulation efficiency (a minimum of 500 cells). The following formula was used to determine the sporulation efficiency.a-evolved haploid lines after 600 generations were transformed with a plasmid carrying the HO gene to obtain diploids of the evolved lines by selecting on URA dropout plates. The diploids were then sporulated as described previously. Tetrad dissection was performed via standard zymolyase treatment and manual dissection under the microscope. Haploids of both mating types were obtained from a single ascus and identified using colony PCR and primers described in  and resuspended in 100-μL autoclaved water. A volume of 1 mL of 70% ethanol was added along the sides of the vial while vortexing. The cells were then incubated at room temperature for an hour and then kept at 4°C overnight. The cells were then washed with 500 μL of RNase buffer  and resuspended in 100 μL of the same buffer. RNase A was added to a final concentration of 1 μg/μL. The cells were then incubated at 27°C for 4 hours and then washed with 1-mL PBS (centrifuged at maximum speed for 45 sec) and resuspended in 950 μL of the same buffer. Fifty μL of 1-mg/mL propidium iodide was added to a final concentration of 50 μg/mL. This was incubated at room temperature for 30 minutes. A volume of 500 μL of the cell suspension was gently vortexed and sonicated before analysis through flow cytometer .A 5-mL YPD culture was started from a single colony on a YPD plate. Saturated cultures were sub-cultured 1:100. When the cultures reached an OD of 1.00, a volume of 1.5 mL of the culture in the exponential phase was spun down  to perform all sequence data analyses. Illumina paired-end reads were uploaded into the server. The quality of the reads was assessed using FastQC (version 0.72). Raw reads were trimmed with Trimmomatic (version 0.38.1)   and mapp


KMT2A gene represent highly unfavorable prognostic factors and most commonly occur in patients less than 1 year of age. Rearrangements of the KMT2A gene drive epigenetic changes that lead to aberrant gene expression profiles that strongly favor leukemia development. Apart from this genetic lesion, the mutational landscape of KMT2A-rearranged ALL is remarkably silent, providing limited insights for the development of targeted therapy. Consequently, identifying potential therapeutic targets often relies on differential gene expression, yet the inhibition of these genes has rarely translated into successful therapeutic strategies. Therefore, we performed CRISPR-Cas9 knock-out screens to search for genetic dependencies in KMT2A-rearranged ALL. We utilized small-guide RNA libraries directed against the entire human epigenome and kinome in various KMT2A-rearranged ALL, as well as wild-type KMT2A ALL cell line models. This screening approach led to the discovery of the epigenetic regulators ARID4B and MBD3, as well as the receptor kinase BMPR2 as novel molecular vulnerabilities and attractive therapeutic targets in KMT2A-rearranged ALL.In acute lymphoblastic leukemia (ALL), chromosomal translocations involving the  KMT2A) gene in chromosome 11q23, in which the N-terminus of KMT2A fuses with the C-terminus of one of its translocation partner genes, such as AFF1 , MLLT1  or MLLT3  [KMT2A-rearranged infant ALL patients are at best 40% [Acute lymphoblastic leukemia (ALL) represents the most common type of cancer diagnosed in children and is currently curable in ~90% of patients . UnfortuKMT2A plays an essential role in regulating gene expression during early development and hematopoiesis [Functionally, wild-type opoiesis  regulatiopoiesis ,7,8. In opoiesis ,9,10, leopoiesis ,12.KMT2A-rearranged ALL (KMT2A-r ALL) may be improved by using epigenetic-based drugs targeting epigenetic vulnerabilities that are specifically essential to this type of leukemia. For instance, we recently showed that KMT2A-r ALL responds remarkably well to histone deacetylase (HDAC) inhibition [FLT3, a receptor tyrosine kinase, has been previously identified as a vulnerability in KMT2A-r ALL. A recent clinical trial with the FLT3 inhibitor lestaurtinib revealed that patients whose leukemia blasts exhibited sensitivity to FLT3 inhibition ex vivo experienced benefits from the addition of the FLT3 inhibitor to chemotherapy [KMT2A-r ALL is an epigenetically driven malignancy, combinations of epigenetic-based drugs and kinase inhibitors may well represent effective treatments for this elusive type of leukemia.In addition to the use of immunotherapeutic approaches such as blinatumomab , treatmehibition ,15. Intehibition ,17. Moreotherapy . Given totherapy , and thaKMT2A-r ALL cells by applying in vitro clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9 (Cas9) knockout screens using synthetically designed single guide RNA (sgRNA) libraries [KMT2A-r ALL.Therefore, we set out to identify novel epigenetic regulators and kinases specifically essential to ibraries ,21 direcibraries ,23. CRISibraries . In the KMT2A-r ALL and those essential for ALL in general, we performed screens in KMT2A-r ALL cell lines  as well as wild-type KMT2A (KMT2A-w) B-cell precursor (BCP) ALL cell lines . The experimental outline  and kinome (504 genes). To distinguish between genes that are specifically essential to  outline A is base outline B.Approximately 67% or 76% of the sequenced reads at baseline (day 0) could be mapped to the epigenome or kinome sgRNA library, respectively. Read counts for non-targeting control sgRNAs remained stable between day 0 and day 21, indicating that the transduction of non-targeting sgRNAs did not affect cell viability or proliferation . Read coKMT2A-r ALL cell lines against those of KMT2A-w ALL cell lines , we have selected the receptor that exhibits the most differential response between KMT2A-r and KMT2A-w ALL cells for further validation, i.e., BMPR2.First, we determined the difference in z-scores between ll lines C,D. We i2A-w ALL C. In addLL cells D. Next, ll lines E,F. Thished data ,30,31,32d CREBBP E, as welBP . The location of these sgRNA sequences in ARID4B and MBD3, respectively, are indicated in + leukemic cells was progressively and significantly reduced over time in KMT2A-r ALL cells line SEM, but not in KMT2A-w ALL cell line 697  using the Synthego ICE Analysis tool. When genes that are essential for cell viability are targeted, knockout leads to cell death accompanied by the loss of the sgRNA sequence from the cell pool. In the case of ARID4B, all tested sgRNAs showed low KO scores ranging from 1% to 35% in the KMT2A-r ALL cell line SEM, with the commercially available sgRNA  producing the lowest score  revealed a significant loss of GFP signal in the line 697 E. Moreov97 cells F. Hence, BCP-ALL G–I.KMT2A-r ALL. These efforts and additional validation experiments revealed PSIP1, CREBBP, and kinase FLT3 representing known vulnerabilities in KMT2A-r, as well as ARID4B, MBD3, and BMPR2 as potential candidates to be considered as novel therapeutic targets in this aggressive type of leukemia. While the selected cell lines may not be a complete representation of KMT2A-r and KMT2A-w ALL, it is important to note that this approach successfully pinpointed well-established vulnerabilities specific to KMT2A-r ALL, such as CREBBP, PSIP1, and FLT3. These findings align with existing knowledge about the involvement of these genes in KMT2A-r ALL, supporting the reliability of our approach in detecting relevant targets.In this study we used CRISPR-Cas9 drop-out screens using sgRNA libraries against the human epigenome and kinome in various cell line models to identify novel molecular vulnerabilities in ARID4B, encoding AT-Rich Interaction Domain 4B (a member of the ARID family of chromatin remodeling proteins), is specifically lethal to KMT2A-r ALL cells. ARID4B plays a role in various cellular processes including embryonic development, cell proliferation, differentiation, and apoptosis [ARID4B plays a significant role in cancer development, metastasis, and cancer-related signaling pathways in different types of human cancers, including breast cancer, ovarian cancer, and hepatocellular carcinoma [ARID4B has the capability to interact with DNA sequences rich in AT base pairs, enabling it to recruit the SIN3A complex in specific regions of the genome. Once recruited, the SIN3A complex interacts with histone deacetylases (HDACs), resulting in the deacetylation of histones at the corresponding DNA locus, leading to chromatin condensation and transcriptional repression. Although we show here that ARID4B is essential to KMT2A-r ALL cells, the exact mechanistic role of ARID4B in this aggressive type of leukemia remains to be elucidated and further explored in the context of KMT2A-r ALL patient samples. From a therapeutic perspective, it would obviously be of interest to evaluate small molecule inhibitors of ARID4B in patient samples in vitro and in vivo using patient-derived xenograft (PDX) mouse models. To date, however, ARID4B inhibitors are not available.As shown, loss of poptosis ,49,50,51arcinoma ,47,52,53arcinoma ,55. WithARID4B, loss of MBD3 was not necessarily lethal to KMT2A-r ALL cells, but rather inhibited leukemic cell proliferation. MBD3 encodes a member of the methyl-CpG binding domain (MBD) protein family, which preferably binds to 5-hydroxymethylcytosine-marked genes, and has been implicated in various cellular processes, including cell differentiation, pluripotency, and cellular reprogramming [MBD3 expression or function has been observed in different human cancers, suggesting its involvement in tumorigenesis and cancer progression [MBD3 in KMT2A-r ALL as opposed to KMT2A-w BCP-ALL. Unfortunately, as is the case for ARID4B, no known MBD3 inhibitors are currently available.In contrast to gramming ,57,58. Mgression ,60,61,62BMPR2 to be lethal to KMT2A-r ALL cells. BMPR2 encodes a member of the bone morphogenetic protein (BMP) receptor family of transmembrane serine/threonine kinases. BMP signaling is activated by binding of TGF-beta superfamily ligands and is involved in various cellular processes, including embryonic development, tissue homeostasis, cell differentiation and hematopoietic stem cell (HSC) renewal [ACVR1 and BMPR1A to be potential molecular vulnerabilities in KMT2A-r ALL  mouse models. Moreover, conducting further exploratory experiments involving knockdown (instead of knockout) techniques such as siRNA- or shRNA-mediated RNA interference, might provide valuable insights. Additionally, it would be intriguing to investigate whether the recently identified therapeutic targets are under the influence of the KMT2A fusion complex. Furthermore, exploring the potential of MENIN inhibition, which targets the interaction between MENIN and the KMT2A fusion complex [KMT2A-r ALL.In summary, the present study provides novel molecular vulnerabilities of  complex , to effeKMT2A-rearranged B-ALL cell lines utilized in this study include SEM , KOPN-8  and ALL-PO [KMT2A::AFF1+). ALL-PO was a gift from the lab of Prof. dr. Cazzaniga . The KMT2A wildtype B-cell precursor (BCP) ALL cell lines utilized include 697  and NALM-6 ; ACC 128 DSMZ). All leukemia cell lines were cultured in RPMI-1640 medium containing GlutaMAXTM supplemented with 10% fetal calf serum, 100 IU/mL Penicillin and Streptomycin, and 0.125 µg/mL Amphotericin B , at 37 °C under a 5% CO2-containing atmosphere. HEK293T cells  were used for virus production and maintained in Dulbecco’s modified Eagle medium  with similar supplements and cultured under similar conditions. All cell lines were routinely tested for the absence of mycoplasma and DNA fingerprinted to assure cell line authenticity.The pediatric d ALL-PO   of <0.3 to minimize the number of cells with more than one genetic editing event. We aimed to obtain at least 500 sequencing reads per sgRNA by using 1000 cells per sgRNA at the starting point of the screen. Transduction was facilitated using 4 µg/mL polybrene (Millipore #TR-1003-G). To find the optimal virus volume for achieving an MOI of <0.3, each new cell type and virus lot was tested by a titration, which was assessed by measuring cell viability  on a Cytoflex S flow cytometer . 24 h after transduction, the medium was replaced to remove polybrene and puromycin selection (1 µg/mL) was initiated one day later. Then, 48 h after puromycin selection, the cells were harvested, representing baseline (Day 0) samples. The remaining surviving cells carrying the sgRNA library were maintained for another 21 days and passaged every three to four days, to harvest cells on day 21. Genomic DNA was isolated using the Trizol reagent  according to the manufacturer’s instructions. The sgRNA sequences were recovered by a first round of PCR  by using 5 µg genomic DNA to ensure the sgRNA pool complexity. The PCR primer sequences were forward 5′-ACACTCTTTCCCTACACGACGCTCTTCCGATCT NNNNNNGGCTTTATATATCTTGTGGAAAGGACG-3′ with NNNNNN as barcode and reverse 5′-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTACTGACGGGCACCGGAGCCAATTCC-3′ as described previously ,23,75. TKMT2A-rearranged and wild-type KMT2A ALL cell line models were assessed to identify genes specifically essential to either type of ALL.Analysis of the data was performed on the web-based analysis platform Galaxy version 0.5.8.4 , using default parameters. Sequencing reads were analyzed in a model-based analysis of genome-wide CRISPR-Cas9 knockout (MAGeCK) , a publiThe candidate molecular vulnerabilities emerging from our CRISPR-Cas9 sgRNA library screens were validated by examining cell survival upon gene knockout using a competition assay .https://ice.synthego.com (accessed on 22 March 2022)). The primers used are listed in The doxycyclin (DOX) inducible pCW-Cas9 vector (Addgene #50661) was utilized to express CAS9. For each target gene, one or two gRNAs were chosen from the CRISPR KO screen sgRNA libraries, complemented with one or two commercially available gRNAs against the genes of interest , IDT. EaThe levels of protein expression were determined by immunoblot analysis. For this, protein was extracted using RIPA buffer supplemented with protease inhibitors (ThermoScientific), and resolved on precast TGX™ gels and transferred to an 0.2 µm nitrocellulose membrane using a Transblot Turbo Transfer System (Bio-Rad). Blots were then probed with antibodies against MBD3 , ARID4B (#A302-233A-M Bethyl labs), B-actin (#ab6276 Abcam) or GAPDH . The membranes were then probed with infrared-labeled secondary antibodies IRDye 800 CW goat-anti-rabbit antibody  and IRDye 680 goat anti-mouse antibody . Proteins were visualized using an Odyssey Infrared Imaging System , and protein expression was quantified using the Odyssey software Image Studio Lite version 4.0.FACS analysis experiments for determination of BMPR2 expressing cells were performed on a CytoFlex Flow Cytometer (Beckman Coulter). Cells were fixed with 1% PFA before blocking with Human TruStain FcX™  and subsequently labeled with eBioscience™ Fixable Viability Dye eFluor™ 450  to select for viable cells. Subsequently the cells were stained with BMPR2 antibody  and PE goat anti mouse IgG as secondary antibody (#405307 BioLegend) according to the manufacturer’s recommendation. Raw CytoFLEX data were processed using CytExpert version 2.3 (Beckman Coulter).t-tests as indicated in the figure legends. All statistical analyses were conducted using GraphPad Prism8, version 8.3.4. p < 0.05 was considered statistically significant.Statistical significance of independent experimental replicates in the graphs were determined using two-sided Student’s


More than half of CRC patients develop metastatic disease (mCRC) with an average 5-year survival rate of 13%. Circular RNAs (circRNAs) have recently emerged as important tumorigenesis regulators; however, their role in mCRC progression remains poorly characterized. Further, little is known about their cell-type specificity to elucidate their functions in the tumor microenvironment (TME). To address this, we performed total RNA sequencing (RNA-seq) on 30 matched normal, primary and metastatic samples from 14 mCRC patients. Additionally, five CRC cell lines were sequenced to construct a circRNA catalog in CRC. We detected 47 869 circRNAs, with 51% previously unannotated in CRC and 14% novel candidates when compared to existing circRNA databases. We identified 362 circRNAs differentially expressed in primary and/or metastatic tissues, termed circular RNAs associated with metastasis (CRAMS). We performed cell-type deconvolution using published single-cell RNA-seq datasets and applied a non-negative least squares statistical model to estimate cell-type specific circRNA expression. This predicted 667 circRNAs as exclusively expressed in a single cell type. Collectively, this serves as a valuable resource, TMECircDB (accessible at   Tumor growth in the colon and rectum, classified as colorectal cancer (CRC), currently ranks third in terms of incidence and second in terms of mortality globally ,2. In thTo date, CRC research has primarily focused on the dysregulation of protein-coding genes to identify oncogenes and tumor suppressors as potential diagnostic and therapeutic targets . One emeet al. discovered that it may be able to function through alternative mechanisms depending on its specific location within the TME. They reported that ciRS-7 was completely absent in CRC cancer cells, but highly expressed in stromal cells. The putative ‘sponging’ target miR-7, on the other hand, was only found to be expressed in cancer cells, rendering the miRNA sponge mechanism implausible  sponges that counteract their target miRNA’s inhibitive effects . Howeverlausible . As thisMany recent studies have shown that many circRNAs are implicated in crucial hallmarks of CRC tumorigenesis, such as sustaining proliferative signaling, avoiding immune destruction, activating invasion and metastasis, and evading cell death and senescence ,14,19–58Furthermore, multiple obstacles have yet to be overcome in the study of cell-type specific expression of circRNAs. Most known single-cell RNA-seq (scRNA-seq) protocols require poly(A) selection, without which the sequencing data tend to be very noisy . As circhttps://www.maherlab.com/tmecircdb-overview.To address this, we performed RNA-seq of matched normal, primary and distant metastatic tissues from CRC patients and conducted a meta-analysis to discover differentially expressed (DE) circRNAs in metastatic tumors compared to primary tumors, termed circular RNAs associated with metastasis (CRAMS). Further, to overcome the limitations of determining cell-type specific expression of circRNAs in the TME, we performed cell-type deconvolution followed by a non-negative least squares (NNLS) model to estimate cell-type specific expression of circRNAs ,63. FromN = 14) were enrolled at Washington University School of Medicine in St Louis (WUSTL) and informed consent was obtained under an Institutional Review Board (IRB)-approved protocol. Adjacent normal colon , primary CRC , liver metastasis  and normal liver  tissues were resected from 14 stage IV mCRC patients and fresh frozen prior to RNA extraction. Input RNA quantity was 1 μg except for samples 15 (0.5 μg) and 27 (0.24 μg) due to low volume  supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin. SW480 and SW620 cells were grown in DMEM (Invitrogen) supplemented with 10% fetal bovine serum  and 1% penicillin/streptomycin. LoVo cells were grown in DMEM/F12 (Invitrogen) supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin. Total RNA inputs of 1 μg were used to generate libraries using the New England Biolabs NEB Ultra II Directional RNA Library Prep for Illumina with rRNA Depletion kits. To construct the cell line reference database, 200 ng aliquots of each library were pooled and used as input for cDNA capture. The IDT xGen Exome Research Panel v2 including 415 115 exon probes was used for exome capture and paired-end sequencing was performed on Illumina HiSeq X.For each cell line sample, uniquely mapped reads and spliced reads were compared with exon probes from IDT xGen Exome Research Panel v2 using BEDtools v2.29.0 overlap to identify on- and off-target reads that were subsequently used to calculate cDNA-capture sequencing on-target rates .et al.  catalog as described in Silva-Fisher et al. . RedundaAs backspliced reads do not map to the linear transcriptome, chimeric alignments were extracted as a BED file from alignment results via STAR v2.7.3a . The chiA total of 740 513 nonredundant circRNAs were downloaded from circAtlas v2.0, which encompasses annotations (with necessary liftover from previous genome assemblies) from circbank, circBase, CIRCpedia v2, CircRiC, circRNADb, MiOncoCirc, deepBase v2.0, TCSD and CSCD . AdditioP < 0.05, |log fold change (FC)| > 0 and FDR adjusted P ≤ 0.15 was considered differentially expressed.To identify circRNAs associated with CRC tumor and metastatic progression, DE analysis was carried out comparing normal, primary and metastatic tissues via edgeR v3.30.3, using raw counts of linear genes and circRNAs . All linP < 0.05) in the same comparison group and in the same direction (up- or downregulated) and/or circRNAs found by CircTest to be differentially expressed (P < 0.05) in the same comparison group were considered validated in parallel.As means of orthogonal validation, the CIRI_DE_replicate function of CIRIquant v1.1.2  and the R package CircTest v0.1.1 were also run on the same cohort of detected circRNAs ,80. Circet al.  values Figure , step 6.To assess the performance of the NNLS models, we reconstructed ‘bulk’ expression from each model’s estimated cell-type specific expression of both linear genes and circRNAs, which was then compared with the ground truth to evaluate how accurately the model recaptures the real expression values Figure .L) of every gene in every sample was calculated through matrix multiplication of NNLS estimated coefficients (matrix B) and CIBERSORT estimated cell-type proportions (matrix p). The modeled ‘bulk’ expression (matrix L) was compared with the ground truth bulk expression (matrix l). A linear regression model was used (R function lm) to evaluate the correlation of expression between modeled and ground truth bulk data for each gene . Pearson correlation coefficients were calculated using R function cor.test for each sample  of every circRNA in every sample was calculated through matrix multiplication of NNLS estimated coefficients (matrix C) and CIBERSORT estimated cell-type proportions (matrix p). Modeled ‘bulk’ expression (matrix N) was then compared with the ground truth bulk expression (matrix n). A linear regression model was used (R function lm) to evaluate the correlation of expression between modeled and ground truth bulk data for each circRNA . Pearson correlation coefficients were calculated using R function cor.test for each sample ; 3360 were shared by normal and tumor samples . Overall, 1644 unique circRNAs were shared between all tissue types Figure .For downstream analyses of DE and cell-type specificity, RNA-seq reads mapping to linear genes were also quantified . A totalWe established a cell line reference catalog by sequencing five common CRC cell lines, including three derived from primary tumors  and two from metastatic tumors (SW620 and LoVo), termed WUSTL cell lines Figure . The tarA total of 32 469 unique circRNAs were detected in all cell lines, originating from 6666 parental genes. A total of 1325 circRNAs were found in common in all cell lines . Out of To identify circRNAs that have yet to be characterized in CRC patient samples, we compared our results with the CRC cohort from the pan-cancer circRNA consortium MiOncoCirc Figure  74). In. In74). To investigate novel circRNAs unannotated to date, we compared all unique backsplice junctions detected in the WUSTL patient cohort and WUSTL cell lines with the integrative circRNA resource circAtlas encompassing multiple existing circRNA databases, dubbed ‘other databases’ Figure  69–77)..69–77). It is worth noting that many circRNA databases only characterize unique circRNAs by their backsplice junction coordinates, without taking into consideration possible different isoforms arising from the same backsplice junction. Hence, the true percentage of novel, unannotated circRNAs in our datasets may be >14%.To characterize circRNAs altered in the mCRC progression, we performed DE analysis using edgeR comparing normal, primary and metastatic tissues from the WUSTL patient cohort Figure . All expTo provide additional evidence for the DE status of these candidates, we performed two parallel DE tests using CIRIquant and CircTest on the same cohort of circRNAs detected from the WUSTL patient cohort ,80. AmonSeveral previously characterized circRNAs that are associated with mCRC or other types of metastatic cancer were shown to be differentially expressed with supporting evidence from at least one other DE test Figures . This inIn general, more circRNAs showed lowered expression in primary or metastatic tumors compared with normal tissues Figure . For exaTo better understand circRNAs dysregulated in CRC in the context of TME, we evaluated their cell-type specific expression in bulk patient RNA-seq. To do so, we leveraged existing patient scRNA-seq data from two independent cohorts, termed the Lee 2020 scRNA-seq cohort  and the Li 2017 scRNA-seq cohort , to build signature matrices Figure  81,82)..82). UsiThe resulting cell-type proportion estimates from CIBERSORT were then used to train an NNLS statistical model to estimate the cell-type specific expression value of circRNAs based on their bulk expression  values Figure , step 6.P < 0.05) was predicted by the Lee 2020 model (93%) than by the Li 2017 model (90%). The percentage of circRNAs (P < 0.05) from the Lee 2020 model (55%) was also greater than that from the Li 2017 model (46%). There was an overall increase in the linear regression R2 values in the Lee 2020 model as compared to the Li 2017 model in linear genes with significant P-values, with R2 ranging from 0.126 to 0.997 with a mean of 0.592 in the Lee 2020 model and R2 ranging from 0.126 to 0.997 with a mean of 0.586 in the Li 2017 model  in the Lee 2020 model and 1725 (6%) in the Li 2017 model, and a consensus of 1217 (4%) between models  unique circRNAs only showed expression in one cell type, while the Li 2017 model predicted 7458 (26%). The consensus between both models showed 4123 (14%) circRNAs with exclusive expression in a single cell type. To select those with significant cell-type specificity, we required the following criteria: s Figure . To furts Figure .P-values, was reported to be specific to CRC cells . Th. Thde noOur study has addressed many limitations of existing CRC research. To date, there has not been a comprehensive, spatial expression profile for circRNAs in mCRC. For example, the pan-cancer database of expressed circRNAs across cancers, MiOncoCirc, consists of 868 patient samples, of which 217 (25%) are from prostate cancer, whereas only 13 (1.5%) samples are from CRC. Further, since these CRC samples were not curated from adjacent sites of normal, primary and metastatic tissues from the same patients, it proved challenging to systematically identify dysregulated circRNAs throughout mCRC progression . Our uniWe performed transcriptome profiling of a matched cohort of mCRC patients and characterized CRAMS. We detected 28 670 unique circRNAs, 51% of which were isoforms unannotated in MiOncoCirc Figure . ThroughA vast majority of DE circRNAs were downregulated in primary or metastatic tumors, which could be attributed to the faster cell proliferation diluting their global abundance as the cancer progresses, or increased export to exosomes due to their high stability ,74. CompIdentifying the spatial origin and cell-type specific expression of circRNAs is paramount to understanding their biological roles as potential TME regulators. To address the lack of cell-type deconvolution methods involving circRNAs, we devised a unique computational approach to identify cell-type specific circRNAs associated with the TME of CRC. By combining deconvolution analysis using two published scRNA-seq datasets and building an NNLS statistical model, we discovered that 667 circRNAs were exclusively expressed in one cell type only. Our results were congruent with earlier findings by highlighting known cell-type specific circRNAs such as ciRS-7 being exclusive to stromal cells Figure  18). In. In18). R2 range was similar to that of the linear genes but with lower maximum R2 values  and GitHub (https://github.com/ChrisMaherLab/TMECircDB). The RNA-seq data generated in this study (WUSTL patient cohort and WUSTL cell lines) have been deposited in the NCBI Gene Expression Omnibus database under the accession code GSE221240. The Lee 2020 scRNA-seq data referenced during the study are available in a public repository from the NCBI Gene Expression Omnibus under the accession code GSE144735. The Li 2017 scRNA-seq data referenced during the study are available in a public repository from the NCBI Gene Expression Omnibus under the accession code GSE81861. All the other data supporting the findings of this study are available within the article and its The cell-type specific expression data of identified circRNAs are available at the TMECircDB resource: zcad021_Supplemental_FilesClick here for additional data file.