The cross-tissue prediction was driven by processes previously linked to diabetes. Genes contributing to this prediction were involved in the electron transport chain as well as kidney development and support oxidative stress as a molecular process underlying the comorbidity between both conditions. Together, the present results suggest a molecular commonality between schizophrenia and glycemic markers of type 2 diabetes.Schizophrenia is substantially comorbid with type 2 diabetes (T2D), but the molecular basis of this effect is incompletely understood. Here, we show that a cortical schizophrenia expression score predicts glycemic control from pancreatic islet cell expression. We used machine learning to identify a cortical expression signature in 212 schizophrenia patients and controls, which explained ~25% of the illness-associated variance. The algorithm was predicted in expression data from 51 subjects (9 with T2D), explained up to 26.3% of the variance in the glycemic control indicator HbA T2D prevalence is increased 2–3-fold compared with the general population and family history of the illness is more common3. While T2D can be a consequence of antipsychotic treatment, glycemic alterations have been found in antipsychotic naive subjects, supporting a disease intrinsic molecular comorbidity between the two conditions5. Although at the genome-wide level schizophrenia and T2D show no genetic correlation6, molecular investigations found shared biological alterations in both illnesses. These include elevated levels of insulin and closely related molecules such as IGF, a metabolic profile also found in treated as well as medication-naive patients with schizophrenia7. On a candidate gene basis, individual risk variants have been implicated in both conditions8, supporting shared underlying genetic determinants. At a systems level, mitochondrial dysfunction has been suggested as unifying biological theme underlying T2D and schizophrenia10. In schizophrenia, increased mitochondrial β oxidation and upregulation of insulin signaling proteins are thought to indicate a state of glucose/energy starvation in the prefrontal cortex that may, in turn, lead to increased oxidative stress9. In T2D, abnormal skeletal glucose transport plays a major part in the molecular etiology of insulin-resistant T2D11. This defect is thought to arise from fatty acid-induced inhibition of insulin receptor (IRS-1) phosphorylation, potentially due to intramyocellular fatty acid accumulation that may result from abnormal mitochondrial fatty acid oxidation11. At the same time, mitochondrial dysfunction in pancreatic β-cells leads to an increase of reactive oxygen species that is thought to underlay the progressive development of β-cell failure, a central part of the T2D pathology12. Despite these intriguing data, it remains unclear whether molecular commonality of these two disorders can be demonstrated at a biological systems level.Patients with schizophrenia die, on average, about two decades earlier than healthy peers, an excess mortality largely due to somatic illnesses such as type 2 diabetes (T2D)1c) levels, a quantitative readout of glycemic control, where values above 6.5% have been suggested as a diagnostic test for diabetes13. The cross-tissue prediction performed here was based on the assumption that (I) schizophrenia is associated with molecular alterations that are at least partially systemic and can be detected in central as well as peripheral tissues and (II) that such alterations are consistent in the direction of their change. We tuned the polygenic model toward schizophrenia relevant biological processes, through pre-selection of genes among gene ontology categories most associated with genetic schizophrenia risk14. This also allowed exploration of whether peripheral effects of the schizophrenia signature were masked by those more strongly linked to risk, which may be more brain specific.To address this, we used a machine learning approach to identify a polygenic schizophrenia signature and explored its impact on T2D  were used to identify a polygenic schizophrenia model in the brain. A data set of pancreatic islet cell expression (GSE38642) was used to test the association of a predicted schizophrenia score with glycemic control. A further data set comprising transcriptome-wide expression data from pancreatic beta cells (GSE25462), acquired from T2D patients and controls using laser capture microdissection, was used for validation of the cross-tissue prediction. Finally, frontal cortex expression data sets from patients with HIV encephalitis (GSE3489) and Alzheimer’s disease (GSE36980) were used as negative controls. Data sets were identified through manual search from the GEO database (freeze date for search February 2017) and details of the data sets can be found in Supplementary Tables 16. Multiple reads mapping to the same gene symbol were averaged. To exclude potential outliers, we visually inspected the first two principal components determined for each data set and excluded one patient in GSE53987 and two in GSE21138. Expression data were filtered to contain only genes that overlapped across all discovery studies, resulting in a total gene number of 17,062. To make overall expression levels comparable between brain and pancreatic islet or negative control data, data were quantile normalized again based on the, respectively, overlapping set of measured genes.Out of six identified cortical post mortem expression data sets comprising schizophrenia patients, two (GSE17612 and GSE21935) were excluded due to high average age (70.6 years vs. 44.7 in the remaining four data sets). Data were preprocessed using the robust multi-array average function of the R package affy, performing background correction, log2 transformation, and quantile normalization17. Pancreatic islet expression data were residualized against the same covariates, except for site. Brain pH was not used as a covariate, since measures were not fully available for all data sets. The pancreatic beta-cell validation data, as well as the Alzheimer’s disease negative control data, were residualized against the same covariates. The former data set was additionally residualized against the first principal component determined from the entire expression data set. This was performed since the authors reporting this study showed the first principal component to capture sample measurement dependent effects on overall variation18. Since no metadata were available for the HIV negative control data set, this data set was residualized only against its first principal component.To prevent an impact of potential covariates on the ability to derive a polygenic profile from expression data, data sets were first normalized with respect to these variables. Specifically, for each of the schizophrenia brain expression data sets, we determined residuals after regressing expression information from each gene against site, gender, age, and post mortem interval (PMI), as well as the second polynomial of age. If a data set contained more controls than patients, this step was preceded by propensity score matching to identify a 1:1 matched sample based on age, gender, and PMI. This was performed using the R library MatchIt19. The selection was performed among the genes linked to schizophrenia  and focused on gene ontology only, since of the 4949 gene sets that were investigated by the PGC and annotated by five databases , 4550 were from the gene ontology14. Among these, we selected the top 200 categories to obtain a likely over-inclusive list of genes. Subsequently, we explored the impact of removing genes in the most schizophrenia-associated categories. For this, we removed genes in the top 0, 20, 40, 60, 80 and 100 categories and corrected the resulting P-values for the family-wise error rate (FWER) according to the method of Bonferroni.To identify genes to be used for algorithm training, we selected those part of gene ontology categories that have previously been associated with schizophrenia, based on genome-wide functional analysis of GWAS data20, available from the Expression Atlas21. The conventional 0.5 FPKM (fragments per kilobase of exon model per million reads mapped) was used as expression cut-off.To compare expression levels between brain and pancreatic tissue, we used RNA-Seq data from human tissue samples from the genotype-tissue expression (GTEx) project22: (I) determine the top n genes associated with case–control status. We used linear correlation for this purpose and chose n to be the sample number divided by 10. This selection was made since expression data were expected to show sufficient effect sizes such that accurate classifiers can be derived from a relatively small number of good predictors23. (II) Build a binomial model for case–control prediction via penalized maximum likelihood to further reduce the variable number to the most important predictors. Five-fold cross-validation was used to identify the optimal lambda value that determines the number of predictors. (III) Residualize the outcome variable against these predictors using a binomial model. (IV) Repeat the procedure from step (I) m times. We chose m = 2, such that the total number of predictors remained below 5024. (V) Using these predictors, build a smoothly clipped absolute deviation-penalized binomial regression model for prediction of diagnosis. Again, cross-validation was used to identify the optimal lambda value. (VI) Predict the final model in the pancreatic islet data.The machine learning strategy employed here was aimed at deriving a signature from a data set with correlated variables and a variable number that greatly exceeded the number of samples. Therefore, we utilized a strategy devised for this purpose that consisted of the following steps25. Since our training data consisted of four different brain expression data sets, we randomly selected two-thirds of patient and control samples from each data set individually and combined them to form the training data. For the brain expression data, classification performance was tested on the samples not used for training, averaged over 1000 repetitions of the procedure. Classification accuracy was measured using non-parametric correlation between predicted and actual glycemic control values and using Nagelkerke’s R2 from logistic regression for classification of schizophrenia.The benefit of this method is its ability to efficiently select important predictors and cope with potential correlation among them. Compared to its original implementation, we made one further adaptation that can substantially improve classifier performance. Instead of training the classifier on the entire data set, we repeatedly trained it on subsets of the training data and averaged the prediction outcome. This method is also known as bootstrap aggregation (“bagging”) and integral part of other powerful machine learning tools, such as random forestsP-value as the frequency of explained variance estimates at least as high as the one observed for the original diagnostic information.To demonstrate the specificity of associations between the schizophrenia polygenic model and glycemic control, the analysis was repeated 1000 times, starting with permuted diagnosis information in the brain expression data. We determined an empirical 26, using only control subjects of the residualized cortical schizophrenia expression data. This analysis was performed on the combined data  since, due to the small sizes of individual data sets, removal of associations with potential confounders may be more robust in the combined data. We identified a soft-threshold  to fit a scale-free topology to the network. The weighted adjacency matrix was then transformed into topological overlap (TOM) and hierarchical clustering (using flashClust28) was used to identify modules from the TOM dissimilarity matrix (1-TOM). We used a relatively small minimum module size of 10 to allow identification of gene sets with low gene numbers that may have high cross-tissue predictiveness. Although small module sizes may lead to biologically less plausible modules, this risk was minimized in the present study by performing WGCNA on a preselected set of genes from few gene ontology categories.Weighted gene co-expression network analysis (WGCNA) was performed using the R package WGCNAP-value of 6.1×10−9 and this did not depend on genes within ontological categories most associated with genetic schizophrenia risk. Specifically, performance was similar when genes part of the top 20, 40, 60, 80, and 100 ontological categories were removed prior to machine learning analysis.Machine learning was used to identify a cortical gene expression signature in 212 schizophrenia patients and controls. The model was built using genes within the 200 ontological categories that have previously been reported to be most strongly associated with genetic schizophrenia risk. Of these, 170 contained at least one gene measured across all investigated data sets (median number of genes was 14). Fig. 1c levels could be predicted for all investigated gene sets below the FWER-corrected P-value threshold of 0.008. Notably, prediction performance peaked after removing genes within the top 60 ontological categories, suggesting that gene sets with the strongest genetic schizophrenia association were adversely affecting the prediction of glycemic control. At this threshold, the polygenic schizophrenia model explained 26.3% of variance in the glycemic control index . Permuting diagnosis demonstrated the specificity of the findings for the real case–control status .Fig. P = 0.17), even though the schizophrenia-control differentiation was not strongly affected .Next, we explored whether the bottom 200 ontological categories regarding genetic schizophrenia risk could also predict glycemic control. This was not the case with an explained variance of 3.9% (WFS1 (P = 1.8×10−7), angiotensinogen , LRP4 (P = 2.7×10−6), and TNS2 (P = 9.3×10−4), all of which were increased in schizophrenia  ontological categories most associated with schizophrenia risk were expressed in the dorsolateral prefrontal cortex (BA9) at a cut-off of 0.5 FPKM. For pancreatic tissue, this was the case for 70% of such genes. For genes part of the two identified ontological categories, the corresponding percentages were 82% (BA9) and 80% (pancreas), respectively. Supplementary Figure P = 2.7×10−8) for classification of schizophrenia. Prediction in pancreatic beta cells showed that T2D patients had significantly lower predicted schizophrenia scores compared to controls  and controls (n = 12) to support the specificity of the schizophrenia signature for prediction in comparable brain tissue samples. As above, the prediction was based on genes part of the two identified ontological categories and which overlapped with the schizophrenia data set (127 genes). This showed that predicted scores did not differ between HIV encephalitis patients and controls (P = 0.54).Two analyses were performed as a negative control. First, schizophrenia scores were predicted in a frontal cortex expression data set of subjects with HIV encephalitis (n = 15) and healthy controls (n = 18) using 162 overlapping genes of the two ontological categories. Also here, predicted schizophrenia scores were not associated with case–control status .Second, scores were predicted in frontal cortex expression data of subjects with Alzheimer’s disease (29). The association between the score and HbA1c levels was negative, meaning that subjects with a pancreatic islet profile more similar to that of schizophrenia patients had lower HbA1c levels. Consistent with this, T2D patients in this cohort as well as in an independent validation cohort had significantly lower schizophrenia scores compared to controls. There are several potential explanations for this finding. First, brain changes underlying the score may be of protective nature or affected by compensatory mechanisms. It is conceivable that such mechanisms are influenced by illness duration and the effects of antipsychotic medication. For example, a shared risk process may initially lead to comparable expression profiles. During later illness stages of schizophrenia, this process may be over-compensated, leading to an inversion of associations with HbA1c levels. This hypothesis could be examined by exploring potential interactions between the predicted schizophrenia score, pancreatic HbA1c levels, and illness duration. Such analysis would be particularly informative if schizophrenia patients would show similar expression profiles in peripheral cells. This would allow testing of samples from living and potentially untreated patients. A second possibility may be a shared risk mechanism that results in opposite expression effects in different tissues. To explore this, it would be interesting to investigate genetic risk overlap between schizophrenia and T2D in the identified processes. Potential joint risk signatures could then be examined regarding their tissue-specific, quantitative effects on expression.Here, we show that a glycemic marker of T2D can be predicted using a schizophrenia brain expression score. This finding supports the presence of a systemic molecular commonality between schizophrenia and T2D. This is in agreement with studies identifying consistent alterations of individual molecules in the periphery and central nervous system of schizophrenia patients . Two SNPs (rs10010131 and rs6446482) with intronic locations in the WFS1 gene are reproducibly associated with a protective effect on risk for T2D. The gene is characterized by a strong LD pattern and, albeit not consistently, evidence for further genetic risk associations has been reported. Meta-analysis investigating rs10010131 and rs734312 has confirmed significant protective effects for the minor alleles of both variants45, although the latter SNP may not be functionally relevant42. The risk allele of rs10010131 is further predictive of future T2D, progression from normal glucose tolerance to T2D46, as well as insulin secretion47. This allele has also been found to interact with a variant in hepatocyte nuclear factor 4 alpha (HNF4A) in an Ashkenazi Jewish Population, suggesting a potential gene–gene interaction effect on T2D risk48.The most strongly implicated gene, 51. Several subsequent genetic studies have investigated potential associations with schizophrenia risk, but these findings have thus far not supported a significant role52. For example, the T2D-associated variant rs10010131 has not been found associated with schizophrenia in a Danish cohort of 410 patients and 820 controls53. Similarly, WFS1 is not harbored by the well-established 108 genetic loci associated with schizophrenia risk54.Interest in WFS1’s role in schizophrenia stems from observations that mutation carriers show an increased likelihood for psychiatric hospitalization55. In T2D, tissue RAS is activated leading to increased oxidative stress and progressive renal pathology [56]. While an angiotensinogen variant (rs699) that has previously been linked to hypertension, has not been found associated with T2D, such association has been identified for a variant in another gene central to the RAS 57.We further identified angiotensinogen as a gene important for the cross-tissue prediction in the present study. Angiotensinogen is a precursor of angiotensin I, a central part of the renin-angiotensin system (RAS), an important regulator of glucose homeostasis58. The etiological role of the schizophrenia candidate gene ACE was initially thought to relate to its involvement in dopamine metabolism60. More recently, an ACE polymorphism has been shown to be associated with plasma glucose concentrations in chronic schizophrenia patients61. Together with the present findings, this may support the involvement of the RAS system in the comorbidity between schizophrenia and T2D, potentially due to their role in regulating glucose homeostasis.In schizophrenia, research into the RAS has focused on angiotensin converting enzyme (ACE), which converts angiotensin I into angiotensin II but genetic association analyses have thus far been inconclusive62. LRP5, a co-receptor of LRP6, has a central role in glucose-induced insulin secretion from pancreatic islets and for maintenance of normal cholesterol metabolism63. Polymorphisms in LRP5 are associated with obesity64 and variants in several other Wnt signaling genes have been linked to the development of T2D, most prominently in TCF7L265. Notably, Wnt signaling plays an important role in kidney development and regulates the expression of hormones essential for glucose homeostasis66.A further gene important for cross-tissue prediction identified here is the LDL receptor-related protein 4 (LRP4). It belongs to the family of lipoprotein receptor-related proteins (LRPs) and antagonizes LRP6-mediated activation of canonical Wnt signaling. Mutations in this gene impact on such signaling and are associated with kidney anomalies in Cenani–Lenz Syndrome67. Interestingly, a T2D risk variant in TCF7L2 has also been found to increase the risk for schizophrenia and this allele is associated with increased expression in pancreatic beta cells53. Further studies should explore the relationship between variation in Wnt signaling-related genes and glucose homeostasis as a potential factor underlying the comorbidity of schizophrenia and T2D. For example, another gene identified here, TNS2 (TENC1), has previously been found to interact with schizophrenia risk gene DISC168 and is involved in the regulation of Akt, an important modulator of Wnt signaling70.Numerous studies have explored the role of Wnt signaling in schizophrenia, mainly due to its involvement in neuronal development71. Such medication effects may have influenced the identified cortical signature. Additional confounders include nicotine, alcohol, or drug use as well as the mode of death . Therefore, the molecular commonality identified in the present study should be interpreted as a state-dependent commonality between schizophrenia and T2D. Further studies are warranted to explore potential associations between this molecular overlap and factors underlying the genetic comorbidity between the conditions.The most significant limitation of the present study is that several confounding effects on the brain expression signature cannot be excluded. Although schizophrenia confers a significant endogenous risk for T2D, this risk is further increased by antipsychotic treatmentThis study supports the presence of a molecular brain signature of schizophrenia that is associated with a glycemic marker of T2D in the periphery. These findings may help to elucidate the biological basis of comorbidity between the two illnesses. They may also aid in uncovering processes underlying the impact of antipsychotic treatment on T2D risk and highlight potential molecular targets for treatment of this clinically relevant comorbidity.Supplementary FiguresSupplementary Tables


PIK3CA in isogenic cancer cell line models. These results have implications for the pancancer use of targeted drugs and potentially for the design of combination therapies.Pancancer studies have identified many genes that are frequently somatically altered across multiple tumour types, suggesting that pathway-targeted therapies can be deployed across diverse cancers. However, the same ‘actionable mutation' impacts distinct context-specific gene regulatory programs and signalling networks—and interacts with different genetic backgrounds of co-occurring alterations—in different cancers. Here we apply a computational strategy for integrating parallel (phospho)proteomic and mRNA sequencing data across 12 TCGA tumour data sets to interpret the context-specific impact of somatic alterations in terms of functional signatures such as (phospho)protein and transcription factor (TF) activities. Our analysis predicts distinct dysregulated transcriptional regulators downstream of somatic alterations in different cancers, and we validate the context-specific differential activity of TFs associated to mutant  Cancer genomic data sets contain a wealth of data that can be used to predict prognosis and further understand disease. Here, the authors integrate multiple genomics data types to identify transcriptional dysregulation in response to somatic mutations. Cancer cells evolve to acquire hallmark capabilities to sustain chronic proliferation, evade growth suppressors and avoid cell death234567891013To this end, we applied a computational strategy for exploiting parallel (phospho)proteomic and mRNA sequencing data for large tumour sets by linking the dysregulation of upstream signalling pathways with altered transcriptional response through the transcriptional circuitry15PIK3CA activating mutations with altered activities of distinct sets of TFs in different cancers. Notably, in isogenic cell line models of breast cancer and head and neck cancer, we validated the altered activity of several TFs in the presence of mutant PIK3CA by measuring promoter occupancy and expression of target genes, confirming the context-specific predictions of our model. These proof-of-principle results suggest a computational strategy for personalized deployment of targeted therapeutics—and potentially for the development of context-specific combination therapies—in a pancancer setting.We next performed a systematic regularized regression analysis to associate frequent somatic aberrations with changes in inferred TF and (phospho)protein activities in each cancer type. This analysis identified key regulators associated with the major mutations in renal clear-cell carcinoma. More generally, we observed that specific molecular aberrations have cancer-specific functional consequences. In particular, we associated interaction matrix between upstream signal-transduction proteins and downstream TFs that predicts target gene expression proteomic and transcriptomic data to learn a model that links alterations in signalling (from RPPA data) with downstream changes transcriptional response (measured by mRNA data) via predicted TF binding sitespression , bottom.pression , top. Inmappings , breast cancer , colorectal adenocarcinoma , glioblastoma multiforme , head and neck squamous carcinoma , kidney renal cell-clear carcinoma , lung adenocarcinoma , lung squamous cell carcinoma , ovarian carcinoma , prostate cancer , uterine corpus endometrial carcinoma , and uterine carcinosarcoma .We trained AR models on tumours from 12 different TCGA cancer studies using samples for which mRNA, RPPA, somatic mutation and copy number variation data were available: bladder urothelial carcinoma . When only the motif hits were randomized, the performance improvement of the true model was modest but still significant , suggesting that the motif data, while noisy, contributes to predictive performance. AR obtained similar performance advantages when assessed using a single held-out test set or when evaluating Pearson correlation or root mean-squared error . Then, we asked whether the value of individual TF/(phospho)protein activities for each sample were significantly low or high relative to the corresponding distribution over permuted data. We corrected for FDR across TFs/(phospho)proteins (see Methods section) and identified significant shared and cancer-specific TF/(phospho)protein regulators .A number of TFs were significantly altered in two or more tumour types, including ZEB1, JUN, ELK1, FOXM1, while others were limited to a single type, such as FOXD1 in HNSC and FOXL1 in KIRC. We identified TFs that are known cancer drivers such as STAT5   as an additional covariate. Indeed, many identified TF regulators had highly significant associations with survival outcome in BLCA, KIRC and UCEC . For insNext, we asked whether our method could identify known and novel TFs that are differentially active in cancer subtypes. TP53. AR analysis suggests that the molecular pathogenesis of HPV(+) head and neck cancer is distinct from HPV(−) tumours. Inferred TF activities of 33 TFs were significantly associated with HPV status  whose activities significantly differed between HPV(+) and HPV(−) tumours; the 33 identified TFs were also enriched among the top-ranked TFs in the new cohort  (Head and neck squamous cancer is frequently associated with human papillomavirus (HPV) infection and mutations in ey test) .TP53 tends to be mutually exclusive with HPV(+) status, but inferred TP53 TF activity and inferred p53 protein activity were not significantly different between HPV(+) and HPV(−) patients . However, it is known that the viral E6 oncoproteins in HPV(+) head and neck cancer form a complex with WT p53 and lead to its degradationAs described previouslyP<10−5, Wilcoxon's rank-sum test used for all tests); ESR1 (estrogen receptor 1) activity was higher in luminal BRCA compared to other BRCA subtypes (P<10−42), consistent with oestrogen receptor serving as a luminal markerP<10−10) and bronchioid subtypes (P<10−5) compared to the magnoid subtype in LUAD.We performed similar analyses for other TCGA cancer studies and in each cancer type could stratify patients by regulator activity profiles . For exaTP53, whereas endometrioid tumours are hormone receptor positive, copy number low, and have a high frequency of PI3K-AKT (phosphatidylinositol 3-kinase-AKT) pathway alterations5P<10−8, Wilcoxon's rank-sum test used for all tests) and increased TGIF1 TF activity and inferred p53 protein activity serous-like tumours  compared to reported TCGA mRNA clusters (P<0.01) and TCGA integrated clusters (P<10−6)  associated CTNNB1 mutant status with altered activity of TFs involved in WNT signalling, epithelial–mesenchymal transition and cancer stem cell transition including TCF4 , NFATC4, JUN, TP53, MAX, MYC, STAT3 and KLF12  . Signifind KLF12 . We confCTNBB1 endometrioid tumours, we developed a systematic statistical approach for modelling the impact of somatic alterations on regulator activity in each tumour type, with the eventual goal of deciphering cancer-specific downstream effects of targeted therapies and potentially discovering secondary targets for combination drug strategies. First, we implemented a regularized regression approach that uses somatic alterations to explain inferred TF/(phospho)protein activity across tumour samples on a regulator-by-regulator basis. For a complex genotype, the model explains TF/(phospho)protein regulator activity across tumours as the sum of effects of individual somatic alterations , and the effect size of each alteration is assigned a nominal P value by a permutation approach (see Methods section). We then corrected for multiple hypotheses across regulator models, treating inferred TF activities and inferred (phospho)protein activities as separate groups of tests (see Methods section). Combining these results identified a set of regulators predicted to be significantly dysregulated by each somatic alteration in each TCGA cancer study.Encouraged by our findings for mutant VHL (von Hippel-Lindau), PBMR1, BAP1, MTOR, ATM, SETD2, KDM5C and PTEN (phosphatase and tensin homolog), as well as copy number changes in MLH1, DUSP1 and RANDBP17 as significantly associated with various TF activity changes across tumours. KIRC is characterized by a high-frequency inactivating mutation in the VHL gene found in ∼54% of tumours in TCGA and likely more prevalentPBRM1, a subunit of the PBAF SWI/SNF chromatin remodelling complex, and in histone deubiquitinase BAP1 define two genetic subtypes of KIRC, while recurrent mutations in the histone methyltransferase SETD2 also occur.PBMR1 and BAP1 mutations showed distinct patterns of TF and protein activities (PBMR1 mutant tumours are associated with increased activity of TFs/(phospho)proteins that have roles in interleukin signalling and MYC, while regulators with increased activity in BAP1 mutant tumours are involved in DNA damage response, apoptosis, insulin signalling and mTOR signalling.KIRC samples with tivities , and regtivities . PBMR1 mBAP1 mutant tumours than PBMR1 mutant tumours. Dysregulation of the KEAP1-NFE2L2 pathway occurs through both genetic and epigenetic mechanisms and induces prosurvival genes promoting proliferation and chemoresistanceKEAP1, NFE2L2 (Nrf2), CUL3 or RBX1 are the most common mechanisms that impair KEAP1-mediated degradation of NFE2L2 and thereby activate the transcriptional effects of NFE2L2. Somatic aberrations in these genes have been described in LUSC, LUAD and HNSC, and indeed we confirmed this activating effect  and KLF12 (interaction P<0.05) activity were significantly decreased. Meanwhile, when VHL was comutated with BAP1, HSF1 activity was significantly decreased (interaction P<0.05), while TIGAR protein activity was significantly increased (interaction P<0.05) . In samples where  P<0.05) .PIK3CA, which encodes the α-isoform of the p110 catalytic subunit of PI3K (PI3Kα); loss of PTEN, which antagonizes PI3K function; and overexpression of membrane-bound receptor tyrosine kinase3714PTEN and PIK3CA by our analysis in BRCA, HNSC, UCEC, KIRC, LUAD and PRAD tumours (see Methods section).The PI3K pathway controls proliferation, metabolism, survival and motility and is frequently activated in many cancers, often via mutations in PIK3CA were present in ∼31% of BRCA tumours and 20% of HNSC tumoursPTEN inactivating mutations, ∼50% have PIK3CA activating mutations and ∼35% have a comutation of PTEN and PIK3CA. PTEN or PIK3CA in BRCA, HNSC or UCEC. Notably, the number of TFs dysregulated by PI3K pathway alterations varied widely across different cancers , with striking changes in ErbB/MAPK, mTOR, HIF-1, VEGF and PI3K-Akt pathways. Only ELK1, a TF downstream of the MAPK/ERK pathway, is dysregulated by PIK3CA or PTEN mutations in all three cancers. Four TF associations were shared in BRCA and HNSC: ZEB1 (EMT activator), TCF4 , RREB1  and FOXM1 ; 35 TFs were common to BRCA and UCEC; and just IRF9 was shared between UCEC and HNSC. There were also associations unique to each cancer type, including USF1 and SMAD1 for BRCA, FOXF2 for HNSC, and NFE2L2 and NR3C1 for UCEC.Activating mutations in PTEN irrespective of PIK3CA status in endometrial cancer  and expression change via quantitative reverse transcription-qPCR (RT-qPCR) of canonical target genes of these TFs -AKT and p-S6K . Linking mutant β-catenin to putative downstream TF effectors could inform future mechanistic studies—for example, short hairpin RNA or CRISPR/Cas screening to identify TFs whose deletion/knockdown leads to changes in proliferation—to develop new therapeutic strategies.Using inferred TF/protein activities in tumours may also reveal clinically relevant patient subgroups. Patients with endometrioid carcinomas display heterogeneous clinical courses and response to therapy, despite similar tumour histopathology. Clustering UCEC tumours by TF activities revealed a subclass of endometrioid tumours that correlated with β-catenin mutation status and had poorer survival , P<0.001cis-regulatory sequences mediating effects on target genesPrevious algorithms to interpret the role of somatic alterations have examined enrichment of mutations in known pathways34106768Overall, current methods cannot translate the mutational landscape of a tumour into a usable model of affected pathways nor use mutational status to predict accurately response to targeted therapies. Our model is designed to capture the causal flow of information from signalling to TFs to target genes; the association analysis is likely to identify causal impacts of mutations and copy number events, since somatic alterations usually alter TF activity/signalling rather than vice versa . Our analysis revealed both known and putative interactions of frequently altered genes with signalling and transcriptional programs in a pancancer context and provides a general strategy for future studies. In cases where a mutation is associated with the altered activity of a targetable TF or (phospho)protein, our analysis may suggest combination therapies.The method we have presented has several limitations. First, our analysis uses predicted TF binding sites based on existing TF motif databases and restricted to promoter sequences; therefore, the TF motif hit matrix is noisy, incomplete and not context-specific. Indeed, due to the strong correlation structure between RPPA and mRNA expression data, AR models trained with the true motif hit matrix achieve only a modest—albeit significant—improvement in prediction performance over models trained on randomized motif data . AdditioDespite these limitations, we have presented a principled integrative strategy for predicting the context-specific impact of somatic alterations on transcriptional programs and signalling pathways. Moreover, our predictions generalize to independent patient cohorts and validate experimentally in isogenic cancer cell line models. We anticipate that such integrative statistical modelling strategies will be crucial for personalizing cancer therapies.http://bioinformatics.mdanderson.org/main/TCPA:Overview). RPPA protein expression data for the UCS study, RNA-seq gene expression data, somatic mutation data and clinical data were downloaded from TCGA's Firehose data run (https://confluence.broadinstitute.org/display/GDAC/Dashboard-Stddata). GISTIC copy number data was downloaded from TCGA's Firehose analyses run . Only the samples ‘whitelisted' by TCGA for the Pan-Cancer Analysis Working Group were used in the study. For our analysis, we restricted to samples with parallel RNA-seq, RPPA, somatic mutation and GISTIC copy number data (We downloaded RPPA protein expression data from TCPA (ber data .q<0.05) by the MutSigCVq<0.001) by the GISTIC2  for all target genes from MSigDBPIK3CA gene—from the originally published Supplementary DataWe obtained SILAC-based quantitative phosphoproteomic data set of a spontaneously immortalized non-tumorigenic breast epithelial cell line MCF10A along with two isogenic derivatives generated by knock-in of mutant alleles—one bearing the E545K mutation and the other bearing the H1047R mutation of the We obtained RPPA data for uterine corpus endometrioid carcinoma25M tumour samples profiled using RNA-seq with N genes, we let YRNxM be the mean-centred log 10 gene expression profiles of tumour samples. Each column of Y corresponds to an RNA-seq experiment. We define each gene's TF attributes in a matrix DRNxQ, where each row represents a gene and each column represent the hit vector for a TF, that is, the bit vector indicating whether there is binding site for the TF in the promoter region of each gene. We define the RPPA attributes of tumour samples as a matrix PRMxS where each row represents a tumour sample and each column represents the (mean-centred) log RPPA protein expression profile for the tumour sample. We set up a bilinear regression problem to learn the weight matrix WRQxS on paired of TF signalling protein features:AR is an algorithm for efficiently solving a regularized bilinear regression problem15We can transform the system to an equivalent system of equations by reformulating the matrix products as Kronecker productswhere ⊗ is a Kronecker product and vec(.) is a vectorizing operator that stacks a matrix and produces a vector, yielding a standard  regression problem. Full details and a derivation of the reduced optimization problem are provided elsewhereYTDW and to infer the TF activities in each sample, we can right-multiply the protein expression profiles through the model by WPT.Given the (phospho)protein profile of a test tumour sample (centred relative to the mean of the training set), we can right multiply the (phospho)protein expression vector through the trained model to predict the similarity of its expression profile to those of the training tumour samples. To recover a reconstruction of the test gene expression profile from the predicted similarities, we assume that the test expression profile is in the linear span of the training profiles. Then, a simple transformation converts the vector of computed similarities into a predicted gene expression variation profilesYTDW and WPT mappings, respectively, we developed an empirical null model as follows. First, we generated random permutations of the gene expression profiles Y for each tumour type. For each permuted Y response matrix, we trained an AR model using true D and P input matrices and computed the corresponding inferred TF and (phospho)protein activities via the YTDW and WPT mappings. Using this permutation and model fitting procedure 5000 times, we generated an empirical null model for TF and (phospho)protein activity distribution for each sample. To identify significant regulator activities (R), we assessed the nominal P value for each sample relative to the empirical null model for the particular regulator (TF/(phospho)protein), and we corrected for multiple hypothesis testing of non-independent hypotheses using the Benjamini–Hochberg–Yekutieli procedure. Then, we reported the significant regulators using an FDR of 0.1 for the largest TCGA studies , an FDR of 0.15 for mid-size studies , and an FDR of 0.25 for small studies  as our thresholds for significance. Then, we calculated, for each TF/signalling regulator, the frequency over samples where the regulator passed its significant threshold for a given cancer. We used this approach to identify significant regulators in each cancer type to identify the shared and cancer-specific roles TF/(phospho)protein regulators.To assess the statistical significance of the inferred (phospho)protein and TF activities obtained from the model via the R) from genomic aberration profiles and used a permutation test approach to assign significance to ridge regression coefficients. Somatic alterations were simply encoded as present/absent. In the permutation test, the elements of the outcome vector of regulator activities R were randomly permuted across samples, and the ridge regression model was fitted using the permuted observations to obtain ridge regression coefficients. By performing 10,000 such permutations, a null distribution of the regression coefficients was generated. The permutation test P value was calculated as the proportion of regression coefficients from the null distribution greater than or equal in absolute value to the absolute value of the coefficient fitted to the true (non-permuted) data. Further we corrected for multiple hypothesis testing of non-independent hypotheses using the Benjamini–Hochberg–Yekutieli procedure across all TF/signalling regulators for each cancer, and we multiplied these FDR-adjusted P values with the sign of the coefficient from the model to calculate a final regulatory–genomic aberration association score. For downstream analysis, we restricted our analysis to regulators identified as significant in at least 1% of samples in each cancer and genomic aberrations with FDR-corrected P<0.15 across regulators from the ridge regression analysis with permutation test.We used ridge regression to predict each TF/(phospho)protein regulator's activity (R) was a modelled function of somatic aberration pairs A and B:We also assessed synergistic/antagonistic effects of pairs of genomic aberrations on TF/protein activity by building linear models with interaction terms for each pair of genomic aberrations. We restricted our analysis to pairs of genes that were altered in at least in 20 tumour samples as well as comutated in at least 10 tumour samples. Here the activity of each TF/(phospho)protein regulator ij is the effect of the interaction in that combination, and ɛk is the error term of the kth observation in that combination. Here the genomic aberrations are binarized (present/absent), so the values are 1 and 0.where P<0.05). If the interaction term was significant, and if its coefficient was greater than zero and greater than the coefficients of the genomic aberration pair, we assumed this pair of somatic aberrations had a synergistic effect on regulator activity. If the interaction term was significant and its coefficient was less than zero and less than the coefficients of the genomic aberration pair, we assumed this pair of somatic aberrations had an antagonistic effect on regulator activity.We first looked for regulator models for which the coefficient of the interaction term was significant  their inferred TF activity profiles and (2) their gene expression profiles corresponding to TFs. We used clinical stage as a background factor for BLCA and KIRC and histological subtype as a background factor for UCEC. Overall survival was calculated from the date of initial diagnosis of cancer- to disease-specific death  and months to last follow-up . Further, we evaluated prognostic accuracy of survival models using a log-rank test. We corrected for multiple hypothesis testing of non-independent hypotheses using the FDR procedure across all models in each cancer type. FDR-corrected P values of models built from inferred TF activities and actual TF mRNA expression profiles were compared using one-sided paired Wilcoxon's signed-rank test.We built Cox proportional hazard regression models for regulators that attained significance by our empirical n=178) and Bergen (n=209) data setsP<0.05 on the TCGA patients. Then, we predicted the risk for each patient in the validation set and calculating concordance index. We reported the P value for the statistical test if the concordance index estimate was different from 0.5.For the validation set, we used the TCGA-trained UCEC model to infer TF activity profiles of MDACC protein activities, we performed two-tailed Wilcoxon's signed-rank tests and determined the direction of shifts by comparing the mean of two populations.−1 penicillin and 100 μg ml−1 streptomycin. Cal27 cell lines were transfected with pbabe control vector, pbabe WT PIK3CA and pbabe E545K PIK3CA vectors (Addgene) using Lipofectamine 3000 according to the manufacturer's instructions.The BRCA cell line, MCF7, which has a PIK3CA E545K mutation, and the targeted correction of the E545K mutation to WT PIK3CA were obtained from the Lauring LabTotal RNA was extracted from MCF7 and Cal27 cell lines using an RNA Extraction Kit from Qiagen. cDNA synthesis was performed using iScript from Bio-Rad, according to the manufacturer's instructions. The Applied Biosystems SYBR green mix (Life Technologies) was used to amplify specific genes listed in ACTR3, 5′-CATTCCTGTGGCTGAAGGGT-3′ and 5′-ATCGCTGCATGTGGTGTGTA-3′; FOXP4, 5′-GACCCTGTGTGAAGACCTGG-3′ and 5′-GTCAGGGGTTTCCAGGATGG-3′; DDX27, 5′-TTGGGGAAGGACATCTGTGC-3′ and 5′-CGGATCCGGATGAACTCCTG-3′ PAPLN, 5′-AGGTCATCTGTGCCATTGGG-3′ and 5′-TGTAGAAGCCACTGCCCTTG-3′; PSMB4, 5′-GACATGCTGGGATCCTACGG-3′ and 5′-CTTTTTCGGTGACAGTGGCG-3′; WNK1, 5′-CTTTTTCGGTGACAGTGGCG-3′ and 5′-CTTGGCTGTTCACTGTTGCC-3′; CDK1, 5′-ACAGGTCAAGTGGTAGCCATG-3′ and 5′-GGAGTGCCCAAAGCTCTGAA-3′; CAMKK1, 5′-CAGGAAGCTATCTGGAGGCG-3′ and 5′-AAGTACTCGAGGCCCAGGAT-3′; TNFSF10, 5-CCTCAGAGAGTAGCAGCTCACA-3′ and 5′-CAGAGCCTTTTCATTCTTGGA-3′; ACTB, 5′-CGTCTTCCCCTCCATCGT-3′ and 5′-GAAGGTGTGGTGCCAGATTT-3′; APC, 5′-CATTTCCAAGAAGAGGGTTTGT-3′ and 5′-GATCAGCAAGAAGCAATGACC-3′; FBXW11, 5′-GGCTGCCGTCAATGTAGTAGA-3′ and 5′-GTGCTCGTGCTCCAGACTT-3′; PPP2R5E, 5′-GTGTGTATCTAGCCCCCATTTT-3′ and 5′-AAACTCATGATGTATTCATTATTCCAA-3′; WNT10B, 5′-ATGCGAATCCACAACAACAG-3′ and 5′-TCCAGCATGTCTTGAACTGG-3′.Primers used for mRNA expression were: et al.et al.et al.MCF7 and Cal27 cell lines were crosslinked with 1% formaldehyde for 10 min at room temperature and quenched with 125 mM glycine for 5 min at room temperature. Cell were lysed and sheared to obtain chromatin fragments of 200–500 bp. Sheared chromatin was incubated overnight with 2 μg of rabbit monoclonal ChIP grade antibody to ELK1  as has been previously used by Zhang Cells were lysed and proteins were extracted in RIPA buffer that was supplemented with phosphatase and protease inhibitors. Proteins were separated by SDS–polyacrylamide gel electrophoresis gels and transferred to a PVDF (polyvinylidene difluoride) membrane. Membranes with blocked with 5% bovine serum albumin and probed using specific antibodies. Actin , pAKT (S473) , pS6K (T389) , HA  were all from Cell Signaling Technology (CST).http://bioinformatics.mdanderson.org/main/TCPA:Overview). RPPA protein expression data for the UCS study, RNA-seq gene expression data, somatic mutation data and clinical data are available in a public repository from TCGA's Firehose data run (https://confluence.broadinstitute.org/display/GDAC/Dashboard-Stddata). GISTIC copy number data is available in a public repository from TCGA's Firehose analyses run . Only the samples ‘whitelisted' by TCGA for the Pan-Cancer Analysis Working Group were used in the study. For our analysis, we restricted to samples with parallel RNA-seq, RPPA, somatic mutation and GISTIC copy number data Supplementary Table 14 and Supplementary Data 1.RPPA protein expression data is available in a public repository from TCPA .Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary Figures, Supplementary Tables and Supplementary ReferencesSignificance analysis (permutation test) - left tailed and right tailed p-values for each transcription factor (TF) and (phospho) protein activities for each cancer type.


Unique molecular identifiers (UMIs) show outstanding performance in targeted high-throughput resequencing, being the most promising approach for the accurate identification of rare variants in complex DNA samples. This approach has application in multiple areas, including cancer diagnostics, thus demanding dedicated software and algorithms. Here we introduce MAGERI, a computational pipeline that efficiently handles all caveats of UMI-based analysis to obtain high-fidelity mutation profiles and call ultra-rare variants. Using an extensive set of benchmark datasets including gold-standard biological samples with known variant frequencies, cell-free DNA from tumor patient blood samples and publicly available UMI-encoded datasets we demonstrate that our method is both robust and efficient in calling rare variants. The versatility of our software is supported by accurate results obtained for both tumor DNA and viral RNA samples in datasets prepared using three different UMI-based protocols. PLOS Computational Biology Software paper.This is a The ability to infer rare variants is important for a large domain of high-throughput genome re-sequencing applications: cancer  and prenConventional pipelines, however, do not suit well for the detection of ultra-rare mutations. Current tools were shown to reliably detect mutations present at ~5% in real data–12, whilRecently introduced unique molecular identifier (UMI) tagging approach–22 showsAt the same time, adapting existing software to the analysis of UMI-tagged data is unfeasible. For example, conventional software tools heavily rely on sequencing quality values to estimate error rates at variant calling stage. Error frequencies, however, are not that straightforward to infer for UMI-assembled consensuses. Moreover, even after consensus assembly, the data is rich for seemingly high-quality errors that are inevitable when using PCR to perform UMI tagging and can arise from 1st cycle PCR errors . This prFig 1). The pipeline corrects errors in the UMI sequences and performs fast and robust consensus assembly able to handle reads with high error load, indels and random offsets. It also takes an advantage of data reduction by consensus assembly and a priori knowledge of target region positions . Software documentation is available here [http://mageri.readthedocs.org/en/latest/]. Description, generated output files and scripts that can be used to reproduce the analysis performed in this paper can be found here: [https://github.com/mikessh/mageri-paper]. MAGERI is free for scientific and nonprofit use. MAGERI analysis can run on a commodity hardware in a reasonable time. For example, processing a sample of 30 million pair-end reads using a 32 GB RAM and 8-core Intel Xeon processor UNIX server takes approximately 30 minutes with the most running time consumed by I/O at the stage of primer matching and sample de-multiplexing. The analysis of duplex sequencing dataset mentioned below takes ~10 minutes using the same hardware setup. Default MAGERI parameters, scripts (R markdown templates) and MAGERI output used to perform the analysis described in this paper can be accessed at [https://github.com/mikessh/mageri-paper].MAGERI is implemented in Java v 1.8 and is distributed as a single cross-platform executable JAR file . contains UMI-tagged sequencing results for cloned FGFR3 exon 7 template sequence. The reported control variant (R248C) is just 1 base away from the first base of the template and was not detected in reads.IonTorrent data was obtained from  and procS2 Table) that were grouped into three tiers , while healthy donor DNA alone served as a negative control.To test the accuracy of MAGERI pipeline we have selected a mutation reference standard with known somatic variant frequencies  that was previously used for similar tasks ,40 as a S1 Table) carrying mutations known to be present in the mutation reference standard. Resulting UMI-tagged libraries were then subject to deep sequencing on Illumina HiSeq2500 platform (Raw sequencing data: PRJNA297719) yielding on average 16,073,484+/-7,149,885 reads per sample. Primers and UMI base positions were identifiable for 87+/-4% of reads; UMI coverage distribution showed a clear peak  sufficient for optimal error correction. The fraction of reads that belong to high-coverage UMIs and were successfully assembled was 99.9+/-0.3%, resulting in 33,911+/-14,203 consensus sequences, 98+/-4% of which were aligned to reference. A comprehensive MAGERI processing summary is provided in S3 Table.UMI-tagged target amplicon libraries were generated using multiplex PCR amplification of genomic regions . Overall, variant frequencies obtained by MAGERI were in good agreement with known variant frequencies provided by the manufacturer . MAGERI variant quality scores (Q scores) for errors observed in healthy donor DNA were also in a good agreement with empirical P-values computed based on error frequencies . MAGERI Q scores for errors observed in control dataset and known variants from reference standard are shown in Fig 2D. These Q scores display a high area under curve (AUC) value when used as a threshold to classify errors and 0.1% tier variants , which is significantly better than the one obtained when using observed variant frequency as a threshold .The number of variants that were identified by MAGERI prior to any variant quality filtering was in a good agreement with the one expected from low-frequency template sampling stochastics arising due to limited coverage . Notably, the first patient’s plasma appear to contain the c.1799T>A mutation at 0.4% frequency, that is detected jointly (i.e. in the same MIGs) with c.1798G>A and together comprise the BRAF V600K variant[Fig 3). The c.1799T>A variant is also present in the corresponding tumor sample, albeit at a far smaller frequency than c.1798G>A. The probability of jointly detecting this mutation pair simply by chance is P < 10−18 (Hypergeometric test), thus the first patient demonstrates an interesting case of a rare subpopulation of tumor cells that is dominant in ctDNA.To demonstrate applicability of MAGERI software to the analysis of patient samples we decided to tackle the problem of detecting circulating tumor DNA (ctDNA)  in peripRAF V600E) mutatioK variant .Materials and Methods, Duplex sequencing data analysis section for details), we were able to reliably call a specific ABL1 mutation used by Schmitt et al. as a control at 0.8% frequency, while MAGERI Q scores were in a good agreement with empirical P-values for remaining erroneous variants . As the duplex sequencing dataset uses ligation for UMI attachment, Q-scores were adjusted to account for the probability of 1st cycle PCR error propagation to become a dominant variant within the consensus . It is necessary to note that the setup that includes just a single test variant with a frequency that by far exceeds that of the most abundant errors is inadequate for performing a comprehensive rare mutation calling benchmark. Nevertheless, MAGERI was able to reliably quantify the distribution of error frequencies in the described case. Using MAGERI and single-strand consensus sequencing can be beneficial, as duplex consensus pairing results in a dramatic decrease of coverage: we observed a median of ~7000 consensuses per position for single-strand molecules and only 1000 consensuses for double-stranded molecules, which is far more than the expected 2x loss.For the sake of an independent validation we have applied our pipeline to a dataset from a recently published study,43 on duMaterials and Methods). MAGERI was able to successfully process data coming from a cDNA-based library sequenced with error-prone long reads with no parameter modifications. Q scores computed by MAGERI for erroneous variants detected in HIV cDNA from 8E5 cell line which serves as a control in this experiment were in good agreement with empirical P-values computed from variant frequencies . On the other hand, HIV cDNA from patient sample that should contain a wealth of mutations displays a drastically different picture with many high-quality variants .To demonstrate the versatility of our software pipeline, we have additionally tested it using a dataset from a completely different domain, HIV amplicon sequencing recently published by Zhou et al. .Erroneous insertions and deletions (indels) at homopolymers are common in high-throughput sequencing performed using Roche 454 and Ion Torrent instruments ,45, and Materials and Methods). Presence of indels in sequencing reads had little effect on the overall assembly efficiency and more than 99.9% of reads successfully assembled into consensuses. Erroneous indels observed in the sequencing data from a cloned FGFR3 exon 7 template can be efficiently filtered by increasing the MIG size threshold: 3 deletions are observed at 5 reads per UMI threshold, 2 deletions are observed at 10–15 reads threshold, and no indels are observed at 20+ threshold. It should be noted, however, that as MAGERI does not implement any indel quality assessment algorithm, indel calls should be manually checked for alignment artefacts and strand bias using MAGERI output in SAM format.We have additionally tested the ability to assemble the indel-prone Ion Torrent data published in Ref.  Click here for additional data file.S2 TableThe table contains coordinates in hg19 assembly, variant type and name, and variant frequency as reported by the vendor. Note that all variants are assayed in two independent experiments and two dilutions (1X and 0.1X).(PDF)Click here for additional data file.S3 TableThe table contains sample name, experiment type (standard for Tru-Q 7 and blank for control DNA), primer set (m1 − 4) used for amplicon sequencing, the ID of independent experiment (replica). The statistics include: total number of reads, fraction of reads in which the UMI and both forward and reverse primers were found unambiguously, number of unique UMIs and number of MIGs that had enough coverage and were successfully assembled into consensus sequences, fraction of reads in assembled UMIs and the total number of aligned consensuses.(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 TableTotal number of variants in each frequency tier coming from two independent experiments and two dilutions (1X and 0.1X) of Tru-Q 7 reference standard.(PDF)Click here for additional data file.S1 FigUMI tagging is ensured by five cycles of linear PCR. Tagging primer is digested by ExoI treatment. Following steps comprise a combination of nested  and step-out  amplification. Illumina adapters for TruSeq sequencing and flow-cell attachment oligonucleotides are included during amplification. During the last step, sample index is inserted for the aims of demultiplexing of different libraries.(PDF)Click here for additional data file.S2 Figa. MIG size distribution, total number of reads in MIGs of a specific size. Each sample is shown with color, two independent experimental replicas are shown as solid and dashed lines. b. Histogram of consensus quality scores  for erroneous variants found in healthy donor DNA. c. Histogram of MIG counts of errors observed in healthy donor DNA and error counts expected from sequencing errors under 5 read MIG size threshold and a sequencing quality Phred score of 20.(PDF)Click here for additional data file.S3 Figa. Fitting Beta distribution to error frequencies observed in UMI-tagged sequencing experiment of a template with a known sequence. Grey area shows the density of observed error frequencies, red line shows the fitting. b. Error counts observed in UMI- tagged sequencing of healthy donor DNA that should (black line and points) and expected from the fitted Beta-Binomial model (red line).(PDF)Click here for additional data file.S4 Figepsilon is the error probability and lambda is the PCR efficiency minus one.Here (PDF)Click here for additional data file.